Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (326 pages)

ISBN: 0-323-90664-8

Series Statement: Intelligent Data-Centric Systems

Content: 5G IoT and Edge Computing for Smart Healthcare addresses the importance of a 5G IoT and Edge-Cognitive-Computing-based system for the successful implementation and realization of a smart-healthcare system. The book provides insights on 5G technologies, along with intelligent processing algorithms/processors that have been adopted for processing the medical data that would assist in addressing the challenges in computer-aided diagnosis and clinical risk analysis on a real-time basis. Each chapter is self-sufficient, solving real-time problems through novel approaches that help the audience acquire the right knowledge. With the progressive development of medical and communication - computer technologies, the healthcare system has seen a tremendous opportunity to support the demand of today's new requirements. Focuses on the advancement of 5G in terms of its security and privacy aspects, which is very important in health care systems Address advancements in signal processing and, more specifically, the cognitive computing algorithm to make the system more real-time Gives insights into various information-processing models and the architecture of layers to realize a 5G based smart health care system.

Note: Front Cover -- 5G IoT and Edge Computing for Smart Healthcare -- Copyright Page -- Contents -- List of contributors -- 1 Edge-IoMT-based enabled architecture for smart healthcare system -- 1.1 Introduction -- 1.2 Applications of an IoMT-based system in the healthcare industry -- 1.3 Application of edge computing in smart healthcare systems -- 1.4 Challenges of using edge computing with IoMT-based system in smart healthcare system -- 1.5 The framework for edge-IoMT-based smart healthcare system -- 1.6 Case study for the application of edge-IoMT-based systems enabled for the diagnosis of diabetes mellitus -- 1.6.1 Experimental results -- 1.7 Future prospects of edge computing for internet of medical things -- 1.8 Conclusions and future research directions -- References -- 2 Physical layer architecture of 5G enabled IoT/IoMT system -- 2.1 Architecture of IoT/IoMT system -- 2.1.1 Sensor layer -- 2.1.2 Gateway layer -- 2.1.3 Network layer -- 2.1.4 Visualization layer -- 2.2 Consideration of uplink healthcare IoT system relying on NOMA -- 2.2.1 Introduction -- 2.2.2 System model -- 2.2.3 Outage probability for UL NOMA -- 2.2.3.1 Outage probability of x1 -- 2.2.3.2 Outage probability of X2 -- 2.2.3.3 Asymptotic -- 2.2.4 Ergodic capacity of UL NOMA -- 2.2.5 Numerical results and discussions -- 2.3 Conclusions -- References -- 3 HetNet/M2M/D2D communication in 5G technologies -- 3.1 Introduction -- 3.2 Heterogenous networks in the era of 5G -- 3.2.1 5G mobile communication standards and enhanced features -- 3.2.2 5G heterogeneous network architecture -- 3.2.3 Intelligent software defined network framework of 5G HetNets -- 3.2.4 Next-Gen 5G wireless network -- 3.2.5 Internet of Things toward 5G and heterogenous wireless networks -- 3.2.6 5G-HetNet H-CRAN fronthaul and TWDM-PON backhaul: QoS-aware virtualization for resource management. , 3.2.7 Spectrum allocation and user association in 5G HetNet mmWave communication: a coordinated framework -- 3.2.8 Diverse service provisioning in 5G and beyond: an intelligent self-sustained radio access network slicing framework -- 3.3 Device-to-Device communication in 5G HetNets -- 3.4 Machine-to-Machine communication in 5G HetNets -- 3.4.1 Machine-to-Machine communication in 5G: state of the art architecture, recent advances and challenges -- 3.4.2 Recent advancement in the Internet of Things related standard: oneM2M perspective -- 3.4.2.1 Advantages of oneM2M -- 3.4.2.2 OneM2M protocols -- 3.4.2.3 OneM2M standard platform: a unified common service-oriented communication framework -- 3.4.3 M2M traffic in 5G HetNets -- 3.4.4 Distributed gateway selection for M2M communication cognitive 5G5G networks -- 3.4.5 Algorithm for clusterization, aggregation, and prioritization of M2M devices in 5G5G HetNets -- 3.5 Heterogeneity and interoperability -- 3.5.1 User interoperability -- 3.5.1.1 Locating the device through identification and classification -- 3.5.1.2 Syntactic and semantic interoperability for interconnecting devices -- 3.5.2 Device interoperability -- 3.6 Research issues and challenges -- 3.6.1 Resource allocation -- 3.6.2 Interference management -- 3.6.3 Power allocation -- 3.6.4 User association -- 3.6.5 Computational complexity and multiaccess edge computing -- 3.6.6 Current research in HetNet based on various technologies -- 3.7 Smart healthcare using 5G5G Inter of Things: a case-study -- 3.7.1 Mobile cellular network architecture: 5th generation -- 3.7.1.1 5G5G system architecture -- 3.7.1.2 Master core technology -- 3.7.2 ZigBee IP -- 3.7.3 Healthcare system architecture using wireless sensor network and mobile cellular network -- 3.7.3.1 System protocol -- 3.7.3.2 Data transmission by 5G terminal in ZigBee network. , 3.7.3.3 Data transmission through 5G terminal by ZigBee network -- 3.8 Conclusions -- References -- 4 An overview of low power hardware architecture for edge computing devices -- 4.1 Introduction -- 4.2 Basic concepts of cloud, fog and edge computing infrastructure -- 4.2.1 Role of edge computing in Internet of Things -- 4.2.2 Edge intelligence and 5G in Internet of Things based smart healthcare system -- 4.3 Low power hardware architecture for edge computing devices -- 4.3.1 Objectives of hardware development in edge computing -- 4.3.2 System architecture -- 4.3.3 Central processing unit architecture -- 4.3.4 Input-output architecture -- 4.3.5 Power consumption -- 4.3.6 Data processing and algorithmic optimization -- 4.4 Examples of edge computing devices -- 4.5 Edge computing for intelligent healthcare applications -- 4.5.1 Edge computing for healthcare applications -- 4.5.2 Advantages of edge computing for healthcare applications -- 4.5.3 Implementation challenges of edge computing in healthcare systems -- 4.5.4 Applications of edge computing based healthcare system -- 4.5.5 Patient data security in edge computing -- 4.6 Impact of edge computing, Internet of Things and 5G on smart healthcare systems -- 4.7 Conclusion and future scope of research -- References -- 5 Convergent network architecture of 5G and MEC -- 5.1 Introduction -- 5.2 Technical overview on 5G network with MEC -- 5.2.1 5G with multi-access edge computing (MEC): a technology enabler -- 5.2.2 Application splitting in MEC -- 5.2.3 Layered service oriented architecture for 5G MEC -- 5.3 Convergent network architecture for 5G with MEC -- 5.4 Current research in 5G with MEC -- 5.5 Challenges and issues in implementation of MEC -- 5.5.1 Communication and computation perspective -- 5.5.1.1 MEC service orchestration and programmability. , 5.5.1.2 MEC service continuity and mobility and service enhancements -- 5.5.1.3 MEC security and privacy -- 5.5.1.4 Standardization of protocols -- 5.5.1.5 MEC service monetization -- 5.5.1.6 Edge cloud infrastructure and resource management -- 5.5.1.7 Mobile data offloading -- 5.5.2 Application perspective -- 5.5.2.1 Industrial IoT application in 5G -- 5.5.2.2 Large scale healthcare and big data management -- 5.5.2.3 Integration of AI and 5G for MEC enabled healthcare application -- 5.6 Conclusions -- References -- 6 An efficient lightweight speck technique for edge-IoT-based smart healthcare systems -- 6.1 Introduction -- 6.2 The Internet of Things in smart healthcare system -- 6.2.1 Support for diagnosis treatment -- 6.2.2 Management of diseases -- 6.2.3 Risk monitoring and prevention of disease -- 6.2.4 Virtual support -- 6.2.5 Smart healthcare hospitals support -- 6.3 Application of edge computing in smart healthcare system -- 6.4 Application of encryptions algorithm in smart healthcare system -- 6.4.1 Speck encryption -- 6.5 Results and discussion -- 6.6 Conclusions and future research directions -- References -- 7 Deep learning approaches for the cardiovascular disease diagnosis using smartphone -- 7.1 Introduction -- 7.2 Disease diagnosis and treatment -- 7.3 Deep learning approaches for the disease diagnosis and treatment -- 7.3.1 Artificial neural networks -- 7.3.2 Deep learning -- 7.3.3 Convolutional Neural Networks -- 7.4 Case study of a smartphone-based Atrial Fibrillation Detection -- 7.4.1 Smartphone data acquisition -- 7.4.2 Biomedical signal processing -- 7.4.3 Prediction and classification -- 7.4.4 Experimental data -- 7.4.5 Performance evaluation measures -- 7.4.6 Experimental results -- 7.5 Discussion -- 7.6 Conclusion -- References -- 8 Advanced pattern recognition tools for disease diagnosis -- 8.1 Introduction. , 8.2 Disease diagnosis -- 8.3 Pattern recognition tools for the disease diagnosis -- 8.3.1 Artificial neural networks -- 8.3.2 K-nearest neighbor -- 8.3.3 Support vector machines -- 8.3.4 Random forests -- 8.3.5 Bagging -- 8.3.6 AdaBoost -- 8.3.7 XGBoost -- 8.3.8 Deep learning -- 8.3.9 Convolutional neural network -- 8.3.10 Transfer learning -- 8.4 Case study of COVID-19 detection -- 8.4.1 Experimental data -- 8.4.2 Performance evaluation measures -- 8.4.3 Feature extraction using transfer learning -- 8.4.4 Experimental results -- 8.5 Discussion -- 8.6 Conclusions -- References -- 9 Brain-computer interface in Internet of Things environment -- 9.1 Introduction -- 9.1.1 Components of BCI -- 9.1.2 Types of BCI -- 9.1.3 How does BCI work? -- 9.1.4 Key features of BCI -- 9.1.5 Applications -- 9.2 Brain-computer interface classification -- 9.2.1 Noninvasive BCI -- 9.2.2 Semiinvasive or partially invasive BCI -- 9.2.3 Invasive BCI -- 9.3 Key elements of BCI -- 9.3.1 Signal acquisition -- 9.3.2 Preprocessing or signal enhancement -- 9.3.3 Feature extraction -- 9.3.4 Classification stage -- 9.3.5 Feature translation or control interface stage -- 9.3.6 Device output or feedback stage -- 9.4 Modalities of BCI -- 9.4.1 Electrical and magnetic signals -- 9.4.1.1 Intracortical electrode array -- 9.4.1.2 Electrocorticography -- 9.4.1.3 Electroencephalography -- 9.4.1.4 Magnetoencephalography -- 9.4.2 Metabolic signals -- 9.4.2.1 Positron emission tomography -- 9.4.2.2 Functional magnetic resonance imaging -- 9.4.2.3 Functional near-infrared spectroscopy -- 9.5 Computational intelligence methods in BCI/BMI -- 9.5.1 State of the prior art -- 9.5.1.1 Preprocessing -- 9.5.1.2 Feature extraction -- 9.5.1.3 Feature classification -- 9.5.1.4 Performance evaluation of BCI systems -- 9.6 Online and offline BCI applications -- 9.7 BCI for the Internet of Things. , 9.8 Secure brain-brain communication.

Additional Edition: Print version: Bhoi, Akash Kumar 5G IoT and Edge Computing for Smart Healthcare San Diego : Elsevier Science & Technology,c2022 ISBN 9780323905480

Language: English

UID:

Format: 1 online resource (402 pages)

Edition: 1st ed.

