11047nam 22004693 450 991086524720332120240614084508.09783031614712(electronic bk.)9783031614705(MiAaPQ)EBC31471664(Au-PeEL)EBL31471664(CKB)32274112300041(EXLCZ)993227411230004120240614d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierProceedings of 4th International Conference on Artificial Intelligence and Smart Energy ICAIS 2024, Volume 11st ed.Cham :Springer,2024.©2024.1 online resource (0 pages)Information Systems Engineering and Management Series ;v.3Print version: Manoharan, S. Proceedings of 4th International Conference on Artificial Intelligence and Smart Energy Cham : Springer,c2024 9783031614705 Intro -- Preface -- Contents -- A Hybrid Machine Learning Approach for Enhanced Prediction of Breast Cancer with Lasso Method for Feature Extraction -- 1 Introduction -- 2 Related Work -- 3 Dataset Description -- 3.1 Dataset -- 3.2 System Architecture -- 4 Methodology -- 4.1 Data Preprocessing -- 4.2 Handling Missing Values -- 4.3 Selection Operator and Minimum Absolute Shrinkage (Lasso) -- 4.4 Evaluation Metrics -- 5 Result Discussion -- 5.1 Random Forest Algorithm -- 5.2 Artificial Neural Network Algorithm -- 5.3 Hybrid Approach -- 6 Results and Discussions -- 6.1 Result Using ANN -- 6.2 Result Using RF -- 6.3 Result Using Hybrid Model -- 7 Conclusion -- References -- A Comparative Overview of Deep Learning Aided Image Generation -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 DCGAN -- 3.2 SDXL -- 3.3 CVAE-GAN -- 3.4 ANI-GAN -- 3.5 CycleGAN -- 4 Result and Discussion -- 5 Conclusion and Future Scope -- References -- Enhancing Pneumonia Detection in Chest X-Rays: A Combined GAN and CNN Approach -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 GAN -- 3.2 AC GAN -- 3.3 Data Augmentation -- 3.4 CNN -- 4 Result Analysis -- 4.1 Feature Extraction and Classification Process -- 4.2 Data Augmentation -- 4.3 AC GAN -- 5 Conclusion -- References -- A Data Driven AI Framework for Conversational Bot by Vision Transformers in Health Care Systems -- 1 Introduction -- 2 Literature Review -- 3 Datasets -- 3.1 Indian Medicinal Plants Dataset -- 3.2 Floramed Data -- 4 Methodology -- 4.1 Proposed Framework -- 4.2 Vision Transformers (ViT) -- 4.3 Large Language Model- Mistral-7B-V0.1 -- 4.4 Frontend Framework -- 4.5 Implementation -- 5 Comparison and Results -- 6 Conclusion -- References -- Predictive Modelling of Cardiac Disease: Enhancing Accuracy Through Machine Learning Algorithms and Borderline-SMOTE Technique.1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Data Balancing Technique -- 3.4 Feature Selection -- 4 ML Models -- 4.1 LR -- 4.2 SVC -- 4.3 RF -- 4.4 KNN -- 4.5 GNB -- 4.6 AdaBoost Classifier -- 4.7 CatBoost Classifier -- 4.8 Gradient Boosting -- 5 Model Evaluation -- 5.1 Accuracy (A) -- 5.2 Precision (P) -- 5.3 Recall (R) -- 5.4 F1 Score (F1) -- 5.5 Specificity (S) -- 5.6 ROC-AUC Curve -- 6 Results and Discussions -- 7 Conclusion and Future Work -- References -- An AI-Driven Model for Decision Support Systems -- 1 Introduction -- 2 Related Work -- 3 AI in Management -- 4 MCDM and GP -- 5 Experimental Results -- 6 Conclusion -- References -- Exploring Music Genres Through Facial Emotions: Intelligent Data Processing and Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Existing System -- 4 Proposed System -- 5 Methodology -- 5.1 Identification of Emotions on the Face -- 5.2 Classifying Music Genres -- 5.3 Integration and Recommendations -- 6 Results and Discussion -- 7 Conclusion -- 8 Future Work -- References -- Optimizing Cloud Task Scheduling Through Innovative Metaheuristic Algorithm and Impulsive Fuzzy C-Means -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Credit Task Length -- 3.2 Credit Makespan -- 3.3 Credit Task Priority -- 3.4 Credit Deadline -- 3.5 Credit Degree of Imbalance -- 3.6 Execution Cost of the Task -- 4 IGFCM Clustering Method -- 4.1 Impulsive Genetic FCM Algorithm -- 4.2 Impulsive Technique in GA Design -- 4.3 IGA Operation -- 4.4 Clustering with FCM Method -- 5 Load Balancing Using Proposed Deep Q-Learning -- 5.1 Metaheuristic Elements - Fire Hawk Optimization (FHO) Algorithm -- 5.2 Deep Q-Learning Algorithm -- 5.3 Experimental Setup -- 5.4 Makespan Comparison Results -- 5.5 Response Time Comparison Results.5.6 Resource Utilization Comparison Results -- 5.7 Average Waiting Time Comparison Results -- 5.8 Total Cost Comparisons Results -- 5.9 Degree of Imbalance Performance Comparisons -- 6 Scalability and Applicability in Real-World Cloud Environment -- 6.1 Scalability -- 6.2 Applicability -- 7 Conclusion and Future Work -- References -- Cirrhosis Patient Survival Prediction Analysis Using ML Algorithms -- 1 Introduction -- 2 Literature Survey -- 3 Methods -- 3.1 Supervised Machine Learning Algorithm -- 3.2 Logistic Regression Algorithm -- 3.3 Support Vector Machine (SVM) -- 3.4 Decision Tree Algorithm -- 3.5 Random Forest Algorithm -- 4 Experiment -- 4.1 Experiment Environment -- 4.2 Cirrhosis Disease Dataset -- 4.3 Data Visualization -- 4.4 Imputing Missing Values in Numerical