This research article has explored the electronic behaviour of CdTe nanowire. The present study has evolved the structural dependence of electronic properties of CdTe nanowire. The shapes for which this dependence has been studied are 2 atoms linear, 2 atoms zigzag, 4 atoms square and 6 atoms hexagonal for CdTe nanowire. We have used ABINIT code for this study. We have explored the geometrical optimization, band structure and stability of proposed structures. The structure which has come out to be the most stable amongst the all is 4 atom square nanowire where as the findings of the study for band structure reveal that CdTe nanowires may have insulating as well semiconducting nature depending on the shape of the nanowire.
{"title":"First Principles Study of Structural Stability and Electronic Properties of CdTe Nanowires","authors":"S. Kaushik, S. Singh, R. Thakur","doi":"10.1166/JCTN.2020.9631","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9631","url":null,"abstract":"This research article has explored the electronic behaviour of CdTe nanowire. The present study has evolved the structural dependence of electronic properties of CdTe nanowire. The shapes for which this dependence has been studied are 2 atoms linear, 2 atoms zigzag, 4 atoms square and\u0000 6 atoms hexagonal for CdTe nanowire. We have used ABINIT code for this study. We have explored the geometrical optimization, band structure and stability of proposed structures. The structure which has come out to be the most stable amongst the all is 4 atom square nanowire where as the findings\u0000 of the study for band structure reveal that CdTe nanowires may have insulating as well semiconducting nature depending on the shape of the nanowire.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5210-5214"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43374890","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}
Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the reviewed methods.
{"title":"An Extensive Review on Machine Learning and Deep Learning Based Cervical Cancer Diagnosis and Classification Models","authors":"C. Suguna, S. Balamurugan","doi":"10.1166/JCTN.2020.9437","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9437","url":null,"abstract":"Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in\u0000 the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently\u0000 ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the\u0000 reviewed methods.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5438-5446"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42244009","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}
In the era of technology advancement and COVID-19 outbreak period, all physical classes have been converted to online classes through social network platforms. Having online classes through social networks are actually very comfortable and flexible for students as they can have their classes at various places. This paper is focuses on the relationship between usages of social network and the quality of education during COVID-19 outbreak.
{"title":"The Usage of Social Network to Students: Does It Improve Student’s Education Quality During COVID-19 Outbreak","authors":"Indraah Kolandaisamy, Raenu Kolandaisamy","doi":"10.1166/JCTN.2020.9412","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9412","url":null,"abstract":"In the era of technology advancement and COVID-19 outbreak period, all physical classes have been converted to online classes through social network platforms. Having online classes through social networks are actually very comfortable and flexible for students as they can have their\u0000 classes at various places. This paper is focuses on the relationship between usages of social network and the quality of education during COVID-19 outbreak.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5224-5228"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48072941","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}
P. Meenal, P. Gowr, A. Ram, A. Rajini, B. Abishek, D. Ravikumar
Excess amount of insulin in human blood might affect the retina in eyes and cause abnormalities in human vision, which is generally termed as Diabetic Retinopathy (DR). Many diabetic patients are often saved by the earlier diagnosis of Diabetic Retinopathy. The surface of retinal layer that has the earlier signs of Diabetic Retinopathy. This type of abnormalities are detected using traditional image processing methods which includes stages such as capturing fundus images, preprocessing, feature extraction and finally classification is performed to classify it as retinal and healthy images. (The proposed system, this detection is completed by Fuzzy-C Means (FCM) clustering). The proposed automated system consists of four phases which includes, preprocessing of the captured fundus images in which the image is resized and the second stage involves CLAHE. Images has to enhanced in order to boost up the features for which Contrast adjustment is performed in the third phase and before classification the grey and green channels of the images are extracted from the processed images. This detection process provides better results than the prevailing method. SVM classifier has been used in the proposed framework which classified the malady level of diabetic retinopathy in eye. The proposed system manages to provide better classification rates compared to the previous methodologies. The accuracy, sensitivity and specificity of the developed automated system was found to be 94.4%, 100% and 85.7%, which was promising than the compared methods.
