Pub Date : 2024-01-01DOI: 10.3844/jcssp.2024.10.32
Thomas Nagunwa
: Attackers are increasingly using Name Server IP Flux Networks (NSIFNs) to run the domain name services of their phishing websites in order to extend the duration of their phishing operations. These networks host a name server that manages the Domain Name System (DNS) records of the websites on a network of compromised machines with frequently changing IP addresses. As a result, blacklisting the machines has less impact on stopping the services, lengthening their lifespan and that of the websites they support. High detection delays and the use of fewer, lesser varied detection features limit the proposed solutions for identifying the websites hosted in these networks, making them more susceptible to detection evasions. This study suggests a novel set of highly diverse features based on DNS, network, and host behaviors for fast and highly accurate detection of phishing websites hosted in NSIFNs using a Machine Learning (ML) approach. Using a variety of traditional and deep learning ML algorithms, the prediction performance of our features was assessed in the context of binary and multi-class classification tasks. Our approach achieved optimal accuracy rates of 98.59% and 90.41% for the binary and multi-class classification tasks, respectively. Our approach is a crucial step toward monitoring NSIFN components to mitigate phishing attacks efficiently.
:攻击者越来越多地使用名称服务器 IP 流量网络（NSIFN）来运行其钓鱼网站的域名服务，以延长其钓鱼行动的持续时间。这些网络托管一个名称服务器，该服务器在IP地址经常变化的受攻击机器网络上管理网站的域名系统（DNS）记录。因此，将这些机器列入黑名单对停止服务的影响较小，从而延长了它们及其所支持网站的寿命。高检测延迟和使用较少、变化较少的检测功能限制了所提出的识别这些网络中托管的网站的解决方案，使其更容易受到检测规避的影响。本研究提出了一套基于 DNS、网络和主机行为的高度多样化的新特征，可使用机器学习 (ML) 方法快速、高度准确地检测 NSIFN 中托管的钓鱼网站。利用各种传统和深度学习 ML 算法，我们在二元和多类分类任务中评估了特征的预测性能。在二元分类和多类分类任务中，我们的方法分别实现了 98.59% 和 90.41% 的最佳准确率。我们的方法为监控 NSIFN 组件以有效缓解网络钓鱼攻击迈出了关键一步。
{"title":"Detection of Phishing Websites Hosted in Name Server Flux Networks Using Machine Learning","authors":"Thomas Nagunwa","doi":"10.3844/jcssp.2024.10.32","DOIUrl":"https://doi.org/10.3844/jcssp.2024.10.32","url":null,"abstract":": Attackers are increasingly using Name Server IP Flux Networks (NSIFNs) to run the domain name services of their phishing websites in order to extend the duration of their phishing operations. These networks host a name server that manages the Domain Name System (DNS) records of the websites on a network of compromised machines with frequently changing IP addresses. As a result, blacklisting the machines has less impact on stopping the services, lengthening their lifespan and that of the websites they support. High detection delays and the use of fewer, lesser varied detection features limit the proposed solutions for identifying the websites hosted in these networks, making them more susceptible to detection evasions. This study suggests a novel set of highly diverse features based on DNS, network, and host behaviors for fast and highly accurate detection of phishing websites hosted in NSIFNs using a Machine Learning (ML) approach. Using a variety of traditional and deep learning ML algorithms, the prediction performance of our features was assessed in the context of binary and multi-class classification tasks. Our approach achieved optimal accuracy rates of 98.59% and 90.41% for the binary and multi-class classification tasks, respectively. Our approach is a crucial step toward monitoring NSIFN components to mitigate phishing attacks efficiently.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"85 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139125134","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 : 2024-01-01DOI: 10.3844/jcssp.2024.88.95
Kayal Padmanandam, Kvn Sunitha, Behafarid Mohammad Jafari, Ali Jafari, Mengyuan Zhao, Nikitha Pitla
{"title":"Customized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworking","authors":"Kayal Padmanandam, Kvn Sunitha, Behafarid Mohammad Jafari, Ali Jafari, Mengyuan Zhao, Nikitha Pitla","doi":"10.3844/jcssp.2024.88.95","DOIUrl":"https://doi.org/10.3844/jcssp.2024.88.95","url":null,"abstract":"","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139129033","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 : 2023-12-01DOI: 10.3844/jcssp.2023.1450.1504
Khanittha Klangburam, Charuay Savithi
{"title":"Wi-Fi Network Quality Assessment Towards a Smart University: A Case Study of Mahasarakham University","authors":"Khanittha Klangburam, Charuay Savithi","doi":"10.3844/jcssp.2023.1450.1504","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1450.1504","url":null,"abstract":"","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":" 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138616748","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 : 2023-12-01DOI: 10.3844/jcssp.2023.1549.1560
Hani M. Al-Mimi, Nesreen A. Hamad, Mosleh M. Abualhaj, S. Al-Khatib, Mohammad O. Hiari