ISBN: 0-443-19414-9

Note: Front Cover -- Deep Learning in Personalized Healthcare and Decision Support -- Deep Learning in Personalized Healthcare and Decision Support -- Copyright -- Contents -- Contributors -- Preface -- Acknowledgments -- 1 - The future of health diagnosis and treatment: an exploration of deep learning frameworks and innovative applica ... -- 1. Introduction -- 2. Computational deep learning frameworks for health monitoring -- 3. Advanced architectures and core concepts of deep learning in smart health -- 4. Comparative analysis of deep learning frameworks for different disease detection -- 5. Advantages of deep learning in smart medical healthcare analytics -- 6. Deep leering applications for disease prediction -- 7. Deep learning in research and development -- 8. Future challenges of deep learning in smart health diagnosis and treatment -- 9. Limitations of deep learning frameworks -- 10. Conclusion and future scope -- References -- 2 - Fermatean fuzzy approach of diseases diagnosis based on new correlation coefficient operators -- 1. Introduction -- 2. Fermatean fuzzy sets and their correlation operators -- 2.1 Fermatean fuzzy sets -- 2.2 Existing correlation operators for Fermatean fuzzy sets -- 3. New Fermatean fuzzy correlation operators -- 3.1 Computational example -- 4. Application example of medical diagnosis -- 5. Conclusion -- References -- 3 - Application of Deep-Q learning in personalized health care Internet of Things ecosystem -- 1. Introduction -- 2. Related work -- 3. Proposed mechanism -- 4. Experimental results -- 5. Future directions -- 6. Conclusion -- References -- 4 - Dia-Glass: a calorie-calculating spectacles for diabetic patients using augmented reality and faster R-CNN -- 1. Introduction -- 2. Related works -- 3. Diabetes: categories, concerns and prevalence -- 3.1 Categories -- 3.2 Concerns -- 3.3 Diabetes prevalence. , 4. System methodology -- 4.1 User information insertion module -- 4.2 Data acquisition through spectacles -- 4.3 Food recognition using faster R-CNN -- 4.4 Calorie estimation -- 4.5 Information rendering and user notification -- 5. Result and analysis -- 5.1 Dataset -- 5.2 Performance analysis -- 6. Conclusion -- References -- 5 - Synthetic medical image augmentation: a GAN-based approach for melanoma skin lesion classification with deep le ... -- 1. Introduction -- 1.1 Background and motivation -- 1.2 Contributions -- 1.3 Related works -- 2. Skin lesion classification methodology -- 2.1 Dataset -- 2.2 CNN architecture with modified VGG16 -- 3. Generation of synthetic skin lesions -- 3.1 Traditional data augmentation -- 3.2 Generative adversarial networks for skin lesion synthesis -- 3.3 Conditional skin lesion synthesis -- 4. Experimental results -- 4.1 Dataset evaluation and performance metrics -- 4.2 Implementation specifications -- 4.3 Training and test data -- 5. Performance comparison -- 6. Conclusion and future work -- 7. Conflict of interest -- References -- 6 - Artificial intelligence representation model for drug-target interaction with contemporary knowledge and develo ... -- 1. Introduction -- 1.1 AI will challenge the status Quo in healthcare -- 2. AI privacy and security challenges -- 2.1 Ensuring transparency, explain ability, and intelligibility -- 2.1.1 Algorithmic fairness and biases -- 2.1.2 Data availability -- 2.1.3 Privacy concerns -- 3. Ensuring transparency, explain ability, and intelligibility -- 3.1 Data availability -- 3.2 Concerns regarding privacy -- 4. Drug discovery and precision medicine with deep learning -- 4.1 Discrimination and unequal treatment -- 4.2 The production of data and its availability -- 4.3 The supervision of quality -- 5. Clinical decision support and predictive analytics. , 5.1 Natural language processing could translate EHR jargon for patients -- 5.2 Faster drug screening in the future -- 5.3 Machine learning predictions rely on input data -- 5.4 Connection between quantifiable construction and function -- 5.4.1 Support for clinical decision-making and predictive analytics are included in this section -- 5.4.2 In order to make prescriptive modeling useful, they must be put into practice -- 6. Natural language processing in drug -- 7. Predictive analytics has a wide range of practical applications, including the following -- 7.1 Efforts made to lessen potential dangers to healthcare organizations' security -- 7.2 Future of deep learning in healthcare -- 8. Conclusion -- References -- 7 - Review of fog and edge computing-based smart health care system using deep learning approaches -- 1. Introduction -- 2. Literature review -- 3. Healthcare using artificial intelligence -- 4. Efficient health care system with improved performance -- 4.1 Dataset -- 5. Conclusion -- References -- 8 - Deep learning in healthcare: opportunities, threats, and challenges in a green smart environment solution for s ... -- 1. Introduction -- 1.1 Major findings and motivation -- 1.2 The coal crisis creates the need for alternatives [2] -- 1.3 With minor delays caused by COVID-19, renewable volumes at auction continue to break records [3] -- 2. Green infrastructure measures in the legislature [4] -- 2.1 Approaches within the direction of a green economy -- 2.1.1 An account of the ongoing power situation in Iceland: a global paradigm [5] -- 2.1.2 Saudi Arabia's Vision 2030 [6,7] -- 2.1.3 NEOM [8] -- 2.1.4 Saudi Arabia, Line [9,10] -- 3. Employment creation as part of the sustainable recovery -- 3.1 Organic agriculture has the ability to create jobs [12] -- 3.1.1 Brief explanation -- 4. Carbon power. , 4.1 There are many rehabilitation strategies that have a favorable impact on the environment -- 4.2 Benefits -- 5. Smart buildings [15] -- 6. Climate change disclosure laws [16] -- 7. The action of biodiversity [17] -- 7.1 Smart houses and smart buildings: weather [17] -- 7.1.1 Measures -- 7.1.2 Benefits -- 7.1.3 Concise description -- 8. Case study -- 8.1 Acceptable approved cases of integrating biodiversity in response to COVID-19 and rehabilitation programs [18] -- 8.2 Securing Georgia's forest by space [19,20] -- 8.3 Kazakhstan needs waste management and community well-being technologies [21] -- 9. Future pandemic preparedness [22] -- 9.1 We should be concerned about the legal wildlife trade in order to prevent the next pandemic [23] -- 10. Recent literature -- 10.1 A comparative analysis of data-driven based optimization models for energy-efficient buildings [24] -- 10.2 Machine learning forecasting model for the COVID-19 pandemic in India [25] -- 10.3 AI-based building management and information system with multi-agent topology for an energy-efficient building: toward occu ... -- 11. Conclusions -- 11.1 Advantages -- 11.2 Limitations -- References -- Further reading -- 9 - Hybrid and automated segmentation algorithm for malignant melanoma using chain codes and active contours -- 1. Introduction -- 1.1 Motivation and contribution -- 1.2 Related works -- 1.3 Paper organization -- 2. Materials and methods -- 2.1 Datasets -- 2.2 Image enhancement -- 3. Proposed methodology -- 3.1 Preprocessing phase -- 3.2 h-CEAC segmentation -- 3.2.1 Feature extraction and chain codes -- 3.2.2 Formulation of the chain code -- 3.2.3 Pixel interdependence -- 3.2.4 EDRS and active contours -- 4. Results and discussions -- 4.1 Comparison with existing algorithms -- 5. Conclusion and future scope -- References. , 10 - Development of a predictive model for classifying colorectal cancer using principal component analysis -- 1. Introduction -- 2. Related works -- 3. Methodology -- 3.1 Experimental dataset -- 3.2 Dimensionality reduction tool -- 3.3 Classification -- 3.3.1 Support vector machine -- 3.3.2 K-nearest neighbor -- 3.3.3 Random forest -- 3.4 Research tool -- 3.5 Performance evaluation metrics -- 4. Results and discussions -- 5. Conclusion -- References -- 11 - Using deep learning via long-short-term memory model prediction of COVID-19 situation in India -- 1. Introduction -- 1.1 Research gaps and motivation -- 2. Literature review -- 2.1 Symptoms of COVID-19 -- 3. How to protect yourself from COVID-19 -- 3.1 High-risk groups -- 4. Facts about the vaccine against the COVID-19 -- 4.1 Vaccines in COVID-19 -- 4.2 Here's a peek at some of the initiatives -- 4.3 Immunology and antigen detection for the COVID-19 vaccine -- 4.4 Potential vaccine-related threats -- 4.5 Vaccines have been approved in India -- 4.5.1 Covishield -- 4.5.2 Covaxin -- 4.5.3 Sputnik V -- 5. Materials and methods -- 5.1 Artificial neural network (ANN) -- 5.2 Model of a neuron -- 5.2.1 Recurrent neural network (RNN) -- 6. Results discussion -- 6.1 Top 10 states (confirmed cases and cured cases in Covid-19) -- 7. Conclusion -- References -- 12 - Post-COVID-19 Indian healthcare system: Challenges and solutions -- 1. Creation of robust healthcare system-A nationwide priority and its emergence -- 2. Pandemonium scenes -- 3. Efforts for healthcare system development -- 4. Providing treatment to all amidst difficulties -- 5. Corona warriors and their woes -- 6. Transformation of Indian healthcare sector post COVID-19 -- 7. Healthcare component 1-Hospitals -- 8. Healthcare component 2-Pharmaceutical industry -- 9. Healthcare component 3-Medical devices and equipment. , 10. Healthcare component 4-Diagnostics.

Additional Edition: Print version: Garg, Harish Deep Learning in Personalized Healthcare and Decision Support San Diego : Elsevier Science & Technology,c2023 ISBN 9780443194139

Language: English

UID:

Format: 1 online resource (296 pages)

ISBN: 0-12-821456-2

Series Statement: Woodhead Publishing Series in Biomaterials

Content: "Biomaterials have existed for millennia as mechanical replacement structures following disease or injury. Biomaterial design has changed markedly from structural support with an "inert" immune profile as the primary objective to designs that elicit an integrative local tissue response and a pro-repair immune cell phenotype. Immunomodulatory Biomaterials: Regulating the Immune Response with Biomaterials to Affect Clinical Outcome offers a single, comprehensive reference on biomaterials for modulation of the host response, for materials scientists, tissue engineers and those working in regenerative medicine. This book details methods, materials and strategies designed to regulate the host immune response following surgical implantation and thus facilitate specific local cell infiltration and tissue deposition. There has been a dramatic transformation in our understanding of the role of the immune system, both innate and adaptive; these changes include recognition of the plasticity of immune cells, especially macrophages, cross-talk between the immune system and stem cells, and the necessity for in situ transition between inflammatory and regulatory immune cell phenotypes. The exploitation of these findings and the design and manufacture of new biomaterials is occurring at an astounding pace. There is currently no book directed at the interdisciplinary principles guiding the design, manufacture, testing, and clinical translation of biomaterials that proactively regulate the host tissue immune response. The challenge for academia, industry, and regulatory agencies to encourage innovation while assuring safety and maximizing efficacy has never been greater. Given the highly interdisciplinary requirements for the design, manufacture and use of immunomodulatory biomaterials, this book will prove a useful single resource across disciplines."

