Tag: Computer Science

Deepali Gupta, Computer Science, Best Innovator Award

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Prof. Dr. Deepali Gupta: Professor at Chitkara University Institute of Engineering and Technology, Chitkara University, India

Title/Designation: Prof. Dr.
Name: Deepali Gupta
Current Role/Designation: Professor
Organization/Institution Details: Chitkara University Institute of Engineering and Technology, Chitkara University
Country: India
Subject Track: Computer Science
Key Areas of Expertise: AI, IoT, Cloud Computing
Award Categories: Best Innovator Award — Submitted: Oct 1, 2025

Dr. Deepali Gupta is a highly respected academician, researcher, and technology leader in the field of Computer Science and Engineering with over 22 years of experience spanning teaching, research, and academic administration. She currently serves as a Professor – Research at Chitkara University Research and Innovation Network (CURIN), where she leads several cutting-edge research initiatives across domains like artificial intelligence, healthcare, and smart systems. Dr. Gupta is known for her contributions to interdisciplinary research, her active involvement in national and international conferences, and her mentorship of Ph.D. scholars and postgraduate students. Her commitment to academic excellence and innovation has earned her several awards, prestigious memberships, and editorial positions across reputed journals and institutions.

Online Profiles

Google Scholar Profile

Dr. Deepali Gupta has made a significant impact in the research community, as reflected by her Google Scholar metrics. She has amassed a total of 3,192 citations, with 3,113 of those since 2020, demonstrating the continued relevance and influence of her recent work. She holds an h-index of 33, indicating a strong foundation of widely cited publications, and an i10-index of 78, with 76 publications having received at least ten citations since 2020. These figures underline her consistent scholarly output and growing recognition in the fields of artificial intelligence, software engineering, and smart technologies.

Education

Dr. Deepali Gupta holds a Ph.D. in Computer Science and Engineering from Kurukshetra University, awarded in 2015 for her contributions to emerging computational technologies. Prior to that, she earned her M.Tech in Computer Science from Punjab Technical University (PTU), Jalandhar in 2008, graduating with first-class honors and a distinction (70.2%). She completed her B.Tech in Information Technology from Maharishi Dayanand University, Rohtak in 2003 with a strong academic record (69.84%). Her foundational education under the CBSE board in Delhi included a first-class performance at both 10+2 (65.4%) and matriculation (72.2%) levels, reflecting a consistent academic track record from an early stage.

Research Focus

Dr. Gupta’s research interests lie at the intersection of artificial intelligence and real-world applications, particularly in areas such as machine learning, deep learning, genetic algorithms, software engineering, blockchain, cloud computing, and the Internet of Things (IoT). Her recent work focuses on AI-based healthcare diagnostics, smart agriculture using drones, and educational technology for underserved communities. She has led multiple interdisciplinary research teams and has successfully aligned her research goals with societal needs, submitting high-value project proposals to agencies like SERB, DST, and CSIR. Through her research, she aims to bridge the gap between technological advancement and social impact by developing scalable, intelligent systems for healthcare, education, and rural development.

Experience

Over her two-decade-long career, Dr. Gupta has held numerous academic and leadership roles. She is currently Professor – Research at CURIN, Chitkara University since 2019, where she oversees innovation-led research, project development, and Ph.D. mentoring. She previously served as Principal at MMGI and Professor & Head (CSE) at MMU Sadopur, leading academic reforms and strategic initiatives. At GIMT Kurukshetra, she played multiple roles from Assistant Professor to Dean Academics, heading the Computer Science and IT departments. Early in her career, she contributed to institutions like SKIET, Swami Devi Dayal Institute, and Doon Valley Institute. Her vast experience spans curriculum design, faculty mentoring, accreditation processes, and industry collaboration, making her a versatile and impactful academic leader.

Research Timeline & Activities

Dr. Gupta’s research journey is marked by a progressive trajectory of idea conceptualization, project submission, execution, and academic dissemination. Since 2019, she has submitted over nine high-value research proposals to prominent funding bodies including SERB, DST-SEED, CSIR, and the Spencer Foundation, with a cumulative proposed budget exceeding ₹3 crore. These proposals range from AI-driven healthcare diagnostics (e.g., diabetic retinopathy detection, skin disease analysis) to educational technology and smart agriculture using drones. She has also taken lead roles in ongoing consultancy projects with industry partners, bridging academia and real-world needs. Additionally, she regularly chairs technical sessions, mentors researchers, and serves as a reviewer for international journals, contributing holistically to the academic ecosystem.

Awards & Honors

Throughout her career, Dr. Deepali Gupta has been recognized for her excellence in teaching, research, and institutional leadership. She has been honored with the Research Excellence Award by Chitkara University for her contributions in publications and patents, and was the Best Teacher and Best Employee at GIMT. She has received over 25 appreciation awards for organizing technical festivals, workshops, and national events such as Engineers’ Day and Annual Sports Meets. In 2021, she received a Certificate of Appreciation from the Institution of Engineers (India), Haryana for her technical service during the COVID-19 pandemic. Her contributions extend beyond academics into community engagement, faculty development, and capacity building, making her a role model for young educators and researchers alike.

Top Noted Publication

Among Dr. Gupta’s extensive list of over 60 high-impact publications, one of her most notable works is titled “Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification”, published in Sustainability (2022), an SCIE-indexed journal with an impact factor of 3.889. This work presents a novel deep learning-based model for biometric verification and is frequently cited in related AI literature. Other top publications include her 2022 paper in Diagnostics on Alzheimer’s disease detection using hybrid AI models (IF 3.992), and a deep learning model for chest X-ray image analysis published in Frontiers in Oncology (IF 5.738). These works not only reflect her research depth but also her ability to address real-world challenges through innovative computational approaches.

  • Efficient automated disease diagnosis using machine learning models
    N Kumar, N Narayan Das, D Gupta, K Gupta, J Bindra
    Journal of Healthcare Engineering, 2021
    Cited by: 183

  • A systematic review on extreme programming
    A Shrivastava, I Jaggi, N Katoch, D Gupta, S Gupta
    Journal of Physics: Conference Series, 2021
    Cited by: 125

  • Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images
    N Kumar, M Gupta, D Gupta, S Tiwari
    Journal of Ambient Intelligence and Humanized Computing, 2023
    Cited by: 112

  • Using identity‐based cryptography as a foundation for an effective and secure cloud model for e‐health
    S Mittal, A Bansal, D Gupta, S Juneja, H Turabieh, MM Elarabawy, …
    Computational Intelligence and Neuroscience, 2022
    Cited by: 109

  • Weighted average ensemble deep learning model for stratification of brain tumor in MRI images
    V Anand, S Gupta, D Gupta, Y Gulzar, Q Xin, S Juneja, A Shah, A Shaikh
    Diagnostics, 2023
    Cited by: 91

Nikita, Computer Science, Best Innovator Award

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Miss. Nikita: Research Scholar at Sardar Vallabhbhai National Institute of Technology, Surat, India

Nikita is a passionate and innovative Research Scholar at the National Institute of Technology Surat, specializing in Natural Language Processing (NLP) and Natural Language Understanding (NLU). Her work primarily revolves around developing advanced machine learning and deep learning models, with a keen focus on applying these techniques to domain-specific unstructured text documents, especially within the legal field. Nikita’s research aims to bridge the gap between complex textual data and actionable insights by creating intelligent systems that improve information retrieval, summarization, and recommendation for specialized domains.

