The paper presents an insightful overview of the intricate legal challenges posed by the proliferation of Artificial Intelligence (AI) and Robotics. This comprehensive review explores the multifaceted dimensions of the evolving legal landscape, addressing issues at the intersection of technology and law. Key focal points include the accountability and liability frameworks for autonomous AI systems, ethical considerations in the deployment of intelligent machines, and the complex dynamics of data privacy in the age of pervasive automation. The review delves into the intricate legal nuances surrounding intellectual property rights, particularly as AI systems contribute to creative outputs and innovation. It navigates the blurred lines between human and machine authorship, raising fundamental questions about ownership and protection in this digital era. Moreover, the paper emphasizes the global nature of these challenges, highlighting the imperative for international cooperation to formulate harmonized legal standards. As AI and robotics revolutionize industries and societal frameworks, the analysis underscores the critical need for adaptive and anticipatory legal frameworks. It explores how existing legal paradigms are grappling with the unprecedented speed of technological advancements and the ethical dilemmas arising from the delegation of decision-making to intelligent algorithms. The paper sets the stage for a thorough examination of the legal intricacies surrounding AI and robotics. It advocates for a proactive and collaborative approach, involving legal experts, technologists, ethicists, and policymakers in crafting robust frameworks that balance innovation with ethical, privacy, and accountability considerations. This review serves as a foundational resource for understanding and addressing the legal challenges inherent in the transformative era of Artificial Intelligence and Robotics. Keywords: Artificial intelligence, Robotics, Legal, AI challenges, Ethics, Review.
{"title":"LEGAL CHALLENGES OF ARTIFICIAL INTELLIGENCE AND ROBOTICS: A COMPREHENSIVE REVIEW","authors":"Chidiogo Uzoamaka Akpuokwe, Adekunle Oyeyemi Adeniyi, Seun Solomon Bakare, Nkechi Emmanuella Eneh","doi":"10.51594/csitrj.v5i3.860","DOIUrl":"https://doi.org/10.51594/csitrj.v5i3.860","url":null,"abstract":"The paper presents an insightful overview of the intricate legal challenges posed by the proliferation of Artificial Intelligence (AI) and Robotics. This comprehensive review explores the multifaceted dimensions of the evolving legal landscape, addressing issues at the intersection of technology and law. Key focal points include the accountability and liability frameworks for autonomous AI systems, ethical considerations in the deployment of intelligent machines, and the complex dynamics of data privacy in the age of pervasive automation. The review delves into the intricate legal nuances surrounding intellectual property rights, particularly as AI systems contribute to creative outputs and innovation. It navigates the blurred lines between human and machine authorship, raising fundamental questions about ownership and protection in this digital era. Moreover, the paper emphasizes the global nature of these challenges, highlighting the imperative for international cooperation to formulate harmonized legal standards. As AI and robotics revolutionize industries and societal frameworks, the analysis underscores the critical need for adaptive and anticipatory legal frameworks. It explores how existing legal paradigms are grappling with the unprecedented speed of technological advancements and the ethical dilemmas arising from the delegation of decision-making to intelligent algorithms. The paper sets the stage for a thorough examination of the legal intricacies surrounding AI and robotics. It advocates for a proactive and collaborative approach, involving legal experts, technologists, ethicists, and policymakers in crafting robust frameworks that balance innovation with ethical, privacy, and accountability considerations. This review serves as a foundational resource for understanding and addressing the legal challenges inherent in the transformative era of Artificial Intelligence and Robotics. \u0000Keywords: Artificial intelligence, Robotics, Legal, AI challenges, Ethics, Review.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"269 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140255873","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 Review provides an overview of the comparative review of data privacy laws and compliance, focusing on the European Union's General Data Protection Regulation (EU GDPR) and data protection regulations in the United States. The analysis explores key similarities and differences, emphasizing their implications for businesses and individuals. The EU GDPR, implemented in 2018, stands as a landmark regulation governing data protection and privacy for individuals within the European Union and the European Economic Area. In contrast, the United States lacks a comprehensive federal data privacy law. Instead, it relies on a patchwork of sector-specific laws and state regulations, such as the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA). One major distinction lies in the overarching principles of these regulations. The EU GDPR adopts a comprehensive and rights-based approach, emphasizing individual rights to privacy, data portability, and the "right to be forgotten." In contrast, the U.S. system often focuses on specific industries or types of data, leading to a more fragmented regulatory landscape. Both regulatory frameworks incorporate principles of transparency, consent, and data breach notification. However, differences in enforcement mechanisms and penalties exist. The EU GDPR imposes significant fines for non-compliance, reaching up to 4% of a company's global annual revenue. In the U.S., penalties vary by state, and enforcement is often reactive, triggered by data breaches. Businesses operating globally must navigate these distinct regulatory landscapes, necessitating a nuanced approach to data privacy compliance. Multinational corporations must adhere to the more stringent requirements when handling EU citizens' data while also considering the diverse regulations within the U.S. This review underscores the ongoing evolution of data privacy laws worldwide and the critical importance for organizations to stay abreast of these developments. It emphasizes the need for a proactive and adaptive approach to data privacy compliance, taking into account the unique requirements and expectations of both the EU GDPR and U.S. regulations. Keywords: Data Privacy, Laws, Compliance, EU GDPR, Regulations.
