Sakthivel Ramalingam | Computer Science | Editorial Board Member

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Assist. Prof. Dr. Sakthivel Ramalingam | Computer Science | Editorial Board Member

Vellore Institute of Technology Chennai | India

Assist. Prof. Dr. Sakthivel Ramalingamthe researcher’s work spans advanced control theory, nonlinear systems, and complex dynamical networks with a strong emphasis on cyber-physical security, resilient control design, and intelligent fuzzy systems. Their contributions focus on developing robust, finite-time, and event-triggered control and filtering strategies for Takagi–Sugeno fuzzy models, Markovian jump systems, networked control systems, and multi-agent networks subjected to uncertainties, delays, cyber attacks, actuator faults, and communication constraints. Their research advances include designing synchronization mechanisms for fractional-order systems, creating hybrid-triggered and observer-based state estimation methods, and proposing fault-tolerant and non-fragile control algorithms for large-scale intelligent systems. With more than thirty-eight SCIE-indexed publications in high-impact journals such as IEEE Transactions on Fuzzy Systems, Neural Networks, Communications in Nonlinear Science and Numerical Simulation, Applied Mathematics and Computation, Nonlinear Dynamics, and the Journal of the Franklin Institute, their work significantly contributes to resilient autonomous systems, intelligent vehicles, stochastic complex networks, and distributed optimization. Their research extends to sampled-data control, interval type-2 fuzzy systems, polynomial fuzzy models, semi-Markovian jump systems, and fractional-order complex networks. They also engage in experimental validation, synchronization analysis, and stability theory, aiming to enhance the reliability, safety, and robustness of modern intelligent systems in uncertain and adversarial environments.

Featured Publications

Sakthivel, R., Sakthivel, R., Kaviarasan, B., & Alzahrani, F. (2018). Leader-following exponential consensus of input saturated stochastic multi-agent systems with Markov jump parameters. Neurocomputing, 287, 84–92.

Sakthivel, R., Sakthivel, R., Kaviarasan, B., Lee, H., & Lim, Y. (2019). Finite-time leaderless consensus of uncertain multi-agent systems against time-varying actuator faults. Neurocomputing, 325, 159–171.

Sakthivel, R., Sakthivel, R., Nithya, V., Selvaraj, P., & Kwon, O. M. (2018). Fuzzy sliding mode control design of Markovian jump systems with time-varying delay. Journal of the Franklin Institute, 1–15.

Sakthivel, R., Kwon, O. M., Park, M. J., Choi, S. G., & Sakthivel, R. (2021). Robust asynchronous filtering for discrete-time T–S fuzzy complex dynamical networks against deception attacks. IEEE Transactions on Fuzzy Systems, 30(8), 3257–3269.

Vaibhav Tummalapalli | Machine learning | Excellence in Innovation Award

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Mr. Vaibhav Tummalapalli l Machine learning
| Excellence in Innovation Award

Epsilon Data Management, LLC | United States

Mr. Vaibhav Tummalapalli’s research focuses on the advancement of applied machine learning methodologies, predictive modeling, and data-driven optimization across large-scale industrial domains, particularly automotive and telecommunications. His work emphasizes the integration of artificial intelligence in lifecycle analytics, customer engagement, and personalization strategies to enhance business intelligence and operational efficiency. His studies explore innovative modeling frameworks such as EV Conquest modeling, VIN-level mileage prediction, and vehicle recommendation systems, which apply behavioral, telematics, and demographic data to drive precision marketing and service optimization. Additionally, his contributions to outlier detection, cohort-based stratified sampling, and KNN imputation distance metrics extend theoretical and applied understanding in data preprocessing and imbalanced learning. His research also addresses model monitoring and drift management using SAS Viya and PySpark-based architectures, ensuring robust model performance in production environments. Through the development of scalable ML pipelines, channel propensity models, and retention-focused predictive systems, his work demonstrates the transformative potential of AI in driving measurable business outcomes, customer retention, and ethical personalization. His scholarly and technical pursuits collectively aim to advance the design of intelligent, explainable, and sustainable machine learning systems for real-world, high-impact applications

Featured Publications

Tummalapalli, V. (2025). Understanding distance metrics in KNN imputation: Theoretical insights and applications. Journal of Mathematical & Computer Applications, 4(4), 1–4. https://doi.org/10.47363/JMCA

Tummalapalli, V. (2025). Machine learning pipeline for automotive propensity models. International Journal of Core Engineering & Management, 8(3), [Issue-03].