{"title":"Automatic Detection of Diabetic Retinopathy Using Support Vector Machine","authors":"P. Meenal, P. Gowr, A. Ram, A. Rajini, B. Abishek, D. Ravikumar","doi":"10.1166/JCTN.2020.9456","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9456","url":null,"abstract":"Excess amount of insulin in human blood might affect the retina in eyes and cause abnormalities in human vision, which is generally termed as Diabetic Retinopathy (DR). Many diabetic patients are often saved by the earlier diagnosis of Diabetic Retinopathy. The surface of retinal layer\u0000 that has the earlier signs of Diabetic Retinopathy. This type of abnormalities are detected using traditional image processing methods which includes stages such as capturing fundus images, preprocessing, feature extraction and finally classification is performed to classify it as retinal\u0000 and healthy images. (The proposed system, this detection is completed by Fuzzy-C Means (FCM) clustering). The proposed automated system consists of four phases which includes, preprocessing of the captured fundus images in which the image is resized and the second stage involves CLAHE. Images\u0000 has to enhanced in order to boost up the features for which Contrast adjustment is performed in the third phase and before classification the grey and green channels of the images are extracted from the processed images. This detection process provides better results than the prevailing method.\u0000 SVM classifier has been used in the proposed framework which classified the malady level of diabetic retinopathy in eye. The proposed system manages to provide better classification rates compared to the previous methodologies. The accuracy, sensitivity and specificity of the developed automated\u0000 system was found to be 94.4%, 100% and 85.7%, which was promising than the compared methods.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5582-5589"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44628913","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}
Early detection of heart disease may prevent myocardial infarction. Electrocardiogram (ECG) is the most widely used signal in clinical practice for the diagnosis of cardiovascular diseases such as arrhythmias and myocardial infarction. Human interpretation is time-consuming, and long-term ECG records are difficult to detect in small differences.Therefore, automated recognition of myocardial infarction using a Computer-Aided Diagnosis (CAD) system is the research interest, which can be used effectively to reduce mortality among cardiovascular disease patients. The most important step in the analysis of complex R-peak/QRS signals using an automated process of ECG signal. To automate the cardiovascular disease detection process, an adequate mechanism is required to characterize ECG signals, which are unknown features according to the similarities between ECG signals. If the classification can find similarities accurately and the probability of arrhythmia detection increases, the algorithm can become an effective method in the laboratory. In this research work, a new classification strategy is proposed to all the more precisely order ECG signals dependent on a powerful model of ECG signals. In this proposed method, a Nonlinear Vector Decomposed Neural Network (NVDN) is developed, and its simulation results show that this classifier can isolate the ECGs with high productivity. This proposed technique expands the exactness of the ECG classification concerning increasingly exact arrhythmia discovery.
{"title":"ECG Classification Framework for Cardiac Disease Prediction Using Nonlinear Vector Decomposed Neural Network","authors":"M. Suhail, T. .. Razak","doi":"10.1166/JCTN.2020.9453","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9453","url":null,"abstract":"Early detection of heart disease may prevent myocardial infarction. Electrocardiogram (ECG) is the most widely used signal in clinical practice for the diagnosis of cardiovascular diseases such as arrhythmias and myocardial infarction. Human interpretation is time-consuming, and long-term\u0000 ECG records are difficult to detect in small differences.Therefore, automated recognition of myocardial infarction using a Computer-Aided Diagnosis (CAD) system is the research interest, which can be used effectively to reduce mortality among cardiovascular disease patients. The most important\u0000 step in the analysis of complex R-peak/QRS signals using an automated process of ECG signal. To automate the cardiovascular disease detection process, an adequate mechanism is required to characterize ECG signals, which are unknown features according to the similarities between ECG signals.\u0000 If the classification can find similarities accurately and the probability of arrhythmia detection increases, the algorithm can become an effective method in the laboratory. In this research work, a new classification strategy is proposed to all the more precisely order ECG signals dependent\u0000 on a powerful model of ECG signals. In this proposed method, a Nonlinear Vector Decomposed Neural Network (NVDN) is developed, and its simulation results show that this classifier can isolate the ECGs with high productivity. This proposed technique expands the exactness of the ECG classification\u0000 concerning increasingly exact arrhythmia discovery.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5563-5569"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43871500","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}
Twitter Sentiment Study is a difficult task that comprises the various kind of preprocessing phases, including reduction in dimensionality. The reduction in dimensionality ensures minimum computational complexity and improved performance in the classification course. In Twitter data, each tweet has functionality values that may or may not reflect an individual’s response. As a result, when tweets are signified as feature matrices, many sparse data points are created and possibly overhead and error rates increase in sentiment analysis on Twitter. This paper proposes a novel kind of algorithm as Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm. The ABC-PIO combines with the characteristics that ABC can produce various samples, PIO can reach the best value rapidly and Cauchy perturbation strategy can improve optimal solution. The proposed technique archive Accuracy of 99.01% for Decision tree, 77.34% for Navy Bias and 60.89% Random Forest. The comparative analysis show that the proposed ABC-PIO with Decision tree archive much better results compared to other existing techniques.