{"title":"DATA PRIVACY LAWS AND COMPLIANCE: A COMPARATIVE REVIEW OF THE EU GDPR AND USA REGULATIONS","authors":"Seun Solomon Bakare, Adekunle Oyeyemi Adeniyi, Chidiogo Uzoamaka Akpuokwe, Nkechi Emmanuella Eneh","doi":"10.51594/csitrj.v5i3.859","DOIUrl":"https://doi.org/10.51594/csitrj.v5i3.859","url":null,"abstract":"This Review provides an overview of the comparative review of data privacy laws and compliance, focusing on the European Union's General Data Protection Regulation (EU GDPR) and data protection regulations in the United States. The analysis explores key similarities and differences, emphasizing their implications for businesses and individuals. The EU GDPR, implemented in 2018, stands as a landmark regulation governing data protection and privacy for individuals within the European Union and the European Economic Area. In contrast, the United States lacks a comprehensive federal data privacy law. Instead, it relies on a patchwork of sector-specific laws and state regulations, such as the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA). One major distinction lies in the overarching principles of these regulations. The EU GDPR adopts a comprehensive and rights-based approach, emphasizing individual rights to privacy, data portability, and the \"right to be forgotten.\" In contrast, the U.S. system often focuses on specific industries or types of data, leading to a more fragmented regulatory landscape. Both regulatory frameworks incorporate principles of transparency, consent, and data breach notification. However, differences in enforcement mechanisms and penalties exist. The EU GDPR imposes significant fines for non-compliance, reaching up to 4% of a company's global annual revenue. In the U.S., penalties vary by state, and enforcement is often reactive, triggered by data breaches. Businesses operating globally must navigate these distinct regulatory landscapes, necessitating a nuanced approach to data privacy compliance. Multinational corporations must adhere to the more stringent requirements when handling EU citizens' data while also considering the diverse regulations within the U.S. This review underscores the ongoing evolution of data privacy laws worldwide and the critical importance for organizations to stay abreast of these developments. It emphasizes the need for a proactive and adaptive approach to data privacy compliance, taking into account the unique requirements and expectations of both the EU GDPR and U.S. regulations. \u0000Keywords: Data Privacy, Laws, Compliance, EU GDPR, Regulations.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"216 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140256005","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}
Pub Date : 2024-02-26DOI: 10.51594/csitrj.v5i2.837
Musa Abubakar, Abba Hamman Maidabara, Yusuf Musa Malgwi, Abdulrahman Mohammed
The cases of heart diseases are increasing at a rapid rate and it’s very important to take precaution to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on wen based heart disease prediction technique based on various medical attributes. Heart disease prediction system were prepared to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and Naïve Bayes to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using Naïve Bayes and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease. Keywords: Web Based, Heart, Disease, Prediction Model, Machine Learning.