Online Profiles

Google Scholar Profile

Since 2020, Nikita’s research has garnered 20 citations, with 18 citations coming in the recent years, reflecting growing recognition in the field. She currently holds an h-index of 3, indicating her publications have consistently contributed to advancing knowledge in natural language processing and related domains.

Education

Nikita is currently pursuing her PhD in Natural Language Processing at the National Institute of Technology Surat, where she has been engaged since 2021. She earned her Master of Technology (M.Tech) degree in Computer Science and Engineering from the National Institute of Technology Durgapur in 2018, where she specialized in network optimization. Her undergraduate degree, Bachelor of Technology (B.Tech) in Computer Science and Engineering, was awarded by BPUT, Odisha in 2015. Nikita also completed her schooling with strong academic performance, laying the foundation for her technical and research career.

Research Focus

Nikita’s research focuses on harnessing the power of Large Language Models (LLMs) like GPT and BERT to tackle challenges in domain-specific text processing. She is actively working on semantic structuring of unstructured documents, developing few-shot and zero-shot prompting techniques, and building robust information retrieval systems tailored to legal and technical domains. Her projects involve creating annotated corpora for semantic tasks, designing chatbots for legal assistance, and innovating recommendation systems that improve user query understanding through keyphrase extraction and thematic classification.

Experience

Nikita’s professional journey includes her current role as a Senior Research Fellow at the National Institute of Technology Surat, which she assumed in August 2023. Prior to this, she worked as a Junior Research Fellow at the same institute from September 2021 to July 2023, where she contributed extensively to her PhD research projects. She also has significant teaching experience, having served as an Assistant Professor at Presidency University, Bengaluru, and earlier at Smt. S. R. Patel Engineering College, Ahmedabad, where she taught computer science courses and mentored students in research projects.

Research Timeline & Activities

Starting in 2017, Nikita’s research trajectory has spanned from traffic grooming in hybrid optical and wireless networks during her M.Tech studies to sophisticated NLP-based projects in her doctoral work. Since 2021, she has been developing LAWBOT, a legal assistant chatbot, pioneering research on recommendation systems based on document keyphrases, and curating semantic theme-based annotated corpora. Her recent activities include leveraging few-shot prompting methods to improve intent-based information retrieval systems, contributing to both theoretical frameworks and practical applications.

Awards & Honors

Nikita has been honored with the MHRD Fellowship for her doctoral studies (2021-2026) and previously for her master’s degree (2016-2018), recognizing her academic excellence and research potential. She qualified the Graduate Aptitude Test in Engineering (GATE) in 2015, which is a testament to her technical proficiency. Additionally, she has earned certifications from prestigious platforms such as NPTEL and Coursera in areas including Machine Learning, Soft Computing, Deep Learning Specialization, and Data Science, reflecting her commitment to continuous learning and expertise development.

Top Noted Publication

One of Nikita’s significant publications is “Layout features and semantic similarity-based hybrid approach for thematic classification of paragraphs in documents,” published in Knowledge and Information Systems in 2025. This work introduces a novel hybrid methodology that combines document layout features with semantic similarity measures to effectively classify thematic content within paragraphs. It offers valuable advancements for automated document analysis, especially relevant for legal and technical documents where structure and semantics play a crucial role.

  • Congestion aware traffic grooming in elastic optical and WiMAX network
    D Naik, T De, Nikita
    2018 Technologies for Smart-City Energy Security and Power (ICSESP), pp. 1-9 — 8 citations (2018)

  • Normalized uplink bandwidth scheduling algorithm for WiMAX networks
    D Naik, S Dora, Nikita, T De
    Advances in Computer, Communication and Control: Proceedings of ETES 2018 — 5 citations (2019)

  • Research challenges for legal document summarization
    DP Rana, RG Mehta, Nikita
    2023 IEEE World Conference on Applied Intelligence and Computing (AIC), pp. 307-312 — 3 citations (2023)

  • LAWBOT: A Smart User Indian Legal Chatbot using Machine Learning Framework
    E Srivastav, A Patel, A Singh, R Sharma, DP Rana, RG Mehta, Nikita
    2024 IEEE 9th International Conference for Convergence in Technology (I2CT), pp. 1-7 — 2 citations (2024)

  • Traffic aggregation in elastic optical and wireless networks
    D Naik, Nikita, A Bauri, T De
    International Conference on Communication, Devices and Computing, pp. 55-70 — 2 citations (2019)

Strengths for the Best Innovator Award

  1. Pioneering Research in Domain-Specific NLP Applications
    Nikita has developed innovative NLP systems specifically tailored for complex and specialized domains such as legal text processing. Her work on LAWBOT, a legal assistant chatbot using machine learning, exemplifies her ability to create cutting-edge solutions that bridge academic research and real-world applications.

  2. Advanced Use of Large Language Models (LLMs) and Few-Shot Prompting
    Leveraging state-of-the-art transformer models like GPT-4 and BERT, Nikita explores novel techniques such as few-shot and zero-shot prompting to enhance semantic understanding and information retrieval from unstructured texts. This positions her at the forefront of AI innovation.

  3. Interdisciplinary Expertise Combining NLP and Network Optimization
    Her unique background spanning both network communication (elastic optical and WiMAX networks) and natural language processing allows her to approach problems with a broad, interdisciplinary perspective—enabling the design of innovative, resource-efficient algorithms and systems.

  4. Contributions to Research Infrastructure through Annotated Corpora
    By curating semantic theme-based annotated corpora for domain-specific tasks, Nikita significantly contributes to the research community’s ability to train and benchmark intelligent systems, fostering further innovation in semantic structuring and text processing.

  5. Strong Research Impact with Growing Recognition
    With a total of 20 citations since 2020 and an h-index of 3, Nikita’s research output has consistently contributed to advancing knowledge in NLP and related fields. Her multiple well-cited publications and prestigious fellowships, including the MHRD fellowship, underscore her academic excellence and innovative contributions.

Gubba Balakrishna, Computer Science, Sustainable Technology Award

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Dr. Gubba Balakrishna: Assistant professor at Anurag University, India

Dr. Gubba Balakrishna is a seasoned academician and researcher with over 15 years of experience in teaching, research, and consultancy. He currently serves as an Associate Professor at Anurag University, where he is deeply involved in advancing the academic and research capabilities of the Computer Science and Engineering department. Dr. Balakrishna specializes in Internet of Things (IoT), Data Analytics, Machine Learning, and their applications in agriculture. His academic journey is complemented by a robust research portfolio that bridges the gap between technology and real-world agricultural challenges, focusing on developing cloud-based frameworks and optimizing agricultural processes. With a passion for research and mentoring, he has guided numerous undergraduate and postgraduate students, fostering a culture of academic excellence. He is also highly involved in organizing and reviewing academic conferences and journals.

Online Profiles

  • Citations: 212 (Since 2020), 210 (Total)

  • h-index: 7

  • i10-index: 7

These metrics highlight Dr. Balakrishna’s influence in the academic community, with over 200 citations since 2020. His h-index of 7 demonstrates a consistent contribution to the field, while his i10-index indicates that several of his papers have been highly cited.

Education

  • Ph.D., Computer Science and Engineering (2021)
    Dissertation: Agriculture as a Service (AAAS): An ESBL cloud service model for IoT-supported agriculture data analytics
    KL University, Guntur.
    Dr. Balakrishna’s doctoral research aimed to enhance agricultural practices by integrating cloud-based services with IoT frameworks, optimizing data analytics, and improving the efficiency of agricultural operations.

  • M.Tech., Computer Science and Engineering (2011)
    Aurora Research Scientific Technology, JNTU, Hyderabad.
    His M.Tech. thesis explored advanced computational methods in data science and their applications in real-time systems.

