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Advances in swarm intelligence : variations and adaptations for optimization problems / / Anupam Biswas, Can B. Kalayci, and Seyedali Mirjalili
| Advances in swarm intelligence : variations and adaptations for optimization problems / / Anupam Biswas, Can B. Kalayci, and Seyedali Mirjalili |
| Autore | Biswas Anupam |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (416 pages) |
| Disciplina | 006.3824 |
| Collana | Studies in Computational Intelligencea |
| Soggetto topico |
Swarm intelligence
Swarm intelligence - Social aspects |
| ISBN | 3-031-09835-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- State-of-the-Art -- A Brief Tutorial on Optimization Problems, Optimization Algorithms, Meta-Heuristics, and Swarm Intelligence -- 1 Optimization Problems -- 2 Optimization Algorithms -- 2.1 Derivative Dependent Algorithms -- 2.2 Derivative Free Algorithms -- 3 Meta-Heuristics -- 4 Swarm Intelligence Algorithms -- References -- Introductory Review of Swarm Intelligence Techniques -- 1 Introduction -- 2 Generic Framework of Swarm Intelligence Techniques -- 3 Evolution of Swarm Intelligence Techniques -- 4 Prominent Swarm Intelligence Algorithms -- 4.1 Particle Swarm Optimization -- 4.2 Firefly Algorithm -- 4.3 Bacteria Colony Algorithm -- 4.4 Crow Search Algorithm -- 4.5 Grey Wolf Optimization -- 4.6 Sperm Whale Algorithm -- 5 Applications of SI Techniques -- 6 Discussions and Conclusion -- References -- Swarm Intelligence for Deep Learning: Concepts, Challenges and Recent Trends -- 1 Introduction -- 2 Swarm Intelligence Algorithms: An Overview -- 3 Deep Learning: An Overview -- 4 Swarm Intelligence with Deep Learning -- 4.1 Neuroevolution -- 4.2 Other Popular Deep Frameworks with SI -- 5 Challenges and Future of Intelligent Deep Learning -- 6 Conclusion -- References -- Advances on Particle Swarm Optimization in Solving Discrete Optimization Problems -- 1 Introduction -- 2 Particle Swarm Optimization (PSO) -- 3 Knapsack Problem (KP) and Solution Using PSO -- 3.1 KP Basics and Constraints -- 3.2 Solution of KP Using PSO -- 4 Traveling Salesman Problem (TSP) and Solution Using PSO -- 4.1 TSP Basics and Constrains -- 4.2 Solution of TSP Using PSO -- 5 Vehicle Routing Problem (VRP) and Solution Using PSO -- 5.1 CVRP Basics and Constrains -- 5.2 Solution of CVRP Using PSO -- 6 University Course Scheduling Problem (UCSP) and Solution Using PSO -- 6.1 UCSP Basics and Constraints -- 6.2 Solution of UCSP Using PSO.
