2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)最新文献_第6页

Comparative Study of Thresholding Techniques for Thermographic Diagnosis of Electrical Equipment 电气设备热成像诊断阈值技术的比较研究

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641557

Asaduzzaman Abir, Md. Rifat Islam Joy, M. Fuad, A. S. Nazmul Huda, Mohaimenul Islam

引用次数: 0

Smart Waste Segregation 智能废物分类

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641526

G. Sanathkumar, K. Nagesh, Guruprasad Hadimani, Laxman, B. R. Charanraj, Prasad B. Honnavalli

{"title":"Smart Waste Segregation","authors":"G. Sanathkumar, K. Nagesh, Guruprasad Hadimani, Laxman, B. R. Charanraj, Prasad B. Honnavalli","doi":"10.1109/R10-HTC53172.2021.9641526","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641526","url":null,"abstract":"The amount of waste generated is increasing at a rapid rate. This is due to the growth in population which in turn leads to urbanization and economic development. According to an estimate, the world generates 2.01 billion tonnes of solid waste annually. By 2050, the expected growth in the amount of waste generated will be increased to 3.04 billion. Many countries, at present are facing lots of issues regarding the proper collection, segregation, and disposal of the solid waste generated. Improper methods adopted for the same can lead to various environmental hazards and can impact the health of the inhabitants. The economic value of waste is realized only after it is segregated. As the slogan states “Recycle today, for a better tomorrow”, the most important step in recycling waste is its proper segregation. This is better utilized if the segregation is at the source itself. In the present scenario, there is no one-stop solution that segregates solid waste into categories such as glasses, plastics, metals, and wet waste at the source of their generation itself. Most of the solid waste namely, glass and plastics are recyclable. Therefore, the proposed solution aims to segregate the wastes into the aforementioned categories with the help of various sensors available and automate the process of segregation, thereby reducing the manpower required for segregation which in turn reduces the occupational hazards of the manual workers.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121282254","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

Deep Learning Approach for an early stage detection of Neurodevelopmental Disorders 神经发育障碍早期检测的深度学习方法

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641691

Lakshmi Boppana, Nikhat Shabnam, Tadikonda Srivatsava

{"title":"Deep Learning Approach for an early stage detection of Neurodevelopmental Disorders","authors":"Lakshmi Boppana, Nikhat Shabnam, Tadikonda Srivatsava","doi":"10.1109/R10-HTC53172.2021.9641691","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641691","url":null,"abstract":"Neurodevelopmental disorders are highly heterogeneous disorders. The symptoms are not same amongst all the individuals and cannot be detected easily by looking at the physiological changes in the individuals. The cause of these disorders may be genetics related but exact causes are not known till date. These disorders can last through one's life if proper treatment is not provided at early stages. Neurodevel-opmental disorders mainly include Autism, ADHD, Schizophrenia. Neurodevelopmental disorders can be identified by using sophisticated technologies. The Functional Magnetic Resonance Imaging(fMRI) is preferred to identify the neurodevelopmental disorders since it allows to measures the activity of brain by detecting changes associated with the blood flow. In this paper, we present a deep learning based system developed to detect the Autism, ADHD, Schizophrenia disorders. The proposed system is trained using ABIDE, ADHD 200, COBRE, UCLA, WUSTL datasets. It is observed that the proposed system is able to produce the results with 71.16% accuracy, 70.13% precision, 69% sensitivity, 80.80% specificity, and 69.56% F1-score.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"62 26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125229351","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

Addressing the purity and purification methods of pond water in Kalinagar, West Bengal 探讨西孟加拉邦加里那加池塘水的纯度和净化方法

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641640

A. Manjunath, S. Anusha, Sagar Basavaraiu, Niharika Raghunath, Aravind V Nair, C. Kavitha, Vishnu Sai Teja, S. Sani

引用次数: 0

Performance Analysis of Deep Neural Networks for Tomato Leaf Disease Classification with Server-Based Computing 基于服务器计算的深度神经网络番茄叶片病害分类性能分析

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641733

Kartik E. Cholachgudda, R. Biradar, Kouame Yann Olivier Akansie, R. Lohith, Aras Amruth.Raj Purushotham

{"title":"Performance Analysis of Deep Neural Networks for Tomato Leaf Disease Classification with Server-Based Computing","authors":"Kartik E. Cholachgudda, R. Biradar, Kouame Yann Olivier Akansie, R. Lohith, Aras Amruth.Raj Purushotham","doi":"10.1109/R10-HTC53172.2021.9641733","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641733","url":null,"abstract":"In recent years, automatic plant disease recognition has gained huge interest in academia and industry. It is considered one of the promising technologies in precision agriculture. With the advancement of deep neural networks (DNNs), it is possible to develop various solutions for plant disease recognition. This paper analyzes the feasibility of using CPU-based desktop computers and GPU-based cloud-hosted services as back-end systems to develop tomato leaf disease classification models. The paper conducts a comprehensive analysis of state-of-the-art DNN architectures proposed for image classification. For each DNNs, various performance indices are measured. The attributes of these indices and their combinations are analyzed and discussed. The results show that EfficientNetBO and MobileNetV2 will provide the best results under most of the circumstances compared to other DNNs considered. In comparison with CPU-based systems, GPU-based systems perform better in almost every analysis performed in this study. The experiments conducted in this paper will help researchers and practitioners to select appropriate DNN architectures that better fit their resource constraints for practical deployment and applications.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130673952","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: 5

