Zhixiang Wang | Data Science and Deep Learning | Best Researcher Award

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Dr. Zhixiang Wang | Data Science and Deep Learning | Best Researcher Award

Research Intern at Beijing Friendship Hospital | China

Dr. Zhixiang Wang is a distinguished researcher in Clinical Data Science with a strong background in Artificial Intelligence, Medical Imaging, and Machine Learning. A PhD graduate from Maastricht University under the mentorship of Professor Andre Dekker, Wang has demonstrated a consistent commitment to bridging computational innovation with clinical application. His prolific research output spans over 29 internationally recognized journal publications, reflecting expertise in multimodal imaging, large language models, and radiomics. His representative works include Performance of GPT-4 for Automated Prostate Biopsy Decision-Making Based on mpMRI: A Multi-Center Evidence Study, Radiomics and Dosiomics Signature from Whole Lung Predicts Radiation Pneumonitis: A Model Development Study with Prospective External Validation and Decision-Curve Analysis, and Computed Tomography and Radiation Dose Images-Based Deep-Learning Model for Predicting Radiation Pneumonitis in Lung Cancer Patients After Radiation Therapy. He further contributed to Development and Performance of a Large Language Model for the Quality Evaluation of Multi-Language Medical Imaging Guidelines and Consensus, A Radiomics Nomogram for the Ultrasound-Based Evaluation of Central Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma, An Applicable Machine Learning Model Based on Preoperative Examinations Predicts Histology, Stage, and Grade for Endometrial Cancer, and Generation of Synthetic Ground Glass Nodules Using Generative Adversarial Networks (GANs). His studies such as CycleGAN Clinical Image Augmentation Based on Mask Self-Attention Mechanism and GAN-Based One-Dimensional Medical Data Augmentation highlight his skill in generative models for data enhancement. Zhixiang Wang’s research also explores Enhancing Diagnostic Accuracy and Efficiency with GPT-4-Generated Structured Reports and Assessing the Role of GPT-4 in Thyroid Ultrasound Diagnosis and Treatment Recommendations: Enhancing Interpretability with a Chain of Thought Approach. With extensive experience in AI-driven diagnostic imaging, multimodal model development, and LLM fine-tuning for clinical reporting, Wang continues to lead innovation at the intersection of data science and precision medicine, contributing impactful advancements toward intelligent, interpretable, and efficient clinical decision-support systems.

Profile: Scopus

Featured Publications:

Wang, Z., Zhang, Z., Luo, T., Yan, M., & Dekker, A. (2026). A cross-modal fine-grained retrieval method based on LAGC and contrastive learning. Expert Systems with Applications.

Wang, Z., Sun, J., Liu, H., & Chen, Y. (2026). Experience-guided multi-agent interpretable framework for radiology report summarization. Computer Methods and Programs in Biomedicine.

Wang, Z., Li, J., Feng, Y., & Qian, L. (2025). Machine learning model based on preoperative MRI and clinical data for predicting pancreatic fistula after pancreaticoduodenectomy. BMC Medical Imaging.

Shi, M. J., Wang, Z. X., & Wang, Z. C. (2025). Performance of GPT-4 for automated prostate biopsy decision-making based on mpMRI: A multi-center evidence study. Military Medical Research.

Animesh Kairi | Biometrics and Human-Machine Interaction | Excellence in Research Award

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Prof. Animesh Kairi | Biometrics and Human-Machine Interaction | Excellence in Research Award

