A vital step in automation of plant root disease diagnosis is to extract root region from the input images in an automatic and consistent manner. However, performance of segmentation algorithm over root images directly depends on the quality of input images. During acquisition, the captured root images are distorted by numerous external factors like lighting conditions, dust and so on. Hence it is essential to incorporate an image enhancement algorithm as a pre-processing step in the plant root disease diagnosis module. Image quality can be improved either by manipulating the pixels through spatial or frequency domain. In spatial domain, images are directly manipulated using their pixel values and alternatively in frequency domain, images are indirectly manipulated using transformations. Spatial based enhancement methods are considered as favourable approach for real time root images as it is simple and easy to understand with low computational complexity. In this study, real time banana root images were enhanced by attempting with different spatial based image enhancement techniques. Different classical point processing methods (contrast stretching, logarithmic transformation, power law transformation, histogram equalization, adaptive histogram equalization and histogram matching) and fuzzy based enhancement methods using fuzzy intensification operator and fuzzy if-then rule based methods were tried to enhance the banana root images. Quality of the enhanced root Article History Received: 7 April 2020 Accepted: 19 May 2020
{"title":"Classical and Fuzzy Based Image Enhancement Techniques for Banana Root Disease Diagnosis: A Review and Validation","authors":"D. Suryaprabha, J. Satheeshkumar, N. Seenivasan","doi":"10.13005/ojcst13.01.05","DOIUrl":"https://doi.org/10.13005/ojcst13.01.05","url":null,"abstract":"A vital step in automation of plant root disease diagnosis is to extract root region from the input images in an automatic and consistent manner. However, performance of segmentation algorithm over root images directly depends on the quality of input images. During acquisition, the captured root images are distorted by numerous external factors like lighting conditions, dust and so on. Hence it is essential to incorporate an image enhancement algorithm as a pre-processing step in the plant root disease diagnosis module. Image quality can be improved either by manipulating the pixels through spatial or frequency domain. In spatial domain, images are directly manipulated using their pixel values and alternatively in frequency domain, images are indirectly manipulated using transformations. Spatial based enhancement methods are considered as favourable approach for real time root images as it is simple and easy to understand with low computational complexity. In this study, real time banana root images were enhanced by attempting with different spatial based image enhancement techniques. Different classical point processing methods (contrast stretching, logarithmic transformation, power law transformation, histogram equalization, adaptive histogram equalization and histogram matching) and fuzzy based enhancement methods using fuzzy intensification operator and fuzzy if-then rule based methods were tried to enhance the banana root images. Quality of the enhanced root Article History Received: 7 April 2020 Accepted: 19 May 2020","PeriodicalId":270258,"journal":{"name":"Oriental journal of computer science and technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123031875","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}
Cybersecurity systems are required at the application, network, host, and data levels. The research is purposed to evaluate Artificial Intelligence paradigms for use in network detection and prevention systems. This is purposed to develop a Cybersecurity system that uses artificial intelligence paradigms and can handle a high degree of complexity. The Pragmatism paradigm is elaborately associated with the Mixed Method Research (MMR), and is the research philosophy used in this research. Pragmatism recognizes the full rationale of the congruence between knowledge and action. The Pragmatic paradigm advocates a relational epistemology, a non-singular reality ontology, a mixed methods methodology, and a value-laden axiology. A qualitative approach where Focus Group discussions were held was used. The Artificial Intelligence paradigms evaluated include machine learning methods, autonomous robotic vehicle, artificial neural networks, and fuzzy logic. A discussion was held on the performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms.