Tummalapalli, V. (2025). Outlier detection & treatment for machine learning models. International Journal of Innovative Research and Creative Technology, 11(3).

Tummalapalli, V. (2025). Stratified sampling in cohort-based data for machine learning model development. International Scientific Journal of Engineering and Management, 4.

Sharmila More | Machine Learning | Women Researcher Award

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Dr. Sharmila More | Machine Learning | Women Researcher Award

MIT Arts, Commerce & Science College| India

Dr. Sharmila More is an accomplished academician and researcher, currently serving as Assistant Professor in the Department of Science and Computer Science at MIT ACSC, Alandi (D), Pune. With over nine years of teaching and administrative experience, along with eight years of dedicated research expertise, she has significantly contributed to the fields of computer science, data science, cyber security, and artificial intelligence. She holds a Ph.D. in Computer Science from MATS University, Raipur, an MCA from Pune University, and a Postgraduate Diploma in Core Competency from Shivaji University. Dr. More has published 18 research papers in reputed journals, presented 20 papers at national and international conferences, and authored a book titled Solving Security Issues in Personal Identification using Fuzzy Approach and Multimodal Images. She has also guided several students in research and academic projects. Her innovations are reflected in multiple patents, including an Indian patent at the FER stage, one design patent, and granted patents in Australia, Germany, and the UK. She is actively engaged in academic committees, curriculum development under the NEP framework, and serves as an editorial board member for journals. Her professional memberships include the Computer Society of India, Indian Science Congress Association, and Soft Computing Research Society. Recognized for her excellence, she has received prestigious honors such as the International Research Excellence Awards for Outstanding Researcher and Distinguished Researcher, Best Teacher Award, and multiple prizes at research competitions. A sought-after resource person and reviewer, she has delivered expert lectures and contributed as co-supervisor and examiner for Ph.D. programs. Her citation index stands at 67, reflecting her impactful scholarship. Dr. More continues to advance interdisciplinary research in computer science, focusing on biometric systems, machine learning, cryptography, and emerging technologies, while inspiring future scholars through teaching, mentorship, and innovation.

Profile: Google Scholar

Featured Publications

More, S. S. (2025). Revolutionizing military operations: The role of deep learning with YOLO v7 in the evolution of drones. Advancements in Intelligent Systems, 33–43. Computing & Intelligent Systems.

More, S. S. (2024). Pythonic learning: Advancements and innovations in machine intelligence. International Journal of Advanced Research in Science, Communication and Technology.

More, S. S. (2023). Statistical and fuzzy inference system analysis of multimodal. Solovyov Studies ISPU.

More, S. S. (2023). Statistical and fuzzy inference system analysis of multimodal images using NMRSA. Solovyov Studies ISPU, 71(11), 122–127.

More, S., Narain, B., & Jadhav, B. T. (2023). Privacy conserving using fuzzy approach and blowfish algorithm for malicious personal identification. In Institute of Engineering (pp. xx–xx). Springer.

 

Manasvi Aggarwal | Artificial Intelligence | Young Scientist Award

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Ms. Manasvi Aggarwal | Artificial Intelligence | Young Scientist Award

Senior Data Scientist, Mastercard

Name: Manasvi Aggarwal  Gender: Female Designation: Senior Data Scientist Department: AI Garage Organization: Mastercard Specialization: Artificial IntelligenceExpertise: Graph Neural Networks (GNNs), Spatio-Temporal Forecasting, Neural Algorithmic Reasoning Industry Experience: Microsoft, Myntra, Mastercard Notable Work: Developed MRP-GNN, identifying $17M in fraud and increasing fraud detection rates by 20% Research Collaborations: Oxford, Cambridge (INAR project) Publications: ICPR, IEEE BigData, ICML workshops, RecSys, Springer book Awards: Business Excellence Award (Mastercard), Google-sponsored EEML travel grant Professional Contributions: ICLR, IEEE BigData, and NeurIPS program committee member Vision: Advancing scalable AI solutions for real-world impact

profile:

Scholar

🎓 Education 

B.Tech: Computer Science, University of Delhi (Ranked 4th in cohort) Competitive Exams: GATE (99.47 percentile), JEST (All India Rank 35) M.Tech (Research): Computer Science, IISc Bengaluru Thesis: “Embedding Networks: Node and Edge Representations” (Graph-based learning, NLP, Computer Vision) PhD Offers: Fully funded PhD admissions from Canada and the USA (deferred due to financial reasons) Key Research Areas: Graph Neural Networks, Spatio-Temporal Forecasting, Neural Algorithmic Reasoning Notable Academic Achievements: IISc Research Contributions in GNNs and self-supervised learning

