A brief summary of robotic ultrasound. Initials: Convolutional neural network (CNN), magnetic resonance imaging (MRI), support vector machine (SVM), reinforcement learning (RL).
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Nowadays, ultrasound imaging can quickly detect diseases of different anatomical structures, including liver [1], gallbladder [2], bile duct [3], spleen [4], pancreas [5], kidney [6], adrenal gland [7], bladder [8], prostate [9], and thyroid [10]. Besides, during the global pandemic caused by COVID-19, ultrasound is largely used for the diagnosis of infected persons by detecting pleural effusion [11, 12]. However, the performance of ultrasound examination is highly dependent on the ultrasound skills of sonographers, in terms of ultrasound images, probe pose, and contact force (Figure 1). In general, the training of an eligible sonographer requires a relatively large amount of time and cases [13, 14]. In addition, the high-intensity repetitive scanning process causes a heavy burden on sonographers’ physical condition, further leading to the scarcity of ultrasound practitioners.
The medical ultrasound examination (as left figure shown) needs the dexterous manipulation of ultrasound probe (as right figure shown), which is caused by the environmental complexity in terms of ultrasound images, probe pose and contact force. (a) Clinical medical ultrasound examination. (b) Ultrasound probe.
To address these issues, many previous studies in robotics have attempted to use robots to help or even replace sonographers [15, 16, 17]. According to the extent of the system autonomy, robotic ultrasound can be categorized into three levels—teleoperated, semi-autonomous, and full-autonomous. A teleoperated robotic ultrasound system usually contains two main parts—teacher site and student site [18, 19, 20]. The motion of the student robot is completely determined by the teacher, usually a trained sonographer, through different kinds of interaction devices, including a 3D space mouse [18], inertial measurement unit (IMU) handle [20, 21], and haptic interface [21]. While for a semi-autonomous robotic ultrasound system, the motion of the student robot is only partly determined by the teacher [22, 23, 24].
For a full-autonomous robotic ultrasound system, the student robot is supposed to perform the whole process of local ultrasound scanning by itself [25, 26, 27] and the teacher robot is only used for emergencies. Until today, only part full-autonomous robotic ultrasound system has been reported in the literature [28, 29]. These robotic ultrasound systems usually focus on the scanning of certain anatomical structures, such as the abdomen [28], thyroid [26], and vertebra [29]. A comprehensive survey on robotic ultrasound is given in Table 1. Despite these achievements, there are still many obstacles to the development of the robotic ultrasound system. For example, the robustness of most systems is poor and some preparations are required before performing the examination. The key is that there is not a high-dimensional model to learn ultrasound skills (Figure 2) from the sonographer, further to guide the adjustment of the ultrasound probe.
Paper | Autonomy degree | Specific target | Modality | Guidance | Publication year |
---|---|---|---|---|---|
[18] | teleoperated | no | force, orientation, position | human | 2015 |
[19] | teleoperated | no | force, orientation, position | human | 2016 |
[20] | teleoperated | no | force, orientation, position | human | 2017 |
[21] | teleoperated | no | force, orientation, position | human | 2020 |
[22] | semi-autonomous | no | force, orientation, position, elastogram | elastogram, human | 2017 |
[23] | semi-autonomous | no | force, orientation, position, vision | CNN, human | 2019 |
[24] | semi-autonomous | yes | force, orientation, position | trajectory, human | 2019 |
[30] | semi-autonomous | yes | force, orientation, position, image | CNN, human | 2020 |
[25] | full-autonomous | yes | force, orientation, position, vision, image, MRI | vision, MRI, confidence map | 2016 |
[26] | full-autonomous | yes | force, orientation, position, image | SVM | 2017 |
[27] | full-autonomous | no | force, orientation, position, vision | vision | 2018 |
[28] | full-autonomous | yes | force, orientation, position, vision, MRI | vision, MRI | 2016 |
[29] | full-autonomous | yes | force, position, vision | RL | 2021 |
A brief summary of robotic ultrasound. Initials: Convolutional neural network (CNN), magnetic resonance imaging (MRI), support vector machine (SVM), reinforcement learning (RL).
The feedback information from three different modalities during a free-hand ultrasound scanning process. The first row represents ultrasound images. The second row represents the contact force in the z-axis between the probe and the skin, collected using a six-dimensional force/torque sensor. The third row represents the probe pose, which is collected using an inertial measurement unit (IMU).
