Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2023, Volume 1
| Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2023, Volume 1 |
| Autore | Das Swagatam |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (553 pages) |
| Altri autori (Persone) |
SahaSnehanshu
Coello CoelloCarlos A BansalJagdish C |
| Collana | Lecture Notes in Networks and Systems Series |
| ISBN | 981-9995-24-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Editors and Contributors -- Influences of Specimen and Fiber Sizes on the Direct Tensile Resistance of Ultra-High-Performance Fiber-Reinforced Concretes -- 1 Introduction -- 2 Experimental Program -- 2.1 Materials -- 2.2 Test Setup -- 3 Test Results and Discussion -- 3.1 Effects of Specimen Size on the Tensile Resistance of UHPFRCs -- 3.2 Influences of Fiber Size on the Tensile Performance of UHPFRC -- 4 Conclusion -- References -- Conceptual Model for Data Collection and Processing in a Smart Medical Ward -- 1 Introduction -- 2 Related Work -- 3 Conceptual Model -- 4 Simulation -- 5 Conclusion -- References -- Parts-of-Speech Tagger in Assamese Using LSTM and Bi-LSTM -- 1 Introduction -- 2 Literature Review -- 2.1 International Language -- 2.2 National Language -- 3 Approaches Used -- 3.1 Long Short-Term Memory -- 3.2 Bidirectional Long Short-Term Memory -- 4 Methodology -- 4.1 Tagset -- 4.2 Preprocessing -- 4.3 Assamese Corpus -- 4.4 Training and Testing -- 5 Experimental Result -- 6 Performance Analysis -- 7 Conclusion and Future Work -- References -- Detection of Explicit Lyrics in Hindi Music Using Different Machine Learning Algorithms -- 1 Introduction -- 2 Related Work -- 2.1 Study of International Music -- 2.2 Study of Hindi Music -- 3 Data -- 3.1 Data Collection -- 3.2 Data Description -- 4 Methodology -- 4.1 Data Preprocessing -- 4.2 Proposed Approaches -- 4.3 Training and Testing -- 5 Experimental Results -- 5.1 Evaluation of the Proposed Models in Hindi Lyric Detection -- 5.2 Detection of User Input Hindi Lyrics for Explicitness -- 6 Conclusion -- References -- Does the Resilience Learning Game Foster Workforce Open Innovation and Sustainability Attributes? Empirical Evidence from Greek Food Industry -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Research Design -- 3.2 Description of the Game.
3.3 Data Analysis and Results -- 4 Discussion -- 5 Conclusion -- References -- Seizure Detection by Analyzing EEG Signals Using Deep Learning Networks -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Dataset Description -- 3.2 The DLN-SD: A Proposed Model -- 4 Results and Discussions -- 5 Conclusion and Future Scope of the Work -- References -- Enhancing Intelligent Video Surveillance: Deep Learning Approaches for Human Anomalous Behavior Recognition -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Results and Discussion -- 5 Conclusion -- References -- GujFormer: A Vision Transformer-Based Architecture for Gujarati Handwritten Character Recognition -- 1 Introduction -- 2 Literature Survey -- 3 Materials and Models -- 3.1 Vision Transformer (ViT) -- 3.2 Encoder and Decoder -- 4 Methodology -- 4.1 Patch Embedding -- 4.2 Multihead Self-Attention -- 4.3 Classification -- 5 Experiments and Result -- 5.1 Dataset and Data Augmentation -- 5.2 Simulation Details -- 5.3 Results and Analysis -- 6 Conclusion -- References -- Prediction of Soil Properties for Agriculture Using Ensemble Learning Techniques -- 1 Introduction -- 2 Literature Survey -- 3 Overview of Machine Learning -- 3.1 Machine Learning Tasks -- 3.2 Datasets -- 3.3 Preparing the Data -- 3.4 Learning Model -- 4 Results -- 5 Conclusion -- References -- Classification of Organic and Recyclable Waste Using a Deep Learning Approach -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Dataset and Preprocessing -- 3.2 Deep Learning Approach -- 3.3 Transfer Learning Approaches -- 4 Results and Discussion -- 5 Conclusion and Future Scope -- References -- Machine Learning and its Application in Food Safety -- 1 Introduction -- 1.1 Supervised Learning -- 1.2 Unsupervised Learning -- 1.3 Reinforcement Learning -- 2 Relevance of ML in Food Safety. 