G. Siddesh, S. R. M. Sekhar, Srinidhi Hiriyannaiah, G. SrinivasaK.
The stock market volume and price are an active area of research for the past many years. Behind every dollar of investment, the customer will be hoping for profit in one or the other way. There is a positive correlation between investor sentiment and stock volume. Predicting the stock market is the most difficult task due to the dynamic fluctuation of volume and price. The traditional analysis methods carried out leads to satisfactory results. In this paper, the proposed system uses real-time data from Twitter to detect the user opinion about the product along with the stock volume for prediction. The stock volume data and the Twitter data are collected first and then the classification of the polarity is carried out using the SentiWordnet dictionary. The algorithm for the prediction of the stock prices uses Long-short term memory, a neural network as the prices are sequential evolving in nature. The results of the proposed system are correlated between the stock market and Twitter data to obtain better insights that are positive.
{"title":"Forecasting Stock Market Volume Price Using Sentimental and Technical Analysis","authors":"G. Siddesh, S. R. M. Sekhar, Srinidhi Hiriyannaiah, G. SrinivasaK.","doi":"10.4018/jitr.299383","DOIUrl":"https://doi.org/10.4018/jitr.299383","url":null,"abstract":"The stock market volume and price are an active area of research for the past many years. Behind every dollar of investment, the customer will be hoping for profit in one or the other way. There is a positive correlation between investor sentiment and stock volume. Predicting the stock market is the most difficult task due to the dynamic fluctuation of volume and price. The traditional analysis methods carried out leads to satisfactory results. In this paper, the proposed system uses real-time data from Twitter to detect the user opinion about the product along with the stock volume for prediction. The stock volume data and the Twitter data are collected first and then the classification of the polarity is carried out using the SentiWordnet dictionary. The algorithm for the prediction of the stock prices uses Long-short term memory, a neural network as the prices are sequential evolving in nature. The results of the proposed system are correlated between the stock market and Twitter data to obtain better insights that are positive.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127419554","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}
This paper proposes a new training algorithm for artificial neural networks based on an enhanced version of the grey wolf optimizer (GWO) algorithm. The proposed model is used for classifying the patients of diabetes disease. The results showed that the proposed training algorithm enhanced the performance of ANNs with a better classification accuracy as compared to the other state of art training algorithms for the classification of diabetes on publicly available “Pima Indian Diabetes (PID) dataset”. Several experiments have been executed on this dataset with variation in size of the population, techniques to handle missing data, and their impact on classification accuracy has been discussed. Finally, the results are compared with other nature-inspired algorithms trained ANN. EGWO attained better results in terms of classification accuracy than the other algorithms. The convergence curve proved that EGWO had balanced the local and global search abilities because it was faster to reach better positions than the original GWO.
{"title":"A Hybrid Approach for Enhancing the Classification Accuracy for Diabetes Disease","authors":"Maryam Mohammed Al-Nussairi, M. A. Eljinini","doi":"10.4018/jitr.298024","DOIUrl":"https://doi.org/10.4018/jitr.298024","url":null,"abstract":"This paper proposes a new training algorithm for artificial neural networks based on an enhanced version of the grey wolf optimizer (GWO) algorithm. The proposed model is used for classifying the patients of diabetes disease. The results showed that the proposed training algorithm enhanced the performance of ANNs with a better classification accuracy as compared to the other state of art training algorithms for the classification of diabetes on publicly available “Pima Indian Diabetes (PID) dataset”. Several experiments have been executed on this dataset with variation in size of the population, techniques to handle missing data, and their impact on classification accuracy has been discussed. Finally, the results are compared with other nature-inspired algorithms trained ANN. EGWO attained better results in terms of classification accuracy than the other algorithms. The convergence curve proved that EGWO had balanced the local and global search abilities because it was faster to reach better positions than the original GWO.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129081260","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}
Indian Govt has taken broad step and declared lock down to reduce the community-transmission of the novel “Coronavirus”.Many people tried to utilize this period by doing online work and household work simulateneouly. Many small scale industries,shops ,agencies,school colleges shut their door following Govt rules and regulations to avoid spreading of virus.People working or engaged in these activities or duties became unemployed .As man is a social animal and feels safe and secured in society due to increase in distance from society from office space and due to financial crises , day by day negative thought impacts their mind and they are mental in stability or pressure . In this study, an attempt was made to prioritize the cause of mental pressure faced by common people. Such that precautionary measures can be taken for the public-health such that appropriate steps can be taken to protect their health from the transmission of this virus. By using the “Grey-technique for order of preference by similarity to ideal solution (Grey-TOPSIS)”method .
