International journal of machine learning and computing最新文献_第4页

Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm 基于机器学习的基于改进Yolov3和Mobilenet算法的视频目标检测和分类性能分析

International journal of machine learning and computing Pub Date : 2023-07-05 DOI: 10.53759/7669/jmc202303025

Mohandoss T, Rangaraj J

{"title":"Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm","authors":"Mohandoss T, Rangaraj J","doi":"10.53759/7669/jmc202303025","DOIUrl":"https://doi.org/10.53759/7669/jmc202303025","url":null,"abstract":"Detecting foreground objects in video is crucial in various machine vision applications and computerized video surveillance technologies. Object tracking and detection are essential in object identification, surveillance, and navigation approaches. Object detection is the technique of differentiating between background and foreground features in a photograph. Recent improvements in vision systems, including distributed smart cameras, have inspired researchers to develop enhanced machine vision applications for embedded systems. The efficiency of featured object detection algorithms declines as dynamic video data increases as contrasted to conventional object detection methods. Moving subjects that are blurred, fast-moving objects, backdrop occlusion, or dynamic background shifts within the foreground area of a video frame can all cause problems. These challenges result in insufficient prominence detection. This work develops a deep-learning model to overcome this issue. For object detection, a novel method utilizing YOLOv3 and MobileNet was built. First, rather than picking predefined feature maps in the conventional YOLOv3 architecture, the technique for determining feature maps in the MobileNet is optimized based on examining the receptive fields. This work focuses on three primary processes: object detection, recognition, and classification, to classify moving objects before shared features. Compared to existing algorithms, experimental findings on public datasets and our dataset reveal that the suggested approach achieves 99% correct classification accuracy for urban settings with moving objects. Experiments reveal that the suggested model beats existing cutting-edge models by speed and computation.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73903802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Interval Type-2 Fuzzy AHP and COPRAS-G-based trusted neighbour node Discovery in Wireless Sensor Networks 基于混合区间2型模糊层次分析法和copras - g的无线传感器网络可信邻居节点发现

International journal of machine learning and computing Pub Date : 2023-07-05 DOI: 10.53759/7669/jmc202303023

Jyothi Kiranmayi E, R. N.V., Nayanathara K.S.

{"title":"Hybrid Interval Type-2 Fuzzy AHP and COPRAS-G-based trusted neighbour node Discovery in Wireless Sensor Networks","authors":"Jyothi Kiranmayi E, R. N.V., Nayanathara K.S.","doi":"10.53759/7669/jmc202303023","DOIUrl":"https://doi.org/10.53759/7669/jmc202303023","url":null,"abstract":"In Wireless Sensor Networks (WSNs), reliable and rapid neighbour node discovery is considered as the crucial operation which frequently needs to be executed over the entire lifecycle. Several neighbour node discovery mechanisms are proposed for reducing the latency or extending the sensor nodes’ lifetime. But majority of the existing neighbour node discovery mechanisms failed in addressing the critical issues of real WSNs related to energy consumptions, constraints of latency, uncertainty of node behaviors, and communication collisions. In this paper, Hybrid Interval Type-2 Fuzzy Analytical Hierarchical Process (AHP) and Complex Proportional Assessment using Grey Theory (COPRAS-G)-based trusted neighbour node discovery scheme (FAHPCG) is proposed for better data dissemination process. In specific, Interval Type 2 Fuzzy AHP is applied for determining the weight of the evaluation criteria considered for neighbour node discovery, and then Grey COPRAS method is adopted for prioritizing the sensor nodes of the routing path established between the source and destination. It adopted the merits of fuzzy theory for handling the uncertainty and vagueness involved in the change in the behavior of sensor nodes during the process of neighbour discovery. It is proposed with the capability of exploring maximized number of factors that aids in exploring the possible dimensions of sensor nodes packet forwarding potential during the process of neighbour node discovery. The simulation results of the proposed FAHPCG scheme confirmed an improved neighbour node discovery rate of 23.18% and prolonged the sensor nodes lifetime to the maximum of 7.12 times better than the baseline approaches used for investigation.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"136 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86441098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monitoring and Recognition of Heart Health using Heartbeat Classification with Deep Learning and IoT 利用深度学习和物联网的心跳分类监测和识别心脏健康

