2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)最新文献_第4页

Energy Management System for Microgrids 微电网能源管理系统

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9574038

Anu Prakash, A. Tomar, N. Jayalakshmi, Kulwant Singh, A. Shrivastava

引用次数: 1

SBTHCT: Segmentation of Brain Tissues using Hybrid Clustering Technique SBTHCT:混合聚类技术的脑组织分割

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573684

V. Shravya, I. Babu, S. Bachu

{"title":"SBTHCT: Segmentation of Brain Tissues using Hybrid Clustering Technique","authors":"V. Shravya, I. Babu, S. Bachu","doi":"10.1109/RTEICT52294.2021.9573684","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573684","url":null,"abstract":"Because of the complex brain tumour structure, boring bodies and external factors like noise, brain magnet resonance imaging data have difficulty in influencing the tumour and oedema. Apart from the morphological operations, application of an effective hybrid clustering algorithm to segment brain tumors in this project is suggested to ease noise sensitivity and increase segmentation stability. Vienna adaptive filtration is particularly used for denoise and for the removal of non brain tissue morphology, thereby effectively reducing process sensitivity to noise. The most important contributions are: Second, the K-Man++ and Gaussian C-Fuzzy cluster refers to the algorithm of the segment images. This consolidation not only enhances the reliability and sensitivity of the algorithm. The tumor pictures removed are eventually processed after morphological procedure and median filtering in order to achieve correct brain tumor representation. The algorithm proposed was compared to other existing segmentation algorithms. The results show that the proposed algorithm is better accurately, sensitively, specifically and performance retrieval.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131931975","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

Chronic kidney disease prediction using machine learning techniques 使用机器学习技术预测慢性肾脏疾病

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573971

G. Nandhini, J. Aravinth

{"title":"Chronic kidney disease prediction using machine learning techniques","authors":"G. Nandhini, J. Aravinth","doi":"10.1109/RTEICT52294.2021.9573971","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573971","url":null,"abstract":"Early diagnosis and characterization are the important components in determining the treatment of chronic kidney disease (CKD). CKD is an ailment which tends to damage the kidney and affect their effective functioning of excreting waste and balancing body fluids. Some of the complications included are hypertension, anemia (low blood count), mineral bone disorder, poor nutritional health, acid base abnormalities, and neurological complications. Early and error-free detection of CKD can be helpful in averting further deterioration of patient's health. These chronic diseases are prognosticated using various types of data mining classification approaches and machine learning (ML) algorithms. This Prediction is performed using Random Forest (RF) Classifier, Logistic Regression (LR) and K-Nearest Neighbor (K-NN) algorithm and Support Vector Machine (SVM). The data used is collected from the UCI Repository with 400 data sets with 25 attributes. This data has been fed into Classification algorithms. The experimental results show that K-NN, LR, SVM hands out an accuracy of 94%, 98% and 93.75% respectively. The RF classifier gives out a maximum accuracy of 100%","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132191074","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: 15

Energy efficient routing with sleep mode and threshold activation 节能路由与睡眠模式和阈值激活

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573965

M. Pramod, Sanket N Shettar, Mohammed Sazeed, Anirudha Jena, P. Neeraj, Supriya Singh

{"title":"Energy efficient routing with sleep mode and threshold activation","authors":"M. Pramod, Sanket N Shettar, Mohammed Sazeed, Anirudha Jena, P. Neeraj, Supriya Singh","doi":"10.1109/RTEICT52294.2021.9573965","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573965","url":null,"abstract":"Wireless sensor networks (WSNs) is highly in demand in research fields involving topics like energy consumption, routing algorithms, selection of sensor location according to a given premise, robustness, efficiency and so on. Sleep mode and threshold activation process are considered to decrease the energy consumption of wireless sensors and to also control the workload of necessary topology maintenance. In remote sensor networks a high limit of the hub's circulation happens which brings about the broadcasting of difference and energy immoderation of superfluous information. To determine the above disadvantages, an energy-effective rest planning component is proposed. This will schedule the sensors to be in active or sleep mode which would help in the reduction of energy consumption. The optimal radius is organized in such a manner that all the sensor nodes are put into several clusters to balance the energy consumption. The sensor nodes are split into a number of small crew called clusters. One and all clusters have a cluster head (CH), which is in charge of directing the whole cluster and redirecting the data to the base station (BS). The node's energy draining can be reduced by switching the cluster-heads frequently. A fuzzy logic is then acquainted with the deliberate degree and the relationship work dependent on fuzzy hypothesis which helps partition the sensor hubs into various classifications. After which excess hubs would be chosen to place into a rest state which guarantees the information uprightness of the entire organization.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133069021","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

Implementation of Smart Energy Meter through Prepaid Transaction using IoT 利用物联网通过预付费交易实现智能电能表

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9574015

P. Reddy, Rajashekhar Kammanaboina, Dumpa Prasad, Prabhakara Rao Kapula, Asisa Kumar Panigrahy

引用次数: 1

Performance analysis of CPU & GPU for Real Time Image/Video 用于实时图像/视频的CPU和GPU性能分析

