Gaussian Mixture Model (GMM)
\r\n\tEqually, the interlinkages that the adrenal gland has in the human body create the premises both for the description of the intimate mechanisms that induce adrenal diseases on other tissues and organs and also for strategic considerations when it comes to treatment.
\r\n\r\n\tThis book, which is aimed at both endocrinologists and practitioners in other medical fields, therefore offers an insight into the mysteries of adrenal disease and a comprehensive overview of the current state of knowledge of this gland, providing an easy-to-follow format that focuses on the most important developments in the field of etiopathogenesis, clinical and paraclinical diagnosis, and treatment of these conditions.
",isbn:"978-1-80356-687-0",printIsbn:"978-1-80356-686-3",pdfIsbn:"978-1-80356-688-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"86c26879d83ac24206ed5476b6cde7fd",bookSignature:"Dr. Diana Loreta Paun",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11853.jpg",keywords:"Cushing Syndrome, Etiopathogenesis, Diagnosis, Treatment, Minimally Invasive Technique, Adrenalectomy, Adrenal Diseases, Perioperative Management, Adrenal Cancer, Genetics, Adrenal Mass, Imaging",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 22nd 2022",dateEndSecondStepPublish:"May 26th 2022",dateEndThirdStepPublish:"July 25th 2022",dateEndFourthStepPublish:"October 13th 2022",dateEndFifthStepPublish:"December 12th 2022",remainingDaysToSecondStep:"8 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Practitioner endocrinologist, associate professor, researcher, and manager of the National Institute of Endocrinology in Romania, coordinator of investment research and training projects, funded by European funds. Dr. Paun is a member of The Romanian Association of Clinical Endocrinology, member and president(2011-2012, 2017-2019) of The Romanian Chapter of the AACE (American Association of Clinical Endocrinologists). Dr. Păun was appointed State Advise (2015) and was appointed Presidental Advisor (2019).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"190860",title:"Dr.",name:"Diana Loreta",middleName:null,surname:"Paun",slug:"diana-loreta-paun",fullName:"Diana Loreta Paun",profilePictureURL:"https://mts.intechopen.com/storage/users/190860/images/system/190860.jpg",biography:"PĂUN DIANA LORETA, MD, PhD, FACE\r\nBorn on: February 1st, 1968 on Bucharest\r\nEmployed to: “Carol Davila”, University of Medicine and Pharmacy\r\nPosition: endocrinologist, Ph.D, Associate Professor of Endocrinology\r\nFellow of the American College of Endocrinology\r\nExperience: General Manager of “CI Parhon” Institute of Endocrinology, Bucharest, 2006-2015.\r\nMaster in Public Health\r\nQualifications in: Diabetology, Osteoporosis, Endocrine Ultrasonography, Public Health. Training in molecular biology laboratory techniques – Max-Planck-Institut für Psychiatrie, Dept. of Chemie u. Endokrinologie, München, 2002\r\nOccupational field: Clinical, Educational and Research activities, Management, Healthcare services.\r\nPostgraduate courses in: Informatics, Clinical Endocrinology, Infertility, Sexology, Public Health etc.\r\nProfessional career:\r\nChemistry-Biology High School graduated on 1986, Faculty of Medicine graduated on 1992, Th.Burghele Hospital doctor on probation during 1993–1994\r\nendocrinology resident to CI Parhon Institute of Endocrinology 1994-1998\r\nendocrinologist since 1998\r\nAssistant Professor, Lecturer, Associated Professor of Endocrinology, Carol Davila University of Medicine and Pharmacy, Bucuresti, Romania\r\nPublications: papers presented on national and international meetings, articles publishised in well-known journals, author and coauthor in monographs and clinical guides book.\r\nParticipation on research projects and clinical trials: Director and member of the team in research projects and in clinical trials.",institutionString:"Carol Davila University of Medicine and Pharmacy",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"455410",firstName:"Dajana",lastName:"Jusic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/455410/images/20500_n.jpeg",email:"dajana.j@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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As reflected in the literature, some of the most frequently used biometric modalities include fingerprint, face, hand geometry, iris, retina, signature, palm print, voice, ear, hand vein, body odor and DNA. While these traits may be used in an isolated manner by biometric recognition systems, experience has shown that results from biometric systems analyzing a single trait are often insufficiently reliable, precise and stable to meet specific performance demands (Ross et al. 2006). In order to move system performance closer to the level expected by the general public, therefore, novel biometric recognition systems have been designed to take advantaje from taking multiple traits into account.
\n\t\t\tBiometric fusion represents an attempt to take fuller advantage of the varied and diverse data obtainable from individuals. Just as is the case with human recognition activities in which decisions based on the opinions of multiple observers are superior to those made by only one, automatic recognition may also be expected to improve in both precision and accuracy when final decisions are made according to data obtained from multiple sources.
\n\t\t\tits discussion of data fusion in biometric systems, the present chapter will analyze distinct types of fusion, as well as particular aspects related to the normalization process directly preceding data fusion.
\n\t\tBiometric recognition involves the determination of the identity of an individual according to his/her personal qualities in opposition to the classical identification systems which depend on the users’ knowledge of a particular type of information (e.g., passwords) or possession of a particular type of object (e.g., ID cards).
\n\t\t\tIn biometrics, ‘recognition’ may be used to refer to two distinct tasks. In the first one, that is called verification, an individual claims to be certain user who has been previously registered(enrolled) into the system. It is also possible that the individual does not indicate his/her identity but there exist some additional information that allow to suppose it. In this case system operation is reduced to confirm or reject that a biometric sample belongs to the claimed identity. In the second task, identification, it is not available such prior information about individual’s identity, the system must determine which among all of the enrollees the subject is, if any. In the present chapter, only verification will be discussed insofar as identification may be understood as the verification of multiple entities.
\n\t\t\tIn both cases (verification and identification), a sample of a predetermined biometric trait (e.g., face, voice or fingerprint) is captured (e.g., via photo, recording or impression) from a subject under scrutiny (donor), this is done using an adequate sensor for the task (e.g., camera, microphone or reader/scanner). A sample is called genuine when its donor identity and the identity of the claimed user are the same and it is called impostor when they are not. Following its capture, the sample is processed (feature extraction) in order to obtain the values of certain predefined aspects of the sample. This set of values constitute the feature vector. The feature vector is then matched against the biometric model corresponding to the individual whose identity has being claimed The common structure of all biometric recognition systems is performed in two phases: (1) an initial training phase in which one or various biometric models are generated for each subject, and a later one called recognition phase, in which biometric samples are captured and matched against the models. The present chapter interprets scores as representing similarity. While, in practice, scores may also indicate difference, no generality is lost here by interpreting scores in this way since a linear transformation of the type (s’ = K-s) can always be established.
Monobiometric Term derived from the Greek monos (one) + bios (life) + metron (measure) and preferred by the authors of the present chapter over the term “unibiometric”, also found in the literature but involving a mix of Greek and Latin morphological units. The same comment should be made about polybiometric and multibiometric terms.
In the majority of biometric recognition systems currently in use only a single biometric trait is captured in order to confirm or reject the claimed users’s identity. Such systems are known as monobiometric. Nevertheless, at they heart is a pattern recognizer, which arrives at a final decision with the results obtained from a single sample processed according to a single algorithm.