  • B.Tech., Computer Science and Engineering (2007)
    Sree Visvesvaraya Institute of Technology and Sciences, JNTU, Hyderabad.
    A solid foundation in computer science was established during his undergraduate studies, where he was introduced to the basic principles of programming, networks, and algorithms.

Research Focus

Dr. Balakrishna’s research focuses on the intersection of advanced technologies such as the Internet of Things (IoT), Big Data Analytics, Deep Learning, and Agriculture. His work primarily revolves around developing scalable IoT solutions that can improve agricultural practices through data-driven decision-making. This includes the creation of frameworks for cloud-based IoT services for agriculture, energy-efficient data processing, and machine learning models for disease detection in crops. His research also extends to the optimization of IoT device placement, edge computing applications, and natural language processing (NLP) for smart agriculture systems. By leveraging these technologies, his work aims to make agriculture more sustainable, efficient, and data-driven.

Experience

Dr. Balakrishna’s academic career spans over 15 years, during which he has contributed to both teaching and research. As an Associate Professor in the Department of Computer Science and Engineering at Anurag University, he has been instrumental in shaping the department’s curriculum and guiding students through complex research projects. His teaching spans undergraduate and postgraduate courses in subjects such as IoT, Machine Learning, Android Development, Big Data Analytics, and more. In addition to his teaching role, Dr. Balakrishna has also been actively involved in academic administration, curriculum development, and the organization of technical workshops, conferences, and seminars. His leadership extends to mentoring and supervising numerous undergraduate and graduate students, guiding them to achieve successful project outcomes.

Research Timeline & Activities

  • 2021 – Present: Dr. Balakrishna’s current research is focused on advancing the use of IoT in agriculture, particularly in disease detection and crop maintenance. He is developing deep learning models and cloud-based systems for real-time agricultural data analytics.

  • 2019 – 2020: He worked extensively on optimizing cloud services and load balancing for IoT applications. His work in this period led to the design of ESBL, a cloud-integrated framework for IoT load balancing that has implications for agriculture and other IoT-driven sectors.

  • 2017 – 2019: During these years, Dr. Balakrishna researched the integration of edge computing with IoT for optimized data processing in agriculture. This work aimed to reduce latency and improve decision-making processes in real-time applications.

  • 2016 – 2017: Early in his career, Dr. Balakrishna explored the use of machine learning and big data analytics in healthcare and agriculture. His research during this period focused on the applications of data analytics to improve operational efficiencies.

Awards & Honors

  • Best Research Award (2022): Dr. Balakrishna received the Best Research Award from IJIEMR-Elsevier SSRN for his excellence in research contributions, recognizing his innovative work in IoT and agriculture.

  • Reviewer for PeerJ Computer Science and Inderscience: He has been a reviewer for prominent SCIE journals, ensuring the quality and relevance of published research in the field of computer science and engineering.

  • Organizing Committee Member for ICACII-2023: Dr. Balakrishna served as the Co-Convener of the International Conference on Advances in Computational Intelligence and Informatics (ICACII-2023), a Scopus-indexed event that brought together leading researchers and practitioners.

  • Leadership in Academic Outreach: Dr. Balakrishna has organized and coordinated more than 10 Faculty Development Programs (FDPs), working closely with premier institutions like NIT Warangal, IIT Hyderabad, and TCS to enhance academic standards.

Top Noted Publications

  • Moparthi, N.R., Balakrishna, G., Chithaluru, P., Kolla, M., Kumar, M. (2023). An improved energy-efficient cloud-optimized load-balancing for IoT frameworks. Heliyon, 9(11).
    Citations: 39
    This paper presents a novel cloud-optimized load-balancing mechanism designed for IoT frameworks, with a focus on energy efficiency and system scalability.

  • Balakrishna, G., Nageshwara Rao, M. (2019). Study report on using IoT agriculture farm monitoring. Innovations in Computer Science and Engineering: Proceedings of the Sixth International Conference.
    Citations: 38
    This study investigates the use of IoT systems in agricultural farm monitoring, focusing on efficiency, real-time data processing, and the integration of IoT devices for improved farm management.

  • Balakrishna, G., Moparthi, N.R. (2020). Study report on Indian agriculture with IoT. International Journal of Electrical and Computer Engineering, 10(3), 2322.
    Citations: 37
    The publication outlines the potential applications of IoT in the Indian agricultural sector, exploring the benefits and challenges of implementing IoT-based solutions for enhanced agricultural productivity.

  • Balakrishna, G., Moparthi, N.R. (2019). ESBL: Design and Implement A Cloud Integrated Framework for IoT Load Balancing. International Journal of Computers Communications & Control, 14(4), 459-474.
    Citations: 28
    This work introduces the ESBL framework, which integrates cloud services with IoT networks to optimize load balancing and improve system performance in large-scale applications.

  • G. Vishnu Murthy, Swathireddy, M., Balakrishna, G. (2019). Big Data Analytics for Popularity Prediction. Journal of Physics: Conference Series, 1228(1), 012051.
    Citations: 21
    The paper explores the use of big data analytics for predicting the popularity of social media content, demonstrating the application of data science in social media trend forecasting.

  • Balakrishna, G., Murthy, G.V., Nageshwara Rao, M., Narayana, M.V. (2022). Implementing Solar Power Smart Irrigation System. Innovations in Computer Science and Engineering: Proceedings of the Ninth International Conference.
    Citations: 20
    This paper discusses the development of a solar-powered smart irrigation system, focusing on sustainable energy solutions for irrigation in agricultural practices.

Strengths for the Sustainable Technology Award

1. Innovative Cloud-Based IoT Solutions for Agriculture

Dr. Balakrishna’s research has significantly advanced the application of cloud-based IoT solutions in agriculture. His development of the ESBL framework and energy-efficient cloud-optimized load balancing mechanisms has the potential to revolutionize farming practices by improving operational efficiency and reducing energy consumption in IoT systems. These innovations contribute to the sustainability of agricultural practices by optimizing data processing and reducing environmental impacts. His research has already garnered 212 citations (since 2020), highlighting the growing influence of his work in the academic community.

2. Integration of Renewable Energy for Sustainable Irrigation

One of Dr. Balakrishna’s notable contributions is his work on solar-powered smart irrigation systems. By integrating renewable energy sources with IoT-driven systems, his research provides a sustainable solution to the water and energy challenges faced by agriculture. This approach not only enhances water management but also promotes the use of green technologies in farming, making it more eco-friendly and resource-efficient.

3. Use of Big Data and AI for Precision Agriculture

Dr. Balakrishna’s research leverages big data analytics and machine learning to enhance precision agriculture. Through advanced data processing techniques, his work enables farmers to make more informed decisions, optimize resources, and predict crop diseases or failures early. This helps mitigate the environmental impact of farming by reducing waste and promoting sustainable agricultural practices.

4. Edge Computing and Real-Time Data Processing in Agriculture

By incorporating edge computing with IoT networks, Dr. Balakrishna has developed systems that can process agricultural data in real time. This reduces the latency in decision-making, allowing farmers to react quickly to changing environmental conditions and optimize crop management. Real-time data processing improves operational efficiency and reduces the need for resource-intensive infrastructure, contributing to a more sustainable agricultural ecosystem.

5. Educational Outreach and Capacity Building

Dr. Balakrishna’s leadership in organizing faculty development programs and his involvement in academic conferences reflect his dedication to building capacity in sustainable technologies. By collaborating with leading institutions and industries, he has fostered an environment of continuous learning and knowledge transfer, empowering the next generation of researchers and practitioners in sustainable technology fields. His efforts in educating and mentoring students and professionals further the cause of sustainable technological development.