{"title":"Performance of Machine Learning and other Artificial Intelligence paradigms in Cybersecurity","authors":"G. Kabanda","doi":"10.13005/ojcst13.01.01","DOIUrl":"https://doi.org/10.13005/ojcst13.01.01","url":null,"abstract":"Cybersecurity systems are required at the application, network, host, and data levels. The research is purposed to evaluate Artificial Intelligence paradigms for use in network detection and prevention systems. This is purposed to develop a Cybersecurity system that uses artificial intelligence paradigms and can handle a high degree of complexity. The Pragmatism paradigm is elaborately associated with the Mixed Method Research (MMR), and is the research philosophy used in this research. Pragmatism recognizes the full rationale of the congruence between knowledge and action. The Pragmatic paradigm advocates a relational epistemology, a non-singular reality ontology, a mixed methods methodology, and a value-laden axiology. A qualitative approach where Focus Group discussions were held was used. The Artificial Intelligence paradigms evaluated include machine learning methods, autonomous robotic vehicle, artificial neural networks, and fuzzy logic. A discussion was held on the performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms.","PeriodicalId":270258,"journal":{"name":"Oriental journal of computer science and technology","volume":"4320 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116838925","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}
Shahzad Ashraf, Tauqeer Ahmed, Sehrish Saleem, Zeeshan Aslam, S. Shahzad
The sustainability and environmental considerations have slowly become divergences, but having greatest influence in the supply chain management that must be contemplates to examine the environmental and organizational factors. The research considers environmental and sustainable strategies within companies, the efficient supply chain management strategies for manufacturers and consumers, and to the environment friendly product design and services, taking a case-by-case perspective and concentrating on enterprise businesses scale. Our finding reveals that green supply chain management firms are delivering exuberant environmental efficiency at an added cost. Among the identified obstacles we identified different obstacles and conceptual relations and barriers are graded based on dependency and driving sand. In future, green policies have greater customer services avenues thereby, appeal for suppliers, manufacturers and officials.
{"title":"Diverging Mysterious in Green Supply Chain Management","authors":"Shahzad Ashraf, Tauqeer Ahmed, Sehrish Saleem, Zeeshan Aslam, S. Shahzad","doi":"10.13005/ojcst13.01.02","DOIUrl":"https://doi.org/10.13005/ojcst13.01.02","url":null,"abstract":"The sustainability and environmental considerations have slowly become divergences, but having greatest influence in the supply chain management that must be contemplates to examine the environmental and organizational factors. The research considers environmental and sustainable strategies within companies, the efficient supply chain management strategies for manufacturers and consumers, and to the environment friendly product design and services, taking a case-by-case perspective and concentrating on enterprise businesses scale. Our finding reveals that green supply chain management firms are delivering exuberant environmental efficiency at an added cost. Among the identified obstacles we identified different obstacles and conceptual relations and barriers are graded based on dependency and driving sand. In future, green policies have greater customer services avenues thereby, appeal for suppliers, manufacturers and officials.","PeriodicalId":270258,"journal":{"name":"Oriental journal of computer science and technology","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129543795","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}
ERP, or Enterprise Resource Planning systems help business management, which consists of a well-designed interface that incorporates different programs to integrate and manage all company functions at intervals of a company, these sets incorporate applications for human resources, monetary and accounting, sales and distribution, project management, materials management, SCM, or Supply Chain Management and quality management. Currently, organizations are running to improve their ability to survive in the global market competitions of the 21st century. While the organizations try to advance in their level of agility, changing and modifying the process of decisionmaking to make it more efficient and effective to satisfy the successive variations of the market. Different views are gathered regarding ERP implementation of ERP in manufacturing. Even we have taken certain essential components