\n\t\t\t\n\t\t\t\tFig. 1 presents a simple representation of the biometric recognition process in monobiometric systems. After a subject presents the biometric trait which the system’s sensor is designed to process, in the first stage (i.e., capturing sample), a biometric sample is obtained by the sensor and processed by the system to eliminate noise, emphasize certain features of interest and, in general, prepare the sample for the following stage of the process. In the next step (i.e., feature extraction), characteristic parameters of the sample are quantified and a feature vector that groups them is obtained. Following quantification, the system proceeds to match the feature vector (i.e., model matching) against others captured during the training phase that correspond to the individual whose identity is being claimed. These latter vectors are often represented in biometric systems with models that summarizes their variability. As a result of the matching process, a score is obtained quantifying the similarity between the sample and the model. In the final stage (i.e., decision making) and as a result of the score generated, the biometric system makes a decision to accept the sample genuine or to reject it as impostor.
\n\t\tIn polybiometric systems, various data sources are used and combined in order to arrive at the final decision about the donor’s identity. These systems are composed of a set of monobiometric parallel subprocesses that operate the data obtained from the distinct sources in order to finally combine them (i.e., fusing data). This fused data is then processed by system through a single subprocess until th final decision can be made regarding the truth of the claimed identity
\n\t\t\tIn the construction of polybiometric recognitions systems, certain parameters must be set in response to the following questions:
\n\t\t\tWhat are the distinct sources of biometric data being analyzed?
At what point in the biometric process will the data be fused or, said another way, what intermediate data will be used for this fusion?
What algorithm is most adequate for bringing about a particular type of fusion?
The following sections of the present chapter will analyze these different aspects of system design.
\n\t\tIn order to respond to the first question of the previous paragraph regarding multiple data sources, the following specific questions must also be considered (Ross 2007).
\n\t\t\tHow many sensors are to be utilized? In the construction of multi-sensor systems, different sensors (each with distinct performances) are used in order to capture multiple samples of a single biometric trait. In one example of this sort of polybiometric system, simultaneous photographs are captured of a subject’s face using both infrared and visible light cameras.
Multi-sensor system.
How many instances of the same biometric trait are to be captured? Human beings can present multiple versions of particular biometric traits (e.g., fingerprints for different fingers, hand geometry and veins for each hand and irises for each eye). As a result and with a schema similar to that of multi-sensor systems, multi-instance systems are designed to capture various instances of the same biometric trait.
How many times is an instance of a particular trait to be captured? Using a single sensor and a single instance of a particular trait, it is nevertheless possible to obtain distinct samples of that instance under different conditions (e.g., video images taken of a trait instance from different angles or voice recordings taken at different moments and with different speech content). These multi-sample systems may also be represented by a schema similar to that of multi-sensor systems.
How many different biometric traits are to be captured? Biometric recognition systems may be designed to analyze a single biometric trait (i.e., unimodal systems) or various traits (i.e., multimodal systems). The particularities of the latter type of system are represented by the schema below.
Multimodal systems.
How many distinct feature extraction algorithms are to be utilized in the processing of the biometric samples? Multi-algorithm systems are designed to use various algorithms for the feature extraction from biometric samples. In this case, the use of different extraction algorithms may allow the system to emphasize different biometric features of interest (e.g., spectral or prosodic features of a voice sample) and produce different feature vectors for each.
Multi-algorithm systems.
Against how many types of patterns and using how many methods are the feature vectors to be matched? Multi-matching systems are biometric recognition systems that allow match the feature vectors against various types of models or/and. using multiple techniques.
Finally, it is also possible to construct hybrid systems systems of an even greater complexity that incorporate more than one type of the multiple data source discussed above.
Multi-matching schema.
As discussed earlier, biometric fusion is composed by a set of monobiometric subprocesses that work in parallel to operate the data obtained from distinct sources. Once this different data has been operated and fused, it is then handled by the system through a single subprocess until the point where the donor’s identity final decision can be made. This process is represented in Fig. 6 below.
\n\t\t\tBiometric fusion process.
Having considered the biometric fusion schema, it is time to return to the questions articulated earlier in the chapter and analyze now at what level of the process the fusion should be carried out or, in other words, what type of data the system should fuse. The possible responses in the literature to these points allow to establish diverse characterizations of data fusion systems defined as fusion levels(Ross 2007) (Joshi et al. 2009) (Kumar et al. 2010).
\n\t\t\tThe first point at which data fusion may be carried out is at the sample level, that means immediately following sample capture by system sensors. This type of fusion is possible in multi-sensor, multi-instance and multi-sample systems and may be obtained by following a particular sample fusion method. The form that this method takes in each case depends on the type of biometric trait being utilized. While fusion may range from a simple concatenation of the digitalized sample data sequence to more complex operations between multiple sequences, but it is almost always carried out for the same reason: to eliminate as many negative effects as possible associated with the noise encrusted in the data samples during capture. Once the fused sample has been generated, it may be used by the system for feature extraction.
\n\t\t\tThe second point at which data may be combined is immediately following the feature extraction. At the feature level, vectors derived from the different sources are combined, yielding a single, fused vector.
\n\t\t\tSample level fusion.
Feature level fusion.
Another alternative is the fusion of scores obtained following the matching of different sample data against corresponding models. The new score resulting from this fusion is then used by the system to reach the final decision. This sort of fusion is normally carried out according to mathematic classification algorithms ranging in type from decision trees to techniques from the field of artificial intelligence, the latter of which offering learning capabilities and requiring prior training. The present chapter focuses particularly on this latter type of fusion which will be developed in much greater detail in sections below.
\n\t\t\tScore level fusion.
Fusion may also be carried out on the final decisions obtained for each monobiometric process through the use of some kind of Boolean function. The most frequent algorithms used in this type of fusion are AND, OR and VOTING. With the first type, the final, combined decision is
Decision level fusion.
Finally, dynamic classifier selection schema uses scores generated at the data matching level in order to determine what classifier offers the highest degree of confidence. The system then arrives at a final decision through the application of solely the selected classifier. This is represented in Fig. 11 below.
\n\t\t\tDynamic classifier selection.
For the recognition of a individual by a classical recognition system, the data collected (e.g., passwords or ID cards information) from the subject must be identical to the previously recorded data into the system. In biometric recognition systems, however, almost never the data captured from a subject (nor the feature vectors obtained from them) are identical to the previous ones (Ross et al. 2006). The reasons for these variations are manifold and include the following:
\n\t\t\tImperfections in the capture process that create alterations (e.g., noise) in the data;
Physical changes in the capture environment (e.g., changes in lighting and degradation of the sensors used); and
Inevitable changes over time in individual’s biometric traits.
As a result of the unrepeatibility of biometric samples, the process of biometric recognition can not be deterministic and it must be based on the stochastic behaviour of samples. In this way, rather than flatly asserting correspondence between a biometric sample and the corresponding model, biometric systems only permit the assertion that this correspondence has a certain probability of being true.
The differences observed among the distinct biometric samples taken of a single trait from a single donor are known as intra-class variations. On the other hand, inter-class variation refers to the difference existing between the samples captured by the system from one subject and those of others. The level of system confidence in the correctness of its final decision is determined according to these two types of variation. The lesser the intra-class variation and the greater the inter-class variation are, the greater the probability that the final decision is correct.
\n\t\t\tIn the matching model step, the system assigns a score to the sample feature vector reflecting the system’s level of confidence in the correspondence between sample and claimed identity. If this score (
Insofar as score, as understood here, is a random variable, the probability that any particular score corresponds to a genuine sample can be defined by its probability density function (pdf)
When the decision score threshold is established in a system (see Fig. 12), the level of system performance is therefore established, because FAR and FRR directly depend on its value. Wether threshold values increases, FAR will also increase while FRR will decrease [Stan et al. 2009]. The optimal value of
Error rates and pdfs (left). Error rates and cost functions (right).
