Shiladitya Bhattacharjee | Cloud Computing | Best Researcher Award

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Dr. Shiladitya Bhattacharjee | Cloud Computing | Best Researcher Award

Associate Professor | University of Petroleum and Energy Studies, Dehradun | India

Dr. Shiladitya Bhattacharjee is a dynamic researcher and academic in computer and information sciences with strong contributions to cloud computing, big data security, parallel processing, GPU-accelerated systems, and intelligent data management. His work integrates advanced computational techniques with secure, scalable architectures to address complex challenges in big data transmission, cloud resource provisioning, virtual machine migration, and heterogeneous network environments. He has produced impactful research outcomes through funded projects, multidisciplinary collaborations, and the development of innovative methods for data confidentiality, integrity, compression, and encryption. His research has resulted in numerous high-quality journal publications, book chapters, conference papers, patents, copyrights, and intellectual properties that highlight his commitment to technological advancement. His academic portfolio includes authorship of a notable book on high performance cloud computing and the submission of a second book on confidential computing. He has demonstrated long-standing expertise in programming and high-performance computing, contributing to areas such as seismic data processing, intelligent optimization algorithms, video compression security, quantum inspired computation, AI-powered sensing systems, and digital forensics. His professional experience spans teaching a diverse range of computing subjects, mentoring researchers, enhancing institutional research productivity, and driving innovation through technical leadership. As a recognized member of global professional bodies and an active reviewer for reputable journals, he consistently engages with the international research community. His work continues to support advancements in secure data processing, computational efficiency, and emerging intelligent systems, establishing him as a significant contributor to modern computing research.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Bhattacharjee, S., Chakkaravarthy, M., & Midhun Chakkaravarthy, D. (2019). GPU-based integrated security system for minimizing data loss in big data transmission. In Advances in Intelligent Systems and Computing (Chapter).

Bhattacharjee, S., Ab Rahim, L. B., Zakaria, M. N. B., & Ab Aziz, I. B. (2018). A hybrid technique for enhancing data integrity in big data transmission environment. In 2018 4th International Conference on Computer and Information Sciences (ICCOINS). IEEE.

Bhattacharjee, S., Ab Rahim, L. B., Zakaria, M. N. B., & Ab Aziz, I. B. (2018). A protocol for selecting the strongest and authentic hotspot in any wireless infrastructure. In 2018 4th International Conference on Computer and Information Sciences (ICCOINS). IEEE.

Bhattacharjee, S., Ab Rahim, L. B., & Ab Aziz, I. B. (2016). A security scheme to minimize information loss during big data transmission over the internet. In 2016 3rd International Conference on Computer and Information Sciences (ICCOINS). IEEE.

Bhattacharjee, S., Ab Rahim, L. B., & Ab Aziz, I. B. (2016). Hiding of compressed bit stream into audio file to enhance the confidentiality and portability of a data transmission system. In 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC). IEEE.

Jun Tang | Computer Science | Best Researcher Award

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Mr. Jun Tang | Computer Science | Best Researcher Award

AI Algorithm Researcher | Chengdu Zhihui Heneng City Technology | China

Mr. Jun Tang is a researcher specializing in intelligent transportation and autonomous driving, with a strong focus on the integration of computer vision and artificial intelligence to enhance vehicular perception and decision making systems. His research primarily explores large vision foundation models and their applications in object detection, scene understanding, and adaptive driving environments. He has contributed to developing advanced detection frameworks that leverage reinforcement learning to improve recognition accuracy, robustness, and real time responsiveness in dynamic traffic conditions. Mr. Tang’s recent interests include prompt-guided object detection methods that utilize natural language and contextual cues to refine visual understanding within autonomous systems. Through his work at Chengdu Zhihui Heneng City Technology, he plays a key role in bridging the gap between theoretical AI models and practical intelligent mobility applications, fostering innovations that advance the safety, efficiency, and scalability of next generation transportation systems. His interdisciplinary approach combines deep learning, machine perception, and cognitive automation, contributing to the development of more adaptive and human like decision making in autonomous vehicles.

Profile: Orcid

Featured Publications

Tang, J., Li, D., Yang, J., Chen, J., & Yuan, R. (2025). Leveraging large visual models for enhanced object detection: An improved SAM-YOLOv5 model. Knowledge-Based Systems, 114757.

Tang, J. (2025, August 29). RT-DETR-based intelligent transportation object detection optimization method and system with prompt mechanism fusion.

Tang, J. (2025, May 27). Object detection method and system based on prompt engineering and regional text description.

Tang, J. (2025, April 11). Quantitative evaluation method and system for multimodal large models.

Tang, J. (2025, January 17). Evaluation method and system for urban governance multimodal large models based on text labeling.

