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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20750 条记录，以下是81-90 订阅

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Development of Image Classification System to Detect Skin Lesion using Deep learning Techniques 3

Development of Image Classification System to Detect Skin Le...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hariprasath, V. Rangkeshwaran, R. Dhanush, K. Prema, D. Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Skin lesions are abnormal growths or appearances of the skin and may be an indication of several diseases, both benign and malignant. To achieve the correct diagnosis and proper treatment of these lesions, need to be discovered early and diagnosed accurately. Conventional methods of diagnosis, including biopsies and examinations of the dermatologist's eye, are often invasive and expensive, thus limiting access to them in underdeveloped regions. This work proposes a deep learning-based image classification system for the identification of skin lesions. The categorization of dermoscopic images was done using CNN architectures such as ResNet50, DenseNet201, VGG 19 and Xception. ResNet50 performed better than the others in identifying and categorizing skin lesions, achieving the highest accuracy of 87.72%. This non-invasive method and accessibility could greatly improve the diagnostic capabilities while reducing reliance on conventional techniques. © 2025 IEEE.

关键词： Deep learning

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Leveraging Sequential Deep learning Models for Robust Compiler Bug Detection in Software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 IEEE.

关键词： Application programs

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Water Quality Prediction and Suggesting Filter using Machine learning 3

Water Quality Prediction and Suggesting Filter using Machine...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chaudhari, Archana Mane, Rutuja Khot, Anant Kadam, Anup Rajam, Nagraj Department of Instrumentation Engineering Vishwakarma Institute of technology Bibwewadi Maharashtra Pune India

ISBN: (纸本)9798331527549

Survival of all living beings and efficient working of industries and agriculture are largely dependent on the availability of clean water. However, contamination is a major issue that requires dependable methods for assessing the quality of water before its use in drinking, chemical applications, or animal hydration. This research utilizes machine learning to predict water quality based on parameters such as pH, turbidity, hardness, conductivity, and dissolved particles. Based on a broad dataset, models such as time-series analysis and Random Forest classifier are applied to categorize water as safe or unsafe for domestic use. A hardware system equipped with sensors captures real-time data, which enables dynamic assessment of water quality. The proposed solution shows high accuracy in its evaluation using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Regression Analysis, offering a practical approach to ensure water safety in diverse applications. © 2025 IEEE.

关键词： artificial intelligence data visualization dataset filter hardware machine learning preprocessed data Random forest algorithm TDS water quality

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Autism Spectrum Disorder Analysis and Detection System 3

Autism Spectrum Disorder Analysis and Detection System

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ananthajothi, K. Mohanapriya, E. Mukhilan, S.S. Department of CSE Engineering College Chennai India

ISBN: (纸本)9798331527549

Autism Spectrum Disorder is a neurodevelopmental disorder characterized by social interaction, communication impairments, and repetitive behaviors. In recent years, there has been an increase in prevalence, and it is important to identify individuals early for appropriate intervention to improve their quality of life. The following study was conducted to deepen the understanding and management of the disorder by employing a comprehensive two-module approach. Module one emphasizes the detection of ASD through sophisticated machine learning techniques for early and accurate diagnosis. Module two is on improving social and communication skills of children with ASD via game-based assessments, offering them an interactive yet enjoyable learning environment. This project, not only in contributing to the scientific community, but also through practical applications contributes and offers easy solutions for caregivers and educators. Integration of technology with both detection and intervention holds huge advancement in this area, promising better outcomes for ASD individuals. © 2025 IEEE.

关键词： Adversarial machine learning

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An Enhanced Framework for Churn Prediction using Stratifed Bagging and Stacked Multi-Output Random Forests 3

An Enhanced Framework for Churn Prediction using Stratifed B...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Bhardwaj, Anay Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Customer churn prediction is an important task in customer relationship management because it helps businesses know who is at risk of leaving and retain such at-risk *** and time-efficient churn prediction is essential to identify the factors driving churn, enabling businesses to implement timely and effective retention strategies.A framework is proposed which combines stratified bagging with multi-output Random Forest and stacked feature transformation, to enhance prediction accuracy. This approach balances the dataset, trains a custom Random Forest using stratified bagging, and applies stacked learning using Logistic Regression on transformed features. Tested on real-world data, the model demonstrated 99% accuracy, showing improvements over the classic Random Forest in precision, recall, and prediction time. © 2025 IEEE.