The National Institute of Standards and Technology (NIST) proposes as a cost function the one shown in formula 2, which is a weighted sum of both error rates. CFR and CFA correspond to the estimated costs of a false rejection and false acceptance, respectively, and Pg and Pi indicate the probabilities that a sample is genuine or impostor. Is obviously true that
In NIST recognition system evaluations, the costs of a false acceptance and a false rejection are quantified, respectively, at 10 and 1, whereas the probabilities that a sample is genuine or impostor are considered to be 99% and 1%, respectively. With these parameters and normalizing the resulting expression, the following equation is obtained (formula 3):
\n\t\t\tFor reasons of expediency, however, the present chapter utilizes other criteria that nevertheless enjoy wide use in the field. According to these criteria, CFA = CFR and Pg = Pi, such that the resulting cost function may be defined as the following (formula 5):
\n\t\t\tAnother value used in the characterization of biometric systems is the equal error rate (EER) which, as shown below, indicates the point at which the error rates are equal:
\n\t\t\tAs a final concept to consider here, the receiver operating characteristic curve (ROC curve) is a two-dimensional measure of classification performance and is regularly used in the comparison of two biometric systems. The ROC curve represents the evolution of the true acceptance rate (TAR) with respect to that of the FAR (Martin 1997):
\n\t\t\tROC curve and area under the curve (AUC).
Through the analysis of the ROC curve, the evaluation of a recognition system may be carried out by considering the costs associated with errors even where the latter have not been previously established. In particular, using the area under the convex ROC curve (AUC), system performance may be expressed as a single numeric value and evaluated: the system considered the best being that with the greatest AUC (Villegas et al. 2009)(Marzban 2004).
\n\t\t\tLet the simplest case of match score distribution be supposed where, for a single source, scores are distributed according to the following criteria:
\n\t\t\tGaussian distribution of scores.
Given the symmetry of the functions, it can be held that the threshold value minimizing the cost function can be located at
From an estimation, the value
Let it be supposed that match score fusion is to be applied to the results of two processes having generated independent scores (s1 and s2) and with distribution functions identical to those described in the previous section of the present chapter. Thus, a match score vector is formed with Gaussian distribution functions for both genuine and impostor subject samples. This vector will have two components, each of which integrating the results from each of the monobiometric classifiers.
\n\t\t\tRepresentation of two-dimensional Gaussian distribution scores.
In Fig.15, the distribution functions are presented together for both genuine and impostor subject score vectors. Right image represents the contour lines of the distribution functions. Observing it, it seems intuitive that, just as was done in the previous section of the present chapter and applying the criteria for symmetry discussed therein, the best decision strategy is that which takes as a genuine subject score vector any vector found above and to the right of the dotted line which, in this particular case, corresponds to
Following this, the resulting estimation of the EER is shown in formula 12. In the specific case proposed here, the resulting EER is found to be 7.56% indicating an important improvement owing to the fact that the centroids of the distribution functions have been separated here by a factor of\n\t\t\t\t\t
Gaussian mixture model (GMM) classifiers are used in order to create a model of statistical behaviour represented by the weighted sum of the gaussian distributions estimated for the class of genuine training score vectors and another similar model to represent the class of impostor vectors. Using the two models, the vectors are classified using the quotient of the probabilities of belonging to each of the two classes. If this quotient is greater than a given threshold (established during the system training phase), the vector is classified as genuine. If the quotient is below the given threshold, the vector is classified as an impostor. Such a procedure is quite similar to that discussed in the previous section of the chapter.
\n\t\t\tIn a situation such as that described in the paragraph above, the following points indicate the expectations for a training process and test using GMMs:
\n\t\t\tThese models (fg’, fi’) of sums of Gaussian functions should maintain a certain similarity to the generative sample distribution ;
The established threshold may be equivalent to the theoretical decision boundary between genuine and impostor score vectors; and
Test results clearly approach the theoretic FAR and FRR.
In order to test the fitness of these premises, 1000 two-dimensional random vectors (Vg) following the distribution function of the genuine vectors and another 1000 vectors (Vi) following the distribution function of the impostor vectors have been taken as training data. With these vectors, GMMs were created to approximate the distribution functions For the training and tests of the GMMs performed here a version of EM algorithm has been used.
The models obtained in the training phase for 10 Gaussian models (10G) derived from the simulated data training are presented below in Table 1:
\n\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t
Gaussian Mixture Model (GMM)
In Fig.16 (left), genuine and impostor models are presented for the score s1 of the score vector. With red lines indicating the impostor model and black lines indicating the genuine sample model, each of the 10 individual Gaussian distributions with which the GMM classifier approximated the distribution of the training data are represented by the thin lines on the graph. The weighted sums of these Gaussian functions (see Formula 13) are represented by the thick lines on the graph. The result has an appearance similar to two Gaussian distributions around +1 and -1. Fig. 18 (right) shows the contour lines of the two-dimensional models.
\n\t\t\tFor a value of
GMM with 10G. Single axe (left). Contours for two-dimension model (right).
In Fig.16. the decision boundary line, at which the quotient of pdfs is equal to the threshold and which separates genuine and impostor decisions, presented as a dotted line. This line is quite near to the proposed boundary. Then the formula 14 represents a transformation from a two-dimension criterion to a one-dimension threshold, which, of course, is easier to manage.
\n\t\t\tIf the same exercise is repeated for a model with 3 Gaussians (3G) and for another with only 1 Gaussian (1G), the following results are obtained:
\n\t\t\tN Gaussian | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER\n\t\t\t\t\t\t | \n\t\t\t\t\t\tth | \n\t\t\t\t\t\tAUC | \n\t\t\t\t\t
10 | \n\t\t\t\t\t\t8.22% | \n\t\t\t\t\t\t7.46% | \n\t\t\t\t\t\t7.84% | \n\t\t\t\t\t\t0.9045 | \n\t\t\t\t\t\t97.12% | \n\t\t\t\t\t
3 | \n\t\t\t\t\t\t7.97% | \n\t\t\t\t\t\t7.52 % | \n\t\t\t\t\t\t7.75% | \n\t\t\t\t\t\t0.9627 | \n\t\t\t\t\t\t97.05% | \n\t\t\t\t\t
1 | \n\t\t\t\t\t\t7.99% | \n\t\t\t\t\t\t7.56% | \n\t\t\t\t\t\t7.77% | \n\t\t\t\t\t\t0.9907 | \n\t\t\t\t\t\t97.10% | \n\t\t\t\t\t
Gaussian Mixture Model (GMM).
Single axe GMM with 3G (left) and 1G (right).
Contour lines for two-dimension GMM with 3G (left) and 1G (right).
Changing the threshold value (see Fig.19), distinct decision boundaries and their corresponding error rates may be obtained. With these values, a ROC curve may be drawn and its AUC estimated.
\n\t\t\tVarious decision boundaries (left) and ROC Curve (right) for GMM 10G.
A support vector machine (SVM) is a classifier that estimates a decision boundary between two vector sets (genuine and impostor ones) such that maximizes the classification margin. In the training phase of a SVM, a model is constructed that defines this boundary in terms of a subset of data known as support vectors (
v\' indicates the transpose v vector.
\n\t\t\tThe equation above defines the distance of a vector (v) to the boundary, where positive distances indicate genuine samples and negative distances indicate impostor samples For the examples presented in this chapter, SVM-Light software has been used.
Given the data distribution and the fact that the expected separation boundary is a straight line, it may be assumed that the linear kernel is the most adequate kernel function here.
\n\t\t\t\n\t\t\t\tFig.20 shows the distribution of genuine samples (in blue) and impostor samples (in red). Points indicated with circles correspond to the support vectors generated in the training phase. The central black line crossing the figure diagonally represents the set of points along the boundary line, which is also quite close to the theoretical boundary.
\n\t\t\tSVM with linear kernel.