Note: Includes index. , Intro -- Immunomodulatory Biomaterials: Regulating the Immune Response with Biomaterials to Affect Clinical Outcome -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Engineering physical biomaterial properties to manipulate macrophage phenotype: From bench to bedside -- 1.1. Introduction -- 1.2. Role of macrophages in tissue repair and the foreign body response -- 1.3. Modulation of macrophage function via physical biomaterial properties in vitro -- 1.3.1. Stiffness -- 1.3.2. Topography or 3D architecture -- 1.3.3. Ligand presentation or geometry of adhesion -- 1.4. Macrophage response to implanted biomaterials in vivo -- 1.4.1. Non-degradable biomaterials -- 1.4.2. Degradable biomaterials -- 1.5. Clinical insight into the effect of physical biomaterial properties on macrophages during tissue repair -- 1.5.1. Dental implants -- 1.5.2. Wound dressings -- 1.5.3. Materials for cardiovascular repair -- 1.6. Conclusions and future directions -- References -- Chapter 2: Early factors in the immune response to biomaterials -- 2.1. Introduction -- 2.2. Protein adsorption -- 2.2.1. Complement cascade -- 2.2.2. Coagulation -- 2.2.3. Immunoglobulins -- 2.2.4. Innate immunity -- 2.2.4.1. Neutrophils -- 2.2.4.2. Mast cells -- 2.2.4.3. Macrophages/monocytes -- 2.2.5. Adaptive immunity -- 2.2.5.1. Dendritic cells -- 2.2.5.2. T Cells -- 2.2.5.3. B Cells -- 2.3. Foreign body giant cells -- 2.4. Fibrous capsule -- 2.5. Signaling pathways activated -- 2.5.1. TLRs and MyD88-dependent signaling -- 2.5.2. Inflammasome activation -- 2.5.3. JAK/STAT pathway -- 2.6. Conclusion -- References -- Chapter 3: Nanotechnology and biomaterials for immune modulation and monitoring -- 3.1. Introduction -- 3.2. Autoimmunity -- 3.3. Allergy -- 3.4. Transplant rejection -- 3.5. Clinical trials of tolerogenic nanotherapies -- 3.5.1. Liposomal. , 3.5.2. Virus-like particles -- 3.5.3. Metallic -- 3.5.4. Polymeric -- 3.6. Precision diagnostics -- 3.6.1. Liquid biopsy -- 3.6.2. Immunological niches -- 3.7. Outlook and conclusion -- Acknowledgments -- References -- Chapter 4: Immune-instructive materials and surfaces for medical applications -- 4.1. Introduction -- 4.1.1. Immune cells involved in inflammation -- 4.1.2. The foreign body response -- 4.2. Naturally occurring biomaterials with immune modulatory properties and their application in wound healing and reduct ... -- 4.3. Bioinstructive synthetic materials and their application in regenerative medicine -- 4.4. Developing ``immune-instructive´´ biomaterials -- 4.5. Concluding remarks -- References -- Chapter 5: Electrospun tissue regeneration biomaterials for immunomodulation -- 5.1. Introduction -- 5.2. Acknowledging immunomodulation in tissue engineering -- 5.3. Well-studied areas -- 5.3.1. Monocytes and macrophages -- 5.3.2. Platelets -- 5.4. Areas gaining attention -- 5.4.1. Neutrophils -- 5.4.2. Mast cells -- 5.5. Areas needing attention -- 5.5.1. Dendritic cells -- 5.5.2. Eosinophils -- 5.5.3. Basophils -- 5.5.4. Natural killer cells -- 5.5.5. T cells -- 5.5.6. B cells -- 5.6. Future directions -- 5.7. Conclusion -- References -- Chapter 6: Biomaterials and immunomodulation for spinal cord repair -- 6.1. Spinal cord injury -- 6.1.1. Acute phase of SCI -- 6.1.2. Subacute phase of SCI -- 6.1.3. Chronic phase of SCI -- 6.1.4. Self-repair after SCI -- 6.1.5. Translational potential of animal models of SCI -- 6.2. Immune response after SCI -- 6.3. Immunomodulation after spinal cord injury -- 6.4. Biomaterials for spinal cord repair -- 6.5. Immunomodulatory biomaterials for spinal cord injury -- 6.5.1. Immunomodulation by surface chemistry -- 6.5.2. Immunomodulation by topography -- 6.5.3. Immunomodulation by delivering agents. , 6.5.3.1. Immunomodulation by providing biological ligands -- 6.5.3.2. Immunomodulation by delivering drugs -- 6.5.3.3. Immunomodulation by carrying cells -- 6.6. Natural immunomodulatory materials for spinal cord injury -- 6.7. Considerations and future directions -- 6.8. Conclusions and summary -- Acknowledgments -- References -- Chapter 7: Biomaterial strategies to treat autoimmunity and unwanted immune responses to drugs and transplanted tissu -- 7.1. Introduction -- 7.1.1. Burden of disease -- 7.1.2. Current treatment options and challenges -- 7.1.3. Immunological causes of aberrant immune responses -- 7.1.3.1. Immunological basis for autoimmune diseases -- 7.1.3.2. Immunological basis for transplant rejection, anti-drug antibodies, and allergies -- 7.1.4. Antigen-specific tolerance as a treatment goal -- 7.2. Scope -- 7.3. Biomaterials in development for autoimmunity and anti-drug antibodies -- 7.3.1. Lessons from trials of free peptide and free protein -- 7.3.1.1. Type 1 diabetes -- 7.3.1.2. Multiple sclerosis -- 7.3.2. Antigen delivery vehicles without additional regulatory cues -- 7.3.2.1. Antigen depots -- 7.3.2.2. Nanoparticles -- 7.3.2.3. Alternative nanoparticle vehicles -- 7.3.2.4. Targeting liver APCs -- 7.3.2.5. Targeting splenic APCs -- 7.3.3. Antigen delivery vehicles with additional regulatory cues -- 7.3.3.1. Small molecule immunomodulators -- 7.3.3.2. Cytokines -- 7.3.4. Peptide-MHC complexes -- 7.3.4.1. Soluble pMHC complexes -- 7.3.4.2. Multimeric pMHC complexes -- 7.3.4.3. Nanoparticle pMHC complexes -- 7.4. Biomaterials in development for transplant tolerance -- 7.4.1. Transplant ECDI-treated cells -- 7.4.2. PLGA scaffold with transplanted cells and additional immunomodulatory drugs -- 7.5. Future of the field -- 7.5.1. Challenges and future directions -- 7.5.1.1. Standardization of immunological goals and readouts. , 7.5.1.2. Further improvement in nanoparticle design -- 7.5.1.3. Manufacturability -- 7.5.2. Current or upcoming clinical trials -- References -- Chapter 8: Lipids as regulators of inflammation and tissue regeneration -- 8.1. Introduction -- 8.2. LC-MS based approaches to analyze lipids and their oxidation products -- 8.3. Free PUFA and their oxidation products as signals for immunomodulation and tissue regeneration -- 8.4. Oxidized phospholipids as modulators of the inflammatory response -- 8.5. Phospholipid signatures of EV -- 8.6. Hydrolysis of MBV derived oxygenated lipids and their possible role in inflammation and tissue regeneration -- References -- Chapter 9: Biomaterials modulation of the tumor immune environment for cancer immunotherapy -- 9.1. Introduction -- 9.2. Fundamentals of cancer immunology and immunotherapy -- 9.2.1. Cancer biology: Setting the stage -- 9.2.2. The role of immunity in cancer -- 9.3. Immunomodulatory biomaterials in cancer therapy -- 9.3.1. Cancer immunotherapy -- 9.3.2. Immunomodulatory biomaterials -- 9.3.3. Direct interactions between cancer and the biomaterial immune microenvironment -- 9.3.4. Biomaterial scaffold cancer vaccines -- 9.3.5. Biomaterial scaffolds for cell-based cancer immunotherapy -- 9.3.6. Immune tissue engineering -- 9.4. Summary -- References -- Chapter 10: Circumventing immune rejection and foreign body response to therapeutics of type 1 diabetes -- 10.1. Introduction -- 10.1.1. Type 1 diabetes (T1D) -- 10.1.2. Insulin and other injectable therapeutics -- 10.1.3. Biomaterials/devices -- 10.1.4. CGMs and insulin pumps -- 10.1.5. Cellular therapies -- 10.1.6. Protective immunity -- 10.2. Immune rejection for cells/grafts -- 10.2.1. General concepts for graft implementation -- 10.2.2. Transplant procedures -- 10.2.3. Human donor considerations -- 10.2.4. Alternative cell sources. , 10.2.4.1. Xenogeneic grafts -- 10.2.4.2. Allogeneic grafts -- 10.2.4.3. Syngeneic grafts -- 10.2.4.4. Autologous grafts -- 10.3. Biological hurdles to preventing graft rejection -- 10.4. Advances in eliminating rejection of non-encapsulated grafts -- 10.4.1. Edmonton protocol and anti-inflammatory strategies -- 10.4.2. Delivery of antigen/nucleotide-based drugs for rejection suppression -- 10.4.3. Engineering therapeutic cells to modulate immune response -- 10.4.4. Tolerogenic vaccines -- 10.4.5. Artificial antigen-presenting cells for inducing tolerance -- 10.5. Advances in preventing FBR to bulk encapsulation systems -- 10.5.1. Bioresorption vs. lack of biodegradability -- 10.5.2. Non-biodegradable hydrogels/alginate and stable immune isolation -- 10.5.3. Effects of altering physical architecture -- 10.5.3.1. Size and shape -- 10.5.3.2. Surface topography and selective porosity -- 10.5.4. Chemical modification of material devices -- 10.5.4.1. Identification of anti-fibrotic chemistries: Surface vs. bulk modified -- 10.5.4.2. Zwitterionic (and other polymer-based) biocompatibility coatings -- 10.5.5. Long-term controlled release systems for rejection prevention -- 10.6. Pre/clinical observations, and models for translation -- 10.6.1. Choosing the right test animal and transplant site -- 10.6.2. Blood flow and nutrient considerations for graft viability -- 10.7. Future prospects and perceived challenges/difficulties -- 10.7.1. Increasing burdens on healthcare -- 10.7.2. Population expansion and increasing age of the general human populace -- 10.7.3. Increase in emerging diseases -- 10.8. Summary/conclusion -- References -- Chapter 11: Machine learning and mechanistic computational modeling of inflammation as tools for designing immuno -- 11.1. Biomaterials, inflammation, and wound healing. , 11.2. Inflammation and wound healing as prototypical complex systems.

Additional Edition: ISBN 0-12-821440-6

Language: English

UID:

Format: 1 online resource (202 pages)

Edition: 1st ed.

ISBN: 9780443136825

Note: Front Cover -- Innovating Health Against Future Pandemics -- About the WBP book series -- Innovating Health Against Future Pandemics -- Copyright -- Dedication -- Contents -- Contributors -- Editor's biographies -- Foreword -- Acknowledgments -- Introduction -- 1. Introduction: Healthcare Innovation to prevent and manage future outbreaks effectively -- 2. The Women's Brain Project contribution: From neurology to healthcare innovation -- 3. Organization and scope of this book -- REFERENCES -- 1 - Epidemiology, data, and technology -- 1 - Advancing epidemiological understanding: Navigating challenges and embracing innovations in the age of technolo ... -- 1. Introduction: Epidemiology, data, and technology -- 2. Data and technology -- 3. Pitfalls (challenges and biases) -- 4. A future model -- 4.1 Early detection and surveillance -- 4.2 Genomic sequencing and pathogen tracking -- 4.3 Predictive modeling -- 4.4 Global data sharing and collaboration -- 5. Conclusion -- Acknowledgments -- References -- 2 - Treatment/pharmaceutical R& -- D -- 2 - The use of computational biology in protein engineering and drug discovery -- 1. Introduction -- 2. Methodology of the search -- 2.1 Overview of the databases used -- 2.2 Criteria for the data collection and selection -- 2.3 Extraction of data and final list of selected documents -- 3. Target identification for drug discovery -- 4. Protein engineering through algorithms -- 5. De novo drug design toward personalized medicine -- 5.1 De novo drug design: An overview -- 5.2 Methods of de novo drug design -- 5.3 Drug design through multiscale de novo method -- 6. Ligand-based modeling -- 7. Molecular docking -- 8. QSAR modeling -- 9. Clinical translation -- 10. Conclusion -- References -- Further reading. , 3 - Innovative clinical trial design and operation-Implementing hybrid and fully decentralized clinical trials in a . -- 1. Introduction -- 2. Adoption and reach -- 3. Decentralized trial planning and preparation -- 3.1 Study staff -- 3.2 Trial solutioning -- 3.3 Trial preparation and planning -- 3.4 Trial enrollment -- 3.5 Telemedicine in trial conduct -- 4. Hybrid trials -- 5. Fully decentralized clinical trials -- 6. Trial enablement -- 6.1 Platforms -- 6.2 Connected sensor technologies -- 6.3 Key considerations -- 6.4 Challenges -- 6.5 Opportunities -- 7. Conclusion and recommendations -- 7.1 Recommended resources -- Acknowledgments -- References -- 3 - Triage, diagnosis, risk prediction -- 4 - A perspective on Artificial Intelligence for digital pharmacovigilance in pandemics -- 1. Introduction -- 2. Traditional pharmacovigilance -- 3. Artificial Intelligence for traditional pharmacovigilance -- 4. Social media listening for digital pharmacovigilance -- 5. A case study: An NLP system for ADE detection on social media -- 6. Conclusions -- Acknowledgement -- References -- 5 - Consequences of the isolation in vulnerable populations: Brain and mental health in the older population -- 1. Introduction -- 2. The effects of social isolation on mental and brain health among the older population -- 2.1 Impact of social isolation on mental health and mediating factors -- 2.2 Impact of social isolation on brain health and mediating factors -- 3. The effects of social isolation on mental and brain health among vulnerable sub-populations -- 3.1 Women: Social isolation, loneliness, and health outcomes -- 3.2 Caregivers: Caregiver burden, loneliness, and mental health -- 3.3 Older adults at risk of developing Alzheimer's disease: Mental health and pathological changes -- 3.4 Individuals living with dementia: Social isolation and neuropsychiatric symptoms. , 4. Measures and interventions for social isolation and loneliness and actionable recommendations for the future -- 4.1 Actionable recommendations to identify the most effective strategies during lockdowns -- 5. Conclusion -- Acknowledgment -- References -- Further reading -- 6 - Design for isolation, confinement, and mental health during intensive care -- 1. Observations about intensive care units during COVID-19 -- 2. Design for isolation and confinement -- 3. Lessons learned from astronauts training facilities -- 4. Technological and architectural solutions for ICU -- 5. Conclusions -- References -- 7 - Triage, diagnosis and risk prediction: The benefits and challenges of digital health and AI technologies -- 1. Introduction -- 2. From prevention and early diagnose to remote monitoring -- 3. Challenges for development and scale up -- 4. Outlook and recommendations -- Acknowledgments -- References -- 4 - Management and policies -- 8 - How digital solutions can ensure continued high-quality care for patients during pandemics -- 1. Introduction -- 2. Key challenges faced by patients with chronic and serious diseases during a pandemic -- 3. How digital health solutions can help ensure continuous, quality care for those with chronic and serious health conditions -- 3.1 Real-world example: Patient apps -- 3.2 Real-world example: Connected devices -- 3.3 Other applications -- 4. How digital solutions can contribute to population health management during a pandemic? -- 4.1 Personalized risk calculation -- 4.2 Remote testing and self-reporting -- 4.3 Personalized treatment selection -- 4.4 Speed in testing -- 4.5 Vaccine rollout and tracking -- 4.6 Educational tools to combat misinformation -- 4.7 Global data collaboration -- 5. Outlook and recommendations -- 5.1 Recommendations -- 5.1.1 Invest in infrastructure -- 5.1.2 Focus on patient-centric solutions. , 5.1.3 Develop on a platform for adaptability, interoperability, and scalability -- 5.1.4 Governments should encourage digital innovation -- References -- Further reading -- 9 - Beyond one-size-fits-all: Precision medicine and novel technologies for sex- and gender-inclusive COVID-19 pand ... -- 1. Introduction -- 2. The role of new technologies in sex- and gender-sensitive precision medicine -- 2.1 Sex- and gender-sensitive precision medicine-Why we need it? -- 2.2 Novel technologies and precision medicine -- 2.3 Machine learning and deep learning -- 2.4 Convolutional neural networks -- 2.5 AI-driven solutions for clinical trials -- 2.6 Natural language processing -- 2.7 Social and medical robots -- 3. Pandemic management and novel technologies: A sex and gender approach -- 3.1 COVID risks -- 3.2 Outcomes -- 3.3 Sex and gender approaches impact pandemic preparedness -- 3.3.1 Role of sex -- 3.3.2 Role of gender -- 3.3.3 Pandemic preparedness -- 3.4 Sex and gender approaches impact data biases and socio-technical systems -- 4. What have we learned from COVID-19? Call for action -- Acknowledgments -- References -- Sources of further information -- 10 - Future outlook -- 1. A launchpad for healthcare innovations in uncharted waters -- 2. The next big one -- 3. Gearing up for the future -- References -- Index -- Back Cover.

