Pub Date : 2024-06-01DOI: 10.3844/jcssp.2024.610.627
Osman Khairunnisa, J. Zanariah, Ahmad Rabiah
: Much research at present focuses on the ways in which organizations secure their networks and information in the supply chain, ignoring the ways in which organizations construct and understand the core of the cybersecurity risks. Cybersecurity focuses more on defense mechanisms such as anti-virus software, malware protection, firewall and more on securing network and application. More research to understand extremist activity should be conducted by exploring the extremist corpus. It is a good strategy since the web is overloaded with multiple conversations or information since the dependency to the technology has been skyrocketing by people including by the extremist itself. The objectives of this study were to identify the types of rhetoric in cyber extremist’s communication, analyze how the cyber extremists utilized the rhetoric in appealing to the audience, identify the stylistic devices used by the extremists and produce a model of cyber extremists’ rhetorical structure. Therefore, new approaches to study the rhetoric of cyber extremists have been designed. It is the combination of Norreklit’s methodology, Neo-Aristotelian criticism ideologist criticism which were deemed able to pry out the hidden literacy of the extremists. In this study the type of rhetoric that dominated the cyber extremists’ communication was pathos. The category of pathos in the extremists’ postings was more on negative feelings such as sad, anger and hatred. Using the Neo-Aristotle criticism, the stylistic devices used by the extremists were identified such as the metonymy, simile metaphor. The metonymy used was like ‘Jihad’, ‘Mujahidin’, ‘Ansar’ and ‘Muhajirin’. The metonymy ‘Penyembah’ which referred to the opponents’ obsessive materialistic behavior was seen multiplicatively in the extremists’ postings. The simile stylistic devices such as the word ‘Thagut’ and ‘terrorist’ were used by the extremists as direct comparison. As a direct comparison, Meanwhile, the metaphor of death was used consistently by the extremist which can be seen as a scary technique for the opponents. Meanwhile, by using the ideologist criticism, the appearance that was used by most of the extremists was the desire to be seen as a peace ideologist and kind rhetor through the use of cold color such as blue and green with the nature design as the background of the blog. All the phrases were gathered, analyzed and integrated to ascertain the pattern based on the research methodologies to develop a model. A model of cyber extremists' rhetorical structure was developed and established towards protecting the critical infrastructure to ease any parties including the authority, expert and public to identify the possible extremists’ styles as red flags during cyber communication such as social media communication
{"title":"A Model of Cyber Extremists' Rhetorical Structure Towards Protecting Critical Infrastructure","authors":"Osman Khairunnisa, J. Zanariah, Ahmad Rabiah","doi":"10.3844/jcssp.2024.610.627","DOIUrl":"https://doi.org/10.3844/jcssp.2024.610.627","url":null,"abstract":": Much research at present focuses on the ways in which organizations secure their networks and information in the supply chain, ignoring the ways in which organizations construct and understand the core of the cybersecurity risks. Cybersecurity focuses more on defense mechanisms such as anti-virus software, malware protection, firewall and more on securing network and application. More research to understand extremist activity should be conducted by exploring the extremist corpus. It is a good strategy since the web is overloaded with multiple conversations or information since the dependency to the technology has been skyrocketing by people including by the extremist itself. The objectives of this study were to identify the types of rhetoric in cyber extremist’s communication, analyze how the cyber extremists utilized the rhetoric in appealing to the audience, identify the stylistic devices used by the extremists and produce a model of cyber extremists’ rhetorical structure. Therefore, new approaches to study the rhetoric of cyber extremists have been designed. It is the combination of Norreklit’s methodology, Neo-Aristotelian criticism ideologist criticism which were deemed able to pry out the hidden literacy of the extremists. In this study the type of rhetoric that dominated the cyber extremists’ communication was pathos. The category of pathos in