{"title":"WEB BASED HEART DISEASE PREDICTION MODEL USING MACHINE LEARNING TECHNIQUE","authors":"Musa Abubakar, Abba Hamman Maidabara, Yusuf Musa Malgwi, Abdulrahman Mohammed","doi":"10.51594/csitrj.v5i2.837","DOIUrl":"https://doi.org/10.51594/csitrj.v5i2.837","url":null,"abstract":"The cases of heart diseases are increasing at a rapid rate and it’s very important to take precaution to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on wen based heart disease prediction technique based on various medical attributes. Heart disease prediction system were prepared to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and Naïve Bayes to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using Naïve Bayes and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease. \u0000Keywords: Web Based, Heart, Disease, Prediction Model, Machine Learning.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"45 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431273","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}
Pub Date : 2024-02-18DOI: 10.51594/csitrj.v5i2.791
Adebunmi Okechukwu Adewusi, Ugochukwu Ikechukwu Okoli, Ejuma Adaga, Temidayo Olorunsogo, Onyeka Franca Asuzu, Donald Obinna Daraojimba
In the contemporary business landscape, the proliferation of Big Data has revolutionized the way organizations gather, process, and utilize information for strategic decision-making. This paper provides a comprehensive overview of the evolving role of Business Intelligence (BI) in harnessing the potential of Big Data and the subsequent impact on gaining a competitive advantage. The review delves into the arsenal of analytical tools that have emerged to handle the vast volumes of data generated in the digital age. From traditional reporting and querying to advanced analytics, machine learning, and predictive modeling, organizations now have a myriad of options to extract valuable insights from their data reservoirs. This paper investigates the efficiency, scalability, and adaptability of these tools in the context of Big Data, emphasizing their role in transforming raw data into actionable intelligence. Furthermore, the paper explores how the integration of BI and Big Data analytics contributes to the development of a competitive edge for businesses. The ability to harness insights from diverse data sources provides organizations with a holistic view of market trends, consumer behavior, and operational efficiency. This, in turn, empowers decision-makers to make informed and timely choices, enhancing overall organizational agility and responsiveness to market dynamics. The study also highlights the challenges associated with implementing BI in the era of Big Data, including issues related to data quality, security, and the need for skilled professionals. Effective solutions to these challenges are discussed, emphasizing the importance of a robust data governance framework and continuous investment in talent development. This paper underscores the pivotal role of Business Intelligence in leveraging the potential of Big Data for gaining a competitive advantage. As organizations navigate the complexities of the modern business landscape, the judicious use of analytical tools and the integration of BI with Big Data analytics stand as key drivers for informed decision-making and sustainable success. Keywords: Business Intelligence, Big Data, Analytical Tool, Business, AI, Review.
{"title":"BUSINESS INTELLIGENCE IN THE ERA OF BIG DATA: A REVIEW OF ANALYTICAL TOOLS AND COMPETITIVE ADVANTAGE","authors":"Adebunmi Okechukwu Adewusi, Ugochukwu Ikechukwu Okoli, Ejuma Adaga, Temidayo Olorunsogo, Onyeka Franca Asuzu, Donald Obinna Daraojimba","doi":"10.51594/csitrj.v5i2.791","DOIUrl":"https://doi.org/10.51594/csitrj.v5i2.791","url":null,"abstract":"In the contemporary business landscape, the proliferation of Big Data has revolutionized the way organizations gather, process, and utilize information for strategic decision-making. This paper provides a comprehensive overview of the evolving role of Business Intelligence (BI) in harnessing the potential of Big Data and the subsequent impact on gaining a competitive advantage. The review delves into the arsenal of analytical tools that have emerged to handle the vast volumes of data generated in the digital age. From traditional reporting and querying to advanced analytics, machine learning, and predictive modeling, organizations now have a myriad of options to extract valuable insights from their data reservoirs. This paper investigates the efficiency, scalability, and adaptability of these tools in the context of Big Data, emphasizing their role in transforming raw data into actionable intelligence. Furthermore, the paper explores how the integration of BI and Big Data analytics contributes to the development of a competitive edge for businesses. The ability to harness insights from diverse data sources provides organizations with a holistic view of market trends, consumer behavior, and operational efficiency. This, in turn, empowers decision-makers to make informed and timely choices, enhancing overall organizational agility and responsiveness to market dynamics. The study also highlights the challenges associated with implementing BI in the era of Big Data, including issues related to data quality, security, and the need for skilled professionals. Effective solutions to these challenges are discussed, emphasizing the importance of a robust data governance framework and continuous investment in talent development. This paper underscores the pivotal role of Business Intelligence in leveraging the potential of Big Data for gaining a competitive advantage. As organizations navigate the complexities of the modern business landscape, the judicious use of analytical tools and the integration of BI with Big Data analytics stand as key drivers for informed decision-making and sustainable success. \u0000Keywords: Business Intelligence, Big Data, Analytical Tool, Business, AI, Review.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"193 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452509","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}
