Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

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Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

Assistant Professor, Prof. Ramkrishna More Arts, Commerce & Science College, India

Dr. Santosh Jagtap, Assistant Professor at Prof. Ramkrishna More College (Autonomous), is a highly accomplished researcher and academic in the fields of Artificial Intelligence (AI) and Cybersecurity, with extensive expertise in applying AI to smart agriculture, healthcare security, IoT-enabled educational systems, and AI-driven safety solutions. Dr. Jagtap holds advanced academic qualifications and has developed a distinguished research profile that emphasizes practical applications of emerging technologies to address societal challenges. His work integrates machine learning, blockchain, IoT, and real-time data processing, producing innovative solutions in areas such as intelligent irrigation systems, plant disease detection, AI-based emotion recognition for safety alerts, and secure healthcare frameworks. Over his career, Dr. Jagtap has contributed significantly to international research projects and collaborative studies, producing high-impact publications in reputed journals and conference proceedings, such as Materials Today: Proceedings, international conferences on electronics, computing, and applied AI. He has also been recognized for innovation through patent awards, notably for AI-based plant disease identification systems, reflecting his focus on technology transfer and real-world impact. Dr. Jagtap has played an active role in mentoring students, guiding research projects, and participating in professional networks that foster academic and technological growth. He has demonstrated a consistent record of research excellence, with a total of 78 citations across 4 Scopus-indexed publications and an h-index of 3, reflecting the growing impact of his work.

Profile: GOOGLE SCHOLAR | SCOPUS

Featured Publications

  • Jagtap, S. T., Phasinam, K., Kassanu. (2022). Towards application of various machine learning techniques in agriculture. Materials Today: Proceedings, 51, 793–797. 70 citations.

  • Jagtap, S. T., Thakar, (2021). A framework for secure healthcare system using blockchain and smart contracts. Second International Conference on Electronics and Sustainable Technologies. 22 citations.

  • Jagtap, S. T., Jagdale, K. C., & Thakar, C. M. (2023). Identification of plant disease device using artificial intelligence. IN Patent 391523-001. –

  • Pratiksha Bhise, D. S. J., & Jagtap, S. T. (2024). AI-driven emergency response system for women’s safety using real-time location and heart rate monitoring. IJRPR. –

  • Keskar,  A., Jagtap, S. T., et al. (2021). Big data preprocessing frameworks: Tools and techniques. Design Engineering, 1738–1746.

Dr. Farnaz Farid | Healthcare industry | Best Researcher Award

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Dr. Farnaz Farid | Healthcare industry | Best Researcher Award

Multidisciplinary Researcher, Western Sydney University, Australia

Dr. Farnaz Farid is a distinguished and multidisciplinary researcher whose academic journey and professional experience span industry and academia, combining expertise in artificial intelligence, cybersecurity, human-centered systems, and applied computing. She holds a Doctor of Philosophy (PhD) degree from Western Sydney University, where her doctoral research focused on computational modeling, AI-driven predictive systems, and network quality of service frameworks; she also earned earlier degrees in engineering and computing from reputable institutions that shaped her foundation in IT, networks, and systems. Over the years, Dr. Farnaz Farid has served in both industry and academic roles: prior to joining academia, she worked at IBM as an IT Specialist, Application Developer, and Project Manager, contributing to enterprise integration, software development, and digital innovation; subsequently, she entered academia as an Associate Lecturer at the University of Sydney and then moved to Western Sydney University, where she is now a Senior Lecturer and Academic Program Advisor, co-leading global initiatives such as “Realising Digital Futures.” Her professional experience includes overseeing cross-disciplinary projects in AI, cybersecurity, IoT, and smart systems, mentoring postgraduate researchers, guiding curriculum development, and fostering partnerships with industry and community stakeholders. Her research interests encompass explainable AI, human-centred security, AI for healthcare, cyber‐physical systems, distributed networks, federated learning, and digital inclusion. Dr. Farid has received a number of awards and honors, such as the Google exploreCSR grant over multiple years to lead community‐based AI projects, the DVC Education Excellence in Teaching (Partnerships) award at her university, and the Teaching and Learning for Public Good Award in Social Sciences, all of which attest to her excellence in teaching, public engagement, and socially impactful research. Through her editorial service (for journals such as Symmetry and Sustainability), membership in the Asian Council of Science Editors (ACSE), and leadership of cross‐disciplinary grants, she has also contributed to the scientific community.