the extremists’ postings was more on negative feelings such as sad, anger and hatred. Using the Neo-Aristotle criticism, the stylistic devices used by the extremists were identified such as the metonymy, simile metaphor. The metonymy used was like ‘Jihad’, ‘Mujahidin’, ‘Ansar’ and ‘Muhajirin’. The metonymy ‘Penyembah’ which referred to the opponents’ obsessive materialistic behavior was seen multiplicatively in the extremists’ postings. The simile stylistic devices such as the word ‘Thagut’ and ‘terrorist’ were used by the extremists as direct comparison. As a direct comparison, Meanwhile, the metaphor of death was used consistently by the extremist which can be seen as a scary technique for the opponents. Meanwhile, by using the ideologist criticism, the appearance that was used by most of the extremists was the desire to be seen as a peace ideologist and kind rhetor through the use of cold color such as blue and green with the nature design as the background of the blog. All the phrases were gathered, analyzed and integrated to ascertain the pattern based on the research methodologies to develop a model. A model of cyber extremists' rhetorical structure was developed and established towards protecting the critical infrastructure to ease any parties including the authority, expert and public to identify the possible extremists’ styles as red flags during cyber communication such as social media communication","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231152","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 : 2024-06-01DOI: 10.3844/jcssp.2024.641.648
Abdulrahman Alshehri, H. Alshareef, Samah Alhazmi, M. Almasri, Maha Helal
: Cloud computing has become popular in recent years due to the considerable flexibility it provides in terms of its availability and affordability and the reliability of different software and services for remote users. Fog computing has also gained considerable attention in recent years from the research fraternity. Fog computing is an additional layer between the users of the cloud and the cloud infrastructure as a place that stores frequently used data in order to reduce latency, which might occur as a consequence of using cloud computing. It also provides easy access and management mechanisms to the devices located at the edge of the cloud, which leads to better performance when compared with cloud computing. Fog computing does, however, pose certain challenges, related to security, such as data breaches; availability, such as dealing with connectivity interruptions; and the reliability of fog resources and services. This study proposes a lightweight system that adopts the fog computing paradigm and addresses several of its challenges by, for instance, enhancing the security aspects of the whole system by validating nodes that join the fog layer before serving the end user. In addition, the proposed system provides better availability and reliability for fog computing and its associated services by capturing and tracking the progress of tasks and being able to resume once an interruption is detected. Experimental results validate the feasibility of the proposed system in terms of its enhanced security capabilities and time cost. This is achieved by using several security techniques which result in allowing only approved devices to join the fog layer. The results also demonstrate the capability to execute tasks even if an interruption is detected by resuming the remainder of the task through another fog node. The proposed solution is unique in the sense that it provides a simple mechanism for implementation in real-world applications, especially in crowded places or when the mobility of users is high. It can also be enhanced further in several ways to address other predicaments related to fog computing.
{"title":"A Holistic Approach to Security, Availability and Reliability in Fog Computing","authors":"Abdulrahman Alshehri, H. Alshareef, Samah Alhazmi, M. Almasri, Maha Helal","doi":"10.3844/jcssp.2024.641.648","DOIUrl":"https://doi.org/10.3844/jcssp.2024.641.648","url":null,"abstract":": Cloud computing has become popular in recent years due to the considerable flexibility it provides in terms of its availability and affordability and the reliability of different software and services for remote users. Fog computing has also gained considerable attention in recent years from the research fraternity. Fog computing is an additional layer between the users of the cloud and the cloud infrastructure as a place that stores frequently