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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11-20 订阅

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automated Fish Species Classification using Convolutional Neural Networks 3

Automated Fish Species Classification using Convolutional Ne...

引用

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

作者： Yamsani, Nagendar Nasra, Parul Gupta, Sheifali School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

Appropriate classification of species in fish is important for biodiversity conservation, management of fisheries, and ecological research. This paper introduces a novel deep learning method for the automatic classification of aquatic species using CNNs. Testing the proposed model on a large-scale and diverse fish dataset resulted in impressive classification accuracy, reaching 92%. The architecture is made to balance the computational cost and predictability. It uses more complicated techniques, such as data augmentation, batch normalization, and dropout, in order to generalize. The study demonstrated evaluation of the model on multiple categories of the fish species and showed that it is precise and reliable in classification. The results show a potential for using CNN-based models for fully automating fish classification tasks, providing a scalable tool with a high degree of reliability in monitoring marine and freshwater ecosystems. Future work includes incorporating real-time detection capabilities into the model and adapting the same design to classify species in video streams. © 2025 IEEE.

关键词： Invertebrates

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An Efficient Deep learning based Approach for Pneumonia Detection 3

An Efficient Deep Learning based Approach for Pneumonia Dete...

引用

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

作者： Shirisha, M. Sri Sai Durga, M. Bhanu Keerthan Reddy, G. Shanmugasundaram, Hariharan Vaishnavi, Bandar Natarajan, Karuppiah Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9798331527549

Pneumonia is a major global cause of death, hence early detection and precise diagnosis are essential for successful management. Conventional techniques for detecting pneumonia, like radiological interpretation and clinical assessment, are frequently laborious and prone to variation amongst observers. Deep learning models have transformed medical imaging by offering automated, reliable, and accurate diagnostic instruments. The goal of this paper is to use data (image) pre-processing techniques to increase the accuracy of the deep learning model, EfficientNetB0 for pneumonia identification. The dataset utilized in this paper consists of annotated chest X-ray images from the publically accessible source "Chest X-Ray Images (Pneumonia)";Paul Mooney submitted 5,863 photos in 2 categories on Kaggle. To enhance the model’s resilience and ability to generalize, the images undergo preprocessing using techniques such as normalization, gray scaling, median filtering, CLAHE, and data augmentation. A standardized evaluation framework is used to evaluate each model’s performance, and measures like F1 score and confusion matrix analysis are used. The results of this paper demonstrate how pre-processing the data can increase the accuracy of the deep learning model and can enhance medical imaging diagnostic accuracy and dependability. © 2025 IEEE.

关键词： Median filters

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Deep learning-based Malaria Detection: A Comparative Study of Algorithm Performance 3

Deep Learning-based Malaria Detection: A Comparative Study o...

引用

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

作者： Saran, K. Vijay, M. Hariharan, M. Prema, D. Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Malaria is caused by Plasmodium parasites and remains a serious threat to health worldwide, with high morbidity and mortality rates. However, effective treatment depends on the accurate and fast diagnosis of malaria, which traditionally has been through microscopy and rapid diagnostic tests that have limitations, including requiring expert interpretation, limited sensitivity in the early stages of infection, and variable accuracy. By automatically detecting malaria from microscopic blood smear images using deep learning models, our study has overcome the above constraints. Four sophisticated deep learning modelsDenseNet201, NasNet, EfficientNetB7, and AlexNet were tested against performance metrics of accuracy, precision, recall, F1 score, and computational efficiency. With 99% accuracy, NasNet was the most successful model among them and is hence very dependable for the diagnosis of clinical malaria. In addition, AlexNet was faster in inference speed and had moderate accuracy compared to DenseNet201 and EfficientNetB7. Such results are a significant step forward in malaria detection technology, and the potential of NasNet for automated, scalable diagnostic systems is promising. © 2025 IEEE.

关键词： Contrastive learning

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Augmented Reality (AR) and Virtual Reality (VR) for Personalized learning 3

Augmented Reality (AR) and Virtual Reality (VR) for Personal...

