1 10 6 https://repository.horizon.ac.id/files/original/fcfb8c1c07ba5cc080c1d8c91c63f686.pdf 487fec25bbc429336942dc4a5ccbd046 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 3 (2024) SEPTEMBER 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Spam Detection in Emails Using Machine Learning Techniques: A Review Subject The topic of the resource Spam, Class Imbalance, SMOTE, Feature Selection, Ensemble Learning Description An account of the resource Despite the vast amounts of data available within email communication systems, spam remains a persistent issue, posing challenges for both users and organizations. Analyzing this data holds the potential to develop more effective methods for detecting and mitigating spam emails. However, extracting actionable insights from this data and leveraging them to construct robust spam detection systems presents a significant challenge. Traditional approaches to combating spam, such as rule-based filtering and heuristic methods, have become increasingly inadequate due to the evolving tactics of spammers. Machine learning techniques offer a promising solution by enabling the training of predictive models using historical email data. However, the effectiveness of these models is influenced by factors such as class imbalance and the identification of relevant features essential for spam detection. This paper provides a comprehensive review of various machine learning techniques employed in spam detection within email communication systems. By examining the strengths and weaknesses of different approaches, we aim to identify strategies for improving the efficiency and accuracy of spam detection. Additionally, we propose a spam detection framework centered around ensemble learning models trained on balanced datasets using techniques like SMOTE, and featuring only the most relevant features. This approach is intended to enhance detection performance while reducing false positives, thereby offering a more effective solution to the challenge of spam detection in email systems. Creator An entity primarily responsible for making the resource Stanley Munga Ngigi1, Richard Mathenge3, Josphat Karani2, Nicholus Muriithi4 Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource September 2024 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/8838dbdcb30a19a3292c9b4c3f01e959.pdf 2dcb429d0f928c94ea370b2bf2b45a69 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 3 (2024) SEPTEMBER 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Visualization of Prediction of The Spread of Covid-19 in Indonesia using Cellular Automata Subject The topic of the resource cellular automata; covid-19, moore, virus Description An account of the resource The first case of COVID 19 was detected in Indonesia in early March 2020. One way to assist the government in making decisions to deal with COVID-19 is to create a map of the distribution of COVID-19 patients based on which can only be accessed by people who have an interest through the website. The data used in this study is the period, location, total cases. After getting the data, the data is then processed to get weekly rules. After getting the weekly rules, the data is entered into the calculation of the Moore scheme to get the prediction results for the next week. Then the prediction results are poured in the form of a map. The prediction process using CA neighbors is carried out using Moore's formula, a formula that applies the adjacent neighbors of 8 neighbors. The accuracy level of Cellular Automata with Moore's neighbors reaches 431.1466353% using MAPE. The error value in the Cellular Automata method is quite high due to several factors that make the prediction results with the original data results different, but this method can be used to research cases of covid 19 Creator An entity primarily responsible for making the resource Safira Aurellia Azzahra, *Dian Pratiwi, Syandra Sari Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource September 2024 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/565ef2635d3bfb77903012e16970f430.pdf 24c8f9580db64fd2aa5c7d75c3b540b7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 3 (2024) SEPTEMBER 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Optimized Q-Learning-Based Handover Decision Algorithm for Femtocells Using Load Balancing in LTE-A Networks Subject The topic of the resource lte-a; q-learning; load balancing; macrocell; femtocell; cbr; voip Description An account of the resource The rapid growth of mobile devices and demand for mobile data have made maintaining capacity, high coverage, and data speed challenging. With the emergence of small cell networks, the Long-Term Evolution (LTE) system helped to address these issues, Femtocell technology is being deployed to provide improved indoor coverage. However, a major challenge is the frequent handover and unequal distribution of cell loads, which lead to a reduction in call and data rates. Small cells have changing and unplanned load distribution over time, resulting in certain cells suffering high user density and strong resource competition, while others have low user density and wasteful resources due to low consumption. This imbalance in cell load distribution greatly influences overall network performance and prevents Femtocells from realizing their full potential. Despite several efforts by researchers to enhance network communication, handover is still a challenging issue, many related works have been done in the field but still it needs improvement. This research proposes an Optimized Q-learning-based Handover Decision Algorithm for Femtocells using Load Balancing in LTE-A Networks to improve overall network performance. The algorithm learns to prioritize and select cells with low load during target cell selection and