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

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Research on Sports Competition Result Prediction System Based on Computer Big data Machine learning Model 3

Research on Sports Competition Result Prediction System Base...

引用

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

作者： Lu, Xiaolong Li, Ming Ye, Chao Yunnan Vocational College of Sports Yunnan Kunming China Yunnan Youth Sports Training Center Yunnan Kunming China

ISBN: (纸本)9798350380989

Using big data theory to analyze athletes' performance data, this paper finds out the changing characteristics of athletes' performance. Using the principle of big data, we find the performance characteristics of athletes in the training process. The particle swarm optimization (PSO) method is used to predict the network parameters of support vector machine (SVM) online. The validity of this method is verified by testing on college students. This method can track athletes' performance characteristics more accurately, so as to build an excellent sports performance prediction model. The model can detect future trends from athletes' performance data in data mining and machine learning. This kind of system has a wide application prospect, and can greatly improve the efficiency and quality. The method combines machine learning theory with support vector machine network, which can effectively improve the prediction accuracy. This method has a very broad application prospect. © 2024 IEEE.

关键词： Athlete performance changing situation machine learning model prediction model

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An IoT Cloud based Health Monitoring System for Remote Area using Node MCU: E Health Checker 3

An IoT Cloud based Health Monitoring System for Remote Area ...

引用

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

作者： Venkataramanaiah, B. Chittimalla, Sravan Kumar Raveendra Reddy, V. Vani, A. Saidmuratova, Malokhat Khasanova, Surayyo Vinayagam, P. Department of ECE Vel Tech Rangarajan Dr.sagunthala R&D Institute of science and technology Chennai India Enliven Technologies Inc TX United States Department of EEE Ramireddy Subbaramireddy Engineering College SPSR Nellore India Department of ECE Chaitanya Bharathi Institute of Technology Hyderabad India Department of Pedagogy and Psychology Urgench State University Urgench Uzbekistan Department of Psychology and Medicine Mamun University Khiva Uzbekistan Department of ECE Saveetha Engineering College Chennai India

ISBN: (纸本)9798331527549

This research work introduces a real-time health monitoring system using IOT sensors and cloud integration for human health monitoring. The system leverages the Node MCU micro controller for its Wi-Fi capabilities, interfacing with sensors like DHT11 (for temperature and humidity), HBR (for heart rate), and SpO2 (for oxygen saturation). These sensors continuously gather health-related data, which is transmitted to the Thing Speak cloud platform for storage and analysis. The hardware setup involves connecting the Node MCU to the sensors for accurate data collection, while the firmware is programmed to handle real-time data acquisition and cloud communication. ThingSpeak is configured to receive and store the data, organizing it into custom channels for further analysis. Basic data processing algorithms are applied to filter out noise and detect trends in the collected health metrics. The system enables real-time visualization of health data and employs machine learning models to offer personalized healthcare insights. This framework facilitates proactive healthcare interventions, enhancing patient care and supporting chronic condition management. With continuous monitoring and cloud-based analysis, it provides a foundation for personalized treatment plans, improving the overall healthcare experience and people lives in rural areas will benefit. © 2025 IEEE.

关键词： Firmware

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Integrated intelligent Surveillance System Using Deep learning 3

Integrated Intelligent Surveillance System Using Deep Learni...

引用

3rd international conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Paul, Arya Paul, Sona Manikandan, A.R. Jose, Katharin P Sabarinath, M.S. Adi Shankara Institute of Engineering and Technology Department of Electronics and Communication Engineering Kerala India

ISBN: (纸本)9798350346169

Nowadays the surveillance systems are widely used to find out the suspicious events that have occurred. In conventional systems, there are a lot of limitations such as storage, bandwidth, cost, the short lifespan of hardware devices, loading issues, etc. We developed an intelligent surveillance system using deep learning in which the video footage of suspicious events is extracted. Transfer learning, a part of machine learning, is used for face detection which involves the reuse of a pre-trained model on new data. The abnormal activity detection is done using a multi person MoveNet Light model and the face detection is done using VGG16. The suspicious objects found in the frame (gun, mask) are identified using corner detection. This system offers less bandwidth, high security, effective storage, and reduced load-balancing issues. In this paper, we detailed the face detection, object detection and anomaly detection used in our system. © 2023 IEEE.

关键词： Deep learning

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Deep learning Driven X-ray Classification for Pneumonia Detection Using CNN 3

Deep Learning Driven X-ray Classification for Pneumonia Dete...

