Muhammed Zekeriya Gündüz | Cybersecurity | Best Review Paper Award

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Assoc. Prof. Dr. Muhammed Zekeriya Gündüz | Cybersecurity | Best Review Paper Award

Assistant Professor |  Bingöl University | Turkey

Assoc. Prof. Dr. Muhammed Zekeriya Gündüz is an active researcher and academic professional with expertise spanning cybersecurity, software engineering, information systems, and artificial intelligence. His professional experience includes academic teaching, research supervision, and applied projects focused on improving software quality, digital security awareness, and ransomware analysis. His research interests emphasize secure software design, cyber threat mitigation, AI-supported information systems, and technology-driven educational solutions. He possesses strong research skills in data analysis, academic publishing, project development, interdisciplinary collaboration, and technology integration. His scholarly work has earned recognition through academic visibility, impactful citations, and contributions to high-quality journals and review studies. Overall, his work reflects consistent academic productivity, applied relevance, and growing influence within computer science and cybersecurity research domains. He has achieved 746 Citations 8Documents 4h-index.

 

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Featured Publications

Cyber-security on Smart Grid: Threats and Potential Solutions

– Elsevier, Computer Networks, 2020 (Top Cited)

Internet of Things (IoT): Evolution, Components and Application Fields

– Pamukkale University Journal of Engineering Sciences, 2018
Analysis of Cyber-Attacks on Smart Grid Applications

– International Conference on Artificial Intelligence and Data Processing, 2018
Analysis of Cyber-Attacks in IoT-Based Critical Infrastructures

– International Journal of Information Security Science, 2019

Fabien Thomas Brans | Digital Forensics | Research Excellence Award

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Dr. Fabien Thomas Brans | Digital Forensics | Research Excellence Award

PhD in Computer Science  | CRGN / LIRA  | France

Dr. Fabien THOMAS-BRANS is a researcher and practitioner in computer science and digital forensics with a strong focus on the processing, diagnosis, and repair of digital evidence within judicial and investigative contexts. His professional experience centers on forensic analysis of electronic devices, legal data extraction, and failure analysis, with active involvement in collaborative projects alongside academic and applied research institutions. His research interests include forensic science, data extraction methodologies, electronic diagnosis and repair, flash and MMC memory analysis, and CRBNE-related forensic interventions, reflecting an interdisciplinary approach that bridges computer science, mathematics, and security domains. His research skills encompass advanced digital forensics techniques, memory error correction, evidence recovery processes, failure analysis, and the development of specialized forensic procedures and training programs. He has contributed to peer-reviewed indexed journal publications and ongoing research articles, demonstrating consistent scholarly output and applied impact. In addition, his work includes collaboration with internationally recognized institutions, highlighting both academic rigor and practical relevance. His awards and honors are reflected through recognition in research excellence–oriented initiatives and professional affiliations within national forensic and cybersecurity organizations. Overall, his profile illustrates a balanced combination of applied forensic expertise, research innovation, and collaborative engagement, contributing meaningfully to advancements in digital evidence handling and forensic computing. He has achieved 12 Citations 3 Documents 2h-index.

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Featured Publications

Yuan Xiaolin | Machine Learning | Editorial Board Member

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Dr. Yuan Xiaolin | Machine Learning | Editorial Board Member

Professor | Hefei Institute of Physical Sciences, Chinese Academy of Sciences | China

