Muhammad Jawaid | Computer Science | Innovative Research Award

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Innovative Research Award

Muhammad Jawaid
De Montfort University Leicester,United Kingdom

Muhammad Jawaid, De Montfort University Leicester, United Kingdom, is a computer science researcher whose scholarly work encompasses medical imaging, machine learning, intelligent systems, and applied computational methods. His research record includes contributions to multimodal neuroimaging classification, coronary imaging analysis, and smart agriculture applications.

Muhammad Jawaid
Affiliation De Montfort University Leicester
Country United Kingdom
Scopus ID 57188570160
Documents 25
Citations 214
h-index 6
Subject Area Computer Science
Event International Academic Excellence Awards
Google Scholar ID DWR4rb8AAAAJ

Abstract

Muhammad Jawaid’s research profile reflects an interdisciplinary application of computational intelligence to complex scientific and biomedical problems. His documented publications include work on multimodal neuroimaging classification for autism spectrum disorder, non-calcified coronary plaque quantification, and smart agriculture systems. A 2026 Journal of Imaging article applies contrastive and transfer learning to aligned structural and functional neuroimaging data, demonstrating the relevance of machine learning to medical-image analysis.[1]

Keywords

Computer Science; Machine Learning; Medical Imaging; Neuroimaging; Artificial Intelligence; Image Classification; Coronary Imaging; Smart Agriculture; Transfer Learning; Research Innovation.

Introduction

The researcher is associated with computational research addressing practical challenges where data analysis and intelligent algorithms can support scientific interpretation. Recent work demonstrates applications ranging from neuroimaging classification to voxel-level analysis of coronary computed tomography data.[1][2] Earlier research also explored Internet-of-Things technologies and recurrent neural-network forecasting for agricultural monitoring.[3]

Research Profile

The supplied bibliometric profile records 25 documents, 214 citations, and an h-index of 6 in Scopus. These indicators provide a quantitative view of publication and citation activity, while individual publications provide additional evidence of the breadth and application-oriented character of the research.

Research Contributions

  • Multimodal neuroimaging classification using contrastive and transfer-learning strategies.[1]
  • Voxel-based computational analysis for non-calcified coronary plaque quantification in CT images.[2]
  • Application of IoT and neural-network forecasting techniques to smart agriculture.[3]

Publications

Recent publications include Contrastive and Transfer Learning for Aligned Multimodal Neuroimaging Classification of Autism Spectrum Disorder in Journal of Imaging (2026), and Non-calcified coronary plaque quantification in CT images using voxel-based descriptive features in Forensic Imaging (2026).[1][2] His earlier publication, Internet of Plants Application for Smart Agriculture, appeared in 2018.[3]

Research Impact

The reported citation profile indicates established scholarly visibility, while the publication topics show application across medical imaging and intelligent computational systems. The 2026 neuroimaging study reports multimodal classification experiments using an aligned feature-learning framework, while the coronary-imaging study investigates computational quantification of non-calcified plaque in CT data.[1][2]

Award Suitability

The documented combination of publication activity, citation indicators, and research spanning machine learning, medical imaging, and intelligent systems provides a substantive basis for consideration for an Innovative Research Award. The profile is particularly relevant to recognition frameworks that evaluate research originality, interdisciplinary application, documented scholarly output, and potential contribution to advancing computational approaches.

Conclusion

Muhammad Jawaid presents a computer science research profile characterized by applied machine learning, medical-image analysis, and intelligent computational applications. His reported bibliometric indicators and publication record support recognition of sustained scholarly activity and interdisciplinary research development.