In this chapter, we proposed a learning-based approach to represent and learn ultrasound skills from sonographers’ demonstrations, and further guide the scanning process [31]. During the learning process, the ultrasound images together with the relevant scanning variables (the probe pose and the contact force) are recorded and encapsulated into a high-dimensional model. Then, we leverage the power of deep learning to implicitly capture the relation between the quality of ultrasound images and scanning skills. During the execution stage, the learned model is used to evaluate the current quality of the ultrasound image. To obtain a high-quality ultrasound image, a sampling-based approach is used to adjust the probe motion.
The main contribution of this chapter is two-fold: 1. A multi-modal model of ultrasound scanning skills is proposed and learned from human demonstrations, which takes ultrasound images, the probe pose, and the contact force into account. 2. Based on the learned model, a sampling-based strategy is proposed to adjust the ultrasound scanning process, to obtain a high-quality ultrasound image. Note that the goal of this chapter is to offer a learning-based framework to understand and acquire ultrasound skills from human demonstrations [31]. However, it is obvious that the learned model can be ported into a robot system as well, which is our work for the next step [32].
This chapter is organized as follows. Section II presents related work in the field of ultrasound images and ultrasound scanning guidance. Section III provides the methodology of our model, including the learning process of task representation, the data acquisition process through human demonstrations, and the strategy for scanning guidance during real-time execution. Section IV describes the detailed experimental validation, with a final discussion and conclusion in Section V.
The goal of the ultrasound image evaluation is to understand images in terms of classification [33], segmenting [34], recognition [35], etc. With the rise of deep learning, many studies have attempted to process ultrasound images with the help of neural networks.
Liu et al. have summarized the extensive research results on ultrasound image processing with different network structures, including convolution neural network (CNN), recurrent neural network (RNN), auto-encoder network (AE), restricted Boltzmann’s machine (RBM), and deep belief network (DBN) [36]. From the perspective of applications, Sridar et al. have employed CNN for the main plane classification in fetal ultrasound images, considering both local and global features of the ultrasound images [37]. To judge the severity of patients, Roy et al. have collected ultrasound images of the COVID-19 patient’s lesions to train a spatial transformer network [38]. Deep learning is also adopted in the task of segmenting thyroid nodules from real-time ultrasound images [39]. While deep learning provides a superior framework to understand ultrasound images, it generally requires a large number of expert-labeled data, which can be difficult and expensive to collect.
Confidence map provides an alternative method in ultrasound image processing [40]. The confidence map is obtained through pixel-wise confidence estimation using a random walk algorithm. Chatelain et al. have devised a control law based on the ultrasound confidence map [41, 42], with the goal to adjust the in-plane rotation and motion of the probe. Confidence map is also employed to automatically determine the proper parameters for ultrasound scanning [25]. Furthermore, the advantages of the confidence maps have been demonstrated by combining with position control and force control to perform automatic position and pressure maintenance [43]. However, a confidence map is proposed with the hand-coded rules, which can not be directly used to guild the scanning motion.
While the goal of ultrasound image processing is to understand images, learning ultrasound scanning skills aims to obtain high-quality ultrasound images through the adjustment of the scanning operation. Droste et al. have used a clamping device with IMU to obtain the relation between the probe pose and the ultrasound images during ultrasound examination [44]. Li et al. have built a simulation environment based on 3D ultrasound data acquired by a robot arm mounted with an ultrasound probe [45]. However, they did not explicitly learn ultrasound scanning skills. Instead, a reinforcement learning framework is adopted to optimize the confidence map of ultrasound images, by adapting the movement of the ultrasound probe. All of the above-mentioned work only take the pose and the position of the probe as input, while in this chapter, the contact force between the probe and humans is also encoded, which is considered as a crucial factor during the ultrasound scanning process [46].
For the learning of force-relevant skills, a great variety of previous studies in robotic manipulation focused on learning the relation between force information and other task-related variables, such as the position and velocity [47], the surface electromyography [48], the task states and constraints [49], and the desired impedance [50, 51, 52]. A multi-modal representation method for contact-rich tasks has been proposed in ref. [53] to encode the concurrent feedback information from vision and touch. The method was learned through self-supervision, which can be further exploited to improve the sampling efficiency and the task success rate. To the best of our knowledge, for a multi-modal manipulation task, including feedback information from ultrasound, force, and motion, this is the first work to learn the task representation and the corresponding manipulation skills from human demonstrations.
