Reepu | Finance | Best Researcher Award

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Dr. Reepu | Finance | Best Researcher Award

Chandigarh University | India

Dr. Reepu is a prolific interdisciplinary researcher whose work spans financial technology, artificial intelligence, sustainable systems, digital business models, and consumer behavior, supported by 58 publications and 165 citations across 136 documents. Her research consistently integrates advanced technologies—AI, machine learning, blockchain, IoT, and data-driven analytics—into domains such as finance, marketing, healthcare, and sustainability. A major thematic focus of her scholarship is technology adoption and its psychological, operational, and strategic implications, demonstrated through studies on consumer readiness for self-service technologies, mobile wallet acceptance in smart cities, AI-driven value creation, and customer churn forecasting. In fintech and digital finance, Dr. Reepu has contributed influential work on neo-banking models, green stock diversification, Gann-based financial forecasting, sustainability performance in private banking, and perceived risks influencing online banking use. Parallel to this, her research in neuromarketing, deepfake detection, and human–machine interfaces reflects a keen interest in cognitive responses, ethical AI, user experience, and emerging digital risks. Dr. Reepu also extends her expertise to sustainability and circular economy themes through publications on supply chain traceability using blockchain, solid waste management in high-altitude tourist regions, and the role of circular economy frameworks in promoting sustainable innovation. Her work is widely disseminated through journal articles, book chapters, and conference papers, collectively contributing to contemporary debates at the intersection of technology, finance, and societal development. Dr. Reepu’s research portfolio reflects a forward-looking vision, offering both conceptual depth and practical insights aligned with the needs of rapidly evolving digital and sustainable ecosystems.

Profiles: Scopus | Orcid

Featured Publications

  • Reepu, R., Kashif, M., Taneja, S., & Kumar, P. (2025). Forecasting stock values by integrating Gann square analysis with artificial intelligence (AI). In Advanced Trends in Financial Analytics (Book chapter). Springer.

  • Reepu, R., Kaya, R. R., & Türkmen, İ. (2025). A study in understanding the critical effects of consumer readiness towards self-service technology: Mediating effects of situational factors. In Technology-Driven Customer Behavior Studies (Book chapter). IGI Global.

  • Reepu, R., Kaur, G., & Sharma, S. (2025). An empirical investigation in understanding the role of artificial intelligence in enhancing the patient experience. In AI Applications in Healthcare Systems (Book chapter). IGI Global.

  • Reepu, R. (2024). The role of artificial intelligence in neuromarketing in understanding consumer behaviour. In Neuromarketing: Concepts and Ethical Implications (Book chapter). IGI Global.

  • Reepu, R., Kaur, G., & Sharma, S. (2024). Sustainable methods for solid waste management in high-altitude tourist regions. In Environmental Sustainability Practices in Emerging Contexts (Book chapter). IGI Global.

Ndukwe Johnson kalu | Waste Valorization | Excellence in Research Award

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Dr. Ndukwe Johnson kalu | Waste Valorization | Excellence in Research Award

UNESCO International Centre for Biotechnology | Nigeria

Dr. Ndukwe Johnson Kalu is a multidisciplinary microbiologist whose research advances microbial waste valorization, bioconversion processes, sustainable bioenergy, and food security solutions, supported by a growing scholarly impact reflected in 241 citations, an h-index of 8, and an i10-index of 7. His work focuses on transforming agricultural and industrial wastes into high-value products such as bioelectricity, biogas, biofertilizers, biochar, and microbial-derived biomolecules, contributing significantly to circular bioeconomy innovations. Dr. Ndukwe has explored the therapeutic potential of microalgae-derived bioactive compounds for managing human disease conditions, providing new directions in functional microbiology. His influential studies on weak acid–induced stress tolerance in yeasts offer essential insights for improving lignocellulosic bioethanol production, enabling more efficient and robust biofuel systems. He has contributed to solving digester instability challenges in anaerobic treatment processes, promoting advancements in renewable energy technologies. His research on microbial glycolipids supports the development of eco-friendly biomedical and personal care products, while his work on cassava waste valorization demonstrates integrated pathways for generating bioelectricity and organic fertilizers. Dr. Ndukwe has also investigated indigenous probiotic beverages, microbial biofilms in dye degradation for textile wastewater treatment, and the biotechnological relevance of keratinases. Beyond laboratory research, he is contributing to scientific knowledge mapping through bibliometric and systematic reviews that examine microbial waste valorization research in Africa, global bioscience trends, and disease-focused research domains such as Alzheimer’s disease and Plasmodium falciparum. Through these diverse efforts, Dr. Ndukwe Johnson Kalu is emerging as an impactful researcher driving sustainable biotechnology and environmental resilience across the African continent and beyond.

