Social Media addiction is becoming a Commonplace. A user gets deeply involve with an application or website that he share it thoughts , ideas and viewpoints on these platforms more comfortably then communicating with an people face to face . By text mining these viewpoint and thoughts from social Media psychological profiling can b done to improve user experience & personality assessment .The aim is to exhibits the construct validation framework for Personality Analysis using machine learning approaches like Term Frequency-Inverse Document Frequency (TF-IDF) SVM. We put forward the methodology by which user’s personality can be analyzed using publicly available information on user’s personal Twitter account using the Myers_Briggs Type Indicator (MBTI). This study will contribute & help in many ways such as customization of Content displayed and product list , Recruitment and Information Retrieval.
{"title":"Personality Analysis by Tweet Mining","authors":"Hirra Mustafa, Tuba Mansoor, S. Yaqoob, Shahid Mehmood, Md. Mijanur Rahman","doi":"10.56536/jicet.v3i1.60","DOIUrl":"https://doi.org/10.56536/jicet.v3i1.60","url":null,"abstract":"Social Media addiction is becoming a Commonplace. A user gets deeply involve with an application or website that he share it thoughts , ideas and viewpoints on these platforms more comfortably then communicating with an people face to face . By text mining these viewpoint and thoughts from social Media psychological profiling can b done to improve user experience & personality assessment .The aim is to exhibits the construct validation framework for Personality Analysis using machine learning approaches like Term Frequency-Inverse Document Frequency (TF-IDF) SVM. We put forward the methodology by which user’s personality can be analyzed using publicly available information on user’s personal Twitter account using the Myers_Briggs Type Indicator (MBTI). This study will contribute & help in many ways such as customization of Content displayed and product list , Recruitment and Information Retrieval.","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131275311","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}
S. Sobia, Arslan Akram, Tuba Mansoor, Hirra Mustafa
As the COVID-19 virus spreads over the globe, countries all over the world are going to extraordinary measures to combat the disease. To stop it from spreading, it's critical to have a high level of awareness and a well-thought-out COVID-19 recognition approach. By analyzing different methods and image-based detection using chest x-ray images, a technique was proposed in this study that includes preprocessing, texture feature analysis, and support vector machines. X-ray image was augmented to make equal blocks and features were extracted using Curvelet. Finally, extracted features were fed into SVM for classification. Curvelet was based on rotational and slicing texture descriptions which give the most pertinent details for the classification of COVID-19. Results in this experiment showed that the method achieved 97.7 % of accuracy against other methods based on the chest x-ray image.
{"title":"COVID-19 Detection using Curvelet Transformation and Support Vector Machine","authors":"S. Sobia, Arslan Akram, Tuba Mansoor, Hirra Mustafa","doi":"10.56536/jicet.v3i1.55","DOIUrl":"https://doi.org/10.56536/jicet.v3i1.55","url":null,"abstract":"As the COVID-19 virus spreads over the globe, countries all over the world are going to extraordinary measures to combat the disease. To stop it from spreading, it's critical to have a high level of awareness and a well-thought-out COVID-19 recognition approach. By analyzing different methods and image-based detection using chest x-ray images, a technique was proposed in this study that includes preprocessing, texture feature analysis, and support vector machines. X-ray image was augmented to make equal blocks and features were extracted using Curvelet. Finally, extracted features were fed into SVM for classification. Curvelet was based on rotational and slicing texture descriptions which give the most pertinent details for the classification of COVID-19. Results in this experiment showed that the method achieved 97.7 % of accuracy against other methods based on the chest x-ray image.","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124239391","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}
Muhammad Umer, Muhammad Javaid Iqbal, Tuba Mansoor, Muhammad Usman Nasir, Ali Asif, A. Ikram
The development of social networks has altered computer science research. Now, a vast amount of data is available via Twitter, Facebook, emails, and IoT. (Internet of Things). So, storing and analyzing these data has become very difficult for academics. Conventional frameworks have been ineffective in processing massive amounts of data. R is an open-source programming language designed for large-scale data analysis with higher accuracy. Additionally, it offers the chance to implement the R programming language. This essay examines the application of R to classify sizable social network data. The Naive Bayes method is used to categorize massive amounts of Twitter data. The experiment has demonstrated that a sizable portion of data may be adequately classified with positive outcomes utilizing the R framework.