Nirmal Kaur, Computer Science, Best Innovator Award

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Dr. Nirmal Kaur: Associate Professor at UIET, PU, India

Dr. Nirmal Kaur is an Associate Professor in the Department of Computer Science and Engineering at the University Institute of Engineering and Technology (UIET), Panjab University, Chandigarh. With over 20 years of teaching and research experience, Dr. Kaur has made significant contributions to the fields of Machine Learning, Deep Learning, Sentiment Analysis, and Distributed Computing. She has been the Principal Investigator for multiple high-impact research projects, including the development of multimodal emotion detection systems and cybersecurity challenges for women users. In addition to her research, Dr. Kaur has mentored numerous Ph.D. and M.Tech. students, guiding them to success in diverse areas such as deepfake detection, energy-efficient scheduling, and multimodal emotion recognition. Her work is internationally recognized, and she has published extensively in top-tier journals and conferences.

Online Profiles

Citations & Impact Metrics

  • Total Citations: 329

  • h-index: 11

  • i10-index: 11

These metrics reflect Dr. Kaur’s significant contribution to her research field, with numerous citations highlighting the impact and relevance of her work in areas like emotion detection, deepfake identification, and distributed computing.

Education

Dr. Kaur earned her Ph.D. in Computer Science and Engineering from I.K. Gujral Punjab Technical University, Jalandhar, in November 2017. Her doctoral research focused on energy-efficient scheduling techniques in distributed computing systems. She was supervised by Prof. Dr. Savina Bansal, a distinguished researcher in the field. She holds an M.Tech. in Computer Science and Engineering from Baba Banda Singh Bahadur Engineering College, Punjab, where she achieved distinction and was a top scorer in her batch. She completed her B.Tech. in Computer Science and Engineering at Punjabi University, Patiala, in 2003. Throughout her academic career, Dr. Kaur has demonstrated excellence, both in terms of research output and academic achievement.

Research Focus

Dr. Kaur’s research primarily focuses on advanced applications of Machine Learning and Deep Learning, particularly in emotion detection across various modalities, including text, audio, video, and multimodal data. She has made pioneering contributions to the detection of deepfakes in multimedia content, using cutting-edge techniques in computer vision and audio signal processing. Additionally, Dr. Kaur is deeply engaged in distributed computing research, working on energy-efficient scheduling algorithms for heterogeneous systems, cloud computing, and task scheduling. Her work not only advances theoretical frameworks but also focuses on real-world applications, such as improving cybersecurity, enhancing multimedia forensics, and contributing to energy-aware computing systems in large-scale environments.

Experience

Dr. Kaur’s academic career spans over two decades. She is currently serving as an Associate Professor at UIET, Panjab University, since 2024. Prior to this, she was an Assistant Professor at the same institution from 2011 to 2024, where she contributed extensively to teaching and research. Before joining UIET, Dr. Kaur worked as an Assistant Professor at Bhai Gurdas Institute of Engineering and Technology, Sangrur, and Swami Vivekanand Institute of Engineering and Technology. During her career, she has taught various undergraduate and postgraduate courses in Computer Science and Engineering. Her expertise lies in Deep Learning, Distributed Computing, Artificial Intelligence, and Cybersecurity. In addition to her teaching, Dr. Kaur has actively participated in departmental administration and has been a member of several academic committees.

Research Timeline & Activities

  • 2023-2025: Dr. Kaur is leading the “Development of Multimodal and Multilingual Emotion Detection System,” funded by IIT Mandi iHub & HCI Foundation. This project aims to enhance emotion detection systems by incorporating multiple languages and data modalities.

  • 2020-2021: Principal Investigator for a research project sponsored by the National Commission for Women, Govt. of India, focused on cybersecurity challenges and cybercrimes against women in Punjab. The project successfully examined the specific issues faced by women users in cyber space.

  • 2016-Present: Dr. Kaur has been mentoring Ph.D. candidates, guiding their research on topics like deepfake detection, energy-aware scheduling in cloud environments, and human pose estimation.

  • Ongoing: Actively working on deepfake detection algorithms, multimodal emotion recognition, and developing energy-efficient scheduling algorithms for heterogeneous computing environments. Dr. Kaur is also reviewing articles for top-tier journals and contributing to the academic community through invited talks and workshops.

Awards & Honors

Dr. Kaur has been recognized with several prestigious awards throughout her academic career. She was awarded the Best Research Award from the Department of Computer Science and Engineering, UIET, in 2017, in recognition of her outstanding contributions to research. She has also received a Best Paper Award for her work presented at the 4th International Conference on Machine Intelligence and Signal Processing, NIT Raipur, 2022. In addition to these accolades, she was honored with a scholarship by the Punjab Government for her academic performance in matriculation. Dr. Kaur’s research contributions in emotion detection and cybersecurity have earned her recognition both within India and internationally.

Top Noted Publication

Dr. Kaur has authored and co-authored several influential papers, with some of her top publications including:

  • Review of Audio Deepfake Detection Techniques: Issues and Prospects

    • Authors: A. Dixit, N. Kaur, S. Kingra

    • Journal: Expert Systems, 40(8), e13322

    • Cited By: 48

    • Year: 2023

  • Emergence of Deepfakes and Video Tampering Detection Approaches: A Survey

    • Authors: S. Kingra, N. Aggarwal, N. Kaur

    • Journal: Multimedia Tools and Applications

    • Cited By: 35

    • Year: 2022

  • LBPNet: Exploiting Texture Descriptor for Deepfake Detection

    • Authors: S. Kingra, N. Aggarwal, N. Kaur

    • Journal: Forensic Science International: Digital Investigation, 42, 301452

    • Cited By: 32

    • Year: 2022

  • Major Convolutional Neural Networks in Image Classification: A Survey

    • Authors: N. Kumar, N. Kaur, D. Gupta

    • Conference: International Conference on IoT Inclusive Life (ICIIL 2019)

    • Cited By: 25

    • Year: 2020

  • A Survey of Routing Protocols in Wireless Sensor Networks

    • Authors: N. Kaur, S. Verma, D. Kavita

    • Journal: IJET, 7(4.12), 20-25

    • Cited By: 24

    • Year: 2018

Strength for the Best Innovator Award

Dr. Nirmal Kaur’s innovative contributions span several important areas in the fields of Computer Science and Engineering. The following strengths highlight why she is considered an exceptional innovator in her field:

1. Pioneering Work in Deepfake Detection

Dr. Kaur’s innovative research in deepfake detection has led to significant advancements in multimedia forensics. Her approach integrates both audio and video signal processing, helping to address critical issues in identifying manipulated content. By developing cutting-edge techniques for detecting deepfake videos and audio, she has set new standards for accuracy and reliability in this fast-evolving domain.

2. Multimodal Emotion Detection Systems

Dr. Kaur has made groundbreaking strides in the development of multimodal emotion detection systems. By combining various data modalities like text, audio, and video, her research allows for a more comprehensive and nuanced understanding of human emotions. This work has broad applications in areas such as sentiment analysis, human-computer interaction, and mental health monitoring.

3. Energy-Efficient Scheduling in Distributed Computing

Her research on energy-efficient scheduling techniques for distributed computing systems has been transformative. Dr. Kaur’s work focuses on improving the energy consumption of heterogeneous systems, ensuring that computational tasks are optimized for power usage without compromising performance. This has far-reaching implications for cloud computing and large-scale data center management.

