Pub Date : 2019-04-01DOI: 10.1109/ICOEI.2019.8862721
Pooja Ghule, Mansi Kambli
Nowadays people are very concerned about the environment because of the rapid changes in the environment which will harm to human health. Hence it is necessary to monitor environment where the people spend more time like at home, office, industry, any working area in real time and long term manner. Using internet of things we can control system as well as we can access system remotely using IoT. It first take information with help of different sensors and transfer sensors values on thingspeak directly, from which can be accessed at anytime and anywhere. Literature survey is done on use of wireless sensors, Cloud and Internet of things, and connection between devices with sensors and network connection will read sensor value which can be further monitored from the internet with the help of thingspeak. Monitoring environment is done through website & controlled manually and automatically by detecting sensor values. We can controlled it manually through website and it can automatically controlled by sensing values. The main Objective design of cloud storage environment is used to store data and to process the data. Internet of things allows physical devices or things which are not computer system, that only act very smartly and makes collaborations decision which are beneficial for different applications. That application allow things to capture value of devices. They transfer “things from being passively computing” and makes an individually decisions in active manner and communicate and collaborate to form single difficult decision. IoT technologies of computing, embedded sensors, communication protocol and internet protocol for communication allow internet of things to provide significant which impose number of challenges and introduces standards which require to specialize and communication
{"title":"Web Based Environment Monitoring System Using IOT","authors":"Pooja Ghule, Mansi Kambli","doi":"10.1109/ICOEI.2019.8862721","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862721","url":null,"abstract":"Nowadays people are very concerned about the environment because of the rapid changes in the environment which will harm to human health. Hence it is necessary to monitor environment where the people spend more time like at home, office, industry, any working area in real time and long term manner. Using internet of things we can control system as well as we can access system remotely using IoT. It first take information with help of different sensors and transfer sensors values on thingspeak directly, from which can be accessed at anytime and anywhere. Literature survey is done on use of wireless sensors, Cloud and Internet of things, and connection between devices with sensors and network connection will read sensor value which can be further monitored from the internet with the help of thingspeak. Monitoring environment is done through website & controlled manually and automatically by detecting sensor values. We can controlled it manually through website and it can automatically controlled by sensing values. The main Objective design of cloud storage environment is used to store data and to process the data. Internet of things allows physical devices or things which are not computer system, that only act very smartly and makes collaborations decision which are beneficial for different applications. That application allow things to capture value of devices. They transfer “things from being passively computing” and makes an individually decisions in active manner and communicate and collaborate to form single difficult decision. IoT technologies of computing, embedded sensors, communication protocol and internet protocol for communication allow internet of things to provide significant which impose number of challenges and introduces standards which require to specialize and communication","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115100233","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862591
Mitul Sheth, Pinal Rupani
The Global Sensing enabled by Wireless Sensor Network (WSN) cut crosswise over numerous zones of current living. This provides the potentiality to compute, and understand the environmental indicators. In today's digital world, a person expects Automatization which makes the task easy, comfortable, fast and efficient. The idea is to advance our traditional system to a Smart Automated System for supplying water in home gardening, farms fields, etc. In this system, we use soil wetness detector, temperature detector and humidity detector that are mounted at the root space of the plants. The values recognize by the system are conveyed to the base station. The target is to fetch data and sync those values with internet using Wifi. It notifies the user as the water level goes down below the set point. This paper shows that making use of NodeMCU we can do observing of circuit diagrams using wireless technology and shows the result using Blynk App. As it detects low wetness and warm temperature, a message is passed between NodeMCU and Blynk App and it automatically starts the motor in home gardening, farm, etc.
{"title":"Smart Gardening Automation using IoT With BLYNK App","authors":"Mitul Sheth, Pinal Rupani","doi":"10.1109/ICOEI.2019.8862591","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862591","url":null,"abstract":"The Global Sensing enabled by Wireless Sensor Network (WSN) cut crosswise over numerous zones of current living. This provides the potentiality to compute, and understand the environmental indicators. In today's digital world, a person expects Automatization which makes the task easy, comfortable, fast and efficient. The idea is to advance our traditional system to a Smart Automated System for supplying water in home gardening, farms fields, etc. In this system, we use soil wetness detector, temperature detector and humidity detector that are mounted at the root space of the plants. The values recognize by the system are conveyed to the base station. The target is to fetch data and sync those values with internet using Wifi. It notifies the user as the water level goes down below the set point. This paper shows that making use of NodeMCU we can do observing of circuit diagrams using wireless technology and shows the result using Blynk App. As it detects low wetness and warm temperature, a message is passed between NodeMCU and Blynk App and it automatically starts the motor in home gardening, farm, etc.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121327541","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862788
M. Hema, Suhitha Pitta
With the increasing significance of age classification in present days, researchers are working on different methods to classify a persons' age. Facial based and Gait based are the major trail methods for age classification. Actually, the facial based approach is not so accurate if the person is far from the camera. Whereas, gait is a preferable solution because it is quick to respond to age parameters. In this paper, Gait energy image Projection model (GPM) is the proposed method for age classification, which combines both spatiotemporal Gait energy image Longitudinal projection (GLP) and Gait energy image Transverse Projection (GTP). The proposed method mainly focuses on four parameters namely head movement, body size, arm movement and Stride length. Regarding classification of age, OU-ISIR dataset is considered and the SVM is selected as the classifier. Moreover, obtained experimental results are compared with the existing ones like FED, GEI and SM. Further Descriptors are fused to check whether they give better results or not.
