Wireless communication is one of the most in-demand fields of communication nowadays. Due to people's desire to transfer data as fast and broadly as possible, most communication processes are impacted by overloading and spectrum shortage when there is significant network usage. Make use of a cognitive radio network to keep an eye on things automatically, and use the spectrum dynamically to avoid overloading. Instead of evaluating the node's fitness, the CRN will alert the secondary user (SU) of the unused spectrum. To choose the fittest node in an environment with a coverage zone, a cognitive radio network in the proposed work uses a genetic algorithm plus coverset prediction. To address these issues, this paper offers a CRN calculation strategy.
{"title":"EFFICIENTLY UTILIZE THE SPECTRUM AND LOAD BALANCING IN COGNITIVE RADIO NETWORK USING GENETIC ALGORITHM WITH COVERSET PREDICTION","authors":"S. Kavitha, Dr. R. Kaniezhil","doi":"10.53555/cse.v10i1.6058","DOIUrl":"https://doi.org/10.53555/cse.v10i1.6058","url":null,"abstract":"Wireless communication is one of the most in-demand fields of communication nowadays. Due to people's desire to transfer data as fast and broadly as possible, most communication processes are impacted by overloading and spectrum shortage when there is significant network usage. Make use of a cognitive radio network to keep an eye on things automatically, and use the spectrum dynamically to avoid overloading. Instead of evaluating the node's fitness, the CRN will alert the secondary user (SU) of the unused spectrum. To choose the fittest node in an environment with a coverage zone, a cognitive radio network in the proposed work uses a genetic algorithm plus coverset prediction. To address these issues, this paper offers a CRN calculation strategy.","PeriodicalId":130369,"journal":{"name":"IJRDO -Journal of Computer Science Engineering","volume":"109 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140669777","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 implementation of a distributed system for publishing students' academic courses aims to facilitate and standardize the dissemination of information on students' academic paths. The main objective of this system is to provide a secure and reliable platform where students can view their academic history. Relevant information includes degrees earned, grades, research projects, internships completed, publications, and skills acquired. To set up such a system, it is essential to take into account several aspects. First of all, the confidentiality and security of student data must be guaranteed. It is necessary to implement strict access control and data encryption mechanisms to protect sensitive information. Then, it is important to define a standardized publication framework that allows students to present their courses in a coherent and understandable way for third parties. This may include information presentation models, metadata tags and data interchange standards. In addition, collaboration between the various players in the system is essential. Universities should cooperate to share student academic data securely. Employers and other organizations should also be actively involved in using this system to verify and validate student academic background information. Finally, it is crucial to put in place data verification and validation processes to ensure the accuracy and credibility of the information published. Academic authorities can play a key role in verifying the validity of degrees and certifications.
{"title":"MISE EN PLACE D'UN SYSTEME DISTRIBUE DE PUBLICATION DES CURSUS ACADEMIQUES DES ETUDIANTS","authors":"Laurent Kompani Esokeli, Nathan Iungbi Singa","doi":"10.53555/cse.v9i8.5794","DOIUrl":"https://doi.org/10.53555/cse.v9i8.5794","url":null,"abstract":"The implementation of a distributed system for publishing students' academic courses aims to facilitate and standardize the dissemination of information on students' academic paths. \u0000The main objective of this system is to provide a secure and reliable platform where students can view their academic history. Relevant information includes degrees earned, grades, research projects, internships completed, publications, and skills acquired. \u0000To set up such a system, it is essential to take into account several aspects. First of all, the confidentiality and security of student data must be guaranteed. It is necessary to implement strict access control and data encryption mechanisms to protect sensitive information. \u0000Then, it is important to define a standardized publication framework that allows students to present their courses in a coherent and understandable way for third parties. This may include information presentation models, metadata tags and data interchange standards. \u0000In addition, collaboration between the various players in the system is essential. Universities should cooperate to share student academic data securely. Employers and other organizations should also be actively involved in using this system to verify and validate student academic background information. \u0000Finally, it is crucial to put in place data verification and validation processes to ensure the accuracy and credibility of the information published. Academic authorities can play a key role in verifying the validity of degrees and certifications.","PeriodicalId":130369,"journal":{"name":"IJRDO -Journal of Computer Science Engineering","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121366571","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}
This file gives an outline of WI-FI networking and descriptions what's required to construct a general-motive WI-FI community. The literature tries to talk about the maximum not unusual place WI-FI technology and their protocols. It then outlines the benefits of WI-FI networking over stressed era. The white paper additionally addresses a number of the essential protection dangers going through WI-FI networks. Various techniques are for the reason that may be used to mitigate those dangers and guard community privateness and protection. It then outlines how WI-FI networks may be utilized in training and schooling, and suggests that training has benefited from the improvement of WI-FI era and the era's price-effectiveness.
