Raziyeh Pourdarbani | Artificial Intelligence | Best Paper Award

Published on by Computer Science Engineering

Prof. Raziyeh Pourdarbani | Artificial Intelligence | Best Paper Award

Faculty Membr | University of Mohaghegh Ardabili | Iran

Dr. Raziyeh Pourdarbani is a Professor of Biosystems Engineering at the University of Mohaghegh Ardabili and an internationally recognized researcher in precision agriculture, image processing, machine vision, artificial intelligence, and hyperspectral imaging. Her research is dedicated to developing advanced computational approaches that enhance automation, sustainability, and non-destructive assessment within agricultural production systems. She has established a strong scholarly footprint through extensive publications that explore cutting-edge deep learning architectures, including the application of 2D and 3D convolutional neural networks, majority voting ensemble strategies, hybrid neural networks, and metaheuristic optimization techniques for quality evaluation and decision-making in crop and fruit management. Her studies have significantly advanced non-destructive methodologies for detecting bruises, internal defects, and ripening stages in fruits, as well as monitoring excessive nitrogen consumption and estimating chemical and physicochemical properties in plant leaves using hyperspectral, visible, and near-infrared spectral data. In addition to agricultural sensing and classification research, she has contributed impactful work on sustainable bioenergy, including biomethane production from agricultural residues, biodiesel engine performance enhancement using nanomaterials, and advanced exergy and life-cycle analysis of hybrid geothermal–solar power systems. She has authored multiple academic books addressing renewable energy and intelligent grading technologies and has led numerous research projects involving automated fruit identification algorithms, orchard-based robotic systems, video-based fruit maturity estimation, spectral wavelength optimization, agricultural development modeling, and geothermal heating-system design. Dr. Pourdarbani actively disseminates her findings through national and international conferences and contributes to the scientific community through reviewing and collaborative roles in multidisciplinary research initiatives. Her work is widely acknowledged for its scientific value and practical relevance in improving agricultural resource efficiency, enhancing food-quality monitoring, and promoting environmentally responsible production strategies. As a leading figure in the integration of computational intelligence with agricultural engineering, she continues to shape research directions that support global progress toward smart, sustainable, and technologically empowered agriculture.

Profile : Google Scholar

Featured Publication

Alibaba, M., Pourdarbani, R., Manesh, M. H. K., Ochoa, G. V., & Forero, J. D. (2020). Thermodynamic, exergo-economic and exergo-environmental analysis of hybrid geothermal–solar power plant based on ORC cycle using emergy concept. Heliyon, 6(4).

Pourdarbani, R., Sabzi, S., Kalantari, D., Hernández-Hernández, J. L., & Arribas, J. I. (2019). A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties.

Pourdarbani, R., Sabzi, S., García-Amicis, V. M., García-Mateos, G., Hernández-Hernández, J. L., & Arribas, J. I. (2019). Automatic classification of chickpea varieties using computer vision techniques. Agronomy, 9(11), 672.

Ebrahimi, S., Pourdarbani, R., Sabzi, S., Rohban, M. H., & Arribas, J. I. (2023). From harvest to market: Non-destructive bruise detection in kiwifruit using convolutional neural networks and hyperspectral imaging. Horticulturae, 9(8), 936.

Pourdarbani, R., Sabzi, S., Rohban, M. H., Hernández-Hernández, J. L., & Arribas, J. I. (2021). One-dimensional convolutional neural networks for hyperspectral analysis of nitrogen in plant leaves. Applied Sciences, 11(24), 11853

Muhammad Asif Munir | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Mr. Muhammad Asif Munir | Machine Learning | Best Researcher Award