Profiles: Google Scholar | Scopus |Orcid

Featured Publications

  • Eze, C. N., Onyejiaka, C. K., Ihim, S. A., Ayoka, T. O., Aduba, C. C., Ndukwe, J. K., Nwaiwu, O., & Onyeaka, H. (2023). Bioactive compounds by microalgae and potentials for the management of some human disease conditions. AIMS Microbiology, 9(1), 55–69.

  • Ndukwe, J. K., Aliyu, G. O., Onwosi, C. O., Chukwu, K. O., & Ezugworie, F. N. (2020). Mechanisms of weak acid-induced stress tolerance in yeasts: Prospects for improved bioethanol production from lignocellulosic biomass. Process Biochemistry, 90, 118–130.

  • Onwosi, C. O., Eke, I. E., Igbokwe, V. C., Odimba, J. N., Ndukwe, J. K., Chukwu, K. O., Aliyu, G. O., & Nwagu, T. N. (2019). Towards effective management of digester dysfunction during anaerobic treatment processes. Renewable and Sustainable Energy Reviews, 116, 109424.

  • Ndukwe, J. K., Aduba, C. C., Ughamba, K. T., Chukwu, K. O., Eze, C. N., Nwaiwu, O., & Onyeaka, H. (2023). Diet diversification and priming with Kunu: An indigenous probiotic cereal-based non-alcoholic beverage in Nigeria. Beverages, 9(1), 14.

  • Aduba, C. C., Ndukwe, J. K., Onyejiaka, C. K., Onyeiwu, S. C., & Moneke, A. N. (2023). Integrated valorization of cassava wastes for production of bioelectricity, biogas and biofertilizer. Waste and Biomass Valorization, 14(12), 4003–4019.

Sirinya Sitthirak | Cancer | Excellence in Research Award

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Dr. Sirinya Sitthirak | Cancer | Excellence in Research Award

Walailak University | Thailand

Dr. Sirinya Sitthirak is an emerging scientist specializing in cancer genomics, multi-omics integration, and computational biology, with a strong focus on accelerating precision oncology through advanced data analytics. Her research spans cancer genomics, proteomics, phosphoproteomics, metabolomics, microbiome studies, and systems-level multi-omics approaches, allowing her to dissect tumor complexity and uncover biomarkers that drive cancer progression and therapeutic response. She has contributed to impactful research such as predicting cisplatin response in cholangiocarcinoma using chromosome patterns and gene-expression signatures, reflecting her commitment to improving clinical outcomes through translational genomics. Dr. Sitthirak is proficient in genome-wide association studies (GWAS), high-throughput data analysis, and Linux-based bioinformatics workflows using R and Python, enabling her to explore cancer evolution, drug resistance mechanisms, and tumor ecological dynamics. Her scientific engagement is demonstrated through invited oral and poster presentations at prestigious global platforms, including the Cancer Evolution: From Genome to Ecology meeting hosted by Wellcome Connecting Science at the University of Cambridge, the International Conference on Cancer Bioinformatics (ETH Zurich), and the European Association for Cancer Research (EACR) conferences. Dr. Sitthirak has earned multiple competitive honors, including bursaries, travel grants, and international presentation awards, underscoring the significance of her contributions. She is also actively involved in capacity building as an instructor for regional bioinformatics programs, teaching helminth genomics, SARS-CoV-2 bioinformatics, and cancer genome analysis across Asia. Overall, her research integrates multi-omics technologies with computational innovation to advance the understanding of cancer biology and support the development of precision diagnostic and therapeutic strategies.