The results of the test data classification demonstrate the performance indicated below for
\n\t\t\tKernel | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER | \n\t\t\t\t\t\tnSV\n\t\t\t\t\t\t | \n\t\t\t\t\t\tAUC | \n\t\t\t\t\t
Linear | \n\t\t\t\t\t\t8.01% | \n\t\t\t\t\t\t7.56% | \n\t\t\t\t\t\t7.78% | \n\t\t\t\t\t\t1956 | \n\t\t\t\t\t\t95.06% | \n\t\t\t\t\t
Results of SVM test
The classifier establishes a transformation of the vector space into a real value whose module is the distance from the boundary, calculated such that the system be optimized to establish the decision threshold at the distance of 0. Just as in the case of GMMs, system behavior can be analyzed using the ROC curve and, more specifically, the AUC through the adjustment of this threshold value (see Table 3).
\n\t\tAn artificial neural network (ANN) simulates an interconnected group of artificial neurons using a computational model. In this context, a neuron is a computational element that operates n-inputs in order to obtain just one output following a transfer function like the one shown at formula 16. Where s\n\t\t\t\t\t
A typical ANN groups its neurons in a few layers, so that, the certain layer neuron outputs are only connected to the next layer neuron inputs.
\n\t\t\tThe neural network training step gets as a result the weight for every neuron input that minimizes the error rates.
\n\t\t\tThen the simplest network is one which has only one neuron with two input and one output (2-1-1). This way, the transfer function has no effect on the system and at the end decision function becomes a linear combination of the inputs and therefore the training estimates a linear separator similar to the one seem before for SVM with linear kernel.
\n\t\t\tApplying neural networks to above described data, is possible to obtain the following results: For the examples with ANN, Neural Network Toolbox™ have been used.
Struct | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER | \n\t\t\t\t\t\tAUC | \n\t\t\t\t\t
2-1-1 | \n\t\t\t\t\t\t7.94% | \n\t\t\t\t\t\t7.62 % | \n\t\t\t\t\t\t7.78% | \n\t\t\t\t\t\t95.06% | \n\t\t\t\t\t
Results of 3 ANN test
One common way in which monobiometric systems present their scores is through likelihood estimates (the probability that the sample is genuine). In such cases, the score rangeis limited to 0-1 (0-100%). Ideally, instances of genuine subject scores would be grouped together around 1 or a point close to 1, while impostor subject scores would be grouped together around zero or near it. Both would demonstrate beta distributions. An example of this ideal situation is plotted in Fig.23 with the pdf for genuine samples follows
As it was done for the Gaussians, identical distribution functions are established for both dimensions of the two-dimension score space, then a theoretical value of EER= 0.396% would be obtained. Also is possible the same routine and evaluate system performances for GMM, SVM and NN classifiers For the examples, the same number of genuine and imposter vectors were randomly generated as the previous sections
\n\t\t\t\tFig. 21 shows the pdf’s used in this example and de model obtained for them, while table 5 display test results.
\n\t\t\tClassifier. | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER | \n\t\t\t\t\t
GMM 10G | \n\t\t\t\t\t\t0.55% | \n\t\t\t\t\t\t0.31% | \n\t\t\t\t\t\t0.43% | \n\t\t\t\t\t
GMM 3G | \n\t\t\t\t\t\t0.56% | \n\t\t\t\t\t\t0.28% | \n\t\t\t\t\t\t0.42% | \n\t\t\t\t\t
GMM 1G | \n\t\t\t\t\t\t0.54% | \n\t\t\t\t\t\t0.28% | \n\t\t\t\t\t\t0.41% | \n\t\t\t\t\t
SVM Linear | \n\t\t\t\t\t\t0.48% | \n\t\t\t\t\t\t0.34% | \n\t\t\t\t\t\t0.38% | \n\t\t\t\t\t
NN (2-1-1) | \n\t\t\t\t\t\t0.43% | \n\t\t\t\t\t\t0.35% | \n\t\t\t\t\t\t0.39% | \n\t\t\t\t\t
Test results.
Single axe pdfs (left), model with 10G (centre), vector plots and SVM linear model.
Unfortunately, the distribution functions for real scores are not as clear-cut as those presented in Fig. 21. Scores for impostor subject samples, for example, are not grouped around 0, but rather approach 1. Similarly, genuine subject sample scores often tend to diverge from 1. Distributions similar to those in Fig. 22 are relatively common. To illustrate this,
Single axe score1 pdfs (left) and score2 pdfs (right)..
These particular distributions don’t display any symmetrical property then the equilibrium point estimated loking for
In order to further simulate real conditions, score 2 has been supposed here to display a different behavior, to wit,
As can be seen, the equilibrium point is found here at a value of
If these two distributions are combined and a two-dimensional score space is established, the resulting pdfs can be represented as the one in Fig.23. It plots these two-dimensional density distributions where de genuine one is found near the point (1,1) while the impostor one is located farther from it.
\n\t\t\tCombined density distribution, 3D view (left), contour lines (right).
Applying the GMM trainer with 10 Gaussian functions to these distributions, the images in Fig. 24 are obtained representing the set of the 10 Gaussians making up the genuine model; the set of 10 Gaussians making up the impostor model and representing the impostor and genuine models as the weighted sum of each of their Gaussian functions.
\n\t\t\tContour lines for GMM 10G models. Individual genuine Gaussians (left), individual impostor Gaussians (centre), both models right (right).
Equivalent representations can be obtained using a GMM with 3 Gaussians
\n\t\t\tContour lines for GMM 3G models (left),and GMM 1G models (centre), vectors and SVM boundary (right).
As in previous sections tests conducted with GMM, SVM and ANN classifiers yield the following results:
\n\t\t\tClassifier | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER | \n\t\t\t\t\t
GMM 10G | \n\t\t\t\t\t\t10.19% | \n\t\t\t\t\t\t9.64% | \n\t\t\t\t\t\t9.91% | \n\t\t\t\t\t
GMM 3G | \n\t\t\t\t\t\t10.96% | \n\t\t\t\t\t\t8.94% | \n\t\t\t\t\t\t9.96% | \n\t\t\t\t\t
GMM 1G | \n\t\t\t\t\t\t10.92% | \n\t\t\t\t\t\t9.19 % | \n\t\t\t\t\t\t10.06% | \n\t\t\t\t\t
SVM Linear | \n\t\t\t\t\t\t10.44 % | \n\t\t\t\t\t\t9.29 % | \n\t\t\t\t\t\t9.86 % | \n\t\t\t\t\t
ANN (2-1-1) | \n\t\t\t\t\t\t10.49% | \n\t\t\t\t\t\t9.285 % | \n\t\t\t\t\t\t9.87 % | \n\t\t\t\t\t
Results from classifiers test
As described in earlier sections of the present chapter, the data sources in a system of match score fusion are the result of different monobiometric recognition subprocesses working in parallel. For this reason, the scores yielded are often not homogeneous.
\n\t\t\tIn the most trivial case, the source of this lack is different meaning of the scores, they may represent the degree of similarity between the sample and the model or the degree of disimilarity or directly represent the degree of subsystem confidence in the decision made.
\n\t\t\tOther sources of non-homogeneous scores are include the different numeric scales or the different value ranges according to which results are delivered, as well as the various ways in which the non-linearity of biometric features is presented. Finally, the different statistical behavior of scores must also be taken into account when performing the fusion. For these reasons, the score normalization, transferring them to a common domain, is essential prior to their fusion. In this way, score normalization must be seen as a vital phase in the design of a combination schema for score level fusion.
