Mohammed Alenazi | Computer Engineering | Best Researcher Award

Mr. Mohammed Alenazi | Computer Engineering | Best Researcher Award

Assistant Professor | University of Tabuk | Saudi Arabia

Mr. Mohammed M. Alenazi is an accomplished academic and researcher with expertise in electrical and electronics engineering, computer engineering, and artificial intelligence applications in energy-efficient networks. He earned his Ph.D. in Electrical and Electronics Engineering from the University of Leeds, UK (2018–2022), focusing on energy efficiency in AI-powered communication systems. Prior to this, he completed his M.Eng. in Computer Engineering at Florida Institute of Technology, USA (2016–2017), and a B.Eng. in Computer Engineering from University Sultan Bin Fahad (2007–2011), along with an Associate’s degree in Electrical/Electronics Equipment Installation and Repair from Tabuk College of Technology (2002–2004). Professionally, Mr. Alenazi began his career as a Senior Engineer at Saudi Telecom Company (2006–2011), where he gained practical experience in optical fiber networks, before transitioning to academia as a Teaching Assistant at Northern Border University (2012–2013) and later at the University of Tabuk, where he continues to serve since 2013, eventually advancing into an assistant professorship. His research interests include machine learning, IoT networks, energy optimization, and intelligent systems, with key contributions in developing models for energy-efficient ML-based service placement, neural network embedding in IoT, and intelligent sterilization systems, reflected in several IEEE and Scopus-indexed publications. In addition to publications, he has contributed innovative patents, such as systems for vehicle communication during accidents. His research skills encompass advanced AI modeling, simulation of communication networks, and interdisciplinary problem-solving in sustainable technologies. Mr. Alenazi is an active member of IEEE, AAAI (USA), AISB (UK), PMI, and the Saudi Council of Engineers, and he holds prestigious certifications including CCNA, CompTIA Security+ CE, and PMP. He has consistently demonstrated leadership in academia and professional communities, bridging industry and research while mentoring students. With a growing academic profile of 28 citations, 7 documents, and an h-index of 3, he is well-positioned for continued impact and recognition in his field.

Profiles: Google Scholar | Scopus | ORCID  | ResearchGate

Featured Publications

  1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. 2020 22nd International Conference on Transparent Optical Networks (ICTON), 1–6. Cited by: 13

  2. Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Proceedings of the Twelfth ACM International Conference on Future Energy Systems. Cited by: 11

  3. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 935–941. Cited by: 9

  4. Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., & others. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Wireless Personal Communications, 136(1), 123–142. Cited by: 4

  5. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). Cited by: 4

Mohsin Hasan | Management science and engineering | Best Researcher Award

Mr . Mohsin Hasan | Management science and engineering | Best Researcher Award

Student at Nanjing University of Aeronautics and Astronautics , China

Mohsin Hasan is a dedicated and impactful researcher currently pursuing a PhD in Management Science and Engineering at Nanjing University of Aeronautics and Astronautics, China. His research focuses on epileptic seizure prediction using advanced machine learning techniques, including LSTM, SHAP, and deep neural networks, addressing a critical healthcare challenge. With publications in top-tier SCIE-indexed journals such as Engineering Applications of Artificial Intelligence and Annals of Operations Research, he demonstrates strong academic rigor and innovation. Mohsin possesses expertise in Python programming, big data analysis, and research writing, supported by a multi-disciplinary academic background in sociology. He has also actively contributed to community health initiatives in Pakistan, reflecting a blend of technical and social impact. While improved English proficiency and expanded international collaboration could enhance his profile, his current achievements make him a strong candidate for the Best Researcher Award, showcasing both research excellence and real-world relevance.

Professional Profile

Education🎓

Mohsin Hasan has a diverse and interdisciplinary educational background that bridges social sciences and engineering. He is currently pursuing a PhD in Management Science and Engineering at Nanjing University of Aeronautics and Astronautics in China, with a research focus on epileptic seizure prediction using machine learning and deep learning techniques. Prior to his doctoral studies, he completed an M.S. in Rural Sociology from the University of Agriculture Faisalabad and a Master’s degree in Sociology from the University of Sargodha, Pakistan. His academic journey began with a Bachelor of Arts from Government College University Faisalabad, followed by intermediate studies at Government Islamia College Chiniot and matriculation at Government High School Chak No. 152 JB Chiniot. Throughout his education, Mohsin has developed strong skills in Python programming, big data analysis, and research writing, positioning him to apply advanced technological solutions to both social and engineering problems, particularly in healthcare and community development.

