Mesopotamian Journal of Big Data最新文献

{"title":"MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty","authors":"Bushra Ali, Khder Alakkari, Mostafa Abotaleb, Maad M. Mijwil, K. Dhoska","doi":"10.58496/mjbd/2024/005","DOIUrl":"https://doi.org/10.58496/mjbd/2024/005","url":null,"abstract":"In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128727","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}

{"title":"Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach","authors":"Idrees A. Zahid, Shahad Sabbar Joudar","doi":"10.58496/mjbd/2024/003","DOIUrl":"https://doi.org/10.58496/mjbd/2024/003","url":null,"abstract":"Considering the prevailing rule of Large Language Models (LLMs) applications and the benefits of XML in a compiler context. This manuscript explores the synergistic integration of Large Language Models with XML-based compiler tools and advanced computing technologies. Marking a significant stride toward redefining compiler construction and data representation paradigms. As computing power and internet proliferation advance, XML emerges as a pivotal technology for representing, exchanging, and transforming documents and data. This study builds on the foundational work of Chomsky's Context-Free Grammar (CFG). Recognized for their critical role in compiler construction, to address and mitigate the speed penalties associated with traditional compiler systems and parser generators through the development of an efficient XML parser generator employing compiler techniques. Our research employs a methodical approach to harness the sophisticated capabilities of LLMs, alongside XML technologies.  The key is to automate grammar optimization, facilitate natural language processing capabilities, and pioneer advanced parsing algorithms. To demonstrate their effectiveness, we thoroughly run experiments and compare them to other techniques. This way, we call attention to the efficiency, adaptability, and user-friendliness of the XML-based compiler tools with the help of these integrations. And the target will be the elimination of left-recursive grammars and the development of a global schema for LL(1) grammars, the latter taking advantage of the XML technology, to support the LL(1) grammars construction. The findings in this research not only underscore the signification of these innovations in the field of compilation construction but also indicate a paradigm move towards the use of AI technologies and XML in the context of the resolution of programming traditional issues. The outlined methodology serves as a roadmap for future research and development in compiler technology, which paves the way for open-source software to sweep across all fields. Gradually ushering in a new era of compiler technology featuring better efficiency, adaptability, and all CFGs processed through existing XML utilities on a global basis.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388980","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}

{"title":"Leveraging AI and Big Data in Low-Resource Healthcare Settings","authors":"Ahmed Hussein Ali, Saad Ahmed Dheyab, Abdullah Hussein Alamoodi, Aws A. Magableh, Yuantong Gu","doi":"10.58496/mjbd/2024/002","DOIUrl":"https://doi.org/10.58496/mjbd/2024/002","url":null,"abstract":"Big data and artificial intelligence are game-changing technologies for the underdeveloped healthcare industry because they help optimize the entire supply chain and deliver more exact patient outcome information. Machine learning approaches that have recently seen more growing popularity include deep learning models that have brought revolution within the healthcare system in the previous years due to more complicated data compared to previous years . Machine learning is an essential data analysis procedure to describe efficient and effective methods to extract hidden information from large amounts of data that it would take logical analytics too long to manage. Recent years have seen an expansion and growth of advanced intelligent systems that have been able to learn more about clinical treatments and glean untapped medical information emanating from vast quantities of data when it comes to drug discovery and chemistry. The aim of this chapter is, therefore, to assess which big data and artificial intelligence approaches are prevalent in healthcare systems by investigating the most advanced big data structures, applications, and industry trends today available. First and foremost, the purpose is to provide a comprehensive overview of how the artificial intelligence and big data models can allocation in healthcare solutions fill the gap between machine learning approaches’ lack of human coverage and the healthcare data’s complexity. Moreover, current artificial intelligence technologies, including generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, are also increasingly being used for drug discovery and chemistry . Finally, the work presents the existing open challenges and the future directions in the drug formulation development field. To this end, the review will cover on published algorithms/automation tools for artificial intelligence applied to large scale-data in the case of healthcare .","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"183 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140456639","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}