{"title":"An Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm for Twitter Sentiment Analysis","authors":"S. Kasthuri, A. N. Jebaseeli","doi":"10.1166/JCTN.2020.9431","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9431","url":null,"abstract":"Twitter Sentiment Study is a difficult task that comprises the various kind of preprocessing phases, including reduction in dimensionality. The reduction in dimensionality ensures minimum computational complexity and improved performance in the classification course. In Twitter data,\u0000 each tweet has functionality values that may or may not reflect an individual’s response. As a result, when tweets are signified as feature matrices, many sparse data points are created and possibly overhead and error rates increase in sentiment analysis on Twitter. This paper proposes\u0000 a novel kind of algorithm as Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm. The ABC-PIO combines with the characteristics that ABC can produce various samples, PIO can reach the best value rapidly and Cauchy perturbation strategy can improve optimal\u0000 solution. The proposed technique archive Accuracy of 99.01% for Decision tree, 77.34% for Navy Bias and 60.89% Random Forest. The comparative analysis show that the proposed ABC-PIO with Decision tree archive much better results compared to other existing techniques.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5378-5385"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49432500","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}
Owing to the rapid growth of information technologies, a rising need for cybersecurity and biometric technologies is increasingly evolving. Biometrics image protection is an important problem as digital images and medical details are distributed via public networks. This research work proposed a threshold-based share creation scheme for Biometrics images. To enhance the security level of the shares, each shares are encrypted by Light Weight Cryptography (LWC)-Stream Cipher method. To increase the stream cipher encryption efficiency, optimal keys are selected by Ant Lion Optimization (ALO) technique. The benefit of consuming stream ciphers is that the speed of execution is maximum over block cipher and less complex. The benefit of the suggested stream cipher approach is that the decoding of the keys in the keystream and the characters in the plain text denotes decrypted biometrics image will improve device reliability. From the implementation results proposed model achieves the maximum PSNR with the security of Biometrics images, compared to other existing techniques.
{"title":"Light Weight Cryptography Based Encrypted Multiple Secret Share Creation for Biometrics Images","authors":"Elavarasi Gunasekaran, Vanitha Muthuraman","doi":"10.1166/JCTN.2020.9441","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9441","url":null,"abstract":"Owing to the rapid growth of information technologies, a rising need for cybersecurity and biometric technologies is increasingly evolving. Biometrics image protection is an important problem as digital images and medical details are distributed via public networks. This research work\u0000 proposed a threshold-based share creation scheme for Biometrics images. To enhance the security level of the shares, each shares are encrypted by Light Weight Cryptography (LWC)-Stream Cipher method. To increase the stream cipher encryption efficiency, optimal keys are selected by Ant Lion\u0000 Optimization (ALO) technique. The benefit of consuming stream ciphers is that the speed of execution is maximum over block cipher and less complex. The benefit of the suggested stream cipher approach is that the decoding of the keys in the keystream and the characters in the plain text denotes\u0000 decrypted biometrics image will improve device reliability. From the implementation results proposed model achieves the maximum PSNR with the security of Biometrics images, compared to other existing techniques.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5469-5476"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42554218","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}
At present times, the diabetic retinopathy (DR) become high and it is required to design an Internet of Things (IoT) enabled DR diagnosis tool to assist the diagnosis process of remote patients. This study designs and develops IoT and cloud computing based Hybrid Feature Extraction (HFE) with Adaptive Neuro Fuzzy Inference System (ANFIS) for DR detection and classification model, abbreviated as HFE-ANFIS model. The proposed model initially captures the retinal fundus image of the patient using the IoT enabled head mounted camera and transmit the images to the cloud server, which executes the diagnosis process. The image preprocessing takes place using three stages namely color space conversion, filtering, and contrast enhancement. Next, segmentation process takes place using fuzzy c-means (FCM) model to identify the diseased portions in the fundus image. Then, HFE based feature extraction and ANFIS based classification processes are carried out to grade the different levels of DR. The performance validation of the HFE-ANFIS model takes place against MESSIDOR dataset and the results are investigated under different dimensions. The simulation outcome indicated that the HFE-ANFIS model has offered superior performance to other methods with the maximum average sensitivity of 94.55%, specificity of 96.41%, precision of 94.66% and accuracy of 95.97%.
