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

共 20248 条记录，以下是71-80 订阅

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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 ...

引用

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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Optimization of ML Algorithms in CAD Diagnosis using Grid Search CV 3

Optimization of ML Algorithms in CAD Diagnosis using Grid Se...

引用

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

作者： Gokila Brindha, P. Boobesh, R. Yokanandh, S.S. Deparment of Computer Technology-UG Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798331527549

Amongst the world's main causes of death coronary artery disease plays a major role. As an accurate and timely diagnosis is essential for effective treatment many researches focus on the early identification of the disease. The current approach uses machine learning in making the prediction of cardiac illness. The goal is to design an optimized machine learning framework that assists the healthcare system in the coronary artery disease diagnosis. The dataset used here consists of 11 features and 2300 records including the diseased and non-diseased records. The kNN, SVM, Random forest, Gradient Boosting and Decision Tree algorithms are applied on the classification process. The parameters of these models are optimized using the GridSearch optimizer and the models with the optimized parameters are created and tested with the testing data. The optimized Gradient Boosting model with an accuracy of 95% proves the best among the other models in predicting the coronary atherosclerosis. © 2025 IEEE.

关键词： Random forests

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automated Retinal Disease Classification Using Fine-Tuned ResNet50: A Deep learning Approach for Early Diagnosis 3

Automated Retinal Disease Classification Using Fine-Tuned Re...

引用

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

作者： Rani, Ritu Gupta, Sheifali Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

This paper presents a deep learning-based solution for retinal illness categorization using ResNet50 convolutional neural network (CNN) architecture. Retinal diseases including age-related macular degeneration, diabetic retinopathy, diabetic neuropathy, macular hole, and optic disc cupping account globally for most causes of visual loss. Effective treatment depends on early retinal imaging diagnosis;nonetheless, manual ophthalmologist diagnosis can be timeconsuming and error-prone. The researchers enhanced the ResNet50 model for fundus imaging retinal disease classification to help solve this. A total dataset used for model training and validation consisted in five illness categories. Pretrained ResNet50 acted as a feature extractor using extra layers catered for the specific work. Model performance was evaluated by means of accuracy, precision, recall, F1-score, and a confusion matrix, therefore assessing classification accuracy across the disease categories. Strong generalizing ability were shown by the results showing that, after 20 epochs, the tuned ResNet50 attained over 90% accuracy both in training and validation. This work highlights CNN's potential in medical picture categorization and proposes that automated systems such as this could enable early retinal disease diagnosis, therefore enhancing diagnostic accuracy and patient outcomes. © 2025 IEEE.

关键词： Ophthalmology

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The Investigation of Student's Online learning Adaptability Level Prediction Based on AutoML 3

The Investigation of Student's Online Learning Adaptability ...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Zhao, Yan Wang, Pengbo Beijing Polytechnic Beijing China Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350380989

In the rapidly evolving realm of online education, developing an accurate method for assessing students' adaptability is of paramount importance to the educational field. It enables educational institutions to better understand student performance and needs, allowing for the provision of timely support and resources. Therefore, this study employed automated Machine learning (AutoML) technology to predict students' adaptability to online learning. Utilizing Baidu's AI development platform named EasyDL, this research conducted an in-depth analysis of 1, 205 data samples provided by the Kaggle platform. The model's performance was evaluated using key indicators e.g. accuracy, precision, recall, and F1-score, and it was compared with traditional machine learning methods to establish its superiority. The experimental results demonstrate that AutoML technology effectively predicts students' adaptability to online learning, with the model achieving an accuracy rate of 90.9%. While there is still room for improvement in predicting high adaptability levels in students, the model significantly underscored the importance of critical features such as age, location, type of educational institution, and class duration. These findings not only validate the efficacy of AutoML technology in the educational sector but also lay an empirical foundation for future research. © 2024 IEEE.

关键词： adaptability level automated machine learning EasyDL online education

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Generation of Pseudo X-Ray and Digitally Reconstructed Radiograph from CT images 3

Generation of Pseudo X-Ray and Digitally Reconstructed Radio...

引用

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

作者： Arya, R. Kochuvila, Sreeja Dept. of Artificial Intelligence Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Computed Tomography (CT) scanning offers 3D insights into the human body but involves higher radiation exposure than traditional X-Rays, increasing the relevance of 3D reconstructions from 2D data. In this work, datasets of CT- pseudo-X-Ray and CT- Digitally Reconstructed Radiograph (DRR) pairs are generated using ray-sum tracing and projection techniques. This can be helpful for implementing machine learning and deep learning architecture as 2-D data availability is limited. The described approach generates CT–pseudo-X-Ray and CT–Digitally Reconstructed Radiograph (DRR) pairs using ray-sum tracing and projection methods. The pseudo-X-Ray is created by combining bone and soft tissue projections, while the DRR is derived via ray-sum tracing. By retaining the CT images in Digital Imaging and Communications in Medicine (DICOM) format, the created dataset enhances robustness and efficiency, supporting 3D reconstruction—a rapidly advancing field. © 2025 IEEE.