Assistant Professor at Institute of Engineering and Management | India

Prof. Animesh Kairi is an accomplished academician and IT professional currently serving as an Assistant Professor (Grade III) and active member of the Controller of Examinations at the Institute of Engineering and Management (IEM), Salt Lake, where he contributes extensively to both academic and administrative excellence. With a strong foundation in Computer Science and Engineering, he is pursuing his Ph.D. from Aliah University, having successfully completed coursework and registration, and continues to engage in research within areas such as Digital Forensics, Cryptography, Artificial Intelligence, and Computer Networking. Holding a Master of Computer Applications degree from Narula Institute of Technology under WBUT and a Bachelor’s degree in Physics (Honours) from the University of Calcutta, he possesses a multidisciplinary understanding of computational and analytical systems. His academic responsibilities include teaching theoretical and laboratory courses in Digital Forensics, Cryptography, Basic Networking, and C programming, as well as managing examination routines, grading systems, course structures, and institutional accreditation processes under NAAC and NBA teams. Before joining academia, he gained substantial industry experience through technical and network engineering roles at organizations such as Diversified Technologies India, Kotak Securities, Trimax IT Infrastructure Limited, and PCS, where he provided advanced network support, system configuration, and remote infrastructure management for major clients across India. He holds multiple professional certifications, including AWS Fundamentals, MTA Networking, CCNA, and specialized courses in Machine Learning, Cyber Security, AI, and Big Data Analytics from reputed platforms like Coursera, Microsoft, Intel, and NIT Jamshedpur. Renowned for his dedication, punctuality, and technical acumen, Animesh Kairi continues to inspire through his commitment to academic growth, innovation in digital systems, and excellence in computing education.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

Kairi, A., Gagan, S., Bera, T., & Chakraborty, M. (2018). DNA cryptography-based secured weather prediction model in high-performance computing. In Proceedings of the International Ethical Hacking Conference 2018: eHaCON 2018.

Chakraborty, S., Kumar, S., Paul, S., & Kairi, A. (2017). A study of product trend analysis of review datasets using Naive Bayes, K-NN, and SVM classifiers. International Journal of Advanced Engineering and Management (ISSN 2456–3676).

Kairi, A., Bhadra, T., Roy, A., & Saha, T. (2023). An innovative method for DNA cryptography-based digital signature verification. In Proceedings of the International Conference on Cyber Intelligence and Information Retrieval (pp. 3–14).

Kairi, A., & Bhadra, T. (2023). Decoding the future using a novel DNA-based cryptosystem. Journal of European Chemical Bulletin, 12(10), 3597–3609.

Chakraborty, S. M. S., Dey, L., & Kairi, A. (2023). Prediction of winning team in soccer game – A supervised machine learning-based approach. In Advances on Mathematical Modeling and Optimization with Its Applications.

Filip Herzyk | Solid-Fluid Interaction | Best Researcher Award

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Mr. Filip Herzyk | Solid-Fluid Interaction | Best Researcher Award

PhD Student at Wroclaw University of Environmental and Life Sciences | Poland

Mr. Filip Herzyk is a highly accomplished and forward-thinking Lead Research Specialist from Poland, whose academic and professional journey reflects an extraordinary blend of scientific rigor, creative design thinking, and interdisciplinary expertise. With a strong foundation in Food Technology and Human Nutrition from the Wroclaw University of Environmental and Life Sciences, Filip continues to advance his scholarly pursuits through doctoral studies in the same field, exploring innovative solutions that integrate food science, biotechnology, and sustainability. His career path began at the Wroclaw Technology Park, where he steadily evolved from a laboratory assistant to a senior and now lead research specialist, demonstrating exceptional competence in laboratory techniques, prototyping, and applied research management. Filip’s professional experience encompasses a wide range of laboratory operations, advanced food analysis, and experimental design, underpinned by his deep knowledge of materials science and product development. Known for his fluency in English and technical proficiency with tools such as Microsoft Office, PowerQuery, Adobe Creative Suite, and laboratory instrumentation, he bridges the gap between scientific experimentation and digital innovation. His interests extend beyond research into areas such as material design, information technology, music and audio technology, and creative art, enriching his multidisciplinary approach to food innovation. Filip’s qualifications, including professional training in agile time management and modern laboratory methodologies, reinforce his capability to lead complex research initiatives that push the boundaries of food quality, safety, and sensory enhancement. His work embodies a synthesis of scientific precision, creativity, and a passion for advancing the future of food technology and human well-being.

