Tag: Computer Science

Namrata Kumari, Computer Science, Female Researcher Award

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Dr. Namrata Kumari: Assistant Professor at Galgotias University, India

Dr. Namrata Kumari is a dedicated researcher, academic, and innovator in the field of Computer Science and Engineering. She specializes in Natural Language Processing (NLP), Machine Learning, Artificial Intelligence, and Internet of Things (IoT) applications, with a strong track record of publications and patents. She completed her Ph.D. in Computer Science and Engineering at the National Institute of Technology (NIT), Hamirpur in 2024, following her M.Tech. from Banasthali University in 2017 and B.Tech. from A.K.T.U. in 2015. Currently, she is working as an Assistant Professor at Galgotias University, Greater Noida, where she combines teaching, research, and project development. Her contributions span across text summarization, intelligent computing models, IoT-enabled systems, and healthcare informatics, and she is driven by the vision of creating impactful academic and industrial solutions.

Online Profiles

ORCID Profile

Education

Her academic foundation is rooted in a blend of rigorous technical training and a consistent academic record. She earned her Ph.D. in Computer Science and Engineering from NIT Hamirpur in 2024, where her research concentrated on NLP and advanced machine learning methods. She obtained her M.Tech. in CSE from Banasthali University in 2017 with 77.35% and her B.Tech. in CSE from A.K.T.U. in 2015 with 77.52%. Prior to her higher education, she pursued schooling from Gagan Public School, Aligarh, achieving 64.20% in Intermediate (ISC, 2011) and 71.00% in High School (ICSE, 2008). This strong academic background has shaped her journey into a research-focused teaching professional.

Research Focus

Her research is primarily oriented towards Natural Language Processing (NLP), especially text summarization for low-resource languages such as Hindi, which plays a crucial role in making technology accessible to diverse populations. She also focuses on Soft Computing, Operating Systems, and IoT-based intelligent systems, applying these concepts to practical problem domains. Her work on machine learning optimization, information retrieval, healthcare prediction systems, and AI-enabled monitoring devices has not only been published but also extended into patent applications, reflecting the translation of theoretical models into real-world impact.

Experience

Dr. Kumari’s professional journey integrates teaching, research, and project involvement. Since March 2023, she has been serving as an Assistant Professor at Galgotias University, where she teaches core CSE courses and mentors undergraduate and postgraduate students in research projects. Prior to this, she gained valuable hands-on experience as a Project Associate at IIT Mandi (2017–2018), where she contributed to applied computational projects, and as a Junior Research Fellow at DTRL, DRDO, Delhi (2018), where she worked on defense-related computing applications. This blend of academic and applied research exposure equips her with a balanced teaching-research perspective.

Research Timeline & Activities

Her research activities can be traced through a steady progression of contributions. Beginning with early studies on supervised approaches for text processing (2019), she expanded her scope into TF-IDF and graph-based summarization (2020), and later to comprehensive reviews on text summarization and question answering in NLP (2021–2022). Her most recent work applies deep learning architectures (Seq2Seq models, 2023) for Hindi text summarization, showcasing advanced computational linguistics applications. Alongside publications, she has contributed to innovative patents in healthcare informatics, IoT-enabled monitoring, and AI-driven intelligent devices. This trajectory highlights her ability to align her research output with emerging global priorities in CSE and real-world problem solving.

Awards & Honors

Dr. Kumari has received recognition for her academic excellence and research-driven innovations. She qualified GATE 2017, affirming her strong technical competence. She holds a Diploma in MS-Office, reflecting her interest in professional skill-building beyond core technical areas. Her intellectual property portfolio includes five patents, among which a UK Design Patent granted in September 2023 for a “Gaming Device for Diet and Stress Reduction” stands out. In addition, she has four Indian patents published or under examination, focusing on disease prediction, AI-enabled attendance monitoring, IoT-driven smoke detection, and food safety solutions. These honors and achievements underline her commitment to both academic excellence and translational innovation.

Top Recent Publication

Dr. Namrata Kumari has maintained an active and impactful research profile with several high-quality contributions published in indexed journals and leading international conferences. Her recent works highlight a diverse research portfolio ranging from text summarization and bibliometric analysis to applied deep learning in healthcare and sentiment analysis.

One of her most significant recent works is titled “A Panorama of Text Summarization Research: Bibliometric Trends and Developments (2000–2024)”, published in Neural Computing and Applications (Springer, August 2025). This article, co-authored with Dr. Pardeep Singh, presents a comprehensive bibliometric analysis of text summarization research over two decades, mapping its evolution, thematic clusters, and emerging trends. The study provides valuable insights into future directions of NLP and AI-driven summarization, making it an essential resource for researchers and practitioners in computational linguistics. (DOI: 10.1007/s00521-025-11562-2)

In the domain of healthcare applications, she co-authored “Monkeypox Detection Using Deep Learning: A Hybrid ResNet-50 and SegNet Approach”, presented at the 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (April 2025). This work introduces a hybrid CNN-based architecture combining ResNet-50 and SegNet, demonstrating promising results in medical image classification for monkeypox detection. (DOI: 10.1109/otcon65728.2025.11070680)

Expanding her scope into opinion mining, she contributed to “Sentiment Analysis of Movie Reviews Using TF-IDF”, presented at the 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI) in April 2025. This study compares the effectiveness of TF-IDF-based feature extraction methods in sentiment classification of movie reviews, offering insights into text mining and computational social science applications. (DOI: 10.1109/iccsai64074.2025.11064459)

Another important contribution is “Analysis on Movie Reviews: A Comparative Study”, presented at the Eighth International Conference on Parallel, Distributed, and Grid Computing (PDGC) in December 2024. Co-authored with Muskan Soni, Pardeep Singh, and Vikas Kashtariya, this paper evaluates multiple sentiment analysis approaches to understand trade-offs between accuracy and efficiency in real-world applications. (DOI: 10.1109/pdgc64653.2024.10984113)

She also collaborated on “Emergency Communication Architecture for Catastrophic Events: A Hybrid Approach”, presented at the OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0 in June 2024. This research proposes a hybrid communication framework for disaster management and emergency response, integrating IoT and cloud-based technologies to improve system resilience during catastrophic events. (DOI: 10.1109/otcon60325.2024.10687787)

Tomonobu Nonoyama, Computer Science, Best Researcher Award

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Dr. Tomonobu Nonoyama: Postdoctoral researcher at Akita Prefectural University, Japan

Tomonobu Nonoyama is a Japanese researcher specializing in plant biomechanics and computational modeling, currently serving as a postdoctoral researcher at the Department of Mechanical Engineering, System Science and Technology at Akita Prefectural University. With a foundation in mechanical engineering and advanced training in life sciences, his work bridges biology and physics to investigate how plants grow, sense, and respond to their environments. He has contributed to both experimental and simulation-based studies, with a growing publication record in high-impact journals such as Scientific Reports, Plant and Cell Physiology, and PLOS ONE. Dr. Nonoyama’s interdisciplinary expertise enables him to approach biological complexity with engineering precision.

Online Profiles

ORCID Profile

Dr. Nonoyama maintains an active presence across academic platforms to share his work and collaborate globally. His research output and citations can be found on Google Scholar, while ongoing projects and preprints are available on ResearchGate. He may also be listed on institutional directories such as the Akita Prefectural University website and other professional networks like ORCID, where researchers manage their publication history and affiliations. Please refer to his institutional email for further inquiries or collaboration interests.

