物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053418

Shahnawaz Ayoub, N. Behera, Meena Naga Raju, Pankaj Singh, S. Praveena, R. K.

{"title":"Hyperparameter Tuned Deep Learning Model for Healthcare Monitoring System in Big Data","authors":"Shahnawaz Ayoub, N. Behera, Meena Naga Raju, Pankaj Singh, S. Praveena, R. K.","doi":"10.1109/IDCIoT56793.2023.10053418","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053418","url":null,"abstract":"Medical image classifiers roles a crucial play in medical service and teaching tasks. But the classical approach obtained its ceiling on performance. Besides, from their use, much longer and more effort require spent on extracted and selected classifier features. The Deep Neural Network (DNN) is a developing Machine Learning (ML) approach which is verified their potential for distinct classifier tasks. Especially, the Convolutional Neural Network (CNN) leads to optimum outcomes on distinct image classifier tasks. But medical image databases can be hard for collecting as it requires several professional skills to categorize them. This study develops a new Hyperparameter Tuned Deep Learning Model for Healthcare Monitoring Systems (HPTDLM-HMS) in big data environment. The presented HPTDLM-HMS technique concentrates on the examination of medical images in the decision-making process. Initially, the presented HPTDLM-HMS technique derives features using EfficientNet model with Manta Ray Foraging Optimization (MRFO) algorithm as hyperparameter tuner. At last, the classification of medical images takes place by Long Short-Term Memory (LSTM) method. To handle big data, Hadoop MapReduce is utilized. The result analysis of the HPTDLM-HMS technique is tested on medical imaging dataset. The comprehensive study of the HPTDLM-HMS technique highlighted and gives recall value of 87.46% is higher when compared to its promising outcomes over other models.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86761580","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

MSCP Based Stator Fault Identification in Induction Motor Using Power Quality Analyzer 基于电能质量分析仪的MSCP异步电动机定子故障识别

物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053524

W. Rajan Babu, M. Sundaram, A. Kavithamani, S. Sam Karthik, N. Abinaya, V. Bharath Choudry

引用次数: 0

Detection and Classification of License Plate by Neural Network Classifier 基于神经网络分类器的车牌检测与分类

物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053459

Surekha Chalnewad, Arati Manjaramkar

{"title":"Detection and Classification of License Plate by Neural Network Classifier","authors":"Surekha Chalnewad, Arati Manjaramkar","doi":"10.1109/IDCIoT56793.2023.10053459","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053459","url":null,"abstract":"A license plate is alphanumeric rectangular plate. It is fixed on the vehicle and used for identification of the vehicle. Generally, huge numbers of vehicles move-on the road which is the major issue of concern in identifying the vehicle(s) owner, registration place of vehicle, address, etc. The automatic license plate detection is one of the solutions for such kind of problems. There are numerous methodologies available for license plate detection, but certain factors like speed of vehicles, language used on license plate, non-uniform letter effects on license plate, etc. makes the task of recognition difficult. The license plate detection system has many applications like payment of parking fees; toll fee on the highway; traffic monitoring system; border security system; signal system, etc. This research work proposes a novel license plate detection technique with the extension of Sobel mask. In proposed system, first step is acquisition of image. Second step is to detect the vehicle from the acquired image. In third step, segmentation of license plate from vehicle image is done. Finally, neural network classifier is used to classify the vehicle(s) license plate. The proposed system gives promising, robust, and efficient results for license plate detection. Proposed system achieves accuracy of 98% is achieved in detecting the license plate.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79844832","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

Convolutional Neural Networks (CNN)-based Vehicle Crash Detection and Alert System 基于卷积神经网络(CNN)的车辆碰撞检测与报警系统