{"title":"Classification of Large Social Twitter Network Data Using R","authors":"Muhammad Umer, Muhammad Javaid Iqbal, Tuba Mansoor, Muhammad Usman Nasir, Ali Asif, A. Ikram","doi":"10.56536/jicet.v3i1.56","DOIUrl":"https://doi.org/10.56536/jicet.v3i1.56","url":null,"abstract":"The development of social networks has altered computer science research. Now, a vast amount of data is available via Twitter, Facebook, emails, and IoT. (Internet of Things). So, storing and analyzing these data has become very difficult for academics. Conventional frameworks have been ineffective in processing massive amounts of data. R is an open-source programming language designed for large-scale data analysis with higher accuracy. \u0000Additionally, it offers the chance to implement the R programming language. This essay examines the application of R to classify sizable social network data. The Naive Bayes method is used to categorize massive amounts of Twitter data. The experiment has demonstrated that a sizable portion of data may be adequately classified with positive outcomes utilizing the R framework.","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126165904","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}
Muhammad Aadil Butt, Sani Danjuma, M.Saad Bin Ilyas, Umair Muneer Butt, Maimoona Shahid, Iqra Tariq
In order to forecast the need for bike-sharing services, this paper suggests a rule-based regression model. Commuters and tourists alike are taking advantage of public bike sharing programs because of the convenience and low carbon footprint they provide. Used information from the UCI Machine Learning Repository. Repeated cross-validation was used to fine-tune the hyper-parameters of five statistical models. Conditional Inference Tree, K-Nearest Neighbor Analysis, Regularized Random Forest, Classification and Regression Trees, and CUBIST. The predictive accuracy of the regression models was measured by calculating the Root Mean Squared Error, R-Squared, Mean Absolute Error, and Coefficient. For both the Seoul Bike and Capital Bikeshare programs, the rule-based model CUBIST was able to account for 95 and 89% of the Variance (R2), respectively. All models built from the two datasets using WEKA v3.8.6, and are used a variable significance analysis to establish which variables were most crucial. The most important factors in determining the hourly demand for bike rentals are the weather and the time of day.
{"title":"Demand Prediction on Bike Sharing Data Using Regression Analysis Approach","authors":"Muhammad Aadil Butt, Sani Danjuma, M.Saad Bin Ilyas, Umair Muneer Butt, Maimoona Shahid, Iqra Tariq","doi":"10.56536/jicet.v3i1.52","DOIUrl":"https://doi.org/10.56536/jicet.v3i1.52","url":null,"abstract":"In order to forecast the need for bike-sharing services, this paper suggests a rule-based regression model. Commuters and tourists alike are taking advantage of public bike sharing programs because of the convenience and low carbon footprint they provide. Used information from the UCI Machine Learning Repository. Repeated cross-validation was used to fine-tune the hyper-parameters of five statistical models. Conditional Inference Tree, K-Nearest Neighbor Analysis, Regularized Random Forest, Classification and Regression Trees, and CUBIST. The predictive accuracy of the regression models was measured by calculating the Root Mean Squared Error, R-Squared, Mean Absolute Error, and Coefficient. For both the Seoul Bike and Capital Bikeshare programs, the rule-based model CUBIST was able to account for 95 and 89% of the Variance (R2), respectively. All models built from the two datasets using WEKA v3.8.6, and are used a variable significance analysis to establish which variables were most crucial. The most important factors in determining the hourly demand for bike rentals are the weather and the time of day.","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127340467","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}
M. Ilyas, A. Ikram, Muhammad Aadil Butt, Iqra Tariq
Abstract— Sales predictions or forecasting can help in analyzing the current and future sales trends of a big mart company. Based on the sales prediction or forecast, a retailer company can plan its production, marketing and promotional activities. Using several machine learning techniques, the obtained data may then be utilized to predict possible sales for retailers. This paper investigates that which machine learning regression algorithm best predicts big marts sales and which technique has the highest correlation coefficient value and the lowest values of mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), and root relative squared error (RRSE). A comparative analysis of various machine learning regression algorithms such as SMO regression, simple linear regression, linear regression, additive regression, multi-layer perceptron, random forest, and M5P will be provided in this paper. After the experiments are completed, a comparison of various cross validations and splitting ratios for training and testing data will be given.