4. Cybersecurity Challenges for Women Users

Dr. Kaur’s work on cybersecurity, particularly focusing on the challenges faced by women users in the digital space, has addressed a critical gap in the understanding of gender-specific cybercrimes. By leading the National Commission for Women’s project, she has not only identified key security issues but also proposed actionable solutions to protect vulnerable users from online harassment and cybercrimes.

5. Mentorship and Leadership in Research

Dr. Kaur’s dedication to mentoring and guiding students is another area where she demonstrates her innovation. By supervising numerous Ph.D. and M.Tech. candidates on cutting-edge research topics such as multimodal emotion recognition, deepfake detection, and cloud computing, she has fostered a new generation of innovators. Her leadership in research has helped cultivate a robust academic and research environment that encourages creativity and critical thinking.

Namrata Kumari, Computer Science, Female Researcher Award

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Dr. Namrata Kumari: Assistant Professor at Galgotias University, India

Dr. Namrata Kumari is a dedicated researcher, academic, and innovator in the field of Computer Science and Engineering. She specializes in Natural Language Processing (NLP), Machine Learning, Artificial Intelligence, and Internet of Things (IoT) applications, with a strong track record of publications and patents. She completed her Ph.D. in Computer Science and Engineering at the National Institute of Technology (NIT), Hamirpur in 2024, following her M.Tech. from Banasthali University in 2017 and B.Tech. from A.K.T.U. in 2015. Currently, she is working as an Assistant Professor at Galgotias University, Greater Noida, where she combines teaching, research, and project development. Her contributions span across text summarization, intelligent computing models, IoT-enabled systems, and healthcare informatics, and she is driven by the vision of creating impactful academic and industrial solutions.

Online Profiles

ORCID Profile

Education

Her academic foundation is rooted in a blend of rigorous technical training and a consistent academic record. She earned her Ph.D. in Computer Science and Engineering from NIT Hamirpur in 2024, where her research concentrated on NLP and advanced machine learning methods. She obtained her M.Tech. in CSE from Banasthali University in 2017 with 77.35% and her B.Tech. in CSE from A.K.T.U. in 2015 with 77.52%. Prior to her higher education, she pursued schooling from Gagan Public School, Aligarh, achieving 64.20% in Intermediate (ISC, 2011) and 71.00% in High School (ICSE, 2008). This strong academic background has shaped her journey into a research-focused teaching professional.

Research Focus

Her research is primarily oriented towards Natural Language Processing (NLP), especially text summarization for low-resource languages such as Hindi, which plays a crucial role in making technology accessible to diverse populations. She also focuses on Soft Computing, Operating Systems, and IoT-based intelligent systems, applying these concepts to practical problem domains. Her work on machine learning optimization, information retrieval, healthcare prediction systems, and AI-enabled monitoring devices has not only been published but also extended into patent applications, reflecting the translation of theoretical models into real-world impact.

Experience

Dr. Kumari’s professional journey integrates teaching, research, and project involvement. Since March 2023, she has been serving as an Assistant Professor at Galgotias University, where she teaches core CSE courses and mentors undergraduate and postgraduate students in research projects. Prior to this, she gained valuable hands-on experience as a Project Associate at IIT Mandi (2017–2018), where she contributed to applied computational projects, and as a Junior Research Fellow at DTRL, DRDO, Delhi (2018), where she worked on defense-related computing applications. This blend of academic and applied research exposure equips her with a balanced teaching-research perspective.

Research Timeline & Activities

Her research activities can be traced through a steady progression of contributions. Beginning with early studies on supervised approaches for text processing (2019), she expanded her scope into TF-IDF and graph-based summarization (2020), and later to comprehensive reviews on text summarization and question answering in NLP (2021–2022). Her most recent work applies deep learning architectures (Seq2Seq models, 2023) for Hindi text summarization, showcasing advanced computational linguistics applications. Alongside publications, she has contributed to innovative patents in healthcare informatics, IoT-enabled monitoring, and AI-driven intelligent devices. This trajectory highlights her ability to align her research output with emerging global priorities in CSE and real-world problem solving.

Awards & Honors

Dr. Kumari has received recognition for her academic excellence and research-driven innovations. She qualified GATE 2017, affirming her strong technical competence. She holds a Diploma in MS-Office, reflecting her interest in professional skill-building beyond core technical areas. Her intellectual property portfolio includes five patents, among which a UK Design Patent granted in September 2023 for a “Gaming Device for Diet and Stress Reduction” stands out. In addition, she has four Indian patents published or under examination, focusing on disease prediction, AI-enabled attendance monitoring, IoT-driven smoke detection, and food safety solutions. These honors and achievements underline her commitment to both academic excellence and translational innovation.

Top Recent Publication

Dr. Namrata Kumari has maintained an active and impactful research profile with several high-quality contributions published in indexed journals and leading international conferences. Her recent works highlight a diverse research portfolio ranging from text summarization and bibliometric analysis to applied deep learning in healthcare and sentiment analysis.

One of her most significant recent works is titled “A Panorama of Text Summarization Research: Bibliometric Trends and Developments (2000–2024)”, published in Neural Computing and Applications (Springer, August 2025). This article, co-authored with Dr. Pardeep Singh, presents a comprehensive bibliometric analysis of text summarization research over two decades, mapping its evolution, thematic clusters, and emerging trends. The study provides valuable insights into future directions of NLP and AI-driven summarization, making it an essential resource for researchers and practitioners in computational linguistics. (DOI: 10.1007/s00521-025-11562-2)

In the domain of healthcare applications, she co-authored “Monkeypox Detection Using Deep Learning: A Hybrid ResNet-50 and SegNet Approach”, presented at the 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (April 2025). This work introduces a hybrid CNN-based architecture combining ResNet-50 and SegNet, demonstrating promising results in medical image classification for monkeypox detection. (DOI: 10.1109/otcon65728.2025.11070680)

Expanding her scope into opinion mining, she contributed to “Sentiment Analysis of Movie Reviews Using TF-IDF”, presented at the 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI) in April 2025. This study compares the effectiveness of TF-IDF-based feature extraction methods in sentiment classification of movie reviews, offering insights into text mining and computational social science applications. (DOI: 10.1109/iccsai64074.2025.11064459)

Another important contribution is “Analysis on Movie Reviews: A Comparative Study”, presented at the Eighth International Conference on Parallel, Distributed, and Grid Computing (PDGC) in December 2024. Co-authored with Muskan Soni, Pardeep Singh, and Vikas Kashtariya, this paper evaluates multiple sentiment analysis approaches to understand trade-offs between accuracy and efficiency in real-world applications. (DOI: 10.1109/pdgc64653.2024.10984113)

She also collaborated on “Emergency Communication Architecture for Catastrophic Events: A Hybrid Approach”, presented at the OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0 in June 2024. This research proposes a hybrid communication framework for disaster management and emergency response, integrating IoT and cloud-based technologies to improve system resilience during catastrophic events. (DOI: 10.1109/otcon60325.2024.10687787)

Tomonobu Nonoyama, Computer Science, Best Researcher Award

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Dr. Tomonobu Nonoyama: Postdoctoral researcher at Akita Prefectural University, Japan

Tomonobu Nonoyama is a Japanese researcher specializing in plant biomechanics and computational modeling, currently serving as a postdoctoral researcher at the Department of Mechanical Engineering, System Science and Technology at Akita Prefectural University. With a foundation in mechanical engineering and advanced training in life sciences, his work bridges biology and physics to investigate how plants grow, sense, and respond to their environments. He has contributed to both experimental and simulation-based studies, with a growing publication record in high-impact journals such as Scientific Reports, Plant and Cell Physiology, and PLOS ONE. Dr. Nonoyama’s interdisciplinary expertise enables him to approach biological complexity with engineering precision.

