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共 11874 条记录，以下是91-100 订阅

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CNOL: A Convoluted Neural optimization Logic to Predict Leptospirosis Disease Detection based on Medical Data Evaluation Scheme 3

CNOL: A Convoluted Neural Optimization Logic to Predict Lept...

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Sajiv, G. Meenakshisundaram, N. Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Leptospirosis is a widespread zoonotic disease that can cause severe health issues if not detected and treated early. This study presents a Convoluted Neural optimization Logic (CNOL) framework designed to predict Leptospirosis Disease using both structured and unstructured medical data. The proposed model integrates convolutional layers for feature extraction from complex datasets, including clinical information and medical images, and utilizes advanced optimization techniques like Adam to enhance learning. The CNOL framework tested on a Leptospirosis Disease Dataset, and results show a significant improvement in predictive accuracy. The model achieved an accuracy of 95.67%, outperforming traditional machine learning models such as Logistic Regression (85.45%) and Random Forest (89.56%), and deep learning models like Deep Neural Networks (93.10%). Additionally, the CNOL framework exhibited superior precision (94.85%) and recall (96.42%), highlighting its effectiveness in both identifying true positive cases and minimizing false negatives. This study demonstrates the potential of combining convolutional layers and advanced optimization techniques for enhancing disease prediction, providing a robust tool for early diagnosis and intervention. © 2025 IEEE.

关键词： Convolutional neural networks

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A Hybrid Deep learning Model for Violence Detection 3

A Hybrid Deep Learning Model for Violence Detection

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chandira Prabha, S. Kaviyadharshini, S. Annie Micheal, A. Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Current violence detection systems often face challenges such as high computational costs, real-time processing difficulties, and limited precision in detecting violent activities in dynamic environments. This research addresses these issues by proposing a hybrid deep learning model that integrates MobileNetV2 for efficient spatial feature extraction and ConvLSTM (Convolutional Long Short-Term Memory) for capturing temporal patterns. The model effectively discriminates between aggressive and non-aggressive behaviours in real-time video streams. Using the Kaggle Violence Dataset, which comprises 1,000 video clips of violent and non-violent activities, the system achieved an accuracy of 96%, showcasing its high performance. Additionally, an integrated alert system enhances practical application by sending real-time notifications via a Telegram bot, including details such as location, timestamp, and camera ID. This solution ensures timely responses to violent incidents, addressing the core issues of computational efficiency and real-time precision, while offering scalability and applicability in diverse scenarios. © 2025 IEEE.

关键词： Deep learning

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A Novel Generative AI-Powered Approach for Sentiment Analysis: Enhancing Deep learning Models with Contextual Understanding 3

A Novel Generative AI-Powered Approach for Sentiment Analysi...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Naik, Mahendra Shridhar Chaitra, S.N. Shashikala, K.S. New Horizon College of Engineering Department of Electronics & Communication Bengaluru India Gm Institute of Technology Department of Electronics & Communication Davangere India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Sentiment analysis, a pivotal aspect of natural language processing (NLP), has seen notable advancements with the advent of deep learning models. Despite these advancements, challenges persist in accurately capturing nuanced emotions and understanding context-dependent sentiments. This paper introduces an innovative method that integrates generative adversarial networks (GANs) into deep learning architectures to enhance sentiment analysis. By leveraging GAN's ability to generate contextually relevant text, the proposed approach enriches the training data, thereby improving the model's ability to discern subtle sentiment variations. Our extensive experiments on benchmark datasets reveal that our method surpasses traditional deep learning models, achieving a 5% improvement in accuracy, a 7% higher F1-score, and demonstrating superior robustness to sarcasm and irony. © 2025 IEEE.

关键词： Generative adversarial networks

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DemoCraft: Using In-Context learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

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Cyber Threat Attack Level Detection using Machine learning 3

Cyber Threat Attack Level Detection using Machine Learning

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rahul, R. Mythili, S. Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

This study focuses on creating a machine learning-based system for identifying cyber-attacks in real time using network data, system logs, and attack history. The work presents a thorough methodology that includes data collection, preprocessing, feature selection, model training, and real-time monitoring. The system uses a variety of machine learning techniques to classify attack types, including supervised learning methods such as Random Forest, Support Vector Machines (SVM), and Neural Networks, as well as unsupervised learning methods such as clustering to detect anomalies that indicate potential threats. The model was trained and evaluated on a dataset of 477 items, reaching an accuracy of 83.33%, with 470 entries identified as carrying threats. The study effectively proved the ability to detect numerous attack types, such as SQL injection, cross-site scripting (XSS), and DDoS attacks, as well as accurately classify and prioritize threat levels. Furthermore, the system was successfully deployed in a live environment, providing real-time threat detection, automatic response capabilities, and detailed alerts to security personnel. This research resulted in the creation of an efficient and scalable cyber threat detection framework capable of both detecting existing attacks and discovering novel, previously unknown threats. © 2025 IEEE.