{"title":"Mental Risk Faced During Lockdown in COVID-19: A Grey-TOPSIS Approach - A Case Study of Odisha","authors":"S. Satapathy","doi":"10.4018/jitr.299377","DOIUrl":"https://doi.org/10.4018/jitr.299377","url":null,"abstract":"Indian Govt has taken broad step and declared lock down to reduce the community-transmission of the novel “Coronavirus”.Many people tried to utilize this period by doing online work and household work simulateneouly. Many small scale industries,shops ,agencies,school colleges shut their door following Govt rules and regulations to avoid spreading of virus.People working or engaged in these activities or duties became unemployed .As man is a social animal and feels safe and secured in society due to increase in distance from society from office space and due to financial crises , day by day negative thought impacts their mind and they are mental in stability or pressure . In this study, an attempt was made to prioritize the cause of mental pressure faced by common people. Such that precautionary measures can be taken for the public-health such that appropriate steps can be taken to protect their health from the transmission of this virus. By using the “Grey-technique for order of preference by similarity to ideal solution (Grey-TOPSIS)”method .","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127824538","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}
Cardiac Magnetic Resonance Imaging is a popular non-invasive technique used for assessing the cardiac performance. Automating the segmentation helps in increased diagnosis accuracy in considerably less time and effort. In this paper a novel approach has been proposed to improve the automated segmentation process by increasing the accuracy of segmentation and laying focus on efficient pre-processing of the cardiac Magnetic Resonance (MR) image. The pre-processing module in the proposed method includes noise estimation and efficient denoising of images using discrete total variation based Non local means method.Segmentation accuracy is evaluated using measures such as average perpendicular distance and dice similarity coefficient. The performance of all the segmentation techniques is improved. Further segmentation comparison has also been performed using other state-of-the art noise removal techniques for pre-processing and it was observed that the proposed pre-processing technique outperformed other noise removal techniques in improving the segmentation accuracy.
{"title":"Improved Segmentation of Cardiac MRI Using Efficient Pre-Processing Techniques","authors":"N. Joshi, Sarika Jain","doi":"10.4018/jitr.299932","DOIUrl":"https://doi.org/10.4018/jitr.299932","url":null,"abstract":"Cardiac Magnetic Resonance Imaging is a popular non-invasive technique used for assessing the cardiac performance. Automating the segmentation helps in increased diagnosis accuracy in considerably less time and effort. In this paper a novel approach has been proposed to improve the automated segmentation process by increasing the accuracy of segmentation and laying focus on efficient pre-processing of the cardiac Magnetic Resonance (MR) image. The pre-processing module in the proposed method includes noise estimation and efficient denoising of images using discrete total variation based Non local means method.Segmentation accuracy is evaluated using measures such as average perpendicular distance and dice similarity coefficient. The performance of all the segmentation techniques is improved. Further segmentation comparison has also been performed using other state-of-the art noise removal techniques for pre-processing and it was observed that the proposed pre-processing technique outperformed other noise removal techniques in improving the segmentation accuracy.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134003186","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}
Based on iceberg theory and the questionnaire of competency’s elements, hierarchical index system of evaluation of teachers' innovation and entrepreneurship competency in universities is established. Through researches, we think that analytic hierarchy process(AHP) is a more scientific and reasonable evaluation method whose rationality is checked by satisfactory consistency while the evaluation model of artificial neutral network doesn’t consider weighting. If the samples are more than 30, the evaluation of neural network model of teachers' innovation and entrepreneurship competency can achieve the accurate results and satisfactory requirements. Since the method of artificial neutral network has advantages of strong operability, simple rules and minor errors, it can greatly reduce the workload because it not only eliminates human subjectivity of evaluation and greatly simplifies the process of evaluation, but also improves working efficiency and provides a new way of thinking for evaluation of the teachers' innovation and entrepreneurship competency in universities
{"title":"Evaluation of Teachers' Innovation and Entrepreneurship Ability in Universities Based on Artificial Neural Networks","authors":"Xingfeng Liu, An Qin","doi":"10.4018/jitr.299926","DOIUrl":"https://doi.org/10.4018/jitr.299926","url":null,"abstract":"Based on iceberg theory and the questionnaire of competency’s elements, hierarchical index system of evaluation of teachers' innovation and entrepreneurship competency in universities is established. Through researches, we think that analytic hierarchy process(AHP) is a more scientific and reasonable evaluation method whose rationality is checked by satisfactory consistency while the evaluation model of artificial neutral network doesn’t consider weighting. If the samples are more than 30, the evaluation of neural network model of teachers' innovation and entrepreneurship competency can achieve the accurate results and satisfactory requirements. Since the method of artificial neutral network has advantages of strong operability, simple rules and minor errors, it can greatly reduce the workload because it not only eliminates human subjectivity of evaluation and greatly simplifies the process of evaluation, but also improves working efficiency and provides a new way of thinking for evaluation of the teachers' innovation and entrepreneurship competency in universities","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114817170","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}
The novel Corona virus SARS-CoV-2 has started with strange new pneumonia of unknown cause in Wuhan city, Hubei province of China. On March 11, 2020, the World Health Organization declared the COVID-19 outbreak as a pandemic. Due to this pandemic situation, the countries all over the world suffered from economic and psychological stress. To analyze the growth of this pandemic, this paper proposes a supervised LSTM model and its variants to predict the infectious cases in India using a publicly available dataset from John Hopkins University. Experimentation has been carried out using various models and window hyper-parameters to predict the infectious rate ahead of a week, 2 weeks, 3 weeks and a month. The prediction results infer that, every individual in India has to be safe at home and to follow the regulations provided by ICMR and the Indian Government to control and prevent others from this complicated epidemic.
