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All Journal Journal of Multidisciplinary Science: MIKAILALSYS International Journal of Education, Management, and Technology
Victoria, Shittu Sarah
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Physics Social Sciences Other

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Internet of Things (IoT) Driven Water Management System for Efficient Level Control Nzeanorue, Christian Chukwuemeka; Nnana, Ogba Samuel; Victoria, Shittu Sarah; Nzeanorue, Chibuike Godswill; Enabulele, Ewemade Cornelius; Olusola, Raphael Aduramimo; Jamiu, Ridwan Olamilekan; Daramola, Merinuibi Sunday; Stephen, Victor Ikechukwu; Ekunseitan, Kehinde Deji
Journal of Multidisciplinary Science: MIKAILALSYS Vol 2 No 3 (2024): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v2i3.3783

Abstract

Water is an essential and valuable resource in daily life, making its conservation crucial to prevent adverse effects. Storing water for domestic, industrial, agricultural, and other purposes is particularly important. Safe drinking water is increasingly becoming polluted due to the growing population and their demands for urbanization and industrialization. At the household level, some people leave electric water pumps running and either go to work or sleep, forgetting to turn off the pumps when the water container is full. This highlights the need for a reliable and continuous water supply. IoT plays a significant role in environmental monitoring, particularly in disaster management, early warning systems, and environmental data analytics. One major challenge in urban cities is water management, especially with the rapid growth of urbanization, necessitating sustainable urban development plans. To address these issues, we propose a "Water Level Monitoring System" solution. This paper presents an IoT-based water monitoring system for real-time applications. The system's sensors measure the water level in the tank, and the data is sent to a cloud server, allowing users to view it on a remote dashboard. This system can be used efficiently by both homeowners and industrial users, as well as other water utilities.
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.
Co-Authors Agada, Olowu Innocent Akintayo, Taiwo Abdulahi Arinze, Ugochukwu Ukeje Daramola, Merinuibi Sunday David, Fakokunde Babatunde Ekunseitan, Kehinde Deji Enabulele, Ewemade Cornelius Jamiu, Ridwan Olamilekan Joel, Ogundigba Omotunde Ngozi, Egenuka Rhoda Nnadiekwe, Oluchi Anthonia Nnamani, Chinenye Cordelia Nnana, Ogba Samuel Nzeanorue, Chibuike Godswill Nzeanorue, Christian Chukwuemeka Ojemerenvhie, Grace Alele Olayinka, Usman Wasiu Olusola, Raphael Aduramimo Oluwadamilola, Adebesin Adedayo Paul, Chadi Queenet, Madumere Chiamaka Stephen, Victor Ikechukwu