Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.134581
Khaled Bedjou, Faical Azouaou
{"title":"Detection of terrorism's apologies on Twitter using a new bi-lingual dataset","authors":"Khaled Bedjou, Faical Azouaou","doi":"10.1504/ijdmmm.2023.134581","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.134581","url":null,"abstract":"","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135263538","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 : 2023-01-01DOI: 10.1504/ijdmmm.2023.134598
Mohammad Khanbabaei, Pantea Parsi, Najmeh Farhadi
{"title":"Using data mining to integrate recency-frequency-monetary value analysis and credit scoring methods for bank customer behaviour analysis","authors":"Mohammad Khanbabaei, Pantea Parsi, Najmeh Farhadi","doi":"10.1504/ijdmmm.2023.134598","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.134598","url":null,"abstract":"","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135263546","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 : 2022-09-28DOI: 10.46610/jodmm.2022.v07i03.001
Jyoti Upadhyay, Farhat Anjum, Chetna Sahu
Predicting student success in advance can help educational institutions enhance their teaching quality. This research offers insight into predicting student success not only based on academic information but also on their social structure and living area. The goal of this study is to predict students' grades using machine learning based models such as Decision Tree, Linear Regressor, and Random Forest Regressor and to select the best model among these three.
{"title":"Machine Learning-Based Computational Optimization of Performance Prediction Model","authors":"Jyoti Upadhyay, Farhat Anjum, Chetna Sahu","doi":"10.46610/jodmm.2022.v07i03.001","DOIUrl":"https://doi.org/10.46610/jodmm.2022.v07i03.001","url":null,"abstract":"Predicting student success in advance can help educational institutions enhance their teaching quality. This research offers insight into predicting student success not only based on academic information but also on their social structure and living area. The goal of this study is to predict students' grades using machine learning based models such as Decision Tree, Linear Regressor, and Random Forest Regressor and to select the best model among these three.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"33 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77742405","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 : 2022-08-09DOI: 10.46610/jodmm.2022.v07i02.005
S. V, Rohit J Kashyap, R. Oommen, D. ., Bhoomika ., R. Swathi
The given system includes a vast dataset of India's states, but the previous system, only single state was selected. All the farmers will get a better knowledge of the crops to cultivate by using a pictorial depiction. Machine learning features give a detailed structure with the information and it gives the predictions. The main problems like knowing about the crop prediction, rotation techniques, utilization of water, need for fertilizer and safety will be taken care of. Due to varying climatic changes of the surrounding the need to have a proficient techniques are required for development of crops and to help the farmers in their knowledge of production and management features. The project gives the proper results for advanced farming techniques by choosing the land for farming, which can help the farmers to gain huge knowledge about this.
{"title":"Efficient Harvest Prediction in Agriculture using Machine Learning Techniques","authors":"S. V, Rohit J Kashyap, R. Oommen, D. ., Bhoomika ., R. Swathi","doi":"10.46610/jodmm.2022.v07i02.005","DOIUrl":"https://doi.org/10.46610/jodmm.2022.v07i02.005","url":null,"abstract":"The given system includes a vast dataset of India's states, but the previous system, only single state was selected. All the farmers will get a better knowledge of the crops to cultivate by using a pictorial depiction. Machine learning features give a detailed structure with the information and it gives the predictions. The main problems like knowing about the crop prediction, rotation techniques, utilization of water, need for fertilizer and safety will be taken care of. Due to varying climatic changes of the surrounding the need to have a proficient techniques are required for development of crops and to help the farmers in their knowledge of production and management features. The project gives the proper results for advanced farming techniques by choosing the land for farming, which can help the farmers to gain huge knowledge about this.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"14 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82280772","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 : 2022-08-08DOI: 10.46610/jodmm.2022.v07i02.006
S. M, Manoj B R, Neola Sendril Dias, N. Pinto, Padma Prasad H M
Customer Segmentation is the technique of separating customers into different clusters based on their specific characteristics. Segmenting customers is very essential in every business sector because each individual is different from one another and has distinct interests. But with the help of machine learning techniques, the data can be sorted to find the target group by applying algorithms to the dataset. Based on Recency, frequency and monetary (RFM) value customers purchasing behavior is segmented and the scope of this project is to divide customers based on different groups like loyal, new and churned customers and this is done by RFM table which is used to analyze customer value and K means algorithm is used to cluster the data and to determine the optimal clusters, elbow method is used. The obtained data is then used for further analysis by the organizations to improve the quality of the product, services offered to the customers and develop their relation which can help to improve sales and plan marketing strategy. Every person is different from one another and we don’t know what he/she buys or what their likes are but, with the help of machine learning technique one can sort out the data and can find the target group by applying several algorithms to the dataset.