References

  1. Vavekanand, R., Kumar, G., Jawaid, M. M., Memon, S. Q., & Kumar, T. (2026). Contrastive and Transfer Learning for Aligned Multimodal Neuroimaging Classification of Autism Spectrum Disorder. Journal of Imaging, 12(7), 328.
    https://doi.org/10.3390/jimaging12070328
  2. Memon, S. Q., Brown, J. M., & Jawaid, M. M. (2026). Non-calcified coronary plaque quantification in CT images using voxel-based descriptive features. Forensic Imaging, 44, 200664.
    https://doi.org/10.1016/j.fri.2025.200664
  3. Aliev, K., Moazzam, M. M., Narejo, S., Pasero, E., & Pulatov, A. (2018). Internet of Plants Application for Smart Agriculture. International Journal of Advanced Computer Science and Applications, 9(4), 421–429.
    https://doi.org/10.14569/IJACSA.2018.090458
  4. Elsevier. (n.d.). Scopus author details: Muhammad Moazzam Jawaid, Author ID 57188570160. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57188570160
  5. De Montfort University. (n.d.). School of Computer Science and Informatics. De Montfort University.

Vimala Roselin | Computer Science | Research Excellence Award

Published on by Academic Excellence

Research Excellence Award

Vimala roselin
Kristu Jayanti University, India

Vimala roselin
Affiliation Kristu Jayanti University
Country India
Google Scholar ID 6UYBA_UAAAAJ&hl
Documents 5
Citations 1
h-index 1
Subject Area Computer Science
Event International Academic Excellence Awards

Vimala roselin is a researcher associated with Kristu Jayanti University, India, whose documented publication record is focused on computer science and applied artificial intelligence. The supplied scholarly record includes research on generative adversarial networks, machine learning, deep learning, neural-network equalisation, clustering, human freedom indices, and big-data security. The available profile information records five documents, one citation, and an h-index of 1. [1]

Abstract

The research profile presents an interdisciplinary application of computational intelligence to practical problems. Recent work includes synthetic image generation for crop disease classification, automated waste classification, neural-network-based system equalisation, and improved clustering methods. The record also includes earlier work concerning secure sensitive-data sharing on big-data platforms. Collectively, these publications indicate engagement with machine learning, deep learning, computer vision, data security, and computational modelling. [2] [3]

Keywords

Computer science; machine learning; deep learning; generative adversarial networks; image classification; waste classification; neural networks; clustering; big data; data security.

Introduction

The supplied publication record spans 2015 to 2026 and demonstrates an evolving interest in computational methods. Recent publications emphasize artificial intelligence and machine learning applications, while the earlier record addresses information security and big-data environments. The combination illustrates the broad application of computer science techniques to classification, optimisation, infrastructure, and data-management problems. [4]

Research Profile

The stated subject area is Computer Science. The supplied profile metrics identify five documents, one citation, and an h-index of 1. Because bibliometric indicators vary according to database coverage and indexing dates, these figures represent the supplied snapshot rather than a permanent measure of research activity. The Google Scholar record provides the associated publication list used for this article. [1]

Research Contributions

  • Application of generative adversarial networks to synthetic image generation for crop disease classification. [2]
  • Development of automated waste classification approaches using machine learning and deep learning. [3]
  • Investigation of neural-network-based equalisation within narrowband electric-grid systems. [4]
  • Use of clustering and computational methods for comparative analysis and data-oriented applications. [5]

Publications

Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks (2025) examines synthetic image generation for crop disease classification using generative adversarial networks. [2]

Extrapolation against categorisation for neural network-based system equalisation in a narrowband electric grid (2026) addresses computational approaches to system equalisation and appears in the International Journal of Critical Infrastructures. [4]

A Framework for Automated Waste Classification System using Machine Learning and Deep Learning Techniques (2025) focuses on automated classification using machine learning and deep learning techniques. [3]

Comparative Analysis of Improved K-Means Clustering for Human Freedom Index (2024) applies an improved clustering approach to comparative analysis of the Human Freedom Index. [5]

Secure sensitive data sharing on a big data platform (2015) concerns secure information sharing within big-data environments. [6]

Research Impact

The supplied profile reports one citation and an h-index of 1 across five documents. The publication topics nevertheless demonstrate a range of applied computer science problems, from agricultural image analysis and waste classification to infrastructure systems and data security. Citation counts can change as databases are updated and should therefore be interpreted with the relevant publication dates and indexing coverage. [1]

Award Suitability

The documented publication portfolio provides evidence of research activity relevant to a Research Excellence Award in Computer Science. Its themes include artificial intelligence, machine learning, deep learning, clustering, computer vision, networked infrastructure, and data security. The record may therefore be considered alongside the complete academic profile, publication evidence, and applicable criteria of the International Academic Excellence Awards.