of ERP for a better understanding of ERP. Ease of use, usefulness, quality, and trust on ERP services have been taken an independent variable that affects user’s decision to adopt ERP. The role of ERP technology in manufacturing facilities are broken into more categories for detail concept. Quantitative data analysis methods were usually used for questionnaire data analysis which was utilized to analyze statistical data and after that collection of interview data was done. A researcher has applied different statistical tools like Chi-Square Tests, Anova, etc. to analyze the collected data. A researcher essential portion is to analyze and interpret data that relates to modifying data which explains the solution to the research question with some additional future recommendation for more quality research. Article History Received: 19 October 2019 Accepted: 03 December 2019
{"title":"QoS Priorities in ERP Implementation – A Study of Manufacturing Industry of Nepal","authors":"S. Giri, R. Thakur, J. Chatterjee","doi":"10.13005/ojcst12.04.03","DOIUrl":"https://doi.org/10.13005/ojcst12.04.03","url":null,"abstract":"ERP, or Enterprise Resource Planning systems help business management, which consists of a well-designed interface that incorporates different programs to integrate and manage all company functions at intervals of a company, these sets incorporate applications for human resources, monetary and accounting, sales and distribution, project management, materials management, SCM, or Supply Chain Management and quality management. Currently, organizations are running to improve their ability to survive in the global market competitions of the 21st century. While the organizations try to advance in their level of agility, changing and modifying the process of decisionmaking to make it more efficient and effective to satisfy the successive variations of the market. Different views are gathered regarding ERP implementation of ERP in manufacturing. Even we have taken certain essential components of ERP for a better understanding of ERP. Ease of use, usefulness, quality, and trust on ERP services have been taken an independent variable that affects user’s decision to adopt ERP. The role of ERP technology in manufacturing facilities are broken into more categories for detail concept. Quantitative data analysis methods were usually used for questionnaire data analysis which was utilized to analyze statistical data and after that collection of interview data was done. A researcher has applied different statistical tools like Chi-Square Tests, Anova, etc. to analyze the collected data. A researcher essential portion is to analyze and interpret data that relates to modifying data which explains the solution to the research question with some additional future recommendation for more quality research. Article History Received: 19 October 2019 Accepted: 03 December 2019","PeriodicalId":270258,"journal":{"name":"Oriental journal of computer science and technology","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125419465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The security challenge on IoT (Internet of Things) is one of the hottest and most pertinent topics at the moment especially the several security challenges. The Botnet is one of the security challenges that most impact for several purposes. The network of private computers infected by malicious software and controlled as a group without the knowledge of owners and each of them running one or more bots is called Botnets. Normally, it is used for sending spam, stealing data, and performing DDoS attacks. One of the techniques that been used for detecting the Botnet is the Supervised Learning method. This study will examine several Supervised Learning methods such as; Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, k- Nearest Neighbors, Random Forest, Gradient Boosting Machines, and Support Vector Machine for identifying the Botnet in IoT with the aim of finding which Supervised Learning technique can achieve the highest accuracy and fastest detection as well as with minimizing the dependent variable.