{"title":"Comparative Analysis of Regression Algorithms used to Predict the Sales of Big Marts","authors":"M. Ilyas, A. Ikram, Muhammad Aadil Butt, Iqra Tariq","doi":"10.56536/jicet.v3i1.53","DOIUrl":"https://doi.org/10.56536/jicet.v3i1.53","url":null,"abstract":"Abstract— Sales predictions or forecasting can help in analyzing the current and future sales trends of a big mart company. Based on the sales prediction or forecast, a retailer company can plan its production, marketing and promotional activities. Using several machine learning techniques, the obtained data may then be utilized to predict possible sales for retailers. This paper investigates that which machine learning regression algorithm best predicts big marts sales and which technique has the highest correlation coefficient value and the lowest values of mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), and root relative squared error (RRSE). A comparative analysis of various machine learning regression algorithms such as SMO regression, simple linear regression, linear regression, additive regression, multi-layer perceptron, random forest, and M5P will be provided in this paper. After the experiments are completed, a comparison of various cross validations and splitting ratios for training and testing data will be given.","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280787","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}
Arslan Akram, Aalia Tariq, M. Salman Ali, M. Usman Tariq, Abdulrehman Altaf
The goal of this research is to provide a useful technique for better facial emotion recognition, especially across cultural boundaries. Although people communicate both verbally and nonverbally, face expressions are crucial in determining verbal communication. The previous human-computer interface did not take into account thus much nonverbal communication. We need a system that can recognise and comprehend the intentions and feelings expressed by social and cultural cues. In this article, we present a technique for categorising facial photos into six different categories of expressions. Three phases make up the approach; in the first, we used viola Jones to edit off all but the face from the original image and create new ones. Then a HOG histogram was used to extract gradient characteristics. Last but not least, we used SVM to classify picture characteristics and got encouraging results. Comparing the outcomes of the suggested method to other cutting-edge approaches, they are astounding. With regard to combined cross-cultural datasets, it offers accuracy of 99.97%.
{"title":"Recognizing Facial Expressions Across Cultures Using Gradient Features","authors":"Arslan Akram, Aalia Tariq, M. Salman Ali, M. Usman Tariq, Abdulrehman Altaf","doi":"10.56536/jicet.v3i1.54","DOIUrl":"https://doi.org/10.56536/jicet.v3i1.54","url":null,"abstract":"The goal of this research is to provide a useful technique for better facial emotion recognition, especially across cultural boundaries. Although people communicate both verbally and nonverbally, face expressions are crucial in determining verbal communication. The previous human-computer interface did not take into account thus much nonverbal communication. We need a system that can recognise and comprehend the intentions and feelings expressed by social and cultural cues. In this article, we present a technique for categorising facial photos into six different categories of expressions. Three phases make up the approach; in the first, we used viola Jones to edit off all but the face from the original image and create new ones. Then a HOG histogram was used to extract gradient characteristics. Last but not least, we used SVM to classify picture characteristics and got encouraging results. Comparing the outcomes of the suggested method to other cutting-edge approaches, they are astounding. With regard to combined cross-cultural datasets, it offers accuracy of 99.97%.","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128834658","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}
Multimedia has emerged as one of the top entertainment source due to cheap and uninterrupted availability of high internet speeds. “Movie recommendation system have attracted much research interest within the field of recommendation systems. Two widely used techniques, one is collaborative filtering (CF) and second is content-based (CB). However, the accuracy performance of any hybrid system which utilizes more advantage of both systems to better results. Movie recommendation systems has suffered from different problems, such as “, Sparsity, Grey sheep problem, Cold start problem, Long-tail problem” etc. Basic Issues can be solved if we take the right choice on what kind of movies to ignore, what movies to suggest. The suggestions generated using approaches such as Linear Regression, Decision Trees, and Bayesian Analysis are examined in this study. Movie-Lens-1M and Movie-Lens-10M are the dataset considered. The results of this experiment suggest that Decision Tree and Linear Regression & Random Forest work well as compared to Bayesian Learning.
{"title":"Analysis of Movie Recommendation System Data Sets using machine learning techniques","authors":"","doi":"10.56536/jicet.v2i2.27","DOIUrl":"https://doi.org/10.56536/jicet.v2i2.27","url":null,"abstract":"Multimedia has emerged as one of the top entertainment source due to cheap and uninterrupted availability of high internet speeds. “Movie recommendation system have attracted much research interest within the field of recommendation systems. Two widely used techniques, one is collaborative filtering (CF) and second is content-based (CB). However, the accuracy performance of any hybrid system which utilizes more advantage of both systems to better results. Movie recommendation systems has suffered from different problems, such as “, Sparsity, Grey sheep problem, Cold start problem, Long-tail problem” etc. Basic Issues can be solved if we take the right choice on what kind of movies to ignore, what movies to suggest. The suggestions generated using approaches such as Linear Regression, Decision Trees, and Bayesian Analysis are examined in this study. Movie-Lens-1M and Movie-Lens-10M are the dataset considered. The results of this experiment suggest that Decision Tree and Linear Regression & Random Forest work well as compared to Bayesian Learning.","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125278441","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}
Security is major concern for any system. The scammer continually tries to obtain the user's account information by applying different tricks to perform fraud that cost the banking system and user huge loss. Machine learning based techniques are most extensively being used to avoid this risk. Face recognition based systems are not sufficient especially in banking sectors. Teachable Machine is a webbased tool that makes building machine learning models fast, easy, and accessible to everyone. So in order to stop those fraudulent we should need powerful fraud detection method or system by which detect fraud .We have proposed a only method by using face recognition features along with face recognition systems to boot the security level of the society against the fraudsters. It is using the features of face recognition and face recognition authentication which makes the transaction more secure as compared to the traditional payment.