: Cybercriminals continuously devise new and more sophisticated ways to attack their targets’ security and cyberattacks are on the rise. One of the earliest and most vulnerable network services is the Domain Name System (DNS), which has had several security issues that have been repeatedly exploited over time. Building a strong Intrusion Detection System (IDS) that guards against unwanted access to network resources is essential to identify DNS attacks in the network and safeguard data. Recently, a number of interesting approaches have been developed as a cure-all for intrusion detection, but constructing a safe DNS system remains difficult because attackers frequently alter their tactics to move around the system’s security measures. In this study, we provide a self-learning model that detects the new attacks on DNS using machine learning classifiers. Support Vector Machine (SVM), K-nearest neighbor, Naive Bayes, and Decision Tree are used in the proposed model to classify data as intrusive or normal. The UNSW_NB15 dataset is used to assess the model performance. Data are pre-processed to eliminate irrelevant attributes from the dataset given that the dimensions of the data affect the success of an IDS. Empirical findings show that SVM and Decision Tree have the best performance for all the classifiers, with an accuracy rate of 99.99%. The performance of Naive Bayes is 99.89% for all attack types, which is the lowest of all the classifiers.
{"title":"Improved Intrusion Detection System to Alleviate Attacks on DNS Service","authors":"Hani M. Al-Mimi, Nesreen A. Hamad, Mosleh M. Abualhaj, S. Al-Khatib, Mohammad O. Hiari","doi":"10.3844/jcssp.2023.1549.1560","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1549.1560","url":null,"abstract":": Cybercriminals continuously devise new and more sophisticated ways to attack their targets’ security and cyberattacks are on the rise. One of the earliest and most vulnerable network services is the Domain Name System (DNS), which has had several security issues that have been repeatedly exploited over time. Building a strong Intrusion Detection System (IDS) that guards against unwanted access to network resources is essential to identify DNS attacks in the network and safeguard data. Recently, a number of interesting approaches have been developed as a cure-all for intrusion detection, but constructing a safe DNS system remains difficult because attackers frequently alter their tactics to move around the system’s security measures. In this study, we provide a self-learning model that detects the new attacks on DNS using machine learning classifiers. Support Vector Machine (SVM), K-nearest neighbor, Naive Bayes, and Decision Tree are used in the proposed model to classify data as intrusive or normal. The UNSW_NB15 dataset is used to assess the model performance. Data are pre-processed to eliminate irrelevant attributes from the dataset given that the dimensions of the data affect the success of an IDS. Empirical findings show that SVM and Decision Tree have the best performance for all the classifiers, with an accuracy rate of 99.99%. The performance of Naive Bayes is 99.89% for all attack types, which is the lowest of all the classifiers.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138619184","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 : 2023-12-01DOI: 10.3844/jcssp.2023.1520.1540
M. Alhlalat, Abdel-Aziz Sharieh, Mohammed Belal Al-Zoubi
: Radiologists employ X-ray images to differentiate various chest diseases. Given the intricate and meticulous nature of this diagnostic procedure, the assistance of automated models becomes imperative in detecting and diagnosing diseases from X-ray images. This research paper proposed a novel approach called Ensemble Convolutional Neural Network for Diagnosing Chest Diseases (ECDCNet), aimed at accurately and efficiently diagnosing fifteen different chest diseases through the analysis of X-ray images of the lungs. The ECDCNet model comprised a stack of five CNNs: ResNet152V2, DenseNet121, Inceptionv3, Vogg19, and Wavelet transform-CNN with various architectures and hyper-parameters to enhance the overall prediction performance. The proposed model applied the image segmentation for the lung's region using the U-Net model to localize and focus on the relevant space and facilitate the identification of specific radiological signs such as nodules, opacities, cavities, and consolidation. Furthermore, the study exploited three ensemble CNN strategies: Average voting, majority voting, and a proposed CNN-ensemble strategy called the Weighted Performance Metrics Ensemble Strategy (WPME) to set the weights of the prediction stage. The proposed WPME strategy used four evaluation measures for assessing the importance of each base CNN in the ensemble model, including precision, recall, F1-score, and accuracy, to enhance the prediction of the ensemble model. The proposed ECDCNet model achieved an accuracy of 95.3, 95.8 and 96.1% in the average voting, the majority voting, and the WPME strategy on a collected dataset of 110804 images for fifteen chest diseases. Further, it achieved an accuracy of 97.9, 98.2 and 98.9% in the average voting, the majority voting, and the WPME strategy on another public dataset of 13150 images for three chest diseases.