Our goal is to learn free-hand ultrasound scanning skills from human demonstrations. We want to evaluate the multi-modal task quality of combining multiple sensory information, including ultrasound images, the probe pose, and the contact force, with the goal to extract skills from the task representation and even transferring skills across tasks. We formulate the multisensory data by a neural network, where the parameters are trained by the data supervised by human ultrasound experts. In this section, we will discuss the learning process of the task representation, the data collection procedure, and the online ultrasound scanning guidance respectively.
For a free-hand ultrasound scanning task, three types of sensory feedback are available—ultrasound images from the ultrasound machine, force feedback from a mounted F/T sensor, and the probe pose from a mounted IMU. To encapsulate the heterogeneous nature of this sensory data, we propose a domain-specific encoder to model the task, as shown in Figure 3. For the ultrasound imaging feedback, we use a VGG-16 network to encode the
The multi-modal task learning architecture with human annotations. The network takes data from three different sensors as input—The ultrasound images, force/torque (F/T), and the pose information. The data for the task learning is acquired through human demonstrations, where the ultrasound quality is evaluated by sonographers. With the trained network, the multi-modal task can be represented as a high-dimensional vector.
The multi-modal model as shown in Figure 3 has a large number of learnable parameters. To obtain the training data, we design a procedure to collect the ultrasound scanning data from human demonstrations, as shown in Figure 4. A novel probe holder is designed with intrinsically mounted sensors such as IMU and F/T sensors. A sonographer is performing the ultrasound scanning process with the probe, and the data collected during the scanning process is described as follows:
The ultrasound scanning data collected from human demonstrations. The sonographer is performing an ultrasound scanning with a specifically designed probe holder. The sensory feedback during the scanning process is recorded, including the ultrasound images from an ultrasound machine, the contact force and torque from a 6D F/T sensor, and the probe pose from an IMU sensor.
For each recorded data in the dataset
As discussed in related work, it is still challenging to model and plan complex force-relevant tasks, mainly due to the inaccurate state estimation and the lack of a dynamics model. In our case, it is difficult to explicitly model the relations among ultrasound images, the probe pose, and the contact force. Therefore, we formulate the policy of ultrasound skills as a model-free reinforcement learning problem, and the target function is as follows:
where
By choosing model-free, it requires no prior knowledge of the dynamics model of the ultrasound scanning process, namely the transition probabilities from one state (current ultrasound image) to another (next ultrasound image). More specifically, we choose Monte Carlo policy optimization [54], where the potential actions are sampled and selected directly from previous demonstrated experience, as shown in Figure 5. For the sampling, we impose a bound between
Our strategy for scanning guidance takes the current pose
In this section, we use real experiments to examine the effectiveness of our proposed approach to multi-modal task representation learning. In particular, we design experiments to verify the following two questions:
Does the force modality contribute to task representation learning?
Is the sampling-based policy effective for real data?
For the experimental setup, we used a Mindray DC-70 ultrasound machine with an imaging frame rate of 900 Hz. The ultrasound image was captured using MAGEWELL USB Capture AIO with a frame rate of 120 Hz and a resolution of
Experiments setup. (a) the ultrasound machine – Mindray DC-70. (b) the video capture device – MAGEWELL USB capture AIO. (c) Data-acquisition probe holder. (d) the computer for data collection with Intel i5 CPU and Nvidia GTX 1650 GPU, Ubuntu16.04 LTS.
As shown in Figure 4, the IMU mounted on the ultrasound probe was ICM20948 and the microcontroller unit (MCU) was STM32F411. The highest frequency of IMU could reach 200 Hz, with an acceleration accuracy of 0.02 g and a gyroscope accuracy of
To make collected data comparable, the recording program needs to implement two functions—coordinate transformation and gravity compensation. The IMU will start to work as soon as the power is turned on. At that time, the probe pose corresponds to the initial coordinate system, so the quaternion’s values are equal to (1, 0, 0, 0) and the rotation matrix is the identity matrix. However, it will take some time from the wiring of the whole system to recording data, that is, the quaternion’s values at the beginning of recording are never equal to the initial ones. To solve this problem, the coordinate transformation is necessary so that the original pose corresponds to the initial coordinate system. Besides, the force/torque signal contains the contact force with the device’s gravity, which means our program should have the function of gravity compensation.
The real-time quaternion
The gravity components
In this experiment, we mainly consider the influence of force, so simply record original values of torque. The force/torque sensor’s output signal contains real-time force components
It is worth noting that gravity
The recording frequency is 10 Hz and the accuracy of gravity compensation is 0.5 N. The ultrasound data were collected at the Hospital of Wuhan University. The sonographer was asked to scan the left kidneys of
The snapshots of human ultrasound scanning demonstrations and samples of the obtained ultrasound images. Here the images (e) and (f) are labeled as good quality while (g) and (h) are labeled as bad quality.