3 Issues Regarding Food Safety -- 4 Recent Technologies Regarding Food Safety -- 4.1 Metal Detector Automatic Testing System -- 4.2 Electronic Sensors -- 4.3 Automatic Monitoring -- 5 Machine Learning Models -- 5.1 Bayesian Networks -- 5.2 Artificial Neural Network (Reinforcement Learning) -- 6 Applications of ML in Food Safety -- 6.1 Smart Traceability -- 6.2 Antimicrobial Resistance Prediction -- 6.3 Antibiotic Resistance Profiles -- 6.4 Detection of Heavy Metals -- 6.5 Detection of Biological Load -- 6.6 Detection of Food Adulteration -- 7 Advantages and Challenges -- 8 Conclusion -- References -- ISO/IEC 27001 Standard: Analytical and Comparative Overview -- 1 Introduction -- 2 Literature Review -- 3 Overview of ISO 27001 -- 3.1 History -- 3.2 Exploring the Key Clauses of ISO 27001 -- 3.3 Annex A-Reference Control Objectives and Controls -- 4 Comparison with Other Frameworks -- 4.1 ISO 27001 Versus NIST CSF -- 4.2 ISO 27001 Versus COBIT -- 5 Conclusion -- References -- Hybrid Deep Learning-Based Potato and Tomato Leaf Disease Classification -- 1 Introduction -- 2 Literature Survey -- 2.1 CNN-Based Crop Disease Classification -- 2.2 LSTM-Based Crop Disease Classification -- 2.3 Hybrid CNN-LSTM-Based Classification -- 3 Proposed Methodology -- 3.1 Preprocessing -- 3.2 Segmentation -- 3.3 Feature Extraction -- 3.4 Leaf Image Disease Classification -- 4 Result -- 4.1 Dataset Collection -- 4.2 Experimental Setup -- 4.3 Performance Analysis -- 5 Discussion -- 5.1 Comparative Analysis -- 5.2 Training Time Analysis -- 6 Conclusion -- References -- Anti-forensic Analysis for Image Splicing Detection Through Advanced Filters -- 1 Introduction -- 2 Related Work -- 3 Deep Learning-Based Image Splicing Detection -- 3.1 Pre-trained ResNet-Based Deep Learning Model -- 3.2 Pre-trained InceptionNet-Based Deep Learning Model -- 4 Filters Used for Anti-forensic. 4.1 Weighted Average Filter -- 4.2 Bilateral Blur Filter -- 4.3 Kuwahara Filter -- 5 Implementation and Results -- 5.1 Dataset Description -- 5.2 Experimental Setup -- 5.3 Evaluation Metrics -- 5.4 Performance Evaluation -- 6 Conclusion and Future Scope -- References -- Classification and Prediction of Vibration Natural Frequencies of a Circular Plate Using Chladni Patterns and Deep Learning Techniques -- 1 Introduction -- 2 Methodology -- 3 Experimentation -- 3.1 Experimental Results -- 3.2 Experimental Mode Shapes -- 4 Simulation -- 4.1 Simulation Procedure -- 5 Deep Learning Methodology -- 5.1 Transfer Learning to Identify Natural Frequency -- 5.2 Data Preparation -- 5.3 Choosing Model -- 6 Results and Discussion -- 6.1 Validation of the Results -- 6.2 Prediction Results for VGG16 Network -- 6.3 Prediction Results for GoogleNet Network -- 7 Comparison of Results from Deep Learning Techniques for Pretrained Network -- 8 Conclusion -- References -- Multi-sensor Data Fusion and Deep Machine Learning Models-Based Mental Stress Detection System -- 1 Introduction -- 2 Related Work -- 3 Experimental Protocol -- 3.1 Placement of IoMT Device and Sensors -- 3.2 Subjects and Study Protocol for Data Acquisition -- 3.3 Dataset Preprocessing and Feature Extraction -- 3.4 Classification Algorithms -- 4 Experiment Results and Discussion -- 5 Conclusion -- References -- Segmentation-Based Transformer Network for Automated Skin Disease Detection -- 1 Introduction -- 2 Review of Literature -- 3 Dataset -- 4 Methodology -- 4.1 Preprocessing -- 4.2 Binary Image Segmentation -- 4.3 Attention Layer -- 4.4 Vision Transformer -- 5 Results -- 6 Conclusions and Future Work -- References -- FASRGAN: Feature Attention Super Resolution Generative Adversarial Network -- 1 Introduction -- 2 Related Works -- 3 Implementation -- 3.1 Dataset -- 3.2 Network Architecture. 3.3 Losses -- 4 Results -- 4.1 Quantitative Analysis -- 4.2 Qualitative Analysis -- 5 Conclusion -- References -- Mapping Sentiment: A Geospatial Analysis of Twitter Data in Indian Premier League 2023 -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Feature Selection -- 3.4 Model Selection -- 3.5 Implementation of Logistic Regression and Linear SVM -- 3.6 Geospatial Mapping -- 3.7 Evaluation Metrics -- 4 Experimental Results and Its Analysis -- 4.1 Sentiment Prediction -- 4.2 Geospatial Analysis -- 5 Conclusion -- References -- The eXtreme Gradient Boosting Method Optimized by Hybridized Sine Cosine Metaheuristics for Ship Vessel Classification -- 1 Introduction -- 2 Background and Related Works -- 2.1 Vessel Classification -- 2.2 XGBoost Overview -- 2.3 Metaheuristics Optimization -- 3 Proposed Method -- 3.1 Original Sine Cosine Algorithm -- 3.2 The Improved Sine Cosine Algorithm -- 4 Experiments and Discussion -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Simulation Results and Discussion -- 5 Conclusion -- References -- A Stacked Model Approach for Machine Learning-Based Traffic Prediction -- 1 Introduction -- 2 Literature Review -- 2.1 Linear Regression -- 2.2 XGBoost -- 3 Proposed Methodology -- 4 Software Implementation -- 4.1 Importing and Splitting Dataset -- 4.2 Creating New Features -- 4.3 Transforming the Training Data -- 4.4 Development of Model and Predictions -- 5 Results and Analysis -- 6 Conclusion -- References -- Deep Reinforcement Learning for Credit Card Fraud Detection -- 1 Introduction -- 2 Literature Survey -- 2.1 Deep RL Approach for Classification Use-Cases -- 3 Research Methodology -- 3.1 Fraud Detection Markov Decision Procedure -- 3.2 Reward Function for Fraud Detection Categorization -- 3.3 DQN-Based Fraud Detection Algorithm -- 4 Results and Discussions. 4.1 Comparative Analysis and Evaluation. |
