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Computation in bioinformatics : multidisciplinary applications / / editors, S. Balamurugan [et al.]
| Computation in bioinformatics : multidisciplinary applications / / editors, S. Balamurugan [et al.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021 |
| Descrizione fisica | 1 online resource (352 pages) |
| Disciplina | 570.285 |
| Collana | Artificial Intelligence and So Computing for Industrial Transformation |
| Soggetto topico |
Bioinformatics
Computational biology |
| ISBN |
1-119-65476-9
1-119-65480-7 1-119-65475-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Bioinfomatics as a Tool in Drug Designing -- 1.1 Introduction -- 1.2 Steps Involved in Drug Designing -- 1.2.1 Identification of the Target Protein/Enzyme -- 1.2.2 Detection of Molecular Site (Active Site) in the Target Protein -- 1.2.3 Molecular Modeling -- 1.2.4 Virtual Screening -- 1.2.5 Molecular Docking -- 1.2.6 QSAR (Quantitative Structure-Activity Relationship) -- 1.2.7 Pharmacophore Modeling -- 1.2.8 Solubility of Molecule -- 1.2.9 Molecular Dynamic Simulation -- 1.2.10 ADME Prediction -- 1.3 Various Softwares Used in the Steps of Drug Designing -- 1.4 Applications -- 1.5 Conclusion -- References -- 2 New Strategies in Drug Discovery -- 2.1 Introduction -- 2.2 Road Toward Advancement -- 2.3 Methodology -- 2.3.1 Target Identification -- 2.3.2 Docking-Based Virtual Screening -- 2.3.3 Conformation Sampling -- 2.3.4 Scoring Function -- 2.3.5 Molecular Similarity Methods -- 2.3.6 Virtual Library Construction -- 2.3.7 Sequence-Based Drug Design -- 2.4 Role of OMICS Technology -- 2.5 High-Throughput Screening and Its Tools -- 2.6 Chemoinformatic -- 2.6.1 Exploratory Data Analysis -- 2.6.2 Example Discovery -- 2.6.3 Pattern Explanation -- 2.6.4 New Technologies -- 2.7 Concluding Remarks and Future Prospects -- References -- 3 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective -- 3.1 Introduction -- 3.2 Bioinformatics and Drug Discovery -- 3.2.1 Structure-Based Drug Design (SBDD) -- 3.2.2 Ligand-Based Drug Design (LBDD) -- 3.3 Bioinformatics Tools in Early Drug Discovery -- 3.3.1 Possible Biological Activity Prediction Tools -- 3.3.2 Possible Physicochemical and Drug-Likeness Properties Verification Tools -- 3.3.3 Possible Toxicity and ADME/T Profile Prediction Tools -- 3.4 Future Directions With Bioinformatics Tool.
3.5 Conclusion -- Acknowledgements -- References -- 4 Role of Data Mining in Bioinformatics -- 4.1 Introduction -- 4.2 Data Mining Methods/Techniques -- 4.2.1 Classification -- 4.2.1.1 Statistical Techniques -- 4.2.1.2 Clustering Technique -- 4.2.1.3 Visualization -- 4.2.1.4 Induction Decision Tree Technique -- 4.2.1.5 Neural Network -- 4.2.1.6 Association Rule Technique -- 4.2.1.7 Classification -- 4.3 DNA Data Analysis -- 4.4 RNA Data Analysis -- 4.5 Protein Data Analysis -- 4.6 Biomedical Data Analysis -- 4.7 Conclusion and Future Prospects -- References -- 5 In Silico Protein Design and Virtual Screening -- 5.1 Introduction -- 5.2 Virtual Screening Process -- 5.2.1 Before Virtual Screening -- 5.2.2 General Process of Virtual Screening -- 5.2.2.1 Step 1 (The Establishment of the Receptor Model) -- 5.2.2.2 Step 2 (The Generation of Small-Molecule Libraries) -- 5.2.2.3 Step 3 (Molecular Docking) -- 5.2.2.4 Step 4 (Selection of Lead Protein Compounds) -- 5.3 Machine Learning and Scoring Functions -- 5.4 Conclusion and Future Prospects -- References -- 6 New Bioinformatics Platform-Based Approach for Drug Design -- 6.1 Introduction -- 6.2 Platform-Based Approach and Regulatory Perspective -- 6.3 Bioinformatics Tools and Computer-Aided Drug Design -- 6.4 Target Identification -- 6.5 Target Validation -- 6.6 Lead Identification and Optimization -- 6.7 High-Throughput Methods (HTM) -- 6.8 Conclusion and Future Prospects -- References -- 7 Bioinformatics and Its Application Areas -- 7.1 Introduction -- 7.2 Review of Bioinformatics -- 7.3 Bioinformatics Applications in Different Areas -- 7.3.1 Microbial Genome Application -- 7.3.2 Molecular Medicine -- 7.3.3 Agriculture -- 7.4 Conclusion -- References -- 8 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression -- 8.1 Introduction -- 8.2 Data Processing. 