引用

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

作者： Gaikwad, Aditya Rahul Gaikwad, Rohan Maruti Jodh, Dasharathraje Naganath Khiani, Simran Department of Computer Engineering G. H Raisoni College of Engineering and Management Pune India

ISBN: (纸本)9798331527549

To develop a specific learning environment using augmented reality (AR) and virtual reality (VR) technology, we create immersive and interactive educational experiences that enhance engagement, improve understanding of complex concepts, and cater to diverse learning styles for students. The system will be able to meet the needs of each learner by gathering data on their learning objectives, preferences, and past knowledge. Interactive multiple-choice questions will be used to evaluate students' performance and identify areas for development. Based on the findings, the website will recommend relevant study guides and classes. AR and VR technology will be used to develop immersive learning environments that include interactive simulations, 3D visualizations, and collaborative virtual worlds. To improve learning results and student engagement, the platform's architecture will prioritize data security, accessibility, and usability. © 2025 IEEE.

关键词： Virtual environments

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Research on Flight intelligent Sorting Algorithm in AI-based Air Traffic Control Systems 24

Research on Flight Intelligent Sorting Algorithm in AI-based...

引用

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

作者： Wang, Minghua Qiao, Jiantong Air Force Engineering University ATC&GCL College Shaanxi Xi'an China

ISBN: (纸本)9798400718120

Flight delay is a significant challenge faced by air traffic control systems, severely affecting passenger travel experience and airline operational efficiency. This paper addresses this issue by proposing an intelligent flight sorting method based on machine learning and deep learning. By preprocessing and feature engineering of flight historical data, airport data, and route data, a set of high-quality feature sets is constructed. On this basis, models such as random forests and LSTM are employed to predict flight delays and intelligently sort flights according to the prediction results. Experimental results demonstrate that this method significantly enhances the accuracy and efficiency of flight sorting, reducing the impact of flight delays on passenger travel. This study provides a novel and effective approach to addressing flight delay problems, offering broad application prospects and practical value. © 2024 Copyright held by the owner/author(s).

关键词： Delay control systems

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Comprehensive Approaches to Agricultural Intelligence: U-Net, YOLO, and Random Forest in Practice 3

Comprehensive Approaches to Agricultural Intelligence: U-Net...

引用

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

作者： Gavas, Saharsh Kosamkar, Pranali Pune India Dr. Vishwanath Karad Mit World Peace University Dept Computer Engineering and Technology Pune India

ISBN: (纸本)9798331528201

This paper seeks to implement and test advanced precision agricultural monitoring and detection systems derived from machine learning, remote sensing, and sensor technologies to improve pest detection, crop yields assessment, and animal husbandry management practices. The approach involves an automated and hierarchical framework integrating cutting edge machine learning technologies with fine spatial resolution remote sensing images and sensors. For instance, the authors apply CNN (convolutional neural networks) for pest identification;the deep learning models trained on Sentinel-2 images serve as yield predictors;UWB location coordinates and accelerometric measures combined form livestock behavior monitoring. This methodology encompasses the stages of implementation, testing and finally verification of the model. A variety of machine learning systems such as Faster-PestNet or deep semantic segmentation algorithms process data while remote sensing and the sensors' data were used in predictions and classifications. This allows using different performance measures such as accuracy, precision recall rates and RMSE (root mean square error) to test the systems. © 2024 IEEE.

关键词： Semantic Segmentation

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intelligent Parking System to Prevent Unauthorized Parking of Vehicles using IoT and R-CNN with YOLO 3

Intelligent Parking System to Prevent Unauthorized Parking o...

引用

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

作者： Vispute, Sushma Potdar, Manasi Pawara, Pranjal Patil, Shreya Patil, Satvika Ahirrao, Dinesh Bhaskar Dept. of Computer Engineering Pimpri Chinchwad College Of Engineering Pune India KCES’s College Of Engineering and Management Jalgaon India

ISBN: (纸本)9798331527549

In recent years, the proliferation of private vehicles has led to a significant challenge in finding adequate parking space, with unauthorized parking becoming a prevalent issue in many residential societies. To address this problem, we propose a novel solution that integrates machine learning techniques and motion detection technology to effectively manage parking spaces and curb unauthorized parking. Our approach utilizes advanced machine learning models such as RCNN for object detection and YOLOv8 for license plate detection, complemented by optical character recognition (OCR) for character recognition. Additionally, the ESP32-CAM webcam module is employed to capture real-time images, ensuring seamless integration with our system. This combination of technologies allows for the accurate detection of unauthorized parking instances in real time, providing efficient management of parking spaces within societies. Key features of the system include automated vehicle identification, real-time monitoring, and notification alerts, all of which contribute to a robust verification system for resident and guest vehicles. By leveraging these advanced technologies, our solution not only streamlines the parking process but also promotes a more organized and peaceful living environment. This innovative approach addresses a critical issue in urban settings, paving the way for more efficient and sustainable parking management practices. © 2025 IEEE.