Profile: Scopus

Featured Publication

Sitthirak, S., [Co-authors]. (2025). Predicting cisplatin response in cholangiocarcinoma patients using chromosome pattern and related gene expression. Scientific Reports.

Qi Zhan | Computing and Network Convergence | Research Excellence Award

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Mr. Qi Zhan | Computing and Network Convergence | Research Excellence Award

Information Engineering University | China

Mr. Qi Zhan is an emerging researcher whose work focuses on advancing intelligent, adaptive, and resource-efficient solutions across network architecture, network intelligence, and machine learning. His research addresses the increasing complexity of distributed packet-measurement systems within Computing and Network Convergence (CNC), where dynamic traffic patterns and heterogeneous resource conditions require optimized orchestration strategies. Mr. Zhan has developed a reinforcement learning–based orchestration method for distributed sketch deployment, offering a significant advancement over traditional static or heuristic allocation approaches. His method enables an intelligent agent to continuously sense node-level resource states and automatically adjust deployment decisions to achieve global optimization. Through this approach, he successfully reduces maximum resource utilization, minimizes the standard deviation of resource consumption, and achieves balanced packet-measurement load distribution across distributed nodes. These improvements enhance fairness, increase scalability, and promote more stable operational performance within large-scale programmable networks. His recent publication, Enabling Resource-Aware Distributed Sketch Deployment with Reinforcement Learning, demonstrates how reinforcement learning can effectively support fine-grained measurement tasks while reducing overhead and adapting to real-time network changes. By integrating machine learning with network system design, Mr. Zhan’s work contributes to the development of intelligent, self-optimizing network infrastructures capable of supporting future high-performance, data-driven, and computation-intensive distributed applications.

Profiles: Orcid

Featured Publication 

Zhan, Q., Dong, Y., Tian, L., Hu, Y., Xia, J., Zhu, Y., Wang, Z., Guo, X., & Li, H. (2025). Enabling resource-aware distributed sketch deployment with reinforcement learning. Conference paper presented at the ACM.

Joyce Mongai Chindong | Remote Sensing in Agriculture and Vegetation | Research Excellence Award

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Ms. Joyce Mongai Chindong | Remote Sensing in Agriculture and Vegetation | Research Excellence Award

Mohammed VI Polytechnic University | Morocco

Ms. Joyce Mongai Chindong is an emerging researcher in geospatial sciences whose work centers on leveraging GIS, remote sensing, and machine learning to improve environmental monitoring in data-scarce regions. Her research primarily focuses on soil degradation processes, including drought dynamics and soil salinity, by integrating multi-sensor satellite observations, proximal sensing techniques, and advanced analytical frameworks. She applies Earth Observation data—such as remotely sensed precipitation products, vegetation indices like NDVI, and field-based spectral and electrical conductivity measurements—to quantify environmental anomalies and assess ecosystem responses to climatic variability. Ms. Chindong’s work emphasizes methodological rigor through preprocessing workflows, anomaly detection, spatial modeling, and the use of supervised learning algorithms to generate accurate, scalable predictions of land surface conditions. Her recent contribution, a multi-sensor machine learning framework for field-scale soil salinity mapping, demonstrates her commitment to developing robust, transferrable solutions for sustainable land management, particularly in resource-limited environments where environmental data is sparse. By combining optical, radar, and ground-based measurements with algorithmic modeling, she advances the precision and reliability of landscape-level assessments that support agricultural resilience and environmental planning. Motivated by global sustainability goals, Ms. Chindong aims to refine geospatial intelligence to better understand environmental processes across diverse landscapes. Her interdisciplinary approach—blending geoinformation science, environmental monitoring, and computational methods—positions her as a promising contributor to research on land degradation, climate impacts, and data-driven environmental decision-making.

