Hussain Kassim Ahmad
@ruc.edu.iq
Department of Computer Techniques Engineering
Alrafidain University Collage
EDUCATION
• B.Sc. in Electrical Engineering from dept. of Electrical Engineering, university of Technology.
• Master's degree in electrical engineering from the University of Technology.
RESEARCH INTERESTS
Power electric, power electronic, communication, Computer Networks, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Hamid Alshareefi, Bassam H. Habib, Ezzeddin Jabbar Obaid, Hussain Kassim Ahmad, and Jamal Alsaidi
Society of Visual Informatics
In recent years, the burgeoning amount, speed, and variety of data in multiple energy fields have generated new requirements that far exceed those of traditional database systems and especially pose new challenges related to sustainable energy analytics. Classical relational database architectures are usually not capable of satisfying the needs of performance, scalability, and energy efficiency of emerging energy platforms for smart grids, renewable energy forecasting, and real-time monitoring. The study proposes a high-level database optimization framework targeted at improving the performance of large-scale energy analytics infrastructures. Specifically, the system combines the Adaptive Query Optimization (AQO) and Partition-Aware Load Balancing (PALB) to address the issues of query delay, storage limit, and energy consumption. AQO dynamically optimizes execution plans by considering workload statistics and planner feedback, while PALB balances query distribution in concurrent scenarios based on system resource metrics. We demonstrate via experiments on a high-performance computing platform that our DWPRF achieves significant improvements in various aspects, such as query execution time reduction, advanced compression to realize higher storage efficiency, reduced energy consumption, and better scalability of the system under high concurrency. The experimental results indicate the feasibility of scheduling and allocating plans when addressing these challenges and show the power of using intelligent planning with resource-aware execution to optimize databases for energy informatics. Moreover, the study outlines potential avenues for future extensions, including machine learning-driven optimizers and distributed deployment at the edge and cloud. The proposed approach provides a solid basis for constructing high-performance, energy-efficient data management systems, which are a fundamental requirement for sustainable energy systems.
Hussain Kassim Ahma, Sahar Ali Abdulkareem, Alaa Salim Abdalrazzaq, and Mariia Liashchenko
IEEE
Background: Energy theft in developing nations reduces electricity quality and availability for legal consumers and costs energy suppliers much money. Poor power distribution network maintenance and management compound these nations' energy supply issues. Objective: This study will design, develop, and deploy a monitoring and deterrence system for power grid theft. The approach reduces revenue losses for energy providers and improves electricity quality for legitimate users, improving power infrastructure health. Methods: Sensors, communicators, and software algorithms comprise the proposed system. These sensors monitor current and voltage, detect power line tapping attempts, and identify irregular consumption patterns that may indicate energy theft. Software algorithms evaluate sensor data and notify authorities of suspected thefts when these abnormalities are detected. Results: Energy providers should expect lower revenue losses and better power quality for legitimate customers using the system. The technique may also reduce power infrastructure pressure and equipment failures from theft overloads. Conclusion: A robust grid power theft monitoring and prevention system is feasible and essential. Energy suppliers will profit, while genuine consumers will get better power and fewer interruptions. Implementing the entire system offers a reliable, high-quality electrical supply for everybody.
Mohammed Abd. Mohammed, Sarah Haitham Jameel, Ali Jabbar Hussein, Hussain Kassim Ahmad, Laith S. Ismail, and Alina Zapryvoda
IEEE
AI powered smart farming transform agriculture landscape. This article based on statistical data that looks into the future of AI with respect to agriculture business.This article creates a path to shed insights on the significant impact of artificial-intelligence in terms of shaping up and automating agricultural operations that would let the reader focus their visualization on root-level processes within food production business.This study is based on the analysis of market data as well as on research results to identify how artificial intelligence is used, the key benefits and perspectives in smart farming. Computer vision, data analytics and machine learning are among the applications of these technologies.The study found out that AI has really Improved Efficiency. The outcomes as per their study show that via automation, one may succeed in bringing the labor cost to 50%, agriculture yield will be increased by 15%, and can reduce use of irrigation water even low down to 20%, having an average increase in the productivity change up to a mark value around at least 30%. This deserves a chapter of its very own, Intelligent irrigation systems and fertilization choices and pest control protocols driven by data. The system is able to identify plant diseases, monitor animal health, and optimize cattle feed, and these features can be expected to increase the productivity of agricultureAlthough the data obviously reflects challenges such as misuse of data, limited entry for small actors and ongoing technological and infrastructure development. The clear finding is that AI has the potential to dramatically transform food production across primary agriculture. Which can lead to higher yields, increased profitability, sustainable practices and one: of the most powerful tools in addressing global food security. We need to adapt this innovative technology and its ethical and equitable implementation to ensure the future sustainability of agriculture.
Mohammed Abd. Mohammed, Mina Haider Mohammed, Hussein Ali Abed Alsultani, Hussain Kassim Ahmad, Rana Hikmat, Piotr Migo, and Genadiy Zhyrov
IEEE
Background: Urban traffic demands efficient management solutions to reduce congestion and improve flow. Traditional traffic signal systems, mostly static, struggle to track urban activity.Objective: This article uses IoT technologies, Arduino microcontrollers, and LTE connection to create an adaptive traffic light system that constantly adjusts traffic signal lengths to maximize traffic flow.Methodology: We created a prototype adaptive traffic light system using Arduino microcontrollers with LTE modules and sensors. The sensors send Real-time traffic data over LTE to a cloud server. The technology uses machine learning algorithms to assess data and traffic conditions and remotely alter traffic signal timings via IoT.Results: The prototype improved traffic flow and reduced congestion during peak hours at chosen junctions. In quantitative terms, traffic throughput rose 25%, and intersection waiting times decreased by 35%. Idling time reduction was anticipated to lower vehicle emissions.Conclusion: Arduino and LTE connection in an IoT-based adaptive traffic signal system show promise for urban traffic management. Traffic flow, waiting times, and emissions improve, proving its scalability and enabling cities to a sustainable and effective traffic management plan as vehicle loads rise. Further study is needed to determine its efficacy in different metropolitan topologies and traffic patterns.