{"title":"A Robust Ensemble Convolutional Neural Networks for Diagnosing Chest Diseases","authors":"M. Alhlalat, Abdel-Aziz Sharieh, Mohammed Belal Al-Zoubi","doi":"10.3844/jcssp.2023.1520.1540","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1520.1540","url":null,"abstract":": Radiologists employ X-ray images to differentiate various chest diseases. Given the intricate and meticulous nature of this diagnostic procedure, the assistance of automated models becomes imperative in detecting and diagnosing diseases from X-ray images. This research paper proposed a novel approach called Ensemble Convolutional Neural Network for Diagnosing Chest Diseases (ECDCNet), aimed at accurately and efficiently diagnosing fifteen different chest diseases through the analysis of X-ray images of the lungs. The ECDCNet model comprised a stack of five CNNs: ResNet152V2, DenseNet121, Inceptionv3, Vogg19, and Wavelet transform-CNN with various architectures and hyper-parameters to enhance the overall prediction performance. The proposed model applied the image segmentation for the lung's region using the U-Net model to localize and focus on the relevant space and facilitate the identification of specific radiological signs such as nodules, opacities, cavities, and consolidation. Furthermore, the study exploited three ensemble CNN strategies: Average voting, majority voting, and a proposed CNN-ensemble strategy called the Weighted Performance Metrics Ensemble Strategy (WPME) to set the weights of the prediction stage. The proposed WPME strategy used four evaluation measures for assessing the importance of each base CNN in the ensemble model, including precision, recall, F1-score, and accuracy, to enhance the prediction of the ensemble model. The proposed ECDCNet model achieved an accuracy of 95.3, 95.8 and 96.1% in the average voting, the majority voting, and the WPME strategy on a collected dataset of 110804 images for fifteen chest diseases. Further, it achieved an accuracy of 97.9, 98.2 and 98.9% in the average voting, the majority voting, and the WPME strategy on another public dataset of 13150 images for three chest diseases.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"213 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138621393","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 : 2023-12-01DOI: 10.3844/jcssp.2023.1561.1579
Yohannes Kurniawan, Natasha Liberty, Samuel Caesar, Calvin Winardi, N. Anwar
: Technological advancement is accelerating in this Industry 4.0 era, resulting in numerous changes in human life. As university students or so-called agents of change, we expected to adapt quickly. Metaverse is one of the hotly debated topics these days. Thus, the goal of this research is to look at the metaverse's prospects, implications, and sustainability through the eyes of university students in Indonesia. Purposive sampling was used as the research method. We also designed a metaverse environment simulation room and invited our respondents to come in to experience the world of the metaverse there, followed by filling out the questionnaire. The simulation is held to collect valid data on respondents' perceptions related to the ease of use, usefulness, and intention to use metaverse based on their real simulation experience, not just on their assumptions. The findings indicated that the metaverse's prospects are very decent, but the societies and existing infrastructure are still insufficient to implement the metaverse. Meanwhile, the metaverse's ease of use has a significant impact on the intention to use. As a result, we need to prepare several things carefully during transition and adaptation. Especially in terms of infrastructure readiness and accessibility.