{"title":"Identifying Botnet on IoT by Using Supervised Learning Techniques","authors":"Amirhossein Rezaei","doi":"10.13005/ojcst12.04.04","DOIUrl":"https://doi.org/10.13005/ojcst12.04.04","url":null,"abstract":"The security challenge on IoT (Internet of Things) is one of the hottest and most pertinent topics at the moment especially the several security challenges. The Botnet is one of the security challenges that most impact for several purposes. The network of private computers infected by malicious software and controlled as a group without the knowledge of owners and each of them running one or more bots is called Botnets. Normally, it is used for sending spam, stealing data, and performing DDoS attacks. One of the techniques that been used for detecting the Botnet is the Supervised Learning method. This study will examine several Supervised Learning methods such as; Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, k- Nearest Neighbors, Random Forest, Gradient Boosting Machines, and Support Vector Machine for identifying the Botnet in IoT with the aim of finding which Supervised Learning technique can achieve the highest accuracy and fastest detection as well as with minimizing the dependent variable.","PeriodicalId":270258,"journal":{"name":"Oriental journal of computer science and technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128439440","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}
Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach. CONTACT Gabriel Kabanda gabrielkabanda@gmail.com Atlantic International University 900 Fort Street Mall 40 Honolulu,
大数据是管理从内部、外部、结构化和非结构化等多种异构数据类型中获取的大量数据的过程，这些数据可用于收集和分析企业数据。本文的目的是对大数据分析项目进行评估，讨论项目失败的原因，并解释为什么以及如何项目预测分析(PPA)方法可能会对基于数据挖掘，机器学习和人工智能的未来方法产生影响。采用定性研究方法。本研究采用语篇分析为主，文献分析为主的研究设计。拉克劳和墨菲的话语理论是最彻底的后结构主义方法。联系Gabriel Kabanda gabrielkabanda@gmail.com大西洋国际大学900 Fort Street Mall 40檀香山，
{"title":"An Evaluation of Big Data Analytics Projects and the Project Predictive Analytics Approach","authors":"G. Kabanda","doi":"10.13005/ojcst12.04.01","DOIUrl":"https://doi.org/10.13005/ojcst12.04.01","url":null,"abstract":"Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach. CONTACT Gabriel Kabanda gabrielkabanda@gmail.com Atlantic International University 900 Fort Street Mall 40 Honolulu,","PeriodicalId":270258,"journal":{"name":"Oriental journal of computer science and technology","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132602284","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}
An Emotion monitoring system for a call-center is proposed. It aims to simplify the tracking and management of emotions extracted from call center Employee-Customer conversations. The system is composed of four modules: Emotion Detection, Emotion Analysis and Report Generation, Database Manager, and User Interface. The Emotion Detection module uses Tone Analyzer to extract them for reliable emotion; it also performs the Utterance Analysis for detecting emotion. The 14 emotions detected by the tone analyzer are happy, joy, anger, sad and neutral, etc. The Emotion Analysis module performs classification into the 3 categories: Neutral, Anger and Joy. By using this category, it applies the point-scoring technique for calculating the Employee Score. This module also polishes the output of the Emotion Detection module to provide a more presentable output of a sequence of emotions of the Employee and the Customer. The Database Manager is responsible for the management of the database wherein it handles the creation, and update of data. The Interface module serves as the view and user interface for the whole system. The system is comprised of an Android application for conversation and a web application to view reports. The Android application was developed using Android Studio to maintain the modularity and flexibility of the system. The local server monitors the conversation, it displays the detected emotions of both the Customer and the Employee. On the other hand, the web application was constructed using the Django Framework to maintain its modularity and abstraction by using a model. It provides reports and analysis of the emotions expressed by the customer during conversations. Using the Model View Template (MVT) approach, the Emotion monitoring system is scalable, reusable and modular. CONTACT Mr. Anil S Naik anil.nk287@gmail.com Department of Information Technology, Walchand Institute of Technology,
提出了一种面向呼叫中心的情绪监测系统。它旨在简化从呼叫中心员工-客户对话中提取的情绪的跟踪和管理。该系统由四个模块组成:情绪检测、情绪分析与报表生成、数据库管理和用户界面。情绪检测模块使用Tone Analyzer对其进行提取，获得可靠的情绪;它还执行话语分析来检测情绪。语调分析器检测到的14种情绪有高兴、高兴、愤怒、悲伤和中性等。情绪分析模块将情绪分为3类:中性、愤怒和快乐。通过使用这个类别，它应用计分技术来计算员工得分。该模块还改进了情绪检测模块的输出，以提供更美观的员工和客户情绪序列输出。数据库管理器负责管理数据库，其中处理数据的创建和更新。接口模块是整个系统的视图和用户界面。该系统由一个用于对话的Android应用程序和一个用于查看报表的web应用程序组成。为了保持系统的模块化和灵活性，使用Android Studio开发Android应用程序。本地服务器监视对话，它显示检测到的客户和员工的情绪。另一方面，web应用程序是使用Django框架构建的，通过使用模型来维护其模块化和抽象化。它提供客户在对话过程中所表达的情绪的报告和分析。采用模型视图模板(MVT)方法，情绪监测系统具有可扩展性、可重用性和模块化。联系Anil S Naik先生anil.nk287@gmail.com Walchand理工学院信息技术系
