Muhammad Aamir | Artificial Intelligence | Best Researcher Award

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Dr. Muhammad Aamir | Artificial Intelligence | Best Researcher Award

Research Scientist | University of Oxford | United Kingdom

Dr. Muhammad Aamir is a researcher at the University of Oxford, United Kingdom, specializing in Artificial Intelligence and advanced computational modeling. His research focuses on developing intelligent algorithms for data-driven decision-making, machine learning, and real-world AI applications across diverse domains. He has contributed to high-impact studies involving hybrid AI models, neural networks, and intelligent sensing systems. Dr. Aamir’s work emphasizes robustness, scalability, and practical deployment of AI solutions. Through interdisciplinary research, he continues to advance the integration of artificial intelligence into complex scientific and engineering problems.

Citation Metrics (Scopus)

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Citations
926

Documents
50

h-index
14

                            ■ Citation              ■ Documents              ■ h-index


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Featured Publications

Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

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Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

Assistant Professor, Prof. Ramkrishna More Arts, Commerce & Science College, India

Dr. Santosh Jagtap, Assistant Professor at Prof. Ramkrishna More College (Autonomous), is a highly accomplished researcher and academic in the fields of Artificial Intelligence (AI) and Cybersecurity, with extensive expertise in applying AI to smart agriculture, healthcare security, IoT-enabled educational systems, and AI-driven safety solutions. Dr. Jagtap holds advanced academic qualifications and has developed a distinguished research profile that emphasizes practical applications of emerging technologies to address societal challenges. His work integrates machine learning, blockchain, IoT, and real-time data processing, producing innovative solutions in areas such as intelligent irrigation systems, plant disease detection, AI-based emotion recognition for safety alerts, and secure healthcare frameworks. Over his career, Dr. Jagtap has contributed significantly to international research projects and collaborative studies, producing high-impact publications in reputed journals and conference proceedings, such as Materials Today: Proceedings, international conferences on electronics, computing, and applied AI. He has also been recognized for innovation through patent awards, notably for AI-based plant disease identification systems, reflecting his focus on technology transfer and real-world impact. Dr. Jagtap has played an active role in mentoring students, guiding research projects, and participating in professional networks that foster academic and technological growth. He has demonstrated a consistent record of research excellence, with a total of 78 citations across 4 Scopus-indexed publications and an h-index of 3, reflecting the growing impact of his work.

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Featured Publications

  • Jagtap, S. T., Phasinam, K., Kassanu. (2022). Towards application of various machine learning techniques in agriculture. Materials Today: Proceedings, 51, 793–797. 70 citations.

  • Jagtap, S. T., Thakar, (2021). A framework for secure healthcare system using blockchain and smart contracts. Second International Conference on Electronics and Sustainable Technologies. 22 citations.

  • Jagtap, S. T., Jagdale, K. C., & Thakar, C. M. (2023). Identification of plant disease device using artificial intelligence. IN Patent 391523-001. –

  • Pratiksha Bhise, D. S. J., & Jagtap, S. T. (2024). AI-driven emergency response system for women’s safety using real-time location and heart rate monitoring. IJRPR. –

  • Keskar,  A., Jagtap, S. T., et al. (2021). Big data preprocessing frameworks: Tools and techniques. Design Engineering, 1738–1746.

Mr. Soumyapriya Goswami | Industrial Internet of Things | Best Researcher Award

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Mr. Soumyapriya Goswami | Industrial Internet of Things | Best Researcher Award 

IT Researcher, Kalyani Government Engineering College, West Bengal

Mr. Soumyapriya Goswami is a dedicated B.Tech IT researcher at Kalyani Government Engineering College with a strong academic foundation and practical experience in emerging technologies, including Artificial Intelligence, Internet of Things (IoT), Wireless Sensor Networks, edge computing, reinforcement learning, and quantum security for medical devices. His education reflects consistent academic excellence, having completed his secondary and higher secondary studies at Asansol Ramakrishna Mission High School and Dhadka NCLahiri Vidyamandir, followed by his ongoing B.Tech IT program at Kalyani Government Engineering College. Professionally, Soumyapriya has developed expertise in AI/ML model deployment, prompt engineering for generative AI, cloud-based solutions, project management, and team leadership, with proficiency in programming languages (Python, C, C++, Java), AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn), and cloud platforms (Google Cloud, Docker, Jenkins). His research interests encompass energy-efficient scheduling for WSNs, reinforcement learning-based threat detection for IoT devices, quantum-aware security protocols for medical devices, digital twins, and cyber-physical systems.  In conclusion, Mr. Soumyapriya Goswami demonstrates strong potential to bridge academic research with industry applications, delivering innovative solutions in AI, IoT, and quantum technologies, while contributing to knowledge dissemination, mentorship, and technological advancement in emerging research domains, positioning him as a promising early-career researcher with impactful scholarly and practical contributions.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

  1. Goswami, S. (Published). NashDQNSleep: Energy-efficient sleep scheduling for WSN using Nash Equilibrium and Deep Q-Networks. Elsevier EAAI. Citation count: unavailable

  2. Goswami, S. (Under Review). Polaris: Optimized power-aware GPU scheduling framework for cloud environments. IEEE TPDS. Citation count: unavailable

  3. Goswami, S. (Under Review). Qure: Quantum-aware protocols for medical device security using entanglement and root-of-trust designs. IEEE Cybernatics. Citation count: unavailable

  4. Goswami, S. (Ongoing). TinySurvive: Reinforcement Learning-based threat intelligence model for low-power IoT devices in hazardous environments. Citation count: unavailable