7 Conclusions -- References -- Performance Analysis of Hybrid Memory Based Dragonfly Algorithm in Engineering Problems -- 1 Introduction -- 2 Mathematical Interpretation of DADE -- 3 Performance Evaluation -- 3.1 Experimental Results -- 3.2 Analysis of DADE -- 3.3 Statistical Analysis -- 4 Convergence Analysis -- 5 Conclusion -- References -- Engineering Problems -- Optimum Design and Tuning Applications in Structural Engineering via Swarm Intelligence -- 1 Introduction -- 2 Review on Design Optimization via Swarm Intelligence and Metaheuristic Algorithm -- 2.1 Optimization of Truss Structures -- 2.2 Reinforced Concrete Members -- 2.3 Frame Structures -- 3 Review on Tuning Optimization of Structural Control Systems Via Swarm Intelligence and Metaheuristic Algorithms -- 3.1 Passive Tuned Mass Dampers (TMD) -- 3.2 Active Tuned Mass Dampers (TMD) -- 4 Application Results of Several Problems -- 4.1 Optimum Design of Retaining Walls -- 4.2 Span Optimization of Frame Structures -- 4.3 Optimum Design of Passive and Active Mass Dampers -- 5 Conclusion -- References -- Bee Colony Optimization with Applications in Transportation Engineering -- 1 Introduction -- 2 Bee Colony Optimization -- 2.1 Constructive Version of the Algorithm (BCOc) -- 2.2 Improvement Version of the Algorithm (BCOi) -- 3 Review of BCO Applications in Transportation Engineering -- 4 Conclusions -- References -- Application of Swarm Based Approaches for Elastic Modulus Prediction of Recycled Aggregate Concrete -- 1 Introduction -- 2 Data Collection -- 3 Artificial Neural Network -- 4 Elephant Herding Optimization -- 5 Hybrid of ANN and EHO -- 6 Results and Discussions -- 6.1 Performance of Artificial Neural Network -- 6.2 Performance of Artificial Neural Network-Elephant Herding Optimization -- 7 Conclusions -- References. Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimization for Optimizing Photonics Crystals -- 1 Introduction -- 2 Photonic Crystal Waveguide and the Optimization Problem Targeted in This Work -- 3 Swarm Intelligence Algorithm -- 4 Results -- 5 Conclusion -- References -- Intelligent and Reliable Cognitive 5G Networks Using Whale Optimization Techniques -- 1 Introduction -- 2 Challenges in 5G -- 3 Cognitive Radio -- 4 Low-Density Parity-Check (LDPC) in 5G Channel -- 5 Proposed Approach: Integration of CR, LDPC in 5G with Whale Optimization Algorithm -- 5.1 Whale Optimization Technique -- 6 Results and Discussion -- 7 Conclusion -- References -- Machine Learning -- Automatic Data Clustering Using Farmland Fertility Metaheuristic Algorithm -- 1 Introduction -- 2 Related Works -- 3 Farmland Fertility Algorithm -- 4 Proposed Method -- 5 Result and Discussion -- 6 Conclusion and Future Works -- References -- A Comprehensive Review of the Firefly Algorithms for Data Clustering -- 1 Introduction -- 2 Contribution of the FA for Data Clustering -- 2.1 Use of Different Variants of FA Without Hybridization -- 2.2 FA Combined with K-Means Algorithm -- 2.3 FA Combined with Evolutionary Computing Algorithms -- 2.4 FA Combined with the Particle Swarm Optimization Algorithm -- 2.5 FA Combined with Fuzzy C-Means Algorithm -- 2.6 FA Combined with Density Peaks Clustering Algorithms -- 2.7 FA Combined with Markov Clustering Algorithms -- 3 Representations, Initialization of Fireflies and Cluster Validation Measures Used by the FA Based Methods for Data Clustering -- 3.1 Definition of Individuals (Representations) -- 3.2 Initializations -- 3.3 Performance Measures -- 4 Possible Further Enhancements to FA Based Clustering Algorithms -- 5 Conclusion -- References. A Hybrid African Vulture Optimization Algorithm and Harmony Search: Algorithm and Application in