not only provides good Quality of Service (QoS) but also has a low load, resulting in better traffic distribution across the cells. Several simulations were performed using LTE-Sim. Results proved the outperformance of the proposed algorithm over the existing algorithm in terms of QoS with a packet loss ratio for CBR packet transmission of 512 bytes with a rate of 8 packets/second intervals, 88.53%, and VoIP packet transmission of 32 bytes per 20 ms/time interval, 89.24% respectively. Creator An entity primarily responsible for making the resource Babangida Abubakar Albaba1,2, Aliyu Saidu1,2, Muhammad Sani Iliyasu, Abdulhamid Usman Nuruddeen, Kamal Sabo wada Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource September 2024 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/3a19ecb75847720f2db49726b459931d.pdf 2b8198fe6678f779191ef3b7a5a64119 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 3 (2024) SEPTEMBER 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Legal Frameworks for Digital Space Protection in Kenya Subject The topic of the resource Legal framework, digital landscape, cybercrime, cyberspace Description An account of the resource Strong cyber laws are necessary to protect digital spaces in Kenya, where the digital landscape is changing quickly. This need has grown. This article explores Kenya's legal framework for cyberspace, focusing on important topics such as privacy, cybercrime, data protection, and intellectual property rights. It investigates the nation's current state of cyber laws through a thorough assessment, determining whether it is sufficient to handle the problems brought about by emerging technologies and cyber threats. Furthermore, the article acknowledges the interdependence of national legislation with international trends and examines the critical role that international norms and standards have played in influencing Kenya's approach to cyber law. The paper highlights the importance of collaborative efforts between government agencies, private enterprises, and civil society in strengthening cyber resilience and promoting a secure digital environment. This article adds to the ongoing discussion on cyber law in Kenya by critically examining the current framework and suggesting possible improvements. Its goal is to promote the creation of a thorough and functional legal system that guarantees the nation's digital spaces are protected. Creator An entity primarily responsible for making the resource Richard Mathenge, Josphat Karani Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource September 2024 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/f4776a7a2ad656f4b49cad6e2663da6a.pdf 1acf117bdaf5da56178ea1b2c438fb81 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 3 (2024) SEPTEMBER 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource File Carving: Analyzing Data Retrieval in Digital Forensics Subject The topic of the resource Digital Forensic; File Carving; Data Recovery;File Carving Tools and Techniques. Description An account of the resource In the current scenario, mostly the data are stored in digital media. Managing the storage and security of huge volume of data is emerging as a significant challenge for data science researchers and engineers. As data is considered as more costly and powerful than anything else, so during damage or loss of data thousands of dollars are being invested for data recovery. File caving is a technique used for data recovery from the file without the any contextual information when the storage media is formatted or file system got damaged. In this study, we have tried to describe the various types of file caving techniques and the tools used for file caving, along with their limitations and the categories of files which are supported along with the scenario for such recovery. Creator An entity primarily responsible for making the resource Purna Chandra Sethi, Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource September 2024 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/719a8512ea431b286b80ffdbf8e6fbe3.pdf c99f6e62bf8804a0be939c2d19c846eb Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 3 (2024) SEPTEMBER 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Cyber Security: The Role of Networking, Coordination, and Trusted Information Sharing in Organizational Resilience Subject The topic of the resource Cybersecurity, Networking, Coordination, Information Sharing, Security Measures, Security Posture, Incident Response, Threat Intelligence, Network Segmentation, Access Control, Insider Threats, Information Overload, Standardization, Collaboration Defence, Competing Priorities, Trust Relationships, Data Breach, Vulnerability Assessment Description An account of the resource Organizations must take proactive measures to strengthen their cybersecurity posture as cyber-attacks continue to develop and become more sophisticated. Networking, coordination, and the sharing of trustworthy information among entities and organizations have become essential tactics for achieving this goal. This article gives a general overview of how networking, coordination, and trusted information sharing can improve an organization's cybersecurity posture. It covers the most recent advancements and trends in this field and analyses the advantages, difficulties, and best practices for putting these ideas into effect. The article's conclusion emphasizes the necessity of a cooperative, all-encompassing strategy for cybersecurity that incorporates networking, coordination, and reliable information exchange. Creator An entity primarily responsible for making the resource Duncan Nyale, Charles Katila Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource September 2024 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text