8.2.1 Installation of Workflow -- 8.2.2 Importing the Raw Data for Processing -- 8.2.3 Retrieving Sample Annotation of the Data -- 8.2.4 Quality Control -- 8.3 Normalization of Microarray Data Using the RMA Method -- 8.3.1 Background Correction -- 8.3.2 Normalization -- 8.3.3 Summarization -- 8.4 Statistical Analysis for Differential Gene Expression -- 8.5 Conclusion -- References -- 9 Machine Learning in Bioinformatics -- 9.1 Introduction and Background -- 9.1.1 Bioinformatics -- 9.1.2 Text Mining -- 9.1.3 IoT Devices -- 9.2 Machine Learning Applications in Bioinformatics -- 9.3 Machine Learning Approaches -- 9.4 Conclusion and Closing Remarks -- References -- 10 DNA-RNA Barcoding and Gene Sequencing -- 10.1 Introduction -- 10.2 RNA -- 10.3 DNA Barcoding -- 10.3.1 Introduction -- 10.3.2 DNA Barcoding and Molecular Phylogeny -- 10.3.3 Ribosomal DNA (rDNA) of the Nuclear Genome (nuDNA)-ITS -- 10.3.4 Chloroplast DNA -- 10.3.5 Mitochondrial DNA -- 10.3.6 Molecular Phylogenetic Analysis -- 10.3.7 Metabarcoding -- 10.3.8 Materials for DNA Barcoding -- 10.4 Main Reasons of DNA Barcoding -- 10.5 Limitations/Restrictions of DNA Barcoding -- 10.6 RNA Barcoding -- 10.6.1 Overview of the Method -- 10.7 Methodology -- 10.7.1 Materials Required -- 10.7.2 Barcoded RNA Sequencing High-Level Mapping of Single-Neuron Projections -- 10.7.3 Using RNA to Trace Neurons -- 10.7.4 A Life Conservation Barcoder -- 10.7.5 Gene Sequencing -- 10.7.5.1 DNA Sequencing Methods -- 10.7.5.2 First-Generation Sequencing Techniques -- 10.7.5.3 Maxam's and Gilbert's Chemical Method -- 10.7.5.4 Sanger Sequencing -- 10.7.5.5 Automation in DNA Sequencing -- 10.7.5.6 Use of Fluorescent-Marked Primers and ddNTPs -- 10.7.5.7 Dye Terminator Sequencing -- 10.7.5.8 Using Capillary Electrophoresis -- 10.7.6 Developments and High-Throughput Methods in DNA Sequencing -- 10.7.7 Pyrosequencing Method. 10.7.8 The Genome Sequencer 454 FLX System -- 10.7.9 Illumina/Solexa Genome Analyzer -- 10.7.10 Transition Sequencing Techniques -- 10.7.11 Ion-Torrent's Semiconductor Sequencing -- 10.7.12 Helico's Genetic Analysis Platform -- 10.7.13 Third-Generation Sequencing Techniques -- 10.8 Conclusion -- Abbreviations -- Acknowledgement -- References -- 11 Bioinformatics in Cancer Detection -- 11.1 Introduction -- 11.2 The Era of Bioinformatics in Cancer -- 11.3 Aid in Cancer Research via NCI -- 11.4 Application of Big Data in Developing Precision Medicine -- 11.5 Historical Perspective and Development -- 11.6 Bioinformatics-Based Approaches in the Study of Cancer -- 11.6.1 SLAMS -- 11.6.2 Module Maps -- 11.6.3 COPA -- 11.7 Conclusion and Future Challenges -- References -- 12 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression -- 12.1 Introduction -- 12.2 FSHR Gene -- 12.3 IL-10 Gene -- 12.4 IRS-1 Gene -- 12.5 PCR Primers Used -- 12.6 Statistical Analysis -- 12.7 Conclusion -- References -- 13 An Insight of Protein Structure Predictions Using Homology Modeling -- 13.1 Introduction -- 13.2 Homology Modeling Approach -- 13.2.1 Strategies for Homology Modeling -- 13.2.2 Procedure -- 13.3 Steps Involved in Homology Modeling -- 13.3.1 Template Identification -- 13.3.2 Sequence Alignment -- 13.3.3 Backbone Generation -- 13.3.4 Loop Modeling -- 13.3.5 Side Chain Modeling -- 13.3.6 Model Optimization -- 13.3.6.1 Model Validation -- 13.4 Tools Used for Homology Modeling -- 13.4.1 Robetta -- 13.4.2 M4T (Multiple Templates) -- 13.4.3 I-Tasser (Iterative Implementation of the Threading Assembly Refinement) -- 13.4.4 ModBase -- 13.4.5 Swiss Model -- 13.4.6 PHYRE2 (Protein Homology/Analogy Recognition Engine 2) -- 13.4.7 Modeller -- 13.4.8 Conclusion -- Acknowledgement -- References. 14 Basic Concepts in Proteomics and Applications -- 14.1 Introduction -- 14.2 Challenges on Proteomics -- 14.3 Proteomics Based on Gel -- 14.4 Non-Gel-Based Electrophoresis Method -- 14.5 Chromatography -- 14.6 Proteomics Based on Peptides -- 14.7 Stable Isotopic Labeling -- 14.8 Data Mining and Informatics -- 14.9 Applications of Proteomics -- 14.10 Future Scope -- 14.11 Conclusion -- References -- 15 Prospects of Covalent Approaches in Drug Discovery: An Overview -- 15.1 Introduction -- 15.2 Covalent Inhibitors Against the Biological Target -- 15.3 Application of Physical Chemistry Concepts in Drug Designing -- 15.4 Docking Methodologies-An Overview -- 15.5 Importance of Covalent Targets -- 15.6 Recent Framework on the Existing Docking Protocols -- 15.7 SN2 Reactions in the Computational Approaches -- 15.8 Other Crucial Factors to Consider in the Covalent Docking -- 15.8.1 Role of Ionizable Residues -- 15.8.2 Charge Regulation -- 15.8.3 Charge-Charge Interactions -- 15.9 QM/MM Approaches -- 15.10 Conclusion and Remarks -- Acknowledgements -- References -- Index -- Also of Interest -- Check out these published and forthcoming related titles from Scrivener Publishing -- EULA. |
| Record Nr. | UNINA-9910555012203321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Impact of artificial intelligence on organizational transformation / / edited by S. Balamurugan [and five others]
| Impact of artificial intelligence on organizational transformation / / edited by S. Balamurugan [and five others] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , 2022 |
| Descrizione fisica | 1 online resource (439 pages) |
| Disciplina | 006.3 |
| Collana | Artificial Intelligence and So Computing for Industrial Transformation |
| Soggetto topico |
Artificial intelligence
Business - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-71027-8
