Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200194
R. Haloi, Jupitara Hazarika, D. Chanda
Analysis of signal transmission activities of human brain can give fruitful information about its functions. These information are of very importance in detection and diagnosis of different types of neurological disorders. Besides low spatial sensitivity, Electroencephalogram(EEG) signals are used for functional analysis of activities of brain because of the large temporal resolution of it. Identification of an appropriate feature of the EEG used to have a key role for its analysis. This work specifically describes feature extraction of EEG signals of persons with Parkinson’s Disease(PD) by using statistical methods. Mean, standard deviation, energy, kurtosis and skewness are the five statistical features selected for this work. In addition to the extraction of features, comparative analysis of these features are also provided considering the EEGs of both normal (Non PD) and the persons with PD symptoms by using T-test. With the use of T-test, without the application of any classification techniques, the features of any two classes can be well differentiated. In the proposed approach, the results show that the p-assessment of the T-experiment is less than 0.05 and hence it can be considered that the features of the two classes are having less than 5% similarity. This fulfils the objective of detecting PD most efficiently. Out of the five features considered, Mean and Energy are the features, which are capable of differentiating the two categories of the subjects most significantly.
{"title":"Selection of Appropriate Statistical Features of EEG Signals for Detection of Parkinson’s Disease","authors":"R. Haloi, Jupitara Hazarika, D. Chanda","doi":"10.1109/ComPE49325.2020.9200194","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200194","url":null,"abstract":"Analysis of signal transmission activities of human brain can give fruitful information about its functions. These information are of very importance in detection and diagnosis of different types of neurological disorders. Besides low spatial sensitivity, Electroencephalogram(EEG) signals are used for functional analysis of activities of brain because of the large temporal resolution of it. Identification of an appropriate feature of the EEG used to have a key role for its analysis. This work specifically describes feature extraction of EEG signals of persons with Parkinson’s Disease(PD) by using statistical methods. Mean, standard deviation, energy, kurtosis and skewness are the five statistical features selected for this work. In addition to the extraction of features, comparative analysis of these features are also provided considering the EEGs of both normal (Non PD) and the persons with PD symptoms by using T-test. With the use of T-test, without the application of any classification techniques, the features of any two classes can be well differentiated. In the proposed approach, the results show that the p-assessment of the T-experiment is less than 0.05 and hence it can be considered that the features of the two classes are having less than 5% similarity. This fulfils the objective of detecting PD most efficiently. Out of the five features considered, Mean and Energy are the features, which are capable of differentiating the two categories of the subjects most significantly.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"29 1","pages":"761-764"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83679901","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 : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200155
Jyotindra Narayan, Arjun Pardasani, S. K. Dwivedy
Gait analysis is an important criterion, nowadays, to diagnose various medical conditions of any individual. Moreover, the gender based disparities in a gait cycle can be witnessed in the social world. In this work, a Kinect-Labview based experimental setup is used to detect and estimate the gait kinematic parameters of ten healthy participants (5 male: 22.2 ± 2.14 years; 5 female: 21.8 ± 2.14 years) in sagittal plane. Primarily, hip, knee and ankle joint angles of right lower limb are evaluated for both gender groups. Thereafter, to draw a clear state of comparison between the gender groups, two sample t-test based statistical investigations are carried out for five prominent gait events at a significance level of 5%. From statistical results, significant gender based gait differences are found which might be useful in the early assessment of gait abnormalities.