Xiao Lin Yuan is an Associate Professor at the Institute of Plasma Physics, Chinese Academy of Sciences, and an expert in fusion engineering systems, with particular specialization in vacuum pumping, fueling systems, and intelligent diagnostics for fusion devices. He earned a doctoral degree in Nuclear Science and Engineering, following comprehensive academic training that laid a strong foundation in plasma physics and large-scale scientific instrumentation. His professional experience includes long-term research and technical roles at a national fusion research institute, where he has contributed to the design, integration, and optimization of critical subsystems for advanced tokamak facilities, as well as participation in nationally and internationally funded collaborative projects. His research focuses on vacuum system design, leak detection technologies, molecular pump fault diagnosis, and the application of artificial intelligence methods such as support vector machines and deep learning models to enhance reliability and predictive maintenance in fusion devices. He has published extensively in leading peer-reviewed journals and international conference proceedings in the fields of fusion engineering, nuclear science, and vacuum technology, demonstrating both methodological rigor and practical impact. Through his sustained research output, project involvement, and academic leadership, he has earned professional recognition within the fusion research community and actively contributes to the advancement of intelligent control and diagnostic technologies for next-generation fusion systems.

Profile : ORCID

Featured Publications

Yuan, X.-L., Chen, Y., Hu, J.-S., et al. (2016). Development and implementation of flowing liquid lithium limiter control system for EAST. Fusion Engineering and Design, 112, 332–337.

Yuan, X.-L., Chen, Y., Hu, J.-S., et al. (2018). 10 Hz pellet injection control system integration for EAST. Fusion Engineering and Design, 126, 130–138.

Yuan, X.-L., Chen, Y., et al. (2018). Development and implementation of supersonic molecular beam injection for EAST tokamak. Fusion Engineering and Design, 134, 62–67.

Yuan, X.-L., Chen, Y., et al. (2023). A support vector machine framework for fault detection in molecular pump. Journal of Nuclear Science and Technology, 60, 72–82.

Zhou, Y., Jiang, M., Yuan, X.-L., et al. (2024). Fault prediction of molecular pump based on DE-Bi-LSTM. Fusion Science and Technology, 80, 1001–1011.

Amir Reza Rahimi | Artificial Intelligence | Best Research Article Award

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Mr. Amir Reza Rahimi | Artificial Intelligence | Best Research Article Award

University of Valencia | Spain 

Amir Reza Rahimi is distinguished for his unwavering dedication to research excellence, demonstrated through his rigorous scientific investigations, analytical depth, and meaningful contributions to advancing knowledge in his field. His research is grounded in systematic inquiry, where he applies advanced methodologies, precise data interpretation, and comprehensive theoretical perspectives to address complex scientific problems with clarity and a strong sense of academic responsibility. Rahimi consistently integrates interdisciplinary approaches, enabling him to explore scientific questions from multiple angles and generate insights that hold both scholarly value and real world relevance. His body of work, which includes peer reviewed publications, collaborative projects, and active participation in academic discussions, reflects originality, innovation, and a clear commitment to producing high quality evidence based outcomes. These contributions not only enrich scientific literature but also support practical applications in policy development, environmental management, and broader scientific decision making. Rahimi’s engagement with emerging research trends, utilization of modern analytical tools, and strict adherence to ethical principles further highlight his professionalism and commitment to responsible scholarship. His ability to collaborate with international researchers, secure research opportunities, and share knowledge across diverse academic platforms showcases his growing influence and leadership within the scientific community. Additionally, his involvement in mentoring students and early career researchers demonstrates his dedication to nurturing scientific talent and promoting a culture of curiosity, critical thinking, and continuous learning. Through his sustained efforts, Rahimi exemplifies the highest standards of research excellence, characterized by intellectual rigor, scientific creativity, and societal relevance. His work continues to contribute significantly to the advancement of his discipline and supports the development of future research directions that address both present and emerging scientific challenges.

Profiles : Google Scholar | ORCID

Featured Publications

Rahimi, A. R., & Sevilla-Pavon, A. (2025). The role of design thinking skills in artificial-intelligence language learning (DEAILL) in shaping language learners’ L2 grit: The mediator and moderator role of artificial intelligence L2 motivational self-system. Computer Assisted Language Learning.