The trajectories of the recorded force and pose during an ultrasound examination. Force component in (a) X direction (b) Y direction (c) Z direction; rotation axis: (d) X Axis (e) Y Axis (f) Z Axis.
The detailed architecture of our network is shown in Figure 9. In this case, the 256-dimensional vector denotes the feature vector presented in Figure 3. We started the training process with a warm start to classify the ultrasound images. The adopted neural network was VGG-16 with cross-entropy loss. A totla of 5995 sets of recorded data were divided into 8:2 for training and validation. Data for training included ultrasound images and labels. The learning rate was 0.001 and the batch size was 20. For the ultrasound skill evaluation, data for training included images
Framework of the neural network. The ultrasound images were encoded with VGG-16. Four fully connected layers were added to transform
(a) Accuracy and (b) loss in training the neural network for ultrasound image classification. (c) Accuracy and (d) loss in training the neural network for ultrasound skills evaluation.
To confirm the correlation between
Accuracy of four networks in validation. Net1 was trained with
Online ultrasound scanning skill guidance: We selected some continuous data streams from the dataset for verification, which had not been used for training the neural network. The sampling process in Figure 5 was repeated
Figure 12 presents predicted results about components of contact force, compared with ground truth data. Figure 13 presents the predicted probe pose with corresponding ultrasound images. Figure 14 presents predicted and true probe poses with corresponding ultrasound images.
Predicted force’s component in (a) X-axis direction. (b) Y-axis direction. (c) Z-axis direction.
Predicted probe pose and corresponding ultrasound images. The confidence is the probability of label 1.
Predicted and true probe pose, with corresponding ultrasound images. The confidence is the probability of label 1.
This chapter provides a general approach to realizing autonomous ultrasound guidance with some merits as follows: (1) The clinical ultrasound skills are considered as a multi-modal model without any unique factor or parameter, namely, it could be used in most robotic ultrasound systems. (2) The ultrasound skills are mapped into low-dimensional vectors, which makes our approach more flexible with other machine learning methods, such as support vector machine, Gaussian mixture model, and k-nearest neighbors algorithm. (3) The autonomous ultrasound examinations are defined as roughly solving the proposed target function by Monte Carlo method, which provides a newborn and robust method to fulfill autonomous ultrasound.
There are some limitations in this chapter. First, the online guidance method is based on random sampling, which leads to a certain degree of randomness. Therefore, there is a certain difference between forecast results and true values in the short term. Second, to ensure the effectiveness of the sampling, a large number of samples are required, which means a higher task quality improvement would require more computation cost. With the expedition of the dataset, this method is difficult to meet the requirement of timely guidance, which can be solved by denoting the feasible set as a probabilistic model to acquire better sampling efficiency. Finally, we believe that through detailed adjustments to the neural network, the efficiency of this model has the opportunity to be greatly improved without losing too much accuracy.
This chapter presents a framework for learning ultrasound scanning skills from human demonstrations. By analyzing the scanning process of sonographers, we define the entire scanning process as a multi-modal model of interactions between ultrasound images, the probe pose, and the contact force. A deep-learning-based method is proposed to learn ultrasound scanning skills, from which the skill-representing target function with a sampling-based strategy for ultrasound examination guidance is proposed. Experimental results show that this framework for ultrasound scanning guidance is robust, and presents the possibility of developing a real-time learning guidance system. In future work, we will speed up the prediction process by taking advantage of self-supervision, with the goal to port the learned guidance model into a real robot system.
This work was supported by Suzhou key industrial technology innovation project (SYG202121), and the Natural Science Foundation of Jiangsu Province (Grant No. BK20180235).