Profile: Scopus | Orcid

Featured Publications:

Herzyk, F., & Korzeniowska, M. (2025, August 21). Optimisation of supercritical CO₂ extraction from black (Ribes nigrum) and red (Ribes rubrum) currant pomace. Applied Sciences.

Herzyk, F., Piłakowska-Pietras, D., & Korzeniowska, M. (2024, May 30). Supercritical extraction techniques for obtaining biologically active substances from a variety of plant byproducts. Foods.

Muhammad Sanwal Bakhsh | Bio-Mechanics | Young Scientist Award

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Mr. Muhammad Sanwal Bakhsh | Bio-Mechanics | Young Scientist Award

MSc Scholar at Pmas-Arid Agriculture University | Pakistan

Mr. Muhammad Sanwal Bakhsh is a motivated and detail-oriented agriculture graduate specializing in plant pathology, research coordination, and sustainable agricultural innovation, currently serving as an Agriculture Officer at the Agriculture Extension Department, Chiniot, Pakistan. With strong expertise in crop disease diagnostics, pest management, and climate-smart agriculture, he contributes to advancing field-based solutions that promote sustainable farming practices and enhance food security. His academic foundation from PMAS-Arid Agriculture University, Rawalpindi, strengthened through hands-on training at Ayub Agriculture Research Institute, Faisalabad, has provided him with extensive experience in biological control research, particularly in using Trichoderma species for managing wheat and cotton diseases. He is adept in laboratory and molecular techniques, data analysis, GIS applications, and scientific writing, supported by technical proficiency in tools such as SPSS, R, and Python. Sanwal has actively participated in international conferences and training programs on horticulture, precision agriculture, and climate resilience, reflecting his commitment to continuous learning and professional excellence. His research publications focus on improving crop productivity and disease management under semi-arid conditions through integrated and eco-friendly approaches. Recognized as a “Young Environmental Activist” and a member of the Young PhytoDoctor’s Forum, he has demonstrated leadership in academic and environmental initiatives. With strong analytical, organizational, and digital skills, Sanwal aims to integrate innovative research, sustainability, and technology-driven strategies to advance agricultural resilience, contributing meaningfully to global development goals and the mission of institutions such as the FAO and UNEP.

Profile: Orcid 

Featured Publications:

Bakhsh, M. S., Sarfraz, M., Ilahi, N., Rehman, M. U., Shamshad, H., Usman, M., Mobin, I., & Bibi, M. (n.d.). Effects of deficit irrigation on growth, yield, and quality of tomato under semi-arid regions. Biology and Life Sciences Forum.

Sarfraz, M., Mehboob, S., Bakhsh, S., Aleem, S., Sarwar, M., Mobin, I., Sultana, R., & Ahmad, J. (n.d.). Assessment of antifungal potential of biocontrol agents and fungicides against Fusarium wilt of cotton. Journal of Agriculture and Biology, 3(1), 90–97.

Bakhsh, M. S., Sarfraz, M., Mehboob, S., Ilahi, N., & Shahrayz, S. (n.d.). Bio-control of Septoria tritici blotch in wheat using Trichoderma species in Pakistan. Pakistan Journal of Phytopathology, 36(2), 301–311.

Kartik Charania | Data Science and Deep Learning | Best Researcher Award

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Mr. Kartik Charania | Data Science and Deep Learning | Best Researcher Award

Senior Research Fellow at Sardar Vallabhbhai National Institute of Technology Surat | India