💼 Experience 

Microsoft: Developed multi-label categorization for Azure offers using BERT embeddings and web scraping  Myntra: Worked on pricing automation, demand forecasting, and user cohort modeling with graph-based approaches Mastercard: Leading AI-driven fraud detection initiatives, including MRP-GNN (detected $17M fraud, 20% improved detection rates) Industry Collaborations: Worked with teams in Israel, USA, and India on AI-driven solutions Mentorship: Guides junior AI researchers, participates in hiring interviews

🏆 Awards & Honors 

Business Excellence Award – Mastercard Priceless Mentoring and Guidance Award – Mastercard Google-sponsored travel grant – EEML 2024 1st Runner-up – Myntra HackerRamp Hackathon Invited Program Committee Member – ICLR, IEEE BigData, NeurIPS

🔬 Research Focus 

Key Areas: Graph Neural Networks (GNNs), Spatio-Temporal Forecasting, Neural Algorithmic Reasoning Mastercard Research: Developed MRP-GNN, detecting high-risk merchants and preventing financial fraud  AI for Security: Fraud detection, risk prediction, secure transactions Spatio-Temporal AI: Developed GNN for terror activity forecasting in Jammu & Kashmir Academic Collaborations: Works with Oxford & Cambridge on INAR, optimizing CLRS-30 benchmarks Publications: Published in ICPR, IEEE BigData, ICML workshops, RecSys Community Contributions: Co-organized Southeast Asian Learning on Graphs (LoG) 2024 Meetup

✅ Conclusion

Manasvi Aggarwal is a highly competitive candidate for the Best Researcher Award, with a strong blend of academic excellence, industry impact, publications, and global collaborations. If she strengthens her patent portfolio, citation metrics, and leadership in independent research projects, she would be an even stronger contender for distinguished research awards.

publication

Deep Learning – M. Aggarwal, M.N. Murty (2021) – 21 citations

 

Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs – M. Aggarwal, M.N. Murty (2021) – 18 citations

 

Self-supervised Hierarchical Graph Neural Network for Graph Representation – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 4 citations

 

Robust Hierarchical Graph Classification with Subgraph Attention – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 4 citations

 

Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 3 citations

 

A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2021) – 2 citations

 

Region and Relations Based Multi Attention Network for Graph Classification – M. Aggarwal, M.N. Murty (2021) – 2 citations

 

Using Relational Graph Convolutional Networks to Assign Fashion Communities to Users – A. Budhiraja, M. Sukhwani, M. Aggarwal, S. Shevade, G. Sathyanarayana (2022) – 1 citation

 

Node Representations – M. Aggarwal, M.N. Murty (2021) – 1 citation

 

Hierarchically Attentive Graph Pooling with Subgraph Attention – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 1 citation

 

Embedding Graphs – M. Aggarwal, M.N. Murty (2021)

 

Representations of Networks – M. Aggarwal, M.N. Murty (2021)

 

Embedding Networks: Node and Graph Level Representations – M. Aggarwal

Narayan Vyas | Artificial Intelligence | Young Scientist Award

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Mr Narayan Vyas | Artificial Intelligence | Young Scientist Award

Mr Narayan Vyas , Vivekananda Global University, India

Dr. Narayan Vyas is an Assistant Professor at Vivekananda Global University, Jaipur, specializing in Internet of Things (IoT) and application development. With a Ph.D. in Computer Science and multiple publications in peer-reviewed journals, he is recognized for his significant contributions to IoT, machine learning, and remote sensing. Dr. Vyas has a proven track record in academia and industry, including roles as a Technical Trainer and Research Consultant. His research includes developing frameworks for agricultural changes and innovations in mobile app development. He is also an active keynote speaker and workshop organizer, dedicated to advancing technological education.

Publication Profile

Orcid

Strengths for the Award

  1. Extensive Research Contributions: Narayan has a robust research profile with numerous publications in peer-reviewed international journals and conferences. His work spans diverse and cutting-edge fields, including IoT, remote sensing, machine learning, and computer vision. This breadth and depth of research indicate a high level of expertise and a significant contribution to the field.
  2. Active Engagement in Academia: As an Assistant Professor and Research Coordinator, Narayan plays a key role in shaping academic programs and mentoring students. His leadership in developing syllabi and organizing workshops demonstrates a commitment to advancing both education and research.
  3. Industry Experience and Practical Impact: His practical experience in mobile application development and work with clients worldwide show his ability to bridge the gap between academic research and real-world applications. This experience is valuable for translating theoretical research into practical solutions.
  4. Editorial and Consultancy Roles: Editing several books and working as a research consultant highlight his expertise and recognition in the field. These roles suggest a high level of respect and credibility among peers.
  5. National Recognition: Passing the NTA UGC NET & JRF on his first attempt underscores his strong foundational knowledge and commitment to research excellence.