used data in order to reduce latency, which might occur as a consequence of using cloud computing. It also provides easy access and management mechanisms to the devices located at the edge of the cloud, which leads to better performance when compared with cloud computing. Fog computing does, however, pose certain challenges, related to security, such as data breaches; availability, such as dealing with connectivity interruptions; and the reliability of fog resources and services. This study proposes a lightweight system that adopts the fog computing paradigm and addresses several of its challenges by, for instance, enhancing the security aspects of the whole system by validating nodes that join the fog layer before serving the end user. In addition, the proposed system provides better availability and reliability for fog computing and its associated services by capturing and tracking the progress of tasks and being able to resume once an interruption is detected. Experimental results validate the feasibility of the proposed system in terms of its enhanced security capabilities and time cost. This is achieved by using several security techniques which result in allowing only approved devices to join the fog layer. The results also demonstrate the capability to execute tasks even if an interruption is detected by resuming the remainder of the task through another fog node. The proposed solution is unique in the sense that it provides a simple mechanism for implementation in real-world applications, especially in crowded places or when the mobility of users is high. It can also be enhanced further in several ways to address other predicaments related to fog computing.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233649","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 : 2024-05-01DOI: 10.3844/jcssp.2024.487.494
Meer Hazar Khan, Muhammad Imran Sharif, Mehwish Mehmood, Fernaz Narin Nur, Md Palash Uddin, Zahid Akhtar, Kamran Siddique, Sadia Waheed Awan
: Web images represent unstructured data sets which often lead to challenges when users try to locate distinct images via text-based searches on the web. Such difficulties stem from different factors, e
{"title":"Enhancing Semantic Web Retrieval Through Ontology-Driven Feature Extraction: A Novel Proposition","authors":"Meer Hazar Khan, Muhammad Imran Sharif, Mehwish Mehmood, Fernaz Narin Nur, Md Palash Uddin, Zahid Akhtar, Kamran Siddique, Sadia Waheed Awan","doi":"10.3844/jcssp.2024.487.494","DOIUrl":"https://doi.org/10.3844/jcssp.2024.487.494","url":null,"abstract":": Web images represent unstructured data sets which often lead to challenges when users try to locate distinct images via text-based searches on the web. Such difficulties stem from different factors, e","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141027461","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 : 2024-05-01DOI: 10.3844/jcssp.2024.495.510
Arnaud Nguembang Fadja, Giuseppe Cota, Francesco Bertasi, Fabrizio Riguzzi, E. Losi, L. Manservigi, M. Venturini, G. Bechini
: Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations not only helps to take corrective measures to avoid service unavailability but also eases the process of maintenance and considerably reduces maintenance costs. Huge amounts of sensor data are collected from (GTs) making monitoring impossible for human operators even with the help of computers. Machine learning techniques could provide support for handling large amounts of sensor data and building decision models for predicting GT future conditions. The paper presents an application of machine learning based on decision trees and k-nearest neighbors for predicting the rotational speed of gas turbines. The aim is to distinguish steady states (e.g., GT operation at normal conditions) from transients (e.g., GT trip or shutdown). The different steps of a machine learning pipeline, starting from data extraction to model testing are implemented and analyzed. Experiments are performed by applying decision trees, extremely randomized trees, and k-nearest neighbors to sensor data collected from GTs located in different countries. The trained models were able to predict steady state and transient with more than 93% accuracy. This research advances predictive maintenance methods and suggests exploring advanced machine learning algorithms, real-time data integration, and explainable AI techniques to enhance gas turbine behavior understanding and develop more adaptable maintenance systems for industrial applications.