Digital Tool for Prevention, Identification and Emergency Handling of Heart Attacks 预防、识别和紧急处理心脏病发作的数字工具

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641730

Akila Mihiranga, Darvin Shane, Bhagya Indeewari, Akila Udana, D. Nawinna, Buddhima Attanayaka

{"title":"Digital Tool for Prevention, Identification and Emergency Handling of Heart Attacks","authors":"Akila Mihiranga, Darvin Shane, Bhagya Indeewari, Akila Udana, D. Nawinna, Buddhima Attanayaka","doi":"10.1109/R10-HTC53172.2021.9641730","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641730","url":null,"abstract":"Heart attack is one of the most frequent causes of death in adults. The majority of heart attacks lead to death before any treatment is given to patients. The conventional mode of healthcare is passive, whereby patients themselves call the healthcare services requesting assistance. Consequently, if they are unconscious when heart failure occurs, they normally fail to call the service. To prevent patients from further harm and save their lives, the early and on-time diagnosis important. This paper presents an innovative web and mobile solution designed using it as Internet of Things (IoT) technology and Machine learning concepts to effectively manage heart patients, the ‘CARDIIAC’ system. This system can predict potential heart attack based on a set of identified risk factors. The system also can identify an actual heart attack using the readings from a wearable IoT device and notify the patient. The system is also equipped with emergency event coordination functionalities. Therefore, ‘CARDIIAC’ provides a holistic care for heart patients by effectively monitoring and managing emergencies related to heart diseases. This would be a socially important system to reduce the number of heart patients who die due to the inability to get immediate treatment.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122108026","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

A Novel Real-Time False Data Detection Strategy for Smart Grid 一种新的智能电网实时假数据检测策略

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641590

Debottam Mukherjee, Samrat Chakraborty, Ramashis Banerjee, Joydeep Bhunia

{"title":"A Novel Real-Time False Data Detection Strategy for Smart Grid","authors":"Debottam Mukherjee, Samrat Chakraborty, Ramashis Banerjee, Joydeep Bhunia","doi":"10.1109/R10-HTC53172.2021.9641590","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641590","url":null,"abstract":"State estimation algorithm ensures an effective realtime monitoring of the modern smart grid leading to an accurate determination of the current operating states. Recently, a new genre of data integrity attacks namely false data injection attack (FDIA) has shown its deleterious effects by bypassing the traditional bad data detection technique. Modern grid operators must detect the presence of such attacks in the raw field measurements to guarantee a safe and reliable operation of the grid. State forecasting based FDIA identification schemes have recently shown its efficacy by determining the deviation of the estimated states due to an attack. This work emphasizes on a scalable deep learning state forecasting model which can accurately determine the presence of FDIA in real-time. An optimal set of hyper-parameters of the proposed architecture leads to an effective forecasting of the operating states with minimal error. A diligent comparison between other state of the art forecasting strategies have promoted the effectiveness of the proposed neural network. A comprehensive analysis on the IEEE 14 bus test bench effectively promotes the proposed real-time attack identification strategy.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129071099","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: 5

FishEye: A Centroid-Based Stereo Vision Fish Tracking Using Multigene Genetic Programming 鱼眼:多基因遗传规划的基于质心的立体视觉鱼类跟踪

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641654

Maria Gemel B. Palconit, Michael Pareja, A. Bandala, Jason L. Española, R. R. Vicerra, Ronnie S. Concepcion, E. Sybingco, E. Dadios

{"title":"FishEye: A Centroid-Based Stereo Vision Fish Tracking Using Multigene Genetic Programming","authors":"Maria Gemel B. Palconit, Michael Pareja, A. Bandala, Jason L. Española, R. R. Vicerra, Ronnie S. Concepcion, E. Sybingco, E. Dadios","doi":"10.1109/R10-HTC53172.2021.9641654","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641654","url":null,"abstract":"Investigating the sharp movements and habitat use of active fishes has traditionally been difficult in aquaculture. This study focuses on improving the prediction of tracking and tagging fish in the three-dimensional form (stereovision). This study used two identical devices to capture videos represented as left and right cameras. As with the location of the aquaculture tank, it was in an environmental outdoor lighting condition containing the three sampled fish. The recorded videos have 20 seconds duration showing the movements of fish. Here, extraction of frames occurs and applies computer vision to get the $x$ and $y$ centroid components. The use of the triangulation method was employed to generate the $z$ point of fish images. Multigene genetic programming (MGGP) was utilized and explored in fish trajectory prediction resulting in 7.78%, 13.34%, and 8.90% mean absolute percentage error for fish 1, 2, 3, and respectively. These findings have prompted the authors and researchers to expand their research to use these methods to track fish.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128186673","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: 2

Indian Sign Language Translation using Deep Learning 使用深度学习的印度手语翻译

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641599

Pratik Likhar, Rathna G N

引用次数: 0

An Unsupervised Approach for COVID-19 Detection using Chest CT Images 基于胸部CT图像的COVID-19无监督检测方法

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641513

Prashanth S, K. Devika, V. R. Murthy Oruganti

引用次数: 2