Profile: Orcid

Featured Publication

Chindong, J. M., Ouzemou, J.-E., Laamrani, A., El Battay, A., Hajaj, S., Rhinane, H., & Chehbouni, A. (2025). A multi-sensor machine learning framework for field-scale soil salinity mapping under data-scarce conditions. Remote Sensing, 17(22), Article 3778.

Zhiguo Meng | Remote Sensing Geology | Research Excellence

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Prof. Dr. Zhiguo Meng | Remote Sensing Geology | Research Excellence

Jilin University | China

Prof. Dr. Zhiguo Meng is a prominent lunar remote sensing and planetary geoscience scholar whose research has substantially advanced our understanding of the Moon’s surface processes, thermophysical behavior, and geological evolution, supported by an impressive record of 105 scientific publications, 771 citations from 446 citing documents, and an h-index of 16. His work focuses extensively on analyzing multi-frequency microwave radiometer data, thermal radiation characteristics, and regolith properties derived from China’s Chang’e missions, including CE-2, CE-4, CE-5, and CE-6. Meng has contributed major breakthroughs in constructing global lunar brightness temperature datasets, defining effective brightness temperature differences, and developing innovative techniques to detect subsurface anomalies and assess the thermophysical properties of lunar deposits in regions such as Oceanus Procellarum, Mare Imbrium, Lacus Mortis, and the Schiller–Schickard cryptomare. His research reveals new insights into lunar volcanic evolution, crustal structure, and thermal history by studying crater emissions, lava flows, and regolith heat production rates. Meng is also active in developing advanced computational tools, including deep learning frameworks such as MFBTFF-Net, to estimate lunar surface oxide abundances using Chang’e microwave sounder data. His interdisciplinary contributions extend to planetary formation studies, notably using Chang’e-4 data to examine millimeter-scale particle–surface collisions, and to Earth-based applications, including InSAR-based ground deformation monitoring in mining and earthquake-prone regions. Through extensive collaboration, frequent contributions to leading journals, and advanced modeling of scattering, irradiation, and thermal behavior, Prof. Dr. Meng continues to shape the scientific foundation essential for lunar exploration, mission planning, and future planetary research.

Profiles: Scopus | Orcid

Featured Publications

  • Meng, Z., Mei, L., Liu, C., Xu, Y., Zhang, X., Bugiolacchi, R., Zong, Q., Cheng, W., Ping, J., & Zhang, Y. (2025). Definition of effective brightness temperature difference and its geological significance. IEEE Transactions on Geoscience and Remote Sensing.

  • Li, Z., Zhao, Y., Tang, X., & Meng, Z. (2025). Heat production rate of lunar major basins: New insights into lunar thermal evolution. IEEE Transactions on Geoscience and Remote Sensing.

  • Li, Y., Yuan, Z., Mazhar, S., Meng, Z., Zhang, Y., Ping, J., & Nunziata, F. (2025). MFBTFF‐Net: A multi-frequency brightness temperature feature fusion network for lunar surface oxides abundance estimation using Chang’e-2 data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

  • Chang, W., Meng, Z., Xu, Y., Zhang, X., Bugiolacchi, R., Xiao, L., Ping, J., Zhang, H., & Zhang, Y. (2025). Microwave thermophysical properties of surface deposits in the CE-6 landing region and implications for returned samples. Earth and Planetary Science Letters.

  • Lei, L., Zhang, X., Luo, P., Zhang, G., You, J., Liu, J., Xu, Y., Fu, S., Li, X., & Meng, Z. (2025). Planetary formation constrained by collisions between millimeter-sized lunar particles and the lunar surface from the Chang’E-4 mission. The Astrophysical Journal.