{"title":"Human age classification based on gait parameters using a Gait Energy Image projection model","authors":"M. Hema, Suhitha Pitta","doi":"10.1109/ICOEI.2019.8862788","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862788","url":null,"abstract":"With the increasing significance of age classification in present days, researchers are working on different methods to classify a persons' age. Facial based and Gait based are the major trail methods for age classification. Actually, the facial based approach is not so accurate if the person is far from the camera. Whereas, gait is a preferable solution because it is quick to respond to age parameters. In this paper, Gait energy image Projection model (GPM) is the proposed method for age classification, which combines both spatiotemporal Gait energy image Longitudinal projection (GLP) and Gait energy image Transverse Projection (GTP). The proposed method mainly focuses on four parameters namely head movement, body size, arm movement and Stride length. Regarding classification of age, OU-ISIR dataset is considered and the SVM is selected as the classifier. Moreover, obtained experimental results are compared with the existing ones like FED, GEI and SM. Further Descriptors are fused to check whether they give better results or not.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121226312","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862667
S. C. Joshi, J. Lather, Y. Dwivedi
Hyper-pigmentation is a disease in which brown colored spots appear on skin. Hyper-pigmentation occurs mainly due to excess production of Melanin. Melanin is a pigment that gives color to skin and produced by Melanocyte cells. This paper introduces a possible design and development of a phototherapy device whose intensity is controlled wirelessly. Near infrared optical radiations are applied to treat Hyper-pigmentation. The designed device consists of infrared Light Emitting Diode array of 830nm wavelength as emitter placed above an affected area. Intensity of LED array is controlled by the mobile phone.
{"title":"Photo Therapy Based Designed Device For Hyper-Pigmentation","authors":"S. C. Joshi, J. Lather, Y. Dwivedi","doi":"10.1109/ICOEI.2019.8862667","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862667","url":null,"abstract":"Hyper-pigmentation is a disease in which brown colored spots appear on skin. Hyper-pigmentation occurs mainly due to excess production of Melanin. Melanin is a pigment that gives color to skin and produced by Melanocyte cells. This paper introduces a possible design and development of a phototherapy device whose intensity is controlled wirelessly. Near infrared optical radiations are applied to treat Hyper-pigmentation. The designed device consists of infrared Light Emitting Diode array of 830nm wavelength as emitter placed above an affected area. Intensity of LED array is controlled by the mobile phone.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125121764","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862684
Hemali Patel, Milin M Patel, Rashmin B. Prajapati
Image Tagging are important as far as image search engines/databases are concerned viz. Flicker, Picasa, Facebook…etc. Image Tagging is a difficult and highly relevant machine learning task. Image tagging with algorithms based on ‘Nearest neighbor classification’ have achieved considerable attention on the implementation point of view but at the cost of increasing computational complexity both during training and testing. In the existing approaches used single object based tagging. In this research paper we are going to discuss different research related to object mining and tagging. As far as there are shape, color and texture feature are impotent to describe object. The proposed system firstly use KNN for tagging different object features for training. Using color moment, shape and gray level co-occurrence matrix (GLCM) as a texture feature. After that system will use adaboost classifier for classification of objects and final image represented by different object tags.
