Selected applications of feature selection techniques in forensic research.
\r\n\tThe main goal of this book is (1) prevention and treatment of life-related diseases and metabolic syndrome, (2) mechanism of lifestyle-related diseases and metabolic syndrome, (3) effective functional compounds, foods, and diet to lifestyle-related diseases and metabolic syndrome, (4) relationship between metabolic syndrome and various diseases such as cancer, allergy, and aging. This book hopes to bring an overview of the recent advances of metabolic syndrome, and thus we welcome both review chapters and original chapters.
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The evidence is presented to the court of law and the prosecutor must be satisfied with its reliability, credibility and admissibility. Forensic evidence can be extremely sensitive and dangerous for law enforcement to handle and the use of incorrect or unreliable evidence threatens the safety of justice.
\nDigital forensics (DF) was introduced as a means of digitally making use of forensic data for both the discovery and interpretation of electronic evidence [2]. This area has become increasingly important with the surge in the volume, variety and velocity of forensic data. Currently, the major challenges faced by DF investigators are an increase in the number of cases and the complexity of cases [1]. The increase in cases could be due to a societal shift towards faith in DF techniques, with the common belief being that advanced tools are highly useful in skilfully extracting and using forensic information [2]. The increasing complexity of cases is simply a result of advances in technology, storage and applications [1]. Another challenge for DF investigators is the requirement for fast turnaround. Due to the nature of forensic inquiries, investigators wish to have faster, more advanced and more accurate tools, in order to prevent any setbacks that could adversely affect the case. Furthermore, it is expected that new challenges will arise for DF in the near future as pointed out by Mazurczyk et al. [3], p. 10: ‘modern digital forensics is a multidisciplinary effort that embraces several fields, including law, computer science, finance, networking, data mining and criminal justice’.
\nArtificial Intelligence (AI) is a technology that has been used for many decades, with growing importance in the modern day due to its uses for learning and reasoning. AI methods are extremely capable of learning and solving complex computational problems and have subsequently been considered crucial for future developments; from explaining the reasoning process of expert systems, to recognising patterns in artificial neural networks [4, 5]. Although AI models have been developed to support parts of the court cases, current judiciary systems may raise concerns over the reliability of decisions made by AI models. Moreover, these models can be useful but only when explained to judges and jurors, such as in a study by Vlek et al. [6] where they used scenario scheme idioms to construct Bayesian Networks (BN), in order to make the network easier to understand. This method attempted to explain why certain modelling choices were made as well as why the network arrived at the final output, given the choices made along the way. Another paper by Timmer et al. [7] used BNs to formalise the relationship between the hypothesis and the evidence presented in the network, and the authors derived a support graph to assist with interpretation of the BN, which could then be used for argument and evidence about the case.
\nIn view of the importance of explainability, there emerges XAI, a collection of AI methods that focuses on producing outputs and recommendations that can be understood and interpreted by human experts. A focus of the AI community at the moment is to develop XAI methods that have a good balance between both transparency and explainability as well as power, performance and accuracy [8]. The application of XAI models to DF problems is scarce but would open up the possibility of using computer-based analysis for evidence in courts of law. It could become an extremely powerful tool for helping judges and jurors make decisions in the presence of many interconnected pieces of evidence.
\nThis chapter investigates the opportunities and challenges of applying XAI to support DF. First, this chapter discusses DF and the applications of AI in the forensics domain. Second, it reviews existing literature on XAI, feature selection methods built on various types of variables such as images and electrodermal activity for drug and alcohol testing, missing data handling techniques and XAI for multi-criteria and interactive learning and their implementation in DF. Third, it discusses a current case study on drug testing that includes problem formulation, a description of the forensics data collected from questionnaires and analytical testing, and the high-level decision-making process for drug screening. Finally, the chapter presents a conclusion drawn from this study and further work.
\nThis section puts this chapter in context by reviewing the area of XAI and its application to DF, and discussing several data-related challenges one may need to address to make the most out of XAI methods, such as dealing with a large number of variables/features, missing data, multiple (conflicting) output criteria and interactions between the AI system and the practitioner.
\nWith ML being the core technology, AI systems have made remarkable achievements in solving increasingly complex computational tasks and making them critical aspects of the future development of human society [4]. However in case of ML algorithmic models pursuing prediction accuracy and becoming increasingly opaque, the explainability becomes problematic for black-box techniques such as ensemble methods and deep neural networks [9].
\nTo address the trade-off between interpretability and model performance, post-hoc interpretability techniques emerge, which approximate black-box models by techniques such as simplification, feature relevance estimation, or visualisation. Eventually, the opaque models are turned into glass-box, which achieve a good trade-off between interpretability and prediction accuracy. Examples of such techniques include local interpretable model-agnostic explanations (LIME) [10], which explain the predictions by approximating the opaque black-box model with simple models locally, and SHapley Additive exPlanations (SHAP) [11], which calculate the contribution of each feature to the prediction based on three desirable properties (i.e. local accuracy, missingness and consistency). These techniques are referred to as XAI, which propose creating a collection of ML techniques that generate more explainable, understandable and trustworthy models without losing out significantly in prediction accuracy [8]. XAI methods can be classified according to multiple criteria, including intrinsic or post hoc, model-specific or model-agnostic and local or global interpretability [12].
\nThis criterion distinguishes whether XAI is achieved intrinsically or post hoc. Intrinsic interpretability refers to ML models that are interpretable because of their simple structures (e.g. linear models, tree-based models). Post hoc interpretability refers to the use of methods like feature importance and partial dependency plots in explaining the black-box models (e.g. ensemble methods, neural network) after training.
\nFor model-specific techniques, interpretability is incorporated within the internals (i.e. inherent structure and learning mechanisms) and is limited to specific models. In contrast, model-agnostic methods, as named, are irrelevant to the inner processing/structure of the model. They can be seamlessly used on any ML model and are applied after the model has been trained [12].
\nThe scope of the interpretability, global to the model or local to the prediction, is another important criterion [10]. Global interpretability refers to the entire model behaviour and answers ‘show does the trained model make predictions?’. Local interpretation methods explain a single prediction which influences a user’s confidence in the prediction and consequently, the user’s action.