关键词： Sales

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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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Early-Stage Diabetes Risk Prediction using Machine learning Techniques 3

Early-Stage Diabetes Risk Prediction using Machine Learning ...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Aparajita Jena, Junali Jasmine Ghosh, Debolina Jain, Sonal Sardar, Tanvir Habib Gourisaria, Mahendra Kumar School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India School of Economics and Commerce KIIT Deemed to be University Odisha Bhubaneshwar India CSE School of Engineering Dayanand Sagar University Karnataka Bangalore India

ISBN: (纸本)9798331527549

Diabetes is a chronic and metabolic disease caused by an increment in glucose level in the blood, which leads overtime to major serious damage to the heart and kidneys. Millions of people are affected by diabetes in the world. The main aim of this study is the prediction of the risk of diabetes at an early stage. The data set contains information on the symptoms of newly diagnosed diabetic patients, those who are at risk of developing diabetes. The data was collected through direct questionnaire's administer to patients at the hospital in Bangladesh. In this work, 10 supervised machine learning algorithms such as Random Forest (RF), Naive Bayes (NB), etc. are implemented by taking accuracy, precision, F1 score etc. as evaluation criteria. This diabetic risk prediction model has a higher accuracy of 99.12% with Random Forest and Gradient boosting than other models. © 2025 IEEE.

关键词： Self-supervised learning

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An intelligent Machine learning Approach for Prediction of Road Accident Severity 3

An Intelligent Machine Learning Approach for Prediction of R...

引用

3rd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Rahul, Namana Sri Balaji, Pandi Jyothi, Sankati Danaveni, Pavithra School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India VIT-AP University Software Development Cell Andhra Pradesh Amaravati522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety. © 2025 IEEE.

关键词： Highway accidents

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Nutrifit: Smart Food Tracking and Nutritional Guidance System using Sensor-based Monitoring 3

Nutrifit: Smart Food Tracking and Nutritional Guidance Syste...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Krishna, Nanditha Surabhi, A.S. Ashrita Dikshit, V.A. Iliger, Bhagyashree Sreedevi, G. Center for Medical Electronics and Computational Intelligence Department of Medical Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Electronics and Communication RV College of Engineering Bengaluru India Department of Biomedical Engineering ACS College of Engineering Bangalore India

ISBN: (纸本)9798331527549

Proper nutrition is essential for maintaining health and well-being, particularly in children who are in critical stages of growth. Poor dietary habits can lead to deficiencies and increase the risk of chronic conditions such as anorexia and obesity. Children, unlike adults, often struggle to consume well-balanced meals, especially in childcare settings where meal intake may go unnoticed. This study introduces an automated food monitoring system designed to promote healthier eating habits for both children and adults. By considering factors such as age, weight, and location, the system provides personalized dietary recommendations. Using load cell sensors, it measures meal weight and tracks nutrient consumption over time, with data accessible through a user-friendly application and website. A survey conducted on children aged 4 to 10 years highlights the system’s effectiveness in addressing nutritional gaps and fostering healthier dietary choices, supporting a preventive approach to long-term health. © 2025 IEEE.

关键词： Food ingredients

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Deep learning to Effectively Detect and Eliminate Real-Time Network Intrusions 3

Deep Learning to Effectively Detect and Eliminate Real-Time ...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Harikesh, S. Sujatha, G. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India

ISBN: (纸本)9798331527549

Network traffic has been growing exponentially for the past several years. As a result, it has become more and more difficult to detect cyber intrusions - especially in unbalanced environments, where the attacks are usually covered up by normal traffic. This investigation is set to deal with the problem by coming up with an intrusion detection system that uses a deep learning approach to effectively find and eliminate the unbalanced network intrusions. The process includes data preprocessing for balancing the classes, feature engineering for choosing optimal features, and comparison of CNN and RNN models for improving detection accuracy. The tests have shown precision of 93% and recall of 88% which in total brought 99.8% accuracy. Also, the real-time deployment has demonstrated the model’s ability to dynamically traffic patterns, which induced false positives to be reduced remarkably. The results help in the improvement of intrusion detection systems by providing a scalable, adaptive, and efficient way to handle constantly altering cyber threats. © 2025 IEEE.

关键词： Network intrusion

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