关键词： Cyber attacks

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Predictive Modelling of Investor Sentiment and Preferences in Green Bonds Using Machine learning 3

Predictive Modelling of Investor Sentiment and Preferences i...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Karamadi, Swetha Chinnasamy, Gopalakrishnan Vinoth, S. Jain Deemed-to-be University Faculty of Management Cms Business School Bengaluru India

ISBN: (纸本)9798331515911

This research study leverages machine learning algorithms to forecast investors' attitudes and preferences concerning green bonds. Using a sample of 187 investors from Bengaluru, the study explores relevant demographic and experience variables affecting investment decision-making. It employs decision tree analysis, K-means clustering, and SHAP (SHapley Additive exPlanations) values to identify outcomes that matter most regarding sentiment and preference. In addition, to indicate the relative importance of these traits to investment decisions and the long-term sustainability of green bonds, the study utilizes multiple regression analysis and survival analysis. These results provide valuable insight for investors, issuers, and policymakers on how to efficiently and effectively structure their green bond offerings and strategies to improve appeal to the market and ultimately invest in a more sustainable environmental outcome. © 2025 IEEE.

关键词： Green development

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Advanced Machine learning Models for Electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. Performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： Power transmission lines

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Machine learning Approaches for Insect Species Recognition using Pyramid Vision Transformer 3

Machine Learning Approaches for Insect Species Recognition u...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Soundarya, M. Iswarya, G. Shree, P. Pooja Suganthi, Swarna Velammal College of Engineering and Technology Department of It Madurai India

ISBN: (纸本)9798331515911

Insect species recognition systems have significantly advanced with recent machine learning techniques, but many existing models still face challenges such as limited scalability, lower accuracy in diverse environments, and poor generalization across species variations. To address these limitations, we propose an insect species recognition system utilizing the Pyramid Vision Transformer (PVT) algorithm, which excels in handling hierarchical and multi-scale features. By utilizing a hybrid dataset that was produced by merging the Insect Village Synthetic Dataset and the Agricultural Pest Dataset, the proposed approach exceeds traditional models by a wide margin, achieving a recognition accuracy of 99.16%. The PVT-based system also demonstrates robustness in recognizing complex and diverse insect species, making it an effective solution for entomological studies. © 2025 IEEE.

关键词： Invertebrates

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IoT-based Women’s Safety Monitoring in Educational Campuses using WSN and Deep learning 3

IoT-based Women’s Safety Monitoring in Educational Campuses...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pallavi Sinha, Raj Department of Electronics & Communication Jayoti Vidyapeeth Women’s University Rajasthan Jaipur India School of Computer Applications Lovely Professional University Punjab India

ISBN: (纸本)9798331527549

The recognition of human activity is significant in such domains as smart environments, security, and health. A novel approach to IoT-based women's safety monitoring at educational campuses using a hybrid deep learning model that combines CNNs and LSTMs has been proposed in this study. Using wearable inertial sensors, the system classifies six predetermined activities - walking, walking upstairs, walking downstairs, sitting, standing, and laying. The Human Activity Recognition dataset preprocessed with noise filtering and feature extraction was utilized in training and testing. Spatial features are extracted from CNNs while the LSTMs capture temporal dependencies, giving a 95.38% accuracy that is augmented with F1-score, precision, and recall exceeding 95%. This approach offers a robust, scalable safety solution for monitoring of activities that would be accomplished in real-world educational settings. © 2025 IEEE.

关键词： Wearable sensors

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Artificial Intelligence, intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach 3

Artificial Intelligence, Intelligent Manufacturing, and Sust...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shabi, Sathika Begum Mohammad Bhuvaneshwari, M. Kanimozhi, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry. © 2025 IEEE.

关键词： Smart manufacturing

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