Education

Dr. Nonoyama received his Bachelor of Science in Engineering in 2014 from Tohoku University’s Department of Mechanical and Aerospace Engineering, where he developed a strong technical foundation. He pursued graduate studies in the Department of Environmental Life Sciences at the same university, earning his Master of Science in 2016 and Ph.D. in Life Sciences in 2020 under the supervision of Prof. Satoshi Chiba. His graduate work focused on evolutionary biology and simulation of complex plant structures, laying the groundwork for his later studies in plant mechanics and modeling. This combination of mechanical and life sciences education forms the core of his interdisciplinary research approach.

Research Focus

Dr. Nonoyama’s research interests are centered on plant biomechanics, specifically investigating how mechanical forces and cellular structures influence plant growth and morphology. He uses both agent-based simulations and mathematical modeling to study complex biological processes such as cortical microtubule alignment, zygote development, and tip growth. His work also extends to analyzing movement and deformation in sensitive plants like the Venus flytrap, where engineering principles are used to infer internal biological forces. A significant part of his current research explores how plant cells coordinate structural growth through mechanical feedback and material properties, with potential applications in biomimetics and agricultural engineering.

Experience

Since April 2022, Dr. Nonoyama has been affiliated with Akita Prefectural University as a postdoctoral researcher, where he continues to develop computational models of plant development and collaborate on interdisciplinary projects in mechanical systems. Prior to that, he held a research position at the Center for Northeast Asian Studies at Tohoku University from 2020 to 2022. During that time, he transitioned his focus from purely biological systems to integrating mechanical simulations and systems theory, contributing to several cross-disciplinary publications. His academic journey reflects a progression from traditional engineering to cutting-edge life sciences research, where simulation, modeling, and empirical observation come together.

Research Timeline

Dr. Nonoyama began his academic training in 2010 at Tohoku University, completing his undergraduate studies in mechanical and aerospace engineering in 2014. From 2014 to 2016, he pursued a master’s degree in life sciences, followed by doctoral research from 2016 to 2020, focusing on hypothetical evolutionary pathways and mechanical modeling of plant forms. After earning his Ph.D., he joined Tohoku University’s Center for Northeast Asian Studies as a postdoctoral researcher, where he developed computational models of plant tissue growth and behavior. Since 2022, he has continued this work at Akita Prefectural University, broadening his research into plant mechanics and systems engineering.

Top-Noted Publication

Among Dr. Nonoyama’s body of work, one standout publication is “Agent-based simulation of cortical microtubule band movement in Arabidopsis zygotes”, published in Scientific Reports (2025). This paper presents a novel simulation approach to modeling microtubule behavior, which is critical to understanding how plant zygotes establish polarity and growth direction. By collaborating with experts in plant biology and systems modeling, Dr. Nonoyama helped bridge empirical cell biology and computational engineering. The study has been noted for its methodological innovation and relevance to developmental plant biology.

  • Agent-based simulation of cortical microtubule band movement in Arabidopsis zygotes
    Published: Scientific Reports, July 28, 2025
    DOI: 10.1038/s41598-025-11078-8
    Contributors: Tomonobu Nonoyama, Zichen Kang, Hikari Matsumoto, Sakumi Nakagawa, Minako Ueda, Satoru Tsugawa

  • KymoTip: High-throughput Characterization of Tip-growth Dynamics in Plant Cells
    Preprint: July 2, 2025
    DOI: 10.1101/2025.06.27.661917
    Contributors: Zichen Kang, Yusuke Kimata, Tomonobu Nonoyama, Toru Ikeuchi, Kazuyuki Kuchitsu, Satoru Tsugawa, Minako Ueda

  • A viscoelastic–plastic deformation model of hemisphere-like tip growth in Arabidopsis zygotes
    Published: Quantitative Plant Biology, 2024
    DOI: 10.1017/qpb.2024.13
    Contributors: Zichen Kang, Tomonobu Nonoyama, Yukitaka Ishimoto, Hikari Matsumoto, Sakumi Nakagawa, Minako Ueda, Satoru Tsugawa

  • Agent-Based Simulation of Cortical Microtubule Band Movement in Arabidopsis Zygotes
    Preprint: October 18, 2024
    DOI: 10.1101/2024.10.17.618799
    Contributors: Tomonobu Nonoyama, Zichen Kang, Hikari Matsumoto, Sakumi Nakagawa, Minako Ueda, Satoru Tsugawa

  • Temporal changes in surface tension guide the accurate asymmetric division of Arabidopsis zygotes
    Preprint: August 9, 2024
    DOI: 10.1101/2024.08.07.605794
    Contributors: Zichen Kang, Sakumi Nakagawa, Hikari Matsumoto, Yukitaka Ishimoto, Tomonobu Nonoyama, Yuga Hanaki, Satoru Tsugawa, Minako Ueda

Mohamed El-Moussaoui, Computer Science, Best Researcher Award

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Doctorate Mohamed El-Moussaoui: Ph.D graduate at Chouaib Doukkali University, Morocco

Mohamed El-Moussaoui is a distinguished expert with over 15 years of experience in the field of monetic engineering, specializing in the intersection of financial technology, artificial intelligence, and data science. With a Doctorate in Computer Science and AI from Chouaib Doukkali University, he has contributed significantly to both academic research and the development of innovative payment solutions. His professional journey spans leadership roles in major firms such as Hightech Payment Systems, Al Barid Bank, and HPS Group, where he successfully directed projects across Europe, the Americas, and Asia. Mohamed’s research bridges theory and application, focusing on community detection in social networks, using cutting-edge AI methodologies to enhance the security, analysis, and optimization of payment systems.

Online Profiles

Google Scholar Profile

Research Metrics

  • Total Citations: 84

  • Citations Since 2020: 82

  • h-index: 2

  • i10-index: 1

These metrics reflect Mohamed’s growing influence in the field of artificial intelligence and network science. With an h-index of 2, his publications are showing a consistent level of academic impact, and the high number of citations since 2020 indicates increasing recognition of his research on community detection and AI applications.

Education

Mohamed earned his Ph.D. in Computer Science and Artificial Intelligence from the prestigious Chouaib Doukkali University, graduating with the highest honors and commendations from his committee. His thesis was focused on the application of advanced algorithms for community detection in social networks, specifically leveraging association rule learning and fuzzy logic. He also holds a Master’s degree in Networks and Systems from Cadi Ayyad University, where he graduated with honors, and a Professional Bachelor’s degree in Computer Science. In addition to his academic journey, Mohamed completed military training at the École Royale de l’Air (ERA) in Marrakech, which shaped his discipline and leadership skills. His educational foundation has provided him with the analytical skills and technical expertise necessary to excel in both industry and academia.

Research Focus

Mohamed’s research primarily revolves around the development of sophisticated community detection techniques in social networks, utilizing association rule learning, fuzzy logic, and deep learning algorithms. He is especially interested in exploring how these methods can be applied to the field of monetic engineering to enhance payment security, fraud detection, and customer experience. His recent works focus on semantic-driven community models, multi-criteria decision-making techniques, and how AI-driven solutions can optimize both large-scale financial transactions and online social network analysis. His research also delves into systematizing deep learning approaches for network community detection, with a goal to bring practical applications to the field of digital payment systems.

Experience

Mohamed has a wealth of practical experience in both the public and private sectors, with a specific focus on the design and implementation of complex payment systems. Currently, he is serving as the Project Director at Hightech Payment Systems, where he is responsible for overseeing the company’s international projects related to digital payment infrastructures across Europe, the Americas, and Asia. Previously, he worked as the Head of the Monetic Engineering Division at Al Barid Bank, managing the integration and innovation of payment systems, including developing and launching new payment services for both retail and corporate clients. He has also held pivotal roles at HPS Group and S2M, where he contributed to product development, systems integration, and managed teams across various regions, ensuring that their solutions met high security standards and customer requirements. Mohamed’s leadership and technical expertise have consistently resulted in the successful deployment of state-of-the-art monetic technologies.