Conclusion

vimala roselin’s supplied research record reflects activity across several areas of applied Computer Science. The publications demonstrate the use of contemporary computational techniques for image classification, waste management, infrastructure systems, clustering, and secure data sharing. The available bibliometric indicators and publication record provide a concise academic profile for consideration within the International Academic Excellence Awards framework.

References

  1. Google Scholar. (n.d.). Vimala Roselin — publication profile.
    https://scholar.google.com/citations?hl=en&user=6UYBA_UAAAAJ&view_op=list_works&sortby=title
  2. Roselin, J. V., et al. (2025). Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks. International Workshop on AI & ML-Frontiers in Cross Disciplinary Applications.
    Google Scholar publication record
  3. Roselin, J. V., et al. (2025). A Framework for Automated Waste Classification System using Machine Learning and Deep Learning Techniques. 5th International Conference on Expert Clouds and Applications (ICOECA).
    Google Scholar publication record
  4. Renjith, E. J., Roselin, J. V., Ramyadevi, R., Prema, R., & Priscila, S. S. (2026). Extrapolation against categorisation for neural network-based system equalisation in a narrowband electric grid. International Journal of Critical Infrastructures, 22(4), 351–376.
    Google Scholar publication record
  5. Ilyas, F. M., Priscila, S. S., Sheela, K., Vimala Roselin, J., Sona, K. V., & Prema, R. (2024). Comparative Analysis of Improved K-Means Clustering for Human Freedom Index. International Conference on Advancements in Smart Computing and Information.
    Google Scholar publication record
  6. Roselin, V. (2015). Secure sensitive data sharing on a big data platform. Tsinghua Science and Technology.
    Google Scholar publication record

SATEESH GORIKAPUDI | Computer Science | Research Excellence Award

Published on by Academic Excellence

Research Excellence Award

SATEESH GORIKAPUDI
Koneru Lakshmaiah Education Foundation, India

SATEESH GORIKAPUDI
Affiliation Koneru Lakshmaiah Education Foundation
Country India
Scopus ID 58249169300
Documents 13
Citations 68
h-index 4
Subject Area Computer Science
Event International Academic Excellence Awards
ORCID 0000-0002-9280-9581

SATEESH GORIKAPUDI is a computer science researcher affiliated with Koneru Lakshmaiah Education Foundation, India. The supplied research record includes publications addressing anomaly detection, machine learning, image processing, fuzzy logic, clustering, energy-efficient Internet of Things (IoT) communication, and optimization. The available bibliographic information records 13 documents, 68 citations, and an h-index of 4 in the stated Scopus profile data. [1]

Abstract

The research profile of SATEESH GORIKAPUDI reflects work across applied computer science and intelligent computational methods. The documented publications examine practical problems using machine learning, fuzzy logic, clustering, optimization, image processing, and IoT networking. Recent work includes anomaly detection in road traffic analysis and counterfeit currency detection using ensemble machine learning and image processing methods. Earlier studies address disease diagnosis, energy-efficient IoT routing, and optimized clustering. [2] [3]

Keywords

Machine learning; anomaly detection; image processing; fuzzy logic; clustering; optimization; Internet of Things; computer science; disease diagnosis; traffic analysis.

Introduction

The research record spans several computational applications in which data-driven techniques are used to identify patterns, classify information, optimize systems, or improve decision-support processes. The publication portfolio from 2023 to 2025 indicates continuing engagement with contemporary computational problems, including IoT communication, medical diagnosis, image-based recognition, and road-traffic analysis. [4]

Research Profile

The stated subject area is Computer Science. The supplied Scopus information lists 13 documents, 68 citations, and an h-index of 4. These indicators provide a bibliometric snapshot of the indexed research record and should be interpreted in relation to publication year, field, document type, and database coverage. [1]

