Sourav Kumar Purohit | Computer Science | Best Researcher Award

Published on by International Popular Scientist Awards

Best Researcher Award

Sourav Kumar Purohit

SRM University

    Sourav Kumar Purohit
Affiliation SRM University
Country India
Scopus ID 57264226200
Documents 7
Citations 131
h-index 4
Subject Area Computer Science
Event International Popular Scientist Awards
ORCID 0000-0002-3343-6818

Sourav Kumar Purohit is affiliated with SRM University, India, and has established a scholarly profile in the field of Computer Science. His indexed research contributions demonstrate sustained academic activity, interdisciplinary collaboration, and measurable citation impact. Based on the available bibliometric indicators, the researcher has published peer-reviewed scientific works indexed in Scopus while contributing to research areas involving computational methodologies and intelligent systems.[1]

Abstract

This article presents an academic overview of Sourav Kumar Purohit in relation to the Best Researcher Award. The profile summarizes institutional affiliation, publication performance, citation metrics, and scholarly impact within Computer Science. The available bibliometric indicators demonstrate active participation in peer-reviewed research and continued dissemination of scientific knowledge through internationally indexed publications.[1]

Keywords

Best Researcher Award; Sourav Kumar Purohit; SRM University; Computer Science; Artificial Intelligence; Machine Learning; Air Quality Prediction; Scopus Author; Citation Analysis; Research Excellence.

Introduction

Recognition through academic awards is commonly based upon sustained research productivity, publication quality, scholarly influence, and contributions to scientific advancement. Bibliometric indicators such as indexed publications, citation counts, and the h-index provide objective evidence supporting research evaluation. These measures complement qualitative assessments of innovation, collaboration, and scientific significance.[2]

Research Profile

  • Researcher: Sourav Kumar Purohit
  • Institution: SRM University
  • Country: India
  • Primary Subject Area: Computer Science
  • Scopus Author ID: 57264226200
  • Indexed Documents: 7
  • Total Citations: 131
  • h-index: 4

Research Contributions

The research portfolio reflects contributions to computational intelligence, predictive analytics, and data-driven modeling. Published work includes studies involving artificial intelligence, ensemble learning techniques, and environmental data analysis, demonstrating interdisciplinary application of computer science methodologies. The research contributes to scientific understanding while supporting technological innovation and evidence-based decision making.[3]

Publications

  • Accurate Air Quality Index Prediction Using Variational Mode Decomposition and Stacked Ensemble Learning. Book Chapter.
  • A Novel Series-Parallel Hybrid Method Employing Fusion of Deep Features for Air Quality Index Forecasting. Peer-reviewed research publication contributing to intelligent environmental prediction methodologies.

Research Impact

The available bibliometric indicators indicate measurable scholarly visibility with seven indexed publications, 131 citations, and an h-index of four. Citation performance suggests that the published research has been referenced by subsequent scholarly work, reflecting continuing academic relevance within the research community.[1]

Award Suitability

Based on the documented publication record, citation metrics, indexed research output, and contributions to Computer Science, Sourav Kumar Purohit demonstrates characteristics consistent with consideration for the Best Researcher Award. The profile reflects sustained scholarly engagement, peer-reviewed dissemination, interdisciplinary collaboration, and measurable research influence according to internationally recognized bibliometric indicators.[2]

Conclusion

The academic record presented illustrates continued contributions to Computer Science through peer-reviewed publications, citation impact, and interdisciplinary research activities. Objective research metrics and scholarly productivity collectively support recognition within academic award programs emphasizing excellence, innovation, and scientific contribution.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Sourav Kumar Purohit, Author ID 57264226200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57264226200
  2. International Popular Scientist Awards. (n.d.). Best Researcher Award.
    https://popularscientist.com/
  3. Pradhan, S. S., Dora, K. K., & Purohit, S. K. (2026). Accurate Air Quality Index Prediction Using Variational Mode Decomposition and Stacked Ensemble Learning.
    DOI: https://doi.org/10.1007/978-3-032-16830-6_16
  4. Pradhan, S. S., Dora, K. K., & Purohit, S. K. (2026). Accurate air quality index prediction using variational mode decomposition and stacked ensemble learning.

Nael Radwan | Computer Science | Research Excellence Award

Published on by International Popular Scientist Awards

Dr. Nael Radwan | Computer Science | Research Excellence Award

Dr. Nael Radwan is a computer science researcher specializing in Internet of Things, network security, and computer networks, with strong expertise in protocol optimization and distributed systems. His research focuses on securing IoT environments through adaptive flow control, authentication mechanisms, and performance evaluation under high-load conditions. He has contributed multiple peer-reviewed publications addressing MQTT protocol security and system resilience. His academic experience includes teaching, curriculum design, and student mentoring across diverse computing disciplines. He integrates research with teaching, emphasizing outcomes-based education, instructional technology, and ethical computing, while contributing to academic assessment, program development, and innovation in technology-enhanced learning environments.

