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检索条件"任意字段=3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023"

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3rd international conference on intelligent systems and sustainable computing, icissc 2023

3rd International Conference on Intelligent Systems and Sust...

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3rd international conference on intelligent systems and sustainable computing, icissc 2023

ISBN: (纸本)9789819783540

The proceedings contain 52 papers. The special focus in this conference is on intelligent systems and sustainable computing. The topics include: Supervised Machine Learning Method for Bone Cancer Detection and Classification;heart Disease Identification Using Ensemble Learning and Deep Learning;Eigenvalue-Based MRAC for All-Quadrant Vector-Controlled Induction Motor Drives Using Fictitious Resistance;PCOS Screening Using Machine Learning and Ensemble Learning Techniques in a Smart Way;MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm;decentralizing Rides: A Blockchain Approach to Carpooling;a Privacy Preserving Decision Tree Classification Algorithm for Prediction of Health Care Monitoring System;drug Review Classification by Using Sentiment Analysis;dynamic Resource Management of Cloud Resources Using Machine Learning Classification Approaches;deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable Machine Learning Framework;integrating Blockchain for Supply Chain Management of Agricultural and Food Products;a Comprehensive Survey on Traffic Light Detection Sensors for Vehicle Safety and Automatic Emergency Braking systems Using Deep Learning Techniques;a Multi-objective Smart Grid Optimization: Integrating Swarm Intelligence with Domain-Specific Heuristics for Superior Energy Efficiency;generative Adversarial Networks for Semi-Supervised Learning in Clinical Decision Support systems;Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation systems;optimized Task Scheduling in Diverse Distributed systems with a Hybrid Genetic Algorithm Approach;scalable Real-Time Anomaly Detection in Financial Markets Using a Novel Machine Learning Model;AI Enabled Swarm Application for Fire-Fighting Bots Over Web-Bluetooth APIs;disaster Management: Sentimental Analysis Using Tweets Based on Experience and Rehabilitation;movie Sense—Hybrid Movie Recommendation System with Sentimental An

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3rd international conference on intelligent computing Techniques for Smart Energy systems, ICTSES 2023

3rd International Conference on Intelligent Computing Techni...

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3rd international conference on intelligent computing Techniques for Smart Energy systems, ICTSES 2023

ISBN: (纸本)9789819784639

The proceedings contain 50 papers. The special focus in this conference is on intelligent computing Techniques For Smart Energy systems. The topics include: Analysis of Fault and Current-Limiting Methods in the Power System;UDS for Data Communication in Temperature Monitoring;approximation in the Weighted Lipschitz Class by (C,α,θ)(E,q) Mean of Its Fourier Series;demand Response and Load Profile Analysis in Modern Electricity systems;First-Principles Study of the Structural, Optical and Electronic Properties of Zinc Blende GaN Through PBEsol and TB-mBJ Functionals;optimization-Based Cost Analysis for Energy Management System in Microgrids;classification and Prediction of Knee Osteoarthritis by Deep Learning Approach;Predicting Stock Price and Market Direction Using Statistical and LSTM Models;Comprehensive Evaluation of Time Management with Balancing Resistors in Passive Cell Balancing Using the 1RC Model;PAWnnect: Pioneering IoT-ML Driven Pet Monitoring;transactive Energy Management for Microgrids: Peer-To-Peer Trading Using Stackelberg Game Approach;Unravelling the Mind's Emotions: A Review of EEG-Based Emotion Detection Using Machine Learning and Deep Learning;unlocking IoT Security: Enabling the Future with Lightweight Cryptographic Ciphers;Optimal Design of Nano Scale Voltage Amplifier Using Evolutionary Techniques for DG-MOSFET;flower Patterned with Elliptical Petals Multiband Microstrip Fractal Patch Antenna for Wireless Applications;Optimal Scheduling of Solar PV Generators and Network Assets in Distribution Network;revolutionizing Biomedical Engineering with Microcontroller Applications: A Comprehensive Review;investigation of Battery Temperature Impact on High Voltage Cable of Electric Vehicles;deep Learning Technique for Computer Vision-Based Pose Estimation for Augmented Reality;Rectangular Shape Patch Antenna with Defected Ground Plane for 5G Sub 6 GHz Wireless Communication systems.

