Computational Intelligence in Communications and Business Analytics
7th International Conference, CICBA 2025, Kolkata, India, July 4–6, 2025, Revised Selected Papers, Part III
Sorozatcím: Communications in Computer and Information Science;
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A termék adatai:
- Kiadó Springer Nature Switzerland
- Megjelenés dátuma 2026. május 22.
- ISBN 9783032171832
- Kötéstípus Puhakötés
- Terjedelem508 oldal
- Méret 235x155 mm
- Nyelv angol
- Illusztrációk XVIII, 508 p. 255 illus., 196 illus. in color. 700
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Hosszú leírás:
This three-volume set CCIS 2861-2863 constitutes the refereed proceedings of the 7th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2025, held in Kolkata, India, during July 4-6, 2025.
The 100 full papers presented in these volumes were carefully reviewed and selected from 311 submissions. The papers are organized into thematic tracks as follows:Computational Intelligence; Data Communication; and Analytics and Application.
TöbbTartalomjegyzék:
.- Analytic Track
.- Federated Learning in Healthcare: A case study on Covid-19.
.- Development of Online Handwritten Bangla Digit Dataset for Mobile Application.
.- Detection of Fake News In Social Media Using Supervised Machine Learning Approach.
.- Detecting Profile Cloning on Online Social Networks with Machine Learning.
.- Comparative Analysis of Apache Spark and Apache Kafka for Efficient Data Integration for Real-Time Data Processing.
.- A Method On The Impact Of Low-detail Images Versus AI-enhanced Images Using Deep Learning For Facial Emotion Recognition.
.- Enhancing Flight Delay Prediction with a Spatio-Temporal Graph Attention Network: A Graph-Based Approach to Modeling Airport Interdependencies.
.- A Comparative Analysis on Deep Learning Approaches for Sentiment Analysis of Food Review Data with Class Imbalance.
.- A framework in Generative AI for preventing Suicidal Thoughts.
.- ResNet50-based Medicinal Plant Leaf Classification.
.- An Alarming Threat of Light Pollution in Ahmedabad City: Efficient Time-series Forecasting Framework.
.- PulmoConnect: An Integrated Mobile Platform for Enhanced Doctor–Patient Communication at the Institute of Pulmocare and Research.
.- An Enhanced Adaptive Network Intrusion Detection System using Hybrid Approach.
.- A Deep Ensemble Species Distribution Model for Multilabel Species Prediction using Multimodal Data.
.- An Adaptive Reinforcement Learning Framework for Enhancing LLM-Based Medical Diagnostics.
.- An Efficient LSTM-XGBoost Hybrid for Demand Forecasting in Food Supply Chains.
.- Enhanced Short-Term Wind Power Ramp Forecasting: A Multi Dataset Validation Approach.
.- Duplication Fraudulent Detection of Tampered Image using Optimized CNN.
.- Unlocking Transparency: Judicial Clarity through Explainable AI in the Decision Making Process.
.- Sustainable Agriculture Through IoT and Data-Driven Irrigation: Machine Learning for Soil Moisture Prediction.
.- Hybrid Generative Artificial Intelligence Model for Medical Image Synthesis.
.- Safe Step: Real Time AI-Enabled Navigation System for the Visually Impaired.
.- On the Study of Resource-Efficient Sentiment Analysis in Social Media: Leveraging Emoji, Hashtags, and Random Forest Classifier.
.- FinStock-Net – Financial Integration of Short & Medium Trends for Stock Price Prediction.
.- Forecasting Market Turbulence: A Multi-Model Study Using GARCH, Random Forest, and LSTM in the Indian Stock Market.
.- Efficient Audio CODEC for IoT devices – leveraging GANs, Adaptive Quantization and Arithmetic Coding.
.- Automated Smart Irrigation System Using IoT-Enabled Autonomous Vehicle with Predictive Analytics.
.- Fine-Tuning for Code Intelligence: Evaluating LLMs on Custom Programming Benchmarks.
.- FedTabTran: A TabTransformer-Based FL Approach for Prediction of Cardiovascular Diseases.
.- An AI-Powered Fake News Detection System: Analyzing Misinformation in Digital Media.
.- Multi-Modal Fake News Detection on Online Social Media using Machine Learning and Explainable AI.
.- DCM-FL: Decentralized Collaborative Multi-client Federated Learning using Layer-Type Aggregation and Knowledge Distillation Methods.
.- Unmasking the Skies: Statistical and Machine Learning Insights into the Air Quality Shift During COVID-19 Lockdowns in Indian Metropolises.
.- An Exact Rare Rule Mining Approach Using Minimum Item Support Constraint from Heterogeneous Data.
.- Leveraging Explainable AI for Rainfall Prediction and Its Impact on Methane Emissions from Rice Paddies Using Multivariate Environmental Data.
.- Land Use/Land Cover Classification with Spectral Indices and Otsu Thresholding.