Online Profiles

ORCID Profile

Dr. Nonoyama maintains an active presence across academic platforms to share his work and collaborate globally. His research output and citations can be found on Google Scholar, while ongoing projects and preprints are available on ResearchGate. He may also be listed on institutional directories such as the Akita Prefectural University website and other professional networks like ORCID, where researchers manage their publication history and affiliations. Please refer to his institutional email for further inquiries or collaboration interests.

Education

Dr. Nonoyama received his Bachelor of Science in Engineering in 2014 from Tohoku University’s Department of Mechanical and Aerospace Engineering, where he developed a strong technical foundation. He pursued graduate studies in the Department of Environmental Life Sciences at the same university, earning his Master of Science in 2016 and Ph.D. in Life Sciences in 2020 under the supervision of Prof. Satoshi Chiba. His graduate work focused on evolutionary biology and simulation of complex plant structures, laying the groundwork for his later studies in plant mechanics and modeling. This combination of mechanical and life sciences education forms the core of his interdisciplinary research approach.

Research Focus

Dr. Nonoyama’s research interests are centered on plant biomechanics, specifically investigating how mechanical forces and cellular structures influence plant growth and morphology. He uses both agent-based simulations and mathematical modeling to study complex biological processes such as cortical microtubule alignment, zygote development, and tip growth. His work also extends to analyzing movement and deformation in sensitive plants like the Venus flytrap, where engineering principles are used to infer internal biological forces. A significant part of his current research explores how plant cells coordinate structural growth through mechanical feedback and material properties, with potential applications in biomimetics and agricultural engineering.

Experience

Since April 2022, Dr. Nonoyama has been affiliated with Akita Prefectural University as a postdoctoral researcher, where he continues to develop computational models of plant development and collaborate on interdisciplinary projects in mechanical systems. Prior to that, he held a research position at the Center for Northeast Asian Studies at Tohoku University from 2020 to 2022. During that time, he transitioned his focus from purely biological systems to integrating mechanical simulations and systems theory, contributing to several cross-disciplinary publications. His academic journey reflects a progression from traditional engineering to cutting-edge life sciences research, where simulation, modeling, and empirical observation come together.

Research Timeline

Dr. Nonoyama began his academic training in 2010 at Tohoku University, completing his undergraduate studies in mechanical and aerospace engineering in 2014. From 2014 to 2016, he pursued a master’s degree in life sciences, followed by doctoral research from 2016 to 2020, focusing on hypothetical evolutionary pathways and mechanical modeling of plant forms. After earning his Ph.D., he joined Tohoku University’s Center for Northeast Asian Studies as a postdoctoral researcher, where he developed computational models of plant tissue growth and behavior. Since 2022, he has continued this work at Akita Prefectural University, broadening his research into plant mechanics and systems engineering.

Top-Noted Publication

Among Dr. Nonoyama’s body of work, one standout publication is “Agent-based simulation of cortical microtubule band movement in Arabidopsis zygotes”, published in Scientific Reports (2025). This paper presents a novel simulation approach to modeling microtubule behavior, which is critical to understanding how plant zygotes establish polarity and growth direction. By collaborating with experts in plant biology and systems modeling, Dr. Nonoyama helped bridge empirical cell biology and computational engineering. The study has been noted for its methodological innovation and relevance to developmental plant biology.

  • Agent-based simulation of cortical microtubule band movement in Arabidopsis zygotes
    Published: Scientific Reports, July 28, 2025
    DOI: 10.1038/s41598-025-11078-8
    Contributors: Tomonobu Nonoyama, Zichen Kang, Hikari Matsumoto, Sakumi Nakagawa, Minako Ueda, Satoru Tsugawa

  • KymoTip: High-throughput Characterization of Tip-growth Dynamics in Plant Cells
    Preprint: July 2, 2025
    DOI: 10.1101/2025.06.27.661917
    Contributors: Zichen Kang, Yusuke Kimata, Tomonobu Nonoyama, Toru Ikeuchi, Kazuyuki Kuchitsu, Satoru Tsugawa, Minako Ueda

  • A viscoelastic–plastic deformation model of hemisphere-like tip growth in Arabidopsis zygotes
    Published: Quantitative Plant Biology, 2024
    DOI: 10.1017/qpb.2024.13
    Contributors: Zichen Kang, Tomonobu Nonoyama, Yukitaka Ishimoto, Hikari Matsumoto, Sakumi Nakagawa, Minako Ueda, Satoru Tsugawa

  • Agent-Based Simulation of Cortical Microtubule Band Movement in Arabidopsis Zygotes
    Preprint: October 18, 2024
    DOI: 10.1101/2024.10.17.618799
    Contributors: Tomonobu Nonoyama, Zichen Kang, Hikari Matsumoto, Sakumi Nakagawa, Minako Ueda, Satoru Tsugawa

  • Temporal changes in surface tension guide the accurate asymmetric division of Arabidopsis zygotes
    Preprint: August 9, 2024
    DOI: 10.1101/2024.08.07.605794
    Contributors: Zichen Kang, Sakumi Nakagawa, Hikari Matsumoto, Yukitaka Ishimoto, Tomonobu Nonoyama, Yuga Hanaki, Satoru Tsugawa, Minako Ueda

Mohamed El-Moussaoui, Computer Science, Best Researcher Award

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Doctorate Mohamed El-Moussaoui: Ph.D graduate at Chouaib Doukkali University, Morocco

Mohamed El-Moussaoui is a distinguished expert with over 15 years of experience in the field of monetic engineering, specializing in the intersection of financial technology, artificial intelligence, and data science. With a Doctorate in Computer Science and AI from Chouaib Doukkali University, he has contributed significantly to both academic research and the development of innovative payment solutions. His professional journey spans leadership roles in major firms such as Hightech Payment Systems, Al Barid Bank, and HPS Group, where he successfully directed projects across Europe, the Americas, and Asia. Mohamed’s research bridges theory and application, focusing on community detection in social networks, using cutting-edge AI methodologies to enhance the security, analysis, and optimization of payment systems.

Online Profiles

Google Scholar Profile

Research Metrics

  • Total Citations: 84

  • Citations Since 2020: 82

  • h-index: 2

  • i10-index: 1

These metrics reflect Mohamed’s growing influence in the field of artificial intelligence and network science. With an h-index of 2, his publications are showing a consistent level of academic impact, and the high number of citations since 2020 indicates increasing recognition of his research on community detection and AI applications.

Education

Mohamed earned his Ph.D. in Computer Science and Artificial Intelligence from the prestigious Chouaib Doukkali University, graduating with the highest honors and commendations from his committee. His thesis was focused on the application of advanced algorithms for community detection in social networks, specifically leveraging association rule learning and fuzzy logic. He also holds a Master’s degree in Networks and Systems from Cadi Ayyad University, where he graduated with honors, and a Professional Bachelor’s degree in Computer Science. In addition to his academic journey, Mohamed completed military training at the École Royale de l’Air (ERA) in Marrakech, which shaped his discipline and leadership skills. His educational foundation has provided him with the analytical skills and technical expertise necessary to excel in both industry and academia.

Research Focus

Mohamed’s research primarily revolves around the development of sophisticated community detection techniques in social networks, utilizing association rule learning, fuzzy logic, and deep learning algorithms. He is especially interested in exploring how these methods can be applied to the field of monetic engineering to enhance payment security, fraud detection, and customer experience. His recent works focus on semantic-driven community models, multi-criteria decision-making techniques, and how AI-driven solutions can optimize both large-scale financial transactions and online social network analysis. His research also delves into systematizing deep learning approaches for network community detection, with a goal to bring practical applications to the field of digital payment systems.