Research Timeline

  • 2025 (Q1): Published “ARLClustering: An R Package for Community Detection” in Applied Network Science (IF 1.4), marking a significant contribution to the development of user interaction-based community detection algorithms.

  • 2024 (Q2): Published “A Multi-Agent-Based Approach for Community Detection” in The International Journal of Data Science and Analytics (IF 3.4), presenting a novel approach to analyzing user behaviors in online platforms.

  • 2025 (Q2): Currently working on a paper titled “A Fuzzy Logic-Based Approach for Community Detection,” under review at International Journal of Fuzzy Systems.

  • 2025 (Q2): A comprehensive systematic review titled “A Review of Deep Learning Methods for Community Detection” is being published in Frontiers in Artificial Intelligence (IF 3.0), providing an in-depth look at the latest advancements in AI for network analysis.

Awards & Honors

  • 2025: Honored with the “Very Honorable” distinction for his Ph.D. dissertation, awarded by Chouaib Doukkali University. This recognition acknowledges his substantial contributions to the field of AI and monetic engineering.

  • 2024: Best Paper Award at the ICEET’24 International Conference for his development of the ARLClustering R package, praised for its innovation and potential applications in social network analysis.

  • 2022: Selected as the Technical Program Chair for the International Workshop on Modeling and Analysis of Complex Networks with Applications (MACNA’22) held in Ontario, Canada, a significant recognition of his expertise and leadership in the field of network science.

  • 2020: Awarded for Best Communication at the International Conference on Smart City Applications (SCA’20) for his innovative work on community detection using association rule learning.

Top-Noted Publication

  • “A Comprehensive Literature Review on Community Detection: Approaches and Applications”
    M. El-Moussaoui, T. Agouti, A. Tikniouine, M. El Adnani. Procedia Computer Science, Vol. 151, 76 (2019)

    • This paper provides an in-depth review of various community detection methods, summarizing state-of-the-art approaches and their practical applications in diverse domains.

  • “A Novel Approach of Community Detection Using Association Rules Learning: Application to User’s Friendships of Online Social Networks”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. Innovations in Smart Cities Applications, Volume 4 (2021)

    • A groundbreaking study that introduces the application of association rule learning (ARL) for detecting communities based on user friendships in online social networks.

  • “A Multi-Agent-Based Approach for Community Detection Using Association Rules”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. International Journal of Data Science and Analytics, Vol. 18 (4), 379-392 (2024)

    • This paper explores a multi-agent-based framework for community detection, applying association rules to analyze complex social networks and user behavior patterns.

  • “A k-Mean Classification Study of Eight Community Detection Algorithms: Application to Synthetic Social Network Datasets”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. AI and IoT for Sustainable Development in Emerging Countries, 557-572 (2022)

    • A comparative study evaluating the effectiveness of various community detection algorithms using synthetic datasets, with a focus on scalability and performance.

  • “ARLClustering: An R Package for Community Detection in Social Networks Based on User Interaction and Association Rule Learning”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. Applied Network Science, Vol. 10 (25), (2025)

    • Introduces ARLClustering, an R package designed to enhance community detection in social networks by utilizing user interaction data and association rule learning.

  • “ARLClustering: R Package for Community Detection-Based Association Rules Learning”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. International Conference on Engineering and Emerging Technologies (ICEET), 1-5 (2025)

    • Presentation of ARLClustering at ICEET’25, demonstrating its applications and potential for improving community detection processes in dynamic social networks.

  • “Community Detection in Social Networks: A State of the Art”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. International Conference on Mathematics & Data Science (ICMDS’20), 102 (2020)

    • A detailed state-of-the-art review on community detection methods, examining algorithms, challenges, and the impact of machine learning in social network analysis.

Ashish Verma, Computer Science, Best Researcher Award

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Doctorate Ashish Verma: Research Scholar at Birla Institute of Technology & Science, Pilani, India

Ashish Verma is a Ph.D. research scholar in the Department of Computer Science and Information Systems at BITS Pilani, Pilani Campus. His work lies at the intersection of robotics, distributed computing, and AI, with a focus on scalable and fault-tolerant multi-robot task allocation systems. With over four years of intensive research experience, Ashish has contributed to multiple high-impact projects involving real-time task coordination among autonomous mobile robots in time-critical environments. He is passionate about building intelligent robotic frameworks that can operate efficiently in uncertain and communication-constrained scenarios. His long-term vision is to establish a research center that develops robotic solutions for societal and industrial challenges in India and beyond.

Online Profiles

ORCID Profile

Education

Ashish holds a Bachelor’s degree in Computer Engineering from Cochin University of Science and Technology (CUSAT), completed in 2019 with a CGPA of 8.18. Currently, he is pursuing his Doctor of Philosophy (Ph.D.) in Computer Science and Engineering at BITS Pilani, where he has been working since November 2020. His doctoral research focuses on the design and implementation of secure, fault-tolerant frameworks for multi-robot task allocation in imperfect communication settings. Through advanced courses and project work, he has built a strong academic foundation in optimization, AI, reinforcement learning, distributed systems, and embedded robotics.

Research Focus

Ashish’s core research areas include distributed multi-robot task allocation (MRTA), dynamic scheduling, embedded systems, fault-tolerance, and secure robotic communication. He explores solutions to NP-Hard problems in heterogeneous mobile robotics using graph theory, optimization, deep learning (LSTM), and reinforcement learning. His current work integrates predictive modeling with dynamic rescheduling to minimize delivery penalties in time-sensitive domains like healthcare and warehousing. He also plans to integrate adversarial-resilient mechanisms and encryption-based authentication to improve system robustness in real-world robotic deployments.

Experience

Ashish has been working as a Research Scholar at the Embedded Systems and Robotics Laboratory at BITS Pilani since November 2020. Over the past four and a half years, he has contributed to designing, simulating, and evaluating robust multi-robot systems under complex operational constraints. His roles have included research design, simulation testing, writing algorithms, preparing publications, and mentoring students. In addition to research, he has served as a lab instructor for undergraduate courses such as Object-Oriented Programming (OOP), providing hands-on training to students in core computer science concepts. His experience reflects both theoretical depth and practical problem-solving ability.

Research Timeline

Between 2021 and 2025, Ashish Verma has worked on four major funded research projects focusing on real-time robotic systems: (1) CF-HMRTA – addressing coalition formation in heterogeneous robot teams using bipartite graph matching; (2) HMR-ODTA – a framework for diverse online task allocation under time constraints; (3) DTA-HMR-TT – a decentralized approach incorporating task transfer to minimize late deliveries; and (4) Predictive Task Allocation using Deep Learning – employing LSTM models to forecast and optimize task schedules dynamically. Each project built progressively toward creating a secure, scalable, and intelligent multi-robot system capable of operating in dynamic, imperfect environments.

Awards & Honors

Ashish has a consistent academic record and has earned accolades from school to university level. He secured the 1st rank in a Mathematics Olympiad during school and was the winner of an intra-university chess championship, reflecting both analytical ability and strategic thinking. In his research journey, he has participated in multiple national and international workshops, including the EMSC-2021 workshop on smart city energy management and a short course on high-performance and parallel computing. He has also contributed to the research community as a peer reviewer for IEEE Transactions on Intelligent Vehicles, a testament to his growing expertise and academic engagement.

Top-Noted Publication

Among his published works, Ashish’s most impactful publication to date is the 2024 IEEE Access paper titled “DTA-HMR-TT: Dynamic Task Allocation for a Heterogeneous Team of Mobile Robots with Task Transfer.” This work presents a novel decentralized scheduling algorithm that addresses multi-pickup and delivery problems with time windows in real-world environments such as hospitals and warehouses. The paper highlights his contribution to handling task rescheduling, minimizing penalties for late deliveries, and enabling robots to intelligently transfer tasks under changing scenarios. It received positive peer recognition for its scalability and practical value in real-time robotic systems.