\nDF, which requires the intelligent analysis of large amounts of complex data, is benefiting from AI. Mitchell [5] reviewed some of the basic AI techniques that have been applied to the DF arena. These include expert systems in explaining the reasoning process, Artificial Neural Network (ANN) in pattern recognition, and decision trees acting as learning the rules for pattern classification and expert system. Irons and Lallie [13] also identified the use of AI techniques to automate aspects like identification, gathering, preservation and analysis of evidence in DF process. In recent years, the importance and requirement of using explainable methods which achieve both the robustness of algorithms and transparency of reasoning have been increasingly acknowledged in DF. Interpretable ML classifiers like decision trees and rule-based models have been commonly applied to DF problem [14, 15]. To explain a legal case, the community has also applied the idea of BN [6, 7]. AfzaliSeresht et al. [16] presented an XAI model in which event-based rules are created to generate stories for detecting patterns in security event logs for assisting forensic investigators. Mahajan et al. [17] applied LIME towards toxic comment classification in cyber forensics and achieved both high accuracy and interpretability compared to various ML models. However, in terms of automated decision-making in DF, there are very few works that have been made to make it explainable. Figure 1 provides the classification of XAI techniques and their recent applications in DF.
\nClassification of XAI techniques and selected applications in DF.
The increase in the availability of data due to a push in digitisation has led to high-dimensional data sets for training and testing AI algorithms. However, the amount of available data is just as important as the quality of the data. To ensure high-quality data is being filtered out from redundant, irrelevant, or noisy data [18], one can apply feature selection. Selecting the most relevant features has been shown to increase prediction accuracy, since it simplifies the model [19] and removes redundant in features [20]. However, the situation of having too little data needs to be avoided where possible to reduce the risk of overfitting, which occurs when a function is too closely fit to a limited set of data points. It is worthwhile highlighting the difference between feature selection and dimensionality reduction: while both methods reduce the number of features in a dataset, feature selection is achieving this by simply selecting and excluding given features without changing them, dimensionality reduction transforms features into a lower dimension. Our focus is more on feature selection methods. However, commonly used dimensionality reduction methods include Principal Component Analysis (PCA), Random Projection, Partial Least Squares and Information Gain.
\nFeature selection methods are categorised in Figure 2 according to their process of ranking features into filter, wrapper and embedded techniques [21]. Filter methods are techniques that rank the relationship of features with an outcome without learning a model, such as Separability and Correlation Analysis (SEPCOR) [20]. Univariate filters calculate the ranking for each individual feature, while multivariate filters compute the ranking based on the correlation between the variables or between the variables and the outcome [22]. Wrapper techniques select features by comparing all the combinations of the included features before starting the prediction model, to find the most accurately predictive one [22]. Wrapper techniques are more computationally expensive than filters; however, they generally produce more accurate results. Finally, embedded methods are classifier-dependent selection methods, where the selection is built based on the classifiers’ chosen hypotheses [23].
\nClassification of feature selection techniques.
Many comparative studies have been performed to find the best feature selection technique for high-dimensional data. For example, Hua et al. [24] compared a wide range of feature selection techniques for a variety of high-dimensional datasets. The authors followed a two-stage feature selection process to reduce computational time. In the first stage, feature selection methods that are independent from the classification process were applied. Following that, a further feature selection was implemented through classifier-specific feature selection techniques. The results show that wrapper methods have better performance in datasets with large samples, and filters have generally equal error trend. One of the main conclusions of their paper is that there is no feature selection technique performed best across all datasets. Another review of feature selection methods for high-dimensional datasets, which focused on filters, was conducted by Ferreira and Figueiredo [25]. The authors compared, amongst others, the following feature selection techniques for supervised learning: ReliefF, correlation-based filter selection, fast correlation-based filter, Fisher’s ratio and minimum redundancy maximum relevance. Other solutions to tackle high-dimensionality in feature selection are the choice of an adequate evaluation criteria, such as predictive measures designed for small sample datasets and ensemble feature selection methods, including combining multiple feature selection methods and boosting [26].
\n\nTable 1 provides an overview of different feature selection methods applied to forensic science applications. Shri and Sriraam [20] formulated a feature extraction and feature selection problem to detect the difference between alcoholics and control groups through measuring the impact of the use of alcohol in multichannel EEG signal regions. Feature subset selection was performed using separability and correlation analysis, which was proposed in the paper. The results illustrate that the introduced technique improved prediction accuracy, and further validation using other classifiers and cross-validation is recommended. Another feature selection technique to enhance screening of alcohol use disorder was introduced by Mumtaz et al. [27]. The EEG features were recorded in 5-minutes eyes open and 5-minutes eyes closed segments. The implemented feature selection takes two steps. First, the relevance of each feature to the outcome is calculated using the ROC. Then, Markov blanket filtering combined with the ROC is used to remove redundant features. The second step has a high computational cost, which is one of the drawbacks of this method. The paper found that the inter-hemispheric coherence between the brain regions ranked the highest in classifying alcohol use disorder (AUD). Mumtaz et al. [28] designed a rank-based feature selection technique in response to the high-dimensionality in the dataset. Feature ranking was computed based on the area under the curve of that feature and represented the relevance of the feature to the outcome. The minimum number of features was chosen by adding the features to the model sequentially, starting from the highest-ranked features.
\nForensic application | \nType of feature selection | \nAlgorithm | \nType of data | \nReference | \n
---|---|---|---|---|
Alcohol testing | \nFilter method | \nSeparability and correlation analysis | \nEEG signals, eye blink artefact and motion artefact | \n[20] | \n
Feature ranking using area under the curve | \nContinuous data | \n[27] | \n||
Feature ranking using area under the curve | \nCategorical and continuous data | \n[28] | \n||
Linear Discriminant Analysis (LDA) | \nImages | \n[29] | \n||
Screening substance use disorder | \nA discriminant function analysis | \nCategorical and continuous data | \n[30] | \n|
Drug testing | \nLinear Discriminant Analysis (LDA) | \nMass spectral data | \n[31] | \n|
Wrapper method | \nExhaustive search method | \nContinuous and time domain features | \n[32] | \n
Selected applications of feature selection techniques in forensic research.
Another alcohol use detection method based on thermal infrared facial images was examined in [29]. The dimensionality reduction was carried out using PCA combined with Linear Discriminant Analysis (LDA) [33]. It was shown that LDA worked well if the data had no missing data [34]. In an application for feature selection [30], applied discriminant function analysis for substance use disorder detection. This disorder is usually related to P3 amplitude,2 addiction severity and impulsivity in predicting treatment completion. The research found that the P3 amplitude accounts for more variance compared to other variables.
\nMahmud et al. [32] designed a method for quick detection of opioid intake using wrist-worn biosensor-generated data. The exhaustive search method was applied to seek a set of variables that achieved the highest accuracy. It helped to minimise the computational time and increased the prediction accuracy and sensitivity. Feature selection methods have also been applied to identify illegal drugs [31]. PCA followed by LDA was implemented for drug isomer differentiation. Three feature selection models that were tested included the full spectrum, exclusion of selected masses and the selected region, where ions are expected to contribute to the isomeric difference.