{"title":"Comparative Gait Analysis of Healthy Young Male and Female Adults using Kinect-Labview Setup","authors":"Jyotindra Narayan, Arjun Pardasani, S. K. Dwivedy","doi":"10.1109/ComPE49325.2020.9200155","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200155","url":null,"abstract":"Gait analysis is an important criterion, nowadays, to diagnose various medical conditions of any individual. Moreover, the gender based disparities in a gait cycle can be witnessed in the social world. In this work, a Kinect-Labview based experimental setup is used to detect and estimate the gait kinematic parameters of ten healthy participants (5 male: 22.2 ± 2.14 years; 5 female: 21.8 ± 2.14 years) in sagittal plane. Primarily, hip, knee and ankle joint angles of right lower limb are evaluated for both gender groups. Thereafter, to draw a clear state of comparison between the gender groups, two sample t-test based statistical investigations are carried out for five prominent gait events at a significance level of 5%. From statistical results, significant gender based gait differences are found which might be useful in the early assessment of gait abnormalities.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"33 40","pages":"688-693"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91509240","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 : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200167
Pawan Kumar, Varun Kumar, R. Sobti
With the increasing competition, universities are trying to reach out to aspiring students to get them enrolled. However, out of all the students enrolled to a university, many do not actually join. This research study aims to evaluate the potential of applying machine learning to enable educational institutes predict joining status of their freshmen students. Also, we attempt to understand the factors affecting joining behavior using CART algorithm. Obtaining classification accuracy up to 80 percent, it is concluded that machine learning is worth applying in this problem domain. Important factors affecting joining behavior include scholarship offered to student, fee paid so far, status of hostel facility availed and marks in qualifying examination.
{"title":"Predicting Joining Behavior of Freshmen Students using Machine Learning – A Case Study","authors":"Pawan Kumar, Varun Kumar, R. Sobti","doi":"10.1109/ComPE49325.2020.9200167","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200167","url":null,"abstract":"With the increasing competition, universities are trying to reach out to aspiring students to get them enrolled. However, out of all the students enrolled to a university, many do not actually join. This research study aims to evaluate the potential of applying machine learning to enable educational institutes predict joining status of their freshmen students. Also, we attempt to understand the factors affecting joining behavior using CART algorithm. Obtaining classification accuracy up to 80 percent, it is concluded that machine learning is worth applying in this problem domain. Important factors affecting joining behavior include scholarship offered to student, fee paid so far, status of hostel facility availed and marks in qualifying examination.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"13 1","pages":"141-145"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83665770","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 : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200057
Anish Pratap Vishwakarma, Ksh. Milan Singh
The proposed technique employs Shunt Active Power Filter (SAPF) and controller optimization technique through Ant Colony Optimization (ACO) algorithm to reduce current harmonics that appeared in the presence of non-linear loads. Non-linear load devices such as Adjustable Speed Drive (ASD), furnaces, modern power Electronics etc. cause unbound harmonics during their operation. To mitigate the harmonic components, Synchronous Reference Frame Theory (SRFT) is introduced in the controlled circuit to generate gate pulses to the SAPF, and consequently inject equal and opposite harmonics magnitude to the system. The controller parameters are optimized using the ACO algorithm. The comparison analysis shows better performance compared with existing algorithms such as conventional PI controller, Genetic Algorithm (GA), and Eagle Perching (EP).
{"title":"Comparative Analysis of Adaptive PI Controller for Current Harmonic Mitigation","authors":"Anish Pratap Vishwakarma, Ksh. Milan Singh","doi":"10.1109/ComPE49325.2020.9200057","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200057","url":null,"abstract":"The proposed technique employs Shunt Active Power Filter (SAPF) and controller optimization technique through Ant Colony Optimization (ACO) algorithm to reduce current harmonics that appeared in the presence of non-linear loads. Non-linear load devices such as Adjustable Speed Drive (ASD), furnaces, modern power Electronics etc. cause unbound harmonics during their operation. To mitigate the harmonic components, Synchronous Reference Frame Theory (SRFT) is introduced in the controlled circuit to generate gate pulses to the SAPF, and consequently inject equal and opposite harmonics magnitude to the system. The controller parameters are optimized using the ACO algorithm. The comparison analysis shows better performance compared with existing algorithms such as conventional PI controller, Genetic Algorithm (GA), and Eagle Perching (EP).","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"51 1","pages":"643-648"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79998574","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 : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200005
Snehal A. Marathe, B. Patil
Increasing the productivity of the photovoltaic panels is a major problem in recent developments. An existing operative solar panel is far from being optimized, because of the critical problems like weather changes, dust deposition and stains deposited over it. Weather changes are due to temperature, humidity, and cloudy atmosphere, dust deposition due to plants, traffic, air pollution and stains due to birds shit. Brief overview of different existing methods to boost the capacity of these panels is given in this paper. These available existing methods are: tracking system with panel, anti-reflecting coating for solar panels, dust cleaning by various methods and cooling of the panel.