{"title":"A REVIEW ON WIRELESS NETWORK AND ELECTRONIC COMMUNICATION","authors":"Mr. A. Prakash, Dr. T.G. Babu","doi":"10.53555/cse.v9i5.5630","DOIUrl":"https://doi.org/10.53555/cse.v9i5.5630","url":null,"abstract":"This file gives an outline of WI-FI networking and descriptions what's required to construct a general-motive WI-FI community. The literature tries to talk about the maximum not unusual place WI-FI technology and their protocols. It then outlines the benefits of WI-FI networking over stressed era. The white paper additionally addresses a number of the essential protection dangers going through WI-FI networks. Various techniques are for the reason that may be used to mitigate those dangers and guard community privateness and protection. It then outlines how WI-FI networks may be utilized in training and schooling, and suggests that training has benefited from the improvement of WI-FI era and the era's price-effectiveness.","PeriodicalId":130369,"journal":{"name":"IJRDO -Journal of Computer Science Engineering","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126494686","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}
Cybercrime is an unlawful act in which computer is the tools to commit an offense; cyber criminals perform operation in cyber space with the help of the internet. Most existing techniques used in detecting cybercrimes could detect individual attacks but failed in terms of coordinated and distributed attacks. Also, most of the detection system used to curb cybercrimes on web application generates a large number of false alarms. Hence, this research developed an enhanced system which could not only detect individual, coordinated and distributed attacks but also reduce the number of false alarms. The research data for this work which consists of six cards (labeled A, B, C, D, E and F) were sourced from an online shopping store. The six cards contain four attributes with associated two thousand seven hundred (2700) transactions. The number of transactions carried out through each card were 200, 300, 400, 500, 600 and 700 respectively. Sixty percent of transactions carried out on each card were used to train the system while the remaining forty percent were used to test the system. The acquired attributes through each card were used as inputs in developing the system. Radial basis function was used for features extraction and the extracted features were moved to the Modified Ripple Down Rule engine that compared the profiling of the cardholder transaction information. The developed system was implemented on Matrix laboratory environment. The performance of the developed system was evaluated at 0.80 threshold using Sensitivity, Specificity, False Alarm Rate, Accuracy and Computational Time.
网络犯罪是一种以计算机为犯罪工具的非法行为;网络犯罪分子借助互联网在网络空间进行犯罪活动。现有的检测网络犯罪的技术大多可以检测到单个攻击，但在协调和分布式攻击方面却失败了。同时，大多数用于遏制网络犯罪的网络应用检测系统都会产生大量的误报。因此，本研究开发了一种增强的系统，不仅可以检测个人，协调和分布式攻击，还可以减少假警报的数量。这项工作的研究数据由六张卡片(标签为A, B, C, D, E和F)组成，来自一家网上购物商店。这六张牌包含四个属性，与2700笔交易相关。每张卡的交易次数分别为200、300、400、500、600和700次。每张卡上60%的交易被用来训练系统，而剩下的40%被用来测试系统。通过每张卡片获得的属性被用作开发系统的输入。利用径向基函数进行特征提取，将提取的特征移动到Modified Ripple Down Rule引擎中，对持卡人交易信息进行对比分析。开发的系统在Matrix实验室环境下实现。采用灵敏度、特异性、虚警率、准确率和计算时间等指标，以0.80阈值评价系统的性能。
{"title":"DEVELOPMENT OF AN EFFECTIVE SYSTEM FOR DETECTING CYBERCRIMES USING MODIFIED RIPPLE DOWN RULE SYSTEM AND NEURAL NETWORK.","authors":"D. G. Amusan, A. Falohun, Oladiran Tayo Arulogun","doi":"10.53555/cse.v9i5.5663","DOIUrl":"https://doi.org/10.53555/cse.v9i5.5663","url":null,"abstract":"Cybercrime is an unlawful act in which computer is the tools to commit an offense; cyber criminals perform operation in cyber space with the help of the internet. Most existing techniques used in detecting cybercrimes could detect individual attacks but failed in terms of coordinated and distributed attacks. Also, most of the detection system used to curb cybercrimes on web application generates a large number of false alarms. Hence, this research developed an enhanced system which could not only detect individual, coordinated and distributed attacks but also reduce the number of false alarms. The research data for this work which consists of six cards (labeled A, B, C, D, E and F) were sourced from an online shopping store. The six cards contain four attributes with associated two thousand seven hundred (2700) transactions. The number of transactions carried out through each card were 200, 300, 400, 500, 600 and 700 respectively. Sixty percent of transactions carried out on each card were used to train the system while the remaining forty percent were used to test the system. The acquired attributes through each card were used as inputs in developing the system. Radial basis function was used for features extraction and the extracted features were moved to the Modified Ripple Down Rule engine that compared the profiling of the cardholder transaction information. The developed system was implemented on Matrix laboratory environment. The performance of the developed system was evaluated at 0.80 threshold using Sensitivity, Specificity, False Alarm Rate, Accuracy and Computational Time.","PeriodicalId":130369,"journal":{"name":"IJRDO -Journal of Computer Science Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115228751","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}
Computing power has been increasing exponentially, meaning that processing power can be harnessed to solve more complex tasks. Two fields that have emerged alongside this rapid growth are data analytics, machine learning. Data analytic and Machine learning algorithms are two terms used interchangeably in the world of big data and statistical analysis, the line that divide these two related terms is so tiny that most data analyst forget about the existence of such line dividing and providing blur differences between data analytic and machine learning algorithms. In this study, the underlying the difference between these two related terms and their common and different applications is studied in a concise manner. Their impact in decision making for firms, organizations and cooperate bodies, their approaches to problem solutions, as well as their limitations.