{"title":"Fraud Detection System Using Facial Recognition Based On Google Teachable Machine for Banking Applications","authors":"Shehla Shoukat, Sheeraz Akram","doi":"10.56536/jicet.v2i2.30","DOIUrl":"https://doi.org/10.56536/jicet.v2i2.30","url":null,"abstract":"Security is major concern for any system. The scammer continually tries to obtain the user's account information by applying different tricks to perform fraud that cost the banking system and user huge loss. Machine learning based techniques are most extensively being used to avoid this risk. Face recognition based systems are not sufficient especially in banking sectors. Teachable Machine is a webbased tool that makes building machine learning models fast, easy, and accessible to everyone. So in order to stop those fraudulent we should need powerful fraud detection method or system by which detect fraud .We have proposed a only method by using face recognition features along with face recognition systems to boot the security level of the society against the fraudsters. It is using the features of face recognition and face recognition authentication which makes the transaction more secure as compared to the traditional payment.","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130736984","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}
Recommendation System (RS) is procedure that recommends comparative things to a purchaser dependent on his/her prior buys or enjoying. Recommendation framework examine a huge sum information of articles and arranges a record of those items which would accomplish the prerequisites of the buyer. Book Recommendation System is being utilized by Amazon, Barnes and Noble, Flipkart, Goodreads, and so on. Amazon is notable for realization and suggestions, which assist clients with finding things they may somehow or another not have found. Recommendation frameworks are impressively utilized for proposed new things to clients and assume a significant part in the disclosure of relevant new things of books. Books are basically appropriate to content based and sifting as they are presently commonly reachable in computerized designs which can permit diverse content mining ways to deal with uncover content associated data. This paper addresses a structure to encourage a substance based proposal framework for books. The book proposal framework should recommend books that are of buyer's advantage. This paper introduced book suggestion framework dependent on consolidated highlights of substance sifting, collective separating. In Online business today, substance given to clients to investigate are overpowering in light of the fact that a normal online business site is around (70%) in excess of an actual store in whole number of clients and things. The created framework is utilized To work with online buy a shopping basket is given to the client. The clients are suggested dependent on going before clients rating utilizing network factorization strategy. Recommendation frameworks are widely utilized for prescribe new things to clients and assume an dispensable part in the revelation of related new things, it can be books, motion pictures or music. A fruitful suggestion framework should gives heterogeneous outcomes and ought not be one-sided towards just the most famous things..
推荐系统(RS)是根据购买者之前购买或喜欢的东西，向其推荐具有可比性的东西的程序。推荐框架检查大量的物品信息，并安排这些物品的记录，以满足买方的先决条件。图书推荐系统被亚马逊、Barnes and Noble、Flipkart、Goodreads等使用。亚马逊以实现和建议而闻名，它帮助客户找到他们可能以某种方式或其他方式没有找到的东西。推荐框架在向客户提出新事物时得到了广泛的应用，在相关图书新事物的披露中起着重要的作用。书籍基本上适合于基于内容的筛选，因为它们目前在计算机化设计中通常是可访问的，这可以允许不同的内容挖掘方法来处理发现的内容相关数据。本文讨论了一个结构，以鼓励基于物质的建议框架的书籍。图书推荐框架应推荐对买方有利的书籍。本文介绍了以实体筛选、集体分离为重点的图书推荐框架。在今天的在线商业中，给客户调查的内容是压倒性的，因为一个正常的在线商业网站在客户和东西的总数上大约(70%)超过了实际的商店。将创建的框架用于在线购买，并将购物篮提供给客户端。利用网络分解策略，根据客户评级的前瞻来建议客户。推荐框架被广泛用于向客户推荐新事物，在相关新事物的揭示中扮演着不可或缺的角色，它可以是书籍，电影或音乐。一个富有成效的建议框架应该给出不同的结果，而不应该只针对最著名的事物。
{"title":"Online Books Recommendation System","authors":"Saba Bashir, Nawazish Naveed","doi":"10.56536/jicet.v2i2.34","DOIUrl":"https://doi.org/10.56536/jicet.v2i2.34","url":null,"abstract":"Recommendation System (RS) is procedure that recommends comparative things to a purchaser dependent on his/her prior buys or enjoying. Recommendation framework examine a huge sum information of articles and arranges a record of those items which would accomplish the prerequisites of the buyer. Book Recommendation System is being utilized by Amazon, Barnes and Noble, Flipkart, Goodreads, and so on. Amazon is notable for realization and suggestions, which assist clients with finding things they may somehow or another not have found. Recommendation frameworks are impressively utilized for proposed new things to clients and assume a significant part in the disclosure of relevant new things of books. Books are basically appropriate to content based and sifting as they are presently commonly reachable in computerized designs which can permit diverse content mining ways to deal with uncover content