Additional Edition: Print version: Mellino, Simona Innovating Health Against Future Pandemics San Diego : Elsevier Science & Technology,c2024 ISBN 9780443136818

Language: English

UID:

Format: 1 online resource (588 pages)

ISBN: 0-323-90134-4

Series Statement: Computation and analysis of turbulent flows

Content: Modelling Approaches and Computational Methods for Particle-laden Turbulent Flows introduces the principal phenomena observed in applications where turbulence in particle-laden flow is encountered while also analyzing the main methods for analyzing numerically. The book takes a practical approach, providing advice on how to select and apply the correct model or tool by drawing on the latest research. Sections provide scales of particle-laden turbulence and the principal analytical frameworks and computational approaches used to simulate particles in turbulent flow. Each chapter opens with a section on fundamental concepts and theory before describing the applications of the modelling approach or numerical method. Featuring explanations of key concepts, definitions, and fundamental physics and equations, as well as recent research advances and detailed simulation methods, this book is the ideal starting point for students new to this subject, as well as an essential reference for experienced researchers.

Note: Front Cover -- Modeling Approaches and Computational Methods for Particle-laden Turbulent Flows -- Copyright -- Contents -- Contributors -- About the editors -- Preface -- References -- Acknowledgment -- 1 Introduction -- 1.1 Physical description -- 1.2 Scope -- 1.3 Deterministic descriptions -- 1.3.1 Complete deterministic description -- 1.3.2 Filtered fluid description: Euler-Lagrange approach -- 1.3.3 Filtered fluid and particle description: Euler-Euler approach -- 1.4 Statistical descriptions -- 1.4.1 Field-based description -- 1.4.1.1 Second-moment and probability density function closures -- 1.4.2 Point-based description -- 1.4.2.1 Collision-resolved statistical description -- 1.4.2.2 Collision-modeled statistical description -- Boltzmann-Enskog equation for particle-laden flow -- 1.5 Important non-dimensional quantities -- 1.5.1 Particle volume fraction -- 1.5.2 Mass loading -- 1.5.3 Particle Reynolds number -- 1.5.4 Particle Stokes number -- 1.5.5 Knudsen number and Mach number -- 1.6 Multiscale nature of turbulent particle-laden flows -- 1.6.1 Microscale physics -- 1.6.2 Mesoscale physics -- 1.6.3 Macroscale physics -- 1.7 Outline of the book -- 1.7.1 Phenomenology and flow physics -- 1.7.2 Model-free particle-resolved simulations at the microscale -- 1.7.3 Euler-Lagrange models and simulations -- 1.7.4 Euler-Euler models and simulations -- Nomenclature -- References -- 2 Particle dispersion and preferential concentration in particle-laden turbulence -- 2.1 Introduction -- 2.2 Particle dispersion -- 2.2.1 Fluid elements -- 2.2.2 Inertial particles -- 2.2.3 Dispersion in inhomogeneous shear flows -- 2.3 Preferential concentration of particles by turbulence -- 2.3.1 Experimental and numerical observations -- 2.3.2 Physical mechanisms of preferential concentration -- 2.3.2.1 Centrifuge mechanism -- 2.3.2.2 Non-local clustering mechanism. , 2.3.2.3 Sweep-stick mechanism -- 2.3.3 Quantifying preferential concentration -- 2.3.3.1 Radial distribution function -- 2.3.3.2 Box counting methods -- 2.3.3.3 Voronoi tessellation -- 2.3.4 Practical significance -- 2.3.4.1 Gravitational settling -- 2.3.4.2 Two-way and four-way coupling -- 2.3.4.3 Convective heat and mass transfer -- 2.3.4.4 Radiation heat transfer -- 2.3.4.5 Electric fields -- 2.4 Turbophoresis -- 2.4.1 Definition and physical mechanism -- 2.4.2 Modeling implications -- References -- 3 Physics of two-way coupling in particle-laden homogeneous isotropic turbulence -- 3.1 Introduction -- 3.1.1 Classification map for turbulent flows laden with dispersed particles -- 3.1.2 Resolution challenges for DNS of turbulent flows laden with particles whose dp < -- η -- 3.2 Particle-laden flows with dp < -- η -- 3.2.1 First experiments demonstrating the two-way coupling effects in turbulent flows (dp < -- η) -- 3.2.2 Physics of two-way coupling in homogeneous isotropic turbulence -- 3.2.2.1 Particles in a vortex -- 3.2.2.2 Particles in decaying isotropic turbulence -- Decay rate of TKE in the physical space -- Dissipation rate of TKE in the physical space -- Decay rate of TKE in the spectral space -- 3.2.3 Why is DNS of forced particle-laden isotropic turbulence not appropriate for two-way or four-way coupling? -- 3.3 Particle-laden flows with dp > -- η -- 3.3.1 Physics of two-way coupling in particle-laden homogeneous isotropic turbulence (dp > -- η) -- 3.3.1.1 Is the Stokes number an appropriate indicator for turbulence modulation for particles with dp> -- η? -- 3.3.1.2 Physical space -- Turbulence kinetic energy -- Dissipation rate of TKE -- Two-way coupling rate of change of TKE -- Effects of particle rotation -- 3.3.1.3 Spectral space -- 3.A Governing equations -- 3.A.1 Particles with dp< -- η -- 3.A.2 Particles with dp>. , η -- 3.B Equations of conservation of linear and angular momenta for a solid particle moving in an incompressible fluid -- References -- 4 Coagulation in turbulent particle-laden flows -- 4.1 Introduction -- 4.2 Geometric collision kernel -- 4.2.1 Dynamic and kinematic descriptions of the geometric collision kernel -- 4.2.2 Examples of geometric collision kernels -- 4.2.3 Turbulent collision of inertial particles - theoretical considerations -- 4.2.4 Turbulent collision of inertial particles - numerical simulations -- 4.2.5 Turbulent collision of inertial particles - parameterizations -- 4.3 Collision efficiency -- 4.4 Modeling the evolution of particle size distribution -- 4.5 A specific application: turbulent collision coalescence of cloud droplets and its impact on warm rain precipitation -- 4.6 Summary and outlook -- Acknowledgments -- References -- 5 Efficient methods for particle-resolved direct numerical simulation -- 5.1 Introduction -- 5.2 The immersed boundary method in Navier-Stokes-based solvers -- 5.2.1 Direct forcing immersed boundary method -- 5.2.2 How and where to distribute Lagrangian marker points? -- 5.2.3 What level of accuracy can be achieved with the immersed boundary method? -- 5.2.4 Further refinements -- 5.2.5 Describing the motion of rigid particles -- 5.3 Distributed Lagrange multiplier methods -- 5.4 Boltzmann equation-based mesoscopic methods -- 5.4.1 From the Boltzmann equation to Navier-Stokes-Fourier -- 5.4.2 The on-grid lattice-Boltzmann method -- 5.4.3 The generalized off-grid DUGKS method -- 5.4.4 Treating the moving solid-fluid interfaces -- 5.4.5 Remarks -- 5.5 Reference datasets -- 5.5.1 Single particle settling in unbounded ambient fluid -- 5.6 Comparing PR-DNS methods: a difficult exercise -- 5.7 Conclusion and outlook -- Acknowledgment -- References -- 6 Results from particle-resolved simulations. , 6.1 Introduction -- 6.2 PR-DNS of dense fluidized systems for drag force parameterizations based on dynamic simulations -- 6.3 PR-DNS of unbounded flows in the dilute regime -- 6.3.1 Settling in initially ambient fluid -- 6.3.2 Finite-size particles in turbulent background flow -- 6.4 PR-DNS of wall-bounded shear flows -- 6.4.1 Vertical plane channel flow -- 6.4.2 Horizontal plane channel flow -- 6.4.2.1 Focusing of heavy particles in low-speed streaks -- 6.4.2.2 Sediment transport -- 6.4.2.3 Sediment pattern formation -- 6.5 Conclusions and outlook -- References -- 7 Modeling of short-range interactions between both spherical and non-spherical rigid particles -- 7.1 Introduction -- 7.2 Motion of a non-spherical rigid body -- 7.3 Geometric description of a non-spherical rigid body and the problem of collision detection of non-spherical rigid bodies -- 7.4 Non-collisional short-range hydrodynamic interactions: lubrication in dilute regime -- 7.5 Methods for Lagrangian tracking of non-spherical rigid bodies with collisions -- 7.5.1 Governing equations -- 7.5.2 Hard-sphere event-driven model for binary collisions -- 7.5.3 Time-driven rigid body dynamics and non-smooth contact dynamics for multibody collisions with persistent contacts -- 7.5.4 Soft-sphere time-driven molecular dynamics for multibody collisions with persistent contacts -- 7.5.4.1 Contact force models -- In translation -- In rotation -- 7.5.4.2 Time integration schemes in DEM -- Integration of the translational motion -- Integration of the angular motion -- 7.6 Efficient and parallel implementation of granular dynamics solvers and their parallel coupling to the fluid solver -- 7.7 Test cases -- 7.7.1 A single sphere bouncing on a horizontal plane -- 7.7.2 A single cylinder bouncing on a horizontal plane -- 7.7.3 Bedload transport of sediment in laminar open-channel flow -- 7.8 Outlook. , References -- 8 Improved force models for Euler-Lagrange computations -- 8.1 Introduction -- 8.2 Undisturbed quantities -- 8.3 Stochastic effects in Euler-Lagrange simulation for unresolved fields -- 8.4 Fluid equations for dilute flows modeled with the Euler-Lagrange method -- 8.5 Particle equation of motion -- 8.6 Eulerian-Lagrangian data transfer -- 8.7 Correction schemes for the undisturbed quantities -- 8.7.1 The distinction between interpolated and undisturbed fluid velocity in numerical simulation -- 8.7.2 Unification involving discrete Green's functions -- 8.7.3 Gaussian-based corrections -- 8.7.4 Non-Gaussian-based corrections -- 8.7.5 Concluding remarks on undisturbed fluid velocity models -- 8.8 Summary and future directions -- 8.9 Discussion questions -- Acknowledgments -- References -- 9 Deterministic extended point-particle models -- 9.1 Motivation to go beyond the point-particle model -- 9.2 Neighbor influence -- 9.3 Undisturbed flow prediction -- 9.3.1 Superposable wake -- 9.4 Deterministic particle force prediction using the PIEP model -- 9.5 Beyond pairwise approximation using machine learning -- 9.6 Concept and statement of the force coupling method -- 9.6.1 FCM equations -- 9.7 FCM results for individual particles -- 9.7.1 Stokes flow -- 9.7.2 Unsteady Stokes flow -- 9.7.3 Finite Reynolds number -- 9.8 Examples of FCM applications -- 9.9 Comments -- References -- 10 Stochastic models -- 10.1 Motivation for stochastic models -- 10.1.1 Scope -- 10.2 Dispersion of inertial particles from a point source -- 10.2.1 Uniform flow -- 10.2.2 Effect of gravitational settling -- 10.2.3 Homogeneous shear flow -- 10.2.3.1 Extension to general flows at finite slip Reynolds number -- 10.2.4 Sources of randomness -- 10.2.4.1 Fluid-phase turbulence -- 10.2.4.2 Flow perturbation by neighboring particles -- 10.2.5 Presumed PDF methods. , 10.2.6 Stochastic models of dispersion.

Additional Edition: Print version: Subramaniam, Shankar Modeling Approaches and Computational Methods for Particle-Laden Turbulent Flows San Diego : Elsevier Science & Technology,c2022 ISBN 9780323901338

Language: English

UID:

Format: 1 online resource (3707 p.)

Edition: 7th ed.

ISBN: 0-323-32195-X , 0-323-18907-5

Uniform Title: Endocrinology (DeGroot)