Kartik Charania is a dedicated Water Resources Engineer and researcher whose work focuses on hydrological modeling, rainfall variability, and sustainable water distribution systems. Pursuing his Ph.D. in Water Resources Engineering at SVNIT, Surat, his doctoral research emphasizes the spatiotemporal analysis of rainfall variability to support efficient and equitable water distribution network design in semi-arid basins. His expertise integrates advanced statistical and innovative trend analysis techniques with GIS-based spatial mapping to assess temporal rainfall shifts and their hydrological implications. Through his research, he aims to enhance water management practices, optimize reservoir operations, and promote climate-resilient water supply systems. His academic journey includes a Master’s in Water Resources Engineering and a Bachelor’s in Civil Engineering from Gujarat Technological University, where he built a strong foundation in hydraulic and environmental systems. Proficient in tools such as EPANET, ArcGIS, Python, HEC-RAS, HEC-HMS, and Q-GIS, he combines computational and analytical approaches to develop data-driven solutions for sustainable water infrastructure. Kartik has contributed to leading journals like Environmental Science and Pollution Research and World Water Policy, presenting innovative methods for rainfall trend analysis in the Shetrunji Basin, India. His active participation in conferences on hydrology and climate variability highlights his commitment to advancing knowledge in the field. Additionally, he qualified for the GATE examination and participated in specialized training programs like the “Training of Trainer (ToT)” under the MARVI project, reflecting his dedication to groundwater visibility and community-based water management.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

Charania, K. M., & Patel, J. N. (n.d.). Spatiotemporal trends and variability of rainfall patterns using innovative polygon trend analysis method for Shetrunji Basin, India. Environmental Science and Pollution Research, 1–11.

Charania, K. M., & Patel, J. N. (n.d.). Comprehensive trend analysis of monthly and seasonal rainfall in the Shetrunji Basin, India using statistical and innovative techniques. World Water Policy.

Maximilian Gehring | Software Engineering | Best Researcher Award

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Mr. Maximilian Gehring | Software Engineering | Best Researcher Award

Research Assistant at Technical University of Darmstadt | Germany

Mr. Maximilian Gehring is a distinguished German researcher and doctoral candidate at the Technical University of Darmstadt, specializing in the integration of digital technologies into construction logistics and intelligent infrastructure systems. His academic journey combines industrial engineering and civil engineering expertise with a strong focus on digital transformation, automation, and Building Information Modeling (BIM). Through his ongoing doctoral research, Further development of digital construction logistics: Design of a digital twin framework for automated construction logistics management, Gehring advances the field of smart construction by developing scalable frameworks that enhance data-driven decision-making and operational efficiency. His scholarly contributions include the publication IoT-altimeter in smart pallets for material tracking on multi-storey construction sites, which explores innovative Internet of Things solutions for improving material tracking and site logistics. In ConLogAI – Concept for an AI-enabled platform for construction logistics scheduling, he presents a robust artificial intelligence framework that optimizes scheduling and resource allocation in complex construction environments. His work Unlocking BIM Potential: Empowering Collaboration Through an Open Source-Powered BIM API Platform for Building Lifecycle Management highlights his commitment to open-source collaboration and interoperability in BIM-driven ecosystems. Additionally, his paper Data fusion approach for a digital construction logistics twin underscores his expertise in merging multiple data streams to build comprehensive digital representations of construction processes. Recognized for his academic excellence, Gehring achieved second place in the RKW Competence Centre’s ‘AufITgebaut’ competition for his master’s thesis on construction site logistics optimization. With advanced programming proficiency in Python, C#, and SQL, alongside experience in tools such as Revit, Docker, and Unity, he bridges engineering, computer science, and management to drive innovation in smart construction and digital infrastructure systems.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

Gehring, M., & Mantel, H. (2025). Towards a more sustainable re-engineering of heterogeneous distributed systems using cooperating run-time monitors. In Proceedings of the book chapter.

Putz, F., Haesler, S., Völkl, T., Gehring, M., Rollshausen, N., & Hollick, M. (2024, November 11). PairSonic: Helping groups securely exchange contact information. In Proceedings of the Conference. ACM.

See, R. A., Gehring, M., Fischer, M., & Karuppayah, S. (2023, December 4). Binary sight-seeing: Accelerating reverse engineering via point-of-interest-beacons. In Proceedings of the Conference. ACM.

