This study embarks on a comprehensive analysis to evaluate the efficiency and scalability of in-memory computing (IMC) compared to traditional disk-based processing for big data analytics. Utilising the "New York City Taxi Trip Duration" dataset from Kaggle, we designed an experiment focusing on three critical analytical tasks: aggregation, sorting, and filtering. Our objective was to quantify the performance improvements offered by IMC, as facilitated by Apache Spark, against conventional SQL queries executed on a disk-based system. The findings reveal that IMC consistently outperforms disk-based processing in execution time, with significant reductions observed across all tasks. Specifically, the aggregation task highlighted the stark contrast in data retrieval speed, demonstrating IMC's superior efficiency with a completion time of 47.3 seconds, compared to 138.7 seconds for disk-based processing. Similar disparities were noted in sorting and filtering tasks, further accentuating IMC's performance advantage. Resource utilisation analysis, focusing on CPU and RAM consumption, indicated higher demands associated with IMC, underscoring the trade-off between enhanced speed and increased resource usage. The investigation provides a nuanced understanding of the practical implications of adopting IMC for big data analytics, especially considering the resource constraints of home computing environments. By juxtaposing theoretical advantages with empirical data, this paper contributes to the ongoing discourse on optimising data processing methodologies in the era of big data, offering insights into the balance between computational efficiency and resource management. Keywords: in-memory computing, big data analytics, disk-based processing, data processing efficiency, resource utilisation, Apache Spark, data analytics performance DOI: 10.7176/CEIS/15-1-05 Publication date: April 30 th 2024
This study examined effects of credit reference system on the right to privacy of customers of financial institutions. The credit reference system being a medium for the exchange of personal information of customers of financial institutions taking part therein; aims at facilitating credit market efficiency and stability. The right to privacy on other hand tries to keep to the minimum the intrusion to private domain of persons, customers of financial institutions being one of them. This article critically examined how these two seemingly conflicting interests have been compromised under the Ethiopian laws. A doctrinal research approach was employed in the analysis of the Ethiopian laws on the right to privacy and laws governing the credit reference system . The review of literature and analysis of legal instruments of both national and international character have been used to substantiate the arguments set forth in this work.This work concluded that even though violation of the right to privacy of the customers of financial institutions can be justified on the ground of voluntary relinquishment such rights; the Ethiopian credit reference system still needs reconsideration in light of principles recognized internationally for the protection of the right to privacy of persons. This work recommends for the revision of the Ethiopian credit reference system for better protection of the right to privacy of customers of financial institutions. Keywords: rights, privacy, credit reference system, financial institutions, Ethiopia DOI: 10.7176/CEIS/15-1-03 Publication date: January 31 st 2024
Artificial Intelligence (AI), advanced alternative to human mind, created by human mind, to simulate human intelligence in machines to think, perform and mimic human cognitive functions. Like humans, it uses various techniques like visual perception, speech recognition, decision-making, and language translation. One of growing security treats in country is with sleeper cells, a groups of individuals or agents operating undercover, within a society as normal people, until activated for a specific purpose, but identifying sleeper cells among community is a complex task that involves various intelligence and security measures. Artificial Intelligence (AI) can play a role in enhancing some aspects of this process, but it is important to note that AI alone may not be a panacea for such challenges due to ethical, legal, and technical considerations. This paper describes how AI help to identify sleeper cells. Keywords: Sleeper cells, Behavioural analysis, Artificial Intelligence, Defence, Security threats DOI: 10.7176/CEIS/15-1-02 Publication date: January 31 st 2024
This study embarks on a comprehensive analysis to evaluate the efficiency and scalability of in-memory computing (IMC) compared to traditional disk-based processing for big data analytics. Utilising the "New York City Taxi Trip Duration" dataset from Kaggle, we designed an experiment focusing on three critical analytical tasks: aggregation, sorting, and filtering. Our objective was to quantify the performance improvements offered by IMC, as facilitated by Apache Spark, against conventional SQL queries executed on a disk-based system. The findings reveal that IMC consistently outperforms disk-based processing in execution time, with significant reductions observed across all tasks. Specifically, the aggregation task highlighted the stark contrast in data retrieval speed, demonstrating IMC's superior efficiency with a completion time of 47.3 seconds, compared to 138.7 seconds for disk-based processing. Similar disparities were noted in sorting and filtering tasks, further accentuating IMC's performance advantage. Resource utilisation analysis, focusing on CPU and RAM consumption, indicated higher demands associated with IMC, underscoring the trade-off between enhanced speed and increased resource usage. The investigation provides a nuanced understanding of the practical implications of adopting IMC for big data analytics, especially considering the resource constraints of home computing environments. By juxtaposing theoretical advantages with empirical data, this paper contributes to the ongoing discourse on optimising data processing methodologies in the era of big data, offering insights into the balance between computational efficiency and resource management. Keywords: in-memory computing, big data analytics, disk-based processing, data processing efficiency, resource utilisation, Apache Spark, data analytics performance DOI: 10.7176/CEIS/15-1-06 Publication date: April 30 th 2024
Lung cancer is a major contributor to cancer-related mortality globally, and timely identification is essential for enhancing patient prognosis. Recently, deep learning methods, specifically Convolutional Neural Networks (CNN), have demonstrated encouraging outcomes in image-based medical diagnosis. The paper suggests utilising a CNN-based method to diagnose lung cancer from a healthcare image dataset, with a specific focus on histopathological image data. The proposed CNN approach utilises the natural hierarchical characteristics found in healthcare imagery to autonomously acquire distinctive features that indicate lung cancer. Transfer learning from extensive image datasets and improving models that have been trained are used to tackle the limitations of limited healthcare image datasets successfully. The CNN model utilising the VGG-19 architecture is developed and tested on a comprehensive dataset of lung cancer patients. Following extensive testing and evaluation, the model demonstrates high accuracy as well as precision and recall in diagnosing lung cancer using medical imaging. Interpretability techniques are utilised to get insights into the model's decision-making process, hence increasing its transparency and therapeutic relevance. The proposed CNN-based technology has the potential to help radiologists and clinicians discover and diagnose lung cancer earlier, leading to better patient care and treatment outcomes. Keywords: Lung Cancer, Histopathological image, Convolutional Neural Networks, VGG-19, Deep Learning DOI: 10.7176/CEIS/15-1-04 Publication date: March 31 st 2024