1-119-71030-8 1-119-71005-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- 1 Artificial Intelligence Disruption on the Brink of Revolutionizing HR and Marketing Functions -- 1.1 Introduction -- 1.2 Research Methodology -- 1.2.1 Research Objectives -- 1.2.2 Data Collection -- 1.3 Artificial Intelligence in HRM -- 1.3.1 Recruitment -- 1.3.2 Engaging the Applicants and Employees -- 1.3.3 Orientation and Onboarding -- 1.3.4 Performance Appraisal -- 1.3.5 Training -- 1.3.6 Compensation -- 1.3.7 Employee Retention -- 1.4 Artificial Intelligence in Marketing -- 1.4.1 Creation of Customer Profiles/Market Segmentation -- 1.4.2 Cognizance of Consumers Purchase Behavior/Intention -- 1.4.3 Pricing -- 1.4.4 Content/Product/Service Recommendations/ Search Optimization -- 1.4.5 Sales Prediction Based on Consumer's Demographics -- 1.4.6 Virtual Assistants/Real-Time Conversations -- 1.4.7 Visual Searching -- 1.4.8 CRM -- 1.5 Discussion and Findings -- 1.6 Implication for Managers -- 1.7 Conclusion -- References -- 2 Ring Trading to Algo Trading-A Paradigm Shift Made Possible by Artificial Intelligence -- 2.1 Introduction -- 2.2 Ring Trading -- 2.3 Features of Generation 1: Ring Trading -- 2.4 Generation 2: Shifting to Online Platform -- 2.5 Generation 3: Algo Trading -- 2.6 Artificial Intelligence -- 2.7 AI Stock Trading -- 2.8 Algorithmic (Algo Trading) Trading -- 2.9 Conclusion -- References -- 3 AI in HR a Fairy Tale of Combining People, Process, and Technology in Managing the Human Resource -- 3.1 Introduction -- 3.2 Problem Recognition -- 3.3 Journey of AI in HR "From Where Till What" -- 3.4 Work Methodology of AI in HR -- 3.5 Branches of AI in HR -- 3.5.1 Machine Learning -- 3.5.1.1 Variance Detection -- 3.5.1.2 Background Verification -- 3.5.1.3 Employees Abrasion/Attrition -- 3.5.1.4 Personalized Content.
3.5.2 Deep Learning -- 3.5.2.1 Important Use of Deep Learning in HR Context -- 3.5.3 Natural Language Processing -- 3.5.4 Recommendation Engines -- 3.6 Implication Stages of AI in HR -- 3.6.1 Automate -- 3.6.2 Augment -- 3.6.3 Amplify -- 3.7 Process Model of AI in HR -- 3.8 Key Roles of AI in HRM -- 3.9 Broad Area of Uses of AI in HR -- 3.9.1 Recruitment -- 3.9.2 Interviews -- 3.9.3 Reduction in the Human Biases -- 3.9.4 Retention -- 3.9.5 AI in Learning and Advancement -- 3.9.6 Diminish Gender Bias Equality -- 3.9.7 Candidate Engagement -- 3.9.8 Prediction -- 3.9.9 Smart People Analytics -- 3.9.10 Employee Experience -- 3.10 Dark Side of AI -- 3.10.1 Technical Requirements and Acceptance -- 3.10.2 Cost Involvement -- 3.10.3 Machine Biases -- 3.10.4 Job Losses -- 3.10.5 Emotional Turmoil -- 3.10.6 Fake Identity -- 3.10.7 Having an Audit Trail -- 3.10.8 Question on Decisions -- 3.11 Conclusion -- References -- 4 Effect of Artificial Intelligence on Human Resource Profession: A Paradigm Shift -- 4.1 Introduction -- 4.2 Evolution of Artificial Intelligence -- 4.2.1 Phases of Artificial Intelligence -- 4.3 Changing Role of Human Resource Professionals -- 4.4 Effect of Artificial Intelligence on Human Resource Profession -- 4.4.1 Symbiotic Relationship Between Artificial Intelligence and Human Resource Profession -- 4.5 Limitations of Artificial Intelligence in HRM -- 4.6 Conclusion -- References -- 5 Artificial Intelligence in Animal Surveillance and Conservation -- 5.1 History -- 5.2 Introduction -- 5.3 Need of Artificial Intelligence -- 5.4 Applications of AI in Animal Surveillance and Conservation -- 5.4.1 In Livestock Monitoring -- 5.4.1.1 Chip and Sensor (RFID) -- 5.4.1.2 Microchip (GPS Tracker) -- 5.4.1.3 Mobile Application -- 5.4.1.4 Drone With Thermal Camera -- 5.4.2 In Wildlife Animal Monitoring -- 5.4.2.1 Motion Sensor Camera. 5.4.2.2 GPS Base Animal Tracker -- 5.4.2.3 Smart Camera (Thermal Camera) -- 5.4.2.4 Satellite Base Tag (Ringing, Callers) -- 5.4.2.5 Acoustics/Sound Monitoring -- 5.4.2.6 Radio Transmitter (Transponder) -- 5.5 Some Other Tools of Artificial Intelligence -- 5.5.1 Computer Software and Application -- 5.5.1.1 Wildbook Comb (Bot) -- 5.5.1.2 Betty -- 5.5.1.3 Sensing Clues -- 5.5.2 Resolve's Trail Guard -- References -- 6 Impact of Artificial Intelligence on Digital Marketing -- 6.1 Introduction -- 6.2 The Impact That AI Has on Marketing -- 6.2.1 The Data of Artificial Intelligence in Marketing -- 6.2.1.1 The Audience: Highly Targeted Marketing Segmentation -- 6.2.1.2 Journey to: The Customer's Road -- 6.2.1.3 Offer to: Advice-Based Behavioral Marketing -- 6.2.2 Number of Efficiency Powered by the AI Global Consumer Statistics -- 6.2.3 Cloud Computing: How it Interfaces to Marketing Thanks to Big Data -- 6.2.4 AI World is Made Also With BOT. Exactly What Are BOT? -- 6.2.5 The Chatbot: Service Robot as Support of Customer Care -- 6.3 The Community Regulation "GDPE" and Artificial Intelligence: Here's How Technology is Governed -- 6.4 The Case Study Estée Lauder -- 6.5 Conclusion -- References -- 7 Role of Artificial Intelligence in Transforming the Face of Banking Organizations -- 7.1 Objectives -- 7.2 Introduction -- 7.2.1 Three Stages of Artificial Intelligence -- 7.2.2 Different Types of Artificial Intelligence -- 7.2.3 Trends and Need of Artificial Intelligence in Context of Indian Banking -- 7.2.4 Uses and Role of Artificial Intelligence in Banks in the Opinion of [20, 25 26 & -- 31] -- 7.2.5 Importance of Artificial Intelligence in Banking Practices and Operation -- 7.2.5.1 Chat Bots -- 7.2.5.2 Analytics -- 7.2.5.3 Robotics Process Automation -- 7.2.5.4 Generating Reports -- 7.2.6 Impact of AI in Banking Operations. 