{"title":"Assessing the Transformative Influence of ChatGPT on Research Practices among Scholars in Pakistan","authors":"Adnan Ullah, Mehran Ullah Baber, Nayab Arshad","doi":"10.58496/mjbd/2024/001","DOIUrl":"https://doi.org/10.58496/mjbd/2024/001","url":null,"abstract":"This article investigates the transformative impact of ChatGPT on research practices within the scholarly community in Pakistan. ChatGPT, a powerful AI language model, has added significant consideration for its possibility of improving academic research. Survey data was gathered via a structured questionnaire distributed to researchers in Pakistan. A total of 278 questionnaires were distributed for the randomly chosen sample, of which 223 were returned. For calculating descriptive statistics, SPSS was utilized.  Results of the study indicated that 90% of scholars are familiar with the practice of ChatGPT in research activities. 86% of scholars used 3.5 (Basic version) of ChatGPT for their research and only 14% used 4 (Plus version) of ChatGPT for their research work. The overall satisfaction level was 46% response satisfied with the usage of ChatGPT in research activities. The article discusses how ChatGPT's natural language processing capabilities have advanced literature reviews, data analysis, and content generation, thereby saving time and fostering greater productivity. Moreover, it examines how the tool’s accessibility and affordability have democratized research, making it more inclusive and open to a broader range of scholars. By shedding light on these critical aspects, this article provides valuable insights into the evolving landscape of research practices in Pakistan and highlights the potential for ChatGPT to revolutionize academic scholarship in the digital age.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"66 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139534562","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}

{"title":"Navigating the Void: Uncovering Research Gaps in the Detection of Data Poisoning Attacks in Federated Learning-Based Big Data Processing: A Systematic Literature Review","authors":"Mohammad Aljanabi","doi":"10.58496/mjbd/2023/019","DOIUrl":"https://doi.org/10.58496/mjbd/2023/019","url":null,"abstract":"This systematic literature review scrutinizes the landscape of research at the intersection of federated learning, big data processing, and data poisoning attacks. Employing a meticulous search strategy across multiple databases, the study unveils a surge in annual scientific production, emphasizing a growing interest in federated learning and related fields. However, a critical research gap becomes evident during the investigation of data poisoning attacks specifically in the context of federated learning when processing big data. The most relevant keywords and a visually compelling word cloud further illuminate the prevailing themes and emphases within the literature, emphasizing the lack of explicit focus on detecting data poisoning attacks. This identified gap presents a significant avenue for future research, offering opportunities to enhance the security and robustness of federated learning systems against adversarial threats in large-scale data scenarios.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"24 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594270","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}

{"title":"Mapping the Evolution of Intrusion Detection in Big Data: A Bibliometric Analysis","authors":"M. Yaseen, A. S. Albahri","doi":"10.58496/mjbd/2023/018","DOIUrl":"https://doi.org/10.58496/mjbd/2023/018","url":null,"abstract":"This study provides a comprehensive analysis of the dynamic amalgamation of intrusion detection and big data, revealing trends and patterns within cybersecurity research. The investigation reveals a notable surge in scholarly output from 2018 onwards, reflecting heightened interest and exploration within the field. Dominant themes such as \"intrusion detection,\" \"big data,\" and \"machine learning\" underscore the integration of security concerns with advanced technologies. Geographical influences showcase diverse contributions, with varying citation impacts from countries like India, China, and Saudi Arabia. Author contributions reveal a balance between prolific authors and impactful contributions from authors with fewer publications. Recommendations include fostering interdisciplinary collaborations, integrating advanced computational methods, and conducting longitudinal studies to gauge sustained impacts. This research underscores collaboration dynamics, thematic evolution, and global influences as pivotal facets within the realm of intrusion detection and big data, guiding future research to fortify digital security in an ever-evolving technological landscape.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"92 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138605960","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}

{"title":"Climate Changes through Data Science: Understanding and Mitigating Environmental Crisis","authors":"Ahmed Hussein Ali, Rahul Thakkar","doi":"10.58496/mjbd/2023/017","DOIUrl":"https://doi.org/10.58496/mjbd/2023/017","url":null,"abstract":"Climate change represents an urgent environmental crisis with far-reaching risks to ecosystems and human communities worldwide. Rapid development of mitigation strategies and solutions is imperative but relies profoundly on advancements in detection, attribution, and prediction derived from climate data analytics. This paper examines the growing role of data science in not only quantifying anthropogenic climate change but also informing impact assessment and targeted intervention across climate-sensitive sectors. First, we survey established and emerging techniques for climate characterization, including machine learning applications on Earth systems data. Next, we discuss how sophisticated climate models alongside statistical analysis of multi-domain datasets—from migration patterns to crop yields—deepens scientific comprehension of climate change repercussions. Building on these insights, we spotlight data-enabled solution paradigms enabling smart climate action, ranging from high-resolution climate risk mapping, emissions reductions via optimized renewable energy infrastructure, to global warming suppression via solar radiation management. However, we also carefully examine the practical limitations hindering deployment and the ethical concerns posed by certain climate intervention proposals. Ultimately, while data science delivers powerful tools for climate change detection, attribution, and response, this paper underscores how continued climate data gathering alongside cross-disciplinary collaboration is vital to overcome analytical uncertainties, implementation barriers, and moral objections as we work to avert profound environmental breakdown.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"81 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138606152","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}

