2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)最新文献_第2页

Picture fuzzy Einstein geometric aggregate Operators and their Application to Multiple Attribute Decision Making 图片模糊爱因斯坦几何聚集算子及其在多属性决策中的应用

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768550

K. Deva, S. Mohanaselvi

引用次数: 0

Optimization of Multicast Routing Using Forward Chain Algorithm for Internet of Things Application (IoT) Agriculture Application 面向物联网农业应用的前向链算法组播路由优化

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768483

Shreekant Salotagi, J. Mallapur

引用次数: 0

An Ensemble Framework for Improving Brain Stroke Prediction Performance 提高脑卒中预测性能的集成框架

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768579

A. Devaki, C.V. Guru Rao

{"title":"An Ensemble Framework for Improving Brain Stroke Prediction Performance","authors":"A. Devaki, C.V. Guru Rao","doi":"10.1109/ICEEICT53079.2022.9768579","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768579","url":null,"abstract":"Brain stroke detection using data-driven approach has economic benefits. Simple approach using Machine Learning (ML) classification algorithms could provide acceptable accuracy for realizing Clinical Decision Support System (CDSS). From the literature, it is ascertained that making ensemble of multiple brain stroke prediction models could improve prediction performance. This is the hypothesis and motivation for the research carried out and presented in this paper. Another important observation from the literature is that most of the ensemble methods found in the literature for brain stroke prediction are not data-driven approaches. This research gap is filled in this paper by focusing on ensemble of data-driven prediction models. Towards this end, we proposed an ensemble framework based on supervised ML techniques for improving brain stroke prediction performance. The framework is named as Brain Stroke Prediction Ensemble (BSPE). We also proposed an algorithm known as Hybrid Ensemble Learning for Brain Stroke Prediction (HEL-BSP). We also reuse our feature engineering algorithm known as Composite Metric based Feature Selection (CMFS). The ensemble is made up of ML models such as Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), KNeighbours classifier, Gradient Boosting and Stochastic Gradient Descent (SGD). A prototype application is built using Python data science platform to evaluate the proposed framework and the underlying algorithm. The experimental results revealed that the ensemble of the prediction models with majority voting approach could outperform individual prediction models.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124581710","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

DC Component-based Differential Pilot Relaying Scheme for Half-wave Transmission Lines 基于直流分量的半波传输线差分导频继电方案

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768444

J. Sharma, Salauddin Ansari, O. Gupta

{"title":"DC Component-based Differential Pilot Relaying Scheme for Half-wave Transmission Lines","authors":"J. Sharma, Salauddin Ansari, O. Gupta","doi":"10.1109/ICEEICT53079.2022.9768444","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768444","url":null,"abstract":"To optimize the power system operation, different regional grids are usually interconnected with the help of long transmission lines (TLs). Sometimes, depending upon the geographical position, renewable energy sources (like hydro energy plants) have been integrated with the utility grid using long TLs. However, transmitting power over a long distance is not preferred as it creates operational and economic issues. A half-wave transmission line (HTL) is proposed in 1940 to cope with such problems. HTL is very long compared to conventionally used TLs, making the dynamics of HTL completely different during normal and abnormal conditions. The conventional protection schemes find limitations in the case of HTL with these abrupt dynamics. This paper proposes a DC component-based differential pilot relaying system capable of fault discrimination and classification. Moreover, the performance of the presented method is scrutinized under different scenarios, such as variation of fault location and inception angle/time, CT saturation error, evolving faults, cross-country faults, and found to be reliable and precise. All the results have been simulated and validated using PSCAD/EMTDC software.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123614074","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

Vehicle Detection and Counting using Deep Learning basedYOLO and Deep SORT Algorithm for Urban Traffic Management System 基于深度学习的yolo和深度排序算法在城市交通管理系统中的车辆检测与计数