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3rd international conference on intelligent computing Techniques for Smart Energy systems, ICTSES 2023

3rd International Conference on Intelligent Computing Techni...

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3rd international conference on intelligent computing Techniques for Smart Energy systems, ICTSES 2023

ISBN: (纸本)9789819784288

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Integrating sustainable Energy systems and Artificial Intelligence: Transforming the Future of Energy 3rd

Integrating Sustainable Energy Systems and Artificial Intell...

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3rd international conference on intelligent systems and sustainable computing, icissc 2023

作者： Kumar, Raman Kaur, Rupinder Gitanjali Channi, Harpreet Kaur Kaur, Karneet Department of Mechanical and Production Engineering Guru Nanak Dev Engineering College Punjab Ludhiana141006 India Department of Information Technology Guru Nanak Dev Engineering College Punjab Ludhiana141006 India Department of Electrical Engineering Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9789819783540

Integrating sustainable energy systems (SESs) and artificial intelligence (AI) has emerged as a promising approach to address the energy sector's challenges and accelerate the transition to a sustainable future. This review presents the key concepts, implications, and potential applications of integrating SESs and AI. It explores various renewable energy sources (RESs) such as solar, wind, hydro, geothermal, and biomass, highlighting their benefits and advancements in AI integration. It examines the emerging trends and opportunities in integrating AI and SESs. Policy, research, and development implications are discussed, emphasizing the need for supportive regulatory frameworks, research advancements, and stakeholder collaboration. In the end, it highlights the transformative potential of this integration. It calls for concerted efforts to harness its benefits and drive the global energy transition toward a more sustainable and intelligent future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy transition

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IET Chennai 3rd international conference on sustainable Energy and intelligent systems, SEISCON 2012

IET Chennai 3rd International Conference on Sustainable Ener...

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IET Chennai 3rd international conference on sustainable Energy and intelligent systems, SEISCON 2012

ISBN: (纸本)9781849197977

The proceedings contain 77 papers. The topics discussed include: secure and practical threshold RSA;counterfeiting attacks on block-wise dependent fragile watermarking schemes;an authentication framework for peer-to-peer cloud;verification of OSPF vulnerabilities by colored petri net;SAT based analysis of LTE stream cipher ZUC;generalized approach to modeling user activity graphs for network security and public safety monitoring;efficient and robust data aggregation using untrusted infrastructure;biometric verification using brain waves toward on-demand user management systems - performance differences between divided regions in alpha-beta wave band;continuous security evaluation and auditing of remote platforms by combining trusted computing and security automation techniques;and proclick: a framework for testing clickjacking attacks in web applications.

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Movie Sense—Hybrid Movie Recommendation System with Sentimental Analysis 3rd

Movie Sense—Hybrid Movie Recommendation System with Sentime...

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3rd international conference on intelligent systems and sustainable computing, icissc 2023

作者： Sudhane, G. Sai Rao, G. Venkateswara Vani, V. Karthik, N. Department of Electrical and Electronics Engineering Puducherry India Department of Computer Science and Engineering National Institute of Technology Puducherry Puducherry Karaikal609609 India

ISBN: (纸本)9789819783540

Post-pandemic, with the advent of many OTT platforms, there are a number of different movies and web series available which makes it difficult for the users to find a suitable movie to watch. So the movie recommendation system helps the users by recommending some movies based on their past search history, their interests and also considering the movie genre, actors, director and movie ratings. Our system uses a hybrid filtering technique which makes the system suitable for new users as well by considering the interests of various other users using SVD. Sentimental Analysis is done using a Sentiment Intensity Analyzer (SIA). Since it uses the most accurate methods, it makes the system a robust one. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recommender systems

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An intelligent Diagnostic Model for Detecting and Classifying Malaria Parasites Based on Microscopic Images 3rd

An Intelligent Diagnostic Model for Detecting and Classifyin...

