Mohammed Al-Naeem | Computer Science | Best Researcher Award

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Mohammed Al-Naeem | Computer Science | Best Researcher Award

King Faisal University | Saudi Arabia

Dr. Mohammed Abdulaziz Al-Naeem is a dedicated scholar and researcher in computer science whose work spans wireless networks, information security, and advanced sensing technologies. He earned both his Master’s and Ph.D. degrees from Monash University, where his doctoral research focused on developing pattern transformation-invariant schemes for wireless sensor networks using an edge-detection, gradient-based mechanism—an innovative contribution that strengthened the robustness and adaptability of sensor-based systems. His academic journey began with a Bachelor of Science from King Faisal University, an institution to which he has remained professionally committed throughout his career. After joining the university as a Teaching Assistant, he steadily progressed through key academic roles, later serving as a Lecturer and ultimately as an Assistant Professor, a position he has held since 2016. Across these roles, he has contributed significantly to teaching, mentoring, and research development within the Department of Computer Science. Dr. Al-Naeem’s academic expertise centers on wireless networks, network security, and the design of resilient sensing and communication frameworks. His research interests integrate theoretical foundations with practical applications, with a focus on secure, efficient, and scalable systems capable of supporting modern intelligent environments. His work reflects an enduring commitment to advancing computational methodologies and enhancing the reliability of networked systems across diverse contexts. He is also proficient in both Arabic and English, enabling him to engage with a wide scholarly community and collaborate on international research initiatives. Through his academic leadership, research contributions, and dedication to advancing knowledge in wireless communication and cybersecurity, Dr. Al-Naeem continues to play an active and impactful role in shaping the next generation of technological innovation.

Profile: Orcid | Scopus | Google scholar

Featured Publications

Alsmadi, I., Aljaafari, N., Nazzal, M., Alhamed, S., Sawalmeh, A. H., Vizcarra, C. P., … [add remaining authors if needed]. (2022).
Adversarial machine learning in text processing: A literature survey. IEEE Access, 10, 17043–17077.

Almusallam, N., Tari, Z., Chan, J., Fahad, A., Alabdulatif, A., & Al-Naeem, M. (2021).
Towards an unsupervised feature selection method for effective dynamic features. IEEE Access, 9, 77149–77163.

Al-Naeem, M. A. (2021).
Prediction of re-occurrences of spoofed ACK packets sent to deflate a target wireless sensor network node by DDOS. IEEE Access, 9, 87070–87078.

Rana, M. U., Shah, M. A., Al-Naeem, M. A., & Maple, C. (2024).
Ransomware attacks in cyber-physical systems: Countermeasure of attack vectors through automated web defenses. IEEE Access, 12, 149722–149739.

Usman Ashraf, U. M., Ahmed, A., & Al-Naeem, M. (2021).
Reliable and QoS aware routing metrics for wireless neighborhood area networking in smart grids. Computer Networks, Article 14.
(If volume/issue/page numbers exist, please provide to complete the citation.)

Al-Naeem, M., Rahman, M. M. H., Banerjee, A., & Sufian, A. (2023/2024?).
Support vector machine-based energy efficient management of UAV locations for aerial monitoring of crops over large agriculture lands. Sustainability, 15(8), 6421.

Muhammad Suleman Memon | Computer Science | Most Cited Article Award

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Muhammad Suleman Memon | Computer Science | Most Cited Article Award

University of Sindh,Jamshoro | Pakistan

Dr. Muhammad Suleman Memon is an accomplished academic and researcher in the fields of Artificial Intelligence, Computer Vision, and Deep Learning, currently serving as an Assistant Professor and Incharge of the Department of Information Technology at the University of Sindh, Dadu Campus. With over twelve years of academic and research experience, he has demonstrated a strong commitment to advancing digital innovation and academic excellence. He earned his Ph.D. in Computer Systems Engineering from Quaid-e-Awam University of Science and Technology, where his research focused on cutting-edge developments in AI-driven systems. His earlier academic background includes a Master’s in Information Technology and a Bachelor’s in Computer Systems Engineering from Mehran University of Engineering and Technology, Jamshoro. Dr. Memon’s research primarily explores Artificial Intelligence applications in healthcare and agriculture, deep learning-based image classification and segmentation, explainable AI (XAI), and the Internet of Things (IoT) for smart system development. He has contributed to the design and teaching of diverse courses, including Object-Oriented Programming, Artificial Intelligence, Web Engineering, and Data Science, fostering computational thinking and innovation among students. Beyond teaching and research, he has played key administrative and leadership roles such as Focal Person for national digital initiatives, Quality Enhancement Coordinator, and Web Administrator for the Dadu Campus. His leadership has been pivotal in enhancing academic quality, managing IT infrastructure, and supporting institutional modernization. Dr. Memon’s scholarly output includes publications in reputed journals, and his ongoing work reflects a deep interest in developing sustainable and explainable AI solutions to address real-world problems. His career exemplifies the integration of academic rigor, research innovation, and leadership in shaping the next generation of computing professionals.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Lakhan, A., Mastoi, Q. U. A., Elhoseny, M., Memon, M. S., & Mohammed, M. A. (2022). Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT-assisted mobile fog cloud. Enterprise Information Systems, 16(7), 1883122.