Note: Description based upon print version of record. , e9780323189071v1; Front Cover; IFC; Endocrinology; Copyright; CONTRIBUTORS; PREFACE; CONTENTS; Part-01 - Principles of Endocrinology and Hormone Signaling ; chapter 1 - Principles of Endocrinology; DEFINITION AND SCOPE OF ENDOCRINOLOGY; HISTORICAL PERSPECTIVES; PRINCIPLES OF HORMONE ACTION; HORMONE ACTION THROUGH RECEPTORS; ROLE OF THE CLINICAL ENDOCRINOLOGIST; MAJOR UNSOLVED PROBLEMS; REFERENCES; CHAPTER 2 - CONTROL OF HORMONE GENE EXPRESSION; CHROMATIN: ACCESSIBILITY OF DNA TO CONTROL FACTORS; FUNCTIONAL ANATOMY OF A GENE; FUNCTIONAL ANATOMY OF THE PROMOTER-REGULATORY REGION , TRANSCRIPTION: CREATING THE TEMPLATE FOR PROTEIN SYNTHESISPOSTTRANSCRIPTIONAL MODIFICATION OF MRNA; TRANSLATION: REGULATED PROTEIN BIOSYNTHESIS; POSTTRANSLATIONAL MODIFICATIONS; REGULATED SECRETION OF PROTEINS; RNAI REGULATION OF GENE EXPRESSION; HORMONAL CONTROL OF GENE EXPRESSION; FUNCTIONAL GENOMICS AND PROTEOMICS; REFERENCES; CHAPTER 3 - CONTROL OF HORMONE SECRETION; SYNTHESIS, PROCESSING, AND SORTING OF PREPROHORMONE PRECURSORS; COMPOSITION OF MATURE SECRETORY GRANULES; SEQUENTIAL STAGES OF THE REGULATED SECRETORY PATHWAY; ESSENTIAL PROTEIN MACHINERY FOR DENSE-CORE GRANULE EXOCYTOSIS , REGULATION OF EXOCYTOSIS BY CALCIUMMODULATION OF CALCIUM-DEPENDENT HORMONE SECRETION BY PROTEIN KINASE C; REFERENCES; chapter 4 - Applications of Genetics in Endocrinology; THE HUMAN GENOME PROJECT AND ITS IMPACT ON MEDICINE; STRUCTURE OF THE HUMAN GENOME; CATEGORIES OF GENETIC DISORDERS; RELATIONSHIP BETWEEN GENOTYPE AND PHENOTYPE IN GENETIC DISORDERS; VARIATIONS IN SIMPLE MENDELIAN INHERITANCE PATTERNS; PRINCIPLES OF GENETIC LINKAGE AND ASSOCIATION; METHODS USED TO DETECT GENE DELETIONS AND POINT MUTATIONS; OVERVIEW OF INHERITED ENDOCRINE DISORDERS; APPROACH TO THE PATIENT , CURRENT DEVELOPMENTSREFERENCES; Part-02 - Neuroendocrinology and Pituitary Disease ; CHAPTER 5 - DEVELOPMENT OF THE PITUITARY*; PITUITARY DEVELOPMENT; TRANSCRIPTION FACTORS AND PITUITARY DEVELOPMENT; OTHER TRANSCRIPTION FACTORS; SIGNALING PATHWAYS IN PITUITARY DEVELOPMENT; NOTCHS; INTERACTIONS BETWEEN DIFFERENT MORPHOGENIC FACTORS; INDEPENDENT HH AND FGF SIGNALING IN PITUITARY ORGANOGENESIS; PITUITARY AND EPIGENETICS; STEM CELLS; SUMMARY; REFERENCES; chapter 6 - Prolactin; THE EVOLUTIONARY BIOLOGY OF PROLACTIN; THE BIOCHEMISTRY OF PROLACTIN , THE ONTOGENY AND PHYSIOLOGY OF PROLACTIN SECRETIONPATHOPHYSIOLOGY OF PROLACTIN SECRETION; PROLACTIN RECEPTORS AND SIGNAL TRANSDUCTION; THE PHYSIOLOGY AND PATHOPHYSIOLOGY OF PROLACTIN ACTIONS IN MAMMALS; SUMMARY; REFERENCES; CHAPTER 7 - DISORDERS OF PROLACTIN SECRETION AND PROLACTINOMAS; EPIDEMIOLOGY; PATHOGENESIS; PATHOLOGY; DIFFERENTIAL DIAGNOSIS OF HYPERPROLACTINEMIA; DIAGNOSIS OF PROLACTINOMAS; TREATMENT; REFERENCES; chapter 8 - Adrenocorticotropic Hormone; HISTORY; PROOPIOMELANOCORTIN GENE; ADRENOCORTICOTROPIC HORMONE AND RELATED PEPTIDES; REFERENCES , chapter 9 - Endocrine Rhythms, the Sleep-Wake Cycle, and Biological Clocks , English

Language: English

UID:

Format: 1 online resource

Edition: 1st.