associated data. This paper addresses a structure to encourage a substance based proposal framework for books. The book proposal framework should recommend books that are of buyer's advantage. This paper introduced book suggestion framework dependent on consolidated highlights of substance sifting, collective separating. In Online business today, substance given to clients to investigate are overpowering in light of the fact that a normal online business site is around (70%) in excess of an actual store in whole number of clients and things. The created framework is utilized To work with online buy a shopping basket is given to the client. The clients are suggested dependent on going before clients rating utilizing network factorization strategy. Recommendation frameworks are widely utilized for prescribe new things to clients and assume an dispensable part in the revelation of related new things, it can be books, motion pictures or music. A fruitful suggestion framework should gives heterogeneous outcomes and ought not be one-sided towards just the most famous things..","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126596159","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}
There is an interdisciplinary field which is known as the image mining, it has special features like machine vision, picture handling, picture recovery, information mining. Al, data sets, and man-made reasoning. Notwithstanding the way that many examinations have been led in every one of these areas, picture mining and arising issues research is as vet in its outset. Information mining strategies, for instance, can't naturally remove valuable data from a lot of information, like pictures. In this theory, we examined the overall method of the examination and the fundamental procedures of picture recovery by introducing the exceptional highlights of picture recovery and bunching utilizing picture mining. Finally, in order to make progress and development in this area, we investigated various image retrieval and elustering systems, as well as knowledge extraction from images. In the current scenarin, image retrieval is the primary requirement task. The popular image retrieval system is content-based image retrieval, which retrieves the target image based on the useful features of the given image. If images are clustered correctly, they can be retrieved relatively quickly. The concepts of (Content-Based Image Retrieval) CBIR, image clustering, and image mining have been combined in this thesis, and a new clustering technique has been introduced to improve the speed of the image retrieval system. To improve computational efficiency, the CBIR system employs clustering and deep learning. To obtain detailed and valuable information, the Fuzzy C-based algorithm and technique for CBIR will be used for color-based image retrieval.
{"title":"Image Retrieval and Clustering Using Image Mining","authors":"","doi":"10.56536/jicet.v2i2.35","DOIUrl":"https://doi.org/10.56536/jicet.v2i2.35","url":null,"abstract":"There is an interdisciplinary field which is known as the image mining, it has special features like machine vision, picture handling, picture recovery, information mining. Al, data sets, and man-made reasoning. Notwithstanding the way that many examinations have been led in every one of these areas, picture mining and arising issues research is as vet in its outset. Information mining strategies, for instance, can't naturally remove valuable data from a lot of information, like pictures. In this theory, we examined the overall method of the examination and the fundamental procedures of picture recovery by introducing the exceptional highlights of picture recovery and bunching utilizing picture mining. Finally, in order to make progress and development in this area, we investigated various image retrieval and elustering systems, as well as knowledge extraction from images. In the current scenarin, image retrieval is the primary requirement task. The popular image retrieval system is content-based image retrieval, which retrieves the target image based on the useful features of the given image. If images are clustered correctly, they can be retrieved relatively quickly. The concepts of (Content-Based Image Retrieval) CBIR, image clustering, and image mining have been combined in this thesis, and a new clustering technique has been introduced to improve the speed of the image retrieval system. To improve computational efficiency, the CBIR system employs clustering and deep learning. To obtain detailed and valuable information, the Fuzzy C-based algorithm and technique for CBIR will be used for color-based image retrieval.","PeriodicalId":145637,"journal":{"name":"Journal of Innovative Computing and Emerging Technologies","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115814594","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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