{"title":"Machine Learning Approaches for the Prediction of Gas Turbine Transients","authors":"Arnaud Nguembang Fadja, Giuseppe Cota, Francesco Bertasi, Fabrizio Riguzzi, E. Losi, L. Manservigi, M. Venturini, G. Bechini","doi":"10.3844/jcssp.2024.495.510","DOIUrl":"https://doi.org/10.3844/jcssp.2024.495.510","url":null,"abstract":": Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations not only helps to take corrective measures to avoid service unavailability but also eases the process of maintenance and considerably reduces maintenance costs. Huge amounts of sensor data are collected from (GTs) making monitoring impossible for human operators even with the help of computers. Machine learning techniques could provide support for handling large amounts of sensor data and building decision models for predicting GT future conditions. The paper presents an application of machine learning based on decision trees and k-nearest neighbors for predicting the rotational speed of gas turbines. The aim is to distinguish steady states (e.g., GT operation at normal conditions) from transients (e.g., GT trip or shutdown). The different steps of a machine learning pipeline, starting from data extraction to model testing are implemented and analyzed. Experiments are performed by applying decision trees, extremely randomized trees, and k-nearest neighbors to sensor data collected from GTs located in different countries. The trained models were able to predict steady state and transient with more than 93% accuracy. This research advances predictive maintenance methods and suggests exploring advanced machine learning algorithms, real-time data integration, and explainable AI techniques to enhance gas turbine behavior understanding and develop more adaptable maintenance systems for industrial applications.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026970","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 : 2024-05-01DOI: 10.3844/jcssp.2024.522.534
Maryori Bautista, Sebastian Alfaro, Lenis Wong
: During the COVID-19 pandemic, virtual education played a significant role around the world. In post-pandemic Peru, higher education institutions did not entirely dismiss the online education modality. However, this virtual education system maintains a traditional teaching-learning model, where all students receive the same content material and are expected to learn in the same way; as a result, it has not been effective in meeting the individual needs of students, causing poor performance in many cases. For this reason, a framework is proposed for the adaptive learning of higher education students in virtual classes using the Cross-Industry Standard Process for Data Mining (CRISP-DM) and Machine Learning (ML) methodology in order to recommend individualized learning materials. This framework is made up of four stages: (i) Analysis of student aspects, (ii) Analysis of Learning Methodology (LM), (iii) ML development and (iv) Integration of LM and ML models. (i) evaluates the student-related factors to be considered in adapting their learning content material. (ii) Evaluate which LM is more effective in a virtual environment. In (iii), Four ML algorithms based on the CRISP-DM methodology are implemented. In (iv), The best ML model is integrated with the LM in a virtual class. Two experiments were carried out to compare the traditional teaching methodology (experiment I) and the proposed framework (experiment 2) with a sample of 68 students. The results showed that the framework was more effective in promoting progress and academic performance, obtaining an Improvement Percentage (IP) of 39.72%. This percentage was calculated by subtracting the grade average of the tests taken at the beginning and end of each experiment.
:在 COVID-19 大流行期间，虚拟教育在世界各地发挥了重要作用。在大流行后的秘鲁，高等教育机构并没有完全否定在线教育模式。但是，这种虚拟教育系统仍然采用传统的教学模式，所有学生都接受同样的教材内容，并以同样的方式进行学习；因此，这种模式不能有效地满足学生的个性化需求，在许多情况下导致学生成绩不佳。为此，我们提出了一个框架，利用数据挖掘跨行业标准流程（CRISP-DM）和机器学习（ML）方法，为虚拟课堂中的高校学生提供自适应学习，以推荐个性化的学习材料。该框架由四个阶段组成：(i) 分析学生方面，(ii) 分析学习方法 (LM)，(iii) 开发 ML，(iv) 整合 LM 和 ML 模型。(i) 评估在调整其学习内容材料时应考虑的与学生有关的因素。(ii) 评估哪种 LM 在虚拟环境中更有效。(iii) 基于 CRISP-DM 方法实施四种 ML 算法。在(iv)中，将最佳 ML 模型与 LM 集成到虚拟课堂中。以 68 名学生为样本，进行了两次实验，比较传统教学方法（实验一）和提议的框架（实验二）。结果表明，该框架在促进学生进步和提高学习成绩方面更为有效，其提高率（IP）为 39.72%。这个百分比是通过减去每次实验开始和结束时的测试平均成绩计算出来的。