\nTo summarise, feature selection methods have been implemented in forensic research and particularly for the detection of substance use. Their application covers various types of data, including images, EEG signals and time-series. Most of the reviewed methods were based on a filters approach. However, since most of these applications have selected the features for classification purposes, embedded techniques are designed to integrate the selection in the classification process. Therefore, it is important to investigate other embedded and wrapper feature selection methods.
\nForensic data contains a large number of features. A proportion of information in these features could be missing, which would reflect a different level of uncertainty because they are measured independently in laboratories [35]. High-dimensional forensic data presents challenges in establishing unbiased estimation and inference of ML models. Missing and uncertain forensic data must be treated in the data preprocessing stage, before the development of ML models. The deletion of incomplete instances and imputation of missing data is the most frequently used method of handling missing data, however the removal of the incomplete instances results in biased inference due to poor representation of complete samples [36, 37].
\nStatistical methods based on data imputation are largely utilised to handle missing data. The basic idea is to replace the missing values with the predicted values obtained from the observed data. There are three types of missing data—missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) [38]. The missingness mechanism by MCAR is independent of observed and unobserved data whereas, MAR is independent of unobserved data and dependent on the observed data. The missingness mechanism by MNAR is only dependent on unobserved data. The forensic datasets are usually MCAR type.
\nThe missing forensics data can be imputed by methods such as Multivariate imputation by Chained Equations (MICE), Maximum likelihood estimation (MLE), Random Forest (RF), K-nearest neighbour (KNN) and MICE by Regularised regression. MICE run a series of regression models whereby each variable with missing data is modelled conditional upon the other variables in the data [39]. This implies that each variable can be modelled according to its distribution. The missing data can be imputed by MLE using the expectation-maximisation (EM) algorithm [40]. It iteratively solves complete data problems and then intuitively fills the missing data with the best guess under the current estimate of the unknown parameters in E-STEP, then re-estimates the parameters from the observed and filled-in missing data in M-STEP.
\nThe method based on the RF called missForest was presented to impute missing continuous and categorical attributes [41]. It averages the multiple imputed unpruned classification or regression trees and estimates the imputation error by built-in out-of-bag error estimates of RF. A study showed that RF imputation method has less bias estimate and narrower confidence interval compared to MICE [42].
\nKNN imputes the closest instance in a multi-dimensional space by K-nearest neighbour imputation method. The similarity between two instances is measured by distance function such as Euclidean distance function. KNN imputation can handle instances with multiple missing variables without a need for the creation of a separate predictive model for each variable [43].
\nHowever, it suffers from the curse of dimensionality and could be computationally expensive as it searches for similar instances in the entire dataset.
\nA regularised regression model minimises the loss function by imposing some penalties. The superiority of regularised regression in terms of biases in imputed missing values in high-dimensional data is presented in [44]. In MICE by regularised regression the initial missing data are imputed by a simple method such as mean or frequency. The new parameters are estimated in the next iteration through the regression model and then missing values are replaced by predicted values. These steps are repeated for each variable with missing values. This procedure is conducted iteratively until convergence. After convergence, the final imputed data is utilised as input in a ML model.
\nXAI techniques have shown promise in solving complex problems with multiple criteria. For example, decision trees, with tree-like structure in which internal nodes stand for tests on features and leaf nodes represent a class label [45], have been used as interpretable supervised classifiers in handling multi-criteria problems like medical diagnosis [46]. Vuong et al. [47] applied decision trees in forensic investigation to automatically produce detection rules used by the robotic vehicle in cybersecurity based on both cyber criteria (network, CPU, disk data) and physical features (speed, vibration, power consumption).
\nWhile decision trees can be adopted for visual reasons to highlight the most influential features in a classification process [48], rules have a textual description and are also readily seen in multi-criteria decision aiding [49]. The most common rules are IF-THEN which discretise a high-dimensional, multivariate feature space into a series of simple and explainable decision statements [50]. Karabiyik and Aggarwal [51] proposed an automated disk forensic investigation tool that leverages a dynamic knowledge base created using rules in the form of IF-THEN statements. Belief-rule-base (BRB), an extension of the IF-THEN rule base, has also been used to address multi-criteria problems [52, 53]. The inference of BRB system is explained by using the evidential reasoning (ER) approach [54], which allows the representation of both qualitative and quantitative data by using belief distributions and the aggregation of belief-based information. In addition to interpretable models, model-agnostic XAI techniques such as using an extended Shapley Value [55] and augmentation-based surrogate model [56] have been adopted in the multi-criteria decision aiding models to further assist in explaining the result of these models to decision makers.
\nXAI techniques have also been used to solve decision problems with multiple objectives. For example, Pessach et al. [57] proposed a comprehensive analytical framework based on the Variable-Order Bayesian Network (VOBN) model to support HR recruiters in global recruitment scheme in balancing multiple organisational objectives. Other XAI techniques/systems developed to solve multi-objective problems include V2f-MORL (vector value function based multi-objective deep reinforcement learning) [58] and fuzzy rule-based systems with multi-objective evolutionary algorithms [59].
\nIndeed, the goal of XAI techniques is to have the simplest rules which are understandable for humans without sacrificing the performance, although simplicity and performance are often conflicting objectives [60]. To achieve both accuracy and comprehensibility, the two important but conflicting classifier properties, Piltaver et al. [61] proposed multi-objective learning of hybrid classifiers (MOLHC) algorithm in which the sub-trees in the initial classification decision tree are replaced with black-box classifiers so that the complete Pareto set of solutions (a set of solutions that do not dominate each other but are superior to the remaining solutions in the search space) is more likely to be found. Similarly, with objectives of maximising the model ability while minimising the complexity, Evans et al. [60] used multi-objective genetic programming, another tree-based construction method in which trees are evolved from a population of candidates rather than constructed greedily in a top-down manner, to construct model-agnostic representation of black-box estimators.
\nInteractive ML is an iterative process of learning that includes the interaction between humans and ML methods [62]. It has been applied for multiple purposes, such as visual analytics [63], interactive model analysis [64] and event sequence analysis [65]. Jiang et al. [62] reviewed recent research in interactive ML and its application to solve a variety of tasks, discussed research challenges and suggested future work in the area. One of the recommendations for future work is to combine XAI with interactive ML. For example, complex ML algorithms can be simplified by using easy to understand algorithms, which helps the process of model building and parameter tuning.