1. Verma, A., Gautam, A., Dutta, A., Shekhawat, V. S., & Mohan, S. (2025). CF-HMRTA: Coalition Formation for Heterogeneous Multi-Robot Task Allocation. Journal of Intelligent & Robotic Systems. https://doi.org/10.1007/s10846-025-02287-4
This paper introduces a novel coalition formation strategy for task allocation among heterogeneous mobile robots, addressing the computational complexity of NP-Hard problems using a bipartite graph matching technique with a worst-case time complexity of O(|E|). The work significantly enhances scalability, demonstrated through simulations involving up to 2000 robots and 400 tasks executed in under 12 seconds. The coalition mechanism ensures optimal robot-task pairing and showcases efficiency in diverse multi-agent environments. The study reflects Ashish Verma’s deep understanding of algorithm design, optimization under constraints, and real-world simulation modeling.

2. Verma, A., Gautam, A., Shekhawat, V. S., & Mohan, S. (2024). DTA-HMR-TT: Dynamic Task Allocation for a Heterogeneous Team of Mobile Robots With Task Transfer. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3505947
This research presents a decentralized framework for dynamic task allocation in time-sensitive scenarios such as hospital logistics and warehouse automation. The proposed system introduces a task-transfer mechanism to improve delivery reliability and minimize penalties under time window constraints. Using a team of autonomous heterogeneous robots, the system dynamically adjusts task schedules based on environmental changes and robot availability. This publication highlights Ashish’s expertise in decentralized algorithms, dynamic scheduling, and robust robotic coordination in structured indoor environments.

Surendra Solanki, Computer Science, Best Researcher Award

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Doctorate Surendra Solanki: Assistant Professor at Manipal University jaipur, Jaipur, Rajasthan, India

Dr. Surendra Solanki is a passionate academician and data science researcher with extensive experience in artificial intelligence, machine learning, and wireless communication systems. His academic journey is marked by a Ph.D. in Computer Science and Engineering from NIT Bhopal, where his research focused on deep learning-based spectrum sensing for cognitive radio networks. He has hands-on expertise in deep learning frameworks like TensorFlow and PyTorch, along with proficiency in Python, R, and MATLAB. With a multidisciplinary outlook, he actively explores large language models, federated learning, blockchain integration in AI systems, and generative models for vision and NLP tasks. Dr. Solanki combines teaching, research, and practical implementation to address complex real-world problems and empower future technologists.

Online Profiles

Scopus Profile

  • 108 citations from 93 documents, and 15 scanned items

  • An h-index of 4

Education

Dr. Solanki earned his Ph.D. in Computer Science and Engineering from the prestigious Maulana Azad National Institute of Technology (MANIT), Bhopal, where he conducted deep learning-based research on spectrum sensing techniques in cognitive radio systems from 2018 to 2024. He completed his M.Tech. in Computer Science and Engineering from the same institution in 2017 with a CGPA of 7.90; his thesis involved secure data hiding using image steganography. He holds a B.E. degree in Information Technology from the Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya (DAVV), Indore, graduating in 2012 with 69% marks. His strong academic foundation has enabled him to bridge theory with emerging technologies effectively.

Research Focus

Dr. Solanki’s research interests lie at the intersection of deep learning, wireless networks, and privacy-preserving artificial intelligence. His doctoral research contributed to improved performance in cognitive radio networks through the use of CNNs, RNNs, and hybrid DL architectures. More recently, he has worked extensively with large language models such as BERT, GPT, LLaMA, and Falcon for natural language processing, medical diagnostics, and multi-modal AI systems. His ongoing research explores explainable AI using Grad-CAM and LIME, federated learning for secure health data analytics, AI-driven malware detection, and generative AI for synthetic data creation and model interpretability.

Experience

Dr. Solanki is currently employed as an Assistant Professor in the Department of Artificial Intelligence and Machine Learning at Manipal University Jaipur, where he teaches undergraduate and postgraduate courses, supervises student research, and develops AI-focused curriculum (2023–Present). Previously, he served as a Senior Faculty in IT at SAGE University Indore (through iNurture Education Solutions) from 2022 to 2023, where he taught advanced machine learning and deep learning subjects. Earlier in his career, he worked as an Assistant Manager in eGovernance for the Government of Madhya Pradesh (2014–2015), contributing to digital infrastructure development. His roles have balanced academic instruction, administrative leadership, and applied research.

Research Timeline

Between 2018 and 2024, Dr. Solanki conducted his doctoral research at MANIT Bhopal on deep learning architectures for cognitive radio, resulting in significant publications in IEEE Access and Springer. In 2020, he expanded into blockchain-integrated AI and device authentication systems for smart grids, leading to co-authored IEEE conference papers. From 2021 onward, his research embraced interdisciplinary domains like federated learning in healthcare, Android malware detection using graph neural networks, and self-supervised models for chest X-ray diagnosis. The timeline from 2023 to 2025 shows his contributions in LLM applications, multimodal AI, and knowledge distillation techniques for resource-efficient models in healthcare and audio processing.

Awards & Honors

Dr. Solanki has earned several accolades throughout his academic and professional career. He has authored more than 18 papers in high-impact journals indexed in SCI and Scopus, including Q1 journals such as IEEE Access and Scientific Reports. His innovations have led to the successful filing and publication of 6 patents in AI, Blockchain, and IoT domains. He has received multiple invitations to review for reputed journals and serve as a speaker at IEEE international conferences. His contributions in federated learning, modulation recognition, and AI for healthcare have been appreciated by both academic and industry collaborators, marking him as a notable emerging researcher in applied AI.

Top-Noted Publication

One of Dr. Solanki’s top-recognized publications is titled “A deep ensemble learning approach for squamous cell classification in cervical cancer”, published in Scientific Reports (Nature Portfolio, Q1 Journal) in 2025. This paper proposes a robust ensemble model leveraging deep CNNs and transformers to enhance the accuracy of cervical cancer cell classification, showcasing significant improvements over traditional ML approaches. The work stands out for its clinical relevance, explainability, and technical depth, contributing to the field of AI in medical diagnostics. The publication has attracted significant citations and collaborative interest from researchers working in computational pathology. [DOI: https://doi.org/10.1038/s41598-025-91786-3]

Fatma Akalın, Computer Science, AI Innovation Award

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Asst. Prof Fatma Akalın: Assistant Professor at Sakarya University, Turkey

Fatma Akalın is an accomplished Assistant Professor in the Department of Computer Engineering at Sakarya University, specializing in the intersection of artificial intelligence, machine learning, and biomedical engineering. Her research is driven by a deep interest in applying AI algorithms to solve complex challenges in healthcare, particularly for disease diagnosis and personalized medicine. With expertise in data science, bioinformatics, and medical imaging, she has made significant strides in automating processes such as genomic sequence classification, anomaly detection in blood cell images, and the development of AI-driven decision support systems. Dr. Akalın’s commitment to advancing research in healthcare technology positions her as a thought leader in both academic and applied AI communities.

Online Profiles

Google Scholar Profile

Citations, h-index, i10-index

  • Citations: Dr. Akalın’s research has accumulated a total of 66 citations across various academic publications, with 61 citations in the last 5 years, reflecting the growing influence and relevance of her work in the field.

  • h-index: With an h-index of 5, Dr. Akalın has made significant contributions, with at least 5 of her publications being cited at least 5 times each. This indicates a solid body of impactful research in her areas of expertise.