ISBN: 9781119761655 , 1119761654 , 9781119761662 , 1119761662 , 9781119761679 , 1119761670

Note: Preface -- Part 1: Internet of Things -- 1 Voyage of Internet of Things in the Ocean of Technology 3 Tejaskumar R. Ghadiyali, Bharat C. Patel and Manish M. Kayasth -- 1.1 Introduction -- 1.1.1 Characteristics of IoT -- 1.1.2 IoT Architecture -- 1.1.3 Merits and Demerits of IoT -- 1.2 Technological Evolution Toward IoT -- 1.3 IoT-Associated Technology -- 1.4 Interoperability in IoT -- 1.5 Programming Technologies in IoT -- 1.5.1 Arduino -- 1.5.2 Raspberry Pi -- 1.5.3 Python -- 1.6 IoT Applications -- Conclusion -- References -- 2 AI for Wireless Network Optimization: Challenges and Opportunities 25 Murad Abusubaih -- 2.1 Introduction to AI -- 2.2 Self-Organizing Networks -- 2.2.1 Operation Principle of Self-Organizing Networks -- 2.2.2 Self-Configuration -- 2.2.3 Self-Optimization -- 2.2.4 Self-Healing -- 2.2.5 Key Performance Indicators -- 2.2.6 SON Functions -- 2.3 Cognitive Networks -- 2.4 Introduction to Machine Learning -- 2.4.1 ML Types -- 2.4.2 Components of ML Algorithms -- 2.4.3 How do Machines Learn? -- 2.4.3.1 Supervised Learning -- 2.4.3.2 Unsupervised Learning -- 2.4.3.3 Semi-Supervised Learning -- 2.4.3.4 Reinforcement Learning -- 2.4.4 ML and Wireless Networks -- 2.5 Software-Defined Networks -- 2.5.1 SDN Architecture -- 2.5.2 The OpenFlow Protocol -- 2.5.3 SDN and ML -- 2.6 Cognitive Radio Networks -- 2.6.1 Sensing Methods -- 2.7 ML for Wireless Networks: Challenges and Solution Approaches -- 2.7.1 Cellular Networks -- 2.7.1.1 Energy Saving -- 2.7.1.2 Channel Access and Assignment -- 2.7.1.3 User Association and Load Balancing -- 2.7.1.4 Traffic Engineering -- 2.7.1.5 QoS/QoE Prediction -- 2.7.1.6 Security -- 2.7.2 Wireless Local Area Networks -- 2.7.2.1 Access Point Selection -- 2.7.2.2 Interference Mitigation -- 2.7.2.3 Channel Allocation and Channel Bonding -- 2.7.2.4 Latency Estimation and Frame Length Selection -- 2.7.2.5 Handover -- 2.7.3 Cognitive Radio Networks -- References -- 3 An Overview on Internet of Things (IoT) Segments and Technologies 57 Amarjit Singh -- 3.1 Introduction -- 3.2 Features of IoT -- 3.3 IoT Sensor Devices -- 3.4 IoT Architecture -- 3.5 Challenges and Issues in IoT -- 3.6 Future Opportunities in IoT -- 3.7 Discussion -- 3.8 Conclusion -- References -- 4 The Technological Shift: AI in Big Data and IoT 69 Deepti Sharma, Amandeep Singh and Sanyam Singhal -- 4.1 Introduction -- 4.2 Artificial Intelligence -- 4.2.1 Machine Learning -- 4.2.2 Further Development in the Domain of Artificial Intelligence -- 4.2.3 Programming Languages for Artificial Intelligence -- 4.2.4 Outcomes of Artificial Intelligence -- 4.3 Big Data -- 4.3.1 Artificial Intelligence Methods for Big Data -- 4.3.2 Industry Perspective of Big Data -- 4.3.2.1 In Medical Field -- 4.3.2.2 In Meteorological Department -- 4.3.2.3 In Industrial/Corporate Applications and Analytics -- 4.3.2.4 In Education -- 4.3.2.5 In Astronomy -- 4.4 Internet of Things -- 4.4.1 Interconnection of IoT With AoT -- 4.4.2 Difference Between IIoT and IoT -- 4.4.3 Industrial Approach for IoT -- 4.5 Technical Shift in AI, Big Data, and IoT -- 4.5.1 Industries Shifting to AI-Enabled Big Data Analytics -- 4.5.2 Industries Shifting to AI-Powered IoT Devices -- 4.5.3 Statistical Data of These Shifts -- 4.6 Conclusion -- References -- 5 IoT's Data Processing Using Spark 91 Ankita Bansal and Aditya Atri -- 5.1 Introduction -- 5.2 Introduction to Apache Spark -- 5.2.1 Advantages of Apache Spark -- 5.2.2 Apache Spark's Components -- 5.3 Apache Hadoop MapReduce -- 5.3.1 Limitations of MapReduce -- 5.4 Resilient Distributed Dataset (RDD) -- 5.4.1 Features and Limitations of RDDs -- 5.5 DataFrames -- 5.6 Datasets -- 5.7 Introduction to Spark SQL -- 5.7.1 Spark SQL Architecture -- 5.7.2 Spark SQL Libraries -- 5.8 SQL Context Class in Spark -- 5.9 Creating Dataframes -- 5.9.1 Operations on DataFrames -- 5.10 Aggregations -- 5.11 Running SQL Queries on Dataframes -- 5.12 Integration With RDDs -- 5.12.1 Inferring the Schema Using Reflection -- 5.12.2 Specifying the Schema Programmatically -- 5.13 Data Sources -- 5.13.1 JSON Datasets -- 5.13.2 Hive Tables -- 5.13.3 Parquet Files -- 5.14 Operations on Data Sources -- 5.15 Industrial Applications -- 5.16 Conclusion -- References -- 6 SE-TEM: Simple and Efficient Trust Evaluation Model for WSNs 111 Tayyab Khan and Karan Singh -- 6.1 Introduction -- 6.1.1 Components of WSNs -- 6.1.2 Trust -- 6.1.3 Major Contribution -- 6.2 Related Work -- 6.3 Network Topology and Assumptions -- 6.4 Proposed Trust Model -- 6.4.1 CM to CM (Direct) Trust Evaluation Scheme -- 6.4.2 CM to CM Peer Recommendation (Indirect) Trust Estimation (PRx,y(∆t)) -- 6.4.3 CH-to-CH Direct Trust Estimation -- 6.4.4 BS-to-CH Feedback Trust Calculation -- 6.5 Result and Analysis -- 6.5.1 Severity Analysis -- 6.5.2 Malicious Node Detection -- 6.6 Conclusion and Future Work -- References -- 7 Smart Applications of IoT 131 Pradeep Kamboj, T. Ratha Jeyalakshmi, P. Thillai Arasu, S. Balamurali and A. Murugan -- 7.1 Introduction -- 7.2 Background -- 7.2.1 Enabling Technologies for Building Intelligent Infrastructure -- 7.3 Smart City -- 7.3.1 Benefits of a Smart City -- 7.3.2 Smart City Ecosystem -- 7.3.3 Challenges in Smart Cities -- 7.4 Smart Healthcare -- 7.4.1 Smart Healthcare Applications -- 7.4.2 Challenges in Healthcare -- 7.5 Smart Agriculture -- 7.5.1 Environment Agriculture Controlling -- 7.5.2 Advantages -- 7.5.3 Challenges -- 7.6 Smart Industries -- 7.6.1 Advantages -- 7.6.2 Challenges -- 7.7 Future Research Directions -- 7.8 Conclusions -- References -- 8 Sensor-Based Irrigation System: Introducing Technology in Agriculture 153 Rohit Rastogi, Krishna Vir Singh, Mihir Rai, Kartik Sachdeva, Tarun Yadav and Harshit Gupta -- 8.1 Introduction -- 8.1.1 Technology in Agriculture -- 8.1.2 Use and Need for Low-Cost Technology in Agriculture -- 8.2 Proposed System -- 8.3 Flow Chart -- 8.4 Use Case -- 8.5 System Modules -- 8.5.1 Raspberry Pi -- 8.5.2 Arduino Uno -- 8.5.3 DHT 11 Humidity and Temperature Sensor -- 8.5.4 Soil Moisture Sensor -- 8.5.5 Solenoid Valve -- 8.5.6 Drip Irrigation Kit -- 8.5.7 433 MHz RF Module -- 8.5.8 Mobile Application -- 8.5.9 Testing Phase -- 8.6 Limitations -- 8.7 Suggestions -- 8.8 Future Scope -- 8.9 Conclusion -- Acknowledgement -- References -- Suggested Additional Readings -- Key Terms and Definitions -- Appendix -- Example Code -- 9 Artificial Intelligence: An Imaginary World of Machine 167 Bharat C. Patel, Manish M. Kaysth and Tejaskumar R. Ghadiyali -- 9.1 The Dawn of Artificial Intelligence -- 9.2 Introduction -- 9.3 Components of AI -- 9.3.1 Machine Reasoning -- 9.3.2 Natural Language Processing -- 9.3.3 Automated Planning -- 9.3.4 Machine Learning -- 9.4 Types of Artificial Intelligence -- 9.4.1 Artificial Narrow Intelligence -- 9.4.2 Artificial General Intelligence -- 9.4.3 Artificial Super Intelligence -- 9.5 Application Area of AI -- 9.6 Challenges in Artificial Intelligence -- 9.7 Future Trends in Artificial Intelligence -- 9.8 Practical Implementation of AI Application -- References -- 10 Impact of Deep Learning Techniques in IoT 185 M. Chandra Vadhana, P. Shanthi Bala and Immanuel Zion Ramdinthara -- 10.1 Introduction -- 10.2 Internet of Things -- 10.2.1 Characteristics of IoT -- 10.2.2 Architecture of IoT -- 10.2.2.1 Smart Device/Sensor Layer -- 10.2.2.2 Gateways and Networks -- 10.2.2.3 Management Service Layer -- 10.2.2.4 Application Layer -- 10.2.2.5 Interoperability of IoT -- 10.2.2.6 Security Requirements at a Different Layer of IoT -- 10.2.2.7 Future Challenges for IoT -- 10.2.2.8 Privacy and Security -- 10.2.2.9 Cost and Usability -- 10.2.2.10 Data Management -- 10.2.2.11 Energy Preservation -- 10.2.2.12 Applications of IoT -- 10.2.2.13 Essential IoT Technologies -- 10.2.2.14 Enriching the Customer Value -- 10.2.2.15 Evolution of the Foundational IoT Technologies -- 10.2.2.16 Technical Challenges in the IoT Environment -- 10.2.2.17 Security Challenge -- 10.2.2.18 Chaos Challenge -- 10.2.2.19 Advantages of IoT -- 10.2.2.20 Disadvant , ing -- 10.3.1.1 Convolutional Neural Network -- 10.3.1.2 Recurrent Neural Networks -- 10.3.1.3 Long Short-Term Memory -- 10.3.1.4 Autoencoders -- 10.3.1.5 Variational Autoencoders -- 10.3.1.6 Generative Adversarial Networks -- 10.3.1.7 Restricted Boltzmann Machine -- 10.3.1.8 Deep Belief Network -- 10.3.1.9 Ladder Networks -- 10.3.2 Applications of Deep Learning -- 10.3.2.1 Industrial Robotics -- 10.3.2.2 E-Commerce Industries -- 10.3.2.3 Self-Driving Cars -- 10.3.2.4 Voice-Activated Assistants -- 10.3.2.5 Automatic Machine Translation -- 10.3.2.6 Automatic Handwriting Translation -- 10.3.2.7 Predicting Earthquakes -- 10.3.2.8 Object Classification in Photographs -- 10.3.2.9 Automatic Game Playing -- 10.3.2.10 Adding Sound to Silent Movies -- 10.3.3 Advantages of Deep Learning -- 10.3.4 Disadvantages of Deep Learning -- 10.3.5 Deployment of Deep Learning in IoT -- 10.3.6 Deep Learning Applications in IoT -- 10.3.6.1 Image Recognition -- 10.3.6.2 Speech/Voice Recognition -- 10.3.6.3 Indoor Localization -- 10.3.6.4 Physiological and Psychological Detection -- 10.3.6.5 Security and Privacy -- 10.3.7 Deep Learning Techniques on IoT Devices -- 10.3.7.1 Network Compression -- 10.3.7.2 Approximate Computing -- 10.3.7.3 Accelerators -- 10.3.7.4 Tiny Motes -- 10.4 IoT Challenges on Deep Learning and Future Directions -- 10.4.1 Lack of IoT Dataset -- 10.4.2 Pre-Processing -- 10.4.3 Challenges of 6V's -- 10.4.4 Deep Learning Limitations -- 10.5 Future Directions of Deep Learning -- 10.5.1 IoT Mobile Data -- 10.5.2 Integrating Contextual Information -- 10.5.3 Online Resource Provisioning for IoT Analytics -- 10.5.4 Semi-Supervised Analytic Framework -- 10.5.5 Dependable and Reliable IoT Analytics -- 10.5.6 Self-Organizing Communication Networks -- 10.5.7 Emerging IoT Applications -- 10.5.7.1 Unmanned Aerial Vehicles -- 10.5.7.2 Virtual/Augmented Reality -- 10.5.7.3 Mobile Robotics -- 10.6 Common Datasets for Deep Learning in IoT -- 10.7 Discussion -- 10.8 Conclusion -- References -- Part 2: Artificial Intelligence in Healthcare -- 11 Non-Invasive Process for Analyzing Retinal Blood Vessels Using Deep Learning Techniques 217 Toufique A. Soomro, Ahmed J. Afifi, Pardeep Kumar, Muhammad Usman Keerio, Saleem Ahmed and Ahmed Ali -- 11.1 Introduction -- 11.2 Existing Methods Review -- 11.3 Methodology -- 11.3.1 Architecture of Stride U-Net -- 11.3.2 Loss Function -- 11.4 Databases and Evaluation Metrics -- 11.4.1 CNN Implementation Details -- 11.5 Results and Analysis -- 11.5.1 Evaluation on DRIVE and STARE Databases -- 11.5.2 Comparative Analysis -- 11.6 Concluding Remarks -- References -- 12 Existing Trends in Mental Health Based on IoT Applications: A Systematic Review 235 Muhammad Ali Nizamani, Muhammad Ali Memon and Pirah Brohi -- 12.1 Introduction -- 12.2 Methodology -- 12.3 IoT in Mental Health -- 12.4 Mental Healthcare Applications and Services Based on IoT -- 12.5 Benefits of IoT in Mental Health -- 12.5.1 Reduction in Treatment Cost -- 12.5.2 Reduce Human Error -- 12.5.3 Remove Geographical Barriers -- 12.5.4 Less Paperwork and Documentation -- 12.5.5 Early Stage Detection of Chronic Disorders -- 12.5.6 Improved Drug Management -- 12.5.7 Speedy Medical Attention -- 12.5.8 Reliable Results of Treatment -- 12.6 Challenges in IoT-Based Mental Healthcare Applications -- 12.6.1 Scalability -- 12.6.2 Trust -- 12.6.3 Security and Privacy Issues -- 12.6.4 Interoperability Issues -- 12.6.5 Computational Limits -- 12.6.6 Memory Limitations -- 12.6.7 Communications Media -- 12.6.8 Devices Multiplicity -- 12.6.9 Standardization -- 12.6.10 IoT-Based Healthcare Platforms -- 12.6.11 Network Type -- 12.6.12 Quality of Service -- 12.7 Blockchain in IoT for Healthcare -- 12.8 Results and Discussion -- 12.9 Limitations of the Survey -- 12.10 Conclusion -- References -- 13 Monitoring Technologies for Precision Health 251 Rehab A. Rayan and Imran Zafar -- 13.1 Introduction -- 13.2 Applications of Monitoring Technologies -- 13.2.1 Everyday Life Activities -- 13.2.2 Sleeping and Stress -- 13.2.3 Breathing Patterns and Respiration -- 13.2.4 Energy and Caloric Consumption -- 13.2.5 Diabetes, Cardiac, and Cognitive Care -- 13.2.6 Disability and Rehabilitation -- 13.2.7 Pregnancy and Post-Procedural Care -- 13.3 Limitations -- 13.3.1 Quality of Data and Reliability -- 13.3.2 Safety, Privacy, and Legal Concerns -- 13.4 Future Insights -- 13.4.1 Consolidating Frameworks -- 13.4.2 Monitoring and Intervention -- 13.4.3 Research and Development -- 13.5 Conclusions -- References -- 14 Impact of Artificial Intelligence in Cardiovascular Disease 261 Mir Khan, Saleem Ahmed, Pardeep Kumar and Dost Muhammad Saqib Bhatti -- 14.1 Artificial Intelligence -- 14.2 Machine Learning -- 14.3 The Application of AI in CVD -- 14.3.1 Precision Medicine -- 14.3.2 Clinical Prediction -- 14.3.3 Cardiac Imaging Analysis -- 14.4 Future Prospect -- 14.5 PUAI and Novel Medical Mode -- 14.5.1 Phenomenon of PUAI -- 14.5.2 Novel Medical Model -- 14.6 Traditional Mode -- 14.6.1 Novel Medical Mode Plus PUAI -- 14.7 Representative Calculations of AI -- 14.8 Overview of Pipeline for Image-Based Machine Learning Diagnosis -- References -- 15 Healthcare Transformation With Clinical Big Data Predictive Analytics 273 Muhammad Suleman Memon, Pardeep Kumar, Azeem Ayaz Mirani, Mumtaz Qabulio, Sumera Naz Pathan and Asia Khatoon Soomro -- 15.1 Introduction -- 15.1.1 Big Data in Health Sector -- 15.1.2 Data Structure Produced in Health Sectors -- 15.2 Big Data Challenges in Healthcare -- 15.2.1 Big Data in Computational Healthcare -- 15.2.2 Big Data Predictive Analytics in Healthcare -- 15.2.3 Big Data for Adapted Healthcare -- 15.3 Cloud Computing and Big Data in Healthcare -- 15.4 Big Data Healthcare and IoT -- 15.5 Wearable Devices for Patient Health Monitoring -- 15.6 Big Data and Industry 4.0 -- 15.7 Conclusion -- References -- 16 Computing Analysis of Yajna and Mantra Chanting as a Therapy: A Holistic Approach for All by Indian Continent Amidst Pandemic Threats 287 Rohit Rastogi, Mamta Saxena, D.K. Chaturvedi, Mayank Gupta, Mukund Rastogi, Prajwal Srivatava, Mohit Jain, Pradeep Kumar, Ujjawal Sharma, Rohan Choudhary and Neha Gupta -- 16.1 Introduction -- 16.1.1 The Stats of Different Diseases, Comparative Observation on Symptoms, and Mortality Rate -- 16.1.2 Precautionary Guidelines Followed in Indian Continent -- 16.1.3 Spiritual Guidelines in Indian Society -- 16.1.3.1 Spiritual Defense Against Global Corona by Swami Bhoomananda Tirtha of Trichura, Kerala, India -- 16.1.4 Veda Vigyaan: Ancient Vedic Knowledge -- 16.1.5 Yagyopathy Researches, Say, Smoke of Yagya is Boon -- 16.1.6 The Yagya Samagri -- 16.2 Literature Survey -- 16.2.1 Technical Aspects of Yajna and Mantra Therapy -- 16.2.2 Mantra Chanting and Its Science -- 16.2.3 Yagya Medicine (Yagyopathy) -- 16.2.4 The Medicinal HavanSamagri Components -- 16.2.4.1 Special Havan Ingredients to Fight Against Infectious Diseases -- 16.2.5 Scientific Benefits of Havan -- 16.3 Experimental Setup Protocols With Results -- 16.3.1 Subject Sample Distribution -- 16.3.1.1 Area Wise Distribution -- 16.3.2 Conclusion and Discussion Through Experimental Work -- 16.4 Future Scope and Limitations -- 16.5 Novelty -- 16.6 Recommendations -- 16.7 Applications of Yajna Therapy -- 16.8 Conclusions -- Acknowledgement -- References -- Key Terms and Definitions -- 17 Extraction of Depression Symptoms From Social Networks 307 Bhavna Chilwal and Amit Kumar Mishra -- 17.1 Introduction -- 17.1.1 Diagnosis and Treatments -- 17.2 Data Mining in Healthcare -- 17.2.1 Text Mining -- 17.3 Social Network Sites -- 17.4 Symptom Extraction Tool -- 17.4.1 Data Collection -- 17.4.2 Data Processing -- 17.4.3 Data Analysis -- 17.5 Sentiment Analysis -- 17.5.1 Emotion Analysis -- 17.5.2 Behavioral Analysis -- 17.6 Conclusion -- References -- Part 3: Cybersecurity -- 18 Fog Computing Perspective: Technical Trends, Security Practices, and Recommendations 325 C. Kaviyazhiny, P. Shanthi Bala and A.S. Gowri -- 18.1 Int , ensing) -- 18.5.3 Network Layer -- 18.5.4 Service Layer (Support) -- 18.5.5 Application Layer (Interface) -- 18.6 Security Issues in Fog Computing -- 18.6.1 Virtualization Issues -- 18.6.2 Web Security Issues -- 18.6.3 Internal/External Communication Issues -- 18.6.4 Data Security Related Issues -- 18.6.5 Wireless Security Issues -- 18.6.6 Malware Protection -- 18.7 Machine Learning for Secure Fog Computing -- 18.7.1 Layer 1 Cloud -- 18.7.2 Layer 2 Fog Nodes For The Community -- 18.7.3 Layer 3 Fog Node for Their Neighborhood -- 18.7.4 Layer 4 Sensors -- 18.8 Existing Security Solution in Fog Computing -- 18.8.1 Privacy-Preserving in Fog Computing -- 18.8.2 Pseudocode for Privacy Preserving in Fog Computing -- 18.8.3 Pseudocode for Feature Extraction -- 18.8.4 Pseudocode for Adding Gaussian Noise to the Extracted Feature -- 18.8.5 Pseudocode for Encrypting Data -- 18.8.6 Pseudocode for Data Partitioning -- 18.8.7 Encryption Algorithms in Fog Computing -- 18.9 Recommendation and Future Enhancement -- 18.9.1 Data Encryption -- 18.9.2 Preventing from Cache Attacks -- 18.9.3 Network Monitoring -- 18.9.4 Malware Protection -- 18.9.5 Wireless Security -- 18.9.6 Secured Vehicular Network -- 18.9.7 Secure Multi-Tenancy -- 18.9.8 Backup and Recovery -- 18.9.9 Security with Performance -- 18.10 Conclusion -- References -- 19 Cybersecurity and Privacy Fundamentals 353 Ravi Verma -- 19.1 Introduction -- 19.2 Historical Background and Evolution of Cyber Crime -- 19.3 Introduction to Cybersecurity -- 19.3.1 Application Security -- 19.3.2 Information Security -- 19.3.3 Recovery From Failure or Disaster -- 19.3.4 Network Security -- 19.4 Classification of Cyber Crimes -- 19.4.1 Internal Attacks -- 19.4.2 External Attacks -- 19.4.3 Unstructured Attack -- 19.4.4 Structured Attack -- 19.5 Reasons Behind Cyber Crime -- 19.5.1 Making Money -- 19.5.2 Gaining Financial Growth and Reputation -- 19.5.3 Revenge -- 19.5.4 For Making Fun -- 19.5.5 To Recognize -- 19.5.6 Business Analysis and Decision Making -- 19.6 Various Types of Cyber Crime -- 19.6.1 Cyber Stalking -- 19.6.2 Sexual Harassment or Child Pornography -- 19.6.3 Forgery -- 19.6.4 Crime Related to Privacy of Software and Network Resources -- 19.6.5 Cyber Terrorism -- 19.6.6 Phishing, Vishing, and Smishing -- 19.6.7 Malfunction -- 19.6.8 Server Hacking -- 19.6.9 Spreading Virus -- 19.6.10 Spamming, Cross Site Scripting, and Web Jacking -- 19.7 Various Types of Cyber Attacks in Information Security -- 19.7.1 Web-Based Attacks in Information Security -- 19.7.2 System-Based Attacks in Information Security -- 19.8 Cybersecurity and Privacy Techniques -- 19.8.1 Authentication and Authorization -- 19.8.2 Cryptography -- 19.8.2.1 Symmetric Key Encryption -- 19.8.2.2 Asymmetric Key Encryption -- 19.8.3 Installation of Antivirus -- 19.8.4 Digital Signature -- 19.8.5 Firewall -- 19.8.6 Steganography -- 19.9 Essential Elements of Cybersecurity -- 19.10 Basic Security Concerns for Cybersecurity -- 19.10.1 Precaution -- 19.10.2 Maintenance -- 19.10.3 Reactions -- 19.11 Cybersecurity Layered Stack -- 19.12 Basic Security and Privacy Check List -- 19.13 Future Challenges of Cybersecurity -- References -- 20 Changing the Conventional Banking System through Blockchain 379 Khushboo Tripathi, Neha Bhateja and Ashish Dhillon -- 20.1 Introduction -- 20.1.1 Introduction to Blockchain -- 20.1.2 Classification of Blockchains -- 20.1.2.1 Public Blockchain -- 20.1.2.2 Private Blockchain -- 20.1.2.3 Hybrid Blockchain -- 20.1.2.4 Consortium Blockchain -- 20.1.3 Need for Blockchain Technology -- 20.1.3.1 Bitcoin vs. Mastercard Transactions: A Summary -- 20.1.4 Comparison of Blockchain and Cryptocurrency -- 20.1.4.1 Distributed Ledger Technology (DLT) -- 20.1.5 Types of Consensus Mechanism -- 20.1.5.1 Consensus Algorithm: A Quick Background -- 20.1.6 Proof of Work -- 20.1.7 Proof of Stake -- 20.1.7.1 Delegated Proof of Stake -- 20.1.7.2 Byzantine Fault Tolerance -- 20.2 Literature Survey -- 20.2.1 The History of Blockchain Technology -- 20.2.2 Early Years of Blockchain Technology: 1991-2008 -- 20.2.2.1 Evolution of Blockchain: Phase 1--Transactions -- 20.2.2.2 Evolution of Blockchain: Phase 2--Contracts -- 20.2.2.3 Evolution of Blockchain: Phase 3--Applications -- 20.2.3 Literature Review -- 20.2.4 Analysis -- 20.3 Methodology and Tools -- 20.3.1 Methodology -- 20.3.2 Flow Chart -- 20.3.3 Tools and Configuration -- 20.4 Experiment -- 20.4.1 Steps of Implementation -- 20.4.2 Screenshots of Experiment -- 20.5 Results -- 20.6 Conclusion -- 20.7 Future Scope -- 20.7.1 Blockchain as a Service (BaaS) is Gaining Adoption From Enterprises -- References -- 21 A Secured Online Voting System by Using Blockchain as the Medium 405 Leslie Mark, Vasaki Ponnusamy, Arya Wicaksana, Basilius Bias Christyono and Moeljono Widjaja -- 21.1 Blockchain-Based Online Voting System -- 21.1.1 Introduction -- 21.1.2 Structure of a Block in a Blockchain System -- 21.1.3 Function of Segments in a Block of the Blockchain -- 21.1.4 SHA-256 Hashing on the Blockchain -- 21.1.5 Interaction Involved in Blockchain-Based Online Voting System -- 21.1.6 Online Voting System Using Blockchain - Framework -- 21.2 Literature Review -- 21.2.1 Literature Review Outline -- 21.2.1.1 Online Voting System Based on Cryptographic and Stego-Cryptographic Model -- 21.2.1.2 Online Voting System Based on Visual Cryptography -- 21.2.1.3 Online Voting System Using Biometric Security and Steganography -- 21.2.1.4 Cloud-Based Secured Online Voting System Using Homomorphic Encryption -- 21.2.1.5 An Online Voting System Based on a Secured Blockchain -- 21.2.1.6 Online Voting System Using Fingerprint Biometric and Crypto-Watermarking Approach -- 21.2.1.7 Online Voting System Using Iris Recognition -- 21.2.1.8 Online Voting System Based on NID and SIM -- 21.2.1.9 Online Voting System Using Image Steganography and Visual Cryptography -- 21.2.1.10 Online Voting System Using Secret Sharing-Based Authentication -- 21.2.2 Comparing the Existing Online Voting System -- References -- 22 Artificial Intelligence and Cybersecurity: Current Trends and Future Prospects 431 Abhinav Juneja, Sapna Juneja, Vikram Bali, Vishal Jain and Hemant Upadhyay -- 22.1 Introduction -- 22.2 Literature Review -- 22.3 Different Variants of Cybersecurity in Action -- 22.4 Importance of Cybersecurity in Action -- 22.5 Methods for Establishing a Strategy for Cybersecurity -- 22.6 The Influence of Artificial Intelligence in the Domain of Cybersecurity -- 22.7 Where AI Is Actually Required to Deal With Cybersecurity -- 22.8 Challenges for Cybersecurity in Current State of Practice -- 22.9 Conclusion -- References -- Index.

