Dr. Shikha Singh | Immunology | Editorial Board Member

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Dr. Shikha Singh | Immunology | Editorial Board Member

Associate Professor | Rama Devi Women's University | India

Dr. Shikha Singh is a distinguished researcher whose scholarly contributions reflect a deep commitment to advancing Immunology through multidisciplinary scientific inquiry. Her work demonstrates how Immunology can be integrated with environmental sciences, microbial biotechnology, clinical microbiology, agricultural systems, and public health challenges to create broader scientific impact. Throughout her research journey, Immunology has served as a central theme driving her analytical approach, enabling her to explore molecular interactions, pathogenic behavior, and ecological factors with exceptional clarity. As a productive scholar, Dr. Shikha Singh has authored influential publications that highlight the relevance of Immunology in understanding environmental toxicity, microbial contamination, disease-related fungal resistance, genotype characterization, and physiological responses in biological systems. Her research in Immunology extends across collaborations with experts in environmental chemistry, biotechnology, microbiology, agricultural sciences, and clinical research, reflecting a strong interdisciplinary foundation. By applying Immunology principles to complex scientific problems, she has contributed to improved strategies for pollution assessment, pathogen detection, antimicrobial evaluation, and sustainable resource management. Her work in Immunology continues to inspire innovative approaches in both laboratory and real-world applications, demonstrating how scientific evidence can guide public health protection and ecological sustainability. With Immunology guiding her research perspective, Dr. Shikha Singh has strengthened scientific understanding, supported evidence-based solutions, and contributed to global knowledge systems through impactful collaborations. Her scholarly influence within Immunology is further reinforced by her consistent publication record and the measurable reach of her research within the global academic community. Google Scholar profile of 1100 Citations, 13 h-index, 19 i10-index.

Profile: Google Scholar

Featured Publications

1. Mohanty, S., Benya, A., Hota, S., Kumar, M. S., & Singh, S. (2023). Eco-toxicity of hexavalent chromium and its adverse impact on environment and human health in Sukinda Valley of India: A review on pollution and prevention strategies. Environmental Chemistry and Ecotoxicology, 5, 46–54.

2. Mishra, S., Ghosh, S., van Hullebusch, E. D., Singh, S., & Das, A. P. (2023). A critical review on the recovery of base and critical elements from electronic waste-contaminated streams using microbial biotechnology. Applied Biochemistry and Biotechnology, 195(12), 7859–7888.

3. Ponnachan, P., Vinod, V., Pullanhi, U., Varma, P., Singh, S., Biswas, R., & Kumar, A. (2019). Antifungal activity of octenidine dihydrochloride and ultraviolet-C light against multidrug-resistant Candida auris. Journal of Hospital Infection, 102(1), 120–124.

4. Benya, A., Mohanty, S., Hota, S., Das, A. P., Rath, C. C., Achary, K. G., & Singh, S. (2023). Endangered Curcuma caesia Roxb.: Qualitative and quantitative analysis for identification of industrially important elite genotypes. Industrial Crops and Products, 195, Article 116363.

5. Singh, S., Sahoo, S., Dash, S., & Nayak, S. (2014). Association of growth and yield parameters with bioactive phytoconstituents in selection of promising turmeric genotypes. Industrial Crops and Products, 62, 373–379.

Assist. Prof. Dr. Law Kumar Singh | Medical Image Processing | Editorial Board Member

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Assist. Prof. Dr. Law Kumar Singh | Medical Image Processing | Editorial Board Member

Assistant Professor | GLA University | India

Assist. Prof. Dr. Law Kumar Singh is a distinguished researcher whose scientific contributions have significantly shaped advancements in Medical Image Processing, artificial intelligence, and clinical decision-support systems. His work demonstrates a consistent commitment to enhancing healthcare diagnostics through Medical Image Processing, with notable achievements in breast cancer prediction, glaucoma detection, diabetic retinopathy assessment, and automated medical screening technologies. Recognized for integrating nature-inspired algorithms with deep learning, he has established a strong research presence built upon innovation and high-impact publications in Medical Image Processing. Throughout his career, Assist. Prof. Dr. Law Kumar Singh has authored influential studies that advance Medical Image Processing by developing hybrid architectures, robust optimization frameworks, and deep neural network models tailored for medical applications. His collaborations with leading experts across computer vision, machine learning, and biomedical engineering have strengthened the translational impact of Medical Image Processing, ensuring that his research contributes both to academic progress and clinical relevance. He has produced numerous widely cited works, reflecting the global recognition of his contributions to Medical Image Processing. His research portfolio highlights a strong focus on real-world medical challenges, where Medical Image Processing serves as a transformative tool for early disease identification and improved patient outcomes. By blending algorithmic efficiency with diagnostic precision, he has consistently demonstrated how Medical Image Processing can elevate the accuracy and reliability of automated healthcare systems. His collaborative projects further emphasize interdisciplinary integration, reinforcing the essential role of Medical Image Processing in solving complex biomedical problems. Assist. Prof. Dr. Law Kumar Singh continues to expand the frontiers of Medical Image Processing through rigorous research, impactful publications, and sustained contributions to global scientific communities. His work stands as a benchmark for excellence in data-driven healthcare innovation. Google Scholar profile of 1558 Citations, 27 h-index, 36 i10-index.

