2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)最新文献_第7页

Analysis of IoT Big Weather Data For Early Flood Forecasting System 面向洪水早期预报系统的物联网大天气数据分析

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616941

J. M. Antony Sylvia, M. Pushpa Rani, B. Aremu

{"title":"Analysis of IoT Big Weather Data For Early Flood Forecasting System","authors":"J. M. Antony Sylvia, M. Pushpa Rani, B. Aremu","doi":"10.1109/icecct52121.2021.9616941","DOIUrl":"https://doi.org/10.1109/icecct52121.2021.9616941","url":null,"abstract":"Due to technological advancements, the Internet of Things (IoT) has been extensively used in a number of environments during this period. The IoT is being utilized successfully, particularly in the field of weather monitoring. As a result, IoT weather sensors generate massive amounts of weather data on a regular basis. This research aims to efficiently analyze the massive amounts of data generated by IoT climate sensors to develop an effective early flood forecasting system. Numerous methods for forecasting floods using historical data have been invented. However, all of these methodologies are becoming inefficient as a result of climate change and the volume of the data. This research provides a methodology for extracting strongly correlated weather features in order to reduce the error in weather forecasts caused by data volume and climate change. The Feed-Forward Artificial Neural Network (FFANN) is used to forecast early rainfall and floods. Furthermore, Chennai has been chosen as the study area for this research. Finally, two experiments are conducted to demonstrate this early flood forecasting system's prediction accuracy and training efficiency. The experimental results demonstrate that the proposed flood forecasting system outperforms recently developed systems in terms of accuracy and training efficiency.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124069775","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

Load forecasting by the Main grid controller using ANN and the implementation of demand response using Micro-controller 主电网负荷预测采用人工神经网络，需求响应采用微控制器实现

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616839

Seema P N, Gopika Korambil Gopalan, M. Nair

{"title":"Load forecasting by the Main grid controller using ANN and the implementation of demand response using Micro-controller","authors":"Seema P N, Gopika Korambil Gopalan, M. Nair","doi":"10.1109/icecct52121.2021.9616839","DOIUrl":"https://doi.org/10.1109/icecct52121.2021.9616839","url":null,"abstract":"The electric power system is undergoing a wide range of transformation to overcome the shortcomings of the conventional grid system. A smart grid is a modernized version of existing grid which incorporates both electrical and digital networks together. Smart grid is a self healing electrical network which monitors, control and analysis various data using digital technology and can take control actions based on the variations in data. This system can provide clean and sustainable power supply to the consumer and allows the consumer to take part in energy transfer. Smart micro grid is a localized version of smart grid which is limited by electrical and geometrical constraints. To reduce the complexities while opting for a centralized control scheme in a smart micron grid here we are using a hierarchical level control approach. The control levels are classified into primary, secondary and tertiary level control which are associated with smart meter, local micro grid controller and a main grid controller. In this work we are concentrating mainly on tertiary level controller which implies that main grid controller gains the control over consumer premises when the consumption level of consumer increases beyond the predefined limits. This demand response management was done in Mplab using micro controllers. Load forecasting is done in tertiary controller using ANN technique in Matlab.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121313800","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: 3

Attack and Anomaly Prediction in IoT Networks using Machine Learning Approaches 使用机器学习方法的物联网网络攻击和异常预测

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616794

Binu P.K, K. M, Sreehari M. V

{"title":"Attack and Anomaly Prediction in IoT Networks using Machine Learning Approaches","authors":"Binu P.K, K. M, Sreehari M. V","doi":"10.1109/icecct52121.2021.9616794","DOIUrl":"https://doi.org/10.1109/icecct52121.2021.9616794","url":null,"abstract":"Cyber-attacks are becoming a very common problem we face in IoT networks nowadays. These attacks mainly target the comparatively less powerful IoT and gateway devices so that the attacker can compromise the device much faster. As the IoT infrastructure is getting exponential growth recently, the attacks are also getting the same pace. Based on various studies, almost 90% of attacks targeting IoT networks are Denial of Service (DoS) or Distributed Denial of Service (DDoS). So, like any other aspect, security is an inevitable part of IoT networks and devices because this is now deployed in highly critical areas like space technology, healthcare, smart cities, etc. Understanding these attacks, their behavior pattern, and their defense mechanisms will help to implement a highly safe and secure IoT infrastructure. The system proposed here is a prototype model for detecting the most common DoS attacks at the gateway device itself using the Machine Learning approach. ML approaches can recognize the behavior pattern of the attacks based on the previously trained data and can make efficient predictions with a high accuracy factor. This will help us to take proper countermeasures to protect our IoT network from such potential attacks. The need for a safe and secure IoT ecosystem is an important need for today and especially in the future.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116925414","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

