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Artificial Intelligence in Oncology : Cancer Diagnosis and Treatment, Medical Imaging, and Personalized Medicine / / edited by Sachi Nandan Mohanty, Álvaro Rocha, Pushan Kumar Dutta
| Artificial Intelligence in Oncology : Cancer Diagnosis and Treatment, Medical Imaging, and Personalized Medicine / / edited by Sachi Nandan Mohanty, Álvaro Rocha, Pushan Kumar Dutta |
| Autore | Mohanty Sachi Nandan |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (901 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
RochaÁlvaro
DuttaPushan Kumar |
| Collana | Medicine Series |
| Soggetto topico |
Artificial intelligence
Medical informatics Bioinformatics User interfaces (Computer systems) Human-computer interaction Medical radiology Oncology Artificial Intelligence Health Informatics User Interfaces and Human Computer Interaction Radiation Oncology |
| ISBN | 3-031-94302-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: AI in Cancer Prediction and Diagnosis -- Chapter 1 Seer Breast Cancer Prediction and Analysis using a Machine Learning Approach -- Chapter 2 Automated MRI-Based Brain Tumor Classification with CNN Models -- Chapter 3 Personalized Transfer Learning-Based CNN for High-Precision Oral Cancer Classification -- Chapter 4 CloudMedX: Cloud-Based Glioma Detection using Deep Learning -- Chapter 5 Multi-Class Brain Tumor Detection via DieT Transformer and Advanced Feature Selection -- Chapter 6 An investigation of AI-assisted strategies for accurate detection of Oral Cancer in Assam -- Part II: Machine Learning and Deep Learning in Oncology -- Chapter 7 Machine Learning-Based Recommender System for Cancer Patients -- Chapter 8 Deep Learning Techniques to Detect Brain Tumors Using EfficientNet-B0 CNN Architecture -- Chapter 9 Optimized Deep Learning Framework for Lung Cancer Detection in Computed Tomography Scans -- Chapter 10 Hybrid Deep Learning Architectures for Brain Tumor Classification Using Magnetic Resonance Imaging: ViT-GRU and GNet-SVM Models -- Chapter 11 A Combination of CNN and Fuzzy Transform Framework for Accurate Brain Tumor Detection -- Chapter 12 CorPML: A ML-based hybrid model for effective Cancer diagnosis using CFS and PSO feature selection -- Chapter 13 Comparative Analysis of Machine Learning Algorithms for Lung and Colon Cancer Classification Using Deep Feature Extraction -- Chapter 14 A Machine Learning and Deep Learning Approach to Cancer Prediction -- Chapter 15 Enhanced Lung Cancer Classification Using SMOTE and Soft Voting Ensemble of Decision Tree, XGBoost, and Logistic Regression -- Chapter 16 A Comprehensive Analysis of Lung Cancer Prediction Using Machine Learning Models -- Chapter 17 Breast Cancer Prediction based on SMOTE and Ensemble Classifier -- Part III: AI-Driven Medical Imaging and Diagnostic Approaches.-Chapter 18 Novel Method for Assessing the Effectiveness of the Deep Learning-Based Unet Model in Forecasting Brain Tumors Using MRI Scans -- Chapter 19 Binary Algorithm in AI for Early Skin Cancer Identification with 3D-TBP -- Chapter 20 Mammograms Classification Using Deep Neural Networks in Breast Cancer Detection -- Chapter 21 Thermal and Mammographic Image Fusion for Breast Cancer Detection: A Self-Supervised Bi-Pipeline Approach -- Chapter 22 ResNet-152 for Brain Tumor Detection: A Deep Learning Approach for Medical Image Analysis -- Chapter 23 Computational Diagnosis application of Cervical Cancer using Deep Learning Application -- Chapter 24 The Impact of Preprocessing Techniques on Automated Skin Cancer Detection Systems -- Chapter 25 Performance Analysis of Intelligent models for Breast cancer classification -- Part IV: AI in Cancer Treatment and Personalized Medicine -- Chapter 26 AI-Driven Advancements in Oncology: Harnessing Pharmacogenomics for Precision Cancer Treatment and Optimized Therapeutic Outcomes -- Chapter 27 Integrating Deep Learning in Prostate Cancer Grading: Innovations in Computational Pathology -- Chapter 28 AI-Driven Radiotherapy Solutions for Rare and Complex Cancers Using Multi-Omics Approaches -- Chapter 29 Artificial