Clustering -- 1 Introduction -- 2 Related Work -- 3 Background Knowledge -- 3.1 Data Clustering Problem -- 3.2 African Vulture Optimization Algorithm -- 3.3 Harmony Search Algorithm -- 4 AVOAHS Algorithm -- 5 Result and Discussion -- 6 Conclusion and Future Work -- References -- Estimation Models for Optimum Design of Structural Engineering Problems via Swarm-Intelligence Based Algorithms and Artificial Neural Networks -- 1 Introduction -- 2 Artificial Neural Networks (ANNs) -- 3 Applications via ANNs -- 4 Numerical Results -- 5 Results and Conclusion -- References -- A Novel Codebook Generation by Lévy Flight Based Firefly Algorithm -- 1 Introduction -- 2 Codebook Generation Algorithms -- 2.1 Vector Quantization and LBG Algorithm -- 2.2 PSO Algorithm -- 2.3 Fruit Fly Algorithm -- 2.4 Firefly Algorithm -- 3 Lévy Flight Based Firefly Algorithm for Codebook Generation -- 4 Parameters -- 5 Simulations and Results -- 6 Conclusion -- References -- Novel Chaotic Best Firefly Algorithm: COVID-19 Fake News Detection Application -- 1 Introduction -- 2 Background -- 2.1 Feature Selection -- 2.2 Metaheuristic Algorithms -- 3 Proposed Method -- 3.1 The Firefly Algorithm -- 3.2 Drawbacks of the FA -- 3.3 Proposed Chaotic-Based FA Metaheuristics -- 4 Experimental Setup -- 4.1 Dataset Collection -- 4.2 Data Preprocessing and Feature Extraction -- 4.3 Feature Selection -- 4.4 Model Development -- 4.5 Evaluation and Assessment -- 5 Results and Discussion -- 6 Conclusion -- References -- Other Applications -- Artificial Bee Colony and Genetic Algorithms for Parameters Estimation of Weibull Distribution -- 1 Introduction -- 2 Weibull Distribution -- 2.1 Maximum Likelihood Inference -- 2.2 Moment Inference -- 2.3 Proposed Functions -- 3 Swarm Intelligence Methods. 3.1 Artificial Bee Colony -- 3.2 Genetic Algorithms -- 4 Simulation Study -- 4.1 Swarm Parameter Settings -- 4.2 Evaluation Criteria -- 4.3 Computational Implementation -- 4.4 Simulation Results -- 4.5 Real Data Example -- 5 Conclusion -- References -- Graph Structure Optimization for Agent Control Problems Using ACO -- 1 Introduction -- 2 Previous Works -- 3 Proposed Method -- 3.1 GNP Algorithm -- 3.2 Ant Colony Network Programming -- 4 Experimental Results -- 4.1 Pursuit Domain -- 4.2 Experimental Results and Analysis -- 5 Conclusions -- References -- A Bumble Bees Mating Optimization Algorithm for the Discrete and Dynamic Berth Allocation Problem -- 1 Introduction -- 2 Berth Allocation Problem -- 3 Bumble Bees Mating Optimization -- 4 Bumble Bees Mating Optimization for the Discrete and Dynamic Berth Allocation Problem -- 5 Computational Results -- 5.1 Overall Results -- 6 Conclusions -- References -- Applying the Population-Based Ant Colony Optimization to the Dynamic Vehicle Routing Problem -- 1 Introduction -- 2 Dynamic Vehicle Routing Problem -- 2.1 Problem Formulation -- 2.2 Generating Dynamic Test Cases -- 3 Population-based Ant Colony Optimization -- 3.1 Constructing Solutions -- 3.2 Updating Population-List -- 3.3 Updating Pheromone Trails -- 3.4 Responding to Dynamic Changes -- 4 Experimental Results -- 4.1 Experimental Setup -- 4.2 Comparison of P-ACO+H Against ACO+H -- 4.3 Comparison of P-ACO+H Against Other P-ACO Algorithms -- 5 Conclusions -- References -- An Improved Cuckoo Search Algorithm for the Capacitated Green Vehicle Routing Problem -- 1 Introduction -- 2 Literature Review -- 3 Material and Method -- 3.1 A Classical Green VRP Model -- 3.2 Solution Method -- 3.3 The VLCS Algorithm -- 4 Experimental Study -- 5 Conclusion and Future Research -- References -- Multi-Objective Artificial Hummingbird Algorithm -- 1 Introduction. 2 Artificial Hummingbird Algorithm (AHA). |