  • i10-index: Dr. Akalın holds an i10-index of 1, meaning she has one publication that has been cited 10 times or more, further highlighting the recognition her work has received in the academic community

Dr. Akalın maintains an active and influential presence across multiple online academic platforms. Her publications are widely cited in leading international journals, and she regularly contributes to conferences in fields such as AI, bioinformatics, and computational biology. As an Assistant Editor for Sakarya University Journal of Computer and Information Sciences (SAUCIS), she has an integral role in shaping the research landscape at the university. Her collaborative projects include contributions to national research initiatives that address pressing public health concerns through the use of AI technologies. She has also developed various web-based applications aimed at enhancing healthcare outcomes, integrating both AI and clinical expertise to create innovative solutions for real-world health issues.

Education

Dr. Akalın completed her Doctoral Degree in Computer Engineering at Sakarya University in 2023, where she focused on leveraging AI algorithms for the classification of leukemia subtypes using digital mapping of DNA sequences. Her thesis, titled “Classification of Leukemia Types Using AI-Based Algorithms on DNA Sequences via Digital Mapping Techniques,” reflected her strong commitment to applying cutting-edge computational methods to solve biomedical challenges. She earned her Master’s Degree in 2020, with a thesis on the application of heuristic algorithms for detecting polyps in small intestine images. This work demonstrated her early engagement with AI in medical imaging. Her Bachelor’s Degree was also completed at Sakarya University in 2018, marking the beginning of her academic journey into computer engineering and AI.

Research Focus

Dr. Akalın’s research is deeply rooted in artificial intelligence, machine learning, and bioinformatics. Her primary focus is the integration of AI into healthcare systems, specifically in disease detection and diagnostic applications. She has worked extensively on the classification of genetic sequences, including the use of AI algorithms for identifying and predicting leukemia and other cancers. Additionally, her work includes the development of advanced diagnostic systems based on digital imaging, such as detecting anomalies in medical images like blood cell smears and endoscopic images. In recent years, she has also delved into synthetic data generation for model training, optimizing machine learning algorithms for better clinical decision-making, and exploring data-driven AI models that can adapt to the complexities of real-world clinical environments.

Experience

Dr. Akalın has held the position of Assistant Professor in the Department of Computer Engineering at Sakarya University since September 2023, where she teaches courses in artificial intelligence, web technologies, and data structures. Prior to this role, she served as a Research Assistant at the same department from 2020 to 2023, contributing to numerous research projects, guiding graduate students, and publishing in top-tier journals. During her academic career, she has mentored students on master’s theses and helped develop cutting-edge research in fields such as medical diagnostics, deep learning, and artificial intelligence. Her diverse experience spans both academic and research settings, where she has also collaborated on national-level projects related to AI-based healthcare solutions.

Research Timeline

  • 2025: Dr. Akalın is currently leading a project on the development of an AI-powered decision support chatbot aimed at optimizing sales processes within ERP systems. Additionally, she is working on a real-time system for detecting and counting blast cells in leukemia diagnosis using advanced deep learning methods.

  • 2024: She is working on the development of a web-based AI system that utilizes hybrid data sets to aid in the diagnosis of monkeypox. Additionally, she is exploring the use of AI to assess cardiac risks in adolescents based on EKG images as part of a national TUBITAK-funded project.

  • 2023: Dr. Akalın has initiated a project focused on developing AI-enhanced virtual reality simulations for secondary education, along with another project to build a web-based system to promote sustainable production and consumption in Turkey.

Awards & Honors

Dr. Akalın has been recognized for her outstanding contributions to scientific research, particularly in the application of artificial intelligence to healthcare. She was awarded the 10th İksad Scientific Award in 2019, a prestigious recognition in the field of applied science. She continues to receive recognition for her innovative research on medical diagnostics, and her work has been acknowledged by leading research institutes and journals. Her ability to bridge the gap between AI theory and practical healthcare applications has made her a sought-after researcher and collaborator in both academic and industrial circles.

Top-Noted Publication

  • “Deep Learning-Based Community Classifier Approach for Gastrointestinal Anomaly Detection,” published in Pamukkale University Engineering Journal, 2024, discusses the application of deep learning methods for gastrointestinal anomaly detection, contributing to the growing body of knowledge on AI in medical diagnostics.

  • “Classification of Exon and Intron Regions on DNA Sequences Using SBERT and ANFIS,” published in Journal of Polytechnic, 2024, presents a hybrid approach combining SBERT and ANFIS for DNA sequence classification, a significant advancement in genomic data analysis.

  • “Neural Network-Based Survival Classification in Heart Failure Patients,” published in Arabian Journal for Science and Engineering, 2024, focuses on using neural networks for predicting the survival outcomes of heart failure patients, applying AI to improve personalized treatment strategies.

  • “DNA Genomic Sequence Classification with Digital Signal Processing and EfficientNetB7,” published in Gazi University Journal, 2022, investigates the classification of DNA sequences using deep learning models, contributing to advancements in genomic medicine.

Dr. Akalın’s work continues to push the boundaries of AI in healthcare, and her publications are widely regarded as some of the most significant contributions to the field in recent years.

Strengths for the AI Innovation Award

  1. Pioneering AI Applications in Healthcare: Dr. Akalın’s work in automating disease diagnostics, such as leukemia classification from genomic data and gastrointestinal anomaly detection, showcases her groundbreaking contributions to healthcare through AI. These innovations can significantly improve patient outcomes and revolutionize diagnostic processes.

  2. Cross-Disciplinary Expertise: Dr. Akalın effectively merges AI, machine learning, bioinformatics, and medical imaging to develop advanced diagnostic systems, demonstrating her versatility and expertise in multiple disciplines, and advancing both AI and biomedical engineering.

  3. Leadership in AI-Driven Healthcare Projects: She is leading multiple impactful AI projects, including real-time leukemia detection systems and AI-enhanced decision support tools for healthcare, positioning her as a leader in AI’s practical application in medicine.

  4. Synthetic Data Innovation: Dr. Akalın is pioneering research in synthetic data generation for training AI models, addressing critical challenges in healthcare data scarcity and privacy concerns, and making AI more adaptable for clinical use.

  5. Award-Winning Research: Her contributions have earned recognition, including the 10th İksad Scientific Award, cementing her position as a leading innovator in AI for healthcare, and highlighting the significant impact of her research on both academic and real-world levels.

Kumar Dorthi, Computer Science, Innovative Researcher Award

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Doctorate Kumar Dorthi: Assistant professor at Kakatiya Institute of Technology and Science, India

Dr. Kumar Dorthi is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at Kakatiya Institute of Technology & Science (KITS) Warangal, Telangana. He has over 12 years of experience in teaching, research, and industry roles. Dr. Dorthi specializes in the Internet of Things (IoT), Machine Learning (ML), and geotechnical engineering, focusing on applications like slope stability monitoring in coal mining. He completed his Ph.D. at the National Institute of Technology Karnataka, Surathkal, where his research centered on using wireless sensor networks for real-time monitoring of underground coal workings. Dr. Dorthi is also an active contributor to the academic community with several publications, patents, and books in the field.

Online Profiles

Education

Dr. Dorthi holds a Ph.D. in Wireless Sensor Networks-based Monitoring of Slope Stability from the National Institute of Technology Karnataka, Surathkal (2019), where his work integrated Internet of Things (IoT) and Machine Learning (ML) technologies for mining safety. He earned his B.Tech in Computer Science Engineering from Kakatiya Institute of Technology & Science, Warangal (2009), followed by an M.Tech in Software Engineering from the same institution (2011). Dr. Dorthi graduated with distinction in all his academic qualifications, demonstrating strong foundations in both computer science and engineering principles.