Rahimi, A. R., & Daneshvar Ghorbani, B. (2025). Developing and validating the scale of language teachers’ computational thinking competency in Computer Assisted Language Learning (LTCCTCALL): Empowering language teaching by cultivating the heart of the 21st-century digital skill. Asian-Pacific Journal of Second and Foreign Language Education.

Rahimi, A. R., & Sevilla-Pavón, A. (2025). Modeling the relationship between online L2 motivational self-system and EFL learners’ virtual exchange self-regulations: The mediator and moderator roles of L2 grit. ReCALL.

Rahimi, A. R., & Sevilla-Pavón, A. (2025). The role of interactive, constructive, active, and passive learning activities (ICAPCALL) in shaping students’ online engagement and learning approaches to virtual exchange (SAVE): A bisymmetric approach. Smart Learning Environments.

Rahimi, A. R., & Teimouri, R. (2025). Advancing language education with ChatGPT: A path to cultivate 21st-century digital skills. Research Methods in Applied Linguistics.

Rahimi, A. R. (2025). Developing and validating the scale of language teachers’ design thinking competency in artificial intelligence language teaching (LTDTAILT). Computers and Education: Artificial Intelligence.

Raziyeh Pourdarbani | Artificial Intelligence | Best Paper Award

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Prof. Raziyeh Pourdarbani | Artificial Intelligence | Best Paper Award

Faculty Membr | University of Mohaghegh Ardabili | Iran

Dr. Raziyeh Pourdarbani is a Professor of Biosystems Engineering at the University of Mohaghegh Ardabili and an internationally recognized researcher in precision agriculture, image processing, machine vision, artificial intelligence, and hyperspectral imaging. Her research is dedicated to developing advanced computational approaches that enhance automation, sustainability, and non-destructive assessment within agricultural production systems. She has established a strong scholarly footprint through extensive publications that explore cutting-edge deep learning architectures, including the application of 2D and 3D convolutional neural networks, majority voting ensemble strategies, hybrid neural networks, and metaheuristic optimization techniques for quality evaluation and decision-making in crop and fruit management. Her studies have significantly advanced non-destructive methodologies for detecting bruises, internal defects, and ripening stages in fruits, as well as monitoring excessive nitrogen consumption and estimating chemical and physicochemical properties in plant leaves using hyperspectral, visible, and near-infrared spectral data. In addition to agricultural sensing and classification research, she has contributed impactful work on sustainable bioenergy, including biomethane production from agricultural residues, biodiesel engine performance enhancement using nanomaterials, and advanced exergy and life-cycle analysis of hybrid geothermal–solar power systems. She has authored multiple academic books addressing renewable energy and intelligent grading technologies and has led numerous research projects involving automated fruit identification algorithms, orchard-based robotic systems, video-based fruit maturity estimation, spectral wavelength optimization, agricultural development modeling, and geothermal heating-system design. Dr. Pourdarbani actively disseminates her findings through national and international conferences and contributes to the scientific community through reviewing and collaborative roles in multidisciplinary research initiatives. Her work is widely acknowledged for its scientific value and practical relevance in improving agricultural resource efficiency, enhancing food-quality monitoring, and promoting environmentally responsible production strategies. As a leading figure in the integration of computational intelligence with agricultural engineering, she continues to shape research directions that support global progress toward smart, sustainable, and technologically empowered agriculture.

Profile : Google Scholar

Featured Publication

Alibaba, M., Pourdarbani, R., Manesh, M. H. K., Ochoa, G. V., & Forero, J. D. (2020). Thermodynamic, exergo-economic and exergo-environmental analysis of hybrid geothermal–solar power plant based on ORC cycle using emergy concept. Heliyon, 6(4).

Pourdarbani, R., Sabzi, S., Kalantari, D., Hernández-Hernández, J. L., & Arribas, J. I. (2019). A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties.

Pourdarbani, R., Sabzi, S., García-Amicis, V. M., García-Mateos, G., Hernández-Hernández, J. L., & Arribas, J. I. (2019). Automatic classification of chickpea varieties using computer vision techniques. Agronomy, 9(11), 672.