Areas for Improvement

  1. Research Continuity and Focus: While Narayan’s work is extensive, he could benefit from focusing more deeply on a narrower set of research areas. This could lead to more impactful and cohesive contributions in specific domains.
  2. Increased Research Collaboration: Although Narayan has collaborated with others, increasing his participation in interdisciplinary research projects could further enhance his impact and visibility in diverse fields.
  3. Awards and Grants: While Narayan has significant achievements, actively seeking and obtaining prestigious awards and grants could bolster his profile and provide additional validation of his research impact.
  4. Public Engagement: Enhancing efforts to communicate research findings to a broader audience, including the general public, could improve the societal impact of his work.

Education

Dr. Narayan Vyas is pursuing a Ph.D. in Computer Science at Punjabi University, Patiala, focusing on developing an image fusion framework for agricultural detection using remote sensing data. He cleared the NTA UGC NET & JRF in Computer Science on his first attempt. Dr. Vyas holds a Master’s in Computer Application from Maharshi Dayanand Saraswati University, where he achieved the first rank. He completed his Bachelor’s in Computer Applications with a specialization in IoT, where he developed a self-balancing robot as his major project.

Experience

Dr. Narayan Vyas currently serves as an Assistant Professor and Research Coordinator at Vivekananda Global University, Jaipur. Previously, he was a Technical Trainer at Chandigarh University, where he founded the Student Research Cell. His role as Principal Research Consultant at AVN Innovations Pvt. Ltd. involved leading research projects and mentoring junior researchers. As a Senior Mobile App Developer at Flexxited, he developed applications for clients globally. His experience spans from academic roles to hands-on technical work, including freelance and training positions.

Awards and Honors

Dr. Narayan Vyas has been recognized for his contributions to technology and research. He served as a Session Chair at the 1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI-2023). He has reviewed papers for prestigious conferences such as IEEE NMITCON-2023 and ICDSNS-2023. He was a proctor for the IEEEXtreme 17.0 programming competition and conducted notable workshops on mobile app development and IoT at leading universities.

Research Focus

Dr. Narayan Vyas’s research focuses on integrating advanced technologies like IoT, machine learning, and remote sensing to solve complex problems in agriculture, healthcare, and environmental monitoring. His work includes developing frameworks for detecting agricultural changes, improving mobile app development methodologies, and enhancing remote sensing data analysis. He is passionate about applying these technologies to real-world problems and advancing the academic understanding of their applications.

Publication Top Notes

“Applying Machine Learning Techniques to Bioinformatics” 📚

“Innovations in Machine Learning and IoT for Water Management” 💧

“Quantum Innovations at the Nexus of Biomedical Intelligence” 🧬

“AI-Driven Alzheimer’s Disease Detection and Prediction” 🧠

“Secure Energy Optimization: Leveraging IoT and AI for Enhanced Efficiency” ⚡

“Internet of Medicine for Smart Healthcare” 🏥

“Multimodal Data Fusion for Bioinformatics” 🌐

“Elevating IoT Sensor Data Management and Security Through Blockchain Solutions” 🔒

“A Machine Learning Framework for Accurate Prediction of Parkinson’s Disease from Speech Data” 🗣️

“Advancing Precision Agriculture: Leveraging YOLOv8 for Robust Deep Learning Enabled Crop Diseases Detection” 🌾

Conclusion

Narayan Vyas is a compelling candidate for the Research for Young Scientist Award due to his significant contributions to research, his active role in academia, and his practical experience. His extensive publication record and leadership in academia reflect a high level of expertise and dedication. Addressing the areas for improvement, such as focusing research efforts and seeking additional recognition, could further strengthen his candidacy. Overall, his achievements and ongoing contributions make him a strong contender for this award.