Research Focus

Dr. Dorthi’s research lies at the intersection of IoT, Machine Learning, and geotechnical engineering. His focus is on wireless sensor networks (WSNs) and their application in monitoring slope stability in underground coal mining operations. This includes the development of real-time systems for predicting and preventing hazards in mining environments. He is also exploring the application of ML algorithms for healthcare analytics, plant disease prediction, smart farming, and fake news detection. Additionally, his work in smart water management systems and IoT-enabled agricultural monitoring addresses critical challenges in environmental sustainability.

Experience

Dr. Dorthi has accumulated over 12 years of academic, research, and industrial experience. He currently serves as an Assistant Professor at KITS Warangal, where he has been teaching since 2019. Prior to this, he held roles as an Associate Professor at KL University, Vijayawada (2018-2019) and as an Assistant Professor at Apex Engineering College, Warangal (2011-2014). He has also worked as a Ph.D. scholar at NITK, Surathkal from 2014 to 2018, where he undertook cutting-edge research in IoT applications for geotechnical engineering. Throughout his career, Dr. Dorthi has mentored several M.Tech and Ph.D. students, particularly in the fields of IoT and machine learning, and has contributed to the development of IoT laboratories at KITS Warangal.

Research Timeline

  • 2014-2018: Pursued Ph.D. at NITK, Surathkal, focused on Wireless Sensor Networks for slope stability monitoring in mining operations.

  • 2011-2014: Worked as an Assistant Professor at Apex Engineering College, Warangal, and concurrently developed research expertise in machine learning and IoT.

  • 2019-Present: Serving as an Assistant Professor at KITS Warangal, where he has been involved in developing new research in agriculture IoT, healthcare analytics, and geotechnical engineering.

  • 2020-2023: Led research into the smart farming and IoT-based water management systems, contributing to national and international conferences.

Awards & Honors

Dr. Dorthi has been recognized for his significant contributions to research and education in the fields of IoT and Machine Learning. In September 2021, he was awarded the Research Excellence Award by the Institute of Scholars, acknowledging his pioneering work in real-time monitoring and smart system development. In addition to this, he has been a recipient of multiple academic scholarships and has received recognition for his outstanding research publications.

Top-Noted Publications

  1. Soora Narasimha Reddy, Vinay Kumar K, Kumar Dorthi, Swathy V, Santosh Kumar, “A Comprehensive Literature Review of Vehicle License Plate Detection Methods,” Traitment Du Signal, Vol. 41, No. 3, pp. 1129-1141, June 2024 (SCIE)

  2. Sai Rama Krishna Indarapu, Swathy Vodithala, Naveen Kumar, Siripuri Kiran, Soora Narasimha Reddy, Kumar Dorthi, “Exploring Human Resource Management Intelligence Practices Using Machine Learning Models,” Journal of High Technology Management Research, 2023, 34(2), 100466 (Scopus)

  3. Kumar Dorthi, Neelima Bayyapu, Ram Chandar K, “Zigbee-based Wireless Data Acquisition System for Monitoring of Partition Stability Above Old Underground Coal Workings,” Arabian Journal of Geosciences, 13(307), 2020 (SCIE)

  4. Kumar Dorthi, Ram Chandar Karra, “Integrated Slope Monitoring System for Slope Stability Over Old Underground Galleries During Surface Mining Operations Using IoT,” Geotechnical and Geological Engineering, 41(3), pp. 1763-1775, 2023 (Scopus & ESCI)

  5. Kumar Dorthi, Ram Chandar K, “Slope Stability Monitoring in Opencast Coal Mine Based on Wireless Data Acquisition System – A Case Study,” International Journal of Engineering and Technology (UAE), 7(2), pp. 24-28, 2017 (Scopus).

These publications represent some of Dr. Dorthi’s top research efforts that have contributed to advancements in mining safety, IoT-enabled monitoring systems, and machine learning applications in geotechnical engineering.

Dr. Kumar Dorthi in support of the Innovative Researcher Award:

1. Cutting-Edge Application of IoT in Geotechnical Engineering

Dr. Dorthi has introduced pioneering solutions in the field of Geotechnical Engineering by integrating Internet of Things (IoT) technologies for real-time slope stability monitoring in underground coal mines. This innovative use of wireless sensor networks (WSNs) has not only enhanced the safety of mining operations but also provided a sustainable, data-driven approach to prevent disasters, making it a model for future engineering applications.

2. Interdisciplinary Research Impact

Dr. Dorthi’s research stands out for its ability to blend multiple domains, such as Machine Learning (ML), Internet of Things (IoT), healthcare, and agriculture. His interdisciplinary research on applications like plant disease prediction using deep learning, cardiovascular disease detection, and smart farming solutions illustrates his ability to leverage cutting-edge technologies to address a wide variety of global challenges.

3. Innovative Patented Technologies

Dr. Dorthi has consistently demonstrated his innovative mindset through the development of several patented technologies. Notable patents include a breath analyzer for cancer detection, smart waste food management system, and AI-based cargo monitoring system. These patents reflect his ability to bridge theoretical research with real-world applications, solving pressing issues such as healthcare diagnostics, environmental sustainability, and industrial safety.

4. Global Leadership and Collaborative Research

Beyond his research, Dr. Dorthi has shown exceptional leadership in academic settings. He has organized and chaired international conferences and seminars, contributing to the global discourse on IoT and machine learning. His work as a mentor to Ph.D. scholars and his involvement in workshops and faculty development programs have cultivated a new generation of researchers, helping to spread his innovative research across the global academic community.

5. Societal Impact through Technology Integration

Dr. Dorthi’s research is not only theoretical but also highly practical with direct applications in mining safety, healthcare, and agriculture. His use of machine learning and IoT to improve agricultural practices, mining operations, and healthcare diagnostics has had a profound impact on these sectors. His work has led to tangible improvements in safety standards, predictive accuracy, and environmental sustainability, thus contributing to the betterment of society as a whole.

Shiting Wen, Computer Science, Best Researcher Award

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Professor Shiting Wen: Professor at School of Computer and Data Engineering, NingboTech University, China

Professor Shiting Wen is an esteemed faculty member at the School of Computer and Data Engineering at NingboTech University, China. His academic journey has seen him achieve high levels of recognition in the field of Computer Science, where he specializes in Big Data Processing, the Internet of Things (IoT), and Artificial Intelligence (AI). With a BSEng in Computer Science from Northeast Forest University and a Ph.D. from both the University of Science and Technology of China (USTC) and City University of Hong Kong (CityU), Prof. Wen has made extensive contributions to various high-impact research areas. He has published over 60 research papers in top-tier journals and conferences such as ICML, ICDE, Inf. Sci., and TITS, cementing his position as a leading academic in his field. Prof. Wen also has a strong presence in academic governance, serving as a Program Committee (PC) member for various international conferences and contributing as an editor for well-regarded journals. His research has garnered international recognition for advancing technologies in AI and IoT, shaping the future of data science and telecommunications.

Online Profiles

ORCID Profile

Prof. Wen’s work is widely disseminated across several academic platforms, including DBLP, where his extensive list of publications and conference proceedings are readily accessible. He maintains an active online presence in the academic community, contributing to significant journals in the fields of data mining, machine learning, IoT, and cloud computing. His research activities and papers have influenced a broad spectrum of disciplines, from blockchain technology and decentralized federated learning to reinforcement learning and knowledge graph reasoning. These platforms serve as an essential resource for those interested in the cutting-edge developments Prof. Wen is driving in these areas. His academic footprint spans multiple disciplines, reflecting the depth and diversity of his research endeavors.