Data encryption is a critical aspect of modern information security, and understanding the approaches taken by different regions is vital for a comprehensive analysis. In the United States and Europe, data encryption methods vary in implementation, legal frameworks, and overall priorities. In the United States, encryption methods are primarily governed by a combination of federal laws and industry standards. The National Institute of Standards and Technology (NIST) plays a central role in recommending cryptographic standards, while the Department of Commerce oversees export controls on encryption technology. The focus in the U.S. is on balancing national security needs with individual privacy rights. The tension between law enforcement's desire for access to encrypted data for criminal investigations and the right to privacy has sparked debates and legal battles. On the other hand, Europe has taken a more privacy-centric approach to data protection. The General Data Protection Regulation (GDPR) is a cornerstone in the European Union's efforts to safeguard individual privacy rights. GDPR mandates the use of encryption to protect personal data, and failure to implement adequate measures can result in hefty fines. European countries also emphasize the importance of end-to-end encryption in communication services to ensure confidentiality. Both regions prioritize encryption, but their approaches reflect different values and legal philosophies. The U.S. tends to navigate a delicate balance between national security and individual rights, while Europe places a stronger emphasis on the protection of personal data as a fundamental right. In terms of technological implementation, the encryption algorithms adopted in both regions are often aligned with global standards. Advanced Encryption Standard (AES) is widely accepted and implemented in various sectors. However, the choice of key management and the level of regulatory oversight differ, contributing to the nuanced landscape of data protection. In conclusion, a comparative review of data encryption methods in the USA and Europe reveals the complex interplay between security, privacy, and legal frameworks. Understanding these differences is crucial for multinational organizations and individuals navigating the intricate landscape of global data protection. Keywords: Data, Encryption, USA, Europe, Review.
{"title":"A COMPARATIVE REVIEW OF DATA ENCRYPTION METHODS IN THE USA AND EUROPE","authors":"Akoh Atadoga, Oluwatoyin Ajoke Farayola, Benjamin Samson Ayinla, Olukunle Oladipupo Amoo, Temitayo Oluwaseun Abrahams, Femi Osasona","doi":"10.51594/csitrj.v5i2.815","DOIUrl":"https://doi.org/10.51594/csitrj.v5i2.815","url":null,"abstract":"Data encryption is a critical aspect of modern information security, and understanding the approaches taken by different regions is vital for a comprehensive analysis. In the United States and Europe, data encryption methods vary in implementation, legal frameworks, and overall priorities. In the United States, encryption methods are primarily governed by a combination of federal laws and industry standards. The National Institute of Standards and Technology (NIST) plays a central role in recommending cryptographic standards, while the Department of Commerce oversees export controls on encryption technology. The focus in the U.S. is on balancing national security needs with individual privacy rights. The tension between law enforcement's desire for access to encrypted data for criminal investigations and the right to privacy has sparked debates and legal battles. On the other hand, Europe has taken a more privacy-centric approach to data protection. The General Data Protection Regulation (GDPR) is a cornerstone in the European Union's efforts to safeguard individual privacy rights. GDPR mandates the use of encryption to protect personal data, and failure to implement adequate measures can result in hefty fines. European countries also emphasize the importance of end-to-end encryption in communication services to ensure confidentiality. Both regions prioritize encryption, but their approaches reflect different values and legal philosophies. The U.S. tends to navigate a delicate balance between national security and individual rights, while Europe places a stronger emphasis on the protection of personal data as a fundamental right. In terms of technological implementation, the encryption algorithms adopted in both regions are often aligned with global standards. Advanced Encryption Standard (AES) is widely accepted and implemented in various sectors. However, the choice of key management and the level of regulatory oversight differ, contributing to the nuanced landscape of data protection. In conclusion, a comparative review of data encryption methods in the USA and Europe reveals the complex interplay between security, privacy, and legal frameworks. Understanding these differences is crucial for multinational organizations and individuals navigating the intricate landscape of global data protection. \u0000Keywords: Data, Encryption, USA, Europe, Review.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"5 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452053","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}
Pub Date : 2024-02-18DOI: 10.51594/csitrj.v5i2.794
Akoh Atadoga, Ogugua Chimezie Obi, Femi Osasona, Shedrack Onwusinkwue, Andrew Ifesinachi Daraojimba, Samuel Onimisi Dawodu
This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.