{"title":"Study of Metaverse Prospect, Implications and Sustainability Based on Perception of University Students in Indonesia","authors":"Yohannes Kurniawan, Natasha Liberty, Samuel Caesar, Calvin Winardi, N. Anwar","doi":"10.3844/jcssp.2023.1561.1579","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1561.1579","url":null,"abstract":": Technological advancement is accelerating in this Industry 4.0 era, resulting in numerous changes in human life. As university students or so-called agents of change, we expected to adapt quickly. Metaverse is one of the hotly debated topics these days. Thus, the goal of this research is to look at the metaverse's prospects, implications, and sustainability through the eyes of university students in Indonesia. Purposive sampling was used as the research method. We also designed a metaverse environment simulation room and invited our respondents to come in to experience the world of the metaverse there, followed by filling out the questionnaire. The simulation is held to collect valid data on respondents' perceptions related to the ease of use, usefulness, and intention to use metaverse based on their real simulation experience, not just on their assumptions. The findings indicated that the metaverse's prospects are very decent, but the societies and existing infrastructure are still insufficient to implement the metaverse. Meanwhile, the metaverse's ease of use has a significant impact on the intention to use. As a result, we need to prepare several things carefully during transition and adaptation. Especially in terms of infrastructure readiness and accessibility.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138618381","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 : 2023-12-01DOI: 10.3844/jcssp.2023.1423.1437
Shahad Fadhil Abbas, S. Shaker, F. A. Abdullatif
: Facial recognition systems are becoming more prevalent in our daily lives. Based on artificial intelligence, computers play a very important role in the issue of identifying and tracking. This technology is mostly used for security and law enforcement. In view of the COVID-19 pandemic, government directives have been issued to citizens to wear medical masks in crowded institutions and places, which has caused difficulties in identifying and tracking people who are wearing them. This study organizes and reviews work on facial identification and face tracking. Conventional facial recognition technology is unable to recognize people when they are wearing masks. This study proposes a Masked Face Identification and Tracking (MFIT) model using yolov5, attention mechanism, and FaceMaskNet-21 deep learning architectures. Standard datasets such as "CASIA-WEBFACE, Glint360K, and chokepoint, etc." are discussed and used to evaluate the criteria relevant to face mask detection and tracking. However, numerous difficulties such as "different size of facial when movement, identification with/without mask wear and Tracking in frames or cameras" have been encountered. Additionally, consideration of the system limits, observations, and several use cases are provided. This study aims to implement a facial recognition system capable of masked face identification and tracking using deep learning.
{"title":"Masked Face Identification and Tracking Using Deep Learning: A Review","authors":"Shahad Fadhil Abbas, S. Shaker, F. A. Abdullatif","doi":"10.3844/jcssp.2023.1423.1437","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1423.1437","url":null,"abstract":": Facial recognition systems are becoming more prevalent in our daily lives. Based on artificial intelligence, computers play a very important role in the issue of identifying and tracking. This technology is mostly used for security and law enforcement. In view of the COVID-19 pandemic, government directives have been issued to citizens to wear medical masks in crowded institutions and places, which has caused difficulties in identifying and tracking people who are wearing them. This study organizes and reviews work on facial identification and face tracking. Conventional facial recognition technology is unable to recognize people when they are wearing masks. This study proposes a Masked Face Identification and Tracking (MFIT) model using yolov5, attention mechanism, and FaceMaskNet-21 deep learning architectures. Standard datasets such as \"CASIA-WEBFACE, Glint360K, and chokepoint, etc.\" are discussed and used to evaluate the criteria relevant to face mask detection and tracking. However, numerous difficulties such as \"different size of facial when movement, identification with/without mask wear and Tracking in frames or cameras\" have been encountered. Additionally, consideration of the system limits, observations, and several use cases are provided. This study aims to implement a facial recognition system capable of masked face identification and tracking using deep learning.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"358 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138625896","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 : 2023-12-01DOI: 10.3844/jcssp.2023.1438.1449
Kinjal Vijaybhai Deputy, K. Passi, Chakresh Kumar Jain
: Agriculture plays a crucial role in the economic development of many countries and sustains the global population despite facing various challenges like climate change, pollinator decline, and plant diseases. These threats to food security highlight the need for innovative solutions to prevent crop loss. Leveraging smartphone technology for automated image recognition-based disease diagnosis has emerged as a promising approach, thanks to their computing power and high-resolution cameras. To address this issue, we have focused on deep learning-based image detection techniques to identify plant diseases using the "PlantVillage" dataset. Several deep learning architectures, including AlexNet, GoogleNet, ResNet50, and InceptionV3, were employed and trained using two approaches: 'Training from scratch' and 'transfer learning’. The results of the analysis reveal GoogLeNet architecture achieved the highest accuracy of 0.999 for color images and 0.996 for segmented images, whereas InceptionV3 trained from scratch gave the highest accuracy of 0.994 for grayscale images with a train-test ratio of 90:10. All the models trained from scratch achieved the maximum F1-score of 1.0 for color and segmented images whereas for grayscale images, GoogleNet and InceptionV3 achieved the highest F1-score of 0.999 with train-test ratio 90:10. These findings indicate the potential of deep learning methods in detecting and diagnosing plant diseases, which can significantly enhance the efficiency and accuracy of disease diagnosis processes in agriculture. Further research and improvements in image recognition techniques can lead to more robust and effective solutions for securing global food production.