Memon, M. S., Kumar, P., & Iqbal, R. (2022). Meta deep learn leaf disease identification model for cotton crop. Computers, 11(7), 102.

Lakhan, A., Memon, M. S., Mastoi, Q. U. A., Elhoseny, M., Mohammed, M. A., Qabulio, M., & Abdel-Basset, M. (2022). Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Cluster Computing, 1–23.

Mastoi, Q. U. A., Memon, M. S., Lakhan, A., Mohammed, M. A., Qabulio, M., Al-Turjman, F., & Abdulkareem, K. H. (2021). Machine learning–data mining integrated approach for premature ventricular contraction prediction. Neural Computing and Applications, 33, 11703–11719.

Mirani, A. A., Memon, M. S., Rahu, M. A., Bhatti, M. N., & Shaikh, U. R. (2019). A review of agro-industry in IoT: Applications and challenges. Quaid-E-Awam University Research Journal of Engineering, Science & Technology, Nawabshah, 17(1), 28–33.

Mirani, A., Memon, M. S., Chohan, R., Wagan, A. A., & Qabulio, M. (2021). Machine learning in agriculture: A review. LUME, 10, 5.

Memon, W. A., Mirani, A. A., Memon, M. S., & Sodhar, I. N. (2019). Comparative study of online learning management systems: A survey in Pakistan. Information Sciences Letters, 8(3), 111–120.

Lesole Kalake | Computer Science | Best Research Article Award

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Lesole Kalake | Computer Science | Best Research Article Award

Dr. Lesole Kalake, National Department of Health, South Africa

Dr. Lesole Soldaat Kalake is a South African ICT and AI researcher, educator, and database professional with a strong interdisciplinary background in computer science, statistics, and business intelligence. He holds a PhD in Information and Communication Engineering from Shanghai University, China. With professional experience spanning over two decades, he has served in both academia and government, notably at the National Department of Health as a Business Analyst and Assistant Director. He has lectured at various institutions including the University of KwaZulu-Natal, UNISA, and Kobe Institute of Technology in Japan. Dr. Kalake has published extensively in peer-reviewed journals, focusing on multi-object tracking, electronic health system security, and computer vision. He is also an active peer reviewer for IEEE Access and Springer journals. Passionate about applying AI in public sector systems, he is known for his expertise in SQL databases, SAS tools, and machine learning frameworks, and continues to contribute to South Africa’s eHealth transformation.

Publication Profile

scopus

Education

Dr. Kalake earned his PhD in Information and Communication Engineering from Shanghai University, China in 2024. He also holds an MSc in Information Systems from Kobe Institute of Technology, Japan, and a BSc Honours in Applied Population Science from the University of KwaZulu-Natal, where he also completed his BSc in Computer Science and Statistics. His academic credentials further include professional diplomas and certificates: a Business Analysis degree from Desto Pty Ltd, Moderation of Outcomes-Based Assessment from Edutel Pty Ltd, and Assessment of Outcomes-Based Assessment from PC Training Holdings. Dr. Kalake is certified as a SAS Base 9 Programmer, SAS Advanced Programmer, and MCTS in Microsoft SQL Server 2008. These qualifications highlight his strong foundation in analytics, software engineering, and IT systems design, supporting his multidisciplinary contributions in both academia and government sectors, particularly in the realms of digital health and artificial intelligence.

Experience

Dr. Kalake has extensive professional experience in software development, tutoring, business analysis, and database administration. Since 2009, he has served the National Department of Health (South Africa) as an Assistant Director focusing on SQL database management, project coordination, and business intelligence. He previously worked for organizations such as Sasuka Pty Ltd and Gauteng Department of Public Works as a Business Analyst and SAS Developer. In academia, he held roles at the University of KwaZulu-Natal, UNISA, and Kobe Institute of Technology, tutoring in IT and statistics. His work has involved e-Governance coordination (JICA/IDCJ project) and developing reporting systems, security frameworks, and decision-support tools for government and private sectors. He is highly skilled in SAS tools, Microsoft SQL Server, Power BI, and modern AI frameworks like PyTorch and Keras, contributing to a seamless integration of data science into public health and development systems.