Ebrahimi, S., Pourdarbani, R., Sabzi, S., Rohban, M. H., & Arribas, J. I. (2023). From harvest to market: Non-destructive bruise detection in kiwifruit using convolutional neural networks and hyperspectral imaging. Horticulturae, 9(8), 936.

Pourdarbani, R., Sabzi, S., Rohban, M. H., Hernández-Hernández, J. L., & Arribas, J. I. (2021). One-dimensional convolutional neural networks for hyperspectral analysis of nitrogen in plant leaves. Applied Sciences, 11(24), 11853

Yihew Biru Woldegiorgis | Ecology | Editorial Board Member

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Mr. Yihew Biru Woldegiorgis | Ecology | Editorial Board Member

Researcher | Gullele Botanic Garden | Ethiopia

Yihew Biru is a wildlife ecologist and conservation scientist serving as a faculty member at a leading Ethiopian higher-education institution, specializing in animal ecology, human–wildlife interactions, and biodiversity conservation. He holds advanced degrees in biology and ecology with specialization in wildlife management, complemented by professional training in conservation planning and ecological monitoring. His experience spans academic teaching, field-based research, community-centered conservation projects, and leadership roles in collaborative initiatives addressing biodiversity loss, ecosystem health, and sustainable natural-resource management. His research focuses on the ecology of large mammals, avian diversity, insect biodiversity, zootherapeutic knowledge, and human–wildlife conflict, producing widely cited publications on elephant feeding ecology, pastoralist perceptions of wildlife, bird species composition in wetlands and community-managed conservation areas, butterfly diversity, zootherapeutic animal use, and conservation challenges across multiple Ethiopian landscapes. He has contributed to capacity-building programs, interdisciplinary conservation platforms, and stakeholder-engagement efforts within protected areas and community-based conservation projects. His achievements have earned him institutional recognitions for academic excellence, merit-based research awards, and invitations to serve as reviewer and editorial contributor for reputable ecological and biodiversity journals. He is an active member of national and international professional societies in ecology and conservation biology and holds certifications in ecological field methods, data analysis, and community-based conservation approaches, demonstrating sustained commitment to advancing scientific knowledge and supporting evidence-based biodiversity conservation in Ethiopia.

Profile : ORCID

Featured Publications

Biru, Y. (2012). Food habits of African elephant (Loxodonta africana) in Babile Elephant Sanctuary, Ethiopia. Tropical Ecology.

Mohamed Moncef Ben Khelifa | Computer Vision | Best Researcher Award

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Mr. Mohamed Moncef Ben Khelifa | Computer Vision | Best Researcher Award

Associate Professor | University of Toulon France | France

Dr. Mohamed Moncef Ben Khelifa, Maître de Conférences des Universités (HC, 61e CNU) au département MMI de l’IUT de Toulon et membre du laboratoire J-AP2S, est un spécialiste reconnu en vision assistée par ordinateur, intelligence artificielle appliquée à la santé, et interfaces intelligentes homme-machine, avec une expertise consolidée par plus de deux décennies d’enseignement, de recherche appliquée et d’innovation technologique. Titulaire d’une double compétence en traitement du signal et de l’image ainsi qu’en neurotechnologie, il a contribué à un ensemble significatif de travaux portant sur la biométrie, l’optimisation multi-objectifs, la classification d’images médicales, l’analyse prédictive de la marche et les interfaces cerveau-machine, totalisant de nombreuses publications indexées et plusieurs projets collaboratifs internationaux, notamment dans le cadre de coopérations scientifiques franco-tunisiennes. Ses travaux récents portent sur la classification biométrique avancée, l’optimisation par essaims intelligents, l’analyse markerless de la démarche pour la détection des pathologies musculosquelettiques, ainsi que sur la modélisation prédictive des troubles de la posture. En neuroergonomie et en cognition, il a proposé des approches intégrant signaux EEG, indices musculaires et paramètres oculaires afin de mesurer le stress, la fatigue mentale et la charge cognitive dans des environnements immersifs. Son engagement en mobilité assistée est également notable, illustré par ses recherches sur la navigation de fauteuils roulants via la fusion de données cérébrales et visuelles, ainsi que par ses innovations brevetées (France et États-Unis) dédiées au contrôle d’appareils mobiles. Lauréat du Prix Var Terre d’Innovation 2014 pour le projet BEWHEELI – Brain Eyes Wheelchair Interface, il œuvre pour la conception de technologies inclusives visant à améliorer l’autonomie des personnes à besoins spécifiques. Par son rôle de coordinateur de projets transméditerranéens, il contribue activement aux avancées en santé numérique pédiatrique, systèmes embarqués intelligents et traitement multimodal de données cliniques, renforçant l’impact sociétal de ses recherches au service de la santé publique et de l’innovation biomédicale.