{"title":"Framework for the Adaptive Learning of Higher Education Students in Virtual Classes in Peru Using CRISP-DM and Machine Learning","authors":"Maryori Bautista, Sebastian Alfaro, Lenis Wong","doi":"10.3844/jcssp.2024.522.534","DOIUrl":"https://doi.org/10.3844/jcssp.2024.522.534","url":null,"abstract":": During the COVID-19 pandemic, virtual education played a significant role around the world. In post-pandemic Peru, higher education institutions did not entirely dismiss the online education modality. However, this virtual education system maintains a traditional teaching-learning model, where all students receive the same content material and are expected to learn in the same way; as a result, it has not been effective in meeting the individual needs of students, causing poor performance in many cases. For this reason, a framework is proposed for the adaptive learning of higher education students in virtual classes using the Cross-Industry Standard Process for Data Mining (CRISP-DM) and Machine Learning (ML) methodology in order to recommend individualized learning materials. This framework is made up of four stages: (i) Analysis of student aspects, (ii) Analysis of Learning Methodology (LM), (iii) ML development and (iv) Integration of LM and ML models. (i) evaluates the student-related factors to be considered in adapting their learning content material. (ii) Evaluate which LM is more effective in a virtual environment. In (iii), Four ML algorithms based on the CRISP-DM methodology are implemented. In (iv), The best ML model is integrated with the LM in a virtual class. Two experiments were carried out to compare the traditional teaching methodology (experiment I) and the proposed framework (experiment 2) with a sample of 68 students. The results showed that the framework was more effective in promoting progress and academic performance, obtaining an Improvement Percentage (IP) of 39.72%. This percentage was calculated by subtracting the grade average of the tests taken at the beginning and end of each experiment.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141043923","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 : 2024-05-01DOI: 10.3844/jcssp.2024.574.584
Paolino Di Felice, Gaetanino Paolone
.
.
{"title":"Papers Mentioning Things Board: A Systematic Mapping Study","authors":"Paolino Di Felice, Gaetanino Paolone","doi":"10.3844/jcssp.2024.574.584","DOIUrl":"https://doi.org/10.3844/jcssp.2024.574.584","url":null,"abstract":".","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141025618","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 : 2024-05-01DOI: 10.3844/jcssp.2024.585.593
Sobha Xavier P., Sathish P. K., Raju G.
: Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net.
{"title":"Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis","authors":"Sobha Xavier P., Sathish P. K., Raju G.","doi":"10.3844/jcssp.2024.585.593","DOIUrl":"https://doi.org/10.3844/jcssp.2024.585.593","url":null,"abstract":": Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141032862","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 : 2024-05-01DOI: 10.3844/jcssp.2024.511.521
Sivaranjini Nagarajan, Gomathi Muthuswamy
: Diagnosing Non-Hodgkin Lymphoma (NHL) is difficult and often requires specialised training and expertise as well as extensive morphological investigation and, in certain cases, costly immunohistological and genetic techniques. Computational approaches enabling morphological-based decision making are necessary for bridging the existing gaps. Histopathological images can be accurately classified using deep learning approaches, however data on NHL subtyping is limited. However, there is a lack of data about the categorization of lymph nodes affected by Non-Hodgkin Lymphoma. Here in this study, initially image preprocessing was done using the maximal Kalman filter which helps in removing the noise, data augmentation was done to improve the dataset, then the lymph nodal area was segmented using the sequential fuzzy YOLACT algorithm. Finally we trained and optimized an Convolutional Lymphnet model to classify and grade tumor level from tumor-free reference lymph nodes using the grey wolf optimized model by selecting the fitness parameters and optimize it for identifying the patient risk score. The overall experimentation was carried out under python framework. The findings demonstrate that the recommended strategy works better than the state-of-the-art techniques by having excellent detection and risk score prediction accuracy