Features -- 4.5 Encoding Categorical Variables -- 4.6 Encoding Target Variable for Training and Test Datasets. -- 5 Experimental Results -- 5.1 Results of Each Model -- 5.2 Accuracy of All Models -- 5.3 Final Results -- 6 Conclusion -- 7 Future Research Directions -- References -- Learnable Discrete Wavelet (LDW) Pooling in CNN for Multidisciplinary Disease Prediction in Healthcare -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Structuring the Paper -- 4 Experimental Study -- 5 Result and Analysis -- 6 Conclusions and Future Scope -- References -- Autonomous Human Computer Interaction System in Windows Environment Using YOLO and LLM -- 1 Introduction -- 2 Methodology -- 2.1 Data Collection -- 2.2 YOLO V8 Model Training -- 2.3 Tesseract OCR Implementation -- 2.4 Edge and Contour Detection -- 2.5 Large Language Model (LLM) GPT-4 with Instruction Fine-Tuning -- 3 Implementation and Algorithm -- 3.1 Screen Image Pre-processing -- 3.2 Large Language Model (LLM) Integration and Its Capabilities -- 3.3 User Command and Screen Image Processing.3.4 Integrate Large Language Model -- 4 Results and Discussion -- 4.1 Optical Character Recognition (OCR) Performance -- 4.2 Edges and Contours Detection -- 4.3 You Only Look Once (YOLO V8) Results -- 4.4 Large Language Model (LLM) Performance and Interpretation -- 4.5 Discussion -- 5 Conclusion and Future Work -- References -- Deep Learning Based Animal Intrusion Detection System -- 1 Introduction -- 2 Problem Statement -- 3 Related Work -- 4 Methodology -- 4.1 Process Involved in Designing -- 5 Performance Analysis -- 6 Conclusion -- References -- Sustainable Crop Monitoring and Management for Enhanced Agricultural Productivity Through IoT, AI&amp -- ML: Case Studies and Innovations -- 1 Introduction -- 1.1 Study of Agriculturalist -- 1.2 Agricultural Landscape -- 1.3 Importance to Livelihood -- 1.4 Problems and Challenges -- 1.5 Governmental Programs -- 1.6 Technological Acceptance -- 1.7 Agricultural Exports -- 1.8 Prospects for the Future -- 1.9 Sustainability and Organic Agricultural -- 2 Agrıcultural Integratıon of Internet of Thıngs and the Artıfıcıal Intellıgence -- 2.1 Agriculture and IoT -- 2.2 AI in Agriculture -- 3 Problem Statement -- 4 Lıterature Revıew of Contrıbutıons -- 5 Materıals and Methods -- 6 Frame Work -- 6.1 Results -- 7 Conclusion -- References -- Design of an Auto Evaluation Model for Subjective Answers Using Natural Language Processing and Machine Learning Techniques -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Proposed Methodology for Student Module -- 3.2 Proposed Methodology for Teacher Module -- 4 Results -- 5 Conclusion -- References -- Navigating the Radiological Landscape: A Cutting-Edge Hybrid VGG16-EfficientNet Model for Improved CT Scan Interpretation -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Data Pre-processing -- 3.3 Data Augmentation.3.4 Model Evaluation -- 4 Proposed Methodology -- 4.1 Working Process of Proposed Model -- 4.2 VGG16-EfficientNet Model -- 5 Results and Discussion -- 6 Conclusion -- References -- Optimized Scene Text Detector and Paddle Optical Character Recognizer Techniques to Extract Text from Images -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Results and Discussion -- 5 Conclusion and Future Scope -- References -- A Cost-Sensitive Sparse Auto-encoder Based Feature Extraction for Network Traffic Classification Using CNN -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Pre-processing -- 3.2 Feature Extraction Using Sparse Autoencoder -- 3.3 Deep Learning Based Traffic Classification Using CNN -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Evaluation Measures -- 4.3 Results and Comparisons -- 5 Conclusion -- References -- Comparative Analysis of Deep Learning Architectures for Rice Crop Image Classification -- 1 Introduction -- 2 Design and Methodology -- 3 Result Analysis -- 4 Discussions -- 5 Conclusion and Future Scope -- References -- A Comprehensive Survey on Real-Time Animal (Dog) Detection System Using Artificial Intelligence Methods -- 1 Introduction -- 2 Literature Review -- 2.1 Objectives (OBJ) of the Review -- 2.2 Observation Table -- 2.3 Major Techniques Used -- 3 Observations and Outcomes of the Survey -- 4 Limitations of the Survey -- 5 Conclusion -- References -- Natural Language Processing Approach to Real-Time Multilingual Speech Conversion -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Proposed System -- 5 Results -- 6 Future Scope -- 7 Conclusion -- References -- Automatic Road Accident Detection Using Deep Learning -- 1 Introduction -- 2 Related Works -- 3 Dataset -- 3.1 Dataset Description -- 3.2 Dataset Preparation for YOLO Model -- 3.3 Dataset Preparation for CNN Model -- 4 Implementation.4.1 Architecture for Accident Detection.Information Systems Engineering and Management SeriesManoharan S1742007Tugui Alexandru1742008Baig Zubair1742009MiAaPQMiAaPQMiAaPQ9910865247203321Proceedings of 4th International Conference on Artificial Intelligence and Smart Energy4168633UNINA