Pub Date : 2019-01-01DOI: 10.1109/ICNTE44896.2019.8946032
Jayamala Adsul, J. Nair, P. Vaidya
This paper presents the design of a reconfigurable Analog-to-Digital Converter (ADC). The design employs a sub-ranging technique and implements a reconfigurable ADC which can be configured to give 8-bit, 12-bit and 16-bit resolution. This ADC can be used for a variety of applications since its resolution and conversion time can be varied depending upon the application. The design has been simulated using NI Multisim 14.1 and results have been presented in this paper. It achieves 8-bit resolution with the sampling rate of 100MHz, 12-bit resolution with the sampling rate of 250KHz and16-bit resolution with the sampling rate of 50KHz.
{"title":"Design and Simulation of a New Reconfigurable Analog to Digital Converter based on Multisim","authors":"Jayamala Adsul, J. Nair, P. Vaidya","doi":"10.1109/ICNTE44896.2019.8946032","DOIUrl":"https://doi.org/10.1109/ICNTE44896.2019.8946032","url":null,"abstract":"This paper presents the design of a reconfigurable Analog-to-Digital Converter (ADC). The design employs a sub-ranging technique and implements a reconfigurable ADC which can be configured to give 8-bit, 12-bit and 16-bit resolution. This ADC can be used for a variety of applications since its resolution and conversion time can be varied depending upon the application. The design has been simulated using NI Multisim 14.1 and results have been presented in this paper. It achieves 8-bit resolution with the sampling rate of 100MHz, 12-bit resolution with the sampling rate of 250KHz and16-bit resolution with the sampling rate of 50KHz.","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130511281","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 : 2019-01-01DOI: 10.1109/ICNTE44896.2019.8945843
S. Mithbavkar, M. Shah
Emotion recognition play important role in human-computer interfacing and a treatment of a person under depression. Facial expressions of a person reflect his emotional status. Electromyogram (EMG)based emotion recognition systems able to recognize true emotions of a person. Current research on EMG based emotion recognition reports overall accuracy in the range 69% to 91 % in a particular emotional environment. In case of posed expressions, emotions were recognized with accuracy range from 91 % to 97%. There is a scope for improvement for enhancing accuracy of emotion recognition in emotional environment. In this research work EMG dataset acquired under emotional environment by Augsburg University is analyzed. From 96 EMG signals representing four emotions, four features including Root mean square, Variance, Mean absolute value and Integrated EMG are calculated. These parameters are given to 3 different classifier namely Elman neural network (ENN) classifier, Back propagation neural network (BPNN), and Nonlinear autoregressive exogenous network (NARX) for classification of emotion. NARX neural network gave maximum overall accuracy of 99.1 %.
{"title":"Recognition of Emotion Through Facial Expressions Using EMG Signal","authors":"S. Mithbavkar, M. Shah","doi":"10.1109/ICNTE44896.2019.8945843","DOIUrl":"https://doi.org/10.1109/ICNTE44896.2019.8945843","url":null,"abstract":"Emotion recognition play important role in human-computer interfacing and a treatment of a person under depression. Facial expressions of a person reflect his emotional status. Electromyogram (EMG)based emotion recognition systems able to recognize true emotions of a person. Current research on EMG based emotion recognition reports overall accuracy in the range 69% to 91 % in a particular emotional environment. In case of posed expressions, emotions were recognized with accuracy range from 91 % to 97%. There is a scope for improvement for enhancing accuracy of emotion recognition in emotional environment. In this research work EMG dataset acquired under emotional environment by Augsburg University is analyzed. From 96 EMG signals representing four emotions, four features including Root mean square, Variance, Mean absolute value and Integrated EMG are calculated. These parameters are given to 3 different classifier namely Elman neural network (ENN) classifier, Back propagation neural network (BPNN), and Nonlinear autoregressive exogenous network (NARX) for classification of emotion. NARX neural network gave maximum overall accuracy of 99.1 %.","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128494973","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 : 2019-01-01DOI: 10.1109/ICNTE44896.2019.8945830
S. Mohurle, M. Devare
This work illuminates the machine learning based fuzzy probability model for calculating the compost usability index and measuring its quality. The work reviews the basic concept of fuzzy theory and probability; status of Municipal waste and applications of fuzzy probability. Further work proposes Fuzzy-Probability Model to quantifying the compost quality by analyzing the compost data. Input variables are set of mineral nutrients and its composition in a sample. The output of (FPM) proposed system is the Quality Index of Compost $(C_{i})$ (i.e. measures of the proportion of all elements available in compost and generate an index accordingly, a numeric value) that describes the quality of compost asserting that even if the expertise describes suitability of values in a particular sample of compost, the quality decided by expert may be approximate, assumed or predicted. The results and conclusion show that the proposed FPM system gives a programming model that helps to generate a quality index for agriculture stakeholders to believe in a particular type of compost.