关键词： X ray radiography

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Unsupervised Vehicle Re-Identification with Clustering and Appearance Style Exploration 24

Unsupervised Vehicle Re-Identification with Clustering and A...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Taleb, Hamdan Zhu, Xiaobo Wang, Chao College of Electronic and Information Engineering Tongji University Shanghai China School of Informatics and Engineering Suzhou University Jiangsu Suzhou China

ISBN: (纸本)9798400718120

Vehicle re-identification (Re-ID) has emerged as a critical challenge in the fields of computer vision and intelligent transportation systems, driven by the need to accurately identify vehicles across multiple camera views in diverse urban environments. Traditional methods relying on appearance-based features such as color and resemblance often fall short due to the variability in vehicle appearances caused by factors like illumination changes, camera angles, and occlusions. To address these limitations, this paper proposes a novel approach that leverages unsupervised learning, integrating agglomerative clustering techniques and style transfer via the StarGAN2 model. By treating each camera as a unique style domain, the proposed method generates new training pictures with diverse styles, reducing camera-induced variability and enhancing cross-camera vehicle identification. The repeated learning framework further refines vehicle identities by clustering similar pictures, thereby improving the system’s ability to distinguish between vehicles in real-world scenarios. This approach not only minimizes the need for manual annotation but also enhances the robustness and adaptability of vehicle Re-ID systems, making it a valuable contribution to urban surveillance and intelligent transportation systems. © 2024 Copyright held by the owner/author(s).

关键词： Unsupervised learning

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An automated Deep learning-based VAR for Enhancing Referee Decision-Making in Football 3

An Automated Deep Learning-based VAR for Enhancing Referee D...

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

作者： Zaken, Hamza Abu Prasanth, A. Jahjah, Mohamad Ali Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

While football is the most popular sport the world has ever seen, the question of referee decision-making has always been a shadow that hampers the spirit and justice of the game. Video Assistant Referee (VAR) was introduced to solve officiating errors through video reviews. Nonetheless, this technology has drawn heavy criticism for causing huge delays and not completely eradicating referee bias. In this research, a Tube Convolutional Neural Network (T-CNN) model is incorporated into a new VAR system as an advanced solution. By allowing for the input of spatial and temporal features for football footage, the T-CNN model processes features which allows for rapid and accurate analysis of complex match events such as offsides, fouls, and goal-line incidents. The proposed system makes gameplay more real-time while ensuring fairness by minimizing human involvement and the consequent delays in the decision-making process. Additionally, its unbiased AI-driven decisions also eliminate the challenge of home team bias. Comparative analyses with traditional VAR methods established that the T-CNN system achieves greater accuracy rate, real-time responsiveness, and fairness, ultimately transforming football officiating. © 2025 IEEE.

关键词： Convolutional neural networks

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Implementation of a Human-Following Smart Trolley using RFID with automated Product Scanning 3

Implementation of a Human-Following Smart Trolley using RFID...

引用

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

作者： Ramasubramanian, G. Raja, Suvarna Smita Dixit, Teja V. Sheethal, R. Sri Balaji, S. Sivadharishana, T.D. Dept.AIML Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331527549

Shopping convenience and automation have gained much priority in the retail industry, especially with the advent of RFID and IoT. This paper presents a Human-Following Smart Trolley using RFID, together with automated product scanning, to remove general shopping hassles in the form of manual handling of a cart and delay during checkout. It proposes the integration of RFID technology for authentication, real-time scanning of products, ultrasonic sensors for tracking humans autonomously, and a microcontroller-based system to handle all operations together. This trolley follows the user at a distance and detects the items that are placed inside it with the help of an RFID tag. The overall bill is projected to calculate the value of the LCD display. The implementation demonstrates the capability of the system to work autonomously in dynamic retail environments for accurate billing and efficient navigation. It cuts down shopping time while increasing customer convenience. The system design is modular;hence, it is scalable and easy to adapt for future improvements. Besides assured accuracy, the system is cost-effective, with Arduino microcontrollers, RFID modules, and ultrasonic sensors fairly inexpensive and hence suitable for small to mid-scale retail setups. The present study investigates how the combination of automation and smart technologies is enabling the redefinition of the retail shopping experience. Further development will likely focus on obstacle avoidance, machine learning for personalization, and full checkout-free capability through mobile payment integrations that will characterize smarter, efficient retail automation in the years to come. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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Deep learning-Based Analysis of Pediatric Pneumonia Detection in Children using Fine-tuned NasNetMobile Model 3

Deep Learning-Based Analysis of Pediatric Pneumonia Detectio...

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3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Tammanashastri, Prabhav Ravi Bhat, Suhas S Sai Jagadeesh, Dumpa Venkata Padam Singh, Rimjhim Bengaluru Amrita Vishwa Vidyapeetam Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798331528201

Pediatric pneumonia is a significant cause of morbidity and mortality in children worldwide. Traditional methods for detecting the disease are time-consuming, thereby necessitating automated detection methods. Hence, the paper fine-tunes the light-weight NasNetMobile model architecture using multiple normalization approaches and six recent activation functions like Swish, HardSwish, Mish, etc. to enhance model performance and generalization in the efficient detection of pediatric pneumonia in children. The work also conducts a comparative analysis of six state-of-the-art model CNN model architectures to prove the efficacy of the proposed model. The outcomes show that fine-tuned NasNetMobile model using the Mish activation function, combined with batch normalization and dropout, outperforms other configurations with an F-score of 92%. © 2024 IEEE.

关键词： Deep learning

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