Assistant Professor| Swedish College of Engineering and Technology | Pakistan

Dr. Muhammad Asif Munir is an accomplished researcher and academic in the field of Electrical Engineering, currently serving as an Assistant Professor at the Swedish College of Engineering and Technology, District Rahim Yar Khan, Punjab, Pakistan, and pursuing his Ph.D. at The Islamia University of Bahawalpur. His research primarily focuses on machine learning and deep learning applications in biomedical image analysis, with a particular emphasis on addressing the challenges of small and imbalanced radiomics datasets. With six peer-reviewed publications indexed in SCI and Scopus journals, including IEEE Access and Future Internet (MDPI), and a growing citation record of 56 citations (h-index: 4, i10-index: 2), Dr. Munir has demonstrated consistent academic excellence and research innovation. His notable contribution, the GSRA-KL framework, introduces a novel sparse regularized autoencoder–based methodology that significantly enhances synthetic data generation and improves the predictive accuracy of gene mutation analysis in lung cancer radiomics. This work not only contributes to the evolution of precision oncology but also exemplifies the integration of AI-driven data synthesis with clinical applications. His ongoing research explores the incorporation of explainable artificial intelligence (XAI) into radiomics for more interpretable, transparent, and reliable predictive modeling, fostering clinically explainable AI systems in healthcare. Dr. Munir’s interdisciplinary approach bridges data science, medical imaging, and clinical decision support, aiming to make AI tools both scientifically robust and ethically transparent. A member of professional organizations such as IEEE and IAENG, he remains actively engaged in promoting research collaboration and advancing the global discourse on intelligent healthcare systems. Through his scholarly contributions, Dr. Munir is significantly impacting the development of data-efficient, interpretable, and patient-centered AI frameworks, reinforcing the global transition toward smart healthcare technologies and next-generation precision medicine. His commitment to research excellence and translational impact continues to position him as a promising figure in the convergence of engineering and medical AI research.

Featured Publication

Aslam, M. A., Munir, M. A., & Cui, D. (2020). Noise removal from medical images using hybrid filters of technique. Journal of Physics: Conference Series, 1518(1), 012061.

Aslam, M. A., Xue, C., Wang, K., Chen, Y., Zhang, A., Cai, W., Ma, L., Yang, Y., Sun, X., & Munir, M. A. (2020). SVM based classification and prediction system for gastric cancer using dominant features of saliva. Nano Biomedicine and Engineering, 12(1), 1–13.

Munir, M. A., Aslam, M. A., Shafique, M., Ahmed, R., & Mehmood, Z. (2022). Deep stacked sparse autoencoders – A breast cancer classifier. Mehran University Research Journal of Engineering and Technology, 41(1), 41–52.

Aslam, M. A., Munir, M. A., Ahmad, R., Samiullah, M., Hassan, N. M., & Mahnoor, S. (2022). Deep neural networks for prediction of cardiovascular diseases. Nano Biomedicine and Engineering, 14(1).

Munir, M. A., Shah, R. A., Ali, M., Laghari, A. A., Almadhor, A., & Gadekallu, T. R. (2024). Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis. IEEE Access.

Dr. Muhammad Asif Munir’s research advances intelligent healthcare by integrating machine learning and explainable AI to enhance diagnostic accuracy and transparency in medical imaging. His innovations in radiomics and synthetic data generation foster data-efficient, interpretable, and globally applicable solutions that strengthen precision oncology and next-generation healthcare systems.

Bincy Baburaj Kaluvilla | Machine Learning | Best Researcher Award

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Dr. Bincy Baburaj Kaluvilla | Machine Learning | Best Researcher Award

Head of Academics | Learners University College | United Arab Emirates

Dr. Bincy B. Kaluvilla is an accomplished academic and researcher specializing in sustainable finance, investment management, and hospitality education, with a particular emphasis on integrating environmental, social, and governance (ESG) principles into financial and hospitality frameworks. She currently serves as Head of Academics and Partnerships at Learners University College, UAE, and previously worked as Assistant Professor and Undergraduate Program Manager at the Emirates Academy of Hospitality Management, where she played a central role in program leadership, faculty coordination, and industry collaboration. Holding a Ph.D. in Accounting from the University of Leicester, an M.Res in Accounting and Finance from the University of Glasgow, and professional recognition as a Fellow of the Higher Education Academy (UK) and CPA Australia, Dr. Kaluvilla combines strong academic foundations with practical insight. Her research encompasses real estate finance, green finance, ESG reporting, and digital transformation in hospitality, contributing over fifteen peer-reviewed publications and book chapters in leading journals such as Frontiers in Computer Science, Asia Pacific Journal of Tourism Research, and Library Hi Tech News, with growing citation impact across Scopus and Web of Science databases. She has authored chapters for major publishers including Springer Nature, Emerald, IGI Global, and Apple Academic Press, addressing emerging issues in sustainable investment, digital currencies, and responsible finance. Her academic influence extends globally through conference presentations at EuroCHRIE in Vienna, GHLS in Dubai, and IPoE in the UAE. Beyond research, she has led significant corporate training initiatives with the Jumeirah Group, Omran Group, and the UAE Ministry of Foreign Affairs, advancing professional development and gender empowerment within the hospitality industry. Through her research, teaching, and leadership, Dr. Kaluvilla continues to advance global understanding of sustainable finance and investment practices, fostering stronger links between academia, industry, and community development.