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573554

Sunil S. Harakannanavar, G. S, S. Ramachandran, Thribhuvan Gupta S, R. C

{"title":"Performance analysis of CPU & GPU for Real Time Image/Video","authors":"Sunil S. Harakannanavar, G. S, S. Ramachandran, Thribhuvan Gupta S, R. C","doi":"10.1109/RTEICT52294.2021.9573554","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573554","url":null,"abstract":"Computer vision algorithms are used in applications which require the given system to process and display images or video inputs. However, this will be computationally intensive on the machine that is performing the automated task based on the visual inputs and results in a large overhead or a lag between input and output processed video. To address this issues, parallel processing is a very useful strategy and can be achieved by dividing the computational tasks among the given hardware of the system which would make use of it efficiently and would work around the limitations of the hardware in the system. In this paper, the parallelism is achieved by multi-threading the algorithm that will divide the computation among the number of cores in the CPU and further optimize by dividing the load with both CPU and GPU for an efficient use of the system hardware and to obtain an optimized result. When the algorithms are executed without any optimization, the obtained output video fps is high and is undesirable. In this proposed method, a speedup factor of 91 times is recorded in AIELI algorithm.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134098112","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

Optimal PMU Placement for Power System State Estimation Using Improved Binary Flower Pollination Algorithm 基于改进二值花授粉算法的电力系统状态估计PMU优化配置

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573518

Suresh Babu Palepu, M. D. Reddy

引用次数: 2

A Comparative Analysis of Low Energy Adaptive Clustering Hierarchyand Power Efficient Gathering in Sensor Information System Progressive Conventions 传感器信息系统递进惯例中低能量自适应聚类层次与高效聚类的比较分析

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573558

M. Sumathi, H.S Lakmisagar, S. Sandeep, H. Kelagadi

{"title":"A Comparative Analysis of Low Energy Adaptive Clustering Hierarchyand Power Efficient Gathering in Sensor Information System Progressive Conventions","authors":"M. Sumathi, H.S Lakmisagar, S. Sandeep, H. Kelagadi","doi":"10.1109/RTEICT52294.2021.9573558","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573558","url":null,"abstract":"A Wireless Sensor Network (WSN) is a web of sensors installed in a random fashion over an area that monitors the physical environmental conditions and parameters like pressure, temperature and moisture. The sensor nodes initially fetch the data from the immediate surroundings which is then aggregated in order to send it to the base station or the sink node. As sensors characteristically are resource-constrained in nature, reducing the energy consumed by the node becomes a challenge. The process used to transfer data from a source to what is known as a route protocol. The three types of succession channels are flat protocols, high-level and local-based protocols. Numerous studies have shown that high-speed traffic improvements improve tracking protocols in terms of deterioration and energy efficiency. Two factors namely energy efficiency and network life time play a very important role in determining the efficiency and capability of the route protocol. In this paper, we emphasize two such principles - LEACH and PEGASIS. In the LEACH protocol, the sensor nodes restructure themselves into groups called clusters and each of these structures are designated with a cluster head node. On the other hand, PEGASIS uses a technique where each individual sensor node communicates solely with its respective neighbour node and inherently. PEGASIS facilitates a chain-based communication structure where turns are taken by each node to transmit data to the base station. In this paper, comparisons between LEACH and PEGASIS protocols are based on network characteristics such as power consumption, packet delivery rate, transmission delays, passing, high percentage and dead node.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117120117","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

Classification of soil types from GPR B Scans using deep learning techniques 使用深度学习技术从GPR B扫描中分类土壤类型

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573702

Nairit Barkataki, Sharmistha Mazumdar, P. Singha, Jyoti Kumari, B. Tiru, Utpal Sarma

引用次数: 6

Prognosis of Supervised Machine Learning Algorithms in Healthcare Sector 监督机器学习算法在医疗保健领域的预测

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573665

Bhavya Shah, Dev Rajdev, Riya Salunkhe, Pooja Ramrakhiani, Himani S. Deshpande

{"title":"Prognosis of Supervised Machine Learning Algorithms in Healthcare Sector","authors":"Bhavya Shah, Dev Rajdev, Riya Salunkhe, Pooja Ramrakhiani, Himani S. Deshpande","doi":"10.1109/RTEICT52294.2021.9573665","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573665","url":null,"abstract":"Medical care is a fundamental liberty. The conquering application of Machine Learning (ML) in this computerized world is noticeable. With the increase in medical data, ML is penetrating in medical care industry resulting in the integration of Machine Learning algorithms and knowledge of medical personnel and designing of prognostic models which can help doctors and patients to analyze risks of any health compilation. Researchers from health domain are exploring ML algorithms to reach out to some useful conclusions. With this paper we aim to help the researchers to understand the efficiency of available ML algorithms on medical datasets, thus helping them to decide which one to choose from the existing methods. This paper implements 5 Supervised Machine Learning algorithms on four different datasets from health domain on Heart Disease, Diabetes, Dermatology, and Breast Cancer. Results of each of the implemented ML algorithms are compared in terms of prediction accuracy and AUC value on medical datasets. Implementation results suggests that Logistic Regression and Random Forest have shown better results with almost all the datasets used for experiment purpose with accuracy (85%-88%) and AUC value (0.89-0.92). The yield of this paper will add to a better understanding of the use of Machine Learning in the Medical Domain.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116082083","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