{"title":"CONSERVATIVE SURVEY OF MACHINE LEARNING AND DATA ANALYTICS","authors":"O. Omotosho","doi":"10.53555/cse.v7i5.5621","DOIUrl":"https://doi.org/10.53555/cse.v7i5.5621","url":null,"abstract":"Computing power has been increasing exponentially, meaning that processing power can be harnessed to solve more complex tasks. Two fields that have emerged alongside this rapid growth are data analytics, machine learning. Data analytic and Machine learning algorithms are two terms used interchangeably in the world of big data and statistical analysis, the line that divide these two related terms is so tiny that most data analyst forget about the existence of such line dividing and providing blur differences between data analytic and machine learning algorithms. In this study, the underlying the difference between these two related terms and their common and different applications is studied in a concise manner. Their impact in decision making for firms, organizations and cooperate bodies, their approaches to problem solutions, as well as their limitations.","PeriodicalId":130369,"journal":{"name":"IJRDO -Journal of Computer Science Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131698399","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}
Human personality plays a vital role in individual's life as well as in the development of an organization. Common ways to evaluating human personality is by using standard questionnaires or by analyzing the Curriculum Vitae (CV). Traditionally, recruiters manually shortlist/filters a candidate’s CV as per their requirements. In this work, a system that automates the eligibility check and aptitude evaluation of candidates in a recruitment process is developed. To meet this need an automated system module is developed for the analysis of aptitude or personality test based on candidate’s CV. The work presented in this paper determines the personality trait of applicants through CV analysis using Python upon which the Personality prediction Model is built. The result helps in evaluating the qualities in the candidates by analyzing personality trait and character of such candidate. The system provides serves as a better option for the recruitment process so that candidate’s data can extracted from CV and shortlisted for the best decision via fair judgment.
{"title":"AUTOMATED PERSONALITY PREDICTIVE MODEL FOR E-RECRUITMENT USING LOGISTIC REGRESSION TECHNIQUE","authors":"O. Omotosho","doi":"10.53555/cse.v8i5.5620","DOIUrl":"https://doi.org/10.53555/cse.v8i5.5620","url":null,"abstract":"Human personality plays a vital role in individual's life as well as in the development of an organization. Common ways to evaluating human personality is by using standard questionnaires or by analyzing the Curriculum Vitae (CV). Traditionally, recruiters manually shortlist/filters a candidate’s CV as per their requirements. In this work, a system that automates the eligibility check and aptitude evaluation of candidates in a recruitment process is developed. To meet this need an automated system module is developed for the analysis of aptitude or personality test based on candidate’s CV. The work presented in this paper determines the personality trait of applicants through CV analysis using Python upon which the Personality prediction Model is built. The result helps in evaluating the qualities in the candidates by analyzing personality trait and character of such candidate. The system provides serves as a better option for the recruitment process so that candidate’s data can extracted from CV and shortlisted for the best decision via fair judgment.","PeriodicalId":130369,"journal":{"name":"IJRDO -Journal of Computer Science Engineering","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127290991","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}
Technology improvements have made it possible to communicate more effectively. New technologies have improved existing communication routes. Some real-time communication systems include limitations, such as the need for additional software plugins and downloads to enable real-time communication, as well as security problems. Web Real-time Communication (WebRTC) is a technology that may be able to assist in the resolution of these issues. Thanks to advancements in internet technology, people may now easily access the internet. With the help of technological improvements, the internet is providing an increasing variety of services, all of which can be virtualized. People's internet communication has become ingrained in their daily lives. People used to communicate with one another by exchanging voice chat messages via the internet.In order to achieve scalability, the platform also makes use of cloud computing. The iterative platform architectural design is revealed, as well as some preliminary scalability analysis results.
