Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2022, Volume 1 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish Chand Bansal

• Autore: Das Swagatam
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
• Edizione: 1st ed. 2023.
• Descrizione fisica: 1 online resource (885 pages)
• Disciplina: 006.33
• Soggetto topico: Telecommunication; Electronic circuits; Cloud Computing; Artificial intelligence; Signal processing; Communications Engineering, Networks; Electronic Circuits and Systems; Artificial Intelligence; Signal, Speech and Image Processing
• Altri autori: SahaSnehanshu Coello CoelloCarlos A BansalJagdish Chand
• Nota di contenuto: Intro -- Preface -- Contents -- Editors and Contibutors -- Adaptive Volterra Noise Cancellation Using Equilibrium Optimizer Algorithm -- 1 Introduction -- 2 Problem Formulation -- 3 Proposed Equilibrium Optimizer Algorithm-Based Adaptive Volterra Noise Cancellation -- 3.1 Gbest -- 3.2 Exploration Stage (F) -- 3.3 Exploitation Stage (Rate of Generation G) -- 4 Simulation Outcomes -- 4.1 Qualitative Performance Analysis -- 4.2 Quantitative Performance Analysis -- 5 Conclusion and Scope -- References -- SHLPM: Sentiment Analysis on Code-Mixed Data Using Summation of Hidden Layers of Pre-trained Model -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 BERT -- 3.2 RoBERTa -- 3.3 SHLPM -- 4 Implementation Details -- 4.1 Dataset and Pre-processing -- 4.2 SHLPM-BERT -- 4.3 SHLPM-XLM-RoBERTa -- 5 Results and Discussion -- 6 Conclusion -- References -- Comprehensive Analysis of Online Social Network Frauds -- 1 Introduction -- 1.1 Statistics of Online Social Network Frauds -- 2 Interrelationship between OSN Frauds, Social Network Threats, and Cybercrime -- 3 Types of Frauds in OSN -- 3.1 Social Engineering Frauds (SEF) -- 3.2 Human-Targeted Frauds (Child/Adults) -- 3.3 False Identity -- 3.4 Misinformation -- 3.5 E-commerce Fraud (Consumer Frauds) -- 3.6 Case Study for Facebook Security Fraud -- 4 OSN Frauds Detection Using Machine Learning -- 4.1 Pros and Cons -- 5 Conclusion -- References -- Electric Vehicle Control Scheme for V2G and G2V Mode of Operation Using PI/Fuzzy-Based Controller -- 1 Introduction -- 2 Motivation -- 3 System Description -- 4 Mathematical Model Equipments Used -- 4.1 Bidirectional AC-DC Converter -- 4.2 Bidirectional Buck-Boost Converter -- 4.3 Battery Modeling -- 4.4 Control of 1-∅-Based Bidirectional AC-DC Converter Strategy -- 5 Fuzzy Logic Controller -- 6 Control Strategy -- 6.1 Constant Voltage Strategy. ; 6.2 Constant Current Strategy -- 7 Results and Discussion -- 7.1 PI Controller -- 7.2 Fuzzy Logic Controller -- 7.3 Comparison of Harmonic Profile -- 8 Conclusion -- References -- Experimental Analysis of Skip Connections for SAR Image Denoising -- 1 Introduction -- 2 Related Works -- 2.1 Residual Network -- 2.2 Existing ResNet-Based Denoising Works -- 3 Implementation of the Different Patterns of Skip Connections -- 3.1 Datasets and Pre-processing -- 3.2 Loss Function -- 4 Results and Discussions -- 4.1 Denoising Results on Synthetic Images -- 4.2 Denoising Results on Real SAR Images -- 5 Conclusion -- References -- A Proficient and Economical Approach for IoT-Based Smart Doorbell System -- 1 Introduction -- 2 Literature Review -- 3 System Design and Implementation -- 3.1 System Design -- 3.2 Implementation -- 4 Results and Discussion -- 4.1 Performance Results -- 4.2 Comparison with an Existing System -- 4.3 Cost Analysis -- 5 Limitations -- 6 Conclusion -- References -- Predicting Word Importance Using a Support Vector Regression Model for Multi-document Text Summarization -- 1 Introduction -- 2 Related Work -- 3 Description of Dataset -- 4 Proposed Methodology -- 4.1 Preprocessing -- 4.2 Word Importance Prediction Using Support Vector Regression Model -- 4.3 Sentence Scoring -- 4.4 Summary Generation -- 5 Evaluation, Experiment, and Results -- 5.1 Evaluation -- 5.2 Experiment -- 5.3 Results -- 6 Conclusion and Future Works -- References -- A Comprehensive Survey on Deep Learning-Based Pulmonary Nodule Identification on CT Images -- 1 Introduction -- 2 Datasets and Experimental Setup -- 2.1 LIDC/IDRI Dataset -- 2.2 LUNA16 Dataset -- 2.3 NLST Dataset -- 2.4 KAGGLE DATA SCIENCE BOWL (KDSB) Dataset -- 2.5 VIA/I-ELCAP -- 2.6 NELSON -- 2.7 Others -- 3 CAD System Structure -- 3.1 Data Acquisition -- 3.2 Preprocessing -- 3.3 Lung Segmentation. ; 3.4 Candidate Nodule Detection -- 3.5 False Positive Reduction -- 3.6 Nodule Categorization -- 4 CNN -- 4.1 Overview -- 4.2 CNN Architectures for Medical Imaging -- 4.3 Unique Characteristics of CNNs -- 4.4 CNN Software and Hardware Equipment -- 4.5 CNNs versus Conventional Models -- 5 Discussion -- 5.1 Research Trends -- 5.2 Challenges and Future Directions -- 6 Conclusion -- References -- Comparative Study on Various CNNs for Classification and Identification of Biotic Stress of Paddy Leaf -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Proposed Methods -- 3 Experimental Results -- 3.1 Hardware Setup -- 3.2 Time Analysis with respect to GPU and CPU -- 3.3 Performance Analysis for Keras and PyTorch -- 3.4 Performance Analysis of CNN Models -- 3.5 Comparison of the Proposed CNN with Other State-of-the-Art Works -- 4 Conclusion -- References -- Studies on Machine Learning Techniques for Multivariate Forecasting of Delhi Air Quality Index -- 1 Introduction -- 2 Materials and Methodology -- 2.1 Delhi AQI Multivariate Data -- 2.2 Methodology -- 3 Experimental Setup and Simulation Results -- 4 Contrast Analysis Considering Dimensionality Reduction -- 5 Conclusions -- References -- Fine-Grained Voice Discrimination for Low-Resource Datasets Using Scalogram Images -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Collection of Voice Dataset -- 3.2 Preprocessing of Available Dataset to Increase the Trainable Samples -- 3.3 Classification of Phonemes Using Deep Convolutional Neural Network (DCNN)-Based Image Classifiers -- 4 Implementation Result and Analysis -- 5 Conclusion and Future Work -- References -- Sign Language Recognition for Indian Sign Language -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 Data Splitting -- 3.4 Data Augmentation -- 3.5 Model Compilation. ; 3.6 Model Training and Testing -- 4 Results -- 5 Novelty and Future Work -- 6 Conclusion -- References -- Buffering Performance of Optical Packet Switch Consisting of Hybrid Buffer -- 1 Introduction -- 2 Literature Survey -- 3 Description of the Optical Packet Switch -- 4 Simulation Results -- 4.1 Bernoulli Process -- 4.2 Results -- 5 Conclusions -- References -- Load Balancing using Probability Distribution in Software Defined Network -- 1 Introduction -- 2 Related Work -- 3 Grouping of Controllers in SDN -- 4 Load Balancing in SDN -- 4.1 Simulation and Evaluation Result -- 5 Conclusion -- References -- COVID Prediction Using Different Modality of Medical Imaging -- 1 Introduction -- 2 Principles of Support Vector Machine (SVM) -- 2.1 Linear Case -- 2.2 Nonlinear Case -- 3 Material and Methods -- 3.1 CT Image Dataset -- 3.2 X-Ray Image Dataset -- 3.3 Ultrasound Image Dataset -- 4 The Proposed Model -- 5 Experimental Result -- 6 Conclusion -- References -- Optimizing Super-Resolution Generative Adversarial