Education

Prof. Wen’s educational background is a strong foundation for his success in the field of computer science. After completing his Bachelor’s Degree in Computer Science and Technology from Northeast Forest University in 2007, he went on to pursue graduate studies at the University of Science and Technology of China (USTC) and City University of Hong Kong (CityU). Under the guidance of renowned experts Prof. Lihua Yue (USTC) and Prof. Qing Li (CityU), he earned his Ph.D. in Computer Science in 2012. His doctoral research focused on pioneering algorithms and methodologies related to data mining, big data, and intelligent systems, which led to several publications in top-tier conferences. Prof. Wen’s education journey reflects not only academic rigor but also a continuous drive to contribute to the advancement of data engineering and AI technologies.

Research Focus

Prof. Wen’s research interests are highly interdisciplinary, with a focus on Big Data Processing and Mining, the Internet of Things (IoT), and Artificial Intelligence. Specifically, his work examines how big data can be processed efficiently, using novel algorithms for data mining and pattern recognition. He explores decentralized systems and federated learning as solutions to large-scale data analysis in distributed environments, such as IoT devices. His AI research includes the application of machine learning algorithms to real-world problems like medical image analysis, blockchain technology, and time series classification. Prof. Wen’s innovative approach to cross-cutting challenges in IoT and AI is advancing both theoretical frameworks and practical applications, particularly in smart healthcare systems, intelligent transportation, and secure data exchange. His research has become crucial in addressing emerging global challenges in data analytics, automation, and security.

Experience

Prof. Wen has extensive professional experience, both in academia and in administrative roles. In addition to his academic role at NingboTech University, where he teaches and mentors graduate students, he has held a significant leadership position as the deputy director of the Bureau of Science and Technology in Yinzhou District, Ningbo City. His contributions to the local scientific community, particularly in the areas of technology innovation and policy development, have further solidified his influence in the region. Prof. Wen has been a key reviewer and editor for top journals such as IEEE Transactions on Knowledge and Data Engineering (TKDE), Knowledge-Based Systems (KBS), and Web Mining and Data Mining (WWWJ). As an editor for Information Technology and Telecommunications, he continues to shape the academic discourse in his fields of expertise. His participation in numerous program committees for leading conferences like WSDM, DASFAA, WISE, ICML, and CIKM has allowed him to contribute to the shaping of future research agendas.

Research Timeline

Prof. Wen’s research trajectory spans over a decade, marked by several key milestones:

  • 2010-2014: Prof. Wen laid the groundwork for his academic career by focusing on foundational topics such as the Internet of Things (IoT), human-centric computing, and data engineering. During this period, he made early contributions to the development of algorithms for data analysis and processing.

  • 2014-2020: This phase marked a shift towards more specialized research, particularly in big data processing and federated learning. Prof. Wen began exploring decentralized data systems, a trend that would become central to his work in AI and blockchain technologies. His research in this period was focused on the intersection of IoT, cloud computing, and machine learning.

  • 2021-Present: Prof. Wen has taken on a leadership role in research surrounding AI safety, reinforcement learning, and blockchain applications. His work in federated learning and decentralized AI models continues to evolve, with applications in healthcare, transportation, and smart cities. His recent contributions have been widely recognized, with papers published in conferences such as ICML, CIKM, and ADMA.

Awards & Honors

Throughout his career, Prof. Wen has received several prestigious awards and honors in recognition of his outstanding contributions to the fields of computer science and data engineering. He has received recognition from key research institutions for his innovation in AI, IoT, and Big Data. He has also earned accolades for his service in academic governance, including his active role as a Program Committee member and editorial board member for well-respected journals. Additionally, Prof. Wen has been the recipient of various research grants, helping him drive forward cutting-edge projects in machine learning, IoT, and blockchain technology. His work continues to be instrumental in advancing the boundaries of knowledge in these transformative fields.

Top-Noted Publications

Among Prof. Wen’s many publications, the following are especially notable for their impact on both academia and industry:

  • “Overcoming Heterogeneous Data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach” (AAAI 2025) – This paper addresses the critical challenge of data heterogeneity in federated learning and proposes novel approaches to enhance model training in medical AI applications.

  • “ExClique: An Express Consensus Algorithm for High-Speed Transaction Processing in Blockchains” (INFOCOM 2025) – A groundbreaking paper on blockchain transaction processing, offering new solutions for enhancing consensus algorithms in decentralized systems.

  • “Towards Efficient Decentralized Federated Learning: A Survey” (ADMA 2024) – This survey paper delves into the methodologies and challenges of decentralized federated learning, providing insights into efficient approaches for distributed AI systems.

  • “Facilitating Feature Selection and Extraction in Clinical Trials with Large Language Models” (ADMA 2024) – This work highlights how AI and large language models can revolutionize clinical trials by automating and improving feature extraction for medical data analysis.

These publications reflect Prof. Wen’s focus on AI-driven innovation, decentralized learning systems, and applications in healthcare and blockchain technology, which continue to influence future research in these areas.

Muhammad Ahmad, Computer Science, Best Researcher Award

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Doctorate Muhammad Ahmad: Student at University of Electronic Science and Technology of China, China

Muhammad Ahmad is a passionate AI researcher and software engineer with expertise in generative AI, deep learning, and medical image analysis. Currently, he is pursuing a Master’s degree in Information and Communication Engineering at the University of Electronic Science and Technology of China (UESTC), where he maintains a strong academic record with a GPA of 3.54/4.0. Muhammad combines his solid theoretical background with hands-on experience in developing innovative AI models, focusing on healthcare applications to improve diagnostic accuracy and patient outcomes.

Online Profiles

ORCID Profile

Education

Muhammad completed his Bachelor of Science in Computer Science from the University of South Asia in Lahore, Pakistan, graduating with a CGPA of 3.16/4.0. During his undergraduate studies, he conducted a notable final year project on Walmart Weekly Sales Prediction using machine learning techniques. Currently, he is advancing his knowledge and research capabilities through a fully funded Master’s program at UESTC, China, where his studies focus on cutting-edge topics such as large language models, generative AI, and medical imaging technologies.

Research Focus

His research is concentrated on the application of deep learning and generative AI methods to medical imaging, particularly focusing on neurodegenerative diseases and brain tumor diagnostics. Muhammad explores the fusion of local and global image features using hybrid architectures and leverages large language models (LLMs) to improve the interpretability and accuracy of medical image analysis, aiming to develop scalable AI solutions for early disease detection and prognosis.

Experience

Muhammad has garnered practical industry experience as a Software Engineer specializing in Artificial Intelligence at E-teleQuote Inc. in Florida, USA, where he led projects on generative AI, real-time chatbot development, and speech-to-text and sentiment analysis technologies. Prior to this, he completed a Machine Learning internship at Quid Sol in Lahore, Pakistan, where he built data pipelines, implemented machine learning algorithms for object detection and tracking, and developed custom deep learning models, strengthening his skills in feature engineering and AI optimization.

Research Timeline

In June 2025, Muhammad published a peer-reviewed article on a hybrid deep learning architecture with adaptive feature fusion for Alzheimer’s disease classification in Brain Sciences. In May 2025, he submitted a manuscript detailing a novel method for dynamic fusion of local and global features aimed at improving brain tumor diagnosis to the International Journal of Machine Learning and Cybernetics. These works reflect his commitment to advancing AI-driven medical research through innovative model designs and rigorous evaluation.

Awards & Honors

Muhammad’s academic excellence and community contributions have been recognized through several awards, including a fully funded scholarship for his Master’s degree at UESTC. He earned a semester scholarship for excellence in a machine learning workshop and received volunteer recognition for his active role in community welfare initiatives through the Rooh Foundation. Additionally, he has demonstrated his technical prowess by winning first and second positions in competitive web development contests hosted by COMSATS University and Superior University, respectively.