 

 

Alireza Nazemi | Neural network | Best Researcher Award

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Prof. Alireza Nazemi | Neural network | Best Researcher Award

Prof at Shahrood University of Technology, Iran

Dr. Alireza Nazemi is a Professor of Applied Mathematics at Shahrood University of Technology, specializing in control and optimization. He holds a Ph.D. in Applied Mathematics from Ferdowsi University of Mashhad. His research interests include optimal control, nonlinear and convex optimization, portfolio optimization, and neural network theory. Dr. Nazemi has published extensively, with recent works addressing neural network applications in optimization and control. He teaches courses such as Optimal Control, Nonlinear Optimization, and Neural Networks & Optimization. Dr. Nazemi is dedicated to advancing mathematical methods to solve complex engineering and financial problems.

profile:

Scopus

Scholar

 

🎓 Education

B. Sc. in Applied Mathematics Sharif University of Technology, Tehran, Iran (1997-2001). M. Sc. in Applied Mathematics (Field: Control & Optimization)  Hakim Sabzevari University, Sabzevar, Iran (2001-2003). Dissertation: “To solve some nonlinear programming problems by using measure theory and neural network models” Supervisor: Prof. Sohrab Effati. Ph.D. in Applied Mathematics (Field: Control & Optimization)  Ferdowsi University of Mashhad, Mashhad, Iran (2005-2009). Dissertation: “To solve some optimal shape design problems with free boundary” Supervisor: Prof. Mohammad Hadi Farahi Advisor: Prof. Ali Vahidian Kamyad

🔍 Research Interests

  • Optimal Control
  • Nonlinear Optimization
  • Convex Optimization
  • Portfolio Optimization
  • Optimization of PDE’s
  • Neural Network Theory

Publication:📝

  • Title: Neural network models and its application for solving linear and quadratic programming problems
    Authors: S. Effati, A.R. Nazemi
    Journal: Applied Mathematics and Computation
    Year: 2006
    Volume: 172
    Issue: 1
    Pages: 305-331
    Citations: 84

 

  • Title: A dynamic system model for solving convex nonlinear optimization problems
    Author: A.R. Nazemi
    Journal: Communications in Nonlinear Science and Numerical Simulation
    Year: 2012
    Volume: 17
    Issue: 4
    Pages: 1696-1705
    Citations: 73

 

  • Title: A gradient-based neural network method for solving strictly convex quadratic programming problems
    Authors: A. Nazemi, M. Nazemi
    Journal: Cognitive Computation
    Year: 2014
    Volume: 6
    Pages: 484-495
    Citations: 72

 

  • Title: Analytical solution for the Fokker–Planck equation by differential transform method
    Authors: S. Hesam, A.R. Nazemi, A. Haghbin
    Journal: Scientia Iranica
    Year: 2012
    Volume: 19
    Issue: 4
    Pages: 1140-1145
    Citations: 62

 

  • Title: Müntz–Legendre spectral collocation method for solving delay fractional optimal control problems
    Authors: S. Hosseinpour, A. Nazemi, E. Tohidi
    Journal: Journal of Computational and Applied Mathematics
    Year: 2019
    Volume: 351
    Pages: 344-363
    Citations: 59

 

  • Title: A neural network model for solving convex quadratic programming problems with some applications
    Author: A. Nazemi
    Journal: Engineering Applications of Artificial Intelligence
    Year: 2014
    Volume: 32
    Pages: 54-62
    Citations: 59

 

  • Title: An efficient dynamic model for solving the shortest path problem
    Authors: A. Nazemi, F. Omidi
    Journal: Transportation Research Part C: Emerging Technologies
    Year: 2013
    Volume: 26
    Pages: 1-19
    Citations: 59

 

  • Title: Application of projection neural network in solving convex programming problems
    Authors: S. Effati, A. Ghomashi, A.R. Nazemi
    Journal: Applied Mathematics and Computation
    Year: 2007
    Volume: 188
    Issue: 2
    Pages: 1103-1114
    Citations: 52

 

  • Title: A dynamical model for solving degenerate quadratic minimax problems with constraints
    Author: A.R. Nazemi
    Journal: Journal of Computational and Applied Mathematics
    Year: 2011
    Volume: 236
    Issue: 6
    Pages: 1282-1295
    Citations: 49

 

  • Title: Solving general convex nonlinear optimization problems by an efficient neurodynamic model
    Author: A. Nazemi
    Journal: Engineering Applications of Artificial Intelligence
    Year: 2013
    Volume: 26
    Issue: 2
    Pages: 685-696
    Citations: 45

 

  • Title: A capable neural network model for solving the maximum flow problem
    Authors: A. Nazemi, F. Omidi
    Journal: Journal of Computational and Applied Mathematics
    Year: 2012
    Volume: 236
    Issue: 14
    Pages: 3498-3513
    Citations: 44