{"title":"Hybrid Feature Based Object Mining And Tagging","authors":"Hemali Patel, Milin M Patel, Rashmin B. Prajapati","doi":"10.1109/ICOEI.2019.8862684","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862684","url":null,"abstract":"Image Tagging are important as far as image search engines/databases are concerned viz. Flicker, Picasa, Facebook…etc. Image Tagging is a difficult and highly relevant machine learning task. Image tagging with algorithms based on ‘Nearest neighbor classification’ have achieved considerable attention on the implementation point of view but at the cost of increasing computational complexity both during training and testing. In the existing approaches used single object based tagging. In this research paper we are going to discuss different research related to object mining and tagging. As far as there are shape, color and texture feature are impotent to describe object. The proposed system firstly use KNN for tagging different object features for training. Using color moment, shape and gray level co-occurrence matrix (GLCM) as a texture feature. After that system will use adaboost classifier for classification of objects and final image represented by different object tags.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1975 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128188233","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862529
B. Ajith, S. Adlinge, Sudin Dinesh, U. Rajeev, E. S. Padmakumar
Airport runway detection and tracking can play an important role in landing an aircraft. In some situations the runway may not be visible to pilot due to adverse weather condition. Considering the case of Unmanned aerial vehicles, the runway detection and tracking algorithm is one of its essential part which enable them to position itself and land safely. This paper explains an algorithm which will track the runway when it is visible using a camera. The algorithm is based on identification of runway colour and runway characteristics. This method ensures the detection of runway accurately. Algorithm detects the runway boundaries by selecting the appropriate hough lines using runway characteristics and runway colour. Once the runway is detected it tracks the runway using feature matching techniques. In tracking phase the algorithm will track the runway and it will find out the accurate runway boundary and threshold stripes. This algorithm can be used to assist pilot during landing and it can be also used to detect runways in UAVs.
{"title":"Robust Method to Detect and Track the Runway during Aircraft Landing Using Colour segmentation and Runway features","authors":"B. Ajith, S. Adlinge, Sudin Dinesh, U. Rajeev, E. S. Padmakumar","doi":"10.1109/ICOEI.2019.8862529","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862529","url":null,"abstract":"Airport runway detection and tracking can play an important role in landing an aircraft. In some situations the runway may not be visible to pilot due to adverse weather condition. Considering the case of Unmanned aerial vehicles, the runway detection and tracking algorithm is one of its essential part which enable them to position itself and land safely. This paper explains an algorithm which will track the runway when it is visible using a camera. The algorithm is based on identification of runway colour and runway characteristics. This method ensures the detection of runway accurately. Algorithm detects the runway boundaries by selecting the appropriate hough lines using runway characteristics and runway colour. Once the runway is detected it tracks the runway using feature matching techniques. In tracking phase the algorithm will track the runway and it will find out the accurate runway boundary and threshold stripes. This algorithm can be used to assist pilot during landing and it can be also used to detect runways in UAVs.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128481584","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862696
T. Bhagya, K. Anand, D. S. Kanchana, Ajai A S Remya
Image segmentation plays a vital role in medical image processing. Different pre-processing methods yield different results. The pre-processing methods such as histogram stretching with erosion and dilation, average filter and median filter along with histogram stretching is applied to the four different segmentation algorithms which are Otsu's thresholding, Watershed based segmentation, Canny edge detection and K-mean clustering. These algorithms are used to segment Acute Lymphoblastic Leukemia datasets and the parameters such as precision, accuracy and sensitivity of the results are calculated so as to find a better algorithm which is suitable for segmentation of the leukemic cells.
{"title":"Analysis of Image Segmentation Algorithms for the Effective Detection of Leukemic Cells","authors":"T. Bhagya, K. Anand, D. S. Kanchana, Ajai A S Remya","doi":"10.1109/ICOEI.2019.8862696","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862696","url":null,"abstract":"Image segmentation plays a vital role in medical image processing. Different pre-processing methods yield different results. The pre-processing methods such as histogram stretching with erosion and dilation, average filter and median filter along with histogram stretching is applied to the four different segmentation algorithms which are Otsu's thresholding, Watershed based segmentation, Canny edge detection and K-mean clustering. These algorithms are used to segment Acute Lymphoblastic Leukemia datasets and the parameters such as precision, accuracy and sensitivity of the results are calculated so as to find a better algorithm which is suitable for segmentation of the leukemic cells.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129501372","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862689
Rahul Sunchu, Srichandrahaas Palli, V.V. Sai Rama Datta, M. Shanmugasundaram
This Internet of Things is the interconnection of everyday objects with each other via the Internet of computing devices embedded in them, enabling them to send and receive data. This paper presents the design of smart office systems for controlling, monitoring and automation of electrical appliances depending on the entry and exit of the individual employee into the workspace. This design also simultaneously maintains and monitors the information in the cloud about the entry and exit timings, number of cabins electrified and electricity usage of the employees using the RFID tags scans at the entry point of the office, It also allows and assists the employees to control and monitor his cabin electricity status through a smartphone. The Intelligent system for office environment project mainly focuses on the use of convenience for the users and to save electricity. To optimize office administrative management, a webpage is created which gives the administrative management the information about how many cabins. The equipment used for this is RC522 reader which is a Radio Frequency Identification system (RFID) which will send the data to the raspberry pi which controls the office environment using IoT. A mobile phone application is designed which works by integrating the concoction of both IoT and the RFID system to monitor and control the devices through the application ‘MQTT’ which remotely gives the flexibility to the users.