\n\t\t\tScore normalization may be understood as the change in scale, location and linearity of scores obtained by distinct monobiometric recognition subprocesses. In a good normalization schema, estimates of transformation parameters must not be overly sensitive to the presence of outliers (robustness) and must also obtain close to optimal results (efficiency) (Nandakumar et al. 2005)(Jain et al 2005)(Huber 1981)
\n\t\t\tThere are multiple techniques that can be used for score normalization. Techniques such as min-max, z-score, median and MAD, double sigmoid and double linear transformations have been evaluated in diverse publications (Snelick et al. 2003) (Puente et al. 2010).
\n\t\tPerhaps the simplest of currently existing score normalization techniques is min-max normalization. In min-max normalization, the goal is to reduce dynamic score ranges to a known one (tipically: 0-1) while, at the same time, retaining the form of the original distributions.
\n\t\t\tFor the use this technique, it is necessary that maximum and minimum values ( Where match scores indicate the difference between a sample and reference, 1 should be assigned to the minimum value and 0 to the maximum.
Applying this transformation to the observations generated in a previous section of the current chapter (see ’13. More realistic distributions’), the following results are obtained:
\n\t\t\tClassifier | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER | \n\t\t\t\t\t
GMM 10G | \n\t\t\t\t\t\t17.62% | \n\t\t\t\t\t\t17.67 % | \n\t\t\t\t\t\t17.64% | \n\t\t\t\t\t
GMM 3G | \n\t\t\t\t\t\t17.27% | \n\t\t\t\t\t\t18.47 % | \n\t\t\t\t\t\t17.87% | \n\t\t\t\t\t
GMM 1G | \n\t\t\t\t\t\t19.49% | \n\t\t\t\t\t\t16.36% | \n\t\t\t\t\t\t17.93% | \n\t\t\t\t\t
SVM Linear | \n\t\t\t\t\t\t18.22% | \n\t\t\t\t\t\t17.31 % | \n\t\t\t\t\t\t17.77 % | \n\t\t\t\t\t
ANN (2-1-1) | \n\t\t\t\t\t\t16.70% | \n\t\t\t\t\t\t18.80% | \n\t\t\t\t\t\t17.75% | \n\t\t\t\t\t
Result after min-max normalization
Due to its conceptual simplicity, one of the most frequently used transformations is z-score normalization. In z-score normalization, the statistical behavior of the match scores is homogenized through their transformation into other scores with a mean of 0 and a standard deviation of 1.
\n\t\t\tClearly, it is necessary that the mean and standard deviation of the original match scores be known prior to normalization or, as in min-max normalization, they should be estimated from training data.
\n\t\t\tZ-score distributions do not retain the forms of the input distributions, save in cases of scores with a Gaussian distribution, and this technique does not guarantee a common numerical range for the normalized scores.
\n\t\t\tTest results from z-score normalization are shown below:
\n\t\t\tClassifier | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER | \n\t\t\t\t\t
GMM 10G | \n\t\t\t\t\t\t10.13% | \n\t\t\t\t\t\t9.76 % | \n\t\t\t\t\t\t9.94% | \n\t\t\t\t\t
GMM 3G | \n\t\t\t\t\t\t10.96% | \n\t\t\t\t\t\t8.96% | \n\t\t\t\t\t\t9.96% | \n\t\t\t\t\t
GMM 1G | \n\t\t\t\t\t\t10.92% | \n\t\t\t\t\t\t9.19% | \n\t\t\t\t\t\t10.06% | \n\t\t\t\t\t
SVM Linear | \n\t\t\t\t\t\t10.38% | \n\t\t\t\t\t\t9.33% | \n\t\t\t\t\t\t9.86% | \n\t\t\t\t\t
ANN (2-1-1) | \n\t\t\t\t\t\t9.43% | \n\t\t\t\t\t\t10.36% | \n\t\t\t\t\t\t9.90% | \n\t\t\t\t\t
Result after z-score normalization
The median and MAD (median absolute deviation) normalization technique uses the statistical robustness resulting from the median of a random distribution to make it less sensitive to the presence of outliers. Nevertheless, median and MAD is generally less effective than z-score normalization, does not preserve the original distribution and does not guarantee a common range of normalized match scores.
\n\t\t\tClassifier | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER | \n\t\t\t\t\t
GMM 10G | \n\t\t\t\t\t\t10.15% | \n\t\t\t\t\t\t9.64 % | \n\t\t\t\t\t\t9.89% | \n\t\t\t\t\t
GMM 3G | \n\t\t\t\t\t\t10.40% | \n\t\t\t\t\t\t9.51% | \n\t\t\t\t\t\t9.96% | \n\t\t\t\t\t
GMM 1G | \n\t\t\t\t\t\t10.49% | \n\t\t\t\t\t\t9.44% | \n\t\t\t\t\t\t9.97% | \n\t\t\t\t\t
SVM Linear | \n\t\t\t\t\t\t9.94% | \n\t\t\t\t\t\t9.77% | \n\t\t\t\t\t\t9.85% | \n\t\t\t\t\t
ANN (2-1-1) | \n\t\t\t\t\t\t9.53% | \n\t\t\t\t\t\t10.28% | \n\t\t\t\t\t\t9.90% | \n\t\t\t\t\t
Test results wiht mediam-MAD notmalization
In one particular study from the literature, a double sigmoid transformation is proposed as a normalization scheme (Cappelli et al. 2000):
\n\t\t\tAccording to the double sigmoid normalization technique, match scores are converted to the interval [0,1]. While the conversion is not linear, scores located on the overlap are nevertheless mapped onto a linear distribution (Fahmy et al. 2008).
\n\t\t\tClassifier | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER | \n\t\t\t\t\t
GMM 10G | \n\t\t\t\t\t\t11.40% | \n\t\t\t\t\t\t10.04% | \n\t\t\t\t\t\t10.72% | \n\t\t\t\t\t
GMM 3G | \n\t\t\t\t\t\t10.32% | \n\t\t\t\t\t\t9.94% | \n\t\t\t\t\t\t10.13% | \n\t\t\t\t\t
GMM 1G | \n\t\t\t\t\t\t11.19% | \n\t\t\t\t\t\t10.20% | \n\t\t\t\t\t\t10.70% | \n\t\t\t\t\t
SVM Linear | \n\t\t\t\t\t\t11.10% | \n\t\t\t\t\t\t9.66% | \n\t\t\t\t\t\t10.38% | \n\t\t\t\t\t
ANN (2-1-1) | \n\t\t\t\t\t\t10.70% | \n\t\t\t\t\t\t9.65% | \n\t\t\t\t\t\t10.06% | \n\t\t\t\t\t
Test results for double sigmoid normalization.
Scores yielded by monobiometric classifiers are interpreted as pair of a decision and confidence. The decision, thus, is made according to the location side of the score is located respect to the threshold while confidence is de distance between them. Thus, the greater the distance to the threshold, the greater will be the weight assigned to the score for the final decision.
\n\t\t\tGenerally, this distance does not enjoy a homogeneous distribution for scores of genuine and impostor observations. As a result, in distributions such as that presented in Fig.23, scores of impostor samples tend to have a greater likelihood than those of genuine scores.
\n\t\t\tIn order to compensate that kind of heterogeneity, a transformation has been proposed in (Puente et al. 2010) to make distributions more uniform around the decision threshold:
\n\t\t\tGMM | \n\t\t\t\t\t\tFAR | \n\t\t\t\t\t\tFRR | \n\t\t\t\t\t\tMER | \n\t\t\t\t\t
GMM 10G | \n\t\t\t\t\t\t11.32% | \n\t\t\t\t\t\t9.61% | \n\t\t\t\t\t\t9.92% | \n\t\t\t\t\t
GMM 3G | \n\t\t\t\t\t\t11.03% | \n\t\t\t\t\t\t8.90% | \n\t\t\t\t\t\t9.96% | \n\t\t\t\t\t
GMM 1G | \n\t\t\t\t\t\t10.92% | \n\t\t\t\t\t\t9.19% | \n\t\t\t\t\t\t10.06% | \n\t\t\t\t\t
SVM Linear | \n\t\t\t\t\t\t10.44% | \n\t\t\t\t\t\t9.29% | \n\t\t\t\t\t\t9.86% | \n\t\t\t\t\t
ANN (2-1-1) | \n\t\t\t\t\t\t9.98% | \n\t\t\t\t\t\t9.78% | \n\t\t\t\t\t\t9.88% | \n\t\t\t\t\t
Test results for double linear normalization.