Research Contributions

  • Road-traffic research addressing anomaly detection and computational analysis. [2]
  • Machine-learning and image-processing approaches for counterfeit currency detection. [3]
  • Fuzzy logic and machine learning applied to early disease diagnosis. [4]
  • Optimization and clustering methods for energy-efficient IoT routing and network applications. [5]

Publications

A Comprehensive Review of Anomaly Detection in Road Traffic Analysis (2025), International Journal of Computing and Digital Systems. [2]

Detection of counterfeit currency using ensemble machine learning models and image processing methods (2025), AIP Conference Proceedings. [3]

Fuzzy Logic-Driven Machine Learning Algorithms for Improved Early Disease Diagnosis (2024), International Journal of Advanced Computer Science and Applications. [4]

An Optimized Clustering Model for Energy-Efficient Routing in IoT Networks (2023), 2023 IEEE International Conference on Contemporary Computing and Communications. [5]

A novel clustering model via optimized fuzzy C-means algorithm and sandpiper optimization with cycle crossover process in IoT (2023), Concurrency and Computation: Practice and Experience. [6]

Research Impact

The supplied bibliometric record reports 68 citations and an h-index of 4 across 13 documents. The publication portfolio also demonstrates application-oriented research across multiple computational contexts. Citation indicators are database-dependent and can change as new publications and citations are indexed. [1]

Award Suitability

The documented research themes provide a substantive basis for consideration under a Research Excellence Award in the Computer Science category. The portfolio contains peer-reviewed journal and conference publications covering machine learning, intelligent systems, optimization, IoT, image processing, and anomaly detection. This assessment is based on the supplied publication and bibliometric information rather than an independent evaluation of the complete academic record.

Conclusion

SATEESH GORIKAPUDI’s documented research profile represents an application-focused body of work in Computer Science. Publications from 2023–2025 demonstrate engagement with machine learning, optimization, clustering, IoT networks, image processing, medical diagnosis, and anomaly detection. The supplied Scopus indicators and publication record provide a concise basis for academic recognition within the International Academic Excellence Awards framework.

References

  1. Elsevier. (n.d.). Scopus author details: SATEESH GORIKAPUDI, Author ID 58249169300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58249169300
  2. Gorikapudi, S. (2025). A Comprehensive Review of Anomaly Detection in Road Traffic Analysis. International Journal of Computing and Digital Systems. DOI: 10.12785/ijcds/1571111482
  3. Gorikapudi, S. (2025). Detection of counterfeit currency using ensemble machine learning models and image processing methods. AIP Conference Proceedings. DOI: 10.1063/5.0296454
  4. Gorikapudi, S. (2024). Fuzzy Logic-Driven Machine Learning Algorithms for Improved Early Disease Diagnosis. International Journal of Advanced Computer Science and Applications. DOI: 10.14569/IJACSA.2024.0151111
  5. Gorikapudi, S. (2023). An Optimized Clustering Model for Energy-Efficient Routing in IoT Networks. 2023 IEEE International Conference on Contemporary Computing and Communications. DOI: 10.1109/inc457730.2023.10263199
  6. Gorikapudi, S. (2023). A novel clustering model via optimized fuzzy C-means algorithm and sandpiper optimization with cycle crossover process in IoT. Concurrency and Computation: Practice and Experience. DOI: 10.1002/cpe.7776

Fengrui Hao | Computer Science | Best Researcher Award

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Dr. Fengrui Hao | Computer Science | Best Researcher Award

Jinan University, China

Dr. Fengrui Hao is an emerging researcher in the field of computer science, currently pursuing his Ph.D. in Cyber Security at the School of Information Science and Technology, Jinan University, Guangzhou, China. He holds a B.S. degree in Information Management and Information Systems and an M.S. degree in Computer Technology from Guilin University of Electronic Technology, which laid the foundation for his deep engagement with advanced computing and security research. His primary focus lies in adversarial machine learning and trustworthy artificial intelligence, where he has made significant contributions to strengthening AI systems against vulnerabilities and ensuring fairness, transparency, and robustness in their applications. With more than ten publications in prestigious journals and conferences such as IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Industrial Informatics (TII), and IEEE Transactions on Knowledge and Data Engineering (TKDE), Dr. Hao has established himself as a promising scholar. His research contributions include the development of novel attack and defense mechanisms, fairness-aware algorithms, and privacy-preserving techniques for graph data publishing, all of which are shaping the foundation of secure and ethical AI. His work has been recognized through two completed or ongoing research projects, one consultancy project, and an impressive record of sixteen patents under process. With a citation index of twenty, his influence in the field continues to expand as he pursues innovative research directions. Dr. Hao’s dedication to advancing adversarial learning and trustworthy AI reflects his vision of creating resilient, secure, and human-centered AI technologies for the future.