7.2.6.1 Front Office Operations/Customer Centric -- 7.2.6.2 Middle Office/Operation Centric -- 7.2.6.3 Back Office/Decision Centric -- 7.2.7 Future of Artificial Intelligence in Banks -- 7.3 Existing Technology -- 7.4 Methodology -- 7.4.1 Search Process -- 7.4.2 Selection Criteria and Review Process -- 7.5 Findings -- 7.6 Conclusion -- 7.7 Suggestions -- References -- 8 Artificial Intelligence and Energy Sector -- 8.1 Introduction -- 8.1.1 Increase in the Emission of Greenhouse Gases -- 8.1.2 Increase in the Financial Burden -- 8.1.3 Huge Power Deficit -- 8.1.4 Water Scarcity -- 8.2 Challenges of Indian Power Sector -- 8.2.1 Global Warming -- 8.2.2 Depletion of Coal -- 8.2.3 Huge Financial Stress -- 8.2.4 Power Crisis -- 8.2.5 Health Issues -- 8.2.6 Plant Load Factor -- 8.2.7 Transmission and Distribution (T& -- D) Losses -- 8.3 Artificial Intelligence for Energy Solutions -- References -- 9 Impact of Artificial Intelligence on Development and Growth of Entrepreneurship -- 9.1 Introduction -- 9.2 Entrepreneurship -- 9.3 Artificial Intelligence -- 9.4 Artificial Intelligence and Entrepreneurship -- 9.5 Process of Entrepreneurship -- 9.5.1 Entrepreneurial Recognition -- 9.5.2 Human Capital -- 9.5.3 Technology Requirements and Idea Generation -- 9.5.4 Opportunity Recognition Phase -- 9.5.5 Opportunity Development -- 9.5.6 Resource Requirements -- 9.5.7 Entrepreneurship -- 9.5.8 Financial Resources -- 9.5.9 Opportunity Exploitation -- 9.5.10 Knowledge Networks -- 9.5.11 Validation of the Product -- 9.6 The Need of Artificial Intelligence for Business Development -- 9.6.1 Consumer Satisfaction -- 9.6.2 Cybercrime Protection -- 9.6.3 CRMs -- 9.6.4 AI-Based Analytics -- 9.6.5 Demand and Supply Management -- 9.6.6 Improved Maintenance and Better Equipment Safety -- 9.6.7 Searching Capable Employees -- 9.6.8 Virtual Assistance for Sales. 9.6.9 Improvements With Self-Driven Technologies -- 9.7 Some Important Facts About AI -- 9.8 Opportunities for Artificial Intelligence in Business -- 9.8.1 AI in the Field of Marketing -- 9.8.2 For Track Competitors -- 9.8.3 Make Less Work of Huge Data -- 9.8.4 AI as Customer Support System -- 9.8.5 Artificial Intelligence in CRMs -- 9.9 Further Research Possibilities -- 9.10 Conclusion -- References -- 10 An Exploratory Study on Role of Artificial Intelligence in Overcoming Biases to Promote Diversity and Inclusion Practices -- 10.1 Introduction -- 10.1.1 Objectives of the Study -- 10.1.2 Background of the Study -- 10.1.3 Relevance and Scope of the Study -- 10.2 Research Gaps Identified -- 10.3 Experiential Work -- 10.3.1 Hypothetical Research Model -- 10.3.2 Methodology -- 10.3.3 Search Process -- 10.3.4 Selection Criteria and Review Process -- 10.3.5 Systematic Representation of Literature Review -- 10.3.6 Understanding Workforce Diversity -- 10.3.7 Benefits and Challenges of Workforce Diversity -- 10.3.8 Biases as Obstacles in Diversity and Inclusion Practices -- 10.3.9 AI as a Tool to Prevent Bias and Promote D& -- I Practices -- 10.4 Synthesis of the Study -- 10.5 Managerial Implications and Conclusion -- References -- 11 Artificial Intelligence: Revolutionizing India Byte by Byte -- 11.1 Introduction -- 11.2 Objectives of the Chapter -- 11.3 AI for India's Transformation -- 11.4 Economic Impact of Artificial Intelligence -- 11.5 Artificial Intelligence and its Impact on Various Sectors -- 11.5.1 AI in Healthcare -- 11.5.2 AI in Banking and Finance -- 11.5.3 Artificial Intelligence in Education -- 11.5.4 Artificial Intelligence in Agriculture Sector -- 11.5.5 Artificial Intelligence in Smart Cities and Infrastructure -- 11.5.6 AI in Smart Mobility and Transportation -- 11.6 SWOT Analysis of Artificial Intelligence -- 11.6.1 Strength. 11.6.2 Weakness. |
| Record Nr. | UNINA-9910554826303321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc., , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Impact of artificial intelligence on organizational transformation / / edited by S. Balamurugan [and five others]
| Impact of artificial intelligence on organizational transformation / / edited by S. Balamurugan [and five others] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , 2022 |