Jinglin Li | Computer Science | Best Researcher Award

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Mr. Jinglin Li | Computer Science | Best Researcher Award

Engineer | China National Nuclear Corporation | China

Li Jinglin is a researcher specializing in intelligent systems, reinforcement learning, and energy-efficient technologies for industrial and service applications. He holds advanced degrees in Instrument Science and Technology, Electrical Engineering, and Vehicle Engineering with a focus on new energy systems. His research encompasses the development of intelligent interactive service technologies for elderly care, optimization of energy-harvesting wireless sensor networks, and multi-task scheduling for energy-secured unmanned vehicles. He has led projects on digital twin platform technologies and vertical displacement control of nuclear fusion plasma, applying deep reinforcement learning to enhance system performance and replace traditional control methods. Li has extensive experience in algorithm design, including MATLAB-based reinforcement learning, adaptive dynamic programming, and multi-level exploration deep Q-network scheduling, with applications in optimal microgrid transmission, mobile charging sequence scheduling, and network monitoring. His work has resulted in multiple first-author publications in high-impact journals covering reinforcement learning, wireless sensor networks, and energy management, as well as conference contributions in control and automation. Beyond his technical expertise, he demonstrates strong analytical, problem-solving, and team collaboration skills, with experience in summarizing complex research findings and implementing practical solutions. Li actively engages in academic presentations and has earned recognition for his research achievements. In addition to his research, he maintains leadership roles in university sports teams, reflecting his commitment to teamwork, discipline, and resilience. His professional approach combines a proactive mindset, logical thinking, and a dedication to advancing intelligent and sustainable technological solutions across both industrial and service domains.

Profile: Scopus

Featured Publications

Li, J. (2024). A deep reinforcement learning approach for online mobile charging scheduling with optimal quality of sensing coverage in wireless rechargeable sensor networks. Ad Hoc Networks, 156, 103431.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal sensing coverage in wireless rechargeable sensor networks. Journal of Ambient Intelligence and Humanized Computing, 15(6), 2869–2881.

Li, J. (2023). Mobile charging sequence scheduling for optimal sensing coverage in wireless rechargeable sensor networks. Applied Sciences, 13(5), 2840.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. IEEE Transactions on Network and Service Management.

Li, J. (2024). A swarm deep reinforcement learning based on-demand mobile charging-scheduling and charging-time control joint algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. Expert Systems with Applications.

Sangeeta Sangani | Cloud Computing | Women Researcher Award

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Dr. Sangeeta Sangani | Cloud Computing | Women Researcher Award

Asst.Prof. at Manipal Academy of Higher Education | Karnataka | India

Dr. Sangeeta Sangani is a dedicated academician and researcher with over 17 years of experience in the field of Computer Science and Engineering. She is currently serving as Assistant Professor (Senior Scale) at Manipal Institute of Technology, MAHE, Bengaluru. Previously, she held long-term academic roles at KLS Gogte Institute of Technology, S.G. Balekundri Institute of Technology, R.V. College of Engineering, and Basaveshwara Engineering College.

Profile:

Academic Qualifications:

Dr. Sangani holds a Master of Technology (M.Tech) degree in Computer Science & Engineering from Basaveshwar Engineering College, Bagalkot under VTU Belagavi. She has also completed her Bachelor of Engineering (B.E) from Hirasugar Institute of Technology, Nidasoshi. She has submitted her Ph.D. thesis under VTU Belagavi and is awaiting the defense viva.

Research & Publications:

Dr. Sangani has a strong research background with numerous publications in Scopus, Web of Science, and Q2-indexed journals. Her research areas include machine learning, IoT security, fake news detection, cloud computing, and healthcare AI applications. Notable publications include works on ensemble learning for IoT security, emotion analysis in music, and workflow scheduling in edge-cloud platforms. She has also authored IEEE and Springer-indexed papers and contributed significantly to applied computing domains.

Patents:

She has filed two patents:

  • Santulan – A car safety device (202041053586, 2020)

  • AI-based Pregnancy Health Monitoring System (2021107441, 2021)

Technical Skills:

Dr. Sangani is proficient in a wide array of technologies and tools such as Python, R, Power BI, Tableau, SQL, ReactJS, NodeJS, MongoDB, and more. She also conducts technical training in Software Testing, Full Stack Web Development, and Python Programming.

Workshops & Events Organized:

She has organized several technical workshops including topics like Cyber Security, Django Application Development, and Deep Learning research avenues. She has also coordinated institutional-level events such as Avalanche 2016 and Aura 2018.

Academic Roles & Leadership:

She has served as a member of the Research Committee, IQAC coordinator, NBA committee member, and TCS Tech Bytes Event Coordinator. She has also taken on duties for academic invigilation and examination coordination with VTU and KLECET.

Soft Skills:

Dr. Sangani is recognized for her strong problem-solving skills, diplomatic interpersonal abilities, leadership qualities, and commitment to teamwork and professional ethics.

Publication Top Notes:

  1. A feature-level ensemble machine learning approach for attack detection in IoT networks

  2. Efficient algorithm for error optimization and resource prediction to mitigate cost and energy consumption in a cloud environment

  3. Reliable and efficient webserver management for task scheduling in edge-cloud platform

  4. Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment

  5. Diagnosis of melanomas by check-list method