Citation Metrics (Google Scholar)

1200

1000

800

600

400

200

0

Citations
1111

h-index
22

🟦 Citations    🟥 i10-index    🟩 h-index


View Google Scholar Profile

Featured Publications


A Study: The Future of the Internet of Things and Its Home Applications

– International Journal of Computer Science and Information Security


Big Data Ethics

– International Journal of Computer Science and Information Security


MQTT in Focus: Understanding the Protocol and Its Recent Advancements

– International Journal of Computer Science and Security


Underwater Communication through Medium Access Control

– International Journal of Computer Science

 

Ouiem Bchir | Computer Science | Research Excellence Award

Published on by International Popular Scientist Awards

Prof. Ouiem Bchir | Computer Science | Research Excellence Award

Professor | Computer Science Department, King Saud University | Saudi Arabia

Prof. Ouiem Bchir is a distinguished researcher in computer science with expertise in machine learning, deep learning, computer vision, and pattern recognition. Her research focuses on clustering techniques, semi-supervised and unsupervised learning, hyperspectral image analysis, and intelligent systems for healthcare, security, and multimedia applications. She has contributed extensively to advanced methodologies such as autoencoders, convolutional neural networks, and fuzzy clustering models. With a strong publication record, she has achieved an h-index of 12, with 527 citations across 65 documents. Her research approach integrates theoretical innovation with practical applications, significantly advancing intelligent data analysis and decision-making systems.

Citation Metrics (Scopus)

600

450

300

150

0

Citations
527

Documents
65

h-index
12

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View Scopus Profile
   View Orcid Profile
    View Google Scholar Profile

Featured Publications

Hsueh-Chan Lu | Computer Science | Research Excellence Award

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Dr. Hsueh-Chan Lu | Computer Science | Research Excellence Award

Professor | National Cheng Kung University | Taiwan

Dr. Hsueh-Chan Lu is a senior academic and researcher specializing in geomatics, intelligent transportation systems, and spatial information science. His research integrates visual localization, indoor positioning, deep learning, and multi-modal signal adaptation to address challenges in intelligent mobility and location-based services. He has produced high-impact contributions in routing optimization, localization algorithms, bike-sharing systems, and predictive spatial analytics, published in leading international journals and conferences. His scholarly output includes 57 documents, achieving an h-index of 17 with 1,130 citations from 995 citing documents, reflecting strong academic influence and sustained research impact alongside leadership, mentorship, and professional service.

Citation Metrics (Scopus)

1200

900

600

300

0

Citations
1,130

Documents
57

h-index
17

🟦 Citations    🟥 Documents    🟩 h-index


View Scopus Profile
    View Orcid Profile
   View Google Scholar Profile

Featured Publications

Stefanos Nikiforos | Computer Science | Research Excellence Award

Published on by International Popular Scientist Awards

Dr. Stefanos Nikiforos | Computer Science | Research Excellence Award

Ionian University | Greece

Dr. Stefanos Nikiforos is a researcher in informatics and education whose work integrates artificial intelligence, natural language processing, and digital pedagogy to enhance learning environments. His research focuses on intelligent educational technologies, virtual learning communities, behavioral analysis, and the detection of bullying and harmful behaviors through language data, with particular expertise in Greek-language corpora. He has contributed to scholarly journals, conference proceedings, and book chapters, and actively participates in editorial and peer-review activities. His scholarly output includes 15 documents with 104 citations across 83 citing sources and an h-index of 5, reflecting growing academic impact alongside extensive professional experience in academic research, higher education teaching, educational leadership, and collaborative digital learning platform development.

Citation Metrics (Scopus)

120

90

60

30

0

Citations
104

Documents
15

h-index
5

🟦 Citations    🟥 Documents    🟩 h-index


View Scopus Profile
   View Orcid Profile
    View Google Scholar Profile

Featured Publications

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

Published on by International Popular Scientist Awards

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.

Nooshin Nemati | Computer Science | Best Researcher Award

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Ms. Nooshin Nemati | Computer Science | Best Researcher Award

Ankara University, Turkey

Dr. Nooshin Nemati is a dedicated researcher in the fields of Artificial Intelligence, Deep Learning, and Medical Image Processing, currently pursuing her PhD in Computer Engineering at Ankara University, where she also contributes to multiple AI-based cancer detection projects. She holds a Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University and a Bachelor’s from Qazvin Azad University.

Profile:

Educational Background:

Nooshin Nemati is currently a PhD candidate in Computer Engineering at Ankara University. She earned her Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University with a completed her undergraduate studies at Qazvin Azad University in Iran.

Research Areas:

Her main research interests lie in Artificial Intelligence, Deep Learning, Medical Image Processing, and Computer Vision, particularly applied to cancer detection in histopathology images. She focuses on segmentation, classification, and detection tasks using advanced deep learning frameworks.

Projects and Contributions:

She has actively contributed to significant research initiatives such as the TUBITAK 1001 Project, focused on deep learning methodologies for breast cancer detection, and the BAP Project, which deals with cancer region detection in histopathology images. She has also been involved in the development of important datasets such as NuSeC and MiDeSeC, aimed at supporting machine learning in medical imaging. In addition, she has applied her technical skills in software development projects including system analysis and automation tools for banks.