Featured Publication

Fahad, Z., Kaluvilla, B. B., & Mulla, T. (2024). Embracing the new era: Artificial intelligence and its multifaceted impact on the hospitality industry. Journal of Open Innovation: Technology, Market, and Complexity, 10(4), 100390.

Ghazanfar, U., Kaluvilla, B. B., & Zahidi, F. (2023). The post-COVID emergence of dark kitchens: A qualitative analysis of acceptance and the advantages and challenges. Research in Hospitality Management, 13(1), 23–30.

Kaluvilla, B. B. (2024). Cultural preservation through technology in UAE libraries. Library Hi Tech News, 41(8), 6–9.

Kalarikkal, S. A., Thamilvannan, G., & Kaluvilla, B. B. (2024). Enhancing access to missionary archives: The role of digital libraries and online repositories. Library Hi Tech News.

Kaluvilla, B. B., Mulla, T., Zahidi, F., & Wondirad, A. (2024). Driving sustainable choices through understanding consumer behaviour and underlying factors that influence the purchasing intention of refurbished furniture. SSRN Electronic Journal.

Md. Habibullah Shakib | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Mr. Md. Habibullah Shakib | Machine Learning | Best Researcher Award

Researcher| World University of Bangladesh| Bangladesh

Mr. Md. Habibullah Shakib is an emerging researcher and analyst from Bangladesh with over 3.5 years of research experience in artificial intelligence, supervised and deep learning, genetic AI, and foundation models. He holds a Bachelor of Science in Computer Science and Engineering from the World University of Bangladesh and a Diploma in Computer Technology from the National Polytechnic Institute. His research focuses on developing intelligent and secure computing systems, with significant contributions to Android malware detection, federated learning, autonomous systems, and IoT-based smart home automation. Among his key projects are the Active Federated YOLOR Model for enhancing autonomous vehicle safety, deep learning and genetic AI approaches for Android malware detection, and the integration of Conformer, Active Learning, and Federated Learning models for encrypted malware traffic detection. His ongoing work on Autonomous Generative AI for Android malware detection reflects his interest in advancing cutting-edge AI-driven cybersecurity solutions. Recognized for his scholarly engagement, he received a Certificate of Reviewing from the Information Processing and Management journal (Elsevier, 2024). He has built a growing academic presence with profiles on Google Scholar, ORCID, SSRN, GitHub, and the AD Scientific Index. Fluent in Bangla and English, he combines strong analytical and organizational skills with a commitment to innovation and teamwork. Through his dedication to ethical AI development, quantitative data analysis, and research collaboration, Md. Habibullah Shakib aims to contribute globally to the progress of intelligent systems, data-driven decision-making, and digital security for sustainable technological advancement.

Featured Publication

Shakib, M. (2023). Android malware detection approach based on genetic AI, CNN, RNN, LSTM, GRU, and active learning. SSRN. Cited by: 1

Shakib, M. H., Yeasin, M., Rahman, M. H., Rahman, K. M., Hossain, S., & Mahi, F. F. (2025). Active learning model used for Android malware detection. Machine Learning with Applications, 100680. Cited by: 8

Shakib, M. D. H. (2024). Android malware detection using transformer and encoder models. SSRN. Cited by: 5

Shakib, M. H. (2024). Comparing conformer, genetic artificial intelligence conformer, and active learning conformer approaches for encrypted Android malware traffic detection. SSRN. Cited by: 4