{"title":"Day-Level Forecasting of COVID-19 Transmission in India Using Variants of Supervised LSTM Models: Modeling and Recommendations","authors":"E. Ramanujam, C. Santhiya, S. Padmavathi","doi":"10.4018/jitr.299376","DOIUrl":"https://doi.org/10.4018/jitr.299376","url":null,"abstract":"The novel Corona virus SARS-CoV-2 has started with strange new pneumonia of unknown cause in Wuhan city, Hubei province of China. On March 11, 2020, the World Health Organization declared the COVID-19 outbreak as a pandemic. Due to this pandemic situation, the countries all over the world suffered from economic and psychological stress. To analyze the growth of this pandemic, this paper proposes a supervised LSTM model and its variants to predict the infectious cases in India using a publicly available dataset from John Hopkins University. Experimentation has been carried out using various models and window hyper-parameters to predict the infectious rate ahead of a week, 2 weeks, 3 weeks and a month. The prediction results infer that, every individual in India has to be safe at home and to follow the regulations provided by ICMR and the Indian Government to control and prevent others from this complicated epidemic.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121440600","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}
It is necessary to systematically review the literature since the information and communication technology (ICT) assisted flipped pedagogical approach in English education has been increasingly popular. By way of visualization through CiteSpace and other qualitative research methods, we arrived at the conclusion that most of the studies support the ICT-assisted flipped pedagogical approach in EFL education although there are still some different findings. The flipped classroom pedagogy in EFL education may bring many advantages to students, teachers, and researchers. Disadvantages of the ICT-assisted flipped pedagogical approach include difficulty in supervising students’ learning activities, organization of in-class academic activities, cautious attitudes, and some negative learning outcomes. We also explored the number of recent publications and citations, co-citation clusters of the cited literature, citation counts, and rankings by bursts, centrality, and sigma. Future research may focus on the interdisciplinary research into the ICT-assisted flipped pedagogical approach.
{"title":"Visualizing the ICT-Assisted Flipped Pedagogical Approach in EFL Education","authors":"Min Wang, Zhonggen Yu","doi":"10.4018/jitr.298328","DOIUrl":"https://doi.org/10.4018/jitr.298328","url":null,"abstract":"It is necessary to systematically review the literature since the information and communication technology (ICT) assisted flipped pedagogical approach in English education has been increasingly popular. By way of visualization through CiteSpace and other qualitative research methods, we arrived at the conclusion that most of the studies support the ICT-assisted flipped pedagogical approach in EFL education although there are still some different findings. The flipped classroom pedagogy in EFL education may bring many advantages to students, teachers, and researchers. Disadvantages of the ICT-assisted flipped pedagogical approach include difficulty in supervising students’ learning activities, organization of in-class academic activities, cautious attitudes, and some negative learning outcomes. We also explored the number of recent publications and citations, co-citation clusters of the cited literature, citation counts, and rankings by bursts, centrality, and sigma. Future research may focus on the interdisciplinary research into the ICT-assisted flipped pedagogical approach.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132970991","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}
The present papers aims at analyzing the actions of the publishing industry in connection with the transposition of Latin American and Caribbean literature written by women from printed to digital format. It presents some results obtained from searches and it detects and analyzes the strategies implemented by the main commercial platforms, digital libraries and bookstores in transposing literary works by women. Likewise, it describes the mechanisms negatively impacting on their representation in the general catalog of Latin American and Caribbean literature. The irruption of works in the public domain and best-selling works by female writers from the region is also discussed.