Profiles: Scopus | Orcid

Featured Publications

  • FBA: Fairness Backdoor Attack on Graph Neural Networks – IEEE Transactions on Dependable and Secure Computing, 2025, DOI: 10.1109/TDSC.2025.3563668

  • dK-DGDP: A Differential Privacy Approach on Directed Social Network Graphs – Computers & Security, 2025, DOI: 10.1016/j.cose.2025.104647

  • GCPA: GAN-Based Collusive Poisoning Attack in Federated Recommender Systems – IEEE Transactions on Knowledge and Data Engineering, 2025, DOI: 10.1109/TKDE.2025.3579807

  • CBAs: Character-level Backdoor Attacks against Chinese Pre-trained Language Models – ACM Transactions on Privacy and Security, 2024, DOI: 10.1145/3678007

  • Collusive Model Poisoning Attack in Decentralized Federated Learning – IEEE Transactions on Industrial Informatics, 2024, DOI: 10.1109/TII.2023.3342901

 

Ashok Ghimire | Computer Science | Research Excellence Award

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Mr. Ashok Ghimire | Computer Science | Research Excellence Award

Mr. Ashok Ghimire | Computer Science | Westcliff University | United States

Ashok Ghimire is a dynamic researcher and professional specializing in artificial intelligence, data analytics, and financial technology. Born in Nepal and currently based in Anaheim, California, he has demonstrated a strong commitment to advancing computer science applications in banking, finance, and business intelligence. With over a decade of academic and professional experience, he has transitioned from banking operations and financial management in Nepal to advanced research in AI-driven data analytics in the United States. His work emphasizes leveraging machine learning, quantum computing, and big data to address critical challenges such as fraud detection, financial inclusion, and risk management. Ashok has earned recognition for his scholarly contributions, securing multiple scholarships and publishing extensively in reputed journals. In addition to his academic excellence, he contributes to the research community as a peer reviewer and conference evaluator. His forward-looking vision integrates technology and finance to create sustainable and secure digital ecosystems.

Author Profiles

Orcid | Google Scholar

Education 

Ashok Ghimire has pursued a strong academic journey combining business, finance, and technology. He completed his high school and higher secondary education in Nepal with distinction before receiving an ICCR-funded scholarship to study for a Bachelor of Business Administration in Finance at MITSOM, Pune, India, where he graduated with distinction. Continuing his academic path, he earned an MBA in Finance and Marketing at Surya World, India, achieving high distinction and receiving support through Surya Pharmaceuticals’ CSR scholarship. Ashok’s academic excellence has been recognized with multiple scholarships at each stage of his education. Building on this foundation, he is currently pursuing a Doctor of Business Administration (DBA) in Business Intelligence and Data Analytics at Westcliff University, Irvine, California. His doctoral studies reflect his growing specialization in artificial intelligence, machine learning, and big data analytics, preparing him to make innovative contributions to the U.S. financial technology and banking sectors.

Experience 

Ashok Ghimire has built a diverse professional background, beginning his career as a Management Trainee at CG Electronics, Nepal, where he gained experience in sales enhancement, product imports, and brand management. He later advanced to Assistant Manager and then Deputy Manager at Nepal Bank Limited, one of the country’s leading financial institutions. During his tenure, he managed credit assessments, financial risk evaluations, loan negotiations, and approvals of major projects, including hydroelectric power initiatives. His responsibilities also extended to customer relations, compliance, staff supervision, and branch operations. Ashok’s professional expertise lies in analyzing business proposals, conducting industry research, and making strategic financial recommendations. Since moving to the U.S., he has focused on academic research and peer reviewing for international journals and conferences, strengthening his global engagement in computer science, AI, and data analytics. His career path reflects a seamless integration of financial management and emerging digital technologies.