Intelligence Future in Oncology for Breast Cancer: Risk Prediction and Monitoring -- Part V: AI in Public Health and Oncology Nursing -- Chapter 30 AI in Public Health -- Chapter 31 The Impact of Artificial Intelligence on Oncology Nursing: Enhancing Patient Care, Symptom Management, and Decision Support -- Chapter 32 Empowering Oncology Healthcare Professionals: Evaluating the Effect of AI-Driven Training Modules on Awareness, Knowledge, Clinical Competence, and Patient Care in Cancer Management -- Chapter 33 Revolutionizing Oncology Education: The Impact of AI-Driven Tools on Patient Knowledge, Adherence, and Satisfaction -- Chapter 34 Harnessing Artificial Intelligence in Oncology Palliative Care: Current Status, Challenges, and Recommendations with Reference to India -- Chapter 35 The Transformative Role of AI in Public Health for Cancer Prevention, Early Detection, and Management -- Part VI: AI and Predictive Analytics in Cancer Research -- Chapter 36 Health Care Professionals’ Opinions on the Role of Artificial Intelligence (AI) in Preventing Cancer -- Chapter 37 Transforming Oncology Care: The Role of Artificial Intelligence in Improving Diagnostic Accuracy and Treatment Decisions -- Chapter 38 Artificial Intelligence in Oncology: Comparative Analysis and Insights into Diagnostics, Treatment, Challenges, and Future Prospects -- Chapter 39 Utilizing Artificial Intelligence to Revolutionize Cancer Screening Through the Application of Predictive Analytics in Public Health -- Chapter 40 Translating Hybrid ANN-ARIMA Diagnostic Models for Early Detection of Oncological Biomarkers -- Chapter 41 Correlating the Hallmarks of Cancer: A Study Using Conditional Dependency Networks -- Chapter 42 Oncology in the AI Era: Transforming Cancer Care Through Intelligent Diagnosis and Treatment -- Chapter 43 Identifying Key Survival AI based Predictors in Breast Cancer for Indian Women: A Retrospective Cohort Analysis -- Chapter 44 Artificial Intelligence in Cancer Management: Bridging Gaps in Global Healthcare Systems -- Chapter 45 Insights into Women's Sentiments on Breast Cancer Detection, Causes, and Treatments: A Comprehensive Analysis -- Chapter 46 A Comprehensive Study of Artificial Intelligence in Oncology -- Chapter 47 Potential Use of Artificial Intelligence in Diagnosing Acute Myeloid Leukaemia: A Haematological Disorder -- Chapter 48 ANN-Based Binary Classification for Breast Cancer: A Comparative Study with Machine Learning Models. |
| Record Nr. | UNINA-9911031661103321 |
Mohanty Sachi Nandan
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Explainable IoT Applications: A Demystification / / edited by Sachi Nandan Mohanty, Suneeta Satpathy, Xiaochun Cheng, Subhendu Kumar Pani
| Explainable IoT Applications: A Demystification / / edited by Sachi Nandan Mohanty, Suneeta Satpathy, Xiaochun Cheng, Subhendu Kumar Pani |
| Autore | Mohanty Sachi Nandan |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (517 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
SatpathySuneeta
ChengXiaochun PaniSubhendu Kumar |
| Collana | Information Systems Engineering and Management |
| Soggetto topico |
Computational intelligence
Engineering - Data processing Artificial intelligence Computational Intelligence Data Engineering Artificial Intelligence |
| ISBN |
9783031748851
3031748859 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Essential Uses of IoT and Machine Learning -- IoT Pro-Interventions: Transforming Industries and Enhancing Quality of Life -- A Comprehensive Review of Machine Learning Approaches in IoT and Cyber Security for Information Systems Analysis -- Application of Machine learning in the Internet of Things -- Empowering Industries with IoT and Machine Learning Innovations -- A Framework for Sustainable Smart Healthcare Systems in Smart Cities -- Cloud Computing Applications in Digital Health: Challenges related to Privacy and Safety -- An IoT-Based Blockchain-Enabled Secure Storage for Healthcare Systems -- Block-Chain Technology in Smart Telemedicine using IOT -- Securing the Future of IoT-Based Smart Healthcare: Challenges, Innovations, and Best Practice -- Smart City: Challenges & Opportunities Detection and Identification of Autonomous Vehicles Using Sensor Synthesis -- AN IOT BASED REAL TIME TRAFFIC MONITORING SYSTEM -- Internet of Things enabled Technological devices empowering expertise in improve Smart City operations -- Enhancing Smart City Retail: An Innovative IoT Driven Smart Billing-Enabled Shopping Cart -- Smart City: Challenges and Issues -- IoT based Real-Time Ecological Monitoring System Deploying an Arduino Board and Cloud Computing -- IoT Based Monitoring Of Waste Management And Air Pollutants -- IOT based smart dustbin design and implementation for monitoring under uncertain environments -- Smart garbage monitoring system using IOT for commercial purpose -- IoT Based Smart Home Systems -- A Survey on Various Secure Access Control and Authentication in a Block Chain -Enable Cloud IoT -- Uncovering the Truth: A Machine Learning Approach to Detect Fake Product Reviews and analyze Sentiment -- Real Time Fall Detection monitoring on elderly using IoT and Deep Learning -- CNN’s augmented with IoT for Traffic Optimization and Signal Regulation -- CVLSTMLW-CNN:A IoT-Enabled Hybrid CNN model for Heart Disease Prediction -- Advancements in Security Technologies for Smart Cities: A Comprehensive Overview -- A Deep Learning Framework based on Convolutional Neural Network for Automatic Detection of Cyberattacks in IoT Use Cases -- Digital Attack Identification for the Internet of Things Using Machine Learning -- IoT Applications and Cyber Threats: Mitigation Strategies for a Secure Future -- Internet of Things and OpenCV-Based Smart Posture Recognition Chair -- Security concerns in low power networks for Internet of Things (IoT) -- Comprehensive Review of Security Challenges and issues in Wireless Sensor Networks Integrated with IoT. |
| Record Nr. | UNINA-9910983327603321 |
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Mohanty Sachi Nandan
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fuzzy Computing in Data Science : Applications and Challenges
| Fuzzy Computing in Data Science : Applications and Challenges |
| Autore | Mohanty Sachi Nandan |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (363 pages) |
| Altri autori (Persone) |
ChatterjeePrasenjit
HungBui Thanh |
| Collana | Smart and Sustainable Intelligent Systems Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-394-15688-X
1-394-15687-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgement -- Chapter 1 Band Reduction of HSI Segmentation Using FCM -- 1.1 Introduction -- 1.2 Existing Method -- 1.2.1 K-Means Clustering Method -- 1.2.2 Fuzzy C-Means -- 1.2.3 Davies Bouldin Index -- 1.2.4 Data Set Description of HSI -- 1.3 Proposed Method -- 1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid -- 1.3.2 Band Reduction Using K-Means Algorithm -- 1.3.3 Band Reduction Using Fuzzy C-Means -- 1.4 Experimental Results -- 1.4.1 DB Index Graph -- 1.4.2 K-Means-Based PSC (EEOC) -- 1.4.3 Fuzzy C-Means-Based PSC (EEOC) -- 1.5 Analysis of Results -- 1.6 Conclusions -- References -- Chapter 2 A Fuzzy Approach to Face Mask Detection -- 2.1 Introduction -- 2.2 Existing Work -- 2.3 The Proposed Framework -- 2.4 Set-Up and Libraries Used -- 2.5 Implementation -- 2.6 Results and Analysis -- 2.7 Conclusion and Future Work -- References -- Chapter 3 Application of Fuzzy Logic to the Healthcare Industry -- 3.1 Introduction -- 3.2 Background -- 3.3 Fuzzy Logic -- 3.4 Fuzzy Logic in Healthcare -- 3.5 Conclusions -- References -- Chapter 4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database -- 4.1 Introduction -- 4.2 Data Extraction and Interpretation -- 4.3 Results and Discussion -- 4.3.1 Per Year Publication and Citation Count -- 4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic -- 4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas -- 4.3.4 Major Contributing Countries Toward Fuzzy Research Articles -- 4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis -- 4.3.6 Coauthorship of Authors -- 4.3.7 Cocitation Analysis of Cited Authors -- 4.3.8 Cooccurrence of Author Keywords.