Experience

Mohamed has a wealth of practical experience in both the public and private sectors, with a specific focus on the design and implementation of complex payment systems. Currently, he is serving as the Project Director at Hightech Payment Systems, where he is responsible for overseeing the company’s international projects related to digital payment infrastructures across Europe, the Americas, and Asia. Previously, he worked as the Head of the Monetic Engineering Division at Al Barid Bank, managing the integration and innovation of payment systems, including developing and launching new payment services for both retail and corporate clients. He has also held pivotal roles at HPS Group and S2M, where he contributed to product development, systems integration, and managed teams across various regions, ensuring that their solutions met high security standards and customer requirements. Mohamed’s leadership and technical expertise have consistently resulted in the successful deployment of state-of-the-art monetic technologies.

Research Timeline

  • 2025 (Q1): Published “ARLClustering: An R Package for Community Detection” in Applied Network Science (IF 1.4), marking a significant contribution to the development of user interaction-based community detection algorithms.

  • 2024 (Q2): Published “A Multi-Agent-Based Approach for Community Detection” in The International Journal of Data Science and Analytics (IF 3.4), presenting a novel approach to analyzing user behaviors in online platforms.

  • 2025 (Q2): Currently working on a paper titled “A Fuzzy Logic-Based Approach for Community Detection,” under review at International Journal of Fuzzy Systems.

  • 2025 (Q2): A comprehensive systematic review titled “A Review of Deep Learning Methods for Community Detection” is being published in Frontiers in Artificial Intelligence (IF 3.0), providing an in-depth look at the latest advancements in AI for network analysis.

Awards & Honors

  • 2025: Honored with the “Very Honorable” distinction for his Ph.D. dissertation, awarded by Chouaib Doukkali University. This recognition acknowledges his substantial contributions to the field of AI and monetic engineering.

  • 2024: Best Paper Award at the ICEET’24 International Conference for his development of the ARLClustering R package, praised for its innovation and potential applications in social network analysis.

  • 2022: Selected as the Technical Program Chair for the International Workshop on Modeling and Analysis of Complex Networks with Applications (MACNA’22) held in Ontario, Canada, a significant recognition of his expertise and leadership in the field of network science.

  • 2020: Awarded for Best Communication at the International Conference on Smart City Applications (SCA’20) for his innovative work on community detection using association rule learning.

Top-Noted Publication

  • “A Comprehensive Literature Review on Community Detection: Approaches and Applications”
    M. El-Moussaoui, T. Agouti, A. Tikniouine, M. El Adnani. Procedia Computer Science, Vol. 151, 76 (2019)

    • This paper provides an in-depth review of various community detection methods, summarizing state-of-the-art approaches and their practical applications in diverse domains.

  • “A Novel Approach of Community Detection Using Association Rules Learning: Application to User’s Friendships of Online Social Networks”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. Innovations in Smart Cities Applications, Volume 4 (2021)

    • A groundbreaking study that introduces the application of association rule learning (ARL) for detecting communities based on user friendships in online social networks.

  • “A Multi-Agent-Based Approach for Community Detection Using Association Rules”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. International Journal of Data Science and Analytics, Vol. 18 (4), 379-392 (2024)

    • This paper explores a multi-agent-based framework for community detection, applying association rules to analyze complex social networks and user behavior patterns.

  • “A k-Mean Classification Study of Eight Community Detection Algorithms: Application to Synthetic Social Network Datasets”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. AI and IoT for Sustainable Development in Emerging Countries, 557-572 (2022)

    • A comparative study evaluating the effectiveness of various community detection algorithms using synthetic datasets, with a focus on scalability and performance.

  • “ARLClustering: An R Package for Community Detection in Social Networks Based on User Interaction and Association Rule Learning”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. Applied Network Science, Vol. 10 (25), (2025)

    • Introduces ARLClustering, an R package designed to enhance community detection in social networks by utilizing user interaction data and association rule learning.

  • “ARLClustering: R Package for Community Detection-Based Association Rules Learning”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. International Conference on Engineering and Emerging Technologies (ICEET), 1-5 (2025)

    • Presentation of ARLClustering at ICEET’25, demonstrating its applications and potential for improving community detection processes in dynamic social networks.

  • “Community Detection in Social Networks: A State of the Art”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. International Conference on Mathematics & Data Science (ICMDS’20), 102 (2020)

    • A detailed state-of-the-art review on community detection methods, examining algorithms, challenges, and the impact of machine learning in social network analysis.

Ashish Verma, Computer Science, Best Researcher Award

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Doctorate Ashish Verma: Research Scholar at Birla Institute of Technology & Science, Pilani, India

Ashish Verma is a Ph.D. research scholar in the Department of Computer Science and Information Systems at BITS Pilani, Pilani Campus. His work lies at the intersection of robotics, distributed computing, and AI, with a focus on scalable and fault-tolerant multi-robot task allocation systems. With over four years of intensive research experience, Ashish has contributed to multiple high-impact projects involving real-time task coordination among autonomous mobile robots in time-critical environments. He is passionate about building intelligent robotic frameworks that can operate efficiently in uncertain and communication-constrained scenarios. His long-term vision is to establish a research center that develops robotic solutions for societal and industrial challenges in India and beyond.

Online Profiles

ORCID Profile

Education

Ashish holds a Bachelor’s degree in Computer Engineering from Cochin University of Science and Technology (CUSAT), completed in 2019 with a CGPA of 8.18. Currently, he is pursuing his Doctor of Philosophy (Ph.D.) in Computer Science and Engineering at BITS Pilani, where he has been working since November 2020. His doctoral research focuses on the design and implementation of secure, fault-tolerant frameworks for multi-robot task allocation in imperfect communication settings. Through advanced courses and project work, he has built a strong academic foundation in optimization, AI, reinforcement learning, distributed systems, and embedded robotics.

Research Focus

Ashish’s core research areas include distributed multi-robot task allocation (MRTA), dynamic scheduling, embedded systems, fault-tolerance, and secure robotic communication. He explores solutions to NP-Hard problems in heterogeneous mobile robotics using graph theory, optimization, deep learning (LSTM), and reinforcement learning. His current work integrates predictive modeling with dynamic rescheduling to minimize delivery penalties in time-sensitive domains like healthcare and warehousing. He also plans to integrate adversarial-resilient mechanisms and encryption-based authentication to improve system robustness in real-world robotic deployments.

Experience

Ashish has been working as a Research Scholar at the Embedded Systems and Robotics Laboratory at BITS Pilani since November 2020. Over the past four and a half years, he has contributed to designing, simulating, and evaluating robust multi-robot systems under complex operational constraints. His roles have included research design, simulation testing, writing algorithms, preparing publications, and mentoring students. In addition to research, he has served as a lab instructor for undergraduate courses such as Object-Oriented Programming (OOP), providing hands-on training to students in core computer science concepts. His experience reflects both theoretical depth and practical problem-solving ability.

Research Timeline

Between 2021 and 2025, Ashish Verma has worked on four major funded research projects focusing on real-time robotic systems: (1) CF-HMRTA – addressing coalition formation in heterogeneous robot teams using bipartite graph matching; (2) HMR-ODTA – a framework for diverse online task allocation under time constraints; (3) DTA-HMR-TT – a decentralized approach incorporating task transfer to minimize late deliveries; and (4) Predictive Task Allocation using Deep Learning – employing LSTM models to forecast and optimize task schedules dynamically. Each project built progressively toward creating a secure, scalable, and intelligent multi-robot system capable of operating in dynamic, imperfect environments.