Professional Experience📝

Mohsin Hasan has a well-rounded professional background that spans academic research and community development. Currently, he is engaged in cutting-edge research as a PhD scholar, working on epileptic seizure prediction using machine learning, with multiple SCIE-indexed publications to his name. His earlier professional experience includes various social outreach and coordination roles across Pakistan. As a Social Outreach Worker with UNODC, he led awareness campaigns and community mobilization for drug addiction treatment. He also served as Supervisor for the Sehat Sahulat Insaaf Card project with RCDP, managing field staff and overseeing healthcare card distribution. As a Dosti Coordinator with Muslim Hands International, he trained teachers and encouraged school enrollment and student participation in extracurricular activities. Additionally, he worked as an Assistant Constituency Coordinator for the FAFEN Election Project, monitoring electoral processes and data collection. His experience demonstrates a strong blend of technical expertise, leadership, and community-oriented service.

Research Interest🔎

Mohsin Hasan’s research interests lie at the intersection of artificial intelligence, healthcare, and data science, with a strong focus on real-world applications that enhance human well-being. His primary area of interest is the prediction and classification of epileptic seizures using advanced machine learning and deep learning techniques, including Long Short-Term Memory (LSTM), Kolmogorov Arnold Network Theorem, SHAP-driven feature analysis, and attention-based neural networks. He is particularly passionate about leveraging electroencephalography (EEG) data to develop interpretable and accurate models for early seizure detection. His research also extends to reliability engineering, operational research, and the integration of AI in medical diagnostics. With a background in sociology and rural development, Mohsin brings a unique, human-centered approach to technological innovation, aiming to bridge the gap between data-driven solutions and community health challenges. His interdisciplinary perspective fuels his commitment to creating scalable, impactful tools for healthcare and beyond, particularly in under-resourced and developing contexts.

Award and Honor🏆

Mohsin Hasan has earned recognition for his dedication to academic excellence and impactful research, positioning him as a strong candidate for prestigious honors. His most notable achievement is his contribution to high-impact, SCIE-indexed journals such as Engineering Applications of Artificial Intelligence and Annals of Operations Research, where his research on epileptic seizure prediction has gained international attention. In addition to academic publications, Mohsin has been involved in global policy discussions and training sessions, including regional dialogues hosted by the Asian Institute of Technology and certification courses by the World Health Organization on emerging health threats and COVID-19 response. His ability to translate complex data science techniques into meaningful healthcare solutions reflects both innovation and social commitment. These accomplishments highlight his exceptional talent, work ethic, and relevance in critical global issues. Such recognition not only underscores his scholarly contributions but also establishes him as a deserving candidate for awards celebrating research excellence and societal impact.

Research Skill🔬

Mohsin Hasan possesses a comprehensive set of research skills that enable him to conduct advanced, data-driven investigations with real-world impact. He is highly proficient in Python programming and well-versed in tools such as Jupyter Notebook, PyCharm, and Google Colab, which he utilizes for building and testing machine learning models. His core expertise lies in deep learning, particularly in applying algorithms like Long Short-Term Memory (LSTM), 1D-ResNet, and attention mechanisms for medical data analysis, especially EEG-based epileptic seizure prediction. Mohsin is skilled in big data analytics, neural network development, and SHAP-based model interpretation, which enhances the transparency and usability of AI models. Additionally, he is experienced in academic research writing, LaTeX formatting, and data visualization using software like Edraw Max and Visio. His ability to integrate technical depth with scientific communication, along with a strong foundation in statistical methods and real-time problem-solving, marks him as a capable and innovative researcher.

Conclusion💡

Yes, Mohsin Hasan is a strong and deserving candidate for the Best Researcher Award.