{"title":"Mechanisms for Improving the Productivity of the Existing Photovoltaic Panels: A Review","authors":"Snehal A. Marathe, B. Patil","doi":"10.1109/ComPE49325.2020.9200005","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200005","url":null,"abstract":"Increasing the productivity of the photovoltaic panels is a major problem in recent developments. An existing operative solar panel is far from being optimized, because of the critical problems like weather changes, dust deposition and stains deposited over it. Weather changes are due to temperature, humidity, and cloudy atmosphere, dust deposition due to plants, traffic, air pollution and stains due to birds shit. Brief overview of different existing methods to boost the capacity of these panels is given in this paper. These available existing methods are: tracking system with panel, anti-reflecting coating for solar panels, dust cleaning by various methods and cooling of the panel.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"30 1","pages":"087-090"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83417481","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 : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200018
B. S. Venkat Raman, P. Tripathi, G. Gupta, R. Keshri
This paper presents an analytical study of the effect of injected current harmonics on induction motor torque ripples. A three-phase induction motor (IM) when fed by an ideal three-phase sinusoidal voltage source produces a ripple-free torque output at the shaft. For better speed and torque control of induction motor, several methods have been proposed; such as V/F control, direct torque control, Field oriented control. All such control schemes involve pulse width modulation schemes with high frequency switching such as SPWM and SVPWM. These techniques provide excellent power optimization and control but induce harmonic currents at the stator winding, which in turn gives rise to torque ripples. In the present work, an analysis of the effects of injected harmonic currents on torque ripple is reported. Case study on SPWM by varying switching frequency and optimal switching frequency for least torque ripple are presented. It is reported that only higher frequency switching does not guarantee lower torque ripple. MATLAB Simulink Simscape toolbox is used for verifying the case study. Torque ripple corresponding to injected stator current ripple is presented for validation of the proposed analysis.
{"title":"Effects of Injected Harmonics on Torque Pulsations of a Three Phase Induction Motor: Study on SPWM","authors":"B. S. Venkat Raman, P. Tripathi, G. Gupta, R. Keshri","doi":"10.1109/ComPE49325.2020.9200018","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200018","url":null,"abstract":"This paper presents an analytical study of the effect of injected current harmonics on induction motor torque ripples. A three-phase induction motor (IM) when fed by an ideal three-phase sinusoidal voltage source produces a ripple-free torque output at the shaft. For better speed and torque control of induction motor, several methods have been proposed; such as V/F control, direct torque control, Field oriented control. All such control schemes involve pulse width modulation schemes with high frequency switching such as SPWM and SVPWM. These techniques provide excellent power optimization and control but induce harmonic currents at the stator winding, which in turn gives rise to torque ripples. In the present work, an analysis of the effects of injected harmonic currents on torque ripple is reported. Case study on SPWM by varying switching frequency and optimal switching frequency for least torque ripple are presented. It is reported that only higher frequency switching does not guarantee lower torque ripple. MATLAB Simulink Simscape toolbox is used for verifying the case study. Torque ripple corresponding to injected stator current ripple is presented for validation of the proposed analysis.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"74 1","pages":"637-642"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85960736","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 : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200118
Vijay L. Agrawal, Dr. Sanjay Vasant Dudul
The aim of this research is to develop an Optimal Classifier based on computational intelligence techniques for the precise diagnosis of deadly Lung Cancer disease. The proposed system provides maximum classification accuracy along with minimum number of connection weights, processing elements, time elapsed per epoch per exemplar and MSE on CV data sets. The Classifiers based on MLP, GFF, MNN Neural Networks and SVM with different learning rules on different transform domains such as DCT, FFT and WHT have been simulated on two different datasets. The optimized single hidden layer Multilayer Perceptron Neural Network with QP learning rule on Histogram knowledge-base for Data-base I and Data-base II resulted into the reasonable and optimal classifier based on C.I. techniques for the diagnosis of Lung Cancer.