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In this chapter the implications of ECM in tissue remodeling are mainly discuss on the neuronal regeneration and wound healing mechanism in the presence of human umbilical mesenchymal conditioned medium (HU-MSCM).",book:{id:"6475",slug:"tissue-regeneration",title:"Tissue Regeneration",fullTitle:"Tissue Regeneration"},signatures:"Dwi Liliek Kusindarta and Hevi Wihadmadyatami",authors:null},{id:"61602",title:"The Role of Vascular Smooth Muscle Cells in the Physiology and Pathophysiology of Blood Vessels",slug:"the-role-of-vascular-smooth-muscle-cells-in-the-physiology-and-pathophysiology-of-blood-vessels",totalDownloads:2807,totalCrossrefCites:17,totalDimensionsCites:30,abstract:"Vascular smooth muscle cells (VSMCs) play important roles not only in the physiological functions of the blood vessels, such as vasoconstriction, vasodilatation and extracellular matrix production, but also in the pathogenesis of vascular diseases, particularly atherosclerosis and hypertension. VSMCs are mostly of mesodermal origin, although some are of neuroectodermal origin, for example, VSMCs present in the aorta and in blood vessels arising from the aortic arch. VSMCs of neuroectodermal origin are implicated in defects of cardiovascular morphogenesis, such as bicuspid aortic valve, coarctation of the aorta, patent ductus arteriosus and tetralogy of Fallot. The origin, location in the vascular tree, gender, species, strain and age influence the phenotype of VSMCs and their propensity to migration and growth. In a healthy adult organism, VSMCs have a quiescent and differentiated contractile phenotype characterized by early markers (e.g., SM α-actin, SM22-α), intermediate markers (h-caldesmon, calponin) and late markers (SM myosins, smoothelin) of VSMC differentiation. 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He also has an honorary appointment to serve as a Collaborative Professor at Kanazawa University, Japan, from Mar 2015 to the present. \nFormerly, Dr. Rahman was a faculty member of the University of Chittagong, Bangladesh, affiliated with the Department of Chemistry (Oct 2002 to Mar 2012) and the Department of Applied Chemistry and Chemical Engineering (Mar 2012 to Sep 2015). Dr. Rahman was also adjunctly attached with Kanazawa University, Japan (Visiting Research Professor, Dec 2014 to Mar 2015; JSPS Postdoctoral Research Fellow, Apr 2012 to Mar 2014), and Tokyo Institute of Technology, Japan (TokyoTech-UNESCO Research Fellow, Oct 2004–Sep 2005). \nHe received his Ph.D. degree in Environmental Analytical Chemistry from Kanazawa University, Japan (2011). He also achieved a Diploma in Environment from the Tokyo Institute of Technology, Japan (2005). Besides, he has an M.Sc. degree in Applied Chemistry and a B.Sc. degree in Chemistry, all from the University of Chittagong, Bangladesh. \nDr. Rahman’s research interest includes the study of the fate and behavior of environmental pollutants in the biosphere; design of low energy and low burden environmental improvement (remediation) technology; implementation of sustainable waste management practices for treatment, handling, reuse, and ultimate residual disposition of solid wastes; nature and type of interactions in organic liquid mixtures for process engineering design applications.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"201020",title:"Dr.",name:"Zinnat Ara",middleName:null,surname:"Begum",slug:"zinnat-ara-begum",fullName:"Zinnat Ara Begum",profilePictureURL:"https://mts.intechopen.com/storage/users/201020/images/system/201020.jpeg",biography:"Zinnat A. 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He was also invited to serve as an associate editor for special issues of the Journal of the American Water Resources Association. He has served as an editorial member for international journals such as Hydrology, Journal of Ecology & Natural Resources, and Hydro Science & Marine Engineering, among others. He has chaired or acted as a technical committee member for twenty-five international forums (conferences). Dr. Shang graduated from Tsinghua University, China, in 2010 with a Ph.D. in Engineering. Prior to that, he worked as a research fellow at Harvard University from 2008 to 2009. 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Pelser and Rujeko Samanthia Chimukuche",slug:"climate-change-rural-livelihoods-and-human-well-being-experiences-from-kenya",totalDownloads:17,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Vegetation Dynamics, Changing Ecosystems and Human Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11663.jpg",subseries:{id:"40",title:"Ecosystems and Biodiversity"}}}]},overviewPagePublishedBooks:{paginationCount:1,paginationItems:[{type:"book",id:"10843",title:"Persistent Organic Pollutants (POPs)",subtitle:"Monitoring, Impact and Treatment",coverURL:"https://cdn.intechopen.com/books/images_new/10843.jpg",slug:"persistent-organic-pollutants-pops-monitoring-impact-and-treatment",publishedDate:"April 13th 2022",editedByType:"Edited by",bookSignature:"Mohamed Nageeb Rashed",hash:"f5b1589f0a990b6114fef2dadc735dd9",volumeInSeries:1,fullTitle:"Persistent Organic Pollutants (POPs) - Monitoring, Impact and