4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries -- 4.4.1 Bibliographic Coupling of Documents -- 4.4.2 Bibliographic Coupling of Sources -- 4.4.3 Bibliographic Coupling of Authors -- 4.4.4 Bibliographic Coupling of Countries -- 4.5 Conclusion -- References -- Chapter 5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling -- 5.1 Introduction -- 5.2 History of Fuzzy Logic and Its Applications -- 5.3 Approximate Reasoning -- 5.4 Fuzzy Sets vs Classical Sets -- 5.5 Fuzzy Inference System -- 5.5.1 Characteristics of FIS -- 5.5.2 Working of FIS -- 5.5.3 Methods of FIS -- 5.6 Fuzzy Decision Trees -- 5.6.1 Characteristics of Decision Trees -- 5.6.2 Construction of Fuzzy Decision Trees -- 5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment -- 5.8 Conclusion -- References -- Chapter 6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model -- 6.1 Introduction -- 6.1.1 Aim and Scope -- 6.1.2 R-Tool -- 6.1.3 Application of Fuzzy Logic -- 6.1.4 Dataset -- 6.2 Model Study -- 6.2.1 Introduction to Machine Learning Method -- 6.2.2 Time Series Analysis -- 6.2.3 Components of a Time Series -- 6.2.4 Concepts of Stationary -- 6.2.5 Model Parsimony -- 6.3 Methodology -- 6.3.1 Exploratory Data Analysis -- 6.3.1.1 Seed Types-Analysis -- 6.3.1.2 Comparison of Location and Seeds -- 6.3.1.3 Comparison of Season (Month) and Seeds -- 6.3.2 Forecasting -- 6.3.2.1 Auto Regressive Integrated Moving Average (ARIMA) -- 6.3.2.2 Data Visualization -- 6.3.2.3 Implementation Model -- 6.4 Result Analysis -- 6.5 Conclusion -- References -- Chapter 7 Modified m-Polar Fuzzy Set ELECTRE-I Approach -- 7.1 Introduction -- 7.1.1 Objectives -- 7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon's Entropy Weight Calculations. 7.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon's Entropy Weight Calculation Method -- 7.3 Application to Industrial Problems -- 7.3.1 Cutting Fluid Selection Problem -- 7.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem -- 7.3.3 FMS Selection Problem -- 7.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection -- 7.4 Conclusions -- References -- Chapter 8 Fuzzy Decision Making: Concept and Models -- 8.1 Introduction -- 8.2 Classical Set -- 8.3 Fuzzy Set -- 8.4 Properties of Fuzzy Set -- 8.5 Types of Decision Making -- 8.5.1 Individual Decision Making -- 8.5.2 Multiperson Decision Making -- 8.5.3 Multistage Decision Making -- 8.5.4 Multicriteria Decision Making -- 8.6 Methods of Multiattribute Decision Making (MADM) -- 8.6.1 Weighted Sum Method (WSM) -- 8.6.2 Weighted Product Method (WPM) -- 8.6.3 Weighted Aggregates Sum Product Assessment (WASPAS) -- 8.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) -- 8.7 Applications of Fuzzy Logic -- 8.8 Conclusion -- References -- Chapter 9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India) -- 9.1 Introduction -- 9.2 Objectives and Methodology -- 9.2.1 Objectives -- 9.2.2 Methodology -- 9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants -- 9.3.1 Psychological Variables Identified -- 9.3.2 Fuzzy Logic for Solace to Migrants -- 9.4 Findings -- 9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid -- 9.6 Conclusion -- References -- Chapter 10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow -- 10.1 Significance of Machine Learning in Healthcare -- 10.2 Cloud-Based Artificial Intelligent Secure Models -- 10.3 Applications and Usage of Machine Learning in Healthcare. 