{"title":"Fuzzy Probability Model for Quantifying the Effectiveness of the MSW Compost","authors":"S. Mohurle, M. Devare","doi":"10.1109/ICNTE44896.2019.8945830","DOIUrl":"https://doi.org/10.1109/ICNTE44896.2019.8945830","url":null,"abstract":"This work illuminates the machine learning based fuzzy probability model for calculating the compost usability index and measuring its quality. The work reviews the basic concept of fuzzy theory and probability; status of Municipal waste and applications of fuzzy probability. Further work proposes Fuzzy-Probability Model to quantifying the compost quality by analyzing the compost data. Input variables are set of mineral nutrients and its composition in a sample. The output of (FPM) proposed system is the Quality Index of Compost $(C_{i})$ (i.e. measures of the proportion of all elements available in compost and generate an index accordingly, a numeric value) that describes the quality of compost asserting that even if the expertise describes suitability of values in a particular sample of compost, the quality decided by expert may be approximate, assumed or predicted. The results and conclusion show that the proposed FPM system gives a programming model that helps to generate a quality index for agriculture stakeholders to believe in a particular type of compost.","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132445524","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 : 2019-01-01DOI: 10.1109/ICNTE44896.2019.8945885
S. Chaudhari, Saurabh Killekar, Aishwarya Mahadik, Nair Meerakrishna, M. Divya
Optimization of unit commitment can be performed in various ways which include Dynamic Programming as one of the most reliable method. The precise meaning of unit commitment is scheduling of generators to increase the efficiency of generation while keeping the cost of generation to be minimum. This can be achieved by systematic following of the procedures of dynamic programming. Constraints related to dynamic programming enfolds the practical situations. Ideal situations can be often misinterpreted and certain factors of conditioning can be ignored. Hence, reliability constraint pertaining to risk of generation limit incapability is also important and down to be looked upon. Simulation of dynamic programming offers a better insight of the nominal and the actual values induced by using this programming method.
{"title":"A Review of Unit Commitment Problem Using Dynamic Programming","authors":"S. Chaudhari, Saurabh Killekar, Aishwarya Mahadik, Nair Meerakrishna, M. Divya","doi":"10.1109/ICNTE44896.2019.8945885","DOIUrl":"https://doi.org/10.1109/ICNTE44896.2019.8945885","url":null,"abstract":"Optimization of unit commitment can be performed in various ways which include Dynamic Programming as one of the most reliable method. The precise meaning of unit commitment is scheduling of generators to increase the efficiency of generation while keeping the cost of generation to be minimum. This can be achieved by systematic following of the procedures of dynamic programming. Constraints related to dynamic programming enfolds the practical situations. Ideal situations can be often misinterpreted and certain factors of conditioning can be ignored. Hence, reliability constraint pertaining to risk of generation limit incapability is also important and down to be looked upon. Simulation of dynamic programming offers a better insight of the nominal and the actual values induced by using this programming method.","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128214313","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 : 2019-01-01DOI: 10.1109/ICNTE44896.2019.8945909
Sejal Chopra, Atharva Aglawe, S. Singh, Amogh Ambekar
With the advent of social media the variety of digital media is growing exponentially. One of the purposes of media is to introduce a sense of joy among users. Multimedia customization and recommendations depend on emotional traits of the content a user is watching. The levels of media enjoyment of a normal person are usually extracted using various surveys or feedback gathering. Enjoyment level varies from an individual to another, the presence of techniques for gathering this kind of feedback from paralyzed will make a breakthrough in media content feedback of such people. In this paper, EEG data is acquired from Neurosky Mindwave headset. The data is then transformed to frequency domain using Fourier transform and various feature extraction steps are performed. Enjoyment level is divided into four levels based on the attention of the user. Finally, an attempt is made to draw a conclusion from the achieved result that this enjoyment level detection mechanism can be integrated with a web application which can help paralyzed people customize their choices of media they are interested in watching.