物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053471

Jayashree M, Rachana P, Ashin Kunjumon, Meena Thamban, Athul Roy

引用次数: 0

Highway Collision Avoidance by Detection of Animal’s Images 基于动物图像检测的公路防撞技术

物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053391

Mahima R, M. M, Manjari K, Rovenal S, K. S, Sruthi M. P

{"title":"Highway Collision Avoidance by Detection of Animal’s Images","authors":"Mahima R, M. M, Manjari K, Rovenal S, K. S, Sruthi M. P","doi":"10.1109/IDCIoT56793.2023.10053391","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053391","url":null,"abstract":"Traffic-related injuries and deaths are a serious problem that all industrialized nations are dealing with today. Object recognition techniques are employed in this study to develop a low cost and simple solution for automated detection and tracking on highways in order to avoid animal-vehicle collisions. In real-world units, a technique for measuring the animal distances from the camera mounted vehicle is also developed. Wild animal monitoring in their natural settings must be efficient and trustworthy in order to update manage decisions. Because of their effectiveness and accuracy in capturing wildlife data in an inconspicuous, continuous, and massive volume, automatic covert camera traps or cameras are becoming extremely popular as a tool for monitoring wildlife. Hand-taking a massive number of photos and films from camera setups is very costly and tedious. It is a significant barrier for researchers and environmental scientists who want to observe wildlife in a natural setting. This research presents a structure for developing automated animal detection in the wild, with the goal of creating an automated wildlife monitoring system, based on current breakthroughs in deep learning methods. In aspects of recognition, the suggested method attains a total precision of about 85.51 percent.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82821596","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

Smart Dimensional Measurement and Material Transportation (SDMMT) System using Artificial Intelligence 基于人工智能的智能尺寸测量与物料输送(SDMMT)系统

物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053429

C. M. Raj, W. R. Babu, Karparthi Uday, R. Senthilkumar, V. Sudha, V. Anandhakumar

引用次数: 0

An Efficient Genetic Algorithm based Auto ML Approach for Classification and Regression 一种高效的基于遗传算法的自动ML分类与回归方法

物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053442

Chereddy Spandana, Ippatapu Venkata Srisurya, S. Aasha Nandhini, R. P. Kumar, G. Bharathi Mohan, Parathasarathy Srinivasan

引用次数: 1

CyberHelp: Sentiment Analysis on Social Media Data Using Deep Belief Network to Predict Suicidal Ideation of Students 网络帮助:基于深度信念网络的社交媒体数据情绪分析预测学生自杀意念

物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053425

U. Sakthi, Thomas M. Chen, Mithileysh Sathiyanarayanan

{"title":"CyberHelp: Sentiment Analysis on Social Media Data Using Deep Belief Network to Predict Suicidal Ideation of Students","authors":"U. Sakthi, Thomas M. Chen, Mithileysh Sathiyanarayanan","doi":"10.1109/IDCIoT56793.2023.10053425","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053425","url":null,"abstract":"Suicide is a very critical and important issue in modern society. Suicide is the third-leading cause of death for college and high school students. Social media allows students in the digital environment to share their suicidal ideas and thoughts with others. Accurate and early detection and prevention of suicidal ideation in students can save the students' lives. To identify the risk factor for suicidal attempts, a suitable method of analysing the suicidal behaviour of students using their sentiment text posted on social media can be used. This paper presents an optimized Dragonfly algorithm (DFA) using a Deep Belief Network (DBN) for the automatic detection of suicidal ideation in students. In our CyberHelp Solution, the proposed DFA-based DBN model analyses student social media data, predicts suicidal behavior, and treats students appropriately. The sentiment analysis performs automated categorization of online messages and makes accurate predictions of the student’s suicidal behaviors. The dragonfly heuristic optimization algorithm is used for tuning the hyperparameter in the deep belief network. The proposed DFA-DBN technique has been implemented to predict suicidal ideation in students with a higher accuracy of 95.5% compared with other classification models.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78908706","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

Rule-based Intrusion Detection System using Logical Analysis of Data 基于规则的数据逻辑分析入侵检测系统

物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053505

Anjanee Kumar, T. Das

引用次数: 0

Data Integration and Transformation using Artificial Intelligence 使用人工智能的数据集成和转换

物联网技术 Pub Date : 2023-01-05 DOI: 10.1109/IDCIoT56793.2023.10053513

Surya Pandey, A. K, M. R. Shaikh, Dhanush Y P, Yajat Vishwakarma

引用次数: 0

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