Pub Date : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732759
G. Ramya, R. Nagarajan, S. Kannadhasan
Wireless Sensor Network (WSN) is a group of compact, low-energy nodes that have become an integral component of modern connectivity networks and are very important in industry and academia. Energy is critical in WSN, and the architecture of WSN is focused on energy conservation in the research group and node power usage presents a big challenge for improving the existence of WSN. Because of the challenging climate it can be expensive or perhaps hard to charge or repair consumed batteries. In this report, various strategies of energy management are implemented to decrease energy usage, boost network capacity and maximise network life.
{"title":"Energy Efficient Wireless Sensor Networks Using LEACH Network","authors":"G. Ramya, R. Nagarajan, S. Kannadhasan","doi":"10.1109/ICATME50232.2021.9732759","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732759","url":null,"abstract":"Wireless Sensor Network (WSN) is a group of compact, low-energy nodes that have become an integral component of modern connectivity networks and are very important in industry and academia. Energy is critical in WSN, and the architecture of WSN is focused on energy conservation in the research group and node power usage presents a big challenge for improving the existence of WSN. Because of the challenging climate it can be expensive or perhaps hard to charge or repair consumed batteries. In this report, various strategies of energy management are implemented to decrease energy usage, boost network capacity and maximise network life.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122149947","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}
Pub Date : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732719
D. Pandey, V. Namdeo
The detection of ALS disease is very critical due to control of nervous system. The nervous system cannot recognize all signal of physical behaviors of human body. For the monitoring of unusual physical behavior of human body identify with biomedical engineering process. Motor imagery EEG classification is the way to detection ALS disease and some other critical disease such as brain stroke and epilepsy. The electropherogram (EEG) is electric recoded signal stored in computer and converted into digital signal with A/D converter. The whole process of signal recoding proceeds by brain computer interface. The complex structure of EEG signal faced a problem of prediction of critical illness. In this paper proposed ensemble-based classifier for the prediction of critical disease. The extraction of features of EEG signals is also challenging task, for the extraction of features used wavelet transform function. The extracted features with transform are very-high dimension and noises. For the optimization of features applied BEE scout algorithm. The BEE scout algorithms reduce the unwanted component of features and provide better component of features for the classifier. The proposed algorithm simulated in MATLAB software and tested with BCI competition IV dataset.
{"title":"Feature Optimization of Motor Imagery EEG Classification using BEE Scout Algorithms and Machine Learning for ALS Disease Predication","authors":"D. Pandey, V. Namdeo","doi":"10.1109/ICATME50232.2021.9732719","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732719","url":null,"abstract":"The detection of ALS disease is very critical due to control of nervous system. The nervous system cannot recognize all signal of physical behaviors of human body. For the monitoring of unusual physical behavior of human body identify with biomedical engineering process. Motor imagery EEG classification is the way to detection ALS disease and some other critical disease such as brain stroke and epilepsy. The electropherogram (EEG) is electric recoded signal stored in computer and converted into digital signal with A/D converter. The whole process of signal recoding proceeds by brain computer interface. The complex structure of EEG signal faced a problem of prediction of critical illness. In this paper proposed ensemble-based classifier for the prediction of critical disease. The extraction of features of EEG signals is also challenging task, for the extraction of features used wavelet transform function. The extracted features with transform are very-high dimension and noises. For the optimization of features applied BEE scout algorithm. The BEE scout algorithms reduce the unwanted component of features and provide better component of features for the classifier. The proposed algorithm simulated in MATLAB software and tested with BCI competition IV dataset.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131866690","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}
Pub Date : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732774
Ishu Sharma
Unmanned Aerial Vehicles (UAVs) are promising choice for smart agriculture, monitoring in military and civil areas, upgrading network capacity in both cellular and wireless network, industry 4.0 and many other areas. The huge applications involved with UAV inspire researchers to boost the working of UAV technology. The resources in UAV technology are required to be used in well-organized manner due to energy and transmitting power constraints. Machine learning is the key to develop and give probabilistic solutions based on the real time data for user's problem. Recently machine learning has taken boom in the evolution of UAV technology also. This paper presents the survey and comparative analysis of the latest research work which has been conducted for the development of UAV with machine learning techniques. The research work carried for different domains of UAV systems is chosen for covering the diversity of the topic.
{"title":"Evolution of Unmanned Aerial Vehicles (UAVs) with Machine Learning","authors":"Ishu Sharma","doi":"10.1109/ICATME50232.2021.9732774","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732774","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are promising choice for smart agriculture, monitoring in military and civil areas, upgrading network capacity in both cellular and wireless network, industry 4.0 and many other areas. The huge applications involved with UAV inspire researchers to boost the working of UAV technology. The resources in UAV technology are required to be used in well-organized manner due to energy and transmitting power constraints. Machine learning is the key to develop and give probabilistic solutions based on the real time data for user's problem. Recently machine learning has taken boom in the evolution of UAV technology also. This paper presents the survey and comparative analysis of the latest research work which has been conducted for the development of UAV with machine learning techniques. The research work carried for different domains of UAV systems is chosen for covering the diversity of the topic.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127970303","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}
Pub Date : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732748
R. Malik, Megha Shrivastava, Vikaram Singh Takur
Image processing plays a vital role in diagnosing medical diseases for the prediction of critical problems such as diabetes, the vascular problem of heart, and heart attack. For the prediction of severe, such a problem used automatic blood vessel segmentation. For automatic blood segmentation, various algorithms and techniques are used. But some sensitivity and accuracy are a significant issue in blood vessel segmentation. In this paper proposed blood vessel segmentation using Gabor transform function, FCM algorithm, and ant colony optimization. Our designed algorithm is very efficient in terms of the accuracy and sensitivity of the retinal image. The utility of the blood vessel segmentation process demands the improvement of the segmentation area and increase the value of efficiency-the development of the image-segmentation method used threshold method with some objective function optimization method. The accurate function optimization method increases the segmentation area and increases the value of sensitivity.
{"title":"Analysis of Retinal Image for Blood Vessel Using Swarm Intelligence and Transform Function","authors":"R. Malik, Megha Shrivastava, Vikaram Singh Takur","doi":"10.1109/ICATME50232.2021.9732748","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732748","url":null,"abstract":"Image processing plays a vital role in diagnosing medical diseases for the prediction of critical problems such as diabetes, the vascular problem of heart, and heart attack. For the prediction of severe, such a problem used automatic blood vessel segmentation. For automatic blood segmentation, various algorithms and techniques are used. But some sensitivity and accuracy are a significant issue in blood vessel segmentation. In this paper proposed blood vessel segmentation using Gabor transform function, FCM algorithm, and ant colony optimization. Our designed algorithm is very efficient in terms of the accuracy and sensitivity of the retinal image. The utility of the blood vessel segmentation process demands the improvement of the segmentation area and increase the value of efficiency-the development of the image-segmentation method used threshold method with some objective function optimization method. The accurate function optimization method increases the segmentation area and increases the value of sensitivity.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130787901","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}
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