Pradeep Kumar Badiya | Materials Science and Nanotechnology | Best Researcher Award

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Dr. Pradeep Kumar Badiya | Materials Science and Nanotechnology | Best Researcher Award

Assistant Professor at Dayananda Sagar University | India

Dr. Pradeep Kumar Badiya, M.Sc., M.Phil., Ph.D. (Chemistry), is a distinguished researcher and academician currently serving as an Assistant Professor at Dayananda Sagar University, Karnataka, India. His scholarly journey reflects a deep commitment to advancing interdisciplinary chemical sciences, integrating nanotechnology, materials science, clinical diagnostics, and device development. He has contributed significantly to nanoelectro-mechanical systems (NEMS) and atom-scale devices using two-dimensional materials such as graphene, focusing on innovative gas sensing applications. His postdoctoral research at the Japan Advanced Institute of Science and Technology under the Mizuta Laboratory emphasized cutting-edge nano-device fabrication and material characterization. Previously, at the STAR Laboratory, Sri Sathya Sai Institute of Higher Learning, he played a pivotal role in developing indigenous biomedical devices, including REsCUE and DuRaT kits for rapid COVID-19 detection, validated by the Indian Council of Medical Research (ICMR), demonstrating his impact in translational biomedical innovation. His research spans clinical research design, semiconductor-device fabrication, fluorescence and plasmonics, surface chemistry, bioprocessing, and biostatistics, with extensive experience in analytical instrumentation such as AFM, SEM, TEM, XRD, UV-Vis, Raman, and ELISA-based systems. He has authored 19 peer-reviewed articles, 18 conference papers, 2 patents, and 1 book chapter, reflecting his active role in academic dissemination and technology transfer. His doctoral work pioneered low-cost biomaterials for bioprocessing and plasmonic applications, combining biological and material sciences for sustainable technological solutions. Dr. Badiya’s leadership in laboratory establishment, mentoring, and project management has shaped the next generation of chemists and interdisciplinary researchers, while his innovative spirit continues to bridge the gap between chemical science and healthcare technology.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

Bhaskar, S., Moronshing, M., Srinivasan, V., Badiya, P. K., Subramaniam, C., & Ramamurthy, S. S. (2020). Silver soret nanoparticles for femtomolar sensing of glutathione in a surface plasmon-coupled emission platform. ACS Applied Nano Materials, 3(5), 4329–4341.

Shubhashree, K. R., Reddy, R., Gangula, A. K., Nagananda, G. S., Badiya, P. K., & Ramamurthy, S. S. (2022). Green synthesis of copper nanoparticles using aqueous extracts from Hyptis suaveolens (L.). Materials Chemistry and Physics, 280, 125795.

Venkatesh, S., Badiya, P. K., & Ramamurthy, S. S. (2015). Low-dimensional carbon spacers in surface plasmon-coupled emission with femtomolar sensitivity and 1000-fold fluorescence enhancements. Chemical Communications, 51(37), 7809–7811.

Thota, S. P., Badiya, P. K., Yerram, S., Vadlani, P. V., Pandey, M., Golakoti, N. R., & Ramamurthy, S. S. (2017). Macro-micro fungal cultures synergy for innovative cellulase enzymes production and biomass structural analyses. Renewable Energy, 103, 766–773.

Venkatesh, S., Badiya, P. K., & Ramamurthy, S. S. (2016). Purcell factor based understanding of enhancements in surface plasmon-coupled emission with DNA architectures. Physical Chemistry Chemical Physics, 18(2), 681–684.

Kumari | Deep Learning | Best Researcher Award

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Mrs. G. Kumari | Deep Learning | Best Researcher Award

Senior Assistant Professor at Vignan’s Institute Of Information Technology | India