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768653

Rahul Kejriwal, Ritika H J, Arpit Arora, Mohana

{"title":"Vehicle Detection and Counting using Deep Learning basedYOLO and Deep SORT Algorithm for Urban Traffic Management System","authors":"Rahul Kejriwal, Ritika H J, Arpit Arora, Mohana","doi":"10.1109/ICEEICT53079.2022.9768653","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768653","url":null,"abstract":"Vehicle counting is a process to estimate traffic density on roads to assess the traffic conditions for intelligent transportation systems (ITS). Real-time traffic management systems have become popular recently due to the availability of high end cameras and technology. The present traffic management systems focus on speed detection, signal jumping, zebra crossing but not on traffic density estimation. Proposed video-based vehicle counting and tracking method using a video captured on CCTV and handheld mobile cameras. The system can be used in smart cities to create smart traffic light signals, in which duration of each signal depends on real time vehicle density in a particular lane of road. Vehicle counting is performed in two steps: the captured video is sent to You Only Look Once (YOLO) based deep learning framework to detect, count and classify the vehicles. Multi vehicular tracking is adopted using Deep SORTalgorithm to track the vehicles in video frames. Model was trained for six different classes, using Google Colaboratory. Datasets of vehicles specifically pertaining to Indian roads environment is considered for implementation. The performance of the model was analyzed, proposed model has tested and obtained an average counting accuracy of 86.56% while the average precision is 93.85%. The model can be implemented for ascertaining the traffic density on roads and this provides knowledge for infrastructural development to authorities. It can also be an integral part of smart city projects to develop intelligent and smart traffic surveillance system.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124150256","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

Solar Roof top PV panel in home - A case study 家用太阳能屋顶光伏板-案例研究

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768553

P. Venkatesh

引用次数: 0

IFC_Health: Three-Layer Integrated Framework for Secure Smart Healthcare System IFC_Health：安全智能医疗系统的三层集成框架

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768435

Priyanka kumari Bhansali, Dilendra Hiran

{"title":"IFC_Health: Three-Layer Integrated Framework for Secure Smart Healthcare System","authors":"Priyanka kumari Bhansali, Dilendra Hiran","doi":"10.1109/ICEEICT53079.2022.9768435","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768435","url":null,"abstract":"The Internet of Things (IoT) is a model that allows objects to monitor and collect data from their surroundings and then transmit that data over the Internet to be evaluated and used for various purposes. Healthcare is one of the IoT application fields that has attracted much attention from industry, academia, and government. The rise of IoT, fog and cloud computing in the medical sector enhances patient safety, staff happiness, and operational efficiency. A three-layer integrated framework for a secure, intelligent healthcare system is proposed in this research. The first layer is the IoT layer, which acquires and transmits healthcare data. The IoT layer uses healthcare embedded sensors and wearable's to communicate to exchange sensitive data with an aggregating node, which can then share data with the Fog server. The second layer is the Fog layer, which retrieves the measured value from the IoT layer and saves them in a local repository. Ensemble learning is used in the Fog layer to forecast diabetes and stroke problems. The third layer is the Cloud layer which provides secure data storage and access control for a smart healthcare system. For fine-grained data access, security, authentication, and user privacy of medical data, the cloud layer use hash-based ciphertext policy attribute-based encryption with signature. The suggested work's performance is tested for each layer using a different set of parameters, and the combination of Io'T, Fog, and Cloud improves the healthcare system's efficiency.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130144529","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

Analysis of Bit Error Rate for Multi-user TDMA-based Communication System 基于tdma的多用户通信系统误码率分析

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768648

Gowri Sai Priya, G. Sai, Gowthami, I. Singh, Shubham Tayal, Mohd Javed Khan

引用次数: 0

Compression and Decompression of Biomedical Signals Using Chinese Remainder Theorem 基于中国剩余定理的生物医学信号压缩与解压缩

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768455

M. K. Rasheed, T. Padma, C. Kumari, N. Rao

{"title":"Compression and Decompression of Biomedical Signals Using Chinese Remainder Theorem","authors":"M. K. Rasheed, T. Padma, C. Kumari, N. Rao","doi":"10.1109/ICEEICT53079.2022.9768455","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768455","url":null,"abstract":"The biomedical data signals captured from the patient is sent for compression where the size of the digital data is 24 bits. Compression is executed for two consecutive data elements. One is current data where data is shared for compression and previous data stores old values. In the starting, previous data storage contains no data. Compression is accomplished using log2 sub-band methodology that involves the identification of changes by making use of XOR logic. The XOR-generated data are further calculated using OR gates that are required to generate flags. Using the Chinese remainder theorem that involves the division method the 24-bit data is compressed into 12 bits. According to the theorem, the dividend should be divided with divisor where the degree of divisor must be less than that of dividend. In our work, the compressed data generated before the division process can be up to 26 bits which is divided only with a prime number as per the theorem rule. The division process produces 12-bit data which is given to the decoder along with the quotient and remainder obtained after division for decompression. It should satisfy the division rule where the decompressed result should be the same as the data to be compressed then the overall compression & decompression is correct and is satisfied in our project.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130716295","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

Design and Analysis of Microstrip Patch Antenna for Sub-6GHz Applications Sub-6GHz微带贴片天线的设计与分析

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2022-02-16 DOI: 10.1109/ICEEICT53079.2022.9768490

K. R. Prabha, B. Nataraj, M. Jagadeeswari

引用次数: 0