| Descrizione fisica | 1 online resource (439 pages) |
| Disciplina | 006.3 |
| Collana | Artificial Intelligence and So Computing for Industrial Transformation |
| Soggetto topico |
Artificial intelligence
Business - Data processing |
| ISBN |
1-119-71027-8
1-119-71030-8 1-119-71005-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- 1 Artificial Intelligence Disruption on the Brink of Revolutionizing HR and Marketing Functions -- 1.1 Introduction -- 1.2 Research Methodology -- 1.2.1 Research Objectives -- 1.2.2 Data Collection -- 1.3 Artificial Intelligence in HRM -- 1.3.1 Recruitment -- 1.3.2 Engaging the Applicants and Employees -- 1.3.3 Orientation and Onboarding -- 1.3.4 Performance Appraisal -- 1.3.5 Training -- 1.3.6 Compensation -- 1.3.7 Employee Retention -- 1.4 Artificial Intelligence in Marketing -- 1.4.1 Creation of Customer Profiles/Market Segmentation -- 1.4.2 Cognizance of Consumers Purchase Behavior/Intention -- 1.4.3 Pricing -- 1.4.4 Content/Product/Service Recommendations/ Search Optimization -- 1.4.5 Sales Prediction Based on Consumer's Demographics -- 1.4.6 Virtual Assistants/Real-Time Conversations -- 1.4.7 Visual Searching -- 1.4.8 CRM -- 1.5 Discussion and Findings -- 1.6 Implication for Managers -- 1.7 Conclusion -- References -- 2 Ring Trading to Algo Trading-A Paradigm Shift Made Possible by Artificial Intelligence -- 2.1 Introduction -- 2.2 Ring Trading -- 2.3 Features of Generation 1: Ring Trading -- 2.4 Generation 2: Shifting to Online Platform -- 2.5 Generation 3: Algo Trading -- 2.6 Artificial Intelligence -- 2.7 AI Stock Trading -- 2.8 Algorithmic (Algo Trading) Trading -- 2.9 Conclusion -- References -- 3 AI in HR a Fairy Tale of Combining People, Process, and Technology in Managing the Human Resource -- 3.1 Introduction -- 3.2 Problem Recognition -- 3.3 Journey of AI in HR "From Where Till What" -- 3.4 Work Methodology of AI in HR -- 3.5 Branches of AI in HR -- 3.5.1 Machine Learning -- 3.5.1.1 Variance Detection -- 3.5.1.2 Background Verification -- 3.5.1.3 Employees Abrasion/Attrition -- 3.5.1.4 Personalized Content.
3.5.2 Deep Learning -- 3.5.2.1 Important Use of Deep Learning in HR Context -- 3.5.3 Natural Language Processing -- 3.5.4 Recommendation Engines -- 3.6 Implication Stages of AI in HR -- 3.6.1 Automate -- 3.6.2 Augment -- 3.6.3 Amplify -- 3.7 Process Model of AI in HR -- 3.8 Key Roles of AI in HRM -- 3.9 Broad Area of Uses of AI in HR -- 3.9.1 Recruitment -- 3.9.2 Interviews -- 3.9.3 Reduction in the Human Biases -- 3.9.4 Retention -- 3.9.5 AI in Learning and Advancement -- 3.9.6 Diminish Gender Bias Equality -- 3.9.7 Candidate Engagement -- 3.9.8 Prediction -- 3.9.9 Smart People Analytics -- 3.9.10 Employee Experience -- 3.10 Dark Side of AI -- 3.10.1 Technical Requirements and Acceptance -- 3.10.2 Cost Involvement -- 3.10.3 Machine Biases -- 3.10.4 Job Losses -- 3.10.5 Emotional Turmoil -- 3.10.6 Fake Identity -- 3.10.7 Having an Audit Trail -- 3.10.8 Question on Decisions -- 3.11 Conclusion -- References -- 4 Effect of Artificial Intelligence on Human Resource Profession: A Paradigm Shift -- 4.1 Introduction -- 4.2 Evolution of Artificial Intelligence -- 4.2.1 Phases of Artificial Intelligence -- 4.3 Changing Role of Human Resource Professionals -- 4.4 Effect of Artificial Intelligence on Human Resource Profession -- 4.4.1 Symbiotic Relationship Between Artificial Intelligence and Human Resource Profession -- 4.5 Limitations of Artificial Intelligence in HRM -- 4.6 Conclusion -- References -- 5 Artificial Intelligence in Animal Surveillance and Conservation -- 5.1 History -- 5.2 Introduction -- 5.3 Need of Artificial Intelligence -- 5.4 Applications of AI in Animal Surveillance and Conservation -- 5.4.1 In Livestock Monitoring -- 5.4.1.1 Chip and Sensor (RFID) -- 5.4.1.2 Microchip (GPS Tracker) -- 5.4.1.3 Mobile Application -- 5.4.1.4 Drone With Thermal Camera -- 5.4.2 In Wildlife Animal Monitoring -- 5.4.2.1 Motion Sensor Camera. 5.4.2.2 GPS Base Animal Tracker -- 5.4.2.3 Smart Camera (Thermal Camera) -- 5.4.2.4 Satellite Base Tag (Ringing, Callers) -- 5.4.2.5 Acoustics/Sound Monitoring -- 5.4.2.6 Radio Transmitter (Transponder) -- 5.5 Some Other Tools of Artificial Intelligence -- 5.5.1 Computer Software and Application -- 5.5.1.1 Wildbook Comb (Bot) -- 5.5.1.2 Betty -- 5.5.1.3 Sensing Clues -- 5.5.2 Resolve's Trail Guard -- References -- 6 Impact of Artificial Intelligence on Digital Marketing -- 6.1 Introduction -- 6.2 The Impact That AI Has on Marketing -- 6.2.1 The Data of Artificial Intelligence in Marketing -- 6.2.1.1 The Audience: Highly Targeted Marketing Segmentation -- 6.2.1.2 Journey to: The Customer's Road -- 6.2.1.3 Offer to: Advice-Based Behavioral Marketing -- 6.2.2 Number of Efficiency Powered by the AI Global Consumer Statistics -- 6.2.3 Cloud Computing: How it Interfaces to Marketing Thanks to Big Data -- 6.2.4 AI World is Made Also With