\nPrevious research combining XAI with interactive learning was done, for example, by Spinner et al. [63]. This research used XAI to explain the output of a ML algorithm, searches for limitations within the models and optimises them. In addition, global monitoring and steering mechanisms were applied. A user study with nine participants was included to test the system, and the results indicated positive feedback from the users. Many other applications of XAI for interactive ML were applied in the form of visual analytics. A modular visual analytics framework was developed for topic modelling, which allows users to compare, evaluate and optimise topic models using a visual analytics dashboard [66]. The design of the framework is interpretable by users and adjusts to their optimisation goal, which is based on time-budget, analysis goal, expertise and the noisiness of the document collection.
\nA review of visual interaction, supporting dimensionality reduction systems and covering interpretable models, was conducted by Sacha et al. [67]. The paper constructed seven possible scenarios for the application of interactive ML in dimensionality reduction. These scenarios included: interactive feature selection, dimensionality reduction parameter tuning, defining constraints and dimensionality reduction type selection. The paper found that some of the previous studies investigated a combination of these scenarios and the maximum number of combined scenarios in a paper was four. The paper also observed that some of these scenarios were studied more in the literature, such as the feature selection, data selection and parameter tuning scenarios. The application of XAI for interactive learning in forensic science has not been explored yet but it is easy to see that this approach can be beneficial in this domain; for example, where collection of evidence can be controlled (e.g. if is obtained experimentally) but is expensive and/or time-consuming, then a suitable approach may be to use XAI in an interactive fashion with a user, who can decide to terminate evidence collection prematurely upon retrieval of sufficient evidence.
\nThis case study describes the process of forensic investigation by experts from an existing forensic science company. It will explore the challenges faced by forensic experts in making decisions based on factual and heuristic knowledge gained through years of experience. It will discuss the opportunity to utilise the forensic data to develop an interpretable and trustworthy system for automation of the decision-making process [68, 69, 70, 71, 72, 73, 74, 75, 76, 77].
\nCurrently, a trained expert in this company makes a decision based on a combination of factors, including the analysis of the testing sample and other, external factors such as chemical treatments and more. The expert then produces a report explaining the reasoning behind their decision, outlining different standards and classifying their decisions into one of a plurality of outcomes surrounding likelihood of drug use and exposure to drugs.
\nThe decision regarding likelihood of drug use or exposure is based on a multitude of considerations, including the level of drug detected, the specific metabolites, the client’s self-declarations and many more factors. When the decision process and report writing is conducted by individual experts, there can be some variability in the final decisions and reports that are produced. One of the main reasons for this is the high volume of features to be taken into consideration, which may all have different levels of importance. Another is that with so many features, it is not possible to cover every potential case that may arise and therefore it is difficult to set specific guidelines for the experts to follow. There is also the potential for subjectivity of the expert when making the final decision—an issue which is difficult to eradicate when relying on human judgement. This can result in disagreement amongst individual experts, or uncertainty where experts may find it difficult to draw conclusions based on the evidence provided. Such differences in subjectivity could be due to personal experience, length of time in the role, previous encounters in different cases and many other potential effects.
\nWhen a metabolite is detected the machine generates information on the amount that was present in the sample or, in other words, the level. This is a continuous value which can be used by the experts to make decisions on whether the client was using a particular substance, whether they were exposed or if the client has not been in contact with a drug at all. The levels at which the expert defines use or exposure are up for debate. It can be difficult for them to pinpoint exact values where the judgement tips from likely exposure to likely use, and further problems arise when considering different levels within each category (e.g. highly likely, likely, etc.). Without set levels experts are using their own judgement to decide which category the client falls into, which again leaves room for disagreement across the board.
\nThe most significant problem from a business-efficiency point of view is the length of time that it takes to write a report. A significant increase of new report instructions has resulted in the need for automation, as the current personnel are under high levels of pressure and demand for quick turnaround.
\nThe need for automation is therefore not only to improve accuracy and reliability, but also to speed up delivery times and free up the time of the experts to allow them to undertake other key responsibilities such as research, training and dealing with abnormal cases. The current problem requires a system for automatic decision-making and report writing for the outcome of drug testing, to produce reports suitable for presentation in legal cases.
\nThe features in the forensic data are collected through a combination of questionnaire data—which is completed by the client being tested—and the outcome of tests using forensic laboratory equipment. Each row represents an individual case and each column represents a feature. The forensic investigator collects the essential evidence such as hair and nails, as well as carrying out a structured questionnaire. The questionnaire consists of a number of sections, with a combination of multiple-choice options and Likert scale questions. The document collects information about medical history, drug and alcohol use, hair and nail care.
\nHair and nail samples are an easy, non-invasive way of collecting the evidence required to detect the chemical and biological substances, which identify substance use or exposure. Depending on hair growth and the length of strands this can show up to 1 year of drug history, although typically only a maximum of 6 months is used during testing. Body hair is taken if there is less than 1 cm of hair available on the scalp. A nail sample is taken if scalp and body hair are both unavailable and can show up to 3–6 month of drug history. The evidence from hair and nail samples may fail the forensic test (false-negative results) if a suspect repeatedly cuts hair and nails, or uses certain chemical treatments. The forensic data from the questionnaire could gather missing features when some of the follow-up questions do not apply to a client. For example, follow-up questions for pregnancy would only apply only to females. The data could also be subject to inconsistencies due to inaccurate or false self-reporting. This could be due to inability to remember and answer the questions. Drug and alcohol intoxication can inhibit memory alone, making it difficult to obtain accurate information on both the quantity of the substance used/exposed to, and the number of days use/exposure, as the client is asked to recall over a period of up to 12 months. The analytical data collected through forensic laboratory tests could also be missing if the metabolites are not present in the client’s body, as this would mean further testing is not required. The reason for this is that the testing equipment looks for every possible substance in the sample, rather than selecting those that have been instructed for analysis. The false-positive and false-negative test results affect the data quality. It could be due to external contamination in hair and nail samples, or having little to no body hair.
\nThis type of forensic data can be used to develop decision support tools to fully automate the decision-making process and validation of the experts’ assessments against empirical data. The XAI model supports complex decision-making and can process large amount of data in minutes. The steps for the development of automated decision-making system in the forensic investigation are shown in Figure 3, where the relevant techniques are described in detail in Section 2 of this chapter.
\nAutomated decision-making process.
The decision-making process for testing Drug X3 follows a hierarchical structure with binary outcomes, which has been simplified into a small decision tree shown in Figure 4. The specific metabolites have been anonymised, instead these have been renamed as ‘Metabolite 1’, ‘Metabolite 2’ and ‘Metabolite 3’. It is a snapshot of an interactive-decision-tree that allows visualisation and assessment of the entire decision-making process followed by an expert when drawing conclusions on whether or not a client has used or been exposed to Drug X.