International journal of machine learning and computing Pub Date : 2023-07-05 DOI: 10.53759/7669/jmc202303028

Arulkumar V, Mohammad Arif, Vinod D, Devipriya A, C. G, Surendran S

{"title":"Monitoring and Recognition of Heart Health using Heartbeat Classification with Deep Learning and IoT","authors":"Arulkumar V, Mohammad Arif, Vinod D, Devipriya A, C. G, Surendran S","doi":"10.53759/7669/jmc202303028","DOIUrl":"https://doi.org/10.53759/7669/jmc202303028","url":null,"abstract":"The advancement and innovations in the field of science and technology paved way for various advanced treatments in the field of medicine. They are implemented using sensors, and computer-aided designs with artificial intelligence techniques. This helps in the detection of serious health constraints at an earlier stage with appropriate treatments using decision-making techniques. One of the important health concerns that are increasing rapidly is cardiovascular disorders. This includes Arrhythmia and Myocardial Infarction. Earlier prediction and classification can protect them from serious constraints. They are diagnosed using the Electrocardiogram (ECG). To obtain accurate results, artificial intelligence techniques are implemented to extract the optimum output. The proposed system includes the detection and classification using deep learning techniques with the Internet of Things (IoT). The existing heartbeat detection system is overcome using a deep convolutional neural network. This helps in the implementation of automatic heartbeat detection and identification of abnormalities. The ECG signals are pre-processed with segmentation and feature extraction techniques. The classification and identification of constraints in the functioning of the heart are identified using optimization algorithms. The proposed system is trained, tested, and evaluated using the MIT-BIH arrhythmia database. The accuracy and efficiency of the proposed system are 99.98% using the MIT-BIH dataset.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87148525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Performance Analysis of Machine Learning Models in Solar Energy Forecasting 机器学习模型在太阳能预测中的性能分析

International journal of machine learning and computing Pub Date : 2023-07-01 DOI: 10.18178/ijml.2023.13.3.1140

引用次数: 0

Deep-Racing: An Embedded Deep Neural Network (EDNN) Model to Predict the Winning Strategy in Formula One Racing 深度赛车:一个用于预测f1赛车获胜策略的嵌入式深度神经网络(EDNN)模型

International journal of machine learning and computing Pub Date : 2023-07-01 DOI: 10.18178/ijml.2023.13.3.1135

引用次数: 0

JoyBot: RASA-Trained Chatbots to Provide Mental Health Assistance for Australians JoyBot: rasa训练的聊天机器人为澳大利亚人提供心理健康援助

International journal of machine learning and computing Pub Date : 2023-07-01 DOI: 10.18178/ijml.2023.13.3.1137

引用次数: 0

Employing the Exponentiated Magnitude Spectrogram in the Deep Learning-Based Mask Estimation for Speech Enhancement 利用指数化幅度谱在基于深度学习的语音增强掩模估计中的应用

International journal of machine learning and computing Pub Date : 2023-07-01 DOI: 10.18178/ijml.2023.13.3.1136

引用次数: 0

Prediction of Mental Health: Heuristic Subjective Well-Being Model on Perceived Stress Scale Using Machine Learning Algorithms 心理健康预测:基于机器学习算法的感知压力量表的启发式主观幸福感模型

International journal of machine learning and computing Pub Date : 2023-07-01 DOI: 10.18178/ijml.2023.13.3.1138

引用次数: 0

Resilience Evaluation of Automakers after 2008 Financial Crisis by UMAP 基于UMAP的2008年金融危机后汽车制造商弹性评估

International journal of machine learning and computing Pub Date : 2023-07-01 DOI: 10.18178/ijml.2023.13.3.1139

引用次数: 0

Analysis of Optical Communications, Fiber Optics, Sensors and Laser Applications 光通信、光纤、传感器和激光应用分析

International journal of machine learning and computing Pub Date : 2023-04-05 DOI: 10.53759/7669/jmc202303012

T. Kim

引用次数: 2