His profile demonstrates a rare and valuable combination of technical AI research, medical applications, and community-level engagement. His high-quality publications, technical skills, and international academic involvement position him as a rising researcher with significant impact potential.

Publications Top Noted✍

  • Title: Long Short-Term Memory and Kolmogorov Arnold Network Theorem for Epileptic Seizure Prediction

  • Authors: Mohsin Hasan, Xufeng Zhao, Wenjuan Wu, Jiafei Dai, Xudong Gu, Asia Noreen

  • Year: 2025

  • Journal: Engineering Applications of Artificial Intelligence

  • Volume and Issue: Volume 154

  • Pages: Article 110757

  • Publisher: Elsevier

  • Indexing: SCIE

  • Citation Format (APA Style):
    Hasan, M., Zhao, X., Wu, W., Dai, J., Gu, X., & Noreen, A. (2025). Long Short-Term Memory and Kolmogorov Arnold Network Theorem for epileptic seizure prediction. Engineering Applications of Artificial Intelligence, 154, 110757. https://doi.org/10.1016/j.engappai.2025.110757 (DOI placeholder if needed)

 

Bechoo Lal | Computer Science | Best Researcher Award

Dr. Bechoo Lal | Computer Science | Best Researcher Award

Associate Professor of KLEF- KL University Vijayawada Campus Andhra Pradesh, India

Dr. Bechoolal 🌟 is an esteemed Associate Professor in Computer Science/Data Science with a passion for inspiring students through a deep understanding of technology and research. With a solid academic foundation that includes a PGP in Data Science from Purdue University and multiple PhDs in Information Systems and Computer Science 🎓, he brings a wealth of expertise to his teaching and research. Dr. Bechoolal has extensive experience in various institutions, from KLEF KL Deemed University to Western College 🏫, and has made significant contributions through his numerous research publications and certifications 🏅. His interests span Machine Learning, Data Science, and programming languages, and he actively engages in projects that explore digital transformation and its societal impacts 💻🔍. Fluent in English and Hindi 🇬🇧🇮🇳, he continues to advance knowledge and inspire the next generation of tech professionals.

Publication profile

Education

Dr. Bechoolal 🎓 is a distinguished academic with a rich educational background in Computer Science and Data Science. He earned a PGP in Data Science from Purdue University 🌟, where he specialized in data regression models and predictive data modeling. Dr. Bechoolal holds multiple PhDs—one in Information Systems from the University of Mumbai and another in Computer Science from SJJT University 🧠. His foundational studies include a Master of Technology in Computer Science from AAI-Deemed University, a Master of Computer Applications from Banaras Hindu University, and an undergraduate degree in Statistics from MG. Kashi Vidyapeeth University 📚. His continuous quest for knowledge is also reflected in his various certifications, including Machine Learning from Stanford University and an IBM Data Science Professional Certificate 🏅.

Academic Qualification

  • 📜 PGP in Data Science (2020-2021) from Purdue University, USA – Specializing in data regression models, predictive data modeling, and accuracy analyzing using machine learning.
  • 📜 PhD in Information System (2015-2019) from the University of Mumbai, India – Research Area: Data Science.
  • 📜 PhD in Computer Science (2011-2015) from SJJT University, India – Research Area: Machine Learning.
  • 📜 Master of Technology (M. Tech) in Computer Science and Engineering (2004-2006) from AAI-Deemed University, Allahabad, India.
  • 📜 Master of Computer Application (MCA) (1995-1998) from Institute of Science, Banaras Hindu University (BHU), India.
  • 📜 Graduation (Statistics-Hons) (1990-1993) from the Department of Mathematics and Statistics, MG Kashi Vidyapeeth University, India.

