Advancements in Smart Computing and Information Security : Second International Conference, ASCIS 2023, Rajkot, India, December 7-9, 2023, Revised Selected Papers, Part II

• Autore: Rajagopal Sridaran
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (515 pages)
• Altri autori: PopatKalpesh MevaDivyakant BajejaSunil
• Nota di contenuto: Intro -- Preface -- Organization -- Abstract of Keynotes -- Generative AI vs Chat GPT vs Cognitive AI Impact on Cyber Security Real World Applications -- Empowering Smart Computing Through the Power of Light -- Optimal Transport Algorithms with Machine Learning Applications -- Some Research Issues on Cyber Security -- Smart Infrastructure and Smart Agriculture- Japan Use Cases -- Unveiling the Dynamics of Spontaneous Micro and Macro Facial Expressions -- AI Advancements in Biomedical Image Processing: Challenges, Innovations, and Insights -- Emerging Technologies and Models for Data Protection and Resource Management in Cloud Environments -- Artificial Intelligence and Jobs of the Future 2030 -- New Age Cyber Risks Due to AI Intervention -- Challenges of 5G in Combat Networks -- Dark Side of Artificial Intelligence -- Blockchain Integrated Security Solution for Internet of Drones (IoD) -- Generative Intelligence: A Catalyst for Safeguarding Society in the Age of GenAI -- Contents - Part II -- Artificial Intelligence and Machine Learning -- Classification of Rule Mining for Biomedical and Healthcare Data -- 1 Introduction -- 1.1 Specification of Software -- 1.2 Data Pre-processing -- 2 The Process of Classification Rule Mining -- 2.1 Random Forest Classifier Algorithm -- 2.2 Logistic Regression -- 2.3 Naive Bayes Algorithm -- 2.4 K-Nearest Neighbor Algorithm -- 3 Conclusion -- References -- Multimodal Sentiment Analysis Using Deep Learning: A Review -- 1 Introduction -- 2 Literature Review -- 3 Result and Discussion -- 4 Conclusion -- References -- Machine Learning Technique for Deteching Leaf Disease -- 1 Introduction -- 1.1 Rice Leaf Blast Diseases -- 1.2 Bacterial Blight Disease -- 1.3 Sheath Blight Disease -- 1.4 Brown Spot Disease -- 2 Related Works -- 3 Pre-trained CNN -- 3.1 Transfer Learning -- 3.2 Inception-v3 Model. ; 3.3 Factorization into Smaller Convolutions -- 3.4 Auxiliary Classifiers -- 4 Modeling the Features -- 4.1 Random Forest -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Pre-trained Inception-v3 Used as a Feature Extractor -- 5.3 Classification Using Random Forest with Inception v3 -- 6 Conclusion -- References -- Cardio Vascular Disease Prediction Based on PCA-ReliefF Hybrid Feature Selection Method with SVM -- 1 Introduction -- 2 Types of CVDs -- 3 Significance of Computing Techniques in CVD Prediction -- 4 Related Works -- 5 Proposed Methodology -- 5.1 Dataset Collection and Preprocessing -- 5.2 Feature Extraction Techniques -- 5.3 Data Splitting -- 5.4 Classification -- 6 Results and Discussion -- 6.1 Dataset Description -- 6.2 Evaluation Metrics Analysis -- 7 Conclusion -- References -- DRL-CNN Technique for Diabetes Prediction -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Feature Selection -- 3.2 Decision Tree (DT) -- 3.3 Random Forest (RF) -- 3.4 Delta Rule Learning Based Optimized CNN(DRL-OCNN) -- 4 Result and Discussion -- 4.1 Dataset Description -- 4.2 PID (Pima Indians Diabetes) Dataset Analysis -- 5 Conclusion -- References -- A Novel Method for Predicting Kidney Disease using Optimized Multi-Layer Perceptron (PKD-OMLP) Classifier -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Z-score for Preprocessing -- 3.2 Feature Selection -- 3.3 Linear Regression (LR) -- 3.4 Support Vector Machine (SVM) -- 3.5 Multi-Layer Perceptron (MLP) -- 3.6 PKD-OMLP Classifier -- 4 Result and Discussions -- 4.1 Dataset Description -- 4.2 Chronic Kidney Disease (CKD) Analysis -- 5 Conclusion and Future Work -- References -- Classification of Heart Diseases Using Logistic Regression with Various Preprocessing Techniques -- 1 Introduction -- 2 Types of Heart Diseases. ; 3 Significance of Predicting Heart Diseases Using ML Models -- 4 Related Studies -- 5 Proposed Model -- 5.1 Data Collection -- 5.2 Preprocessing -- 5.3 Model Training -- 6 Results and Discussion -- 6.1 Heart Failure Clinical Records -- 6.2 Evaluation Metrics -- 7 Conclusion -- References -- Plant Disease Detection Automation Using Deep Neural Networks -- 1 Introduction -- 2 Literature Review -- 3 Base Architecture: ConvNet -- 3.1 Input Layer -- 3.2 Convolution and Pooling Layer -- 3.3 Fully Connected Layer -- 4 Approaches and Results -- 4.1 Dataset -- 4.2 Data Augmentation -- 4.3 Alexnet -- 4.4 Resnet-50 -- 4.5 Proposed Model: ProliferateNet -- 5 Prevention and Recommendation -- 6 Conclusion -- References -- CT and MRI Image Based Lung Cancer Feature Selection and Extraction Using Deep Learning Techniques -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 4 Performance Analysis -- 