Treatment",editors:[{id:"63465",title:"Prof.",name:"Mohamed Nageeb",middleName:null,surname:"Rashed",slug:"mohamed-nageeb-rashed",fullName:"Mohamed Nageeb Rashed",profilePictureURL:"https://mts.intechopen.com/storage/users/63465/images/system/63465.gif",biography:"Prof. Mohamed Nageeb Rashed is Professor of Analytical and Environmental Chemistry and former vice-dean for environmental affairs, Faculty of Science, Aswan University, Egypt. He received his Ph.D. in Environmental Analytical Chemistry from Assiut University, Egypt, in 1989. His research interest is in analytical and environmental chemistry with special emphasis on: (1) monitoring and assessing biological trace elements and toxic metals in human blood, urine, water, crops, vegetables, and medicinal plants; (2) relationships between environmental heavy metals and human diseases; (3) uses of biological indicators for monitoring water pollution; (4) environmental chemistry of lakes, rivers, and well water; (5) water and wastewater treatment by adsorption and photocatalysis techniques; (6) soil and water pollution monitoring, control, and treatment; and (7) advanced oxidation treatment. Prof. Rashed has supervised several MSc and Ph.D. theses in the field of analytical and environmental chemistry. He served as an examiner for several Ph.D. theses in analytical chemistry in India, Kazakhstan, and Botswana. He has published about ninety scientific papers in peer-reviewed international journals and several papers in national and international conferences. He participated as an invited speaker at thirty international conferences. Prof. Rashed is the editor-in-chief and an editorial board member for several international journals in the fields of chemistry and environment. He is a member of several national and international societies. He received the Egyptian State Award for Environmental Research in 2001 and the Aswan University Merit Award for Basic Science in 2020. Prof. Rashed was recognized in Stanford University’s list of the World’s Top 2% Scientists in 2020 and 2021.",institutionString:null,institution:{name:"Aswan University",institutionURL:null,country:{name:"Egypt"}}}]}]},openForSubmissionBooks:{paginationCount:1,paginationItems:[{id:"11478",title:"Recent Advances in the Study of Dyslexia",coverURL:"https://cdn.intechopen.com/books/images_new/11478.jpg",hash:"26764a18c6b776698823e0e1c3022d2f",secondStepPassed:!1,currentStepOfPublishingProcess:2,submissionDeadline:"June 30th 2022",isOpenForSubmission:!0,editors:[{id:"294281",title:"Prof.",name:"Jonathan",surname:"Glazzard",slug:"jonathan-glazzard",fullName:"Jonathan Glazzard"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{paginationCount:54,paginationItems:[{id:"81595",title:"Prosthetic Concepts in Dental Implantology",doi:"10.5772/intechopen.104725",signatures:"Ivica Pelivan",slug:"prosthetic-concepts-in-dental-implantology",totalDownloads:22,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Current Concepts in Dental Implantology - From Science to Clinical Research",coverURL:"https://cdn.intechopen.com/books/images_new/10808.jpg",subseries:{id:"2",title:"Prosthodontics and Implant Dentistry"}}},{id:"80963",title:"Pain Perception in Patients Treated with Ligating/Self-Ligating Brackets versus Patients Treated with Aligners",doi:"10.5772/intechopen.102796",signatures:"Farid Bourzgui, Rania Fastani, Salwa Khairat, Samir Diouny, Mohamed El Had, Zineb Serhier and Mohamed Bennani Othmani",slug:"pain-perception-in-patients-treated-with-ligating-self-ligating-brackets-versus-patients-treated-wit",totalDownloads:21,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Current Trends in Orthodontics",coverURL:"https://cdn.intechopen.com/books/images_new/10780.jpg",subseries:{id:"2",title:"Prosthodontics and Implant Dentistry"}}},{id:"80964",title:"Upper Airway Expansion in Disabled Children",doi:"10.5772/intechopen.102830",signatures:"David Andrade, Joana Andrade, Maria-João Palha, Cristina Areias, Paula Macedo, Ana Norton, Miguel Palha, Lurdes Morais, Dóris Rocha Ruiz and Sônia Groisman",slug:"upper-airway-expansion-in-disabled-children",totalDownloads:35,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Oral Health Care - An Important Issue of the Modern Society",coverURL:"https://cdn.intechopen.com/books/images_new/10827.jpg",subseries:{id:"1",title:"Oral Health"}}},{id:"80839",title:"Herbs and Oral Health",doi:"10.5772/intechopen.103715",signatures:"Zuhair S. 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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. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. 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. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. 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. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. 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:"24",type:"subseries",title:"Computer Vision",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11420,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. 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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,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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