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Record Nr. |
UNINA9910746299603321 |
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Autore |
Biswas Anupam |
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Titolo |
Artificial Intelligence for Societal Issues |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing AG, , 2023 |
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©2023 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (359 pages) |
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Collana |
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Intelligent Systems Reference Library ; ; v.231 |
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Altri autori (Persone) |
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SemwalVijay Bhaskar |
SinghDurgesh |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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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 -- |
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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 -- |
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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. |
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