{"title":"Optimizing Big Data Analytics for Reliability and Resilience: A Survey of Techniques and Applications","authors":"El-Houcine El Baqqaly, Alaa Hussein Khaleel","doi":"10.58496/mjbd/2023/016","DOIUrl":"https://doi.org/10.58496/mjbd/2023/016","url":null,"abstract":"The advent of big data has revolutionized various industries, enabling organizations to make data- driven decisions and gain valuable insights. However, the sheer volume, velocity, and variety of big data pose significant challenges in ensuring the reliability and resilience of big data analytics pipelines. In this context, optimization techniques play a crucial role in enhancing the reliability and resilience of big data analytics. This paper provides a comprehensive survey of optimization techniques for reliable and resilient big data analytics. The paper first discusses the motivation for optimizing big data analytics for reliability and resilience. Then, it presents a detailed overview of various optimization techniques, including resource optimization, data partitioning, data compression, load balancing, and fault detection and tolerance. Finally, the paper discusses the applications of optimization techniques in various big data analytics domains, such as real-time analytics, fraud detection, recommendation systems, predictive analytics, and risk management.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"61 24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139261677","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}

{"title":"A Novel Approach of Reducing Energy Consumption by utilizing Big Data analysis in Mobile Cloud Computing","authors":"Mostafa Abdulghafoor Mohammed, Nicolae Țăpuș","doi":"10.58496/mjbd/2023/015","DOIUrl":"https://doi.org/10.58496/mjbd/2023/015","url":null,"abstract":"With the rapid proliferation of smart mobile devices and increasing adoption of cloud computing services, energy efficiency has become an important issue in mobile cloud environments. High energy consumption not only results in higher operational costs but also creates sustainability concerns related to cloud infrastructure and services. This paper proposes leveraging big data techniques such as machine learning and predictive analytics to optimize resource allocation and reduce energy consumption in mobile cloud computing. The massive amount of data on factors like user behavior, mobility patterns, network availability, and resource utilization can provide key insights to improve energy efficiency. We present an intelligent predictive framework to forecast mobile cloud resource demands and enable dynamic scaling of cloud configurations aligned to current needs. By proactively adapting cloud resources based on learned models and detected usage patterns, over-provisioning and under-utilization can be minimized. Specifically, we demonstrate how clustering, classification, regression, and times series models derived from contextual usage data can significantly improve energy efficiency when integrated with mobile cloud management systems. The proposed approaches are validated experimentally using simulated workloads and real-world trajectory data sets. Results indicate average energy savings of 42% and up to 62% for certain user groups compared to conventional cloud resource allocation techniques. This work provides an important contribution toward building more sustainable and energy efficient mobile cloud computing systems to meet the mobility and computing demands of the future through the transformative power of big data analytics.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139288633","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}

{"title":"From Machine Learning to Artificial General Intelligence: A Roadmap and Implications","authors":"O. I. Obaid","doi":"10.58496/mjbd/2023/012","DOIUrl":"https://doi.org/10.58496/mjbd/2023/012","url":null,"abstract":"The prospect of developing artificial general intelligence (AGI) with the same comprehensive capabilities as the human mind presents humanity both tremendous opportunities and dire risks. This paper explores the potential applications and implications of AGI across diverse domains including science, healthcare, education, security, and the economy. However, realizing AGI's benefits requires proactive alignment of its goals and values with those of humanity through responsible governance. As AGI approaches and possibly surpasses human-level intellectual abilities, we must grapple with complex ethical issues surrounding autonomy, consciousness, and disruptive societal impacts. The exact timeline for achieving AGI remains uncertain, but its emergence will likely stem from the convergence of advanced technologies like big data, neural networks, and quantum computing. Ultimately, the creation of AGI represents humanity's greatest opportunity to profoundly enhance flourishing, as well as our greatest challenge to steer its development toward benevolence rather than catastrophe. With sage preparation and foresight, AGI could usher in an unparalleled era of insight and invention for the betterment of all people. But without adequate safeguards and alignment, its disruptive potential could prove catastrophically destabilizing. This paper argues prudently governing the transition to AGI is essential for harnessing its transformative power to elevate rather than endanger our collective future.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129154095","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}