Profile: Google Scholar

Featured Publications

1. Thawkar, S., Sharma, S., Khanna, M., & Singh, L. K. (2021). Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and ant lion optimizer. Computers in Biology and Medicine, 139, 104968.

2. Singh, L. K., Khanna, M., & Singh, R. (2023). Artificial intelligence based medical decision support system for early and accurate breast cancer prediction. Advances in Engineering Software, 175, 103338.

3. Singh, L. K., Pooja, Garg, H., Khanna, M., & Bhadoria, R. S. (2021). An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus. Medical & Biological Engineering & Computing, 59(2), 333–353.

4. Khanna, M., Singh, L. K., Thawkar, S., & Goyal, M. (2024). PlaNet: A robust deep convolutional neural network model for plant leaves disease recognition. Multimedia Tools and Applications, 83(2), 4465–4517.

5. Singh, L. K., Pooja, Garg, H., & Khanna, M. (2022). Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets. Evolving Systems, 13(6), 807–836.

Dr. Tahmineh Azizi | Neuroscience | Editorial Board Member

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Dr. Tahmineh Azizi | Neuroscience | Editorial Board Member

Research Associate | University of Wisconsin-Madison | United States

Dr. Tahmineh Azizi is a distinguished researcher whose work spans mathematical modeling, biomathematics, biostatistics, dynamical systems, and advanced neuroscience applications. Her scholarship brings a unique interdisciplinary synergy, using quantitative frameworks to address complex challenges in biological systems, physiology, and neuroscience. Dr. Azizi’s research encompasses fractal geometry, neural oscillations, physiological modeling, biodistribution analysis, and computational interpretations of human brain activity, all of which contribute significantly to global neuroscience discourse. Her contributions extend to innovative investigations in neural bursting, spiking behavior, seizure recognition, and stress modeling, strengthening the integration of applied mathematics with modern neuroscience. Through impactful publications on neuro-degenerative disease dynamics, fetal ECG complexity, epileptic network organization, and mental arithmetic–related brain activity, she continually advances the analytical depth of computational neuroscience. Dr. Azizi has collaborated widely across multidisciplinary teams, contributing to studies involving nanoparticle bioimaging, pharmacokinetics, environmental dynamics, urban climate analysis, and diverse physiological systems, demonstrating the far-reaching societal impact of her work. Her research in neuroscience is recognized for methodological rigor, conceptual innovation, and practical relevance, providing valuable insights to laboratories, clinical researchers, and computational scientists. She has authored multiple highly cited works and remains an influential figure in advancing global neuroscience research through her integration of mathematical tools with biological and neurological complexity. Dr. Azizi’s body of work exemplifies a commitment to precision, scientific integrity, and continued advancement of neuroscience, establishing her as a respected contributor in the international research community. Google Scholar profile of 427 Citations, 11 h-index, 13 i10-index.

Profile: Google Scholar

Featured Publications

1. Pitchaimani, A., Nguyen, T. D. T., Marasini, R., Eliyapura, A., Azizi, T., Jaberi-Douraki, M., & Aryal, S. (2019). Biomimetic natural killer membrane camouflaged polymeric nanoparticle for targeted bioimaging. Advanced Functional Materials, 29(4), 1806817.

2. Azizi, T. (2022). On the fractal geometry of gait dynamics in different neuro-degenerative diseases. In Clinical Applications of Fractals and Fractional Order Systems (pp. 129-149).

3. Riviere, J. E., Jaberi-Douraki, M., Lillich, J., Azizi, T., Joo, H., Choi, K., Thakkar, R., & Monteiro-Riviere, N. A. (2018). Modeling gold nanoparticle biodistribution after arterial infusion into perfused tissue: Effects of surface coating, size and protein corona. Nanotoxicology, 12(10), 1093–1112.

4. Azizi, T., & Mugabi, R. (2020). Global sensitivity analysis in physiological systems. Applied Mathematics, 11(3), 119–136.

5. Azizi, T., & Kerr, G. (2020). Chaos synchronization in discrete-time dynamical systems with application in population dynamics. Journal of Applied Mathematics and Physics, 8(03), 406.