{"title":"Segmentation of Mall Customers Using RFM Analysis and K-Means Algorithm","authors":"S. M, Manoj B R, Neola Sendril Dias, N. Pinto, Padma Prasad H M","doi":"10.46610/jodmm.2022.v07i02.006","DOIUrl":"https://doi.org/10.46610/jodmm.2022.v07i02.006","url":null,"abstract":"Customer Segmentation is the technique of separating customers into different clusters based on their specific characteristics. Segmenting customers is very essential in every business sector because each individual is different from one another and has distinct interests. But with the help of machine learning techniques, the data can be sorted to find the target group by applying algorithms to the dataset. Based on Recency, frequency and monetary (RFM) value customers purchasing behavior is segmented and the scope of this project is to divide customers based on different groups like loyal, new and churned customers and this is done by RFM table which is used to analyze customer value and K means algorithm is used to cluster the data and to determine the optimal clusters, elbow method is used. The obtained data is then used for further analysis by the organizations to improve the quality of the product, services offered to the customers and develop their relation which can help to improve sales and plan marketing strategy. Every person is different from one another and we don’t know what he/she buys or what their likes are but, with the help of machine learning technique one can sort out the data and can find the target group by applying several algorithms to the dataset.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"18 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84495060","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 : 2022-08-08DOI: 10.46610/jodmm.2022.v07i02.002
Suchetha N V, S. ., Susheel C. Nagur, V. B S, Varun S Hiremat
Stroke is one of the major causes of mortality all over the world. Stroke is caused when the blood flow to the brain is obstructed. The poor blood flow causes death of brain cells and eventually, it may result in death of the person. In this work, three different machine learning algorithms are being used for the prediction of stroke risk, Decision Tree, K Nearest Neighbors and Random Forest. Among these, Random Forest model provides better accuracy of 94.1%. As Compared to traditional methods, using machine learning for the prediction of stroke is convenient and also economical.
{"title":"Performance Analysis of Stroke Prediction using Robust Machine Learning Algorithms","authors":"Suchetha N V, S. ., Susheel C. Nagur, V. B S, Varun S Hiremat","doi":"10.46610/jodmm.2022.v07i02.002","DOIUrl":"https://doi.org/10.46610/jodmm.2022.v07i02.002","url":null,"abstract":"Stroke is one of the major causes of mortality all over the world. Stroke is caused when the blood flow to the brain is obstructed. The poor blood flow causes death of brain cells and eventually, it may result in death of the person. In this work, three different machine learning algorithms are being used for the prediction of stroke risk, Decision Tree, K Nearest Neighbors and Random Forest. Among these, Random Forest model provides better accuracy of 94.1%. As Compared to traditional methods, using machine learning for the prediction of stroke is convenient and also economical.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"108 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85653766","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 : 2022-02-28DOI: 10.46610/jodmm.2022.v07i01.003
Kaaviasudhan V S, Saran Nithish T S, K. S, N. Y. Devi
Technology creates the generation gap by how well older people can learn and use new technology. Each generation have different values and opinions. Due to innovation develop its leads to the generation gap. A difference in the attitude of people from different generations leads to lack of understanding. And also, generation gap is also referred to as difference in the point of view between young and old generations specially between parents and children.