Ranadheer Reddy | Environmental Science | Excellence in Research Award

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Mr. Ranadheer Reddy | Environmental Science | Excellence in Research Award

King Mongkut Institute of Technology | Thailand

Mr. Ranadheer Reddy is an emerging researcher whose multidisciplinary work spans geoinformatics, remote sensing, geostatistics, environmental monitoring, public health analytics, and data-driven decision-support systems, supported by a growing academic footprint that includes 6 documents, 15 citations, and an h-index of 3. His publications demonstrate a strong commitment to applying spatial technologies and computational methods to address real-world environmental and societal challenges across India and Southeast Asia. His research on soil erosion estimation and sediment retention in the Lam Phra Phloeng watershed showcases his expertise in watershed assessment and environmental risk modelling, while his recent open-access study on flood mapping and damage assessment during the 2024 Chiang Rai Flood in Thailand highlights his proficiency in leveraging UN-SPIDER-recommended remote sensing methodologies within Google Earth Engine for rapid disaster analysis. Mr. Ranadheer Reddy has also contributed significantly to spatial public health research through a geostatistical analysis of child malnutrition determinants in India, integrating demographic and socio-economic factors to generate policy-relevant insights. His work on spatio-temporal land use and land cover changes in smart cities along the Delhi–Mumbai Industrial Corridor reflects his interest in sustainable urban development and long-term environmental change assessment. Expanding into applied data science, his book chapters explore machine learning approaches for early rice disease diagnosis and big data analytics in the healthcare sector, demonstrating his focus on technological innovation for agricultural resilience, healthcare improvement, and sustainable development. Collectively, his emerging body of work contributes to advancing geospatial intelligence, environmental modelling, and data-driven policy formulation.

Profile: Scopus

Featured Publications

  • Seebonruang, U., Mandadi, R., Thammaboribal, P., Gonzales, A. L., & Bharadwas, G. S. V. S. A. (2025). Estimation of soil erosion and enhancing sediment retention in the Lam Phra Phloeng watershed. Water, 17, 3339.

  • Thammaboribal, P., Tripathi, N., Lipiloet, S., & Mandadi, R. (2025). Flood mapping and damage assessment using UN-SPIDER recommended practices in Google Earth Engine: A case study of the 2024 Chiang Rai Flood, Thailand. International Journal of Geoinformatics, 165–179.

  • Mandadi, R. R., Tripathi, N. K., Pal, I., Mozumder, C., & Gonzales, A. L. (2023). Geostatistical exploratory analysis on child malnutrition and its determinants in India. International Journal of Geoinformatics.

  • Kanchan, A., Nitivattananon, V., Tripathi, N. K., Winijkul, E., & Mandadi, R. R. (2024). A spatio-temporal examination of land use and land cover changes in smart cities of the Delhi–Mumbai Industrial Corridor. Land, 13.

  • Sakhamuri, S., Tatini, N. B., Krishna, P. G., Mandadi, R. R., RajaSekhar, J., Gunnam, L. C., & Teja, K. R. (2025). Machine learning approach to ensure rice nutrition through early diagnosis of rice diseases. In V. Jain, M. Raman, A. Agrawal, M. Hans, & S. Gupta (Eds.), Achieving Sustainability with AI Technologies (pp. 297–310). IGI Global.

Giuseppe Osteria | Cherenkov Camera in Space | Research Excellence Award

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Dr. Giuseppe Osteria | Cherenkov Camera in Space | Research Excellence Award

Istituto Nazione di Fisica Nucleare | Italy

Dr. Giuseppe Osteria’s research focuses on advancing astroparticle physics with a strong emphasis on high-precision measurements of cosmic radiation using space-based and balloon-borne detectors. With a citation record of 19,296 citations, an h-index of 63, and an i10-index of 193 (with 5,131 citations, h-index 34, and i10-index 99 since 2020), his work has significantly shaped the field. Over the past 25 years, he has contributed extensively to major international experiments aimed at understanding the composition, origin, and propagation of cosmic rays, antimatter components, and high-energy astrophysical phenomena. His scientific contributions span the MACRO experiment, which provided foundational insights into atmospheric neutrinos and underground muon fluxes, as well as the MINISINGAO/ARGO and NOE/ICANOE projects investigating cosmic-ray interactions and neutrino behavior. A major portion of his career is linked to the WIZARD-PAMELA mission, where his leadership in time-of-flight systems, trigger development, and light-nuclei data analysis enabled groundbreaking results, including the discovery of anomalous positron abundance and precise measurements of proton, helium, electron, positron, and antiproton spectra. Dr. Osteria has also played central roles in the JEM-EUSO program, contributing to the advancement of space-based detection of extreme-energy cosmic rays and serving as an international data processor and operations manager for missions such as EUSO-Balloon, TA-EUSO, and MINI-EUSO. His work extends to next-generation missions—SPB2, PBR, CSES-Limadou, GAPS, and HERD-DMP—where he has overseen instrumentation, trigger electronics, calorimetry systems, and high-level data processing. Collectively, his research drives progress in cosmic-ray physics, antimatter studies, and multi-messenger astrophysics, positioning him as a leading contributor to space-borne astroparticle research.