The principal conclusion that can be drawn from the present chapter is undoubtedly the great advantage provided by score fusion relative to monobiometric systems. In combining data from diverse sources, error rates (EER, FAR and FRR) can be greatly reduced and system stability greatly increased through a higher AUC.
\n\t\t\tThis improvement has been observed with each of the classifiers discussed in the present chapter. Nevertheless and in consideration of comparative studies of normalization techniques and fusion algorithms, it can be noted that the specific improvement produced depends on the algorithms used and the specific case at hand. It is not possible, therefore, to state
One final conclusion that stands out is that improvements in error rates are directly linked to the number of biometric features being combined. From this, it may be deduced that the greater the number of features being fused, the larger the improvement will be in the error rates.
\n\t\tNeglected tropical diseases (NTDs) include a collection of chronic, disabling, and physically disfiguring infectious diseases that usually affect dwellers of poor rural populations in tropical and sub-tropical countries of the world [1]. Apart from their negative impact on the health of victims, NTDs exert an immense socio-economic burden on the society as a result of the social stigma and physical disabilities associated with them. These interrelated negative outcomes perpetuate a cycle of poverty and unproductivity resulting in a consistent decline in economic growth [2]. As a major element of the Millennium Development Goals (MDGs), much effort is being put in for the elimination of the NTDs [3].
Among the NTDs, helminth infections especially soil-transmitted helminthiasis (STHs) and schistosomiasis are among the most prevalent afflictions of humans [4]. About 2 billion people are estimated to suffer from helminth infections worldwide, out of whom 300 million suffer from severe morbidity [5]. The negative impact of helminth infections on human growth and development (including cognitive development in childhood and nutritional status), pregnancy and work performance cannot be overemphasized. Though considered as acute health problems in some developed parts of the world, chronic parasitic infections are common and recurrent in poor communities and usually result in long-lasting complications making them a significant health threat to the populations who are continuously at risk for infection [6].
Over the years, many highly effective chemotherapeutic agents have been developed for treating helminth infections. Unfortunately in the setting of rural poverty where these diseases are mostly prevalent, access to healthcare facilities and the cost of medications are a challenge [7, 8]. Additionally, environmental factors and unavoidable domestic or occupational exposures, strongly favor the process of re-infection even after a successful therapy [9, 10]. Given that these infections also require lengthy treatment regimens with related costs which cannot be afforded by the affected victims, many patients seek for alternative treatment options especially the use of herbal medicines which are readily available and less expensive [9, 11].
Herbal extracts have been used in traditional medicines since ancient times for the effective treatment of human diseases [12]. Ethnobotanical studies in various regions of the world have documented medicinal plants used for the treatment of various parasitic infections. Scientific investigations of selected plants have also revealed remarkable activity of medicinal plants against specific human parasites [13, 14]. In Ghana, numerous medicinal plants play an important role in the healthcare system of rural communities. The Ghanaian flora provides a ready source for new therapeutic interventions for the local population [15, 16, 17]. This chapter provides a review with special focus on medicinal plants collected from Ghana with anthelmintic and anti-schistosomal activity.
Soil transmitted helminth (STH) infections are a group of infections which are acquired by the ingestion of, or contact with, soil containing infectious worm eggs or larvae [18]. STHs have been reported as the most common parasitic infections encountered in humans with an estimation of more than 1 billion people infected with at least one or more helminth parasites. They constitute an important global health challenge in resource deprived parts of the world and are prevalent in areas of poor sanitary conditions [19].
The main species of clinical importance are the intestinal roundworm (
Anthelmintics are a group of antiparasitic drugs that expel worms and other internal parasites out of the body by either stunting or killing them. For the treatment of STHs, the benzimidazoles specifically albendazole and mebendazole are the current treatment drugs of choice [19]. The main challenge with these anthelminthics is the development of resistance due to the intensive use of drugs in both human and live-stock [22]. With few new drugs evolving against helminth infections over the years, the fight against these parasites could become a losing battle, thus the need to search for new alternatives.
Schistosomiasis, widely known as bilharzia, is caused by infection with blood flukes of the genus
Five species of the schistosome parasite namely:
For the eradication of schistosomiasis, control programmes have been based on preventive chemotherapy. The WHO endorsed and advocated for mass drug administration (MDA) especially among school children utilizing a single oral dose of 40 mg/kg praziquantel [27]. Unfortunately, the unavailability of the drugs due to cost, poor drug coverage, inequity of access to chemotherapy and non-compliance to therapy due to adverse side effects have impeded the progress of this approach [7, 28]. The expansion of preventive chemotherapy has also raised concerns about the potential development of resistance to praziquantel (PZQ) which remains the only commercially readily available drug for the control of schistosomiasis [29]. Some studies have reported low cure rates of PZQ attributing this to possible mutation of the schistosome parasite as well as inactivity of PZQ against early stages of the worms [30, 31]. It is thus not a satisfactory situation to have only one single effective treatment. Ideally, other anti-schistosome drugs should be developed so that the classical strategy of avoiding development of resistance could be followed.
Reported anthelmintic and anti-schistosomal activities of medicinal plants collected from various parts of Ghana were obtained from electronic databases including PubMed, SciFinder and Google Scholar. The inclusion criteria were that: (i) plants should be used in Ghanaian traditional medicine for treatment of worm infestations or expulsion of worms and schistosomiasis (urinary and intestinal) or other condition characterized by the symptoms of the above diseases (ii) plant should have been investigated for anthelmintic or anti-schistosomal (cercarididal) activity using one or more validated
The anthelmintic activity of plant extracts was mostly studied by evaluating their effect on worms after direct exposure for a period of time. Earthworms including
The anthelminthic potency of the petroleum ether, chloroform and methanol extracts of
The methanol extracts (50–150 mg/mL) of the stem bark and roots of
In another study, the aqueous and ethanolic stem bark extracts (50–200 mg/mL) of
The anthelmintic activity of the ethanolic extract of
The pawpaw tree is well known for its nutritional and medicinal values. The leaf decoction is used as a galactogogue and in the treatment of tonsillitis, ulcerative stomatitis, hemorrhoids, asthma, urinary tract infections, as poultice for sores and gingivitis and in the treatment of helminth infections. The roots are used as antidote to various poisons. The fruits are used to treat indigestion, chronic diarrhea, ringworm infections, bleeding piles, and amoebic dysentery [39]. Almost all parts of the plant are documented to be used for managing helminth infections. In Ghana, 74% traditional healers used this plant for treating helminth infections [40].