{"title":"Gender and Catalog: How Is Latin American Literature Written by Women Transposed Into Digital Formats?","authors":"Adrián R. Vila, Gloria Alejandra Lynch","doi":"10.4018/jitr.299379","DOIUrl":"https://doi.org/10.4018/jitr.299379","url":null,"abstract":"The present papers aims at analyzing the actions of the publishing industry in connection with the transposition of Latin American and Caribbean literature written by women from printed to digital format. It presents some results obtained from searches and it detects and analyzes the strategies implemented by the main commercial platforms, digital libraries and bookstores in transposing literary works by women. Likewise, it describes the mechanisms negatively impacting on their representation in the general catalog of Latin American and Caribbean literature. The irruption of works in the public domain and best-selling works by female writers from the region is also discussed.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134325222","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}
X. He, Siqi Li, X. He, Wenqiang Wang, Xiang Zhang, Bin Wang
Credit scoring, aiming to distinguish potential loan defaulter, has played an important role in financial industry. To further improve the accuracy and efficiency of classification, this paper develops an ensemble model combined extreme gradient boosting (XGBoost) and deep neural network (DNN). In the method, training set is divided into different subsets by bagging sampling at first. Then, each subset is trained as a feature extractor by DNN and the extracted features is taken as the input of XGBoost to construct the base classifier. At last, the prediction result is the average of outputs of different base classifiers. In the training verification process, three credit datasets from the UCI machine learning repository are used to evaluate the proposed model. The outcome shows that this model is superior with a significant improvement.
{"title":"A Novel Ensemble Learning Model Combined XGBoost With Deep Neural Network for Credit Scoring","authors":"X. He, Siqi Li, X. He, Wenqiang Wang, Xiang Zhang, Bin Wang","doi":"10.4018/jitr.299924","DOIUrl":"https://doi.org/10.4018/jitr.299924","url":null,"abstract":"Credit scoring, aiming to distinguish potential loan defaulter, has played an important role in financial industry. To further improve the accuracy and efficiency of classification, this paper develops an ensemble model combined extreme gradient boosting (XGBoost) and deep neural network (DNN). In the method, training set is divided into different subsets by bagging sampling at first. Then, each subset is trained as a feature extractor by DNN and the extracted features is taken as the input of XGBoost to construct the base classifier. At last, the prediction result is the average of outputs of different base classifiers. In the training verification process, three credit datasets from the UCI machine learning repository are used to evaluate the proposed model. The outcome shows that this model is superior with a significant improvement.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124422445","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}
Phishing attack is a deceitful attempt to steal the confidential data such as credit card information, and account passwords. In this paper, Phish-Shelter, a novel anti-phishing browser is developed, which analyzes the URL and the content of phishing page. Phish-Shelter is based on combined supervised machine learning model.Phish-Shelter browser uses two novel feature set, which are used to determine the web page identity. The proposed feature sets include eight features to evaluate the obfuscation-based rule, and eight features to identify search engine. Further, we have taken eleven features which are used to discover contents, and blacklist based rule. Phish-Shelter exploited matching identity features, which determines the degree of similarity of a URL with the blacklisted URLs. Proposed features are independent from third-party services such as web browser history or search engines result. The experimental results indicate that, there is a significant improvement in detection accuracy using proposed features over traditional features.
{"title":"Phish-Shelter: A Novel Anti-Phishing Browser Using Fused Machine Learning","authors":"Rizwan Ur Rahman, Lokesh Yadav, D. Tomar","doi":"10.4018/jitr.2022010104","DOIUrl":"https://doi.org/10.4018/jitr.2022010104","url":null,"abstract":"Phishing attack is a deceitful attempt to steal the confidential data such as credit card information, and account passwords. In this paper, Phish-Shelter, a novel anti-phishing browser is developed, which analyzes the URL and the content of phishing page. Phish-Shelter is based on combined supervised machine learning model.Phish-Shelter browser uses two novel feature set, which are used to determine the web page identity. The proposed feature sets include eight features to evaluate the obfuscation-based rule, and eight features to identify search engine. Further, we have taken eleven features which are used to discover contents, and blacklist based rule. Phish-Shelter exploited matching identity features, which determines the degree of similarity of a URL with the blacklisted URLs. Proposed features are independent from third-party services such as web browser history or search engines result. The experimental results indicate that, there is a significant improvement in detection accuracy using proposed features over traditional features.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"121 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123178607","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：