{"title":"Intelligent System for Office Environment Using Internet of Things","authors":"Rahul Sunchu, Srichandrahaas Palli, V.V. Sai Rama Datta, M. Shanmugasundaram","doi":"10.1109/ICOEI.2019.8862689","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862689","url":null,"abstract":"This Internet of Things is the interconnection of everyday objects with each other via the Internet of computing devices embedded in them, enabling them to send and receive data. This paper presents the design of smart office systems for controlling, monitoring and automation of electrical appliances depending on the entry and exit of the individual employee into the workspace. This design also simultaneously maintains and monitors the information in the cloud about the entry and exit timings, number of cabins electrified and electricity usage of the employees using the RFID tags scans at the entry point of the office, It also allows and assists the employees to control and monitor his cabin electricity status through a smartphone. The Intelligent system for office environment project mainly focuses on the use of convenience for the users and to save electricity. To optimize office administrative management, a webpage is created which gives the administrative management the information about how many cabins. The equipment used for this is RC522 reader which is a Radio Frequency Identification system (RFID) which will send the data to the raspberry pi which controls the office environment using IoT. A mobile phone application is designed which works by integrating the concoction of both IoT and the RFID system to monitor and control the devices through the application ‘MQTT’ which remotely gives the flexibility to the users.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130440486","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862666
S. Nandhini, Sharma S Mrinal, Naveen Balachandran, K. Suryanarayana, D. Ram
Increasing urbanization has led to a major waste management crisis with the proliferation of improperly planned structures having no proper facility to collect, segregate and process waste. Domestic waste has increasing chemical and plastic content. These chemicals do not perish unless treated properly. The treatment also necessitates timely collection, segregation and if possible decomposition, reuse or recycling. Human intervention has been the most popular way to segregate waste, but when it comes to working with a mixture of wastes, it puts their health and hygiene at stake. It is always better to treat waste through the help of robots which can handle waste in any hazardous environment. An automated waste collection and segregation system based on a robotic assembly and machine learning based classification is developed. A robotic arm with a distance sensor will pick up the waste and place it on a binary classifier platform which has a camera attached to capture the image and an algorithm to classify the waste as biodegradable or non-biodegradable into their respective bins.
{"title":"Electronically assisted automatic waste segregation","authors":"S. Nandhini, Sharma S Mrinal, Naveen Balachandran, K. Suryanarayana, D. Ram","doi":"10.1109/ICOEI.2019.8862666","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862666","url":null,"abstract":"Increasing urbanization has led to a major waste management crisis with the proliferation of improperly planned structures having no proper facility to collect, segregate and process waste. Domestic waste has increasing chemical and plastic content. These chemicals do not perish unless treated properly. The treatment also necessitates timely collection, segregation and if possible decomposition, reuse or recycling. Human intervention has been the most popular way to segregate waste, but when it comes to working with a mixture of wastes, it puts their health and hygiene at stake. It is always better to treat waste through the help of robots which can handle waste in any hazardous environment. An automated waste collection and segregation system based on a robotic assembly and machine learning based classification is developed. A robotic arm with a distance sensor will pick up the waste and place it on a binary classifier platform which has a camera attached to capture the image and an algorithm to classify the waste as biodegradable or non-biodegradable into their respective bins.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130464066","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862601
C. J. Mariya, K. A. Nyni
This paper mainly focuses on various feature selection methods that is followed for achieving accurate diagnosis of neuromuscular diseases such as Amyotrophic Lateral Sclerosis (ALS) and Myopathy. Since both of these has similarity in the Electromyography (EMG) waveform of normal patients, this will create more difficulties in terms of diagnosis. Hence, proper feature selection is the essential part in the diagnosis. Two feature selection methods were adopted for evaluation. In the first method, time domain and frequency domain features are taken from each frame of EMG signal and in the second method, Discrete Wavelet Transform (DWT) features like maximum DWT coefficient and mean value of high energy DWT coefficients were analysed. For the purpose of classification, the Multi-Support Vector Machine (MSVM) classifier is employed.
{"title":"Review on Feature Extraction Methods in Neuromuscular Disease Diagnosis","authors":"C. J. Mariya, K. A. Nyni","doi":"10.1109/ICOEI.2019.8862601","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862601","url":null,"abstract":"This paper mainly focuses on various feature selection methods that is followed for achieving accurate diagnosis of neuromuscular diseases such as Amyotrophic Lateral Sclerosis (ALS) and Myopathy. Since both of these has similarity in the Electromyography (EMG) waveform of normal patients, this will create more difficulties in terms of diagnosis. Hence, proper feature selection is the essential part in the diagnosis. Two feature selection methods were adopted for evaluation. In the first method, time domain and frequency domain features are taken from each frame of EMG signal and in the second method, Discrete Wavelet Transform (DWT) features like maximum DWT coefficient and mean value of high energy DWT coefficients were analysed. For the purpose of classification, the Multi-Support Vector Machine (MSVM) classifier is employed.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114511096","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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