Featured Publications

Abellard, A., & Ben Khelifa, M. M. (2004). A Petri net modelling of a neural human–machine interface. In IEEE International Conference on Industrial Technology (ICIT).

Abellard, A., Ben Khelifa, M. M., & Bouchouicha, M. (2005). A Petri net modelling of an adaptive learning control applied to an electric wheelchair. In Computational Intelligence in Robotics and Automation.

Abellard, A., Ben Khelifa, M. M., Bouchouicha, M., & Abellard, P. (2003). Modélisation par réseaux de Petri pour une programmation VHDL. Exemple d’application en robotique mobile d’assistance au handicap. ISDM Journal, 1–7.

Abellard, A., Randria, I., Franceschi, M., Abellard, P., & Ben Khelifa, M. M. (2018). Feasibility study of a technical programme for electric wheelchair steering aid.

Abellard, A., Randria, I., & Ben Khelifa, M. M. (2006). Utilisation des réseaux de Petri architecturaux pour la modélisation des algorithmes de commande d’une plateforme technologique d’aide aux handicapés. In SETIT 2005.

Dr. Ben Khelifa’s work bridges artificial intelligence, neurotechnologies, and predictive biomechanics to design inclusive solutions for healthcare and mobility assistance. His research drives innovation in pediatric digital health, assistive robotics, and multimodal clinical data analysis, improving quality of life for populations with specific needs.

Joung hwan mun | Machine learning | Best Scholar Award

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Prof. Dr. Joung hwan mun | Machine learning | Best Scholar Award

Professor | Sungkyunkwan University | South Korea

Professor Joung Hwan Mun, Ph.D., is a distinguished Professor in the Department of Biomechatronic Engineering at Sungkyunkwan University, Korea, where he also serves as Director of the Institute of Biotechnology and Bioengineering and the Center for Bio-Information & Communication Technology. He earned his B.S. and M.S. degrees in Biomechatronic Engineering from Sungkyunkwan University and a Ph.D. in Mechanical Engineering from The University of Iowa, USA. With a prolific academic career spanning over two decades, Dr. Mun has significantly contributed to advancing biomechatronics, biomedical engineering, and intelligent healthcare technologies. His primary research interests encompass embedded systems in healthcare, artificial intelligence applications in medical devices, Internet of Things (IoT) integration for medical systems, and wearable sensor technologies for human motion analysis. He has authored more than 250 peer-reviewed publications, including 151 journal articles and 105 conference papers, reflecting his extensive influence in biomechanics, gait analysis, and machine learning-driven motion prediction. His work on AI-based gait and fall detection models, center of pressure trajectory prediction, and exoskeleton design has been widely recognized for improving human mobility, rehabilitation, and clinical diagnostics. Dr. Mun holds over 30 international and national patents, including innovations in surgical navigation, wearable exoskeletons, and fall detection systems, demonstrating his commitment to translational research with direct societal benefits. His leadership in integrating AI, sensor fusion, and biomechanical modeling has fostered interdisciplinary collaborations across Korea, the United States, and Japan. A former Adjunct Associate Professor at The University of Iowa and Invited Associate Professor at Tokyo Denki University, Dr. Mun continues to advance next-generation biomedical systems that merge artificial intelligence and human biomechanics to enhance healthcare accessibility, safety, and quality worldwide.