Profiles: Google Scholar | Orcid | Scopus

Featured Publications

  • Adriani, O., Barbarino, G. C., Bazilevskaya, G. A., Bellotti, R., Boezio, M., et al. (2009). An anomalous positron abundance in cosmic rays with energies 1.5–100 GeV. Nature, 458(7238), 607–609.

  • Adriani, O., Barbarino, G. C., Bazilevskaya, G. A., Bellotti, R., Boezio, M., et al. (2011). PAMELA measurements of cosmic-ray proton and helium spectra. Science, 332(6025), 69–72.

  • Adriani, O., Barbarino, G. C., Bazilevskaya, G. A., Bellotti, R., Boezio, M., et al. (2010). PAMELA results on the cosmic-ray antiproton flux from 60 MeV to 180 GeV in kinetic energy. Physical Review Letters, 105(12), 121101.

  • Picozza, P., Galper, A. M., Castellini, G., Adriani, O., Altamura, F., Ambriola, M., … Barbarino, G. C. (2007). PAMELA–A payload for antimatter matter exploration and light-nuclei astrophysics. Astroparticle Physics, 27(4), 296–315.

  • Ambrosio, M., Antolini, R., Aramo, C., Auriemma, G., Baldini, A., Barbarino, G. C., … MACRO Collaboration. (1998). Measurement of the atmospheric neutrino-induced upgoing muon flux using MACRO. Physics Letters B, 434(3–4), 451–457.

M. R. Rajan | Nanoparticles | Excellence in Research Award

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Prof. Dr. M. R. Rajan | Nanoparticles | Excellence in Research Award

The Gandhigram Rural Institute- Deemed to be University | India

Prof. Dr. M. R. Rajan is a distinguished researcher whose work spans aquaculture toxicology, green nanotechnology, environmental biotechnology, and fish health management. His research primarily explores the synthesis, characterization, and safe application of diverse metal and metal oxide nanoparticles—including selenium, silver, copper oxide, manganese oxide, magnesium oxide, chromium oxide, aluminum oxide, zinc oxide, carbon quantum dots, and graphene quantum dots—and their physiological impacts on numerous freshwater fish species. A major focus of his work is the incorporation of nanoparticles into fish diets to enhance growth performance, haematological and biochemical responses, antioxidant activity, and overall aquatic health, helping advance the emerging field of nanonutrition. He contributes significantly to the development of green-synthesized nanoparticles using plant extracts and waste-derived materials, assessing their antibacterial, larvicidal, and biomedical potentials, as well as their suitability for aquaculture applications. His studies also address key environmental challenges, including wastewater toxicity, tannery effluent pollution, and eco-friendly remediation strategies, while other works explore the role of probiotic gut microbiota in improving fish immunity and disease resistance. With 44 scientific publications and more than 220 citations, Prof. Dr. M. R. Rajan research offers valuable insights into sustainable aquaculture practices, nanoparticle biosafety, environmental health, and innovative nanobiotechnological solutions for aquatic systems.

Profile: Scopus

Featured Publications

  • Varghese Edwin Hillary, V., Dayana Senthamarai, M., Antony Ceasar, A., Ignacimuthu, S., & Rajan, M. R. (2025). Characterization of a silver nanoparticle derived from the fruit peel of Myristica fragrans on mosquito control. International Journal of Tropical Insect Science.