:非霍奇金淋巴瘤（NHL）的诊断非常困难，通常需要专业培训和专业知识以及广泛的形态学调查，在某些情况下还需要昂贵的免疫组织学和遗传学技术。要弥补现有的差距，就必须采用计算方法，做出基于形态学的决策。使用深度学习方法可以对组织病理学图像进行准确分类，但有关 NHL 亚型的数据却很有限。然而，关于受非霍奇金淋巴瘤影响的淋巴结的分类数据却很缺乏。在这项研究中，我们首先使用最大卡尔曼滤波器进行图像预处理，该滤波器有助于去除噪声；然后进行数据扩增以改进数据集；最后使用序列模糊 YOLACT 算法分割淋巴结区域。最后，我们训练并优化了一个卷积淋巴网络模型，利用灰狼优化模型从无肿瘤的参考淋巴结中对肿瘤程度进行分类和分级，方法是选择适配参数并进行优化，以确定患者的风险评分。整个实验在 python 框架下进行。实验结果表明，所推荐的策略比最先进的技术效果更好，具有极高的检测和风险评分预测准确度。
{"title":"Non-Hodgkin Lymphoma Risk Grading Through the Pathological Data by Using the Optimized Convolutional Lymphnet Model","authors":"Sivaranjini Nagarajan, Gomathi Muthuswamy","doi":"10.3844/jcssp.2024.511.521","DOIUrl":"https://doi.org/10.3844/jcssp.2024.511.521","url":null,"abstract":": Diagnosing Non-Hodgkin Lymphoma (NHL) is difficult and often requires specialised training and expertise as well as extensive morphological investigation and, in certain cases, costly immunohistological and genetic techniques. Computational approaches enabling morphological-based decision making are necessary for bridging the existing gaps. Histopathological images can be accurately classified using deep learning approaches, however data on NHL subtyping is limited. However, there is a lack of data about the categorization of lymph nodes affected by Non-Hodgkin Lymphoma. Here in this study, initially image preprocessing was done using the maximal Kalman filter which helps in removing the noise, data augmentation was done to improve the dataset, then the lymph nodal area was segmented using the sequential fuzzy YOLACT algorithm. Finally we trained and optimized an Convolutional Lymphnet model to classify and grade tumor level from tumor-free reference lymph nodes using the grey wolf optimized model by selecting the fitness parameters and optimize it for identifying the patient risk score. The overall experimentation was carried out under python framework. The findings demonstrate that the recommended strategy works better than the state-of-the-art techniques by having excellent detection and risk score prediction accuracy","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141024894","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 : 2024-05-01DOI: 10.3844/jcssp.2024.548.563
Samsul Arifin, Dwi Wijonarko, Suwarno, Edwin K Sijabat
: Text encryption is one of the techniques used to maintain the confidentiality of information in digital communications. In this study, we propose to apply a combination of the Unimodular Hill Cipher and RSA methods to a text encryption algorithm using the Python programming language. The Unimodular Hill Cipher method uses an unimodular matrix to replace text characters with encrypted characters, while RSA (Rivest-Shamir-Adleman) is a public key encryption algorithm that relies on modulo arithmetic properties. The purpose of this research is to combine the strengths of the two methods and produce a more secure text encryption system. Unimodular Hill Cipher provides the advantage of randomizing text characters by using matrix modulo operations, while RSA provides a high level of security through the use of public and private key pairs. In this study, we explain in detail the basic theory and algorithms of the Unimodular Hill Cipher and RSA. We also describe the implementation steps of both methods in the Python programming language. The text data used in this study went through a preprocessing stage before being encrypted. We also analyze the results of the encryption using several statistical methods to measure how close the relationship between the original text and the result of the encryption is. In a comparative analysis with the previous paper, in this study, the use of the Unimodular Hill Cipher and RSA methods in the context of Python provides additional insight into the performance and level of security of both. The experimental results show that the combination of the Unimodular Hill Cipher and RSA methods can produce a higher level of security in text encryption. It is hoped that this research can contribute to the development of a more effective and secure text encryption algorithm.