| Record Nr. | UNINA-9910841865203321 |
Das Swagatam
|
||
| Singapore : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Data-Driven Computing and Intelligent Systems [[electronic resource] ] : Selected Papers from ADCIS 2023, Volume 2 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish C. Bansal
| Advances in Data-Driven Computing and Intelligent Systems [[electronic resource] ] : Selected Papers from ADCIS 2023, Volume 2 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish C. Bansal |
| Autore | Das Swagatam |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (536 pages) |
| Disciplina | 621.382 |
| Altri autori (Persone) |
SahaSnehanshu
Coello CoelloCarlos A BansalJagdish C |
| Collana | Lecture Notes in Networks and Systems |
| 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 |
| ISBN | 981-9995-21-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep learning models for classification of remotely sensed data of sugarcane -- Detection and Analysis of Wormhole Attacks in the AODV Routing Protocol with IEEE 802.11p for the Internet of Vehicles -- A Systematic Review of NLP Applications in Clinical Healthcare: Advancement and Challenges -- An Investigational Analysis of Automatic Speech Recognition on Deep Neural Networks and Gated Recurrent Unit Model -- Matched Filter and Kirsch’s Template based approach for Retinal Vessel Segmentation -- Prediction of abnormality in kidney function using classification techniques and fuzzy systems -- Implementation of Parallel Applications on the Hypercube Topology by Using Multistage Network -- Integrating Artificial Intelligence for Adaptive Decision-Making in Complex System -- Qualitative Research Reasoning on Dementia Fore-cast using Machine Learning Techniques -- Implementation of Vision Transformers on SPECT Heart Dataset: A Comparative Study -- CSR U-Net: A Novel Approach for Enhanced Skin Cancer Lesion Image Segmentation -- Automatic Detection and Classification System for Mesothelioma Cancer using Deep Learning Models with HPO -- A systematic literature survey on IoT in Healthcare: Security and Privacy Threats -- Hybrid Deep Learning Framework for Glaucoma Detection Using Fundus Images -- Sunflower Optimization with Elite Learning Strategy (SFO-ELS) for Antenna Selection in Massive MIMO Sub Array Switching Architecture -- Machine Learning Models for Human Activity Recognition: A Comparative Study. |
| Record Nr. | UNINA-9910841868803321 |
|
Das Swagatam
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Data-Driven Computing and Intelligent Systems [[electronic resource] ] : Selected Papers from ADCIS 2023, Volume 3 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish C. Bansal
| Advances in Data-Driven Computing and Intelligent Systems [[electronic resource] ] : Selected Papers from ADCIS 2023, Volume 3 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish C. Bansal |
| Autore | Das Swagatam |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (567 pages) |
| Disciplina | 006.33 |
| Altri autori (Persone) |
SahaSnehanshu
Coello CoelloCarlos A BansalJagdish C |
| Collana | Lecture Notes in Networks and Systems |
| 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 |
| ISBN | 981-9995-18-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A comprehensive review: Sentiment Analysis for Indian local Languages -- Face Recognition-Based Surveillance System -- Comparative Analysis of Malware Classification using Supervised Machine Learning Algorithms -- Noise filtering algorithm based on Machine learning for identification of ground hitting photons in Jaipur city -- Data-driven interior plan generation for residential buildings in Vietnam -- A weekly scheduling of operating theaters using Multi Agent Planner -- Using website content for detecting phishing URLs: A Novel approach -- Attention and Residual-Atrous Convolutional Learning-Based CNN Architecture for Lung Nodule Segmentation and Classification -- Adaptive Segmentation on Extracting Textural and Fractal Patterns for Assessing