Awards & Honors

Ashish has a consistent academic record and has earned accolades from school to university level. He secured the 1st rank in a Mathematics Olympiad during school and was the winner of an intra-university chess championship, reflecting both analytical ability and strategic thinking. In his research journey, he has participated in multiple national and international workshops, including the EMSC-2021 workshop on smart city energy management and a short course on high-performance and parallel computing. He has also contributed to the research community as a peer reviewer for IEEE Transactions on Intelligent Vehicles, a testament to his growing expertise and academic engagement.

Top-Noted Publication

Among his published works, Ashish’s most impactful publication to date is the 2024 IEEE Access paper titled “DTA-HMR-TT: Dynamic Task Allocation for a Heterogeneous Team of Mobile Robots with Task Transfer.” This work presents a novel decentralized scheduling algorithm that addresses multi-pickup and delivery problems with time windows in real-world environments such as hospitals and warehouses. The paper highlights his contribution to handling task rescheduling, minimizing penalties for late deliveries, and enabling robots to intelligently transfer tasks under changing scenarios. It received positive peer recognition for its scalability and practical value in real-time robotic systems.

1. Verma, A., Gautam, A., Dutta, A., Shekhawat, V. S., & Mohan, S. (2025). CF-HMRTA: Coalition Formation for Heterogeneous Multi-Robot Task Allocation. Journal of Intelligent & Robotic Systems. https://doi.org/10.1007/s10846-025-02287-4
This paper introduces a novel coalition formation strategy for task allocation among heterogeneous mobile robots, addressing the computational complexity of NP-Hard problems using a bipartite graph matching technique with a worst-case time complexity of O(|E|). The work significantly enhances scalability, demonstrated through simulations involving up to 2000 robots and 400 tasks executed in under 12 seconds. The coalition mechanism ensures optimal robot-task pairing and showcases efficiency in diverse multi-agent environments. The study reflects Ashish Verma’s deep understanding of algorithm design, optimization under constraints, and real-world simulation modeling.

2. Verma, A., Gautam, A., Shekhawat, V. S., & Mohan, S. (2024). DTA-HMR-TT: Dynamic Task Allocation for a Heterogeneous Team of Mobile Robots With Task Transfer. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3505947
This research presents a decentralized framework for dynamic task allocation in time-sensitive scenarios such as hospital logistics and warehouse automation. The proposed system introduces a task-transfer mechanism to improve delivery reliability and minimize penalties under time window constraints. Using a team of autonomous heterogeneous robots, the system dynamically adjusts task schedules based on environmental changes and robot availability. This publication highlights Ashish’s expertise in decentralized algorithms, dynamic scheduling, and robust robotic coordination in structured indoor environments.

Surendra Solanki, Computer Science, Best Researcher Award

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Doctorate Surendra Solanki: Assistant Professor at Manipal University jaipur, Jaipur, Rajasthan, India

Dr. Surendra Solanki is a passionate academician and data science researcher with extensive experience in artificial intelligence, machine learning, and wireless communication systems. His academic journey is marked by a Ph.D. in Computer Science and Engineering from NIT Bhopal, where his research focused on deep learning-based spectrum sensing for cognitive radio networks. He has hands-on expertise in deep learning frameworks like TensorFlow and PyTorch, along with proficiency in Python, R, and MATLAB. With a multidisciplinary outlook, he actively explores large language models, federated learning, blockchain integration in AI systems, and generative models for vision and NLP tasks. Dr. Solanki combines teaching, research, and practical implementation to address complex real-world problems and empower future technologists.

Online Profiles

Scopus Profile

  • 108 citations from 93 documents, and 15 scanned items

  • An h-index of 4

Education

Dr. Solanki earned his Ph.D. in Computer Science and Engineering from the prestigious Maulana Azad National Institute of Technology (MANIT), Bhopal, where he conducted deep learning-based research on spectrum sensing techniques in cognitive radio systems from 2018 to 2024. He completed his M.Tech. in Computer Science and Engineering from the same institution in 2017 with a CGPA of 7.90; his thesis involved secure data hiding using image steganography. He holds a B.E. degree in Information Technology from the Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya (DAVV), Indore, graduating in 2012 with 69% marks. His strong academic foundation has enabled him to bridge theory with emerging technologies effectively.

Research Focus

Dr. Solanki’s research interests lie at the intersection of deep learning, wireless networks, and privacy-preserving artificial intelligence. His doctoral research contributed to improved performance in cognitive radio networks through the use of CNNs, RNNs, and hybrid DL architectures. More recently, he has worked extensively with large language models such as BERT, GPT, LLaMA, and Falcon for natural language processing, medical diagnostics, and multi-modal AI systems. His ongoing research explores explainable AI using Grad-CAM and LIME, federated learning for secure health data analytics, AI-driven malware detection, and generative AI for synthetic data creation and model interpretability.

Experience

Dr. Solanki is currently employed as an Assistant Professor in the Department of Artificial Intelligence and Machine Learning at Manipal University Jaipur, where he teaches undergraduate and postgraduate courses, supervises student research, and develops AI-focused curriculum (2023–Present). Previously, he served as a Senior Faculty in IT at SAGE University Indore (through iNurture Education Solutions) from 2022 to 2023, where he taught advanced machine learning and deep learning subjects. Earlier in his career, he worked as an Assistant Manager in eGovernance for the Government of Madhya Pradesh (2014–2015), contributing to digital infrastructure development. His roles have balanced academic instruction, administrative leadership, and applied research.

Research Timeline

Between 2018 and 2024, Dr. Solanki conducted his doctoral research at MANIT Bhopal on deep learning architectures for cognitive radio, resulting in significant publications in IEEE Access and Springer. In 2020, he expanded into blockchain-integrated AI and device authentication systems for smart grids, leading to co-authored IEEE conference papers. From 2021 onward, his research embraced interdisciplinary domains like federated learning in healthcare, Android malware detection using graph neural networks, and self-supervised models for chest X-ray diagnosis. The timeline from 2023 to 2025 shows his contributions in LLM applications, multimodal AI, and knowledge distillation techniques for resource-efficient models in healthcare and audio processing.

Awards & Honors

Dr. Solanki has earned several accolades throughout his academic and professional career. He has authored more than 18 papers in high-impact journals indexed in SCI and Scopus, including Q1 journals such as IEEE Access and Scientific Reports. His innovations have led to the successful filing and publication of 6 patents in AI, Blockchain, and IoT domains. He has received multiple invitations to review for reputed journals and serve as a speaker at IEEE international conferences. His contributions in federated learning, modulation recognition, and AI for healthcare have been appreciated by both academic and industry collaborators, marking him as a notable emerging researcher in applied AI.

Top-Noted Publication

One of Dr. Solanki’s top-recognized publications is titled “A deep ensemble learning approach for squamous cell classification in cervical cancer”, published in Scientific Reports (Nature Portfolio, Q1 Journal) in 2025. This paper proposes a robust ensemble model leveraging deep CNNs and transformers to enhance the accuracy of cervical cancer cell classification, showcasing significant improvements over traditional ML approaches. The work stands out for its clinical relevance, explainability, and technical depth, contributing to the field of AI in medical diagnostics. The publication has attracted significant citations and collaborative interest from researchers working in computational pathology. [DOI: https://doi.org/10.1038/s41598-025-91786-3]