10.3.1 Detecting Diseases and Diagnosis -- 10.3.2 Drug Detection and Manufacturing -- 10.3.3 Medical Imaging Analysis and Diagnosis -- 10.3.4 Personalized/Adapted Medicine -- 10.3.5 Behavioral Modification -- 10.3.6 Maintenance of Smart Health Data -- 10.3.7 Clinical Trial and Study -- 10.3.8 Crowdsourced Information Discovery -- 10.3.9 Enhanced Radiotherapy -- 10.3.10 Outbreak/Epidemic Prediction -- 10.4 Edge AI: For Smart Transformation of Healthcare -- 10.4.1 Role of Edge in Reshaping Healthcare -- 10.4.2 How AI Powers the Edge -- 10.5 Edge AI-Modernizing Human Machine Interface -- 10.5.1 Rural Medicine -- 10.5.2 Autonomous Monitoring of Hospital Rooms-A Case Study -- 10.6 Significance of Fuzzy in Healthcare -- 10.6.1 Fuzzy Logic-Outline -- 10.6.2 Fuzzy Logic-Based Smart Healthcare -- 10.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems -- 10.6.4 Applications of Fuzzy Logic in Healthcare -- 10.7 Conclusion and Discussions -- References -- Chapter 11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS -- 11.1 Introduction -- 11.2 Video Conferencing Software and Its Major Features -- 11.2.1 Video Conferencing/Meeting Software (VC/MS) for Higher Education Institutes -- 11.3 Fuzzy TOPSIS -- 11.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS -- 11.4 Sample Numerical Illustration -- 11.5 Conclusions -- References -- Chapter 12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming -- 12.1 Introduction -- 12.1.1 Basic Concepts of Fuzzy AHP and Goal Programming -- 12.2 Research Model -- 12.2.1 Average Growth Rate Calculation -- 12.3 Result and Discussion -- 12.4 Conclusion -- References -- Chapter 13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment -- 13.1 Introduction -- 13.2 Proposed Algorithm. 13.3 An Illustrative Example on Ergonomic Design Evaluation -- 13.4 Conclusions -- References -- Chapter 14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic -- 14.1 Introduction -- 14.2 Control Approach in Wave Energy Systems -- 14.3 Related Work -- 14.4 Mathematical Modeling for Energy Conversion from Ocean Waves -- 14.5 Proposed Methodology -- 14.5.1 Wave Parameters -- 14.5.2 Fuzzy-Optimizer -- 14.6 Conclusion -- References -- Chapter 15 The m-Polar Fuzzy TOPSIS Method for NTM Selection -- 15.1 Introduction -- 15.2 Literature Review -- 15.3 Methodology -- 15.3.1 Steps of the mFS TOPSIS -- 15.4 Case Study -- 15.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method -- 15.4.2 Effect of Shannon's Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method -- 15.5 Results and Discussions -- 15.5.1 Result Validation -- 15.6 Conclusions and Future Scope -- References -- Chapter 16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology -- 16.1 Introduction -- 16.2 MCDM Techniques -- 16.2.1 FAHP -- 16.2.2 Entropy Method as Weights (Influence) Evaluation Technique -- 16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches -- 16.3.1 TOPSIS -- 16.3.2 FMOORA Method -- 16.3.3 FVIKOR -- 16.3.4 Fuzzy Grey Theory (FGT) -- 16.3.5 COPRAS -G -- 16.3.6 Super Hybrid Algorithm -- 16.4 Illustrative Example -- 16.5 Results and Discussions -- 16.5.1 FTOPSIS -- 16.5.2 FMOORA -- 16.5.3 FVIKOR -- 16.5.4 Fuzzy Grey Theory (FGT) -- 16.5.5 COPRAS-G -- 16.5.6 Super Hybrid Approach (SHA) -- 16.6 Conclusions -- References -- Chapter 17 Fuzzy MCDM on CCPM for Decision Making: A Case Study -- 17.1 Introduction -- 17.2 Literature Review -- 17.3 Objective of Research -- 17.4 Cluster Analysis -- 17.4.1 Hierarchical Clustering -- 17.4.2 Partitional Clustering -- 17.5 Clustering. 17.6 Methodology. |
| Record Nr. | UNINA-9910632499103321 |
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Mohanty Sachi Nandan
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Protecting and Mitigating Against Cyber Threats : Deploying Artificial Intelligence and Machine Learning
| Protecting and Mitigating Against Cyber Threats : Deploying Artificial Intelligence and Machine Learning |
| Autore | Mohanty Sachi Nandan |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (561 pages) |
| Disciplina | 005.8 |
| Altri autori (Persone) |
SatpathySuneeta
YangMing ValiD. Khasim |
| Soggetto topico | Computer security |
| ISBN |
1-394-30521-4
1-394-30519-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Foundations of AI & -- ML in Security -- Chapter 1 Foundations of AI and ML in Security -- Abbreviations -- 1.1 Introduction -- 1.1.1 The Convergence of AI and ML in Security -- 1.2 Understanding Security Attacks -- 1.2.1 Types of Attacks and Vulnerability -- 1.2.2 How Attacks Exploit Vulnerabilities -- 1.2.3 Real-World Examples of AI and ML for Security -- 1.3 Evolution of Information, Cyber Issues/Threats Attacks -- 1.3.1 Cyber Security Threats -- 1.3.2 The Most Prevalent Security Attacks -- 1.4 Machine Learning for Security and Vulnerability -- 1.4.1 Data Collection and Preprocessing -- 1.4.2 Feature Engineering for Security Attack Detection -- 1.5 Challenges and Future Directions -- 1.6 Summary -- References -- Chapter 2 Application of AI and ML in Threat Detection -- 2.1 Introduction -- 2.2 Foundation of AI and ML in Security -- 2.2.1 Definition and Concepts -- 2.2.2 Types of Artificial Intelligence -- 2.2.3 Algorithms and Models in Machine Learning -- 2.3 AI and ML in Applications in Threat Detection -- 2.3.1 Next-Generation Endpoint Protection -- 2.3.2 Endpoint Detection and Response (EDR) -- 2.4 AI/ML Based Network Intrusion Detection Systems (NIDS) -- 2.5 Threat Intelligence and Predictive Analytics -- 2.6 Challenges and Considerations -- 2.7 Integration and Interoperability -- 2.8 Future Directions -- 2.9 Conclusion -- References -- Chapter 3 Artificial Intelligence and Machine Learning Applications in Threat Detection -- 3.1 Introduction -- 3.2 Foundations of Threat Detection -- 3.2.1 Traditional Threat Detection Methods -- 3.2.2 The Need for Advanced Technologies -- 3.3 Overview of AI and ML -- 3.3.1 Understanding Artificial Intelligence -- 3.3.2 Machine Learning Fundamentals -- 3.4 AI and ML Techniques for Threat Detection.