BOT. Exactly What Are BOT? -- 6.2.5 The Chatbot: Service Robot as Support of Customer Care -- 6.3 The Community Regulation "GDPE" and Artificial Intelligence: Here's How Technology is Governed -- 6.4 The Case Study Estée Lauder -- 6.5 Conclusion -- References -- 7 Role of Artificial Intelligence in Transforming the Face of Banking Organizations -- 7.1 Objectives -- 7.2 Introduction -- 7.2.1 Three Stages of Artificial Intelligence -- 7.2.2 Different Types of Artificial Intelligence -- 7.2.3 Trends and Need of Artificial Intelligence in Context of Indian Banking -- 7.2.4 Uses and Role of Artificial Intelligence in Banks in the Opinion of [20, 25 26 & -- 31] -- 7.2.5 Importance of Artificial Intelligence in Banking Practices and Operation -- 7.2.5.1 Chat Bots -- 7.2.5.2 Analytics -- 7.2.5.3 Robotics Process Automation -- 7.2.5.4 Generating Reports -- 7.2.6 Impact of AI in Banking Operations. 7.2.6.1 Front Office Operations/Customer Centric -- 7.2.6.2 Middle Office/Operation Centric -- 7.2.6.3 Back Office/Decision Centric -- 7.2.7 Future of Artificial Intelligence in Banks -- 7.3 Existing Technology -- 7.4 Methodology -- 7.4.1 Search Process -- 7.4.2 Selection Criteria and Review Process -- 7.5 Findings -- 7.6 Conclusion -- 7.7 Suggestions -- References -- 8 Artificial Intelligence and Energy Sector -- 8.1 Introduction -- 8.1.1 Increase in the Emission of Greenhouse Gases -- 8.1.2 Increase in the Financial Burden -- 8.1.3 Huge Power Deficit -- 8.1.4 Water Scarcity -- 8.2 Challenges of Indian Power Sector -- 8.2.1 Global Warming -- 8.2.2 Depletion of Coal -- 8.2.3 Huge Financial Stress -- 8.2.4 Power Crisis -- 8.2.5 Health Issues -- 8.2.6 Plant Load Factor -- 8.2.7 Transmission and Distribution (T& -- D) Losses -- 8.3 Artificial Intelligence for Energy Solutions -- References -- 9 Impact of Artificial Intelligence on Development and Growth of Entrepreneurship -- 9.1 Introduction -- 9.2 Entrepreneurship -- 9.3 Artificial Intelligence -- 9.4 Artificial Intelligence and Entrepreneurship -- 9.5 Process of Entrepreneurship -- 9.5.1 Entrepreneurial Recognition -- 9.5.2 Human Capital -- 9.5.3 Technology Requirements and Idea Generation -- 9.5.4 Opportunity Recognition Phase -- 9.5.5 Opportunity Development -- 9.5.6 Resource Requirements -- 9.5.7 Entrepreneurship -- 9.5.8 Financial Resources -- 9.5.9 Opportunity Exploitation -- 9.5.10 Knowledge Networks -- 9.5.11 Validation of the Product -- 9.6 The Need of Artificial Intelligence for Business Development -- 9.6.1 Consumer Satisfaction -- 9.6.2 Cybercrime Protection -- 9.6.3 CRMs -- 9.6.4 AI-Based Analytics -- 9.6.5 Demand and Supply Management -- 9.6.6 Improved Maintenance and Better Equipment Safety -- 9.6.7 Searching Capable Employees -- 9.6.8 Virtual Assistance for Sales. 9.6.9 Improvements With Self-Driven Technologies -- 9.7 Some Important Facts About AI -- 9.8 Opportunities for Artificial Intelligence in Business -- 9.8.1 AI in the Field of Marketing -- 9.8.2 For Track Competitors -- 9.8.3 Make Less Work of Huge Data -- 9.8.4 AI as Customer Support System -- 9.8.5 Artificial Intelligence in CRMs -- 9.9 Further Research Possibilities -- 9.10 Conclusion -- References -- 10 An Exploratory Study on Role of Artificial Intelligence in Overcoming Biases to Promote Diversity and Inclusion Practices -- 10.1 Introduction -- 10.1.1 Objectives of the Study -- 10.1.2 Background of the Study -- 10.1.3 Relevance and Scope of the Study -- 10.2 Research Gaps Identified -- 10.3 Experiential Work -- 10.3.1 Hypothetical Research Model -- 10.3.2 Methodology -- 10.3.3 Search Process -- 10.3.4 Selection Criteria and Review Process -- 10.3.5 Systematic Representation of Literature Review -- 10.3.6 Understanding Workforce Diversity -- 10.3.7 Benefits and Challenges of Workforce Diversity -- 10.3.8 Biases as Obstacles in Diversity and Inclusion Practices -- 10.3.9 AI as a Tool to Prevent Bias and Promote D& -- I Practices -- 10.4 Synthesis of the Study -- 10.5 Managerial Implications and Conclusion -- References -- 11 Artificial Intelligence: Revolutionizing India Byte by Byte -- 11.1 Introduction -- 11.2 Objectives of the Chapter -- 11.3 AI for India's Transformation -- 11.4 Economic Impact of Artificial Intelligence -- 11.5 Artificial Intelligence and its Impact on Various Sectors -- 11.5.1 AI in Healthcare -- 11.5.2 AI in Banking and Finance -- 11.5.3 Artificial Intelligence in Education -- 11.5.4 Artificial Intelligence in Agriculture Sector -- 11.5.5 Artificial Intelligence in Smart Cities and Infrastructure -- 11.5.6 AI in Smart Mobility and Transportation -- 11.6 SWOT Analysis of Artificial Intelligence -- 11.6.1 Strength. 11.6.2 Weakness. |
| Record Nr. | UNINA-9910831038903321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc., , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Nature inspired algorithms and their applications / / editors, S. Balamurugan [et al.]