| Record Nr. | UNINA-9910627249103321 |
Biswas Anupam
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| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence for Societal Issues
| Artificial Intelligence for Societal Issues |
| Autore | Biswas Anupam |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2023 |
| Descrizione fisica | 1 online resource (359 pages) |
| Disciplina | 303.4834 |
| Altri autori (Persone) |
SemwalVijay Bhaskar
SinghDurgesh |
| Collana | Intelligent Systems Reference Library |
| ISBN | 3-031-12419-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part I Crime and Security -- 1 Artificial Intelligence for Cybersecurity: Threats, Attacks and Mitigation -- 1.1 Introduction -- 1.2 Cybersecurity -- 1.2.1 Attacks -- 1.2.2 Threats -- 1.2.3 AI as a Tool for Cyber-Attacks -- 1.3 Conventional Solutions -- 1.4 Intervention of AI -- 1.4.1 Recent Trends -- 1.4.2 AI Based Mitigation of Cyberthreats -- 1.5 Conclusion -- References -- 2 A Survey on Deep Learning Models to Detect Hate Speech and Bullying in Social Media -- 2.1 Introduction -- 2.2 Methodology -- 2.2.1 Convolution-Based Methods -- 2.2.2 Sequential Deep Learning Based Methods -- 2.2.3 Transformer-Based Methods -- 2.3 Conclusion -- References -- 3 A Deep Learning Based System to Estimate Crowd and Detect Violence in Videos -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Methodology -- 3.3.1 Crowd Estimation -- 3.3.2 Violence Detection -- 3.4 Implementation -- 3.5 Results and Analysis -- 3.6 Future Enhancement -- 3.7 Conclusion -- References -- 4 Role of ML and DL in Detecting Fraudulent Transactions -- 4.1 Introduction -- 4.1.1 Introduction to Fraudulent Transaction -- 4.1.2 Influence of Online Banking on Fraudulent Transaction -- 4.1.3 Statistics of Fraudulent Transactions -- 4.1.4 Current Preventive Systems -- 4.1.5 Introduction to Artificial Intelligence -- 4.1.6 Introduction to Deep Learning -- 4.2 Different Detection Systems for Fraud -- 4.2.1 Hidden Markov Model -- 4.2.2 Artificial Neural Network (ANN) -- 4.2.3 Autoencoder -- 4.2.4 Convolutional Neural Network -- 4.2.5 Rule-Based Method -- 4.2.6 Generative Adversarial Network -- 4.3 Future Scope -- 4.4 Conclusion -- References -- Part II Agriculture and Education -- 5 Employing Image Processing and Deep Learning in Gradation and Classification of Paddy Grain -- 5.1 Introduction: State of Agriculture Sector in India.
5.1.1 Problems and Challenges Faced by the Agriculture Segment of India -- 5.1.2 Problem Statement and Paper Organization -- 5.2 Background: The Role of Artificial Intelligence in Agriculture Sector -- 5.2.1 Usability of Artificial Intelligence and Machine Learning in Agriculture -- 5.3 Literature Review -- 5.4 Proposed Approach: Image Processing -- 5.4.1 Involved Steps -- 5.4.2 Materials and Tools -- 5.5 Methodology and Implementation -- 5.5.1 Plan and Proposed Architecture -- 5.5.2 The CNN Architecture -- 5.5.3 Implementation -- 5.5.4 GUI Creation and Testing -- 5.6 Results and Discussion -- 5.7 Future Work -- 5.8 Conclusion -- References -- 6 Role of Brand Love in Green Purchase Intention: Analytical Study from User's Perspective -- 6.1 Introduction -- 6.1.1 Green Purchase Intention -- 6.1.2 Brand Love -- 6.1.3 Significance and Scope of Study -- 6.2 Review of Literature -- 6.3 Research Methodology -- 6.3.1 Research Model -- 6.3.2 Description of Variables -- 6.3.3 Research Questions -- 6.3.4 Hypothesis -- 6.4 Results and Discussion -- 6.4.1 Structural Equation Model -- 6.4.2 Multi-group Analysis -- 6.4.3 C. Variances -- 6.5 Findings -- 6.6 Suggestions -- 6.7 Conclusion -- 6.8 Questionnaire -- References -- 7 Effect of Online Review Rating on Purchase