\nDecision process for testing Drug X.
First, based on the questionnaire data the expert will check to see whether the client has declared any use of Drug X in the last 12 months. If this is true then use is confirmed and no further testing is needed. If use has not been declared, based on the analytical data which has been extracted from the hair or nail sample, the expert will consider whether the data shows detection of the Metabolite 1 compound. If Metabolite 1 is detected, further testing is required to determine the levels of Metabolite 1 present in different sections of the hair as this will inform the expert whether the client has used or been exposed to the drug.
\nIf Metabolite 1 is not detected, the expert checks for Metabolite 2. If Metabolite 2 is detected then it is concluded that the client has been exposed, but if it is not detected then the final check is for Metabolite 3. If Metabolite 3 is detected then it is determined that there is no evidence of use or exposure, but if it is detected then the decision is either use or exposure. This is dependent on the levels of each metabolite detected.
\nThis chapter has discussed the application of XAI to digital forensics with a particular focus on forensic drug testing. We provided an overview of data-related challenges one may face when implementing an XAI solution including a large number of features (e.g. pieces of evidence), missing data, multiple conflicting decision criteria and the need for interactive learning. Different techniques for dealing with these challenges were reviewed and applications in digital forensics were highlighted. Finally, we outlined a case study on a forensic science company to demonstrate real challenges of forensic reporting and the potential for XAI to design a trustworthy automated system to present generated evidence in the court of law.
\nThe chapter proposes important future directions for adopting XAI techniques to address challenges in digital forensics. These include, first and foremost, the validation of the manually derived decision trees. It would be interesting to derive decision trees automatically using the available data. These trees could differ from the manually derived trees and thus reveal alternative drivers and potential hidden biases. Another direction is the development of more advanced XAI methods including belief or fuzzy rule based models. To make these data-driven models more accurate, one can also investigate systematic ways of merging with knowledge base and rules provided by experts. Thus, updating the rules can be done in an interactive fashion, for example as and when new scientific insight from chemistry becomes available. Certainly, these directions of future research are relevant for forensics in drug testing but also for digital forensics in general.
\nFungi are eukaryotic organisms thought to have about 4 million species [1]. Although the cell structures of fungi are similar to other eukaryotic cells, they differ from other cells by the presence of ergosterol in their cell membrane and chitin in their cell walls. Cell cytoplasm contains higher concentrations of salt and sugar than other eukaryotes. This regulates cell homeostasis and regulates the exchange of substances. Except for yeasts, most fungi have microscopic structures called hyphae, and these come together to form visible structures called mycelium. The hyphae have apical growth. In the apical growing parts of the hyphae, there are secretory vesicles called “Spitzenkörper” and Wooronin body organs that act as peroxisomes. Because of these properties of hyphae, fungi can live where other eukaryotic cells do not and can use various substrates [2, 3]. Fungi take part in many degradations, transformation, and cycle events in nature. Although they are heterotrophic creatures, they can survive as saprophytic, mutualistic, and parasitic. The fact, that they are found in all parts of the world and live in different environments is due to the superior reproductive abilities of fungi [4, 5, 6].
Fungi can reproduce sexually and asexually. Asexual reproduction of fungi is carried out by vegetative reproduction through hyphae or by the spores they produce. Sexual reproduction is; they form a diploid nucleus with the union of haploid spores, and the cycle continues with the germination of this nucleus. In fungi, asexual reproduction takes place more than sexual reproduction. This event increases the adaptive power of fungi and prevents the accumulation of any harmful mutations that may occur and their transmission from generation to generation. In addition, their chances of survival and competitive advantage are ensured [4, 5, 7].
Sexual reproduction in fungi takes place in three stages. In the first stage; haploid cells fuse, this is called plasmogamy. In the second stage; the fusion of two haploid nuclei, this event is karyogamy. The third stage is; the resulting diploid cells undergo meiosis to form haploid cells, and the cycle continues in this way [8]. These stages are summarized in Figure 1.
Fungal reproduction.
The association or non-union of haploid cells is determined by DNA. The sex of haploid cells is determined by a specific gene region in fungi. This region is known as the mating-type locus and is abbreviated MAT. MATs genetically determine the mating identity of fungi and stimulate the secretion of pheromones. Secreted pheromones provide communication between fungi and realize sexual intercourse [4, 9, 10, 11].
Fungi, like every living thing, need energy and food sources to complete their development and life cycles after sexual and asexual reproduction. These food sources are carbon, nitrogen, vitamins, and minerals. They also need suitable environmental conditions (such as pH, temperature, humidity, oxygen) to grow and develop [12, 13, 14].
Fungi can consume vegetable and animal carbon sources thanks to their hydrolytic enzymes. They can use monosaccharides and polysaccharides such as glucose, fructose, chitin, cellulose, hemicellulose, and lignin [15, 16]. Like all living things, fungi need a nitrogen source for their growth and development, and fungi can metabolize many different nitrogen sources. Especially ammonium and glutamine are the first nitrogen sources they use. In addition, they can easily use other nitrogen sources [17].
Vitamins are cofactors of enzymes and growth factors of many organisms. Fungi need vitamins for their growth and development. Some of these vitamins are; thiamine, biotin, riboflavin, nicotinic acid, vitamin K and pantothenic acid [18].
Like many microorganisms, fungi can survive in varying environmental conditions and under various stress factors. They can survive and reproduce in extreme environments, such as the poles, in extremely cold regions, and in extremely hot regions such as deserts. Fungi are generally; grow better in warm, acidic, and aerobic environments, but they can survive in cold, alkaline, and anaerobic environments. Although the growth temperatures of the fungi are quite wide, the best growth is seen at 25°C. Fungi that live under the temperature at which they develop optimally are called psychrotolerant, and fungi that live at temperatures of 40°C and above are called thermotolerant fungi. Fungi that live in or are exposed to temperatures above 40°C can survive by protecting themselves from heat stress by producing heat shock proteins. Fungi can be found in yeast or mold structures depending on the temperature of the environment they are in, and fungi with this feature are called dimorphic fungi. One of the most important fungi showing this feature is
Fungi are among the largest and most diverse groups of eukaryotic organisms. Because of their complex gene structure, the enzymes they produce, and their ability to use many different carbon sources, they, directly and indirectly, affect human life. They have been used for centuries as a food source and in the production process of many biotechnological products. Today, fungi are used in various fields such as antibiotics, enzyme technology, drug production, pigment production [24, 25, 26]. Although fungi are necessary for the survival of life on earth, they cause serious problems in most organisms. Fungi cause disease in humans, animals, and plants and cause the death of these organisms [27, 28]. Fungi infect many organisms with the secondary metabolites and mycotoxins they produce, even cause their extinction [29].