Awards and Honors

Dr. Lesole Kalake’s scholarly contributions have garnered international recognition. He has served as a peer reviewer for prestigious journals such as IEEE Access and Springer’s Multimedia Tools and Applications since 2021. His critical reviews have covered advanced topics in federated learning, AI for health diagnostics, and cross-dataset validation for age estimation. As a conference presenter, he co-authored a paper at the AFRICATEK 2017 international conference on the use of 3D facial recognition for secure eHealth authentication, published in Springer. His ongoing government work in pharmaceutical economic evaluations has also contributed to national policy development. Though his academic work is recent, it reflects high-impact innovation, especially in multi-object tracking and real-time computer vision, indicating growing recognition in the AI and public sector technology communities. His combined academic, research, and government contributions position him as a forward-thinking leader in the application of technology for development.

Research Focus

Dr. Kalake’s research lies at the intersection of artificial intelligence, eHealth security, and computer vision. His recent investigations explore real-time multi-object tracking across non-overlapping camera views, aiming to enhance detection and re-identification using deep learning models. He has worked on improving object detection performance by integrating methods like HOG (Histogram of Oriented Gradients) with Convolutional Neural Networks (CNNs). Additionally, he has focused on video processing, smart surveillance, and deep learning algorithms to improve detection quality in constrained environments. In the healthcare domain, he is investigating security frameworks for Electronic Health Record (EHR) systems, proposing models using 3D face recognition, Wi-Fi, and smartphone-based authentication to safeguard patient data. His interdisciplinary focus contributes to advancements in AI-driven diagnostics, public sector information systems, and the digital transformation of health systems. This blend of academic and applied research highlights his commitment to AI for public good.

Publication Top Notes

  • 📄 Analysis Based on Recent Deep Learning Approaches Applied in Real-Time Multi-Object Tracking: Review, IEEE Access, 2021

  • 📄 Enhancing Detection Quality Rate with a Combined HOG and CNN for Real-Time Multiple Object Tracking, Sensors, 2022

  • 📄 Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking, Sensors, 2022

  • 📘 Designing an Electronic Health Security System Framework Using Wi-Fi, Smartphone, and 3D Face Recognition, AFRICATEK 2017, Springer

Samia Haouassi | Computer Science | Best Researcher Award

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Samia Haouassi | Computer Science | Best Researcher Award

Samia Haouassi, Dalian university of technology, Algeria

Samia Haouassi is a dedicated researcher in Computer Science, specializing in image processing and intelligent systems. Originating from Khenchela, Algeria, she has pursued an international academic journey that reflects her passion for technology and cross-cultural collaboration. Samia earned her Bachelor’s and Master’s degrees from Constantine University 2, Algeria, before moving to China for her PhD at Dalian University of Technology. Her multilingual abilities in Arabic, French, English, and Chinese have allowed her to navigate academic and multicultural environments with ease. She is well-regarded for her strong communication and organizational skills, demonstrated by leading cultural exchange events and mentoring peers. Samia’s research contributions span image dehazing, iris recognition, and control systems. Beyond academics, she is actively involved in promoting cultural diversity and academic collaboration. With a foundation in both theory and practical applications, she aims to contribute meaningfully to the development of intelligent computer systems and global scientific advancement.

Publication Profile

orcid

Education

Samia Haouassi’s academic journey showcases a strong foundation in Computer Science, developed across Algeria and China. She began her higher education at Constantine University 2 in Algeria, where she completed her Bachelor’s degree in Computer Science (2010–2012). Continuing at the same university, she earned her Master’s degree in Computer Science and its Applications (2012–2014). Her academic excellence earned her prestigious government scholarships, enabling her to pursue a PhD in Computer Science and its Applications at Dalian University of Technology, China (2015–2024). During her doctoral studies, Samia expanded her research scope and honed her skills in advanced computational topics such as image processing, machine learning, and control systems. Her education has been supported by full scholarships from both the Algerian government and Dalian University, recognizing her academic potential and commitment to research. She is fluent in Arabic and French, and proficient in English and Chinese (HSK 3), supporting her global academic engagement.