Top-Noted Publication

Muhammad’s most notable publication, “Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification,” published in Brain Sciences in 2025, presents an innovative approach that integrates multiple feature sets for more accurate staging of Alzheimer’s disease. This research contributes significantly to the field of AI-assisted medical diagnostics by enhancing feature representation and classification performance, offering a promising tool for clinical applications.

Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification
Brain Sciences | 2025-06-06
DOI: 10.3390/brainsci15060612
Contributors: Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar

Abstract (expanded):
This article introduces a novel hybrid deep learning architecture designed to enhance the classification of Alzheimer’s disease (AD) through the integration of adaptive feature fusion. The model combines both global and local features from neuroimaging data, optimizing classification performance across multiple stages of Alzheimer’s disease. The study leverages convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process and fuse brain images for a more accurate disease diagnosis and staging. By enhancing feature extraction with adaptive fusion techniques, the architecture outperforms traditional methods in terms of accuracy and reliability.

Key Contributions:

  1. Hybrid Model Design: The integration of CNNs for feature extraction and RNNs for sequence learning provides a comprehensive framework for Alzheimer’s classification.

  2. Adaptive Feature Fusion: The study proposes a dynamic fusion strategy that adapts to various stages of the disease, improving classification precision.

  3. Stage-Specific Diagnosis: The model successfully categorizes Alzheimer’s at different stages, addressing the clinical challenge of early-stage detection.

Impact & Relevance:
This work is a significant step forward in the use of AI for medical diagnostics, particularly for neurodegenerative diseases. The proposed model has the potential to aid clinicians in diagnosing Alzheimer’s disease at earlier stages, leading to better patient outcomes. It highlights the growing role of deep learning in medical image analysis and opens doors for future research on hybrid architectures in healthcare.

Shashi Bhushan, Computer Science, Best Researcher Award

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Doctorate Shashi Bhushan: Senior Lecturer at University Teknologi PETRONAS, Malaysia

Dr. Shashi Bhushan is a highly accomplished Associate Professor at Universiti Teknologi PETRONAS (UTP), Malaysia, affiliated with the Centre for Intelligent Signal and Imaging Research (CISIR). With a robust background in computer science and computational intelligence, he has established himself as a leading researcher in biomedical image processing and signal analysis. Over the years, Dr. Bhushan has developed an interdisciplinary research portfolio that integrates artificial intelligence, deep learning, and medical diagnostics. His work is dedicated to solving real-world problems in healthcare, focusing on automated systems for disease detection, classification, and decision support. He actively collaborates with national and international institutions and contributes as a reviewer and editor for several high-impact scientific journals.

Online Profiles

Google Scholar Profile

ORCID Profile

  • Citations: 703

  • h-index: 17

  • i10-index: 26

Dr. Shashi Bhushan maintains an active online academic presence through several platforms. His UTP official profile provides detailed information on his teaching, research interests, and institutional contributions. His Scopus profile showcases his indexed publications and citation metrics, reflecting the impact of his research. Through his Google Scholar page, readers can track his h-index, i10-index, and recent scholarly contributions. On ResearchGate, Dr. Bhushan actively shares preprints, project updates, and engages with the broader research community. These platforms collectively reflect his research influence and collaborative efforts.

Education

Dr. Bhushan holds a Doctor of Philosophy (Ph.D.) in Computer Science with a specialization in computational and biomedical systems. His doctoral work focused on the development of hybrid intelligent algorithms for image classification and signal processing, particularly in the domain of healthcare analytics. Prior to his Ph.D., he earned a Master’s degree and a Bachelor’s degree in Computer Science and Engineering, where he laid the foundational knowledge in programming, machine learning, and embedded systems. His academic training has enabled him to bridge the gap between traditional computing and next-generation intelligent systems for practical, high-impact applications.

Research Focus

Dr. Bhushan’s research primarily lies in the areas of computational intelligence, machine learning, biomedical image processing, and intelligent signal interpretation. His key interests include the development of AI algorithms for early disease detection, such as brain tumors, breast cancer, and neurodegenerative conditions. He also investigates EEG and ECG signal analysis for predictive diagnostics and real-time monitoring. His recent work focuses on convolutional neural networks (CNNs), hybrid feature extraction techniques, deep belief networks (DBNs), and fuzzy logic systems. With a commitment to practical relevance, his research often results in prototype systems and software tools for medical professionals and researchers.

Experience

Dr. Bhushan brings over 15 years of academic and research experience to his role as Associate Professor at Universiti Teknologi PETRONAS. Throughout his career, he has taken on responsibilities as a lecturer, supervisor, research leader, and technical committee member. He has supervised multiple Ph.D. and Master’s students, and he has published extensively in reputed journals and conferences. Dr. Bhushan has secured numerous research grants and has led projects involving AI-based healthcare applications and intelligent signal systems. Beyond research, he is also active in academic governance, curriculum development, and mentoring young researchers, contributing holistically to academic excellence.

Research Timeline

Dr. Bhushan’s research trajectory reflects continuous growth and innovation. From 2010 to 2015, during his Ph.D. years, he developed hybrid intelligent models for image processing. Between 2016 and 2019, he expanded his work into biomedical applications, specifically targeting brain imaging and EEG-based analysis. From 2020 onward, his focus has been on integrating deep learning with classical signal processing techniques to enhance the accuracy and speed of automated diagnostic systems. His recent work also includes AI-powered frameworks for real-time healthcare monitoring and cross-disciplinary projects involving industrial and academic partners.

Awards & Honors

Dr. Bhushan has received several accolades in recognition of his scholarly contributions. He has been awarded multiple Best Paper Awards at international IEEE and Scopus-indexed conferences. His innovative work in biomedical signal classification earned him research excellence awards from UTP and other collaborating institutions. He has also received prestigious grants and funding from government and industry for leading-edge projects in artificial intelligence and healthcare technology. His research impact is acknowledged globally through invitations as keynote speaker, session chair, and editorial board member of reputed journals.

Top-Noted Publication

Among Dr. Bhushan’s impactful publications, his paper titled “A hybrid model for brain tumor classification using convolutional neural networks and handcrafted features” published in Biomedical Signal Processing and Control stands out. This work combines deep learning with traditional feature extraction to create a robust diagnostic tool capable of classifying complex brain tumor types with high accuracy. The study is widely cited and has influenced subsequent research in AI-assisted medical imaging. It demonstrates Dr. Bhushan’s unique ability to merge theory with clinical relevance, leading to improved decision support tools in radiology and oncology.

  • Code-Switching ASR for Low-Resource Indic Languages: A Hindi-Marathi Case Study
    Authors: H Palivela, M Narvekar, D Asirvatham, S Bhusan, V Rishiwal, U Agarwal
    Published in: IEEE Access, 2025
  • Design and Study of Single Array and 2 x 2 Array Patch Array Antenna
    Authors: AR Sharmila, AK Singh, S Bhushan
    Published in: Proceedings of the 4th International Conference on Machine Learning, Advances in …, 2025
  • Beyond Blockchain: Reviewing the Impact and Evolution of Decentralized Networks
    Authors: RKYMK Shashi Bhushan, Sharmila Arunkumar, Neha Goel
    Published in: 2024
  • DeepSplice: A Deep Learning Approach for Accurate Prediction of Alternative Splicing Events in the Human Genome
    Authors: M Abrar, D Hussain, IA Khan, F Ullah, MA Haq, MA Aleisa, A Alenizi, …
    Published in: Frontiers in Genetics, 2024
  • Design and Study of Single Array and 2× 2 Array Patch Array Antenna
    Authors: A Rajeev, AK Singh, S Bhushan, DD Dominic
    Published in: International Conference on Machine Learning, Advances in Computing …, 2024