Awards and Honors 

Ashok Ghimire’s academic and professional journey is distinguished by numerous awards and recognitions. He received scholarships at every stage of his higher education, beginning with Einstein Academy in Nepal, where he was awarded for his higher secondary studies. He later received the prestigious Indian Council for Cultural Relations (ICCR) scholarship for his undergraduate degree in Finance at MITSOM, Pune, India. His postgraduate studies were supported by a CSR scholarship from Surya Pharmaceuticals, enabling him to complete his MBA in Finance and Marketing with distinction. At Westcliff University, he has been recognized on the Dean’s List with Distinction and awarded the Founder’s Scholarship for Business during his doctoral studies. Beyond academia, Ashok has secured a UK Design Patent for an AI-based Facial Recognition Device (2025) and has established himself as an active peer reviewer for leading international journals and conferences, highlighting his global recognition as a scholar.

Research Focus 

Ashok Ghimire’s research focus lies at the intersection of computer science, artificial intelligence, and financial technology. His work explores how AI, machine learning, and data analytics can transform the U.S. financial sector, particularly in enhancing fraud detection, anti-money laundering (AML) compliance, financial crime prevention, and risk management. He has published extensively on AI-driven predictive modeling, big data in banking, quantum computing applications in fraud detection, and the role of sentiment analysis in cryptocurrency markets. His research also extends to healthcare, education, and agricultural industries, where AI-driven solutions are shaping innovation and efficiency. Through his doctoral studies, he aims to strengthen the adoption of business intelligence and decision-support systems in organizations, especially in resource-constrained environments. His future vision is to integrate computer science with financial strategies to support economic inclusion, transparency, and security, ultimately contributing to both national priorities and global digital transformation.

Publications 

  • Exploring Benefits, Overcoming Challenges, and Shaping Future Trends of AI in Agriculture.

  • Behavioral Intention to Adopt Artificial Intelligence in Educational Institutions.

  • Exploring the Latest Trends in AI Technologies: Current State and Impacts.

  • Applying TAM in IT Systems to Evaluate Decision Support Adoption.

  • Advances in Smart Health Care: Paradigms, Challenges, Case Studies.

  • Predictive Models Performance in Financial Services for At-Risk Customers.

  • Harnessing Big Data with AI-Driven BI Systems for Real-Time Fraud Detection.

  • AI-Powered Anomaly Detection for AML Compliance in US Banking.

  • Quantum Computing in US Banking: Future of Fraud Prevention.

  • Multi-Factor Forex Hedging Models with Reinforcement Learning.

  • Organizational Factors Influencing Predictive Analytics Adoption for FX Exposure.

  • Role of AI-Based Sentiment Detection in Forecasting Cryptocurrency Market.

  • Leveraging AI for Trade-Based Money Laundering Detection.

  • AI-Driven Drug Repurposing for Oncology Treatments.

  • Meta-Synthesis of Barriers to Decision Tree Analytics in Payment Fraud.

  • Enhancing Real-Time Fraud Detection Using RNNs.

  • Sociotechnical Framework for Business Intelligence Adoption in SMEs.

  • Data Analytics in Judicial Decision-Making.

Conclusion

Ashok Ghimire represents a new generation of scholars who bridge finance, technology, and computer science to drive innovation in real-world contexts. His academic journey, professional expertise, and extensive publications reflect a strong foundation in both theory and practice. Through his pioneering research on artificial intelligence, data analytics, and financial risk management, he is actively shaping the future of digital finance and business intelligence. His achievements, including international scholarships, patents, and peer-review contributions, highlight his credibility and leadership in the field. Positioned at the intersection of academia and industry, Ashok continues to contribute toward building smarter, safer, and more inclusive financial systems. His forward-looking vision makes him a strong candidate for recognition in the domain of Computer Science, particularly where technology and finance converge to address global challenges.