{"title":"Crop Disease Detection Using Deep Learning Techniques on Images","authors":"Kinjal Vijaybhai Deputy, K. Passi, Chakresh Kumar Jain","doi":"10.3844/jcssp.2023.1438.1449","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1438.1449","url":null,"abstract":": Agriculture plays a crucial role in the economic development of many countries and sustains the global population despite facing various challenges like climate change, pollinator decline, and plant diseases. These threats to food security highlight the need for innovative solutions to prevent crop loss. Leveraging smartphone technology for automated image recognition-based disease diagnosis has emerged as a promising approach, thanks to their computing power and high-resolution cameras. To address this issue, we have focused on deep learning-based image detection techniques to identify plant diseases using the \"PlantVillage\" dataset. Several deep learning architectures, including AlexNet, GoogleNet, ResNet50, and InceptionV3, were employed and trained using two approaches: 'Training from scratch' and 'transfer learning’. The results of the analysis reveal GoogLeNet architecture achieved the highest accuracy of 0.999 for color images and 0.996 for segmented images, whereas InceptionV3 trained from scratch gave the highest accuracy of 0.994 for grayscale images with a train-test ratio of 90:10. All the models trained from scratch achieved the maximum F1-score of 1.0 for color and segmented images whereas for grayscale images, GoogleNet and InceptionV3 achieved the highest F1-score of 0.999 with train-test ratio 90:10. These findings indicate the potential of deep learning methods in detecting and diagnosing plant diseases, which can significantly enhance the efficiency and accuracy of disease diagnosis processes in agriculture. Further research and improvements in image recognition techniques can lead to more robust and effective solutions for securing global food production.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138619973","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 : 2023-12-01DOI: 10.3844/jcssp.2023.1410.1422
R. Khan, Ankan Shahriar Islam, Md. Ahosan Hossain Sijan, M. Syeed, Mohammad Faisal Uddin, Md. Shakhawat Hossain
{"title":"Augmented Scope-Based E-Commerce Business Model for Emerging Markets","authors":"R. Khan, Ankan Shahriar Islam, Md. Ahosan Hossain Sijan, M. Syeed, Mohammad Faisal Uddin, Md. Shakhawat Hossain","doi":"10.3844/jcssp.2023.1410.1422","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1410.1422","url":null,"abstract":"","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138626331","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}
: Music recommendation systems can significantly improve the listening and search experiences of a music library or music application. There is simply too much music on the market for a user to navigate tens of millions of songs effectively. Because of the high demand for excellent music recommendations, the field of Music Recommendation Systems (MRS) is rapidly expanding. The main motivation for developing the rating-based recommendation system was to extract relevant information from user reviews of instrumental music. In this study, we suggest an NSGA-II-based music recommendation system based on user interest, popularity of an instrument, and total cost. Our aim is to maximize user interest and popularity while minimizing the costs. We also compared our method to the baseline algorithm and discovered that it outperforms the baseline approaches. We used real-world metrics like precession, recall, and F1-score to compare our method to the baseline approaches.
{"title":"Folk Music Recommendation Using NSGA-II Optimization Algorithm","authors":"Joyanta Sarkar, Anil Rai, Kayala Kiran Kumar, Venkata Nagaraju Thatha, Sowmiya Manisekaran, Sayantan Mandal, Joyanta Sarkar, Sudeshna Das","doi":"10.3844/jcssp.2023.1541.1548","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1541.1548","url":null,"abstract":": Music recommendation systems can significantly improve the listening and search experiences of a music library or music application. There is simply too much music on the market for a user to navigate tens of millions of songs effectively. Because of the high demand for excellent music recommendations, the field of Music Recommendation Systems (MRS) is rapidly expanding. The main motivation for developing the rating-based recommendation system was to extract relevant information from user reviews of instrumental music. In this study, we suggest an NSGA-II-based music recommendation system based on user interest, popularity of an instrument, and total cost. Our aim is to maximize user interest and popularity while minimizing the costs. We also compared our method to the baseline algorithm and discovered that it outperforms the baseline approaches. We used real-world metrics like precession, recall, and F1-score to compare our method to the baseline approaches.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"47 S224","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138622909","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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