{"title":"ParaInfia-An infotainment System for semi-paralyzed using Electroencephalogram","authors":"Sejal Chopra, Atharva Aglawe, S. Singh, Amogh Ambekar","doi":"10.1109/ICNTE44896.2019.8945909","DOIUrl":"https://doi.org/10.1109/ICNTE44896.2019.8945909","url":null,"abstract":"With the advent of social media the variety of digital media is growing exponentially. One of the purposes of media is to introduce a sense of joy among users. Multimedia customization and recommendations depend on emotional traits of the content a user is watching. The levels of media enjoyment of a normal person are usually extracted using various surveys or feedback gathering. Enjoyment level varies from an individual to another, the presence of techniques for gathering this kind of feedback from paralyzed will make a breakthrough in media content feedback of such people. In this paper, EEG data is acquired from Neurosky Mindwave headset. The data is then transformed to frequency domain using Fourier transform and various feature extraction steps are performed. Enjoyment level is divided into four levels based on the attention of the user. Finally, an attempt is made to draw a conclusion from the achieved result that this enjoyment level detection mechanism can be integrated with a web application which can help paralyzed people customize their choices of media they are interested in watching.","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130865556","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 : 2019-01-01DOI: 10.1109/ICNTE44896.2019.8946053
J. Goyal, S. S. Mantha, V. Phalle
The aim of this work is to determine the effective passive measures to enhance the thermal comfort of the building envelop. Passive measures are those measures which reduce electricity demand but do not require any electricity to operate. The building components such as walls, roof and window are exposed to solar radiation and outside air temperature. Heat is conducted from exterior environment into the interior of the building envelop through these channels, wherein majority (approx. 60%) is conducted by walls and roof. Envelop insulation is considered as an efficient retrofitting measure to reduce heat gain from walls and roof. In this study, a techno-economic model has been investigated to obtain optimum insulation thickness (OIT) for the residential buildings in the three climate zones of India. Four types of commonly used insulation materials analysed are: expanded polystyrene (EPS), extruded polystyrene (XPS), fiberglass and polyurethane foam (PUF). The effect of cooling design temperature and the coefficient of performance (COP) of air conditioners on the insulation thickness were also investigated. Fiberglass is observed as the most cost effective insulation material, considering life cycle performance of the insulation material for all the climate zones. It was also concluded that air-conditioner with higher COP can potentially reduce the insulation cost.
{"title":"Analysis of Passive Retrofitting Measures for Reduced Electricity Demand","authors":"J. Goyal, S. S. Mantha, V. Phalle","doi":"10.1109/ICNTE44896.2019.8946053","DOIUrl":"https://doi.org/10.1109/ICNTE44896.2019.8946053","url":null,"abstract":"The aim of this work is to determine the effective passive measures to enhance the thermal comfort of the building envelop. Passive measures are those measures which reduce electricity demand but do not require any electricity to operate. The building components such as walls, roof and window are exposed to solar radiation and outside air temperature. Heat is conducted from exterior environment into the interior of the building envelop through these channels, wherein majority (approx. 60%) is conducted by walls and roof. Envelop insulation is considered as an efficient retrofitting measure to reduce heat gain from walls and roof. In this study, a techno-economic model has been investigated to obtain optimum insulation thickness (OIT) for the residential buildings in the three climate zones of India. Four types of commonly used insulation materials analysed are: expanded polystyrene (EPS), extruded polystyrene (XPS), fiberglass and polyurethane foam (PUF). The effect of cooling design temperature and the coefficient of performance (COP) of air conditioners on the insulation thickness were also investigated. Fiberglass is observed as the most cost effective insulation material, considering life cycle performance of the insulation material for all the climate zones. It was also concluded that air-conditioner with higher COP can potentially reduce the insulation cost.","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127443822","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 : 2019-01-01DOI: 10.1109/icnte44896.2019.8945838
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icnte44896.2019.8945838","DOIUrl":"https://doi.org/10.1109/icnte44896.2019.8945838","url":null,"abstract":"","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121250128","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 : 2019-01-01DOI: 10.1109/ICNTE44896.2019.8945917
Abhilash Mane, Riddhi R. Adhikari, Shreyash Gadgil, N. Raykar
This paper investigates the applications of Machine Learning in recognition of 2D drawings of machine components. Recognition of primitive geometric shapes such as polygons within engineering drawings forms basic element of such approach. Machine learning algorithms are used to identify 3 to 7 sided polygons with random shapes and segmented edges. The uncertainty induced by segmented edges poses a challenge for predicting number of sides using statistical method such as Machine Learning. Different types of datasets with varying amount of uncertainty are used. The recognition of shapes is attempted with different sets of features such as coordinates of points, slopes of lines and geometric parameters such as area, perimeter and centroid. Three machine learning models namely, Random Forest Classifier, K- Nearest Neighbors Classifier and Support Vector Classifier are adopted. The performance of these models for identification of polygons is discussed.