Dr. G. Kumari is a highly dedicated academician and researcher in the field of Computer Science and Engineering, currently serving as an Assistant Professor in the Department of Computer Science and Engineering at Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam. She is pursuing her Ph.D. from Jawaharlal Nehru Technological University, Kakinada (JNTUK), with a research focus on advanced machine learning applications, data-driven predictive systems, and intelligent computing methodologies. Her academic foundation is built upon an M.Tech in Computer Science and Engineering from Godavari Institute of Engineering and Technology (GIET), Rajahmundry, and a B.Tech in Computer Science from Aditya Institute of Technology and Management (AITAM), Tekkali. With an extensive teaching career spanning over a decade and a half across reputed institutions, she has taught core computer science subjects including Machine Learning, Software Engineering, Computer Networks, and Advanced Data Structures. Her research contributions are widely recognized, encompassing publications in reputed international journals and conferences. Her works include Diabetes Prediction using Machine Learning and Deep Neural Models with Hybrid Resampling Techniques, Graph Temporal Hybrid Neural Networks for Enhanced Malware Detection in Banking Systems, Enhancing Liver Disease Detection and Management with Advanced Machine Learning Models, Cancer Detection with Ensemble Learning Model from Novel Precedence-based Algorithms, Statistical Approaches for Forecasting Air Pollution: A Review, and Phish Alert: Phishing Website Detection using XGBoost Algorithm. She has also contributed to numerous applied AI and software engineering domains with publications such as Room Temperature Based Alerting System, Vehicle Number Plate Recognition and Logging using OpenCV and Tesseract-OCR, High-Level Security in Cloud Using Hybridization of Public Key Cryptography, A Novel Approach for Extraction of Dominant Representation Points of the Image, A Trusted New Method for Authentication and Security for Web Applications in Cloud using RSA Algorithm, Classification of Customer to Upgrade Profits in Retail Market with Deep Learning Methodology, Translation and Transliteration of Words, Future of Software Testing: Novel Perspective, Challenges, and Efficient Resource Allocation Algorithm in Dependable Distributed Computing Systems Using A Colony Optimization.

Profile: Orcid 

Featured Publications:

Rao, K. V., Devi, J. A., Anuradha, Y., Kumari, G., Kumar, M. S., & Rao, M. S. (2024, August 30). Enhancing liver disease detection and management with advanced machine learning models. International Journal of Experimental Research and Review, 42, Article 009.

Herbert Jelinek | Structural Health Monitoring | Best Researcher Award

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Assoc. Prof. Dr. Herbert Jelinek | Structural Health Monitoring | Best Researcher Award

Associate Head of Department at Khalifa University | United Arab Emirates

Assoc. Prof. Dr. Herbert Jelinek, PhD, is a distinguished neuroscientist and academic with extensive contributions to biomedical science, neuroscience, and clinical physiology. Born in Vienna, Austria, and educated in Sydney, he holds a Ph.D. in Medicine specializing in Neuroscience from the University of Sydney, with additional qualifications in Human Genetics, Psychology, and Neuroscience. He currently serves as Associate Professor and Associate Chair of Graduate Studies in the Department of Clinical Sciences at Khalifa University, Abu Dhabi, where he also contributes to undergraduate and postgraduate curriculum development, supervision, and research leadership. His impressive research record is reflected in an H-index of 43 on Scopus with nearly 400 publications and over 8,400 citations, and an H-index of 54 on Google Scholar with over 13,900 citations, highlighting his global academic influence. Dr. Jelinek’s expertise spans biomedical engineering, applied neuroscience, and clinical neurophysiology, with specific interests in heart rate variability, neurofeedback, biofeedback, autonomic dysfunction, and diabetic complications. His academic journey includes previous appointments as Associate Professor and Senior Lecturer at Charles Sturt University, where he played a pivotal role in course design, laboratory development, and mentoring of research students, alongside leadership in several academic committees. Internationally recognized for integrating neuroscience, psychology, and biomedical innovation, Dr. Jelinek’s interdisciplinary approach bridges research and clinical application, advancing diagnostic methods for chronic disease and neurophysiological disorders. He is a Senior Member of the IEEE Engineering in Biology and Medicine Society and an active member of the Applied Neuroscience Society of Australasia. Through decades of academic leadership, student mentorship, and scholarly excellence, Dr. Herbert Franz Jelinek continues to shape the evolving landscape of neuroscience, biofeedback, and biomedical education across research and clinical domains.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

Jelinek, H. F., … (2025). Hierarchical random forest model, inflammation and oxidative stress as predictors of the atherogenic index of plasma and diabetes progression. Scientific Reports.

