Abdul Aziz | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Research Hypothesis

Abdul Aziz | Computer Science and Artificial Intelligence | Best Researcher Award

Mr Abdul Aziz, Khulna University of Engineering & Technology, Bangladesh

🧑‍🏫 Abdul Aziz is an Assistant Professor at Khulna University of Engineering & Technology (KUET), specializing in computer science and engineering. With a passion for deep learning 🤖, fuzzy logic, and smart city innovations 🌆, he has presented at major conferences like ICCIT and BIM. A recipient of the Vice-Chancellor Award 🏅 and a University Gold Medalist 🥇, Abdul’s research focuses on AI-driven solutions for real-world problems. His notable works include danger detection for women and children and risk evaluation of hazardous chemicals. Dedicated to education and research, he inspires future engineers at KUET. 📚✨

Publication Profile

Scopus

Academic Qualifications

Mr. Abdul Aziz is an accomplished computer science professional with a strong academic background 🎓. He earned his Master of Science in Computer Science & Engineering from Khulna University of Engineering & Technology (KUET) in 2022, achieving a CGPA of 3.75/4.00 💻. Previously, he completed his Bachelor of Science at KUET in 2017 with an outstanding CGPA of 3.92/4.00, securing 1st place among 59 students and topping the EEE Faculty 🏆. His academic journey began at Shahid Syed Nazrul Islam College, where he completed his Higher Secondary Certificate in 2012 📚. Abdul Aziz exemplifies dedication and excellence in his field.

Professional Experiences

Mr. Abdul Aziz is an accomplished academic in computer science, currently serving as an Assistant Professor at Khulna University of Engineering & Technology (KUET) since December 2020 🎓💻. He began his journey at KUET as an Adjunct Faculty (Lecturer) in August 2017 and later became a Lecturer from January 2018 to December 2020 📚. Prior to this, he contributed to Northern University of Business and Technology Khulna (NUBTK) as a Lecturer from July to December 2017 🏫. With a strong dedication to education and research, Mr. Aziz continues to shape future engineers and drive innovation in computer science 🚀🔍.

Achievements, Awards, and Certifications

Mr. Abdul Aziz is a distinguished academic and researcher recognized for his outstanding achievements🏅. In 2024, he received the Vice-Chancellor Award for High Impact Research Journal Publication📚. He was the University Gold Medalist in 2018 for securing 1st position in his graduating class🥇. From 2013 to 2016, Abdul earned the University Vocational Scholarship and the Dean’s Award for ranking among the top 10% of students for four consecutive years🏆. His programming skills were highlighted in 2014 when he secured 3rd place in one intra-batch contest and 1st place in another💻🥇.

Membership

Mr. Abdul Aziz is a passionate coach, mentor, and trainer in programming and technology 💻. Since 2018, he has coached 25+ teams for ICPC regionals, National Girls Programming Contests, and university competitions. He led the KUET_Effervescent team to the 48th ICPC World Finals in Astana, Kazakhstan (2024) 🏆. Aziz serves as an Associate Member of the Institution of Engineers, Bangladesh ⚙️ and reviews international conferences. As a trainer for BDSET and ITEE programs, he uplifts digital skills 📊. He also organized major events like BitFest 2019 and NHSPC, mentoring future innovators. His journey began as a debate champion 🎤.

Academic Projects

Mr. Abdul Aziz has undertaken diverse academic projects during his undergraduate studies at KUET. In his 3rd semester (2014), he developed a Java-based smart home automation desktop app 🏠💻. In the 4th semester (2014-2015), he created a medical center automation website using PHP, HTML, and MySQL for doctor-patient communication 🏥🌐. His 5th semester (2015) featured hospital DBMS design with PL/SQL and Oracle 📊. By the 6th semester (2015-2016), he built an Android app for real-time object tracking 📱🗺️ and a keypad/Bluetooth-controlled LCD display project using Arduino 📟🔷. In his final semester, he developed a 3D car racing game with C++ and OpenGL 🚗🎮.

Research Focus

Abdul Aziz’s research focuses on applying deep learning 🤖, signal processing 🎵, and fuzzy logic 🔢 to develop innovative solutions in safety, smart cities 🌆, and mobile applications 📱. His work spans danger detection for women and children 🚨, city service task distribution 🏙️, and chemical risk evaluation 🧪. Additionally, Aziz explores advanced error detection and correction in computing 💻. His contributions aim to enhance public safety, improve urban services, and boost system reliability. With publications in top-tier journals 🏆, his research bridges technology and real-world applications, fostering smarter and safer environments.

Publication Top Notes

DangerDet: A mobile application-based danger detection platform for women and children using deep learning

ShopiRound: An Android application-based e-commerce system to find products nearby using travelling salesman problem

A fuzzy logic-based risk evaluation and precaution level estimation of explosive, flammable, and toxic chemicals for preventing damages

Multi-bit error detection and correction technique using HVDK (Horizontal-Vertical-Diagonal-Knight) parity

CitySolution: A complaining task distributive mobile application for smart city corporation using deep learning

 

Cheikh Abdelkader Ahmed Telmoud | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Research Hypothesis

Cheikh Abdelkader Ahmed Telmoud | Computer Science and Artificial Intelligence | Best Researcher Award

Mr Cheikh Abdelkader Ahmed Telmoud, Nouakchott University, Mauritania

Based on Mr. Cheikh Abdelkader Ahmed Telmoud’s academic and professional background, he appears to be a strong candidate for a “Best Researcher Award.