Intention -- 7.1 Introduction -- 7.1.1 Role of Review Rating in Social Media -- 7.1.2 Effect of Review Rating on Purchase Intention -- 7.1.3 Objective of the Study -- 7.2 Literature Review -- 7.2.1 Review Rating on Purchase Intention -- 7.3 Methodology -- 7.4 Analysis and Interpretation -- 7.5 Results and Discussion -- 7.6 Conclusion -- References -- 8 Artificial Intelligence: Paving the Way to a Smarter Education System -- 8.1 Introduction -- 8.2 Education and Its Many Challenges -- 8.2.1 Rising Cost of Education Worldwide -- 8.2.2 Reaching the Less Privileged and Promoting Women's Education. 8.2.3 Addressing Different Learning Needs -- 8.2.4 Learning Needs of the Differently-Abled -- 8.2.5 Setting High Standards and Maintaining Quality of Education -- 8.2.6 Overcoming the Age-Old Problem of Rote Learning -- 8.2.7 The Ever-Increasing Burden on the Education System -- 8.3 The Role of Technology in Transforming the Education Sector -- 8.3.1 Massive Open Online Courses (MOOC) -- 8.3.2 Virtual Reality (VR) in Education -- 8.3.3 Augmented Reality (AR) for Immersive Learning -- 8.3.4 Artificial Intelligence (AI) in Education -- 8.4 Leveraging AI for Transforming the EdTech Space -- 8.4.1 Benefits of AI for Students -- 8.4.2 Benefits for Educators -- 8.4.3 Benefits for Management and Administrators of Education Institutes -- 8.5 Assessing Tech Readiness to Embrace AI Using the SAMR Model -- 8.6 The Challenges and Limitations of AI in Education -- 8.7 Top AI Solutions Their Key Features, and Benefits -- 8.8 Conclusion -- References -- Part III Emotion and Mental Health -- 9 Using Deep Learning to Recognize Emotions Through Speech Analysis -- 9.1 Introduction -- 9.2 Related Works -- 9.3 Proposed Methodology -- 9.3.1 Mel-Frequency Cepstral Coefficients -- 9.3.2 Prediction Models Using Neural Networks -- 9.3.3 Performance Metrics -- 9.4 Experimental Result -- 9.4.1 Dataset Preparation -- 9.4.2 MFCC Extraction -- 9.4.3 Training of Neural Network Model -- 9.4.4 Prediction Using Model -- 9.5 Discussion -- 9.5.1 Performance Comparison of CNN and LSTM on Two Emotions -- 9.5.2 Performance Comparison of CNN and LSTM on Four Emotions -- 9.6 Conclusion -- References -- 10 Face Emotion Detection for Autism Children Using Convolutional Neural Network Algorithms -- 10.1 Introduction -- 10.2 Literature Survey -- 10.3 Background of the Research -- 10.3.1 Existing Classifier -- 10.3.2 Multi-model System -- 10.4 Proposed Emotion Detection Model. 10.4.1 Face Detection -- 10.4.2 Face Cropping -- 10.4.3 Pre-processing and Data Augmentation -- 10.4.4 Convolution Neural Network-Based Emotion Detection -- 10.5 Results and Discussion -- 10.5.1 Evaluation Metrics -- 10.5.2 Comparative Analysis -- 10.5.3 Comparative Analysis with Other Classifiers -- 10.6 Conclusion -- References -- 11 Prevention of Global Mental Health Crisis with Transformer Neural Networks -- 11.1 Introduction -- 11.2 Background -- 11.2.1 Motivation -- 11.2.2 From an Invisible Problem to a Global Crisis -- 11.2.3 Can COVID-19 Pandemic Seed a Global Mental Health Crisis? -- 11.2.4 Call for Action by Editorials and Experts -- 11.2.5 Dimensions of the Global Crisis in Mental Health -- 11.3 Design of Deep Learning Solution for Mental Health -- 11.3.1 Key Ideas in Deep Learning for Mental Health -- 11.3.2 Landscape -- 11.3.3 Design of AI Solution to Avert the Global Mental Heath Crisis -- 11.3.4 Design of AI to Improve Thinking Patterns: Views of Self/Future -- 11.3.5 Detailed Design -- 11.4 Mental Health Screening at Scale -- 11.4.1 Approaches for Pandemic Scale Screening -- 11.4.2 Deep Learning in Mental Health Screening -- 11.5 Mental Health Diagnosis and Resilience Detection -- 11.5.1 