A fungal kingdom is a group that contains the most and most harmful plant pathogens. By infecting all tissues and organs of plants, they damage many herbaceous and woody plants with high economic value and nutritional properties. In cultivated plants such as corn, wheat, sugar beet, potato, banana; causes great harm to farmers by causing diseases such as root rot, wilt, stem softness, gall and rust. They also develop in stored grains and cause product loss. Also; high woody plants are infected by white rot and brown rot fungi, resulting in tissue deterioration and plant death. Plant pathogen fungi can reproduce both sexually and asexually in host plants [30, 31, 32, 33]. Table 1 shows the plants that some fungi cause disease.
Pathogen fungus | Plant | Damage |
---|---|---|
Picea | Root rot | |
Abies | Root rot | |
Maize | Gall | |
Maize, Sugar beet | Ear rot | |
Wheat, barley | Black rust | |
Flaxseed | Red rust | |
Tomato, pepper, watermelon | Root rot | |
Chickpeas, carrots, olives, apples | Pallidness | |
Ginger | Aflatoxin | |
Crucifers | Blackleg | |
Banana | Mildew | |
Soybean | Root rot | |
Pinus | Red band needle blight | |
Pinus | Brown spot needle blight, | |
Tomato, potato, pepper eggplant | Mildew | |
Plants | Vascular pallor |
Some plant pathogenic fungi and infecting plants.
Fungi cause infections not only in plants but also in humans and animals. Bees, insects, frogs, fish, and corals are some organisms affected by fungal infections. Fungi enter the body from the outer shells, trachea, and skin of these creatures and cause the death of these creatures. Fungal diseases have killed more than 1.6 million people annually. It is thought that pandemics caused by fungi may occur with global warming and climate change [34]. Because the stress tolerance and adaptation abilities of the fungi are very high, they have destroyed their existence on earth by infecting many different organism groups (Table 2). In humans, they cause skin infection, lung infection, and intestinal infection. They also cause diseases in animals and humans by reproducing sexually and asexually [35, 36].
Pathogen fungus | Organisms | Damage |
---|---|---|
Horse | Encephalomalacia | |
Human | Liver cancer | |
Human | Brain tissue loss | |
Human | Infection in the blood | |
Human | Infection in the lung | |
Amphibian | ||
Ant | ||
Bird | Lung infection | |
Fish shellfish | ||
Cat |
Diseases are caused by some pathogenic fungi in humans and animals.
Apart from its use as food; the fungi which we use in the production of drugs, antibiotics, anticarcinogenic substances, pigments, alcohol, and biofuels, are indispensable elements for the continuation of our lives. As we stated in this publication, their high reproductive capacity and ability to survive in extreme conditions provide fungi with a competitive advantage and advantage over other organisms. These abilities give it the capability to live in the plant, animal, and human tissue-organs. If the development and growth demand of fungi, whether pathogenic or not, are known, we can make the most of these organisms and prevent the development of fungi that cause disease. This study will enable us to get to know fungi a little more closely and will enable us to take precautions against these organisms.
The authors do not declare any conflict of interest.
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
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\n\n\n\nIntechOpen publishes different types of publications.
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Eventually, the environmental and economic advantages of these systems are presented.",book:{id:"6514",slug:"microfluidics-and-nanofluidics",title:"Microfluidics and Nanofluidics",fullTitle:"Microfluidics and Nanofluidics"},signatures:"Hafiz Muhammad Ali, Tayyab Raza Shah, Hamza Babar and\nZargham Ahmad Khan",authors:[{id:"187624",title:"Dr.",name:"Hafiz Muhammad",middleName:null,surname:"Ali",slug:"hafiz-muhammad-ali",fullName:"Hafiz Muhammad Ali"},{id:"229676",title:"Mr.",name:"Hamza",middleName:null,surname:"Babar",slug:"hamza-babar",fullName:"Hamza Babar"},{id:"241251",title:"Mr.",name:"Tayyab",middleName:"Raza",surname:"Raza Shah",slug:"tayyab-raza-shah",fullName:"Tayyab Raza Shah"},{id:"241252",title:"Mr.",name:"Zargham Ahmad",middleName:null,surname:"Khan",slug:"zargham-ahmad-khan",fullName:"Zargham Ahmad Khan"}]},{id:"59009",doi:"10.5772/intechopen.72505",title:"Thermal Transport and Challenges on Nanofluids Performance",slug:"thermal-transport-and-challenges-on-nanofluids-performance",totalDownloads:1721,totalCrossrefCites:4,totalDimensionsCites:15,abstract:"Progress in technology and industrial developments demands the efficient and successful energy utilization and its management in a greater extent. Conventional heat-transfer fluids (HTFs) such as water, ethylene glycol, oils and other fluids are typically low-efficiency heat dissipation fluids. Thermal management is a key factor in diverse applications where these fluids can be used, such as in automotive, microelectronics, energy storage, medical, and nuclear cooling among others. Furthermore, the miniaturization and high efficiency of devices in these fields demand successful heat management and energy-efficient materials. The advent of nanofluids could successfully address the low thermal efficiency of HTFs since nanofluids have shown many interesting properties, and the distinctive features offering extraordinary potential for many applications. Nanofluids are engineered by homogeneously suspending nanostructures with average sizes below 100 nm within conventional fluids. This chapter aims to focus on a detail description of the thermal transport behavior, challenges and implications that involve the development and use of HTFs under the influence of atomistic-scale structures and industrial applications. Multifunctional characteristics of these nanofluids, nanostructures variables and features are discussed in this chapter; the mechanisms that promote these effects on the improvement of nanofluids thermal transport performance and the broad range of current and future applications will be included.",book:{id:"6514",slug:"microfluidics-and-nanofluidics",title:"Microfluidics and Nanofluidics",fullTitle:"Microfluidics and Nanofluidics"},signatures:"José Jaime Taha-Tijerina",authors:[{id:"182402",title:"Dr.",name:"Jose",middleName:"Jaime",surname:"Taha-Tijerina",slug:"jose-taha-tijerina",fullName:"Jose Taha-Tijerina"}]},{id:"61556",doi:"10.5772/intechopen.74426",title:"Microfluidics and Nanofluidics: Science, Fabrication Technology (From Cleanrooms to 3D Printing) and Their Application to Chemical Analysis by Battery-Operated Microplasmas-On-Chips",slug:"microfluidics-and-nanofluidics-science-fabrication-technology-from-cleanrooms-to-3d-printing-and-the",totalDownloads:1850,totalCrossrefCites:6,totalDimensionsCites:10,abstract:"The science and phenomena that become important when fluid-flow is confined in microfluidic channels are initially discussed. Then, technologies for channel fabrication (ranging from photolithography and chemical etching, to imprinting, and to 3D-printing) are reviewed. The