{"title":"Conventional Neural Network approach for the Diagnosis of Lung Tumor","authors":"Vijay L. Agrawal, Dr. Sanjay Vasant Dudul","doi":"10.1109/ComPE49325.2020.9200118","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200118","url":null,"abstract":"The aim of this research is to develop an Optimal Classifier based on computational intelligence techniques for the precise diagnosis of deadly Lung Cancer disease. The proposed system provides maximum classification accuracy along with minimum number of connection weights, processing elements, time elapsed per epoch per exemplar and MSE on CV data sets. The Classifiers based on MLP, GFF, MNN Neural Networks and SVM with different learning rules on different transform domains such as DCT, FFT and WHT have been simulated on two different datasets. The optimized single hidden layer Multilayer Perceptron Neural Network with QP learning rule on Histogram knowledge-base for Data-base I and Data-base II resulted into the reasonable and optimal classifier based on C.I. techniques for the diagnosis of Lung Cancer.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"34 1","pages":"543-547"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90082972","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 : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200105
Rahul, Manoj, J. K. Verma
Deployment of smart cell’s importance in the LTE system provides support in bandwidth requirement and consumption of power in the network. The system proposes scheduling algorithms for improvement in the throughput of the system. It uses the Hungarian algorithm for optimization and for packet success rate improvement. The main objective is to improve the throughput of the system by using the optimization scheduling method. The new planning calculation will bring about a worthy throughput and gives some reasonableness between clients. The result shows the improvement in throughput distribution and packet success rate (PSR) by the use of Hungarian optimization. All the simulations have been done in the MATLAB software.
{"title":"A User Scheduling in LTE Network having Environment with Mixed Traffic","authors":"Rahul, Manoj, J. K. Verma","doi":"10.1109/ComPE49325.2020.9200105","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200105","url":null,"abstract":"Deployment of smart cell’s importance in the LTE system provides support in bandwidth requirement and consumption of power in the network. The system proposes scheduling algorithms for improvement in the throughput of the system. It uses the Hungarian algorithm for optimization and for packet success rate improvement. The main objective is to improve the throughput of the system by using the optimization scheduling method. The new planning calculation will bring about a worthy throughput and gives some reasonableness between clients. The result shows the improvement in throughput distribution and packet success rate (PSR) by the use of Hungarian optimization. All the simulations have been done in the MATLAB software.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"132 1","pages":"269-274"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77814171","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}
Advent of modern means of living and busy schedule, a person generally forget to look after the health and thereby prone to some of severe health disorders. From various researches it is clear that Parkinson Disease (PD) basically occur due to negligence of an individual. It is a disorderliness that affects a small part of the brain that manages the gesture and mental order. The syndrome vary from person. Generally PD is identified from a certain age level but recent studies shown that people of any age group can suffer from PD. Considering the extremity, if there is an system to identify the major factors of PD then it would be of great importance. Thus an expert system named Transparent Decision Rules for Parkinson Disease (TD-Rules-PD) is proposed in this paper which finds out the high risk factor of PD. TD-Rules-PD consist of four phases: Rule Production, Rule selection, Rule pruning and Merging and Identify High Risk Factor. Rule Production stage uses Decision tree to produce rules. Rule selection step choose the most efficient rules, from the so collected rules Rule Pruning and Merging step drops the confusing and insignificant rules and then combines the flittered rule set to a single rule and finally Identify High Risk Factor stage finds out the most prevailing factor of PD.