{"title":"Text and Voice Based Emotion Monitoring System","authors":"A. S. Naik","doi":"10.13005/ojcst12.04.05","DOIUrl":"https://doi.org/10.13005/ojcst12.04.05","url":null,"abstract":"An Emotion monitoring system for a call-center is proposed. It aims to simplify the tracking and management of emotions extracted from call center Employee-Customer conversations. The system is composed of four modules: Emotion Detection, Emotion Analysis and Report Generation, Database Manager, and User Interface. The Emotion Detection module uses Tone Analyzer to extract them for reliable emotion; it also performs the Utterance Analysis for detecting emotion. The 14 emotions detected by the tone analyzer are happy, joy, anger, sad and neutral, etc. The Emotion Analysis module performs classification into the 3 categories: Neutral, Anger and Joy. By using this category, it applies the point-scoring technique for calculating the Employee Score. This module also polishes the output of the Emotion Detection module to provide a more presentable output of a sequence of emotions of the Employee and the Customer. The Database Manager is responsible for the management of the database wherein it handles the creation, and update of data. The Interface module serves as the view and user interface for the whole system. The system is comprised of an Android application for conversation and a web application to view reports. The Android application was developed using Android Studio to maintain the modularity and flexibility of the system. The local server monitors the conversation, it displays the detected emotions of both the Customer and the Employee. On the other hand, the web application was constructed using the Django Framework to maintain its modularity and abstraction by using a model. It provides reports and analysis of the emotions expressed by the customer during conversations. Using the Model View Template (MVT) approach, the Emotion monitoring system is scalable, reusable and modular. CONTACT Mr. Anil S Naik anil.nk287@gmail.com Department of Information Technology, Walchand Institute of Technology,","PeriodicalId":270258,"journal":{"name":"Oriental journal of computer science and technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116590385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this research was to develop a structure for a network intrusion detection and prevention system based on the Bayesian Network for use in Cybersecurity. The phenomenal growth in the use of internet-based technologies has resulted in complexities in cybersecurity subjecting organizations to cyberattacks. What is required is a network intrusion detection and prevention system based on the Bayesian Network structure for use in Cybersecurity. Bayesian Networks (BNs) are defined as graphical probabilistic models for multivariate analysis and are directed acyclic graphs that have an associated probability distribution function. The research determined the cybersecurity framework appropriate for a developing nation; evaluated network detection and prevention systems that use Artificial Intelligence paradigms such as finite automata, neural networks, genetic algorithms, fuzzy logic, support-vector machines or diverse data-mining-based approaches; analysed Bayesian Networks that can be represented as graphical models and are directional to represent cause-effect relationships; and developed a Bayesian Network model that can handle complexity in cybersecurity. The theoretical framework on Bayesian Networks was largely informed by the NIST Cybersecurity Framework, General deterrence theory, Game theory, Complexity theory and data mining techniques. The Pragmatism paradigm used in this research, as a philosophy is intricately related to the Mixed Method Research (MMR). A mixed method approach was used in this research, which is largely quantitative with the research design being a survey and an experiment, but supported by qualitative approaches where Focus Group discussions were held. The performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms was discussed. Alternative improved solutions discussed include the use of machine learning algorithms specifically Artificial Neural Networks (ANN), Decision Tree C4.5, Random Forests and Support Vector Machines (SVM). CONTACT Gabriel Kabanda gabrielkabanda@gmail.com Atlantic International University 900 Fort Street Mall 40 Honolulu,
本研究的目的是开发一种基于贝叶斯网络的网络入侵检测和防御系统的结构，用于网络安全。基于互联网的技术使用的显著增长导致了网络安全的复杂性，使组织遭受网络攻击。因此，需要一个基于贝叶斯网络结构的网络入侵检测与防御系统。贝叶斯网络(BNs)被定义为用于多变量分析的图形概率模型，并且是具有相关概率分布函数的有向无环图。研究确定了适合发展中国家的网络安全框架;评估使用人工智能范例的网络检测和预防系统，如有限自动机、神经网络、遗传算法、模糊逻辑、支持向量机或各种基于数据挖掘的方法;分析了贝叶斯网络，可以表示为图形模型，并有方向性地表示因果关系;并开发了一个可以处理网络安全复杂性的贝叶斯网络模型。贝叶斯网络的理论框架主要受NIST网络安全框架、一般威慑理论、博弈论、复杂性理论和数据挖掘技术的影响。本研究中使用的实用主义范式作为一种哲学与混合方法研究(MMR)有着复杂的关系。在本研究中使用了混合方法方法，这在很大程度上是定量的，研究设计是一个调查和一个实验，但支持定性方法，焦点小组讨论举行。讨论了支持向量机、人工神经网络、k近邻、朴素贝叶斯和决策树算法的性能。讨论的替代改进解决方案包括使用机器学习算法，特别是人工神经网络(ANN)，决策树C4.5，随机森林和支持向量机(SVM)。联系Gabriel Kabanda gabrielkabanda@gmail.com大西洋国际大学900 Fort Street Mall 40檀香山，