Publication profile

google scholar

Education and Expertise

Mr. Telmoud has a solid educational foundation, currently pursuing a Ph.D. in Computer Sciences with a focus on AI applications in healthcare, food security, similarity learning, and NLP. His Master’s and Bachelor’s degrees in Computer Science with specializations in Data Science, Networks, and Information Systems further reinforce his expertise in these critical fields.

Professional Experience

As a Communication Support Manager, Project Manager, and Full Stack Developer at Next Technology, Mr. Telmoud has demonstrated significant practical experience in the tech industry, managing multiple FinTech projects. His role in developing innovative solutions like CrossPay and BCIpay showcases his capability to apply his academic knowledge to real-world problems.

Research Contributions

Mr. Telmoud’s research contributions are impressive, spanning various domains such as AI in healthcare and agriculture, similarity learning, and natural language processing. His published articles and presentations at international conferences demonstrate his commitment to advancing knowledge in these areas. Notably, his work on optimizing ML models for rice yield prediction and heart disease diagnosis highlights his focus on impactful research with tangible benefits.

Scientific Impact

The breadth and depth of Mr. Telmoud’s research, especially in AI-driven solutions for healthcare and agriculture, reflect his potential to make significant contributions to these fields. His involvement in cutting-edge research projects, such as similarity learning and Arabic dialect identification, further underline his research capabilities.

Conclusion

Mr. Cheikh Abdelkader Ahmed Telmoud’s blend of academic excellence, practical experience, and impactful research makes him a deserving candidate for the Best Researcher Award. His work not only advances scientific knowledge but also addresses real-world challenges, making a positive difference in society.

Publication top notes

Elevating Cardiac Health with ECG Classification Using Machine Learning

Cutting-Edge Predictive Models: A Comparative Study on Heart Disease Diagnosis

EcoSense: A Smart IoT-Based Digital Twin Monitoring System for Enhanced Farm Climate Insights

Harvesting Insights: AI-driven Rice Yield Predictions and Big Data Analytics in Agriculture

REVOLUTIONIZING RICE YIELD PREDICTION: A DATA-DRIVEN APPROACH IN MAURITANIA

ADVANCING HEART DISEASE DIAGNOSIS AND ECG CLASSIFICATION USING MACHINE LEARNING

DeepSL: Deep Neural Network-based Similarity Learning.

Optimizing Machine Learning Models for Enhanced Rice Yield Prediction

Optimizing ML Models for Enhanced Rice Yield Prediction and Development of an Integrated Platform for Monitoring, and Rice Yield Prediction In Precision Agriculture

Revolutionizing Heart Disease Prediction: A Machine Learning Approach

Yasin Fatemi | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Research Hypothesis

Yasin Fatemi | Computer Science and Artificial Intelligence | Best Researcher Award

Mr Yasin Fatemi, Auburn University, United States

Based on the details provided, Mr. Yasin Fatemi is a highly suitable candidate for a Researcher of the Year Award.

Publication profile

google scholar

Educational Background 📚

Mr. Fatemi has a robust academic foundation with a Ph.D. in Industrial and Systems Engineering from Auburn University, where he has maintained a perfect GPA of 4.0. His ongoing M.Sc. in Data Science further complements his expertise, and he also holds an M.Sc. and B.Sc. in Industrial and Systems Engineering from Tarbiat Modares University and the University of Kurdistan, respectively. This diverse and interdisciplinary educational background supports his innovative research in healthcare and systems optimization.

Research Experience and Contributions 🔬

Mr. Fatemi’s research is both extensive and impactful. His recent work involves using machine learning and network analysis to address critical healthcare issues such as low birth weight prediction, racial disparities in maternal outcomes, and cardiovascular death among liver transplant recipients. These projects showcase his ability to apply advanced analytical methods to real-world problems, significantly contributing to the fields of healthcare and data science. His studies have utilized cutting-edge techniques such as Recursive Feature Elimination, SHapley Additive exPlanations (SHAP), and network feature analysis, highlighting his technical prowess and innovation.

Publications and Academic Output 📝

Mr. Fatemi has authored several peer-reviewed articles, contributing to reputable journals like Frontiers in Public Health and Journal of Multidisciplinary Healthcare. His research on the stress and compensation perceptions of frontline nurses during the COVID-19 pandemic, as well as his work on hospital smart notification systems, demonstrates his commitment to improving healthcare environments and outcomes. His publications reflect his ability to tackle diverse and pressing issues, making him a significant contributor to the academic community.

Technical and Academic Skills 🛠️

Mr. Fatemi’s technical skills are impressive, encompassing data analysis tools like Python, R, and SQL, and specialized software for simulation and optimization. His expertise in machine learning, statistical learning, and network analysis is evident in his research outputs, further establishing his credibility as an innovative researcher.

Conclusion

Mr. Yasin Fatemi’s strong educational background, extensive research experience, and impactful contributions to healthcare and data science make him an excellent candidate for a Best Researcher Award. His ability to apply complex analytical techniques to critical issues in healthcare and his consistent academic excellence underscore his suitability for this recognition.

Publication top notes

Investigating frontline nurse stress: perceptions of job demands, organizational support, and social support during the current COVID-19 pandemic

Listening to the Voice of the hospitalized child: comparing children’s experiences to their parents

The Cost of Frontline Nursing: Investigating Perception of Compensation Inadequacy During the COVID-19 Pandemic

ChatGPT in Teaching and Learning: A Systematic Review

Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients

Evaluating a Hospital Smart Notification System in a Simulated Environment: The Method

Machine Learning Approaches for Cardiovascular Death Prediction Among Nash Liver Transplant Recipients