In a comparative assessment of the anthelminthic activity of various parts of the plant, the hydroethanolic extracts of the leaves, stem bark, and seeds of
Ethnopharmacological reports from parts of Ghana revealed the extensive use of the leaves of
In a previous study, the alcoholic leaf extract of
In another study, fractions and purified compounds from
The anthelmintic and helminth resistance modifying activities of methanol extract of
Further the extract at 1, 2 and 5 mg/mL significantly potentiated the activity of albendazole, mebendazole and levamisole against the test organism. In the presence of 2 mg/mL of the extract the paralysis and death times of albendazole (8 mg/mL) against
In a previous study, the methanol stem bark extract of
In a previous study, the methanol stem bark extract of
The foliage of
The hydroethanolic extract of the roots of
In another study, the 70% aqueous acetone extract, solvent fractions and isolated compounds from the roots of
In a previous, observations were made for the time taken for different solvent extracts of the leaves of
The leaves and stem bark of
The anthelmintic activity of the aqueous and ethanolic extracts of the roots of
In a previous study, the anthelmintic activity
In another study, the stem bark extracts (ethanol and chloroform extracts) of
The methanol extracts of the leaves and stem bark
The ethanolic extract of the dried fruits and leaves (300–300 mg/mL) were investigated for anthelmintic activity against earth worms. The anthelmintic activity of the fruit extract was more potent that the leaf extract. Both extracts demonstrated a concentration dependent activity with the fruit extract demonstrating significant paralytic and death times (
See Table 1.
Plant | Family | Common name | Part Investigated | Activity Type |
---|---|---|---|---|
Euphorbiaceae | Christmas Bush | Leaves | Anthelmintic activity against | |
Apocynaceae | Alstonia | Roots, stem bark | Anthelmintic activity against | |
Meliaceae | Neem | Seeds Leaves | Anthelmintic activity against Cercaricidal and adulticidal activity against | |
Caricaceae | Pawpaw | Leaves, stem bark, seeds | Anthelmintic activity against | |
Combretaceae | — | Leaves | Anthelmintic activity against | |
Cyperaceae | — | Whole plant | Anthelmintic activity against | |
Dichapeltaceae | — | Stems, roots | Anti-schistosomal activity against eggs obtained from clinical isolates of | |
— | Leaves, stem bark Roots | Cercaricidal activity against post-infective larvae (schistosomule) and adult parasite of Cercaricidal activity against freshly shed cercariae from | ||
Gutifferae | Bitter kola | Stem bark | Anthelmintic activity against | |
Apocynaceae | — | Stem bark | Cercariae from | |
Rubiaceae | — | Stem bark | Anthelmintic activity against Cercaricidal activity against Adulticidal effect against | |
Moringaceae | Moringa | Foliage | Anthelmintic activity against | |
Rubiaceae | African peach | Stem bark | Cercaricidal activity against Adulticidal effect against | |
Lamiaceae | Basil | Fruits | Anthelmintic activity against | |
Euphorbiaceae | — | Roots | Anthelmintic activity against the free-living nematode | |
Plumbaginaceae | — | Leaves | Anthelmintic activity against | |
Euphorbiaceae | — | Leaves | Cercaricidal activity against | |
Apocynaceae | Snakeroot | Leaves, roots Roots, stem bark | Anthelmintic activity against Cercaricidal activity against Adulticidal effect against | |
Anacardiaceae | Roots | Anthelmintic activity against | ||
Asteraceae | Bitter leaf | Leaves, stem bark Leaves | Anthelmintic activity against Cercaricidal activity against Adulticidal effect against | |
Apocynaceae | — | Leaf, stem bark | Anthelmintic activity against | |
Apocynaceae | African black pepper | Fruits, leaves | Anthelmintic activity against |
Medicinal plants from Ghana with anthelmintic and anti-schistosomal activity.
[Refer to Section 2.3 for plant description].
The methanol leaf extract of
The effect of
The effect of the extract on the weight of spleen and liver of infected mice were all significantly lesser in the
Crude extracts (pet-ether, ethyl acetate and methanol) and isolated triterpenoids from the stems and roots of
For the stem extracts, the ovicidal potency was in the following order petroleum ether (IC50 = 443.70) > EtOAc (IC50 = 638.00) > MeOH (IC50 = 893.70 μg/mL). The IC50 values for the root extracts were 248.60, 546.40, and 566.30 μg/mL respectively for the EtOAc, pet-ether and MeOH extracts.
The isolated compounds (Friedelan-3-one, β-Sitosterol/stigmasterol, Dichapetalin M and Dichapetalin A) showed higher ovicidal activity than the extracts though activities for both extracts and compounds were lower compared to the standard drug, praziquantel. The highest ovicidal potency was exhibited by β-sitosterol/stigmasterol mixture with an IC50 of 177.90 μg/mL which was about 11 times less potent than praziquantel (15.47 ± 0.06 μg/mL). The next highest was dichapetalin A (151.10 μg/mL) whiles friedelan-3-one showed the least potency with IC50 of 378.10 μg/mL. From the root extract, Dichapetalin M showed ovicidal effect with IC50 of 191.00 μg/mL [75].
The cercaricidal activity of the leaf and stem bark extracts of
In another study, the
The hydroethanolic and alkaloidal extracts from the stem bark of
[Refer to Section 2.8 for plant description].
In a previous study, the cercaricidal activity of the methanol stem bark extract of
Previous studies on the cercaricidal activity the methanolic extract of stem bark of
The methanolic extract (250 μg/mL) of
[Refer to Section 2.13 for plant description].
The root and stem bark of
The ethanolic extract of the root and stem bark were both found to be active against the cercariae and adult worms. At a concentration range of 62.5–1000 𝜇g/mL the stem bark extract exhibited significant anti-cercarial activity (
[Refer to Section 2.15 for plant description].
In a previous study, the evaluation of the cercaricidal and schistosomicidal activities of the methanol extract of the leaves of
The ability of the leaf extract (500 mg/kg
The anthelmintic and anti-schistosomal activities of some medicinal plants employed in Ghanaian traditional medicine have been validated. For most of these plants however, the specific bioactive constituents are not yet identified. It is therefore imperative that further studies to isolate and verify the constituents responsible for the observed activities be performed. Further, the evaluation of safety profiles will add substantial value to the reported bioactivities and make these plants attractive for adaptation to pharmaceutical companies for further development.
Authors have no conflict of interest to declare.