Summary Generation Using Deep Learning 使用深度学习生成摘要

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/ICECCT52121.2021.9616945

Perpetua F. Noronha, Madhu Bhan

{"title":"Summary Generation Using Deep Learning","authors":"Perpetua F. Noronha, Madhu Bhan","doi":"10.1109/ICECCT52121.2021.9616945","DOIUrl":"https://doi.org/10.1109/ICECCT52121.2021.9616945","url":null,"abstract":"Summarization is the process of converting any given long text into a lossy compressed version while preserving the overall essence and meaning of the original given text. A problem in natural language processing is the task of summarizing a voluminous text. This research, lays emphasis on abstractive summarization of the text, a process which generates a coherent, short and meaningful text by learning the context from given input source text. This work is a preliminary attempt to generate abstractive summary. The proposed work uses the Attentional Encoder and Decoder based Sequence to Sequence Recurrent Neural Network model. The proposed research’s primary objective is to get insights on the working of the mentioned Sequence architecture and the intricacies of different subcomponents of the encoders and the decoders architecture working together so as to give a particular end result. The scope of this paper covers an experimentation of performing text summarization process using Encoder and Decoder based Sequential Learning Recurrent Neural Network design. Satisfactory summary results are obtained after the model is experimented and analyzed on the Amazon Fine Food reviews.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117113516","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

PMU Based Realtime Fault Detection Algorithm for Transmission Line Faults 基于PMU的传输线故障实时检测算法

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/ICECCT52121.2021.9616969

V. Vishal, P. S. Shenil

引用次数: 1

Design and Comparison of PSO, SA and GA tuned PID Controller for Ball Balancer Arrangement 球平衡器布置的PSO、SA和GA整定PID控制器的设计与比较

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/ICECCT52121.2021.9616686

Apoorv Surana, B. Bhushan

引用次数: 4

Quantum Computing: Challenges and Opportunities 量子计算:挑战与机遇

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/ICECCT52121.2021.9616647

Sachin Aralikatti

引用次数: 0

Chronological Poor and Rich Tunicate Swarm Algorithm integrated Deep Maxout Network for human action and abnormality detection 时序贫富被囊虫群算法将深度Maxout网络集成到人类行为和异常检测中

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/ICECCT52121.2021.9616637

Bhagya Jyothi K L, Vasudeva

{"title":"Chronological Poor and Rich Tunicate Swarm Algorithm integrated Deep Maxout Network for human action and abnormality detection","authors":"Bhagya Jyothi K L, Vasudeva","doi":"10.1109/ICECCT52121.2021.9616637","DOIUrl":"https://doi.org/10.1109/ICECCT52121.2021.9616637","url":null,"abstract":"Human behavior has always been a significant parameter in social interaction and human activity recognition is a major clue that eases the analysis of human character. In recent decades, researchers have gained more attention towards human activity detection that plays a huge part in different domains, like intelligent video surveillance, and human computer communication in order to improve security level. In order to recognize the human action from videos, effective features are required. However, extraction of effective features is still resembles as a challenging process due to changes occurred in human action. In order to counterpart such limitations, this article proposes an effective strategy for human action recognition and abnormality detection using Chronological Poor and Rich Tunicate Swarm Algorithm (CPRTSA)-based Deep Maxout Network. However, the human action recognition and abnormality detection is performed using same Deep Maxout Network, where the network classifier is trained using proposed CPRTSA. The proposed approach provides better accuracy in terms of human action recognition with higher efficiency and the proposed CPRTSA achieved a maximum accuracy of 0.959, maximum sensitivity of 0.963, and a maximum specificity of 0.965.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114605953","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

Usual and Unusual Human Activity Recognition in Video using Deep Learning and Artificial Intelligence for Security Applications 使用深度学习和人工智能进行安全应用的视频中常见和不寻常的人类活动识别

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/ICECCT52121.2021.9616791

Ajeet Sunil, M. Sheth, Shreyas E, Mohana

引用次数: 8

Relay coordination in DG integrated system DG集成系统中的继电器协调

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616874

S. John, M. Ebenezer

引用次数: 0