{"title":"Bayesian Network Model for a Zimbabwean Cybersecurity System","authors":"G. Kabanda","doi":"10.13005/ojcst12.04.02","DOIUrl":"https://doi.org/10.13005/ojcst12.04.02","url":null,"abstract":"The purpose of this research was to develop a structure for a network intrusion detection and prevention system based on the Bayesian Network for use in Cybersecurity. The phenomenal growth in the use of internet-based technologies has resulted in complexities in cybersecurity subjecting organizations to cyberattacks. What is required is a network intrusion detection and prevention system based on the Bayesian Network structure for use in Cybersecurity. Bayesian Networks (BNs) are defined as graphical probabilistic models for multivariate analysis and are directed acyclic graphs that have an associated probability distribution function. The research determined the cybersecurity framework appropriate for a developing nation; evaluated network detection and prevention systems that use Artificial Intelligence paradigms such as finite automata, neural networks, genetic algorithms, fuzzy logic, support-vector machines or diverse data-mining-based approaches; analysed Bayesian Networks that can be represented as graphical models and are directional to represent cause-effect relationships; and developed a Bayesian Network model that can handle complexity in cybersecurity. The theoretical framework on Bayesian Networks was largely informed by the NIST Cybersecurity Framework, General deterrence theory, Game theory, Complexity theory and data mining techniques. The Pragmatism paradigm used in this research, as a philosophy is intricately related to the Mixed Method Research (MMR). A mixed method approach was used in this research, which is largely quantitative with the research design being a survey and an experiment, but supported by qualitative approaches where Focus Group discussions were held. The performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms was discussed. Alternative improved solutions discussed include the use of machine learning algorithms specifically Artificial Neural Networks (ANN), Decision Tree C4.5, Random Forests and Support Vector Machines (SVM). CONTACT Gabriel Kabanda gabrielkabanda@gmail.com Atlantic International University 900 Fort Street Mall 40 Honolulu,","PeriodicalId":270258,"journal":{"name":"Oriental journal of computer science and technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131662753","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}
Body modification (or body alteration) is the wilful altering of the human body by an individual in a way that lasts forever or for a very long time. This is usually for non-medical reasons that include sexual enhancement, a rite of passage, aesthetic reasons, denoting affiliation, trust and loyalty, religious reasons, shock value, and self-expression. It can range from the socially acceptable decoration (e.g., pierced ears or nose in many societies) to the religiously mandated. Body art is the modification of any part of the human body for artistic or aesthetic reasons. Nanotechnology is currently available to implant biometric devices in human beings, which can be monitored by software, satellites and utilized by Government and Industry. In fact several developers are currently bringing these technologies to the public and private sector at affordable prices. The context of “Technology Consumerism” compounded by Intentionality and Free-Will of its consumer’s results in many unintended consequences outlined in this paper. Geometry of Morphogenesis is the proposed theory for decoding body modification.
{"title":"Nanotechnology – Intentionality and Free-Will","authors":"T. Gopal","doi":"10.13005/ojcst12.03.05","DOIUrl":"https://doi.org/10.13005/ojcst12.03.05","url":null,"abstract":"Body modification (or body alteration) is the wilful altering of the human body by an individual in a way that lasts forever or for a very long time. This is usually for non-medical reasons that include sexual enhancement, a rite of passage, aesthetic reasons, denoting affiliation, trust and loyalty, religious reasons, shock value, and self-expression. It can range from the socially acceptable decoration (e.g., pierced ears or nose in many societies) to the religiously mandated. Body art is the modification of any part of the human body for artistic or aesthetic reasons. Nanotechnology is currently available to implant biometric devices in human beings, which can be monitored by software, satellites and utilized by Government and Industry. In fact several developers are currently bringing these technologies to the public and private sector at affordable prices. The context of “Technology Consumerism” compounded by Intentionality and Free-Will of its consumer’s results in many unintended consequences outlined in this paper. Geometry of Morphogenesis is the proposed theory for decoding body modification.","PeriodicalId":270258,"journal":{"name":"Oriental journal of computer science and technology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122301344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：