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It is an acute exaggerated clinical manifestation of thyrotoxic state. The exact incidence is unknown. It occurs in 1–2% of patients admitted for thyrotoxicosis. It has a mortality of 10–20%. This chapter would help us to understand its clinical manifestations, pathophysiology, and effective treatment. Terminal learning objective would be to diagnose impending storm early and start prompt treatment in day-to-day practice. The chapter would cover pathophysiology including triggers, clinical features including various diagnostic criteria, diagnosis, and treatment of thyroid storm. 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It starts as a diffuse enlargement of the thyroid gland and ends in a nodular enlarged thyroid. Though MNG can be sporadic, there is a strong correlation between occurrence of MNG and iodine deficiency. The characteristic feature of MNG is its functional and structural heterogeneity. The MNG usually presents as neck swelling; rarely it may produce pressure symptoms, i.e., dyspnea, hoarseness of voice, and dysphagia. It can also present with symptoms of hyperthyroidism particularly in long-standing goiter. Imaging particularly ultrasound is very useful to define characteristic of MNG and surrounding structure. The incidence of malignancy in MNG is 4–14%, and risk factors are family history of thyroid carcinoma, history of neck radiation, prior surgery, and presence of cervical lymphadenopathies. Management of MNG can be done by drugs, surgery, and radioiodine (I-131) depending on results of diagnostic evaluation and associated complications.",book:{id:"9077",slug:"goiter-causes-and-treatment",title:"Goiter",fullTitle:"Goiter - Causes and Treatment"},signatures:"Sanjay Saran",authors:[{id:"242737",title:"Dr.",name:"Sanjay",middleName:null,surname:"Saran",slug:"sanjay-saran",fullName:"Sanjay Saran"}]},{id:"61473",title:"Nuclear Medicine in the Assessment of Thyrotoxicosis Associated with Increased Thyroid Function and Radioiodine 131 Ablative Therapies",slug:"nuclear-medicine-in-the-assessment-of-thyrotoxicosis-associated-with-increased-thyroid-function-and-",totalDownloads:1422,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Nuclear medicine is directly involved in both the diagnosis and treatment of benign thyroid disease. Thyroid scintigraphy (most commonly with technetium-99 m pertechnetate) should be used as the imaging modality of choice for assessment of thyrotoxicosis, since it demonstrates the functional state of the thyroid gland. An adequate understanding of the pathophysiological mechanisms and characteristics of the patient is essential, as well as the different treatments of thyroid disorders that present with hyperthyroidism (Graves’ disease, toxic multinodular goiter, and toxic adenoma-Plummer’s disease). Therapeutic modalities include antithyroid drugs, radioiodine and surgery. Antithyroid drugs are the first line of therapy and regarding the use of radioiodine, current recommendations consider it a safe and effective therapeutic alternative in hyperthyroidism. Finally, we highlight the existence of some special situations (children, pregnancy, thyroid eye disease, chronic renal failure and dialysis patients) and the importance of radiation protection measures to the patient, the public and professionals.",book:{id:"6791",slug:"thyroid-disorders",title:"Thyroid Disorders",fullTitle:"Thyroid Disorders"},signatures:"Elena Espinosa Muñoz",authors:[{id:"241332",title:"M.Sc.",name:"Elena",middleName:null,surname:"Espinosa Muñoz",slug:"elena-espinosa-munoz",fullName:"Elena Espinosa Muñoz"}]},{id:"71040",title:"Hyperthyroidism",slug:"hyperthyroidism",totalDownloads:881,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Excess level of thyroid hormones in blood is thyrotoxicosis, which is responsible for clinical syndrome of hypermetabolism, sympathetic hyperactivity. Hyperthyroidism is the term used to denote the overproduction of thyroid hormones from the thyroid gland. Hyperthyroidism is possible with hyperactive thyroid gland due to multi/solitary nodular thyroid disease or Grave’s disease. Thyrotoxicosis associated with thyroiditis is not hyperthyroidism. Treatment of hyperthyroidism is with anti-thyroid drugs (ATT), radio-active iodine ablation (RAI), or thyroid surgery; whereas, treatment of thyroiditis is symptomatic.",book:{id:"9077",slug:"goiter-causes-and-treatment",title:"Goiter",fullTitle:"Goiter - Causes and Treatment"},signatures:"Rushikesh Maheshwari",authors:[{id:"300029",title:"Dr.",name:"Rushikesh",middleName:null,surname:"Maheshwari",slug:"rushikesh-maheshwari",fullName:"Rushikesh Maheshwari"}]},{id:"61460",title:"Thyroid Cancer: Diagnosis, Treatment and Follow-Up",slug:"thyroid-cancer-diagnosis-treatment-and-follow-up",totalDownloads:1525,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Thyroid cancer is the most common malignancy of the endocrine system and it is usually presented as nodular goiter, the last being extremely a common clinical and ultrasound finding. The widespread use of ultrasonography during the last decades has resulted in a dramatic increase in the prevalence of clinically inapparent thyroid nodules, which only in 5.0–10.0% harbor thyroid carcinoma. The goal of the initial sonographic assessment of thyroid nodules is to distinguish benign nodules that could be managed conservatively from those with suspicious or malignant features requiring further management, including fine needle aspiration biopsy (FNAB), some axillary molecular techniques and thyroid surgery. Since over 90% of malignant thyroid nodules are differentiated thyroid carcinomas (DTCs) with good prognosis, it is necessary to establish strict criteria for diagnosis, treatment and follow-up in order to minimize the potential harm of over-treatment of low-risk patients and to provide adequate therapy to patients at high risk. This often requires an interdisciplinary approach involving endocrinologists, surgeons, pathologists, radiologists and oncologists.",book:{id:"6791",slug:"thyroid-disorders",title:"Thyroid Disorders",fullTitle:"Thyroid Disorders"},signatures:"Mira Siderova",authors:[{id:"242582",title:"Associate Prof.",name:"Mira",middleName:null,surname:"Siderova",slug:"mira-siderova",fullName:"Mira Siderova"}]}],onlineFirstChaptersFilter:{topicId:"1011",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"May 17th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",isOpenForSubmission:!1,annualVolume:null,editor:null,editorTwo:null,editorThree:null},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",isOpenForSubmission:!0,annualVolume:11400,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. Board Member and Chair of Mycology Group of Chinese Society of Dermatology.",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"5",title:"Parasitic Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",isOpenForSubmission:!0,annualVolume:11401,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},{id:"6",title:"Viral Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",isOpenForSubmission:!0,annualVolume:11402,editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:26,paginationItems:[{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81557",title:"Object Tracking Using Adapted Optical Flow",doi:"10.5772/intechopen.102863",signatures:"Ronaldo Ferreira, Joaquim José de Castro Ferreira and António José Ribeiro Neves",slug:"object-tracking-using-adapted-optical-flow",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Information Extraction and Object Tracking in Digital Video",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",subseries:{id:"24",title:"Computer Vision"}}},{id:"81558",title:"Thresholding Image Techniques for Plant Segmentation",doi:"10.5772/intechopen.104587",signatures:"Miguel Ángel Castillo-Martínez, Francisco Javier Gallegos-Funes, Blanca E. Carvajal-Gámez, Guillermo Urriolagoitia-Sosa and Alberto J. Rosales-Silva",slug:"thresholding-image-techniques-for-plant-segmentation",totalDownloads:13,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Information Extraction and Object Tracking in Digital Video",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",subseries:{id:"24",title:"Computer Vision"}}}]},overviewPagePublishedBooks:{paginationCount:8,paginationItems:[{type:"book",id:"7723",title:"Artificial Intelligence",subtitle:"Applications in Medicine and Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7723.jpg",slug:"artificial-intelligence-applications-in-medicine-and-biology",publishedDate:"July 31st 2019",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"a3852659e727f95c98c740ed98146011",volumeInSeries:1,fullTitle:"Artificial Intelligence - Applications in Medicine and Biology",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null}]},{type:"book",id:"7656",title:"Fuzzy Logic",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7656.jpg",slug:"fuzzy-logic",publishedDate:"February 5th 2020",editedByType:"Edited by",bookSignature:"Constantin Volosencu",hash:"54f092d4ffe0abf5e4172a80025019bc",volumeInSeries:3,fullTitle:"Fuzzy Logic",editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",institutionURL:null,country:{name:"Romania"}}}]},{type:"book",id:"9963",title:"Advances and Applications in Deep Learning",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"0d51ba46f22e55cb89140f60d86a071e",volumeInSeries:4,fullTitle:"Advances and Applications in Deep Learning",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. 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He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. Voyich",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Montana State University",country:{name:"United States of America"}}},{id:"330412",title:"Dr.",name:"Muhammad",middleName:null,surname:"Farhab",slug:"muhammad-farhab",fullName:"Muhammad Farhab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"349495",title:"Dr.",name:"Muhammad",middleName:null,surname:"Ijaz",slug:"muhammad-ijaz",fullName:"Muhammad Ijaz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}}]}},subseries:{item:{id:"23",type:"subseries",title:"Computational Neuroscience",keywords:"Single-Neuron Modeling, Sensory Processing, Motor Control, Memory and Synaptic Pasticity, Attention, Identification, Categorization, Discrimination, Learning, Development, Axonal Patterning and Guidance, Neural Architecture, Behaviours and Dynamics of Networks, Cognition and the Neuroscientific Basis of Consciousness",scope:"Computational neuroscience focuses on biologically realistic abstractions and models validated and solved through computational simulations to understand principles for the development, structure, physiology, and ability of the nervous system. 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A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. 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The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. 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Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. 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