Mangrove Dynamics using Multi-spectral Data -- Exploring Multivariate Chemometric Tool for Simultaneous Determination of Erectile Dysfunction Drugs in Pharmaceutical Formulation -- Mass transport of combined oscillating electroosmotic and pressure drivenflow through cylindrical nanopore considering ion partitioning effects -- Audio Signal Analysis and Classification of Bluetooth Vulnerabilities using Machine Learning Techniques -- Performance Evaluation of Thresholding-based Segmentation Algorithms for Aerial Imagery -- Estimation of particle Froude Number in Deposited Bed Condition using Hybrid Machine Learning Models -- An Image based Automated Potato Leaf Disease Detection Model -- Unsupervised Synthetic Code-Mixed Data Generation -- Automated Building Segmentation in Areal images using Boundary Edge Detection. |
| Record Nr. | UNINA-9910845086203321 |
|
Das Swagatam
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Data-Driven Computing and Intelligent Systems [[electronic resource] ] : Selected Papers from ADCIS 2023, Volume 4 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Hemant Rathore, Jagdish Chand Bansal
| Advances in Data-Driven Computing and Intelligent Systems [[electronic resource] ] : Selected Papers from ADCIS 2023, Volume 4 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Hemant Rathore, Jagdish Chand Bansal |
| Autore | Das Swagatam |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (517 pages) |
| Disciplina | 006.33 |
| Altri autori (Persone) |
SahaSnehanshu
CoelloCarlos A. Coello RathoreHemant BansalJagdish Chand |
| Collana | Lecture Notes in Networks and Systems |
| 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 |
| ISBN | 981-9995-31-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910847582703321 |
|
Das Swagatam
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Data-Driven Computing and Intelligent Systems [[electronic resource] ] : Selected Papers from ADCIS 2022, Volume 1 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish Chand Bansal
| Advances in Data-Driven Computing and Intelligent Systems [[electronic resource] ] : Selected Papers from ADCIS 2022, Volume 1 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish Chand Bansal |
| Autore | Das Swagatam |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (885 pages) |
| Disciplina | 006.33 |
| Altri autori (Persone) |
SahaSnehanshu
Coello CoelloCarlos A BansalJagdish Chand |
| Collana | Lecture Notes in Networks and Systems |
| 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 |
| ISBN | 981-9932-50-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910736981503321 |
|
Das Swagatam
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modeling, machine learning and astronomy : First International Conference, MMLA 2019, Bangalore, India, November 22-23, 2019, revised selected papers / / Snehanshu Saha, Nithin Nagaraj, Shikha Tripathi (editors)
| Modeling, machine learning and astronomy : First International Conference, MMLA 2019, Bangalore, India, November 22-23, 2019, revised selected papers / / Snehanshu Saha, Nithin Nagaraj, Shikha Tripathi (editors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XII, 185 p. 100 illus., 80 illus. in color.) |
| Disciplina | 006.31 |
| Collana | Communications in computer and information science |
| Soggetto topico | Machine learning |
| ISBN | 981-336-463-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Modeling and Foundations -- Machine Learning Applications -- Astronomy and Astroinformatics. |
| Record Nr. | UNINA-9910447240403321 |
| Gateway East, Singapore : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modeling, machine learning and astronomy : First International Conference, MMLA 2019, Bangalore, India, November 22-23, 2019, revised selected papers / / Snehanshu Saha, Nithin Nagaraj, Shikha Tripathi (editors)
| Modeling, machine learning and astronomy : First International Conference, MMLA 2019, Bangalore, India, November 22-23, 2019, revised selected papers / / Snehanshu Saha, Nithin Nagaraj, Shikha Tripathi (editors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XII, 185 p. 100 illus., 80 illus. in color.) |
| Disciplina | 006.31 |
| Collana | Communications in computer and information science |
| Soggetto topico | Machine learning |
| ISBN | 981-336-463-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Modeling and Foundations -- Machine Learning Applications -- Astronomy and Astroinformatics. |
| Record Nr. | UNISA-996465348503316 |
| Gateway East, Singapore : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||