{"title":"QUANTUM COMPUTING IN BIG DATA ANALYTICS: A COMPREHENSIVE REVIEW: ASSESSING THE ADVANCEMENTS, CHALLENGES, AND POTENTIAL IMPLICATIONS OF QUANTUM APPROACHES IN HANDLING MASSIVE DATA SETS","authors":"Akoh Atadoga, Ogugua Chimezie Obi, Femi Osasona, Shedrack Onwusinkwue, Andrew Ifesinachi Daraojimba, Samuel Onimisi Dawodu","doi":"10.51594/csitrj.v5i2.794","DOIUrl":"https://doi.org/10.51594/csitrj.v5i2.794","url":null,"abstract":"This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. \u0000Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"131 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452685","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}
Pub Date : 2024-02-18DOI: 10.51594/csitrj.v5i2.793
Femi Osasona, Andrew Ifesinachi Daraojimba, Akoh Atadoga, Shedrack Onwusinkwue, Ogugua Chimezie Obi, Samuel Onimisi Dawodu
The relentless evolution of Artificial Intelligence (AI) has significantly transformed the landscape of business analytics, offering unparalleled opportunities for organizations to enhance decision-making processes and gain a competitive edge. This study provides a comprehensive review of AI integration in business analytics, focusing on the distinctive trends observed in both the United States (USA) and African business ecosystems. In the United States, a technologically advanced market, the adoption of AI in business analytics has witnessed remarkable strides. Corporations across various sectors leverage AI-driven tools and algorithms to analyze vast datasets, extract meaningful insights, and optimize strategic decision-making. The USA's emphasis on innovation and robust technological infrastructure has propelled AI integration as a cornerstone of modern business strategies. Contrastingly, the African continent is experiencing a unique trajectory in AI adoption within the realm of business analytics. Despite facing challenges related to infrastructure and resource limitations, African businesses are increasingly recognizing the transformative potential of AI. Initiatives promoting AI education and collaboration with global tech partners have contributed to a growing awareness and implementation of AI in business analytics across various African industries. This review explores commonalities and divergences in the trends observed between the USA and Africa, highlighting the factors influencing AI integration in each region. Factors such as regulatory frameworks, cultural nuances, and economic landscapes play a pivotal role in shaping the AI landscape in both contexts. By understanding these trends, businesses can tailor their AI strategies to align with regional dynamics, fostering sustainable growth and innovation. This study provides valuable insights into the evolving landscape of AI integration in business analytics, offering a comparative analysis of trends in the USA and Africa. As organizations navigate the complexities of adopting AI, acknowledging regional variations becomes crucial for developing effective and context-specific strategies. Keywords: AI, Business Analytics, USA, Africa, Business, Innovation.
{"title":"AI INTEGRATION IN BUSINESS ANALYTICS: A REVIEW OF USA AND AFRICAN TRENDS","authors":"Femi Osasona, Andrew Ifesinachi Daraojimba, Akoh Atadoga, Shedrack Onwusinkwue, Ogugua Chimezie Obi, Samuel Onimisi Dawodu","doi":"10.51594/csitrj.v5i2.793","DOIUrl":"https://doi.org/10.51594/csitrj.v5i2.793","url":null,"abstract":"The relentless evolution of Artificial Intelligence (AI) has significantly transformed the landscape of business analytics, offering unparalleled opportunities for organizations to enhance decision-making processes and gain a competitive edge. This study provides a comprehensive review of AI integration in business analytics, focusing on the distinctive trends observed in both the United States (USA) and African business ecosystems. In the United States, a technologically advanced market, the adoption of AI in business analytics has witnessed remarkable strides. Corporations across various sectors leverage AI-driven tools and algorithms to analyze vast datasets, extract meaningful insights, and optimize strategic decision-making. The USA's emphasis on innovation and robust technological infrastructure has propelled AI integration as a cornerstone of modern business strategies. Contrastingly, the African continent is experiencing a unique trajectory in AI adoption within the realm of business analytics. Despite facing challenges related to infrastructure and resource limitations, African businesses are increasingly recognizing the transformative potential of AI. Initiatives promoting AI education and collaboration with global tech partners have contributed to a growing awareness and implementation of AI in business analytics across various African industries. This review explores commonalities and divergences in the trends observed between the USA and Africa, highlighting the factors influencing AI integration in each region. Factors such as regulatory frameworks, cultural nuances, and economic landscapes play a pivotal role in shaping the AI landscape in both contexts. By understanding these trends, businesses can tailor their AI strategies to align with regional dynamics, fostering sustainable growth and innovation. This study provides valuable insights into the evolving landscape of AI integration in business analytics, offering a comparative analysis of trends in the USA and Africa. As organizations navigate the complexities of adopting AI, acknowledging regional variations becomes crucial for developing effective and context-specific strategies. \u0000Keywords: AI, Business Analytics, USA, Africa, Business, Innovation.