Technical Skills:

Nooshin is proficient in AI, Machine Learning, Deep Learning, and programming frameworks such as ASP.NET and WordPress. She also holds certifications like Network+ and CCNA, showcasing her broad technical competence.

Citation Metrics:

  • Total Citations: 75

  • Citations Since 2020: 71

  • h-index: 6

  • h-index Since 2020: 5

  • i10-index: 3

  • i10-index Since 2020: 3

Publication Top Notes:

  • An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images
    2023
    Citations: 22

  • Detection of colorectal cancer with vision transformers
    2022
    Citations: 11

  • Effect of color normalization on nuclei segmentation problem in H&E stained histopathology images
    2022
    Citations: 10

  • A hybridized deep learning methodology for mitosis detection and classification from histopathology images
    2023
    Citations: 8

  • CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images
    2024
    Citations: 7

 

Belal Hamed | Computer Science | Best Researcher Award

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Dr. Belal Hamed | Computer Science | Best Researcher Award

Assistant Lecturer at Department of Computer Science, Faculty of Science, Minia University, Egypt

Belal Ahmed Mohammed Hamed is an Assistant Lecturer at the Department of Computer Science, Faculty of Science, Minia University, and at the Department of Artificial Intelligence, Minia National University, Egypt. He holds a master’s degree in Computer Science, with research expertise in bioinformatics, machine learning, and graph-based disease prediction models. His work focuses on developing advanced algorithms for pattern recognition in DNA sequences and medical data analysis. He has published in Scopus and SCI-indexed journals, and contributed to six research projects, including two funded ones. He also serves as a reviewer for journals such as Scientific Reports and The Journal of Supercomputing. His notable contributions include a high-accuracy Graph Convolutional Network model for Alzheimer’s gene prediction.

Profile:

Academic Background:

Belal holds a Master’s degree in Computer Science. His academic training and research work are rooted in computer science, with a focus on interdisciplinary applications in healthcare and genomics.

Research Areas:

  • Bioinformatics

  • Machine Learning

  • SNP-based Disease Prediction

  • Graph Neural Networks

  • DNA Pattern Matching Algorithms

Research Contributions:

Belal developed a deep learning model that integrates SNP data and Graph Convolutional Networks (GCNs) to predict gene-disease associations, specifically in Alzheimer’s disease. The model achieved 98.04% accuracy and AUROC of 0.996, identifying both known and novel genes. His framework is adaptable for use in other diseases, supporting personalized medicine and clinical research.

Publications & Impact:

  • 4 research papers in SCI/Scopus-indexed journals (Springer Nature, Wiley)

  • Google Scholar Citations: 73

  • h-index: 3

Research & Projects:

  • Participated in 6 research projects, including 2 funded

  • Contributed to 1 industry-academic collaboration in medical data analysis

Editorial Roles:

  • Reviewer for The Journal of Supercomputing, Scientific Reports, and Medical Data Mining Journal

  • Young Scientist – Medical Data Mining Journal

Collaborations:

Active in interdisciplinary research teams, particularly in genomics and artificial intelligence.

Publication Top Notes:

Xiang Ma | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by International Popular Scientist Awards

Mr. Xiang Ma | Computer Science and Artificial Intelligence | Best Researcher Award

Postgraduate sichuan unviersity China

📖 Xiang Ma is a student at Sichuan University specializing in Electronic Information and Control Engineering. His research focuses on developing innovative solutions for image super-resolution reconstruction in construction site scenarios. By leveraging computer vision, machine learning, and engineering principles, Xiang’s work aims to improve image quality, safety, and monitoring efficiency in real-world construction environments.

Profile

Orcid

Education

🎓 Xiang Ma is pursuing a degree in Electronic Information and Control Engineering at Sichuan University. With a strong academic foundation, he integrates principles of electronic systems, computer vision, and machine learning in his research.

Experience

🔧 Xiang Ma has contributed to three completed and ongoing research projects, including collaborations with CSCEC First Bureau Technology R&D Program and the Sichuan Province Major Special Project on Intelligent Manufacturing and Robotics. His work bridges academic research with industrial applications in safety and automation technologies for construction sites.

Research Interest

🔍 Xiang Ma is passionate about Image Super-Resolution Reconstruction, with a focus on enhancing low-resolution images affected by noise in construction scenarios. His research includes proposing the Lightweight Feature Enhancement Network (LFEN) to improve visual perception, edge detection, and noise immunity using advanced machine learning techniques.

Awards

🏆 Xiang Ma is applying for the Best Researcher Award for his contributions to image processing technologies in construction scenarios. His work has been recognized for its innovative approach to leveraging lightweight network designs for practical applications.

Publications Top Notes: 

📚 Xiang Ma has published three research papers in prestigious journals:

Liu, Y., Ma, X. & Cheng, J. (2024). Lightweight Feature Enhancement Network for Image Super-Resolution Reconstruction at Construction Sites. Arab Journal of Science and Engineering. Published Year: 2024. Cited by: 15 articles.