{"title":"VOICE CHAT WEB APP USING WEBRTC","authors":"Avdhesh Tiwari, Harshit Gupta, Sangam Mittal, Mayank Rawat","doi":"10.53555/cse.v8i11.5444","DOIUrl":"https://doi.org/10.53555/cse.v8i11.5444","url":null,"abstract":"Technology improvements have made it possible to communicate more effectively. New technologies have improved existing communication routes. Some real-time communication systems include limitations, such as the need for additional software plugins and downloads to enable real-time communication, as well as security problems. Web Real-time Communication (WebRTC) is a technology that may be able to assist in the resolution of these issues. Thanks to advancements in internet technology, people may now easily access the internet. With the help of technological improvements, the internet is providing an increasing variety of services, all of which can be virtualized. People's internet communication has become ingrained in their daily lives. People used to communicate with one another by exchanging voice chat messages via the internet.In order to achieve scalability, the platform also makes use of cloud computing. The iterative platform architectural design is revealed, as well as some preliminary scalability analysis results.","PeriodicalId":130369,"journal":{"name":"IJRDO -Journal of Computer Science Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122198178","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}
In order to speed up finding of causes of COVID-19 illness, this study developed novel diagnostic platform using profound convolutional neural network (CNN) helping radiologists diagnose COVID-19 pneumonia beside non-COVID-19 pneumonia in patient in Middle more Hospital. As the name suggests, crucial objective of our research is to produce a chest X-ray image classification program which could properly identify a scan's categorization as either "normal," "viral pneumonia," or "COVID-19." Using X-rays, we will train an image classifier to determine whether or not a person has COVID-19. In this data set, there are over 3000 chest X-ray pictures categorized in normal, viral, as well as COVID-19. A picture classifying system which properly identifies which of three categories Chest X-Ray scan corresponds with is purpose of this investigation.
{"title":"DETECTION OF COVID-19 WITH CHEST X-RAY","authors":"Ganesh Yadav, Shobhit Jain, Shikhar Singh, Shivam Shanna","doi":"10.53555/cse.v8i11.5443","DOIUrl":"https://doi.org/10.53555/cse.v8i11.5443","url":null,"abstract":"In order to speed up finding of causes of COVID-19 illness, this study developed novel diagnostic platform using profound convolutional neural network (CNN) helping radiologists diagnose COVID-19 pneumonia beside non-COVID-19 pneumonia in patient in Middle more Hospital. As the name suggests, crucial objective of our research is to produce a chest X-ray image classification program which could properly identify a scan's categorization as either \"normal,\" \"viral pneumonia,\" or \"COVID-19.\" Using X-rays, we will train an image classifier to determine whether or not a person has COVID-19. In this data set, there are over 3000 chest X-ray pictures categorized in normal, viral, as well as COVID-19. A picture classifying system which properly identifies which of three categories Chest X-Ray scan corresponds with is purpose of this investigation.","PeriodicalId":130369,"journal":{"name":"IJRDO -Journal of Computer Science Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122767297","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}
Artificial intelligence (AI) is a discipline of computing that focuses mostly on transferring human intelligence and mental processes into machines that can assist humans in many ways. Machine learning (ML) is the approach of choice in AI for creating useful software for computer vision, speech recognition, natural language processing, robot control, and other applications. Some of the most common analyses of large, complicated and multidimensional data sets in astronomy can be performed by using ML methods. It can be used for automating observatory scheduling to increase the effective utilization and scientific return from telescopes. It is also used for image recognition, classification of galaxies and planet recognition. This paper offers an in-depth review of the evolution of artificial intelligence and the use of AI and ML in the field of astronomy, especially for data analysis, image recognition, astronomical scheduling, classification of galaxies and planet recognition. It adds to the existing literature on use of artificial intelligence for astronomical applications and is a useful resource for students and researchers.
{"title":"HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW","authors":"Manit Rajendrakumar Patel","doi":"10.53555/cse.v8i11.5427","DOIUrl":"https://doi.org/10.53555/cse.v8i11.5427","url":null,"abstract":"Artificial intelligence (AI) is a discipline of computing that focuses mostly on transferring human intelligence and mental processes into machines that can assist humans in many ways. Machine learning (ML) is the approach of choice in AI for creating useful software for computer vision, speech recognition, natural language processing, robot control, and other applications. Some of the most common analyses of large, complicated and multidimensional data sets in astronomy can be performed by using ML methods. It can be used for automating observatory scheduling to increase the effective utilization and scientific return from telescopes. It is also used for image recognition, classification of galaxies and planet recognition. This paper offers an in-depth review of the evolution of artificial intelligence and the use of AI and ML in the field of astronomy, especially for data analysis, image recognition, astronomical scheduling, classification of galaxies and planet recognition. It adds to the existing literature on use of artificial intelligence for astronomical applications and is a useful resource for students and researchers.","PeriodicalId":130369,"journal":{"name":"IJRDO -Journal of Computer Science Engineering","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125471263","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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