reference list is extensive and (within each topic) it is arranged chronologically. Examples (with emphasis on those from the authors’ laboratory) are highlighted. Among them, they involve plasma miniaturization via microplasma formation inside micro-fluidic (and in some cases millifluidic) channels fabricated on 2D and 3D-chips. Questions addressed include: How small plasmas can be made? What defines their fundamental size-limit? How small analytical plasmas should be made? And what is their ignition voltage? The discussion then continues with the science, technology and applications of nanofluidics. The conclusions include predictions on potential future development of portable instruments employing either micro or nanofluidic channels. Such portable (or mobile) instruments are expected to be controlled by a smartphone; to have (some) energy autonomy; to employ Artificial Intelligence and Deep Learning, and to have wireless connectivity for their inclusion in the Internet-of-Things (IoT). In essence, those that can be used for chemical analysis in the field for “bringing part of the lab to the sample” types of applications.",book:{id:"6514",slug:"microfluidics-and-nanofluidics",title:"Microfluidics and Nanofluidics",fullTitle:"Microfluidics and Nanofluidics"},signatures:"Vassili Karanassios",authors:[{id:"60925",title:"Prof.",name:"Vassili",middleName:null,surname:"Karanassios",slug:"vassili-karanassios",fullName:"Vassili Karanassios"}]},{id:"59924",doi:"10.5772/intechopen.75091",title:"Micro/Nanofluids in Sustainable Machining",slug:"micro-nanofluids-in-sustainable-machining",totalDownloads:1320,totalCrossrefCites:3,totalDimensionsCites:8,abstract:"Micro/nanofluids are the recent alternative solutions for cooling lubrication that can be defined as the fluids containing microparticles or nanoparticles, which own superior lubrication and cooling characteristics. For these reasons, they have gained significant attention in industrial applications, such as automotive, machining, and biomedical industries. In this chapter, the authors mainly present the recent progress and applications of nanofluids in machining processes, as well as some initial researches about microfluids. Nanofluids provide an excellent media in cutting zone for enhancing the thermal conductivity and tribological characteristics. Therefore, they help to enhance the cutting performance by reducing the coefficient of friction, cutting temperature tool wear, and improving the surface quality. Moreover, the application of nanoparticles in vegetable oils, which are inherently nontoxic as well as biodegradable, gives them superior lubrication properties suitable for MQL application, especially for difficult-to-cut materials. The novel green technology definitely brings out many new solutions in machining practice.",book:{id:"6514",slug:"microfluidics-and-nanofluidics",title:"Microfluidics and Nanofluidics",fullTitle:"Microfluidics and Nanofluidics"},signatures:"Tran The Long and Tran Minh Duc",authors:[{id:"222893",title:"Dr.",name:"TranThe",middleName:null,surname:"Long",slug:"tranthe-long",fullName:"TranThe Long"},{id:"227561",title:"Prof.",name:"Tran Minh",middleName:null,surname:"Duc",slug:"tran-minh-duc",fullName:"Tran Minh Duc"}]},{id:"63782",doi:"10.5772/intechopen.81123",title:"Convection Flow of MHD Couple Stress Fluid in Vertical Microchannel with Entropy Generation",slug:"convection-flow-of-mhd-couple-stress-fluid-in-vertical-microchannel-with-entropy-generation",totalDownloads:937,totalCrossrefCites:4,totalDimensionsCites:7,abstract:"Entropy generation of fully developed steady, viscous, incompressible couple stress fluid in a vertical micro-porous-channel in the presence of horizontal magnetic field is analysed in this work. The governing equations for the flow are derived, and nondimensionalised and the resulting nonlinear ordinary differential equations are solved via a rapidly convergent technique developed by Zhou. The solution of the velocity and temperature profiles are utilised to obtain the flow irreversibility and Bejan number. The effects of couple stresses, fluid wall interaction parameter (FSIP), effective temperature ratio (ETR), rarefaction and magnetic parameter on the velocity profile, temperature profile, entropy generation and Bejan number are presented and discussed graphically.",book:{id:"7516",slug:"pattern-formation-and-stability-in-magnetic-colloids",title:"Pattern Formation and Stability in Magnetic Colloids",fullTitle:"Pattern Formation and Stability in Magnetic Colloids"},signatures:"Abiodun A. Opanuga, Olasunmbo O. Agboola, Hilary I. Okagbue and Sheila A. Bishop",authors:null}],mostDownloadedChaptersLast30Days:[{id:"67203",title:"Introductory Chapter: Swirling Flows and Flames",slug:"introductory-chapter-swirling-flows-and-flames",totalDownloads:1643,totalCrossrefCites:0,totalDimensionsCites:2,abstract:null,book:{id:"7409",slug:"swirling-flows-and-flames",title:"Swirling Flows and Flames",fullTitle:"Swirling Flows and Flames"},signatures:"Toufik Boushaki",authors:[{id:"101545",title:"Dr.",name:"Toufik",middleName:null,surname:"Boushaki",slug:"toufik-boushaki",fullName:"Toufik Boushaki"}]},{id:"59009",title:"Thermal Transport and Challenges on Nanofluids Performance",slug:"thermal-transport-and-challenges-on-nanofluids-performance",totalDownloads:1724,totalCrossrefCites:4,totalDimensionsCites:15,abstract:"Progress in technology and industrial developments demands the efficient and successful energy utilization and its management in a greater extent. Conventional heat-transfer fluids (HTFs) such as water, ethylene glycol, oils and other fluids are typically low-efficiency heat dissipation fluids. Thermal management is a key factor in diverse applications where these fluids can be used, such as in automotive, microelectronics, energy storage, medical, and nuclear cooling among others. Furthermore, the miniaturization and high efficiency of devices in these fields demand successful heat management and energy-efficient materials. The advent of nanofluids could successfully address the low thermal efficiency of HTFs since nanofluids have shown many interesting properties, and the distinctive features offering extraordinary potential for many applications. Nanofluids are engineered by homogeneously suspending nanostructures with average sizes below 100 nm within conventional fluids. This chapter aims to focus on a detail description of the thermal transport behavior, challenges and implications that involve the development and use of HTFs under the influence of atomistic-scale structures and industrial applications. Multifunctional characteristics of these nanofluids, nanostructures variables and features are discussed in this chapter; the mechanisms that promote these effects on the improvement of nanofluids thermal transport performance and the broad range of current and future applications will be included.",book:{id:"6514",slug:"microfluidics-and-nanofluidics",title:"Microfluidics and Nanofluidics",fullTitle:"Microfluidics and Nanofluidics"},signatures:"José Jaime Taha-Tijerina",authors:[{id:"182402",title:"Dr.",name:"Jose",middleName:"Jaime",surname:"Taha-Tijerina",slug:"jose-taha-tijerina",fullName:"Jose