现代生活方式的到来和繁忙的日程安排，使一个人普遍忘记照顾好自己的健康，从而容易出现一些严重的健康失调。从各种研究来看，帕金森病(PD)基本上是由于个体的疏忽而发生的。这是一种紊乱，影响了大脑中管理手势和精神秩序的一小部分。这种症状因人而异。一般来说，帕金森病是在特定年龄阶段确诊的，但最近的研究表明，任何年龄组的人都可能患有帕金森病。考虑到肢体，如果有一个系统来识别帕金森病的主要因素，那将是非常重要的。为此，本文提出了一个帕金森病透明决策规则(Transparent Decision Rules for Parkinson, TD-Rules-PD)专家系统，用于发现帕金森病的高危因素。TD-Rules-PD包括规则生成、规则选择、规则裁剪与合并和高风险因素识别四个阶段。规则生成阶段使用决策树生成规则。规则选择步骤选择最有效的规则，规则修剪和合并步骤从收集到的规则中剔除混乱和不重要的规则，然后将过滤后的规则集合并为单个规则，最后识别高风险因素阶段找出PD的最主要因素。
{"title":"Expert System to Manage Parkinson Disease by Identifying Risk Factors: TD-Rules-PD","authors":"Arpita Nath Boruah, Saroj Kumar Biswas, Sivaji Bandyopadhyay, Sunita Sarkar","doi":"10.1109/ComPE49325.2020.9200075","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200075","url":null,"abstract":"Advent of modern means of living and busy schedule, a person generally forget to look after the health and thereby prone to some of severe health disorders. From various researches it is clear that Parkinson Disease (PD) basically occur due to negligence of an individual. It is a disorderliness that affects a small part of the brain that manages the gesture and mental order. The syndrome vary from person. Generally PD is identified from a certain age level but recent studies shown that people of any age group can suffer from PD. Considering the extremity, if there is an system to identify the major factors of PD then it would be of great importance. Thus an expert system named Transparent Decision Rules for Parkinson Disease (TD-Rules-PD) is proposed in this paper which finds out the high risk factor of PD. TD-Rules-PD consist of four phases: Rule Production, Rule selection, Rule pruning and Merging and Identify High Risk Factor. Rule Production stage uses Decision tree to produce rules. Rule selection step choose the most efficient rules, from the so collected rules Rule Pruning and Merging step drops the confusing and insignificant rules and then combines the flittered rule set to a single rule and finally Identify High Risk Factor stage finds out the most prevailing factor of PD.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"41 1 1","pages":"001-006"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74694744","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 : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200056
Shaswat Dharaiya, Bhavin Soneji, D. Kakkad, N. Tada
Most customers who prefer buying products online on E-Commerce websites tend to rely on the ratings given to a product by other customers or a summary of the already existing customer reviews. However, a plethora of meaningful data is stored in the review text which eludes representation through customer ratings or the summary of the reviews likewise. But it is inefficient to go through each and every review. Our model thus adopts two approaches to demonstrate and resolve the generated issue - General Approach where the data is sorted based on the ratings, and Specific Approach where the data is sorted based on the products. The subsequent result is the generation of two new corpora followed by the generation of two new Word Clouds consisting of positive and negative features respectively for each existing product. The purpose of these Word Clouds is to highlight the features of products that are mentioned in the reviews. Hence, such a model provides more accurate as well as an efficient analysis of the offered products.
{"title":"Generating Positive and Negative Sentiment Word Clouds from E-Commerce Product Reviews","authors":"Shaswat Dharaiya, Bhavin Soneji, D. Kakkad, N. Tada","doi":"10.1109/ComPE49325.2020.9200056","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200056","url":null,"abstract":"Most customers who prefer buying products online on E-Commerce websites tend to rely on the ratings given to a product by other customers or a summary of the already existing customer reviews. However, a plethora of meaningful data is stored in the review text which eludes representation through customer ratings or the summary of the reviews likewise. But it is inefficient to go through each and every review. Our model thus adopts two approaches to demonstrate and resolve the generated issue - General Approach where the data is sorted based on the ratings, and Specific Approach where the data is sorted based on the products. The subsequent result is the generation of two new corpora followed by the generation of two new Word Clouds consisting of positive and negative features respectively for each existing product. The purpose of these Word Clouds is to highlight the features of products that are mentioned in the reviews. Hence, such a model provides more accurate as well as an efficient analysis of the offered products.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"23 1","pages":"459-463"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79328688","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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