5 Conclusion -- References -- Text Classification with Automatic Detection of COVID-19 Symptoms from Twitter Posts Using Natural Language Programming (NLP) -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 3.1 Dataset -- 3.2 Working of GRU and LSTM for Data Modeling -- 4 Experimental Results -- 5 Conclusion -- References -- A Novel Image Filtering and Enhancement Techniques for Detection of Cancer Blood Disorder -- 1 Introduction -- 2 Literature Survey -- 3 Materials and Methods -- 3.1 Image Preprocessing -- 4 Results and Discussion -- 4.1 Simulation Results -- 4.2 Evaluation of Filtering Algorithms -- 4.3 Peak Signal to Noise Ratio (PSNR) -- 5 Conclusion -- References -- Enhanced Oxygen Demand Prediction in Effluent Re-actors with ANN Modeling -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Architecture and Methodology -- 3.1 Data Pre-processing -- 3.2 Construction of the Model -- 3.3 Model Deployment in Cloud -- 4 Results and Conclusion. ; References -- Comparative and Comprehensive Analysis of Cotton Crop Taxonomy Classification -- 1 Introduction -- 1.1 Overview of Indian Economy and Agriculture -- 1.2 Overview of Cotton Disease Detection -- 1.3 Role of Machine Learning Algorithm in Cotton Disease Detection -- 2 Recent Literature Findings -- 2.1 Literature Review -- 3 Comparative and Comprehensive Analysis -- 4 Discussion and Future Scope -- 5 Conclusion -- References -- Efficient College Students Higher Education Prediction Using Machine Learning Approaches -- 1 Introduction -- 2 Importance of ML in Student Performance Prediction -- 3 Problems in the Prediction Model -- 4 Literature Review -- 5 Proposed Methodology -- 5.1 Support Vector Machine (SVM) -- 5.2 Random Forest (RF) -- 5.3 Convolution Neural Network (CNN) -- 6 Result and Discussion -- 6.1 Accuracy Analysis -- 6.2 Precision -- 6.3 Recall -- 6.4 Precision and Recall Analysis -- 7 Outcome of the Prediction System -- 8 Conclusion and Future Work -- References -- Efficient Lung Cancer Segmentation Using Deep Learning-Based Models -- 1 Introduction -- 2 Traditional Lung Cancer Diagnostic Tests -- 3 Importance of Deep Learning in Lung Cancer Segmentation -- 4 Problem Definition -- 5 Literature Review -- 6 Proposed Methodology -- 6.1 U-Net -- 6.2 Mask R-CNN -- 6.3 V-Net -- 7 Results and Discussion -- 7.1 Dataset Description -- 7.2 Evaluation Metrics -- 8 Conclusion -- 9 Future Scope -- References -- CSDM-DEEP-CNN Based Skin Multi-function Disease Detection with Minimum Execution Time -- 1 Introduction -- 2 Related Works -- 3 CSDM Design and Implementation -- 3.1 Pre-processed Image -- 3.2 Input Image Size Process -- 3.3 Convolutional Neural Network Prediction Process -- 3.4 Optimization Process -- 4 Results and Discussion -- 5 Conclusion -- References. ; Improving Skin Lesion Diagnosis: Hybrid Blur Detection for Accurate Dermatological Image Analysis -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 Hybrid Blur Detection Method: Elliptical Fourier Analysis and Convolutional Neural Networks -- 3.2 Performance Evaluation of the Hybrid Method for Blur Region Identification and Localization -- 3.3 Clinical Impact and Utility Assessment of the Hybrid Method in Skin Lesion Diagnosis and Treatment Decision-Making -- 4 Experimental Results -- 5 Conclusion -- References -- Swarm Based Enhancement Optimization Method for Image Enhancement for Diabetic Retinopathy Detection -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Dataset -- 3.2 Geometrical Correction -- 3.3 RGB Color Space -- 3.4 Noise Elimination -- 3.5 Image Enhancement -- 4 Results and Discussion -- 4.1 Mean Square Error (MSE) Analysis -- 4.2 Peak Signal-to-Noise-Ratio (PSNR) -- 4.3 Structure Similarity Index Measure (SSIM) -- 5 Conclusion and Future Work -- References -- Classification of Intrusion Using CNN with IQR (Inter Quartile Range) Approach -- 1 Introduction -- 2 Literature Survey -- 3 Proposed System -- 3.1 SMOTE -- 3.2 Z-SCORE -- 3.3 Inter Quartile Range (IQR) -- 3.4 CNN -- 4 Results and Discussion -- 4.1 UCI Cyber Hacking Dataset -- 4.2 Accuracy Analysis -- 4.3 Precision Analysis -- 4.4 Recall Analysis -- 4.5 F-Measure Analysis -- 5 Conclusion -- 6 Future Enhancement -- References -- Enhancing Heart Disease Prediction Using Artificial Neural Network with Preprocessing Techniques -- 1 Introduction -- 2 Related Works -- 3 Objectives -- 4 Proposed Methodology -- 4.1 Input Dataset -- 4.2 Preprocessing -- 4.3 Z-Score Normalization (ZS) -- 4.4 Interquartile Range (IQR) -- 4.5 Synthetic Minority Over-Sampling Technique (SMOTE) -- 4.6 Artificial Neural Network (ANN) -- 5 Results and Discussion. ; 5.1 Evaluation Metrics.
• Titolo autorizzato: Advancements in Smart Computing and Information Security
• ISBN: 3-031-59097-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910855366703321
• Lo trovi qui: Univ. Federico II
• Serie: Communications in Computer and Information Science Series