Additional Edition: Print version : ISBN 9781119761648

Language: English

Keywords: Electronic books. ; Electronic books. ; Electronic books.

DOI: 10.1002/9781119761655

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119761655

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119761655

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119761655

UID:

Format: VI, 164 S. : graph. Darst. : 30 cm.

Note: Saarbrücken, Univ., Diss., 2002

Language: English

Keywords: Englisch ; Maschinelle Übersetzung ; Deutsch ; Skopus ; Hochschulschrift

UID:

Format: 1 online resource (651 pages)

Edition: 1st ed.

ISBN: 981-9719-23-2

Series Statement: Lecture Notes in Networks and Systems Series ; v.961

Note: Intro -- Organization -- Preface -- Contents -- Editors and Contributors -- Soft Computing and Computational Intelligence -- Deep Learning Approach to Compose Short Stories Based on Online Hospital Reviews of Tirunelveli Region -- 1 Introduction and Significance -- 2 CVAE Technique for Story Creation -- 3 SSHR Model Approaches -- 4 Evaluation Analysis of SSHR -- 5 Conclusion and Discussion -- References -- Music Emotion Recognition for Intelligent and Efficient Recommendation Systems -- 1 Introduction -- 2 Related Works -- 3 Traditional and Proposed Approach -- 3.1 Recommendation System -- 3.2 Proposed Work -- 4 Methods -- 4.1 Face Recognition and Image Processing -- 4.2 OpenCV Software -- 4.3 Fisherface Classifier -- 5 Results and Discussion -- 5.1 Measurement of Precision, F1-Score, and Recall -- 6 Conclusion -- References -- BScFilter: A Deep Learning Approach for Sports Comments Filtering in a Resource Constraint Language -- 1 Introduction -- 2 Related Work -- 3 Dataset Development -- 4 Methodology -- 5 Results and Analysis -- 6 Conclusion and Future Work -- References -- HABE Secure Access at Cloud-Healthcare Database -- 1 Introduction -- 2 State of the Art -- 3 Problem Formulation -- 4 ABE Framework Overview -- 5 Proposed Mechanism -- 6 Performance Analysis -- 7 Conclusion and Future Work -- References -- Comparative Analysis of Nature-Inspired Algorithms for Task Assignment Problem -- 1 Introduction -- 1.1 Jaya Optimization Algorithm (JOA) -- 1.2 Ant Colony Optimization (ACO) -- 1.3 Genetic Algorithm (GA) -- 1.4 Differential Evolution Algorithm (DE) -- 2 Problem Statement -- 3 Simulation Analysis and Discussion -- 3.1 Experimental Setup -- 3.2 Experimental Outcomes -- 3.3 Discussion of Results -- 4 Conclusion -- References. , A Novel Approach for Object Recognition in Hazy Scenes: Integrating YOLOv7 Architecture with Boundary-Constrained Dehazing -- 1 Introduction -- 2 Literature Review -- 3 Framework -- 4 Implementation and Result -- 5 Conclusion -- References -- Dynamic Multihead Attention for Enhancing Neural Machine Translation Performance -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Results -- 5 Conclusion and Future Scope -- References -- Scratch Vision Transformer Model for Diagnosis Grape Leaf Disease -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Experiment and Analysis -- 4.1 Experimenting with Scratch Vision Transformer -- 4.2 Confusion Matrix -- 4.3 Training Loss and Validation Loss -- 5 Results and Discussion -- 6 Conclusion -- References -- Multilevel Association Mining with Particle Swarm Optimization: A Comprehensive Approach for High-Utility Itemset Discovery -- 1 Introduction -- 2 Related Works -- 3 Methods -- 4 Results and Discussion -- 5 Conclusion -- References -- Attention-Based Neural Machine Translation for Multilingual Communication -- 1 Introduction -- 2 Related Works -- 2.1 Per-language Encoder-Decoder -- 3 Methodology -- 4 Results -- 5 Conclusion -- References -- Use of Deep Learning to Handle Early-Stage Business Data -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Objective -- 1.3 Scope of Study -- 2 Literature Survey -- 3 Research Methodology -- 3.1 Data Collection and Preprocessing -- 3.2 Feature Extraction and Representation -- 4 Early-Stage Business Data Analysis -- 4.1 Data Characteristics and Challenges -- 4.2 Deep Learning Approaches for Early-Stage Data -- 4.3 Predictive Analysis and Forecasting -- 4.4 Anomaly Detection and Risk Management -- 5 Handling Early-Stage Business Data: Implementation Using Python -- 5.1 Exploring the Dataset and Identifying Predictors -- 5.2 Preprocessing of Data. , 5.3 Balancing the Data -- 5.4 Loading of Pre-processed Data -- 5.5 Learning and Interpreting the Result by Creating a Model -- 5.6 Setting an Early Stopping Mechanism -- 5.7 Testing the Model -- 6 Analysis and Limitations -- 7 Conclusion -- References -- An Efficient Text-Based Document Categorization with k-Means and Cuckoo Search Optimization -- 1 Introduction -- 2 Related Works -- 3 Methods -- 4 Results and Discussion -- 5 Conclusion -- References -- Potato Leaf Disease Detection and Classification Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Dataset Overview -- 4 Proposed Methodology -- 4.1 Image Resizing -- 4.2 Image Augmentation -- 4.3 Histogram Equalization -- 4.4 CLAHE -- 5 Experimental Set-Up -- 5.1 ResNet50 -- 5.2 VGG16 -- 5.3 VGG19 -- 5.4 MobileNet -- 6 Result and Discussion -- 7 Conclusion -- 8 Future Scope -- References -- Disease Detection -- Parkinson's Disease Detection Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Implemented Approach -- 3.1 About Dataset -- 3.2 Data Preprocessing -- 3.3 Algorithms Used for Classification -- 3.4 Metrics Used to Access the Performance of  Implementation -- 4 Result and Discussion -- 5 Conclusion and Future Scope -- References -- Comparative Analysis of ML Models for Brain Tumor Detection -- 1 Introduction -- 2 Related Work -- 3 Dataset Description -- 4 Methodology -- 4.1 Model Selection -- 4.2 Model Creation Using Orange -- 5 Result and Discussion -- 6 Conclusion -- References -- Machine Learning-Based Prediction of COVID-19: A Robust Approach for Early Diagnosis and Treatment -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Gathering -- 3.2 Dataset -- 3.3 Data Preparation -- 3.4 Data Normalization -- 3.5 Data Splitting -- 3.6 The Application of Algorithms -- 3.7 Model Assessment -- 3.8 Choosing the Best Algorithm -- 4 Result Analysis. , 4.1 Accuracy -- 4.2 Jaccard Score -- 4.3 Cross-Validated Score -- 5 Conclusion -- References -- The Prediction of Diabetes Using Machine Learning in the Healthcare System -- 1 Introduction -- 2 Literature Study -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Algorithm -- 4 Simulation Result -- 5 Conclusion and Future Scope -- References -- A Fuzzy-Based Vision Transformer Model for Tea Leaf Disease Detection -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset -- 3.2 Data Splitting -- 4 Experimental Result -- 4.1 Accuracy -- 4.2 Precision -- 4.3 Recall -- 4.4 Analysis of Model Accuracy and Training Times -- 4.5 Experimenting with Vision Transformer -- 4.6 Confusion Matrix -- 4.7 Training Loss and Validation Loss -- 5 Contribution -- 6 Conclusion -- 7 Limitations and Future Works -- References -- Towards Federated-Deep Learning-Based Glaucoma Detection from Color Fundus Images -- 1 Introduction -- 2 Literature Review -- 3 CNN and Federated Learning -- 3.1 Convolution Neural Network -- 3.2 Federated Learning -- 3.3 Used Dataset -- 3.4 Data Pre-processing -- 3.5 Federated Learning Technique -- 3.6 AlexNet Architecture -- 4 Proposed Architecture -- 5 Performance Analysis -- 6 Limitations and Future Scopes -- 7 Conclusions -- References -- Inventing the Potential of a High-Frequency EEG, Namely Dodecanogram (DDG): Human Subjects' Study -- 1 Introduction -- 2 Methods -- 2.1 Construction of DDG -- 3 Discussion -- 3.1 Triplet-of-Triplet Electromagnetic Radiation Mapping from the Whole Human Body Using Dodecanogram, DDG -- 3.2 Human Subjects Study Using DDG Technology -- 4 Conclusion -- References -- Real-Time Prediction of Diabetes Complications Using Regression-Based Machine Learning Models -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 3.1 Methods for Measuring Blood Glucose Levels. , 3.2 Data Preprocessing -- 3.3 Machine Learning Classifiers -- 4 Performance Evaluation -- 5 Result Analysis -- 6 Conclusion -- References -- Network and Cyber Security -- Exploring Open Access Cybersecurity Datasets for Machine Learning-Based Cyberattack Detection -- 1 Introduction -- 2 Literature Review -- 3 Basic Security Concepts -- 4 Cybersecurity Datasets -- 4.1 CICID2017 -- 4.2 VIRUS-MNIST: A BENCHMARK MALWARE -- 4.3 Dumpware10: A Dataset for Memory Dump Based Malware Image Recognition -- 4.4 CCCS-CIC-AndMal2020 -- 4.5 Malimg Malware Images -- 4.6 KDD Cup'99 -- 4.7 NSL-KDD -- 4.8 DARPA -- 4.9 UNSW-NB15 -- 4.10 IoT-23 -- 4.11 CTU-13 -- 5 Types of Attack in Cyberspace -- 5.1 Cross-Site Scripting -- 5.2 SQL Injection Attacks -- 5.3 Broken Authentication -- 5.4 Drive-By Download -- 5.5 Password-Based Attacks -- 5.6 Fuzzing -- 5.7 Using Components with Known Vulnerabilities -- 5.8 Distributed Denial-of-Service (DDoS) -- 5.9 Man-in-the-Middle (MiTM) -- 5.10 Directory Traversal -- 6 Machine Learning Algorithms -- 6.1 Random Forest -- 6.2 ECOC-SVM -- 6.3 SVM -- 6.4 KNN -- 6.5 Logistic Regression -- 6.6 CatBoost -- 6.7 MDRL-SLSTM -- 6.8 RC-NN -- 6.9 Voting Extreme Learning Machine (V-ELM) -- 6.10 ExtraTrees Classifier -- 7 Limitations and Future Work -- 8 Conclusion -- References -- Design and Development of a Reliable Blockchain-Based Pension System -- 1 Introduction -- 2 Blockchain -- 3 Methodology Adopted to Implement a Blockchain-Secured Pension System -- 4 Implementation of Blockchain-Secured Pension System -- 5 Results and Discussion -- 6 Conclusion and Future Scope -- References -- Comparative Analysis of CatBoost Against Machine Learning Algorithms for Classification of Altered NSL-KDD -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Preprocessing -- 3.2 Classification and Evaluation. , 3.3 Evaluation Metrics and Model Comparison.