{"title":"Application of Unimodular Hill Cipher and RSA Methods to Text Encryption Algorithms Using Python","authors":"Samsul Arifin, Dwi Wijonarko, Suwarno, Edwin K Sijabat","doi":"10.3844/jcssp.2024.548.563","DOIUrl":"https://doi.org/10.3844/jcssp.2024.548.563","url":null,"abstract":": Text encryption is one of the techniques used to maintain the confidentiality of information in digital communications. In this study, we propose to apply a combination of the Unimodular Hill Cipher and RSA methods to a text encryption algorithm using the Python programming language. The Unimodular Hill Cipher method uses an unimodular matrix to replace text characters with encrypted characters, while RSA (Rivest-Shamir-Adleman) is a public key encryption algorithm that relies on modulo arithmetic properties. The purpose of this research is to combine the strengths of the two methods and produce a more secure text encryption system. Unimodular Hill Cipher provides the advantage of randomizing text characters by using matrix modulo operations, while RSA provides a high level of security through the use of public and private key pairs. In this study, we explain in detail the basic theory and algorithms of the Unimodular Hill Cipher and RSA. We also describe the implementation steps of both methods in the Python programming language. The text data used in this study went through a preprocessing stage before being encrypted. We also analyze the results of the encryption using several statistical methods to measure how close the relationship between the original text and the result of the encryption is. In a comparative analysis with the previous paper, in this study, the use of the Unimodular Hill Cipher and RSA methods in the context of Python provides additional insight into the performance and level of security of both. The experimental results show that the combination of the Unimodular Hill Cipher and RSA methods can produce a higher level of security in text encryption. It is hoped that this research can contribute to the development of a more effective and secure text encryption algorithm.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141050681","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 : 2024-05-01DOI: 10.3844/jcssp.2024.535.547
Nagendra N, Chandra J
: The importance of aspect extraction in text classification, particularly in the e-commerce sector. E-commerce platforms generate vast amounts of textual data, such as comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Aspect extraction involves identifying and classifying individual traits or aspects mentioned in textual reviews to understand customer opinions, improve products, and enhance the customer experience. The role of product reviews in e-commerce is discussed, emphasizing their value in aiding customers' purchase decisions and guiding businesses in product stocking and marketing strategies. Reviews are essential for boosting sales potential, maintaining a good reputation, and promoting brand recognition. Customers extensively research product reviews from different sources before purchasing, making them vital user-generated content for e-commerce businesses. The current work provided an efficient and novel classification model for sentence classification using the ABNAM model. The automated text classification models available cannot categorize the data into sixteen distinct classes. The technologies applied for the mentioned work contain TF-IDF, N-gram, CNN, linear SVM, random forest, Naïve bays, and ABNAM with significant results. The best-performing ML method for the successful classification of a given sentence into one of the sixteen categories is achieved with the proposed model named the based Neural Attention Model (ABNAM), which has the highest accuracy at 97%. The research acclaimed ABNAM as a novel classification model with the highest-class categorizations.
{"title":"Sentence Classification Using Attention Model for E-Commerce Product Review","authors":"Nagendra N, Chandra J","doi":"10.3844/jcssp.2024.535.547","DOIUrl":"https://doi.org/10.3844/jcssp.2024.535.547","url":null,"abstract":": The importance of aspect extraction in text classification, particularly in the e-commerce sector. E-commerce platforms generate vast amounts of textual data, such as comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Aspect extraction involves identifying and classifying individual traits or aspects mentioned in textual reviews to understand customer opinions, improve products, and enhance the customer experience. The role of product reviews in e-commerce is discussed, emphasizing their value in aiding customers' purchase decisions and guiding businesses in product stocking and marketing strategies. Reviews are essential for boosting sales potential, maintaining a good reputation, and promoting brand recognition. Customers extensively research product reviews from different sources before purchasing, making them vital user-generated content for e-commerce businesses. The current work provided an efficient and novel classification model for sentence classification using the ABNAM model. The automated text classification models available cannot categorize the data into sixteen distinct classes. The technologies applied for the mentioned work contain TF-IDF, N-gram, CNN, linear SVM, random forest, Naïve bays, and ABNAM with significant results. The best-performing ML method for the successful classification of a given sentence into one of the sixteen categories is achieved with the proposed model named the based Neural Attention Model (ABNAM), which has the highest accuracy at 97%. The research acclaimed ABNAM as a novel classification model with the highest-class categorizations.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141031297","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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