  • Dayana Senthamarai, M., Edwin Hillary, V., Rajan, M. R., & Antony Ceasar, A. (2025). Phyto-synthesis of selenium nanoparticles using Mentha spicata extract and its larvicidal and antibacterial activities. Journal of Asia-Pacific Entomology, 28(1), 102370.

  • Malavika, V., Rajan, M. R., Soundhariya, N., Prethika, J., & Chandrakala, V. (2025). Comparative evaluation of chemically and green-synthesised carbon quantum dots for aquatic applications. Journal of Environmental Nanotechnology, 14(3), 505–515.

  • Chinnadurai Kaleeswaran, M., Dayana Senthamarai, M., & Rajan, M. R. (2024). Evaluation of disparate multiplicities of copper oxide nanoparticles integrated feed on the growth and hematology of koi carp. Journal of Toxicological Studies, 2(1), 497.

  • Dayana Senthamarai, M., Rekha, M., & Rajan, M. R. (2024). Incorporation of nano selenium in fish diet and assessment of growth performance and biochemical criteria of Labeo rohita. Journal of Environmental Nanotechnology, 13(1), 01–09.*

 

Shoresh Shokoohi | Fault Detection | Editorial Board Member

Published on by Applied Scientist

Dr. Shoresh Shokoohi | Fault Detection | Editorial Board Member

University of Kurdistan | Iran

Dr. Shoresh Shokoohi is a distinguished researcher in power systems, smart microgrids, intelligent control, and condition monitoring, with a strong focus on advanced control strategies for voltage, frequency, and stability enhancement in inverter-based microgrids. His research emphasizes intelligent, data-driven, and model-based approaches—including fuzzy logic controllers, ANN-based self-tuning schemes, neuro-fuzzy systems, and optimization-based droop control—to ensure resilient, reliable, and autonomous operation of microgrids under dynamic conditions. Dr. Shokoohi has made notable contributions to intelligent secondary control, transient stability enhancement, load-frequency control, and robust online tuning methods, publishing influential works in top-tier journals such as IEEE Transactions on Smart Grid, International Journal of Electrical Power & Energy Systems, Electric Power Components and Systems, Optimization and Engineering, and Neural Computing & Applications. Extending beyond microgrid control, he has advanced the field of electric machine diagnostics through innovative model-based techniques, such as Luenberger observer–based fault detection, parity-equation methods, and current-noise cancellation for accurate bearing fault diagnosis. His interdisciplinary work integrates artificial intelligence, optimization algorithms, and intelligent control applications to address emerging challenges in smart grid reliability and inverter-based distributed generation. With a strong publication record across journals and conferences, Dr. Shoresh Shokoohi research continues to support the evolution of next-generation smart grids, providing practical, intelligent, and robust solutions that enhance the performance, stability, and resilience of modern power and energy systems.

Profile: Google Scholar

Featured Publications

  • Bevrani, H., & Shokoohi, S. (2013). An intelligent droop control for simultaneous voltage and frequency regulation in islanded microgrids. IEEE Transactions on Smart Grid, 4(3), 1505–1513.

  • Ahmadi, S., Shokoohi, S., & Bevrani, H. (2015). A fuzzy logic-based droop control for simultaneous voltage and frequency regulation in an AC microgrid. International Journal of Electrical Power & Energy Systems, 64, 148–155.

  • Bevrani, H., Habibi, F., & Shokoohi, S. (2013). ANN-based self-tuning frequency control design for an isolated microgrid. In Meta-heuristics Optimization Algorithms in Engineering, Business, Economics.

  • Khezri, R., Golshannavaz, S., Shokoohi, S., & Bevrani, H. (2016). Fuzzy logic-based fine-tuning approach for robust load frequency control in a multi-area power system. Electric Power Components and Systems, 44(18), 2073–2083.

  • Shokoohi, S., Golshannavaz, S., Khezri, R., & Bevrani, H. (2018). Intelligent secondary control in smart microgrids: An on-line approach for islanded operations. Optimization and Engineering, 19(4), 917–936.