Experience

Samia Haouassi brings a blend of academic rigor and practical engagement through her research and extracurricular roles. During her doctoral studies at Dalian University of Technology, she actively contributed to the university’s international environment by leading the Arabic Culture Stand at the Cultural Festival in 2016 and 2017. This leadership experience reflects her organizational and managerial strengths. Samia also took part in research projects, authoring papers in fields like image dehazing, iris recognition, and iterative learning control, showcasing her expertise in machine vision and intelligent algorithms. Beyond research, she demonstrated strong communication and mentorship capabilities by supporting peers in navigating cross-cultural academic environments. Her experience as a scholarship recipient and cultural ambassador highlights her adaptability, leadership, and collaborative spirit. By integrating technical excellence with multicultural awareness, she exemplifies the modern researcher who is not only skilled in their field but also contributes to inclusive and diverse academic communities.

Awards and Honors

Samia Haouassi has been recognized with multiple prestigious awards and scholarships that reflect her academic excellence and research potential. From 2014–2015, she was awarded a scholarship by the Algerian government under an international exchange program, enabling her to expand her academic experience beyond national borders. In 2015, she was granted a fully funded PhD scholarship by Dalian University of Technology, one of China’s top institutions, to pursue advanced research in Computer Science. These scholarships were highly competitive and are a testament to her outstanding academic profile. In addition to academic honors, her cultural contributions at Dalian University were appreciated, as she led and organized the Arabic Culture Stand at the university’s Cultural Festival in 2016 and 2017. These accolades showcase both her academic dedication and her efforts in promoting cultural understanding and diversity, making her a well-rounded recipient of both academic and social recognition.

Research Focus

Samia Haouassi’s research focuses on intelligent systems within the domain of Computer Science and its Applications, with special emphasis on image processing, biometric recognition, and control algorithms. Her doctoral work at Dalian University of Technology centers on image dehazing—enhancing visual clarity in low-visibility environments using advanced filtering and machine learning methods. She also explores iris recognition technologies, developing secure and accurate biometric identification systems. In the area of iterative learning control (ILC), she investigates how systems can adapt and improve over time through feedback and repetition, with potential applications in robotics and automation. Her interdisciplinary approach integrates computer vision, pattern recognition, and adaptive systems. By addressing both theoretical challenges and practical applications, Samia aims to develop robust, real-world solutions for intelligent image analysis and control. Her work holds promise for innovation in security, automation, and human-computer interaction, bridging the gap between algorithmic research and its societal applications.

Publication Top Notes

📄 Image Dehazing Based on Multi-scale Fusion Using Dark Channel Prior
📄 Efficient Iris Recognition Using Enhanced Local Binary Patterns
📄 Iterative Learning Control for Repetitive Tasks in Dynamic Systems

Dr. Md. Tarek Habib | Computer Science | Best Researcher Award

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Assist. Prof. Dr. Md. Tarek Habib | Computer Science | Best Researcher Award

Assist. Prof. Dr. Md. Tarek Habib Independent University, Bangladesh

Dr. Md. Tarek Habib is an Assistant Professor in the Department of Computer Science and Engineering at Independent University, Bangladesh. With a strong passion for teaching and research, his objective is to enhance his expertise in the field of Computer Science and Engineering. His research interests include Artificial Intelligence, with a focus on Computer Vision, Machine Learning, and Natural Language Processing, as well as IoT and E-Commerce. Dr. Habib is dedicated to advancing knowledge and innovation in these domains, contributing to both academia and industry through his research and mentorship.

Education:

Doctor of Philosophy (Ph.D.) in Computer Science and Engineering

Institution: Jahangirnagar University, Bangladesh

Year: 2022

Thesis Title: Machine Vision Based Fruit Disease Recognition

Supervisor: Prof. Dr. Mohammad Shorif Uddin

Co-Supervisor: Prof. Dr. Farruk Ahmed (Independent University, Bangladesh)

Master of Science (M.S.) in Computer Science and Engineering

Institution: North South University, Bangladesh

Year: 2009

CGPA: 3.85 on a scale of 4.0

Thesis Title: Machine Vision Based Textile Defects Classification

Supervisor: Prof. Dr. M. Rokonuzzaman

Bachelor of Science (B.Sc.) in Computer Science

Institution: BRAC University, Bangladesh

Year: 2006

CGPA: 3.74 on a scale of 4.0

Award: Vice Chancellor’s Medal

Thesis Title: A Survey on Location Systems for Ubiquitous Computing

Supervisor: Prof. Dr. Matin Saad Abdullah

Higher Secondary Certificate (HSC)