Data Science Certifications and Training

  • 🎓 Machine Learning, Stanford University, USA (2020)
  • 🎓 IBM Data Science Professional Certificate (2020)
  • 🎓 Data Science and Big Data Analytics (2019), ICT Academy, Govt. of India
  • 🎓 Security Fundamentals, Microsoft Technology Associate (2017)
  • 🎓 Intelligent Multimedia Data Warehouse and Mining (2009), University of Mumbai
  • 🎓 Python Programming (2017), University of Mumbai, India

 

Teaching Interest 

  • 📘 Data Science/Machine Learning
  • 📘 Database 📘 C/C++/Python Programming Languages
  • 📘 Software Engineering

Research Interest

  • 🔍 Machine Learning
  • 🔍 Data Science

Computer Science/Data Science Skills

💻 Machine Learning, Data Visualization, Big Data Analytics

📊 Predictive Modelling: Supervised Learning (Linear and Logistic Regression, Decision Tree, Support Vector Machine (SVM), Naïve Bayes Classifiers), Unsupervised Learning (K-Means clustering, principal components analysis (PCA))

💻 Programming Languages: Python (NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn), SPSS, R-Programming

💻 Operating Systems/Platforms: UNIX/LINUX, WINDOWS, MS-DOS

💻 C/C++, CORE JAVA Programming Languages

💻 DBMS/RDBMS: Oracle, SQL, MySQL, NoSQL

Publication top notes

  • Improving migration forecasting for transitory foreign tourists using an Ensemble DNN-LSTM model
    Authors: Nanjappa, Y., Kumar Nassa, V., Varshney, G., Pandey, S., V Turukmane, A.
    Journal: Entertainment Computing
    Year: 2024
    Citations: 0 📅
  • Using social networking evidence to examine the impact of environmental factors on social followings: An innovative Machine learning method
    Authors: Murthy, S.V.N., Ramesh, P.S., Padmaja, P., Reddy, G.J., Chinthamu, N.
    Journal: Entertainment Computing
    Year: 2024
    Citations: 0 📅
  • Real-Time Convolutional Neural Networks for Emotion and Gender Classification
    Authors: Singh, J., Singh, A., Singh, K.K., Samudre, N., Raperia, H.
    Conference: Procedia Computer Science
    Year: 2024
    Citations: 0 📅
  • Identification of Brain Diseases using Image Classification: A Deep Learning Approach
    Authors: Singh, J., Singh, A., Singh, K.K., Turukmane, A.V., Kumar, A.
    Conference: Procedia Computer Science
    Year: 2024
    Citations: 0 📅
  • Fake News Detection Using Transfer Learning
    Authors: Singh, J., Sahu, D.P., Gupta, T., Lal, B., Turukmane, A.V.
    Conference: Communications in Computer and Information Science
    Year: 2024
    Citations: 0 📅
  • Reliability Evaluation of a Wireless Sensor Network in Terms of Network Delay and Transmission Probability for IoT Applications
    Authors: Mishra, P., Dash, R.K., Panda, D.K., Lal, B., Sujata Gupta, N.
    Journal: Contemporary Mathematics (Singapore)
    Year: 2024
    Citations: 0 📅
  • TRANSFER LEARNING METHOD FOR HANDLING THE INTRUSION DETECTION SYSTEM WITH ZERO ATTACKS USING MACHINE LEARNING AND DEEP LEARNING
    Authors: Upender, T., Lal, B., Nagaraju, R.
    Conference: ACM International Conference Proceeding Series
    Year: 2023
    Citations: 0 📅
  • Monitoring and Sensing of Real-Time Data with Deep Learning Through Micro- and Macro-analysis in Hardware Support Packages
    Authors: Lal, B., Chinthamu, N., Harichandana, B., Sharmaa, A., Kumar, A.R.
    Journal: SN Computer Science
    Year: 2023
    Citations: 0 📅
  • An Efficient QRS Detection and Pre-processing by Wavelet Transform Technique for Classifying Cardiac Arrhythmia
    Authors: Lal, B., Gopagoni, D.R., Barik, B., Kumar, R.D., Lakshmi, T.R.V.
    Journal: International Journal of Intelligent Systems and Applications in Engineering
    Year: 2023
    Citations: 0 📅
  • IOT-BASED Cyber Security Identification Model Through Machine Learning Technique
    Authors: Lal, B., Ravichandran, S., Kavin, R., Bordoloi, D., Ganesh Kumar, R.
    Journal: Measurement: Sensors
    Year: 2023
    Citations: 3 📅📈