| Nature inspired algorithms and their applications / / editors, S. Balamurugan [et al.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc. : , : Scrivener Publishing, , [2022] |
| Descrizione fisica | 1 online resource (384 pages) |
| Disciplina | 571.0284 |
| Soggetto topico | Nature-inspired algorithms |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-68166-9
1-119-68198-7 1-119-68199-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Introduction to Nature-Inspired Computing -- 1.1 Introduction -- 1.2 Aspiration From Nature -- 1.3 Working of Nature -- 1.4 Nature-Inspired Computing -- 1.4.1 Autonomous Entity -- 1.5 General Stochastic Process of Nature-Inspired Computation -- 1.5.1 NIC Categorization -- 1.5.1.1 Bioinspired Algorithm -- 1.5.1.2 Swarm Intelligence -- 1.5.1.3 Physical Algorithms -- 1.5.1.4 Familiar NIC Algorithms -- References -- 2 Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning -- 2.1 Introduction of Genetic Algorithm -- 2.1.1 Background of GA -- 2.1.2 Why Natural Selection Theory Compared With the Search Heuristic Algorithm? -- 2.1.3 Working Sequence of Genetic Algorithm -- 2.1.3.1 Population -- 2.1.3.2 Fitness Among the Individuals -- 2.1.3.3 Selection of Fitted Individuals -- 2.1.3.4 Crossover Point -- 2.1.3.5 Mutation -- 2.1.4 Application of Machine Learning in GA -- 2.1.4.1 Genetic Algorithm Role in Feature Selection for ML Problem -- 2.1.4.2 Traveling Salesman Problem -- 2.1.4.3 Blackjack-A Casino Game -- 2.1.4.4 Pong Against AI-Evolving Agents (Reinforcement Learning) Using GA -- 2.1.4.5 SNAKE AI-Game -- 2.1.4.6 Genetic Algorithm's Role in Neural Network -- 2.1.4.7 Solving a Battleship Board Game as an Optimization Problem Which Was Initially Released by Milton Bradley in 1967 -- 2.1.4.8 Frozen Lake Problem From OpenAI Gym -- 2.1.4.9 N-Queen Problem -- 2.1.5 Application of Data Mining in GA -- 2.1.5.1 Association Rules Generation -- 2.1.5.2 Pattern Classification With Genetic Algorithm -- 2.1.5.3 Genetic Algorithms in Stock Market Data Mining Optimization -- 2.1.5.4 Market Basket Analysis -- 2.1.5.5 Job Scheduling -- 2.1.5.6 Classification Problem -- 2.1.5.7 Hybrid Decision Tree-Genetic Algorithm to Data Mining.
2.1.5.8 Genetic Algorithm-Optimization of Data Mining in Education -- 2.1.6 Advantages of Genetic Algorithms -- 2.1.7 Genetic Algorithms Demerits in the Current Era -- 2.2 Introduction to Artificial Bear Optimization (ABO) -- 2.2.1 Bear's Nasal Cavity -- 2.2.2 Artificial Bear ABO Gist Algorithm: -- Pseudo Algorithm: -- Implementation: -- 2.2.3 Implementation Based on Requirement -- 2.2.3.1 Market Place -- 2.2.3.2 Industry-Specific -- 2.2.3.3 Semi-Structured or Unstructured Data -- 2.2.4 Merits of ABO -- 2.3 Performance Evaluation -- 2.4 What is Next? -- References -- 3 Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique -- 3.1 Introduction -- 3.1.1 Example of Optimization Process -- 3.1.2 Components of Optimization Algorithms -- 3.1.3 Optimization Techniques Based on Solutions -- 3.1.3.1 Optimization Techniques Based on Algorithms -- 3.1.4 Characteristics -- 3.1.5 Classes of Heuristic Algorithms -- 3.1.6 Metaheuristic Algorithms -- 3.1.6.1 Classification of Metaheuristic Algorithms: Nature-Inspired vs. Non-Nature-Inspired -- 3.1.6.2 Population-Based vs. Single-Point Search (Trajectory) -- 3.1.7 Data Processing Flow of ACO -- 3.2 A Case Study on Surgical Treatment in Operation Room -- 3.3 Case Study on Waste Management System -- 3.4 Working Process of the System -- 3.5 Background Knowledge to be Considered for Estimation -- 3.5.1 Heuristic Function -- 3.5.2 Functional Approach -- 3.6 Case Study on Traveling System -- 3.7 Future Trends and Conclusion -- References -- 4 A Hybrid Bat-Genetic Algorithm-Based Novel Optimal Wavelet Filter for Compression of Image Data -- 4.1 Introduction -- 4.2 Review of Related Works -- 4.3 Existing Technique for Secure Image Transmission -- 4.4 Proposed Design of Optimal Wavelet Coefficients for Image Compression -- 4.4.1 Optimized Transformation Module. 4.4.1.1 DWT Analysis and Synthesis Filter Bank -- 4.4.2 Compression and Encryption Module -- 4.4.2.1 SPIHT -- 4.4.2.2 Chaos-Based Encryption -- 4.5 Results and Discussion -- 4.5.1 Experimental Setup and Evaluation Metrics -- 4.5.2 Simulation Results -- 4.5.2.1 Performance Analysis of the Novel Filter KARELET -- 4.5.3 Result Analysis Proposed System -- 4.6 Conclusion -- References -- 5 A Swarm Robot for Harvesting a Paddy Field -- 5.1 Introduction -- 5.1.1 Working Principle of Particle Swarm Optimization -- 5.1.2 First Case Study on Birds Fly -- 5.1.3 Operational Moves on Birds Dataset -- 5.1.4 Working Process of the Proposed Model -- 5.2 Second Case Study on Recommendation Systems -- 5.3 Third Case Study on Weight Lifting Robot -- 5.4 Background Knowledge of Harvesting Process -- 5.4.1 Data Flow of PSO Process -- 5.4.2 Working Flow of Harvesting Process -- 5.4.3 The First Phase of Harvesting Process -- 5.4.4 Separation Process in Harvesting -- 5.4.5 Cleaning Process in the Field -- 5.5 Future Trend and Conclusion -- References -- 6 Firefly Algorithm -- 6.1 Introduction -- 6.2 Firefly Algorithm -- 6.2.1 Firefly Behavior -- 6.2.2 Standard Firefly Algorithm -- 6.2.3 Variations in Light Intensity and Attractiveness -- 6.2.4 Distance and Movement -- 6.2.5 Implementation of FA -- 6.2.6 Special Cases of Firefly Algorithm -- 6.2.7 Variants of FA -- 6.3 Applications of Firefly Algorithm -- 6.3.1 Job Shop Scheduling -- 6.3.2 Image Segmentation -- 6.3.3 Stroke Patient Rehabilitation -- 6.3.4 Economic Emission Load Dispatch -- 6.3.5 Structural Design -- 6.4 Why Firefly Algorithm is Efficient -- 6.4.1 FA is Not PSO -- 6.5 Discussion and Conclusion -- References -- 7 The Comprehensive Review for Biobased FPA Algorithm -- 7.1 Introduction -- 7.1.1 Stochastic Optimization -- 7.1.2 Robust Optimization -- 7.1.3 Dynamic Optimization -- 7.1.4 Alogrithm. 