{"title":"Internet of Things and Cloud Enabled Hybrid Feature Extraction with Adaptive Neuro Fuzzy Inference System for Diabetic Retinopathy Diagnosis","authors":"K. Parthiban, K. Venkatachalapathy","doi":"10.1166/JCTN.2020.9418","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9418","url":null,"abstract":"At present times, the diabetic retinopathy (DR) become high and it is required to design an Internet of Things (IoT) enabled DR diagnosis tool to assist the diagnosis process of remote patients. This study designs and develops IoT and cloud computing based Hybrid Feature Extraction\u0000 (HFE) with Adaptive Neuro Fuzzy Inference System (ANFIS) for DR detection and classification model, abbreviated as HFE-ANFIS model. The proposed model initially captures the retinal fundus image of the patient using the IoT enabled head mounted camera and transmit the images to the cloud server,\u0000 which executes the diagnosis process. The image preprocessing takes place using three stages namely color space conversion, filtering, and contrast enhancement. Next, segmentation process takes place using fuzzy c-means (FCM) model to identify the diseased portions in the fundus image. Then,\u0000 HFE based feature extraction and ANFIS based classification processes are carried out to grade the different levels of DR. The performance validation of the HFE-ANFIS model takes place against MESSIDOR dataset and the results are investigated under different dimensions. The simulation outcome\u0000 indicated that the HFE-ANFIS model has offered superior performance to other methods with the maximum average sensitivity of 94.55%, specificity of 96.41%, precision of 94.66% and accuracy of 95.97%.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5261-5269"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45963024","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}
Hospital patient record that is stored in public cloud demands high level of security and access control. To guarantee the proper user access, an authentication scheme is required that can follow up data access. In this paper, counter based authentication verification is introduced. This method utilizes token generation and counter strategy. Elliptic curve based digital signature is employed in token generation. Along with the generated token, the data is encrypted and a counter value is appended to it. Whenever an authorized user views or modifies the stored data, the counter value is updated. Thus, this method significantly identifies an unauthorized data access.
{"title":"Counter Based Authentication Verification to Secure Patient Data in Cloud","authors":"A. Vikram, G. Gopinath","doi":"10.1166/JCTN.2020.9447","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9447","url":null,"abstract":"Hospital patient record that is stored in public cloud demands high level of security and access control. To guarantee the proper user access, an authentication scheme is required that can follow up data access. In this paper, counter based authentication verification is introduced.\u0000 This method utilizes token generation and counter strategy. Elliptic curve based digital signature is employed in token generation. Along with the generated token, the data is encrypted and a counter value is appended to it. Whenever an authorized user views or modifies the stored data, the\u0000 counter value is updated. Thus, this method significantly identifies an unauthorized data access.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5516-5519"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49103871","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}
Dadheech Pankaj, R. Sheeba, R. Vidya, P. Rajarajeswari, P. Srinivasan, C. Kumar, Sudhakar Sengan
The Internet is slowly shaping to be the primary information source that fulfils all the needs of a person. Whenever someone plans to buy a product, they tend to consult with the reviews online to get a clear idea of the product in terms of its various aspects. The problem is that the information available about a single product is so much in volume that the users not be able to extract the information they require from this massive amount of data. The paper proposes a system that generates a temporal aspect based text summary of user opinions that are collected from different sources across the Internet with their time-stamp. These comments are broken into sentences and sub-sentences after predefined based classification. Then, Sentiment analysis is performed. The time relationship is taken into account, and the causal relationship is identified at the deflection points or the time frames during which there is a significant opinion change. The major advantage of this system is that the changes in user opinions with time can be traced and the cause of this sentiment change can be found out in addition to offering customers a quick, convenient and easy way to consume information about a product to help them decide whether or not to purchase it. It also helps enterprises to get relevant insights related to their products based on the customer reviews online.
{"title":"Implementation of Internet of Things-Based Sentiment Analysis for Farming System","authors":"Dadheech Pankaj, R. Sheeba, R. Vidya, P. Rajarajeswari, P. Srinivasan, C. Kumar, Sudhakar Sengan","doi":"10.1166/JCTN.2020.9426","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9426","url":null,"abstract":"The Internet is slowly shaping to be the primary information source that fulfils all the needs of a person. Whenever someone plans to buy a product, they tend to consult with the reviews online to get a clear idea of the product in terms of its various aspects. The problem is that the\u0000 information available about a single product is so much in volume that the users not be able to extract the information they require from this massive amount of data. The paper proposes a system that generates a temporal aspect based text summary of user opinions that are collected from different\u0000 sources across the Internet with their time-stamp. These comments are broken into sentences and sub-sentences after predefined based classification. Then, Sentiment analysis is performed. The time relationship is taken into account, and the causal relationship is identified at the deflection\u0000 points or the time frames during which there is a significant opinion change. The major advantage of this system is that the changes in user opinions with time can be traced and the cause of this sentiment change can be found out in addition to offering customers a quick, convenient and easy\u0000 way to consume information about a product to help them decide whether or not to purchase it. It also helps enterprises to get relevant insights related to their products based on the customer reviews online.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"17 1","pages":"5339-5345"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42105272","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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