{"title":"Investigating Application of Machine Learning in Identification of Polygon Shapes for Recognition of Mechanical Engineering Drawings","authors":"Abhilash Mane, Riddhi R. Adhikari, Shreyash Gadgil, N. Raykar","doi":"10.1109/ICNTE44896.2019.8945917","DOIUrl":"https://doi.org/10.1109/ICNTE44896.2019.8945917","url":null,"abstract":"This paper investigates the applications of Machine Learning in recognition of 2D drawings of machine components. Recognition of primitive geometric shapes such as polygons within engineering drawings forms basic element of such approach. Machine learning algorithms are used to identify 3 to 7 sided polygons with random shapes and segmented edges. The uncertainty induced by segmented edges poses a challenge for predicting number of sides using statistical method such as Machine Learning. Different types of datasets with varying amount of uncertainty are used. The recognition of shapes is attempted with different sets of features such as coordinates of points, slopes of lines and geometric parameters such as area, perimeter and centroid. Three machine learning models namely, Random Forest Classifier, K- Nearest Neighbors Classifier and Support Vector Classifier are adopted. The performance of these models for identification of polygons is discussed.","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127989119","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 : 2019-01-01DOI: 10.1109/ICNTE44896.2019.8945898
P. Thakur, J. Khan, Mugdha Dongre, Swarnjayanti Gupta
Inconel 718 has vast range of aeronautical applications. The CNC end milling is the machining processes used for the production of high precision components of aircraft. The machining inconel 718 for high precision always leads to high power consumption and low productivity. This work deals with maximization of productivity and minimization of roughness simultaneously by Taguchi-Grey-Fuzzy logic in cutting of Incone1718 by end milling operation. The control factors considered are depth of cut, feed, speed and type of insert material with roughness and productivity (material removal rate) as responses. Here L18 orthogonal array is utilized as design of experiment with 3 levels of depth of cut, speed, feed and two levels of type of insert material. After the experimentation the optimum levels were obtained by the application of taguchi-grey-fuzzy method. Based on confirmation test, this technique gave 38% increment in multi response performance index (MRPI) which is the combined effect of all the three responses. Also, productivity and roughness increased by 37% and 61 %.
{"title":"Multi Objective Optimization in CNC End Milling of Inconel 718 Super Alloy by Taguchi-Grey-Fuzzy Method","authors":"P. Thakur, J. Khan, Mugdha Dongre, Swarnjayanti Gupta","doi":"10.1109/ICNTE44896.2019.8945898","DOIUrl":"https://doi.org/10.1109/ICNTE44896.2019.8945898","url":null,"abstract":"Inconel 718 has vast range of aeronautical applications. The CNC end milling is the machining processes used for the production of high precision components of aircraft. The machining inconel 718 for high precision always leads to high power consumption and low productivity. This work deals with maximization of productivity and minimization of roughness simultaneously by Taguchi-Grey-Fuzzy logic in cutting of Incone1718 by end milling operation. The control factors considered are depth of cut, feed, speed and type of insert material with roughness and productivity (material removal rate) as responses. Here L18 orthogonal array is utilized as design of experiment with 3 levels of depth of cut, speed, feed and two levels of type of insert material. After the experimentation the optimum levels were obtained by the application of taguchi-grey-fuzzy method. Based on confirmation test, this technique gave 38% increment in multi response performance index (MRPI) which is the combined effect of all the three responses. Also, productivity and roughness increased by 37% and 61 %.","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123985843","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 : 2019-01-01DOI: 10.1109/ICNTE44896.2019.8946029
Akash Maurya, Rahul Wable, Rasika Shinde, S. John, Rahul Jadhav, R. Dakshayani
Chronic kidney disease (CKD) is a type of kidney disease in which there is gradual loss of kidney function over a period of months or years. Prediction of this disease is one of the most important problems in medical fields. So automated tool which will use machine learning techniques to determine the patient's kidney condition that will be helpful to the doctors in prediction of chronic kidney disease and hence better treatment. The proposed system extracts the features which are responsible for CKD, then machine learning process can automate the classification of the chronic kidney disease in different stages according to its severity. The objective is to use machine learning algorithm and suggest suitable diet plan for CKD patient using classification algorithm on medical test records. Diet recommendation for patient will be given according to potassium zone which is calculated using blood potassium level to slow down the progression of CKD.
{"title":"Chronic Kidney Disease Prediction and Recommendation of Suitable Diet Plan by using Machine Learning","authors":"Akash Maurya, Rahul Wable, Rasika Shinde, S. John, Rahul Jadhav, R. Dakshayani","doi":"10.1109/ICNTE44896.2019.8946029","DOIUrl":"https://doi.org/10.1109/ICNTE44896.2019.8946029","url":null,"abstract":"Chronic kidney disease (CKD) is a type of kidney disease in which there is gradual loss of kidney function over a period of months or years. Prediction of this disease is one of the most important problems in medical fields. So automated tool which will use machine learning techniques to determine the patient's kidney condition that will be helpful to the doctors in prediction of chronic kidney disease and hence better treatment. The proposed system extracts the features which are responsible for CKD, then machine learning process can automate the classification of the chronic kidney disease in different stages according to its severity. The objective is to use machine learning algorithm and suggest suitable diet plan for CKD patient using classification algorithm on medical test records. Diet recommendation for patient will be given according to potassium zone which is calculated using blood potassium level to slow down the progression of CKD.","PeriodicalId":292408,"journal":{"name":"2019 International Conference on Nascent Technologies in Engineering (ICNTE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126405033","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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