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"11 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452996","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 study provides a comprehensive review of quantum cryptography and its implications for U.S. national security in the face of emerging quantum technologies. The primary objective is to investigate the potential of quantum cryptographic methods in creating unbreakable encryption and their future role in enhancing digital security. Employing a systematic literature review and content analysis, the study draws on recent peer-reviewed articles, institutional reports, and academic journals from 2013 to 2023. The methodology focuses on evaluating the evolution, current state, and challenges of quantum cryptography, along with its integration into existing security frameworks. Key findings reveal that Quantum Key Distribution (QKD) and post-quantum cryptography (PQC) offer promising solutions against the threats posed by quantum computing to classical encryption methods. However, the practical implementation of these technologies faces significant challenges, including technological limitations and the need for global standardization. The study underscores the urgency for U.S. national security policy to prioritize the development and integration of quantum-resistant cryptographic technologies and to foster international collaboration for standardization. Finally, the study highlights the transformative potential of quantum cryptography in digital security, emphasizing the need for continued research and collaboration to overcome implementation challenges. Future research directions include the development of efficient quantum cryptographic protocols and ethical considerations surrounding the deployment of quantum technologies. This study contributes to the discourse on securing national interests in the face of advancing quantum computing capabilities. Keywords: Quantum Cryptography, Digital Security, Post-Quantum Cryptography, Quantum Key Distribution.
{"title":"QUANTUM CRYPTOGRAPHY AND U.S. DIGITAL SECURITY: A COMPREHENSIVE REVIEW: INVESTIGATING THE POTENTIAL OF QUANTUM TECHNOLOGIES IN CREATING UNBREAKABLE ENCRYPTION AND THEIR FUTURE IN NATIONAL SECURITY","authors":"Sedat Sonko, Kenneth Ifeanyi Ibekwe, Valentine Ikenna Ilojianya, Emmanuel Augustine Etukudoh, Adefunke Fabuyide","doi":"10.51594/csitrj.v5i2.790","DOIUrl":"https://doi.org/10.51594/csitrj.v5i2.790","url":null,"abstract":"This study provides a comprehensive review of quantum cryptography and its implications for U.S. national security in the face of emerging quantum technologies. The primary objective is to investigate the potential of quantum cryptographic methods in creating unbreakable encryption and their future role in enhancing digital security. Employing a systematic literature review and content analysis, the study draws on recent peer-reviewed articles, institutional reports, and academic journals from 2013 to 2023. The methodology focuses on evaluating the evolution, current state, and challenges of quantum cryptography, along with its integration into existing security frameworks. Key findings reveal that Quantum Key Distribution (QKD) and post-quantum cryptography (PQC) offer promising solutions against the threats posed by quantum computing to classical encryption methods. However, the practical implementation of these technologies faces significant challenges, including technological limitations and the need for global standardization. The study underscores the urgency for U.S. national security policy to prioritize the development and integration of quantum-resistant cryptographic technologies and to foster international collaboration for standardization. Finally, the study highlights the transformative potential of quantum cryptography in digital security, emphasizing the need for continued research and collaboration to overcome implementation challenges. Future research directions include the development of efficient quantum cryptographic protocols and ethical considerations surrounding the deployment of quantum technologies. This study contributes to the discourse on securing national interests in the face of advancing quantum computing capabilities. \u0000Keywords: Quantum Cryptography, Digital Security, Post-Quantum Cryptography, Quantum Key Distribution.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"242 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452495","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}
Pub Date : 2024-02-18DOI: 10.51594/csitrj.v5i2.816
Akoh Atadoga, Chinedu Ugochukwu Ike, Onyeka Franca Asuzu, Benjamin Samson Ayinla, Ndubuisi Leonard Ndubuisi, Rhoda Adura Adeleye