Modelling of Neuroplasticity/Resilience Using Deep Learning -- 11.5.2 Diagnosis with Multimodal Deep Learning -- 11.5.3 Modelling of Cognitive Behavior: View of Self and Future -- 11.6 Cognitive Therapy -- 11.6.1 Reinforcement Learning and GPT-n for Therapy Conversations -- 11.6.2 Privacy Safe On-device ML, Distillation Versus Few Shot Learning -- 11.7 Future Directions: AI Architecture for Mental Health -- 11.7.1 Triad-Therapy Using Multimodal Encoder-Decoder Modelling -- 11.7.2 Addressing Needs of Countries with NLP Beyond English Language -- 11.7.3 Implications of Findings and Scope for Future Work -- 11.8 Conclusion -- References. 12 Diagnosis of Mental Illness Using Deep Learning: A Survey -- 12.1 Introduction -- 12.2 Concept of ML and DL -- 12.3 Deep Learning in Mental Health -- 12.3.1 Concept of Bioinformatics in Deep Learning -- 12.4 Mental Health Disorders -- 12.4.1 Anxiety Disorders -- 12.4.2 Mood Disorders -- 12.4.3 Psychotic Disorders -- 12.4.4 Dementia -- 12.5 Diagnosis Using Deep Learning -- 12.6 Challenges and Future Scope -- 12.7 Conclusion -- References -- Part IV Healthcare Informatics and Management -- 13 Skin Disease Detection and Classification Using Deep Learning: An Approach to Automate the System of Dermographism for Society -- 13.1 Introduction -- 13.2 Background -- 13.2.1 Skin Disease Nature -- 13.2.2 Data Set Description -- 13.3 Literature Review -- 13.4 Proposed Method -- 13.4.1 Data Pre-processing -- 13.4.2 Performance Metrics -- 13.4.3 Implementation -- 13.5 Results and Discussion -- 13.6 Conclusions and Future Scope -- References -- 14 A Deep Learning Techniques for Brain Tumor Severity Level (K-CNN-BTSL) Using MRI Images -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Problem Statement -- 14.4 Proposed Work: K-CNN-BTSL (Brain Tumor Severity Level) -- 14.4.1 Preprocessing -- 14.4.2 Image Segmentation -- 14.4.3 Feature Extraction -- 14.5 K-CNN-BTSL -- 14.6 Results and Discussion -- 14.6.1 Testing with Benign Input -- 14.6.2 Testing with MALIGNANT Input -- 14.7 Conclusion -- References -- 15 COVID-19 Detection in X-Rays Using Image Processing CNN Algorithm -- 15.1 Introduction -- 15.2 Method and Materials -- 15.2.1 About X-Rays Dataset -- 15.2.2 CNN Architecture -- 15.2.3 Basic Requirement -- 15.3 Methodology -- 15.4 Experimental Analysis -- 15.5 Discussion -- 15.5.1 Some Issues Handled by Deep Learning -- 15.5.2 Advantage of the Proposed Model -- 15.6 Conclusion and Future Direction -- References. 16 Black Fungus Prediction in Covid Contrived Patients Using Deep Learning. |
| Record Nr. | UNINA-9910746299603321 |
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Biswas Anupam
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| Cham : , : Springer International Publishing AG, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Principles of Social Networking : The New Horizon and Emerging Challenges
| Principles of Social Networking : The New Horizon and Emerging Challenges |
| Autore | Biswas Anupam |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2021 |
| Descrizione fisica | 1 online resource (447 pages) |
| Altri autori (Persone) |
PatgiriRipon
BiswasBhaskar |
| Collana | Smart Innovation, Systems and Technologies Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN | 981-16-3398-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Principles of Social Networking |
| Record Nr. | UNINA-9910497105503321 |
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Biswas Anupam
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| Singapore : , : Springer Singapore Pte. Limited, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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