Taha-Tijerina"}]},{id:"61556",title:"Microfluidics and Nanofluidics: Science, Fabrication Technology (From Cleanrooms to 3D Printing) and Their Application to Chemical Analysis by Battery-Operated Microplasmas-On-Chips",slug:"microfluidics-and-nanofluidics-science-fabrication-technology-from-cleanrooms-to-3d-printing-and-the",totalDownloads:1850,totalCrossrefCites:6,totalDimensionsCites:10,abstract:"The science and phenomena that become important when fluid-flow is confined in microfluidic channels are initially discussed. Then, technologies for channel fabrication (ranging from photolithography and chemical etching, to imprinting, and to 3D-printing) are reviewed. The reference list is extensive and (within each topic) it is arranged chronologically. Examples (with emphasis on those from the authors’ laboratory) are highlighted. Among them, they involve plasma miniaturization via microplasma formation inside micro-fluidic (and in some cases millifluidic) channels fabricated on 2D and 3D-chips. Questions addressed include: How small plasmas can be made? What defines their fundamental size-limit? How small analytical plasmas should be made? And what is their ignition voltage? The discussion then continues with the science, technology and applications of nanofluidics. The conclusions include predictions on potential future development of portable instruments employing either micro or nanofluidic channels. Such portable (or mobile) instruments are expected to be controlled by a smartphone; to have (some) energy autonomy; to employ Artificial Intelligence and Deep Learning, and to have wireless connectivity for their inclusion in the Internet-of-Things (IoT). In essence, those that can be used for chemical analysis in the field for “bringing part of the lab to the sample” types of applications.",book:{id:"6514",slug:"microfluidics-and-nanofluidics",title:"Microfluidics and Nanofluidics",fullTitle:"Microfluidics and Nanofluidics"},signatures:"Vassili Karanassios",authors:[{id:"60925",title:"Prof.",name:"Vassili",middleName:null,surname:"Karanassios",slug:"vassili-karanassios",fullName:"Vassili Karanassios"}]},{id:"61632",title:"Application of Nanofluids for Thermal Management of Photovoltaic Modules: A Review",slug:"application-of-nanofluids-for-thermal-management-of-photovoltaic-modules-a-review",totalDownloads:1570,totalCrossrefCites:10,totalDimensionsCites:18,abstract:"Mounting temperature impedes the conversion efficiency of photovoltaic systems. Studies have shown drastic efficiency escalation of PV modules, if cooled by nanofluids. Ability of nanofluids to supplement the efficiency improvement of PV cells has sought attention of researchers. This chapter presents the magnitude of improved efficiency found by different researchers due to the cooling via nanofluids. The effect of factors (such as, nanoparticle size, nanofluid concentration, flowrate of nanofluid and geometry of channel containing nanofluid) influencing the efficiency of PV systems has been discussed. Collective results of different researchers indicate that the efficiency of the PV/T systems (using nanofluids as coolant) increases with increasing flowrate. Efficiency of these systems increases with increasing concentration of nanofluid up to a certain amount, but as the concentration gets above this certain value, the efficiency tends to decline due to agglomeration/clustering of nanoparticles. Pertaining to the most recent studies, stability of nanoparticles is still the major unresolved issue, hindering the commercial scale application of nanofluids for the cooling of PV panels. Eventually, the environmental and economic advantages of these systems are presented.",book:{id:"6514",slug:"microfluidics-and-nanofluidics",title:"Microfluidics and Nanofluidics",fullTitle:"Microfluidics and Nanofluidics"},signatures:"Hafiz Muhammad Ali, Tayyab Raza Shah, Hamza Babar and\nZargham Ahmad Khan",authors:[{id:"187624",title:"Dr.",name:"Hafiz Muhammad",middleName:null,surname:"Ali",slug:"hafiz-muhammad-ali",fullName:"Hafiz Muhammad Ali"},{id:"229676",title:"Mr.",name:"Hamza",middleName:null,surname:"Babar",slug:"hamza-babar",fullName:"Hamza Babar"},{id:"241251",title:"Mr.",name:"Tayyab",middleName:"Raza",surname:"Raza Shah",slug:"tayyab-raza-shah",fullName:"Tayyab Raza Shah"},{id:"241252",title:"Mr.",name:"Zargham Ahmad",middleName:null,surname:"Khan",slug:"zargham-ahmad-khan",fullName:"Zargham Ahmad Khan"}]},{id:"69672",title:"CFD Application for Gas Turbine Combustion Simulations",slug:"cfd-application-for-gas-turbine-combustion-simulations",totalDownloads:1264,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"The current chapter presents the use of computational fluid dynamics (CFD) for simulating the combustion process taking place in gas turbines. The chapter is based on examples and results from a series of applications developed as part of the research performed by the authors in national and European projects. There are envisaged topics like flame stability, pollutant emission prediction, and alternative fuels in the context of aviation and industrial gas turbines, growing demands for lower fuel consumption, lower emissions, and overall sustainability of such energetic machines. Details on the available numerical models and computational tools are given along with the expectation for further developing CFD techniques in the field. The chapter includes also some comparison between theoretical, numerical, and experimental results.",book:{id:"9276",slug:"computational-fluid-dynamics-simulations",title:"Computational Fluid Dynamics Simulations",fullTitle:"Computational Fluid Dynamics Simulations"},signatures:"Valeriu Vilag, Jeni Vilag, Razvan Carlanescu, Andreea Mangra and Florin Florean",authors:null}],onlineFirstChaptersFilter:{topicId:"1215",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:315,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,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:19,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{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"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,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",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,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. 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He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. 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Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"25",type:"subseries",title:"Evolutionary Computation",keywords:"Genetic Algorithms, Genetic Programming, Evolutionary Programming, Evolution Strategies, Hybrid Algorithms, Bioinspired Metaheuristics, Ant Colony Optimization, Evolutionary Learning, Hyperparameter Optimization",scope:"Evolutionary computing is a paradigm that has grown dramatically in recent years. 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For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11421,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. 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