Featured Publication

Oh, S. E., Choi, A., & Mun, J. H. (2013). Prediction of ground reaction forces during gait based on kinematics and a neural network model. Journal of Biomechanics, 46(14), 2372–2380.

Mun, J. H., & Youn, S. H. (2020). Apparatus and method for discriminating biological tissue, surgical apparatus using the apparatus (U.S. Patent No. 10,864,037).

Choi, A., Kim, T. H., Yuhai, O., Jeong, S., Kim, K., Kim, H., & Mun, J. H. (2022). Deep learning-based near-fall detection algorithm for fall risk monitoring system using a single inertial measurement unit. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 2385–2394.

Park, H. J., Sim, T., Suh, S. W., Yang, J. H., Koo, H., & Mun, J. H. (2016). Analysis of coordination between thoracic and pelvic kinematic movements during gait in adolescents with idiopathic scoliosis. European Spine Journal, 25(2), 385–393.

Choi, A., Lee, J. M., & Mun, J. H. (2013). Ground reaction forces predicted by using artificial neural network during asymmetric movements. International Journal of Precision Engineering and Manufacturing, 14(3), 475–483.

Choi, A., Joo, S. B., Oh, E., & Mun, J. H. (2014). Kinematic evaluation of movement smoothness in golf: Relationship between the normalized jerk cost of body joints and the clubhead. Biomedical Engineering Online, 13(1), 20.

Dr. Joung Hwan Mun’s pioneering research integrates artificial intelligence, biomechanics, and wearable sensing to advance intelligent healthcare systems and human–machine interaction. His innovations in gait analysis, fall detection, and exoskeleton technologies have significantly enhanced mobility, rehabilitation, and safety, driving global progress in personalized healthcare and biomedical engineering.

Niti Kant | Computational Theory | Best Researcher Award

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Prof. Dr. Niti Kant | Computational Theory | Best Researcher Award

Professor | University of Allahabad | India

Prof. Dr. Niti Kant is a distinguished physicist currently serving in the Department of Physics, University of Allahabad, Prayagraj, India. With a Ph.D. from the Indian Institute of Technology (IIT) Delhi (2005) under the supervision of Dr. A. K. Sharma, his research focuses on laser–plasma interaction, self-focusing of lasers, harmonic generation, laser-induced electron acceleration, and terahertz (THz) radiation generation. Over the past two decades, Dr. Kant has made significant contributions to theoretical plasma physics, employing advanced analytical and numerical modeling approaches using Mathematica and Origin. He has published over 150 research papers in reputed international journals indexed by SCI, earning an H-index of 33 on Google Scholar, reflecting the global impact of his research. His academic journey includes postdoctoral research at POSTECH, South Korea, and academic leadership at Lovely Professional University, Punjab, where he served as Professor before joining the University of Allahabad. Dr. Kant has successfully led several sponsored research projects funded by CSIR, SERB, and DST, totaling over ₹50 lakhs, and has guided more than ten Ph.D. scholars in cutting-edge areas such as THz generation, nonlinear optics, and high-power laser–matter interaction. A life member of several prestigious scientific societies, including the Indian Science Congress Association, Optical Society of India, and Plasma Science Society of India, he also serves on editorial and review boards of international journals and as a peer reviewer for top publishers like Elsevier, IOP, and AIP. His work has been recognized with multiple honors, including the Merit Award (2024) by the University of Allahabad, Research Excellence Awards (2020, 2021), and the Outstanding Scientist Award (2020). With active international collaborations across the UK, Czech Republic, South Korea, and the USA, Dr. Kant’s research continues to advance the frontiers of laser–plasma physics, contributing to innovations in photonics, clean energy, and applied plasma technologies with profound implications for scientific and technological progress.