7.1.5 Swarm Intelligence -- 7.2 Related Work to FPA -- 7.2.1 Flower Pollination Algorithm -- 7.2.2 Versions of FPA -- 7.2.3 Methods and Description -- 7.3 Limitations -- 7.4 Future Research -- 7.5 Conclusion -- References -- 8 Nature-Inspired Computation in Data Mining -- 8.1 Introduction -- 8.2 Classification of NIC -- 8.2.1 Swarm Intelligence for Data Mining -- 8.2.1.1 Swarm Intelligence Algorithm -- 8.2.1.2 Applications of Swarm Intelligence in Data Mining -- 8.2.1.3 Swarm-Based Intelligence Techniques -- 8.3 Evolutionary Computation -- 8.3.1 Genetic Algorithms -- 8.3.1.1 Applications of Genetic Algorithms in Data Mining -- 8.3.2 Evolutionary Programming -- 8.3.2.1 Applications of Evolutionary Programming in Data Mining -- 8.3.3 Genetic Programming -- 8.3.3.1 Applications of Genetic Programming in Data Mining -- 8.3.4 Evolution Strategies -- 8.3.4.1 Applications of Evolution Strategies in Data Mining -- 8.3.5 Differential Evolutions -- 8.3.5.1 Applications of Differential Evolution in Data Mining -- 8.4 Biological Neural Network -- 8.4.1 Artificial Neural Computation -- 8.4.1.1 Neural Network Models -- 8.4.1.2 Challenges of Artificial Neural Network in Data Mining -- 8.4.1.3 Applications of Artificial Neural Network in Data Mining -- 8.5 Molecular Biology -- 8.5.1 Membrane Computing -- 8.5.2 Algorithm Basis -- 8.5.3 Challenges of Membrane Computing in Data Mining -- 8.5.4 Applications of Membrane Computing in Data Mining -- 8.6 Immune System -- 8.6.1 Artificial Immune System -- 8.6.1.1 Artificial Immune System Algorithm (Enhanced) -- 8.6.1.2 Challenges of Artificial Immune System in Data Mining -- 8.6.1.3 Applications of Artificial Immune System in Data Mining -- 8.7 Applications of NIC in Data Mining -- 8.8 Conclusion -- References -- 9 Optimization Techniques for Removing Noise in Digital Medical Images -- 9.1 Introduction. 9.2 Medical Imaging Techniques -- 9.2.1 X-Ray Images -- 9.2.2 Computer Tomography Imaging -- 9.2.3 Magnetic Resonance Images -- 9.2.4 Positron Emission Tomography -- 9.2.5 Ultrasound Imaging Techniques -- 9.3 Image Denoising -- 9.3.1 Impulse Noise and Speckle Noise Denoising -- 9.4 Optimization in Image Denoising -- 9.4.1 Particle Swarm Optimization -- 9.4.2 Adaptive Center Pixel Weighted Median Exponential Filter -- 9.4.3 Hybrid Wiener Filter -- 9.4.4 Removal of Noise in Medical Images Using Particle Swarm Optimization -- 9.4.4.1 Curvelet Transform -- 9.4.4.2 PSO With Curvelet Transform and Hybrid Wiener Filter -- 9.4.5 DFOA-Based Curvelet Transform and Hybrid Wiener Filter -- 9.4.5.1 Dragon Fly Optimization Algorithm -- 9.4.5.2 DFOA-Based HWACWMF -- 9.5 Results and Discussions -- 9.5.1 Simulation Results -- 9.5.2 Performance Metric Analysis -- 9.5.3 Summary -- 9.6 Conclusion and Future Scope -- References -- 10 Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis -- 10.1 Introduction -- 10.1.1 NIC Algorithms -- 10.2 Related Works -- 10.3 Dataset: Wisconsin Breast Cancer Dataset (WBCD) -- 10.4 Ten-Fold Cross-Validation -- 10.4.1 Training Data -- 10.4.2 Validation Data -- 10.4.3 Test Data -- 10.4.4 Pseudocode -- 10.4.5 Advantages of K-Fold or 10-Fold Cross-Validation -- 10.5 Naive Bayesian Classifier -- 10.5.1 Pseudocode of Naive Bayesian Classifier -- 10.5.2 Advantages of Naive Bayesian Classifier -- 10.6 K-Means Clustering -- 10.7 Support Vector Machine (SVM) -- 10.8 Swarm Intelligence Algorithms -- 10.8.1 Particle Swarm Optimization -- 10.8.2 Firefly Algorithm -- 10.8.3 Ant Colony Optimization -- 10.9 Evaluation Metrics -- 10.10 Results and Discussion -- 10.11 Conclusion -- References -- 11 Applications of Cuckoo Search Algorithm for Optimization Problems -- 11.1 Introduction -- 11.2 Related Works. 11.3 Cuckoo Search Algorithm. |
| Record Nr. | UNINA-9910555132603321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc. : , : Scrivener Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Tele-healthcare : applications of artificial intelligence and soft computing techniques / / edited by R. Nidhya, Manish Kumar, and S. Balamurugan
| Tele-healthcare : applications of artificial intelligence and soft computing techniques / / edited by R. Nidhya, Manish Kumar, and S. Balamurugan |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , [2022] |
| Descrizione fisica | 1 online resource (418 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Artificial intelligence - Medical applications
Telecommunication in medicine Artificial intelligence |
| ISBN |
1-119-84193-3
1-119-84192-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910831000303321 |
| Hoboken, NJ : , : Wiley, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||