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3rd international conference on intelligent systems and sustainable computing, icissc 2023

作者： Madhu, Golla Meher, Bonasi Avinash Jagadeesh, G.V.S. Kumar, Gunturu Santosh Babu, Kodali Varun Department of Information Technology VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad500090 India

ISBN: (纸本)9789819783540

The presence of Plasmodium parasites causes malaria, which endangers human lives as it is a dangerous disease. Most of the existing work is computationally expensive or is not optimized for detecting miniature parasites and is slow at evaluating results. For prevailing the above-stated problems, we developed a hybrid diagnostic model that uses both Machine Learning and Deep Learning algorithms. This hybrid model uses a Convolutional Neural Network in combination with a Random Forest, where the features of the microscopic images are extracted with the aid of a CNN model. Using these features, we classify Malaria parasite images into parasitized and uninfected cells using a Random Forest algorithm. Over the past decade, models such as R-CNN, VGG-19, SVM, YOLOv4, and IDTL-MPDC have been evaluated and compared. Using a hybrid CNN model, we addressed the problems associated with previous work by splitting the dataset into 20% for testing, achieving 100% training accuracy and 96.04% testing accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Comprehensive Survey on Traffic Light Detection Sensors for Vehicle Safety and Automatic Emergency Braking systems Using Deep Learning Techniques 3rd

A Comprehensive Survey on Traffic Light Detection Sensors fo...

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3rd international conference on intelligent systems and sustainable computing, icissc 2023

作者： Gowri, B. Shyamala Priya, G. Vishnu Anjana, S. Arjun, V. Kodal, Devendraa Department of Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819783540

The integration of Traffic Light Detection (TLD) systems with Advanced Emergency Braking systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl

关键词： Road and street markings

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Deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable Machine Learning Framework 3rd

Deciphering Diabetes: Illuminating Prognostic Insights with ...

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3rd international conference on intelligent systems and sustainable computing, icissc 2023

作者： Rony, Mohammad Abu Tareq Islam, Mohammad Shariful Saha, Pritom Johora, Fatama Tuz Ahammad, Mejbah Alam, Shah Md Nazmul Rahman, Md Saifur Nag, Anindya Department of Statistics Noakhali Science and Technology University Noakhali3814 Bangladesh Computer Science and Telecommunication Engineering Noakhali Science and Technology University NoakhaliNoakhali-3814 Bangladesh Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering University of Chittagong Chittagong Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University Tempe United States Strategic Management University UniversityoftheCumberlands Williamsburg United States Computer Science and Engineering Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9789819783540

Diabetes is a chronic disease that impacts millions of people globally. Early detection and management of diabetes can help mitigate potential complications and enhance patient outcomes. Machine learning (ML) algorithms have become more prevalent for predicting diabetes in recent years. The research paper proposed a machine learning-based approach for predicting diabetes utilizing the Diabetes Health indicator dataset and compared the comprehensive performance of multiple ML algorithms. The study used the LIME (Local Interpretable Model-Agnostic Explanations) package to provide interpretable predictions and acquire insights into the factors contributing to the model’s predictions. Although the ML models were interpretable, they also delivered the highest accuracy 86% demonstrated by the XGBoost classifier in prediction. The LIME produces local explanations for individual predictions, emphasizing the most significant features contributing to the prediction. The study found that, for the XGB model, BMI has a negative influence, while Genhlth positively impacts the outcome. Moreover, Age and Income have a positive impact on the outcome of diabetes. The findings of this research can potentially improve patient outcomes and alleviate the global burden of diabetes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

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3rd international conference on intelligent systems and sustainable computing, icissc 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM Machine learning

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