Featured Publication

Kamboj, O., Azad, T., Rajput, J., & Kant, N. (2025). The effect of density ramp on self-focusing of q-Gaussian laser beam in magnetized plasma. Journal of Optics (India). Citations: 2

Azad, T., Kant, N., & Kamboj, O. (2025). Efficient THz generation by Hermite–cosh–Gaussian lasers in plasma with slanting density modulation. Journal of Optics (India). Citations: 23

Singh, J., Kumar, S., Kant, N., & Rajput, J. (2025). Effect of frequency-chirped ionization laser on accelerated electron beam characteristics in plasma wakefield acceleration. European Physical Journal Plus. Citations: 1

Anshal, L., Kant, N., Azad, T., Rajput, J., & Kamboj, O. (2025). Propagation of Hermite–cosh–Gaussian laser beam in free-electron laser device under upward plasma density ramp. Laser Physics Letters. Citations: 1

Azad, T., Kant, N., & Kamboj, O. (2025). Enhanced third harmonic generation and SRS suppression in magnetized rippled plasma using Hermite cosh–Gaussian laser beam. Journal of Optics (India). Citations: 2

Prof. Dr. Niti Kant’s pioneering research in laser–plasma interaction, nonlinear optics, and terahertz generation has advanced the understanding of high-power laser applications, enabling innovations in photonics, clean energy, and next-generation communication technologies. His work bridges fundamental physics with practical technologies, fostering global scientific collaboration and contributing to sustainable technological progress.

srividhya chandran | climate finance | Best Researcher Award

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Ms. srividhya chandran | climate finance | Best Researcher Award

Research Scholar | Bharathiar University | India

C. Srividhya is a Research Scholar in the Department of Commerce at Bharathiar University, Coimbatore, specializing in climate change economics, financial inclusion, and fintech innovation. Her research integrates sustainability, artificial intelligence, and finance to address global economic and environmental challenges. She has published several articles in reputed international journals, including the International Journal of Engineering Development and Research, International Journal of Advanced Research in Science, Communication and Technology, and International Journal of Progressive Research in Engineering Management and Science. Her notable works such as “AI-Driven Decarbonization: A Machine Learning Framework for Optimising Climate Mitigation Strategies” and “Climate Change Risk and Firm Risk: A Bibliometric Analysis Using Biblioshiny” reflect her interdisciplinary approach and contribution to climate and financial research. Srividhya has participated in more than seventeen national and international conferences organized by prominent institutions like Bharathiar University, Rathinam College, Bishop Heber College, and IIM Jammu, presenting and publishing impactful papers in collaboration with Dr. M. Nirmala on topics such as fintech adoption, women entrepreneurship, and economic transformation. Holding an M.Com in Finance and Accounting with distinction from Bharathiar University and a B.Com (Professional Accounting) from Karunya Institute of Technology and Sciences, she has also completed advanced research methodology and entrepreneurship development programs sponsored by ICSSR and DST. Her research aims to harness the potential of digital finance and artificial intelligence to promote sustainability, inclusive growth, and economic resilience, establishing her as a promising young scholar contributing meaningfully to the intersection of finance, technology, and climate policy.

Featured Publication

Srividhya, C. (2025). Understanding the integration of climate change risk and corporate financial risk: A scientometric analysis. International Journal of Advanced Research in Science, Communication and Technology, 5(1), 45–56.

Srividhya’s research bridges the gap between finance, technology, and sustainability by integrating artificial intelligence into climate risk assessment and financial decision-making. Her work supports evidence-based policies for sustainable economic growth, promoting resilience, inclusivity, and innovation in global financial systems.