引用

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

作者： Saxena, Sarvagya Khandelwal, Siddhartha Chaudhary, Sneha Gupta, Priyanka Gla University Computer Science Engineering Vrindavan India Gla University Computer Science Engineering Mathura India Gla University Computer Engineering & Application Mathura India

ISBN: (纸本)9798331528201

Major deaths (55% or 1.5 million) of LRI (Lower Respiratory Infections) are caused due to Pneumococcal pneumonia. LRI is the 5th highest cause of demise as mentioned by the Global Burden of Disease Study 2015. Health organization suffers from a lack of Physicians. A Physician handles so many cases which leads to misdiagnoses. To resolve all these issues we came up with an easy-to-handle, and implemented in health organizations, the CNN model. The main aim of this model is to help the healthcare professionals by providing them with a tool which will help them detect Pneumococcal pneumonia. With the help of this tool, the work strain of a doctor can be reduced. This model is developed without any pre-trained model and for better accuracy, an immense dataset is used. All the X-ray images were pre processed before being introduced to the neural network. Different layers have been used to make the model more precise and make it adaptable for X-ray images under different conditions. The aim is to combine deep learning concepts with healthcare to make a positive and meaningful impact in the healthcare industry. © 2024 IEEE.

关键词： CNN Models Deep learning Healthcare Pneumococcal pneumonia X-Ray

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A Novel Approach of Image Caption Generator using Deep learning 3

A Novel Approach of Image Caption Generator using Deep Learn...

引用

3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Saini, DilipKumar Jang Bahadur Kumar, Sunil Joshi, Kapil Pathak, Abhishek Kumar Jain, Saksham Singh, Anupam Pimpri Chinchwad University Department of Computer Science and Engineering Pune412106 India Meerut India Uttaranchal Institute of Technology Uttaranchal University Department of Cse Dehradun India Upes Dehradun India Meerut India Graphic Era Hill University Department of Computer Science and Engineering dehradun India

ISBN: (纸本)9798350306989

Image caption generation is an emerging field of study for researchers that mainly focuses on developing systems that can generate captions of an image. In today's World, Image captioning is a very useful tool. Moreover, some systems use machine learning models such as deep learning models which include CNNs and RNNs models used to analyze images and generate captions. There are some late growths in caption generation that have focused on transfer learning, reinforcement learning, and multimodal approaches. The proposed system has 5 phases which are data cleaning, extraction, layering, training, and testing. The proposed model is tested on the recent Flicker8k_dataset for image caption generation and it is implemented using Python software. © 2023 IEEE.

关键词： Long short-term memory

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Unveiling the Intricacies of Cyber Harassment Intentions on Social Media Platforms 3

Unveiling the Intricacies of Cyber Harassment Intentions on ...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Shraya, Bejgum Reddy Dopathi, Hari Charan Anitha, G. Pattanaik, Balachandra Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamilnadu Chennai India Wallaga University Nekemte Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350381931

Cyberbullying is a severe issue that impacts teens and adults alike. Errors like hopelessness and suicide have resulted from it. The demand for material on social media platforms to be regulated is growing. In the work that follows, machine learning and natural language processing are used to construct a model to identify cyberbullying in text data. The program leverages data from two different types of cyberbullying: personal attacks on Wikipedia forums and tweets containing hate speech. To determine the optimal approach, three feature extraction techniques and four classifiers are examined. The method produces more than 80% accurate data from Wikipedia and more than 90% correct data from Twitter. © 2024 IEEE.

关键词： Machine learning

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Bird Species Detection Using Deep learning Techniques 3

Bird Species Detection Using Deep Learning Techniques

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Thangaraj, Rajasekaran Jaisankar, P. Lakshana, S. Devadharshini, G. Jeyabharathi, P. Nandha Engineering College Department of Computer and Engineering Erode India Nandha Engineering College Department of Mathematics Erode India

ISBN: (纸本)9798331523893

Peacocks can significantly impact agricultural fields by damaging crops, necessitating an efficient detection system for timely intervention. This work introduces P-YOLOv5, a modified version of the YOLOv5 architecture designed for real-time object detection in resource-constrained environments. By replacing the original YOLOv5 backbone with a customized Mobile Net, which utilizes depthwise separable convolutions, P-YOLOv5 significantly reduces model size and computational complexity while preserving high detection accuracy. The model is evaluated on a detection dataset, demonstrating its effectiveness in tracking movement patterns related to environmental interactions. Experimental results show that P-YOLOv5 achieves a mean average precision (mAP) of 0.915 and a recall rate of 0.99, outperforming YOLOv5 in both inference speed and efficiency, with a 17% reduction in model size. These findings underscore P-YOLOv5 as a highly efficient solution for real-time object detection, offering a balance of speed, accuracy, and computational efficiency. © 2025 IEEE.