Networks -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Training Dataset -- 3.2 Test Dataset -- 4 Proposed Methodology -- 5 Performance Metrics -- 5.1 Peak Signal-to-Noise Ratio (PSNR) -- 5.2 Structural Similarity Index (SSIM) -- 6 Results and Discussion -- 7 Conclusion -- References -- Prediction of Hydrodynamic Coefficients of Stratified Porous Structure Using Artificial Neural Network (ANN) -- 1 Introduction -- 2 Stratified Porous Structure -- 3 Experimental Setup -- 4 Artificial Neural Network -- 4.1 Dataset Used for ANN -- 4.2 ANN Model -- 5 Results and Discussions -- 6 Conclusions -- References -- Performance Analysis of Machine Learning Algorithms for Landslide Prediction -- 1 Introduction -- 2 Literature Survey -- 3 Methodology of the Performance Analysis Work -- 3.1 Data Acquisition Layer -- 3.2 Fog Layer -- 3.3 Cloud Layer. ; 4 Performance Analysis and Results -- 5 Conclusion -- References -- Brain Hemorrhage Classification Using Leaky ReLU-Based Transfer Learning Approach -- 1 Introduction -- 2 Related Works -- 3 Materials and Method -- 3.1 Dataset -- 3.2 Transfer Learning -- 3.3 ResNet50 -- 4 Proposed Methodology -- 4.1 Input Dataset -- 4.2 Pre-processing -- 4.3 Network Training -- 4.4 Transfer Learning-Based Feature Extraction -- 5 Results -- 6 Conclusion -- References -- Factors Affecting Learning the First Programming Language of University Students -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Experimental Design -- 3.3 Data Analysis -- 4 Result -- 4.1 Findings -- 5 Decision and Conclusion -- References -- Nature-Inspired Hybrid Virtual Machine Placement Approach in Cloud -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Proposed Framework -- 4.1 Intelligent Water Drops (IWD) Algorithm -- 4.2 Water Cycle Algorithm (WCA) -- 4.3 Intelligent Water Drop Cycle Algorithm (IWDCA) -- 5 Result -- 5.1 Experiment Setup -- 5.2 Simulation Analysis of IWDCA -- 6 Conclusion -- References -- Segmented ε-Greedy for Solving a Redesigned Multi-arm Bandit Environment -- 1 Introduction -- 2 Previous Works -- 3 Methodology -- 4 Results -- 5 Conclusion and Future Work -- References -- Data-Based Time Series Modelling of Industrial Grinding Circuits -- 1 Introduction -- 2 Formulation -- 2.1 Grinding Circuit -- 2.2 Least Square Support Vector Regression -- 2.3 Proposed Algorithm -- 3 Results and Discussions -- 3.1 Results of Proposed Algorithm -- 3.2 LS-SVR Model Performance -- 3.3 Comparison with Arbitrarily Selected Model -- 4 Conclusions -- References -- Computational Models for Prognosis of Medication for Cardiovascular Diseases -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Results -- 5 Conclusion -- References. ; Develop a Marathi Lemmatizer for Common Nouns and Simple Tenses of Verbs.
• Sommario/riassunto: The volume is a collection of best selected research papers presented at International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2022) held at BITS Pilani, K K Birla Goa Campus, Goa, India during 23 – 25 September 2022. It includes state-of-the art research work in the cutting-edge technologies in the field of data science and intelligent systems. The book presents data-driven computing; it is a new field of computational analysis which uses provided data to directly produce predictive outcomes. The book will be useful for academicians, research scholars, and industry persons.
• Titolo autorizzato: Advances in Data-Driven Computing and Intelligent Systems
• ISBN: 981-9932-50-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910736981503321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems, . 2367-3389 ; ; 698