3.4.1 Supervised Learning and Unsupervised Learning -- 3.4.2 Deep Learning -- 3.5 Challenges and Solutions -- 3.5.1 Imbalanced Datasets -- 3.5.2 Ability and Interpretability -- 3.6 Future Trends and Innovations -- 3.6.1 Evolving Technologies -- 3.6.2 Ethical Considerations -- Conclusion -- References -- Part II: AI & -- ML Applications in Threat Detection -- Chapter 4 Comparison Study Between Different Machine Learning (ML) Models Integrated with a Network Intrusion Detection System (NIDS) -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Methodology -- 4.3.1 Data Preprocessing -- 4.3.2 Data Splitting -- 4.3.3 Machine Learning Models -- 4.4 Proposed Model -- 4.5 Experimental Result -- 4.5.1 Performance Evaluation Metrics -- 4.5.2 Results of XGBoost Classifier -- 4.5.2.1 Confusion Matrix -- 4.5.2.2 Accuracy/Recall/Precision -- 4.5.2.3 ROC Curve -- 4.5.3 Results of ExtraTrees Classifier -- 4.5.3.1 Accuracy/Recall/Precision/ROC Curve -- 4.5.4 Comparison and Discussion -- 4.6 Conclusion and Future Work -- References -- Chapter 5 Applications of AI, Machine Learning and Deep Learning for Cyber Attack Detection -- 5.1 Introduction -- 5.1.1 Evolution of Cyber Threats and the Need for Advanced Solutions -- 5.1.2 Taxonomy of Cyber Attacks -- 5.2 Background -- 5.2.1 What is Cyber Security? -- 5.2.2 Cyber Security Systems -- 5.2.3 Ten Different Cyber Security Domains -- 5.3 Role of AI for Cyber Attack Detection -- 5.3.1 Machine Learning for Cyber Attack Detection -- 5.3.2 Deep Learning as a Game Changer in Cyber Attack Detection -- 5.4 Cyber Security Data Sources and Feature Engineering -- 5.4.1 Data Sources -- 5.4.2 Feature Engineering -- 5.5 Training Models for Anomaly Detection in Network Traffic -- 5.5.1 Supervised Learning Models -- 5.5.2 Unsupervised Learning Models -- 5.5.3 Deep Learning Models -- 5.5.4 Hybrid Models. 5.6 Case Study: The Use of AI and ML in Combating Cyber Attacks -- 5.6.1 Analysis: Company X's Strategy for Detecting Cyber Attacks -- 5.6.1.1 Implementation -- 5.6.1.2 Results -- 5.7 Challenges of Artificial Intelligence Applications in Cyber Threat Detection -- 5.8 Future Trends -- 5.9 Conclusion -- References -- Chapter 6 AI-Based Prioritization of Indicators of Intelligence in a Threat Intelligence Sharing Platform -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Methodology -- 6.3.1 Brief Code Explanation -- 6.3.1.1 Bringing in Libraries and Modules -- 6.3.1.2 Parting the Dataset -- 6.3.1.3 Making and Preparing the Model -- 6.3.1.4 Assessing the Model -- 6.3.1.5 Saving the Prepared Model -- 6.3.1.6 Stacking the Prepared Model -- 6.3.1.7 Information Assortment and Preprocessing -- 6.3.1.8 Extricating Remarkable IP Locations -- 6.3.1.9 Creating Highlights for IP Locations -- 6.3.1.10 Stacking Highlights Information -- 6.3.1.11 Foreseeing Needs -- 6.3.1.12 Printing IP Locations and Needs -- 6.3.2 Explanation of the Code Step-By-Step -- 6.4 Proposed Model -- 6.4.1 Workflow Model -- 6.4.2 Decision Tree Machine Learning Model and Its Usage in this Study -- 6.5 Experimental Result/Result Analysis -- 6.6 Conclusion -- 6.6.1 High Level AI Calculations -- 6.6.2 Reconciliation of Regular Language Handling (NLP) Strategies -- 6.6.3 Interpretability and Reasonableness -- 6.6.4 Taking Care of Information Changeability -- 6.6.5 Ill-Disposed Assault Recognition -- 6.6.6 Moral Contemplations -- References -- Chapter 7 Email Spam Classification Using Novel Fusion of Machine Learning and Feed Forward Neural Network Approaches -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Proposed Methodology -- 7.4 Experimentation and Results -- 7.4.1 Data Assortment -- 7.4.2 Applying ML Algorithms -- 7.4.3 Apply FFNN -- 7.4.4 Apply Stacking Ensemble of RF and FFNN. 7.4.5 Apply Voting Ensemble of RF and FFNN -- 7.4.6 Comparison of All Models -- 7.5 Conclusion -- References -- Chapter 8 Intrusion Detection in Wireless Networks Using Novel Classification Models -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Methodology -- 8.4 State of the Art -- 8.5 Result Analysis -- 8.6 Conclusion -- References -- Chapter 9 Detection and Proactive Prevention of Website Swindling Using Hybrid Machine Learning Model -- 9.1 Introduction -- 9.2 Related Literature Survey -- 9.3 Proposed Framework -- 9.3.1 Block Diagram -- 9.3.2 Flow Chart -- 9.4 Implementation -- 9.4.1 Random Forest -- 9.4.2 XGBoost -- 9.4.3 CATBoost -- 9.5 Result Analysis -- 9.6 Conclusion -- References -- Part III: Advanced Security Solutions & -- Case Studies -- Chapter 10 Securing the Future Networks: Blockchain-Based Threat Detection for Advanced Cyber Security -- 10.1 Introduction -- 10.1.1 Background and Evolution of Cybersecurity Threats -- 10.1.2 The Need for Advanced Threat Detection -- 10.1.3 Review of Blockchain Technology in Cybersecurity -- 10.2 Understanding Blockchain Technology -- 10.2.1 Basics of Blockchain -- 10.2.2 Decentralization and Security Features -- 10.2.3 Smart Contracts and their Role in Security -- 10.3 Challenges in Traditional Threat Detection -- 10.3.1 Evolving Nature of Cyber Threats -- 10.3.2 The Importance of Proactive Security Solutions -- 10.4 Integrating Blockchain into Cybersecurity -- 10.4.1 Using Blockchain as the Basis for Improved Security -- 10.4.2 Consensus Mechanisms and Trust -- 10.4.3 Decentralized Identity Management -- 10.5 Challenges and Considerations of Blockchain in Cybersecurity -- 10.5.1 Scalability Issues in Blockchain -- 10.5.2 Regulatory and Compliance Challenges -- 10.5.3 Balancing Transparency and Privacy -- 10.6 Future Trends and Innovations and Case Studies of Blockchain Technology. 10.6.1 Emerging Technologies in Blockchain-Based Security Cyber Security -- 10.6.2 Industry Initiatives and Collaborations on Blockchain for Cybersecurity Solutions -- 10.7 Conclusion -- References -- Chapter 11 Mitigating Pollution Attacks in Network Coding-Enabled Mobile Small Cells for Enhanced 5G Services in Rural Areas -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Proposed Model -- 11.4 Results -- 11.5 Conclusion -- References -- Chapter 12 Enhancing Multi-Access Edge Computing Efficiency through Communal Network Selection -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Existing System -- 12.4 Proposed System -- 12.5 Implementation -- 12.6 Results and Discussion -- 12.7 Conclusion -- 12.8 Future Scope -- References -- Chapter 13 Enhancing Cyber-Security and Network Security Through Advanced Video Data Summarization Techniques -- 13.1 Introduction -- 13.1.1 Overview of Video Summarization -- 13.1.2 Importance of Efficient Video Management -- 13.2 Video Summarization Techniques -- 13.2.1 Clustering-Based Methods -- 13.2.2 Deep Learning Frameworks -- 13.2.3 Multimodal Integration Strategies (Audio, Visual, Textual) -- 13.3 Notable Advanced Techniques -- 13.3.1 SVS_MCO Method and Performance -- 13.3.2 Knowledge Distillation (KDAN Framework) -- 13.3.3 Advanced Models (Query-Based, Audio-Visual Recurrent Networks) -- 13.4 Graph-Based and Unsupervised Summarization -- 13.4.1 Graph-Based Summarization Techniques -- 13.4.2 Unsupervised Summarization Methods (Two- Stream Approach for Motion and Visual Features) -- 13.5 Secure and Multi-Video Summarization -- 13.5.1 Secure Video Summarization -- 13.5.2 Multi-Video Summarization -- 13.6 Advanced Scene and Activity-Based Summarization -- 13.6.1 Scene Summarization -- 13.6.2 Activity Recognition -- 13.7 Performance Benchmarking and Evaluation. 13.7.1 Datasets and Evaluation Metrics (e.g., SumMe, TVSum). |
| Record Nr. | UNINA-9911018961503321 |
|
Mohanty Sachi Nandan
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||