关键词： Object detection

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Multimodal Sentiment Analysis using a Hybrid Deep learning Model with Advanced Feature Fusion and Attention Mechanism 3

Multimodal Sentiment Analysis using a Hybrid Deep Learning M...

引用

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

作者： Kuruva, Arjun Nagaraju, C. A.P. Proddatur India

ISBN: (纸本)9798331527549

Multimodal sentiment analysis plays a crucial role in natural language processing by incorporating visual features, such as emojis, to interpret sentiment and mood in text. This paper addresses the challenge of effectively integrating textual and visual modalities, particularly the gap in leveraging emoji data for emotion detection. We propose a hybrid model that combines BERT-based textual embeddings with pre-trained emoji embeddings, advanced feature fusion, and attention mechanisms to enhance sentiment understanding. The attention mechanism enables the model to focus on relevant semantic and emotional cues, while perceptual segmentation allows for improved interpretation of long-term dependencies between text and emojis. When evaluated on the Sentiment140 dataset, the proposed model achieves state-of-the-art performance, reaching an accuracy of 92.8%, F1-score of 92.3%, and AUC of 96.2%, outperforming benchmarks like BERT-LSTM and RoBERTa-GRU. These results underscore the effectiveness of advanced feature fusion and attention mechanisms in multimodal sentiment analysis, providing robust emotion detection in social media data. Despite computational challenges, the model demonstrates strong generalization across different data types, offering valuable insights for future research in multimodal emotion analysis. © 2025 IEEE.

关键词： Semantics

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Butterfly Optimization based Feature Selection with Long-short Term Memory for Web Page Crawling 3

Butterfly Optimization based Feature Selection with Long-sho...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Deepak, H.A. Divya, G. Deepak, R. Keerthi Kumar, M. Navkis College of Engineering Department of Electronics and Communication Engineering Hassan India Bgs Institute of Technology Adichunchanagiri University Department of Electronics and Communication Engineering Mandya India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Gsssietw Department of Electronics and Communication Engineering Mysuru India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508456

Info recovery, data removal, manuscript removal, network explore, and connected under-standing all struggle with organising the enormous number of text documents. Due to rapid expansion growth of the World Wide Web, web crawling has become increasingly promi-nent. Website search engines face challenges in efficiently accessing a vast array of online content. A focused crawler is a specialized type of web crawler designed to specifically gather web pages from the Internet. The atmosphere of online sites, however, can readily affect how well the focused crawling works. This research paper presents an automated web scraping technique that blends a feature selection model with deep learning. The pre-processed data is then utilised to train a text-based Convolutional Neural Network (CNN) model, which is then used to extract features. The characteristics are then picked in the most optimal manner using the Butterfly Optimisation Algorithm (BOA) model. way. The chosen traits are then categorised as Long-Short Term Memory (LSTM). In our studies, the suggested model is assessed using the WebKB benchmark dataset. Our experimental findings demonstrated that the suggested model was frequently able to learn the appropriate decision surface using the area under the Precision-Recall Curve as the performance met-ric. © 2025 IEEE.

关键词： Convolutional neural networks

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Object model mapping and resource sharing methods for intelligent circuit breaker 3

Object model mapping and resource sharing methods for intell...

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3rd international conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Cheng, Xianming Yin, Zhibin Yuan, Jianan Ding, Xiao Beijing Smartchip Microelectronics Technology Co. Ltd Dept. Of Intelligent Distribution Beijing China

ISBN: (纸本)9798350352658

With the large-scale application of device intelligence and IoT, terminal devices need to have the ability to connect multiple IoT device nodes to achieve efficient utilization of power data. This article proposes a method for implementing physical channels and data resource sharing in multi business applications, unifying the channel model, and setting up a data center to uniformly process the collected data at the end, achieving data sharing. Experimental verification has shown that the physical channel and data sharing method proposed in this article effectively improves the utilization rate of power data, reduces business processing time, lowers communication network and resource storage costs, and achieves efficient and stable operation of power equipment under light load, with high practicality. © 2024 IEEE.

关键词： data centers

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