{"title":"A Study on Technology Causes a Gap Between Human Generation","authors":"Kaaviasudhan V S, Saran Nithish T S, K. S, N. Y. Devi","doi":"10.46610/jodmm.2022.v07i01.003","DOIUrl":"https://doi.org/10.46610/jodmm.2022.v07i01.003","url":null,"abstract":"Technology creates the generation gap by how well older people can learn and use new technology. Each generation have different values and opinions. Due to innovation develop its leads to the generation gap. A difference in the attitude of people from different generations leads to lack of understanding. And also, generation gap is also referred to as difference in the point of view between young and old generations specially between parents and children.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"222 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74461001","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 : 2022-01-01DOI: 10.1504/ijdmmm.2022.10046102
Nuhu Yusuf, N. Samsudin, Norfaradilla Wahid, A. Mustapha, Nazri M. Nawi, Mohd Amin Mohd Yunus
{"title":"Arabic text semantic-based query expansion","authors":"Nuhu Yusuf, N. Samsudin, Norfaradilla Wahid, A. Mustapha, Nazri M. Nawi, Mohd Amin Mohd Yunus","doi":"10.1504/ijdmmm.2022.10046102","DOIUrl":"https://doi.org/10.1504/ijdmmm.2022.10046102","url":null,"abstract":"","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"2 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75011423","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 : 2022-01-01DOI: 10.1504/ijdmmm.2022.125265
Paúl Cumba Armijos, Diego Riofrío Luzcando, Verónica Rodríguez Arboleda, Joe Luis Carrión Jumbo
{"title":"Detecting cyberbullying in Spanish texts through deep learning techniques","authors":"Paúl Cumba Armijos, Diego Riofrío Luzcando, Verónica Rodríguez Arboleda, Joe Luis Carrión Jumbo","doi":"10.1504/ijdmmm.2022.125265","DOIUrl":"https://doi.org/10.1504/ijdmmm.2022.125265","url":null,"abstract":"","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"79 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76622276","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 : 2021-11-30DOI: 10.46610/jodmm.2021.v06i03.004
Vijeeta Patil, Shanta Kallur, Vani A. Hiremani
Face recognizable proof has drawn in numerous scientists because of its novel benefit, for example, non-contact measure for include obtaining. Varieties in brightening, posture and appearance are significant difficulties of face acknowledgment particularly when pictures are taken as dim scale. To mitigate these difficulties partially many exploration works have been completed by considering shading pictures and they have yielded better face acknowledgment rate. A strategy for perceiving face utilizing shading nearby surface highlights is depicted. Test results show that Face ID approaches utilizing shading neighborhood surface highlights astonishingly yield preferred acknowledgment rates over Face acknowledgment approaches utilizing just shading or surface data. Especially, contrasted and grayscale surface highlights, the proposed shading neighborhood surface highlights can give great coordinating with rates to confront pictures taken under extreme varieties in enlightenment and furthermore for low goal face pictures. The other biometric framework utilizes palmprint as quality for the recognizable proof and validation of people. The principal point is to extract Haralick highlights and utilization of probabilistic neural organizations for confirmation utilizing palmprint biometric quality. PolyUdatabase tests are taken from around 200 clients every client's 2 examples are gained. This palm print biometric recognizes the phony (fake) palmprint made of POP (Plaster of paris) and separates among living and non-living dependent on the entropy highlight. Test results portray that the eleven Haralick feature values are acquired in execution stage and productive precision is accomplished.
{"title":"Multimodal Fusion of Face and Palm Using Local Color Binary Patterns and Haralick Features","authors":"Vijeeta Patil, Shanta Kallur, Vani A. Hiremani","doi":"10.46610/jodmm.2021.v06i03.004","DOIUrl":"https://doi.org/10.46610/jodmm.2021.v06i03.004","url":null,"abstract":"Face recognizable proof has drawn in numerous scientists because of its novel benefit, for example, non-contact measure for include obtaining. Varieties in brightening, posture and appearance are significant difficulties of face acknowledgment particularly when pictures are taken as dim scale. To mitigate these difficulties partially many exploration works have been completed by considering shading pictures and they have yielded better face acknowledgment rate. A strategy for perceiving face utilizing shading nearby surface highlights is depicted. Test results show that Face ID approaches utilizing shading neighborhood surface highlights astonishingly yield preferred acknowledgment rates over Face acknowledgment approaches utilizing just shading or surface data. Especially, contrasted and grayscale surface highlights, the proposed shading neighborhood surface highlights can give great coordinating with rates to confront pictures taken under extreme varieties in enlightenment and furthermore for low goal face pictures. The other biometric framework utilizes palmprint as quality for the recognizable proof and validation of people. The principal point is to extract Haralick highlights and utilization of probabilistic neural organizations for confirmation utilizing palmprint biometric quality. PolyUdatabase tests are taken from around 200 clients every client's 2 examples are gained. This palm print biometric recognizes the phony (fake) palmprint made of POP (Plaster of paris) and separates among living and non-living dependent on the entropy highlight. Test results portray that the eleven Haralick feature values are acquired in execution stage and productive precision is accomplished.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"110 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86235050","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：