This review explores the intricate and evolving relationship between Artificial Intelligence (AI) and Quantum Computing within the realm of financial markets. As technology continues to advance, the integration of AI and quantum computing has emerged as a paradigm-shifting force, promising unprecedented capabilities to analyze and navigate the complexities of financial systems. This critical review delves into the synergies, challenges, and potential disruptions arising from the intersection of these two transformative technologies. The utilization of AI in financial markets has witnessed remarkable progress in recent years, with machine learning algorithms, deep neural networks, and natural language processing contributing to enhanced data analysis, predictive modeling, and decision-making. However, the computational demands of these sophisticated algorithms often surpass the capabilities of classical computing architectures, paving the way for the exploration of quantum computing as a potential solution. Quantum computing, with its ability to process vast datasets and perform complex calculations at speeds inconceivable by classical computers, presents a revolutionary approach to addressing the computational challenges faced by AI in financial applications. The review critically examines the potential advantages of quantum computing, such as its capacity to solve optimization problems, simulate financial scenarios, and secure data through quantum cryptography. Despite the promises, the integration of AI and quantum computing in financial markets is not without hurdles. The review investigates the current limitations, including hardware constraints, error correction challenges, and the high costs associated with quantum computing infrastructure. Ethical considerations and regulatory frameworks surrounding the implementation of such powerful technologies in financial decision-making also warrant careful examination. This critical review provides a comprehensive analysis of the intersection of AI and quantum computing in financial markets, shedding light on the transformative potential, challenges, and ethical implications that accompany this cutting-edge convergence of technologies. Understanding this intersection is crucial for stakeholders seeking to navigate the evolving landscape of finance and technology. Keywords: AI, Quantum, Computing, Financial Market, Review.
{"title":"THE INTERSECTION OF AI AND QUANTUM COMPUTING IN FINANCIAL MARKETS: A CRITICAL REVIEW","authors":"Akoh Atadoga, Chinedu Ugochukwu Ike, Onyeka Franca Asuzu, Benjamin Samson Ayinla, Ndubuisi Leonard Ndubuisi, Rhoda Adura Adeleye","doi":"10.51594/csitrj.v5i2.816","DOIUrl":"https://doi.org/10.51594/csitrj.v5i2.816","url":null,"abstract":"This review explores the intricate and evolving relationship between Artificial Intelligence (AI) and Quantum Computing within the realm of financial markets. As technology continues to advance, the integration of AI and quantum computing has emerged as a paradigm-shifting force, promising unprecedented capabilities to analyze and navigate the complexities of financial systems. This critical review delves into the synergies, challenges, and potential disruptions arising from the intersection of these two transformative technologies. The utilization of AI in financial markets has witnessed remarkable progress in recent years, with machine learning algorithms, deep neural networks, and natural language processing contributing to enhanced data analysis, predictive modeling, and decision-making. However, the computational demands of these sophisticated algorithms often surpass the capabilities of classical computing architectures, paving the way for the exploration of quantum computing as a potential solution. Quantum computing, with its ability to process vast datasets and perform complex calculations at speeds inconceivable by classical computers, presents a revolutionary approach to addressing the computational challenges faced by AI in financial applications. The review critically examines the potential advantages of quantum computing, such as its capacity to solve optimization problems, simulate financial scenarios, and secure data through quantum cryptography. Despite the promises, the integration of AI and quantum computing in financial markets is not without hurdles. The review investigates the current limitations, including hardware constraints, error correction challenges, and the high costs associated with quantum computing infrastructure. Ethical considerations and regulatory frameworks surrounding the implementation of such powerful technologies in financial decision-making also warrant careful examination. This critical review provides a comprehensive analysis of the intersection of AI and quantum computing in financial markets, shedding light on the transformative potential, challenges, and ethical implications that accompany this cutting-edge convergence of technologies. Understanding this intersection is crucial for stakeholders seeking to navigate the evolving landscape of finance and technology. \u0000Keywords: AI, Quantum, Computing, Financial Market, Review.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"39 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452704","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 study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector. Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.
{"title":"AI-DRIVEN PREDICTIVE ANALYTICS IN AGRICULTURAL SUPPLY CHAINS: A REVIEW: ASSESSING THE BENEFITS AND CHALLENGES OF AI IN FORECASTING DEMAND AND OPTIMIZING SUPPLY IN AGRICULTURE","authors":"Oluwafunmi Adijat Elufioye, Chinedu Ugochukwu Ike, Olubusola Odeyemi, Favour Oluwadamilare Usman, Noluthando Zamanjomane Mhlongo","doi":"10.51594/csitrj.v5i2.817","DOIUrl":"https://doi.org/10.51594/csitrj.v5i2.817","url":null,"abstract":"This study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector. \u0000Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"28 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452960","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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