Additional Edition: ISBN 981-9719-22-4

Language: English

UID:

Format: 1 online resource (569 pages)

Edition: 1st ed.

ISBN: 981-9978-62-9

Series Statement: Lecture Notes in Networks and Systems Series ; v.818

Note: Intro -- Preface -- Contents -- Editors and Contributors -- Climate Change Parameter Dataset (CCPD): A Benchmark Dataset for Climate Change Parameters in Jammu and Kashmir -- 1 Introduction -- 2 Background and Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Forest Cover -- 3.3 Water Bodies -- 3.4 Agriculture and Vegetation -- 3.5 Population -- 3.6 Temperature -- 3.7 Construction -- 3.8 Air Index -- 4 Dataset Results -- 5 Conclusion -- References -- Brain Tumor Classification Using Deep Learning Techniques -- 1 Introduction -- 2 Review of Previous Work -- 3 Information About the Data and Environmental Setup -- 3.1 Data Preparation and Exploration -- 4 Convolutional Neural Networks and Techniques Implemented -- 4.1 AlexNetV2 -- 4.2 VGG-16 -- 4.3 ResNet-50 -- 4.4 Inception-ResNet -- 4.5 MobileNetv2 -- 5 Experimental Approaches -- 5.1 Novel CNN Architecture -- 5.2 Parallel Networks Model -- 5.3 Machine Learning Assisted -- 6 Optimisation Using Nature-Inspired Algorithms -- 6.1 Particle Swarm Optimization -- 6.2 Genetic Algorithm -- 7 Conclusion -- References -- Machine Learning-Based Hardware Trojans Detection in Integrated Circuits: A Systematic Review -- 1 Introduction -- 2 Machine Learning and Models -- 3 A Review of Machine Learning Methods for Hardware Trojan Detection -- 3.1 Reverse Engineering -- 3.2 Circuit Feature Analysis -- 3.3 Side-Channel Analysis -- 3.4 Golden Model Free Analysis -- 3.5 Classification Approaches -- 3.6 Final Result of Hardware Trojan Detection Approaches -- 4 Discussion and Future Work -- 5 Conclusion -- References -- Impact of Technostress on Employee Retention and Employee Turnover -- 1 Introduction -- 2 Literature Review -- 3 Theoretical Background and Hypothesis -- 4 Methodology -- 5 Data Analysis and Discussion -- 6 Limitations and Future Scope -- 7 Conclusion -- References. , Single Image Dehazing Using DCP with Varying Scattering Constant -- 1 Introduction -- 2 Haze Formation Model -- 3 Solution Approach -- 3.1 Estimation of Atmospheric Light Using DCP -- 3.2 Estimating Omega -- 4 Results and Discussion -- 5 Conclusion -- References -- Detecting IoT Malware Using Federated Learning -- 1 Introduction -- 2 Related Works -- 2.1 Signature-Based Detection -- 2.2 Behavior-Based Detection -- 2.3 Machine Learning-Based Detection -- 2.4 Deep Learning-Based Detection -- 2.5 Motivation for Federated Learning -- 3 Federated Learning -- 3.1 Overview of Federated Learning -- 3.2 Mathematical Formulation -- 3.3 Challenges and Considerations in Federated Learning -- 4 Federated Learning Strategy for IoT Malware Recognition -- 4.1 Obstacles and Rationale -- 4.2 Model Design -- 4.3 Procedure of Federated Learning -- 5 Experimental Results and Evaluation -- 6 Conclusion -- References -- A Deep Learning Approach for BGP Security Improvement -- 1 Introduction -- 2 Related Work -- 3 Deep Learning Models -- 3.1 Convolutional Neural Network (CNN) -- 4 Dataset -- 5 Proposed Technique -- 6 Evaluation Metrics -- 7 Experimental Results -- 8 Conclusion -- References -- Wireless Sensor Network Protocols in Underwater Communication -- 1 Introduction -- 1.1 Underwater Wireless Sensor Network Architecture -- 2 Challenges Regarding UWSNs -- 2.1 Propagation Delay -- 2.2 Bandwidth -- 2.3 Energy Consumption -- 2.4 Communication Coverage -- 2.5 Attenuation -- 2.6 Cost -- 2.7 Sophisticated Techniques -- 3 Routing Protocols -- 3.1 Localization-Based Protocols -- 3.2 Localization-Free Protocols -- 3.3 Cooperation Routing -- 4 Conclusion -- References -- A Genetic Algorithm Approach for Portfolio Optimization -- 1 Introduction -- 1.1 Techniques for Portfolio Optimization -- 2 Genetic Algorithm -- 3 Scope of Genetic Algorithm -- 4 Fitness Function. , 5 Genetic Algorithm-Based Portfolio Optimization -- 6 Result and Analysis -- 7 Advantages -- 8 Conclusion -- References -- Security Issues and Solutions in Post Quantum Authenticated Key Exchange for Mobile Devices -- 1 Introduction -- 2 Contribution -- 3 Description of Signal Leakage Attack -- 4 Preliminaries -- 5 Dabra et al. ``Lattice-Based Key Exchange for Mobile Devices ch10dabra2020lba'' -- 6 Statement of Problem -- 6.1 Registration Issues -- 6.2 Password Change Issues -- 6.3 Insider Attack -- 6.4 Signal Leakage Attack -- 7 Changes Required in Dabra et al.'s Registration Phase -- 8 Conclusion/Future Directions -- References -- Towards Decentralized Fog Computing: A Comprehensive Review of Models, Architectures, and Services -- 1 Introduction -- 1.1 Fog Computing, an Extension of Cloud Computing -- 1.2 Fog Over Cloud -- 1.3 Transition from Cloud to Fog -- 2 Motivation -- 3 Literature Survey -- 4 Architectural Model -- 4.1 Architectural Styles -- 4.2 Views -- 4.3 Dimensions -- 4.4 Related Work -- 5 Discussion -- 6 Research Contributions -- 7 Conclusion -- References -- Analysis of Various Mac Protocols in 802.11 AX -- 1 Introduction -- 1.1 OFDMA Multi-user Transmissions in 802.11ax -- 2 Literature Review -- 2.1 Comparison Table -- 3 Conclusion -- References -- Monkeypox Disease Classification Using HOG-SVM Model -- 1 Introduction -- 1.1 Transmission -- 1.2 Symptoms of Monkeypox Disease -- 1.3 Traditional Detection -- 1.4 Traditional Treatments of Monkeypox -- 1.5 AI-Based Detection -- 2 Literature Survey -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Feature Extraction -- 3.3 Classification -- 3.4 Support Vector Machine (SVM) -- 4 Implementation -- 5 Evaluation Matrices -- 6 Result -- 7 Conclusion -- References -- A Deep Learning Model for Automatic Recognition of Facial Expressions Using Haar Cascade Images -- 1 Introduction. , 2 Literature Review -- 3 The Presented Approach -- 3.1 Dataset -- 3.2 Pre-processing -- 3.3 Presented Deep Learning Model -- 3.4 Testing -- 4 Result -- 5 Conclusion and Future Work -- References -- Sensing Performance Analysis Using Choatic Signal-Based SCMA Codebook for Secure Cognitive Communication System in 5G -- 1 Introduction -- 1.1 Related Work -- 2 SCMA with Chaotic Sequence-Based System Analysis -- 2.1 Dynamic Characteristic of Chaotic Sequence -- 2.2 SCMA Modeling -- 3 System Model -- 3.1 SCMA Encoding -- 3.2 SCMA Decoding -- 4 Proposed Detection Method -- 4.1 Binary Hypothesis Testing -- 4.2 Wald Hypothesis Test -- 4.3 Weight Assignment Method -- 4.4 Experimental Setup -- 5 Detection Performance Analysis -- 6 Conclusion -- References -- Identification of Severity Level for Diabetic Retinopathy Detection Using Neural Networks -- 1 Introduction -- 2 Review of Literature -- 3 Motivation of Proposed Research Work -- 4 Existing Datasets -- 4.1 Dataset from Zenodo -- 4.2 EyePACS Dataset from Kaggle -- 4.3 APTOS Dataset from Kaggle -- 5 Proposed Methodology -- 5.1 Flow of the Proposed Work -- 5.2 Preprocessing on the Dataset -- 5.3 Architecture Used for the Proposed Work -- 5.4 Implementation Platform and Performance Matrix -- 6 Results -- 7 Conclusion -- 8 Future Work -- References -- Metaheuristic Optimized BiLSTM Univariate Time Series Forecasting of Gold Prices -- 1 Introduction -- 2 Related Works -- 2.1 BiLSTM Overview -- 2.2 Variation Mode Decomposition -- 2.3 Metaheuristics Optimization -- 3 Methods -- 3.1 Overview of Basic Moth Flame Optimizer Algorithm -- 3.2 Modified MFO -- 4 Experiments and Discussion -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Results and Discussion -- 5 Conclusion -- References -- Improvised Neural Machine Translation Model for Hinglish to English -- 1 Introduction -- 2 Literature Review. , 2.1 Sequence-to-Sequence (STS) -- 2.2 Attention (ATT) Model -- 3 Methodology -- 3.1 Dataset -- 3.2 Processing Data -- 3.3 Proposed System -- 4 Results -- 5 Discussion -- 5.1 No Smoothing -- 5.2 Laplace Smoothing -- 5.3 Additive Smoothing -- 5.4 Exponential Smoothing -- 5.5 Chen and Cherry Smoothing -- 6 Conclusion -- References -- Recording of Class Attendance Using DL-Based Face Recognition Method -- 1 Introduction -- 2 Related Work -- 3 Proposed System -- 3.1 Dataset Creation -- 3.2 Preprocessing -- 3.3 Face Detection and Recognition -- 3.4 Attendance Marking and Report Generation -- 3.5 Implementation -- 4 Result Analysis -- 5 Conclusions -- References -- Machine Learning Enabled Hairstyle Recommender System Using Multilayer Perceptron -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Dataset Description -- 3.2 Data Preprocessing -- 3.3 Facial Landmark Detection -- 3.4 Face Shape Classification -- 3.5 Model Comparison -- 3.6 Recommendation -- 4 Results and Discussion -- 5 Conclusion -- References -- Automated Health Insurance Management Framework with Intelligent Fraud Detection, Premium Prediction, and Risk Prediction -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Dataset Specifics -- 3.2 Exploratory Data Analysis -- 3.3 Feature Engineering and Selection -- 3.4 Machine Learning Models -- 4 Experimental Results and Discussion -- 5 Conclusion and Future Work -- References -- Responsible Artificial Intelligence for Music Recommendation -- 1 Introduction -- 2 Background and Motivation -- 3 Literature Review -- 3.1 Identifying Research Gaps in the Literature -- 3.2 Contributions of This Paper -- 4 Methodology -- 4.1 Data Acquiring and EDA -- 4.2 Model Development and Evaluation -- 4.3 Feature Importance and Model Retrain -- 4.4 Explainable AI -- 5 Results -- 6 Conclusion -- 7 Future Work -- References. , A Robot Mapping Technique for Indoor Environments.

Additional Edition: ISBN 981-9978-61-0

Language: English