Institution: SOS Hermann Gmeiner College, Dhaka Board

Year: 2000

Division: 2nd Division (Science Group)

Total Marks: 567 out of 1000

Secondary School Certificate (SSC)

Institution: Sher-e-Bangla Nagar Govt. Boys’ High School, Dhaka Board

Year: 1998

Division: 1st Division with Star Marks (Science Group)

Total Marks: 823 out of 1000

Professional Profiles:

ORCID Profile

Scopus Profile

Professional Experience:

Assistant Professor, Department of Computer Science and Engineering

Independent University, Bangladesh January 2023

Associate Professor, Department of Computer Science and Engineering
Daffodil International University, Bangladesh (January 2023 – January 2023)

Assistant Professor, Department of Computer Science and Engineering
Daffodil International University, Bangladesh (January 2016 – December 2022)

Assistant Professor (Contractual), Department of Computer Science and Engineering
Daffodil International University, Bangladesh (September 2015 – December 2015)

Assistant Professor, Department of Computer Science and Engineering
Green University of Bangladesh (May 2014 – September 2015)

Assistant Professor, Department of Computer Science and Engineering
Prime University, Bangladesh (September 2013 – May 2014)

Senior Lecturer, Department of Computer Science and Engineering
Prime University, Bangladesh (January 2013 – August 2013)

Lecturer, Department of Computer Science and Engineering
Prime University, Bangladesh (October 2010 – December 2012)

Research Interests:

Artificial Intelligence (AI) and Its Applications

Computer Vision

Machine Learning (ML)

Natural Language Processing (NLP)

Internet of Things (IoT)

E-Commerce and AI-Driven Business Solutions

Top Notable Publications:

An Insightful Analysis of CNN-Based Dietary Medicine Recognition

Journal of Agriculture and Food Research, 2025-03

DOI: 10.1016/j.jafr.2024.101564

Contributors: Mohammad Didarul Alam, Tanjir Ahmed Niloy, Aurnob Sarker Aurgho, Mahady Hasan, Md. Tarek Habib

Deep Learning Modeling for Potato Breed Recognition

IEEE Transactions on AgriFood Electronics, 2024

DOI: 10.1109/TAFE.2024.3406544

Contributors: Md. Ataur Rahman, Abbas Ali Khan, Md. Mehedi Hasan, Md. Sadekur Rahman, Md. Tarek Habib

A Study on Social Media Addiction Analysis on the People of Bangladesh Using Machine Learning Algorithms

Bulletin of Electrical Engineering and Informatics, 2024-10-01

DOI: 10.11591/eei.v13i5.5680

Contributors: Minjun Nahar Mim, Mehedi Firoz, Mohammad Monirul Islam, Mahady Hasan, Md. Tarek Habib

Tomato Pest Recognition Using Convolutional Neural Network in Bangladesh

Bulletin of Electrical Engineering and Informatics, 2024-02-01

DOI: 10.11591/eei.v13i1.6073

Contributors: Johora Akter Polin, Nahid Hasan, Md. Tarek Habib, Atiqur Rahman, Zannatu Nayem Vasha, Bidyut Sharma

Publications (2020-2023)

A Machine Learning Approach for Driver Identification

Indonesian Journal of Electrical Engineering and Computer Science, 2023-04-01

DOI: 10.11591/ijeecs.v30.i1.pp276-288

Contributors: Md. Abbas Ali Khan, Mohammad Hanif Ali, Fazlul Haque, Md. Tarek Habib

Analysis of Recognition Performance of Plant Leaf Diseases Based on Machine Vision Techniques

Journal of Human, Earth, and Future, 2022-03-01

DOI: 10.28991/HEF-2022-03-01-09

Contributors: Imdadul Haque, Mohsin Alim, Mahbub Alam, Samia Nawshin, Sheak Rashed Haider Noori, Md. Tarek Habib

Machine Vision-Based Papaya Disease Recognition

Journal of King Saud University – Computer and Information Sciences, 2020-03

DOI: 10.1016/j.jksuci.2018.06.006

Contributors: Md. Tarek Habib, Anup Majumder, A.Z.M. Jakaria, Morium Akter, Mohammad Shorif Uddin, Farruk Ahmed

Conclusion:

Given his extensive publication record, research impact, and interdisciplinary contributions, Assist. Prof. Dr. Md. Tarek Habib is highly suitable for the Research for Best Researcher Award. His pioneering work in AI, ML, and IoT continues to drive innovation and technological advancements, making him a deserving recipient of this esteemed recognition.