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All Journal Asian Journal of Science, Technology, Engineering, and Art International Journal of Education, Management, and Technology
Ojemerenvhie, Grace Alele
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Arts Computer Science & IT Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Social Sciences

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Transforming Data Analytics with AI for Informed Decision-Making Akintayo, Taiwo Abdulahi; Paul, Chadi; Queenet, Madumere Chiamaka; Nnadiekwe, Oluchi Anthonia; Victoria, Shittu Sarah; David, Fakokunde Babatunde; Joel, Ogundigba Omotunde; Agada, Olowu Innocent; Ngozi, Egenuka Rhoda; Arinze, Ugochukwu Ukeje; Ojemerenvhie, Grace Alele; Oluwadamilola, Adebesin Adedayo; Nnamani, Chinenye Cordelia; Olayinka, Usman Wasiu
International Journal of Education, Management, and Technology Vol 2 No 3 (2024): International Journal of Education, Management, and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ijemt.v2i3.3812

Abstract

This study delves into how advanced data analytics and artificial intelligence (AI) can work together to enhance decision-making processes. As we navigate today’s data-driven environment, discovering the synergy between these fields is crucial, given the growing complexity of datasets. Advanced analytical tools are essential, and AI offers exceptional capabilities in pattern recognition and automation. This research investigates how cosmbining data analytics techniques—such as Predictive Modeling, Clustering, and Trend Analysis—with AI approaches like Machine Learning and Deep Learning can improve decision-making. A key focus of the study is on making AI models more interpretable and transparent. It emphasizes the importance of ensuring that AI-driven decisions are clear and understandable. Additionally, the research addresses ethical considerations and the need for human-centered design, aiming to balance AI’s power with openness. It also strives for responsible AI use by tackling issues such as bias and promoting ethical practices in the application of advanced data analytics and AI. The study demonstrates practical applications in areas like healthcare and finance, showing how these technologies can transform personalized medicine, disease prediction, risk assessment, fraud detection, and market trend analysis. Overall, this research highlights the valuable interaction between advanced data analytics and AI, offering a guide for organizations to enhance their decision-making while adhering to ethical standards and responsible AI use.
The Cloud Security Revolution: Unlocking the Potential of AI and Machine Learning to Stay Ahead of Threats Okereke, Ruth Onyekachi; Ojemerenvhie, Grace Alele; Azeez, Oladimeji Lamina; Oko-odion, Terry Uwagbae; Samson, Iyanu Opeyemi; Anosike, Chijioke Nnaemeka; Owan, Faith Obun; Nnamani, Chinenye Cordelia
Asian Journal of Science, Technology, Engineering, and Art Vol 2 No 5 (2024): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v2i5.3813

Abstract

As we navigate the digital world, cybersecurity has become a top priority. With each technological advancement, new vulnerabilities emerge, making robust defenses essential. The fusion of machine learning and artificial intelligence has become a game-changer in the fight against cyber threats. This paper delves into the latest applications of these technologies in network security, shedding light on their critical roles in addressing pressing concerns and identifying areas for further exploration. We also examine the ethical and legal implications of implementing these technologies. Our research highlights current challenges and open questions, with a focus on recent breakthroughs in network security leveraging AI and ML. The findings are promising, suggesting that further innovation in integrating AI and ML into network security frameworks holds significant potential. Exciting applications include bolstering network security, detecting malware, and responding to intrusions. Interestingly, while 45% of organizations recognize the need to adopt these technologies, half have already done so, while 5% remain hesitant.
Co-Authors Agada, Olowu Innocent Akintayo, Taiwo Abdulahi Anosike, Chijioke Nnaemeka Arinze, Ugochukwu Ukeje Azeez, Oladimeji Lamina David, Fakokunde Babatunde Joel, Ogundigba Omotunde Ngozi, Egenuka Rhoda Nnadiekwe, Oluchi Anthonia Nnamani, Chinenye Cordelia Okereke, Ruth Onyekachi Oko-odion, Terry Uwagbae Olayinka, Usman Wasiu Oluwadamilola, Adebesin Adedayo Owan, Faith Obun Paul, Chadi Queenet, Madumere Chiamaka Samson, Iyanu Opeyemi Victoria, Shittu Sarah