关键词： Convolutional neural networks

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IoT-Enabled Reinforcement learning for Autonomous Robotic Lawn Mowers in Residential Gardens 3

IoT-Enabled Reinforcement Learning for Autonomous Robotic La...

引用

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

作者： Helenprabha, K. Amarnath, Raveendra N. Mohankumar, N. Gopi, B. Aishwarya, N. Murugan, S. Department of Electronics and Communication Engineering R.M.D.Engineering College Tamil Nadu Chennai India Mphasis Campbell Dr MelissaTX United States Nagpur Campus Pune India Innovation Center Department of Biomedical Engineering Sona College of Technology Tamil Nadu Salem India Tamil Nadu Salem India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

Traditional automated lawn mowers sometimes have difficulties in navigating complex garden designs and changing to changing conditions, which causes ineffective efficiency and limited coverage. This research introduces a new way to improve the efficiency and autonomy of robotic lawn mowers in residential settings by combining Reinforcement learning (RL) algorithms with Internet of Things (IoT) technologies. To help the robotic mower respond appropriately to environmental signals it presents a system that uses IoT sensors to collect data on topography, weather, and vegetation in real-time. It uses RL methods to make the mower better at mowing and navigating over time. With the autonomous mower attaining more accuracy in grass cutting while reducing energy consumption and operating expenses, our trial findings show considerable increases in coverage and efficiency compared to traditional techniques. Building on previous work in smart gardening technology to the body of understanding on IoT enabled autonomous systems for home use. The research addresses inefficient lawn management through the integration of IoT and RL, resulting in adaptive, accurate, and dependable robotic mowing for enhanced energy efficiency and coverage. © 2025 IEEE.

关键词： Reinforcement learning

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Streamlined Machine learning Workflow for Accurate Rice Yield Estimation Using Remote Sensing and Meteorological data 3

Streamlined Machine Learning Workflow for Accurate Rice Yiel...

引用

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

作者： Sinchana, H.S. Pallavi, N.R. Bgs Institute of Technology Adichunchanagiri University Department of Computer Science Engineering B.G Nagar Mandya District India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

The goal of this project is to use machine learning (ML), meteorological data, and remote sensing data to create an effective methodology for accurate rice yield estimation. The project entails creating a customized machine learning model with the aim of forecasting rice yields while accounting for the unique agricultural characteristics of the target region. The goal of the research is to determine the best approach for estimating rice yields by utilizing sophisticated data processing techniques and carefully assessing various machine learning models. By combining these strategies, crop production forecasts should become more accurate and provide insightful information for agricultural planning and management in a variety of geographical areas. © 2025 IEEE.

关键词： Adversarial machine learning

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Synergistic Integration of Blockchain Technology and Machine learning Algorithms for Optimization of Agriculture Supply Chain Management 3

Synergistic Integration of Blockchain Technology and Machine...

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

作者： Asalkar, Gayatri Sahane, Prema Pedde, Atharv Hapse, Samarth Ghondage, Tejas Bhosale, Krishna Department Of Computer Engineering JSPM's Rajarshi Shahu College of Engineering Maharashtra Pune411033 India

ISBN: (纸本)9798331527549

This research study focuses on the discussion of blockchain and machine learning optimization in the conventional agricultural supply chain. Optimization of both rising technologies has the possibility of addressing some of the complex and entrenched issues that confront the agricultural industry while receiving calls for more openness, honesty, and productivity. Blockchain, being the technological application that is based on secure and transparent transactions and enables the assurance of immutability and Machine learning as a data driven power in optimization, leads to a significantly better traceability, erased inefficiencies, less substances and time to market. It is however not entirely without some few challenges that will have to be overcome for all these innovations to come into their own. Among the obstacles that need to be overcome for realization of these innovations some of the critical ones are Technology and data issues and Stakeholder issues. © 2025 IEEE.

关键词： Blockchain immutability Machine learning optimization traceability

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