Jelinek, H. F., … (2025). Diabetic foot prevention, assessment, and management using innovative smart wearable technology: A systematic review. [Journal name not specified].

Jelinek, H. F., … (2025). Vm and ζ-potential of Candida albicans correlate with biofilm formation. Scientific Reports.

Jelinek, H. F., … (2025). Enhancing CCTA image quality: A review of deep learning approaches for advanced artifact correction and denoising. Artificial Intelligence Review.

Jelinek, H. F., … (2025). Placebo effect in gameplay: Insights from Reddit crowdsourcing towards intrinsic behavior understanding. [Conference Paper].

Lubna Aziz | Data Science and Deep Learning | Best Researcher Award

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Assoc. Prof. Dr. Lubna Aziz | Data Science and Deep Learning | Best Researcher Award

Associate Professor at Iqra University Karachi | Pakistan

Assoc. Prof. Dr. Lubna Aziz is an accomplished AI and MLOps Engineer, Researcher, and Academic Leader with over fifteen years of multidisciplinary experience in artificial intelligence, machine learning, and higher education leadership, currently serving as Assistant Professor and Head of Artificial Intelligence at Iqra University, Karachi. She holds a PhD in Computer Science from Universiti Teknologi Malaysia and has earned dual Gold Medals in both her MS and BS in Computer Engineering from BUITEMS, reflecting her consistent record of academic excellence. Her professional expertise spans AI model development, scalable ML pipeline automation, MLOps deployment, Explainable AI, Computer Vision, and Generative AI, integrating research-driven innovation with real-world engineering impact. Dr. Aziz has designed and led AI curricula, supervised numerous student projects, and directed institutional initiatives aligned with HEC, NCEAC, and ABET accreditation standards. Her research advances Computer Vision, Large Language Models (LLMs), and Explainable AI (XAI) with applications across healthcare, finance, and creative AI, focusing on interpretable, multimodal, and human-centric intelligent systems. She has contributed to IEEE Access, Nature Scientific Reports, Springer, and MDPI journals, with publications exploring object detection, medical imaging, energy optimization, multimodal AI, and generative modeling. As an active reviewer for leading international journals and a keynote and technical chair for major AI and engineering conferences, she has significantly shaped discourse in emerging technologies. Her research projects include AI-driven healthcare diagnostics, cardiovascular risk modeling, and LLM intelligence benchmarking, funded by HEC, NIH, and the Royal Academy of Engineering UK. Known for her academic leadership, technical depth, and commitment to inclusive innovation, Lubna Aziz continues to bridge the gap between AI research and practical deployment, fostering the next generation of intelligent systems and ethical AI solutions.

Profile: Orcid 

Featured Publications:

Deebani, W., Aziz, L., Alawad, W. M., Alahmari, L. A., Al‐Ahmary, K. M., Alqurashi, Y., & Alwabel, A. S. A. (2025). Advancing electronic noses with transformers: Real‐time classification of hazardous odors and food freshness. Journal of Food Science.

Aziz, L., Adil, H., & Sarwar, R. (2025). Artificial sensing: AI-driven electronic nose for real-time gas leak detection and food spoilage monitoring. Sir Syed University Research Journal of Engineering & Technology.

Deebani, W., Aziz, L., Aziz, A., Basri, W. S., Alawad, W. M., & Althubiti, S. A. (2025). Synergistic transfer learning and adversarial networks for breast cancer diagnosis: Benign vs. invasive classification. Scientific Reports.

Aziz, L., Salam, M. S. B. H., Sheikh, U. U., Khan, S., Ayub, H., & Ayub, S. (2021). Multi-level refinement feature pyramid network for scale imbalance object detection. IEEE Access.

Arfeen, Z. A., Sheikh, U. U., Azam, M. K., Hassan, R., Shehzad, H. M. F., Ashraf, S., Abdullah, M. P., & Aziz, L. (2021). A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems. Concurrency and Computation: Practice and Experience.

Aziz, L., Salam, M. S. B. H., Sheikh, U. U., & Ayub, S. (2020). Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review. IEEE Access.