Profile: GOOGLE SCHOLAR  | SCOPUS | ORCID

Featured Publications

  • Farid, F. (2025). An explainable predictive model for the detection of mental health conditions in the workplace. (citation count: 13)

  • Farid, F. (2025). A threat analysis framework for cyberattacks in smart cities: ransomware in focus. (citation count: 24)

  • Dong, H., & Farid, F. (2024). A deep learning based patient care application for skin cancer detection.

  • Farid, F., & colleagues. (2024). AI technologies in reducing hospital readmission for chronic diseases: a recommended framework.

  • Lai, T., & Farid, F. (2024). Ensemble learning for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis.

Ms. Asma Rehman | ML in Chemistry | AI & Machine Learning Award

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Ms. Asma Rehman | ML in Chemistry | AI & Machine Learning Award 

Accomplished Researcher, University of Agriculture Fasilabad, Pakistan

Ms. Asma Rehman is an accomplished researcher and academic from Pakistan, currently affiliated with the University of Agriculture Faisalabad, where she specializes in Green Chemistry, Organocatalysis, Polymer Degradation, and Artificial Intelligence Applications in Chemical Process Modeling.  Ms. Rehman’s research interests are strongly interdisciplinary, focusing on the integration of Artificial Intelligence and Data Science techniques with Green Chemistry principles to develop predictive models for catalytic reactions, optimize degradation processes of polymers like polystyrene, and design sustainable, energy-efficient pathways for environmental remediation. Her scientific vision aims to utilize machine learning algorithms for reaction kinetics modeling and to establish scalable frameworks for waste management, industrial pollution control, and material upcycling. In her academic journey, she has co-authored several peer-reviewed journal articles published in globally recognized outlets such as RSC Advances and AI (MDPI), highlighting her contributions to both computational and experimental chemistry. Her major research contributions include AI-assisted photocatalysis for wastewater treatment, organocatalyst design for green synthetic chemistry, and studies combining photoredox catalysis with sustainable material science. Ms. Rehman’s technical proficiency encompasses a diverse range of research skills, including statistical data analysis, reaction kinetics modeling, computational chemistry tools, spectral characterization, and data-driven optimization frameworks. Her ongoing work focuses on merging Bayesian inference models and supervised machine learning for predictive chemical engineering applications, reflecting her capability to adapt AI tools within the scientific process. She has been recognized within her institution for her academic excellence and research initiative, mentoring undergraduates and participating in interdisciplinary academic forums that align with the United Nations Sustainable Development Goals (SDGs).

Profile: ORCID

Featured Publications

  • Rehman, A., Iqbal, M. A., Haider, M. T., & Majeed, A. (2025). Artificial intelligence-guided supervised learning models for photocatalysis in wastewater treatment. AI, 6(10), Article 0258. Citations: 6

  • Ahad, A., Majeed, A., Zafar, A., Iqbal, M. A., Ali, S., Batool, M., Rehman, A., & Manzoor, F. (2025). A green marriage: The union of theophylline’s catalytic activity and healing potential. RSC Advances, 15(9), 8479A. Citations: 10

  • Manzoor, F., Majeed, A., Ibrahim, A. H., Iqbal, M. A., Rehman, A., Aziz, S., Shahzadi, A., Fatima, S., Ejaz, S., & Zafar, M. S. (2025). Nickel-photoredox catalysis: Merging photons with metal catalysts for organic synthesis. RSC Advances, 15(12), 4650E. Citations: 8

  • Rehman, A., Iqbal, M. A., & Majeed, A. (2025). Machine learning-assisted modeling of polystyrene degradation using green catalysts for sustainable waste valorization. Journal of Environmental Chemical Engineering, 13(7). Citations: 5

  • Rehman, A., Manzoor, F., & Majeed, A. (2025). Data-driven optimization of organocatalytic pathways for eco-friendly polymer processing. Green Chemistry Letters and Reviews, 18(4). Citations: 4

Mr. Ankush Sharma | Quantile Life Prediction | Young Researcher Award

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Mr. Ankush Sharma | Quantile Life Prediction | Young Researcher Award 

Emerging Research Scholar, Banaras Hindu University, India

Mr. Ankush Sharma is a dynamic and emerging research scholar in the domain of Statistics, specializing in Survival Analysis, Reliability Engineering, Degradation Modeling, Bayesian Estimation, and Functional Modeling. He is currently pursuing his Ph.D. in Statistics from Banaras Hindu University, Varanasi, India, where his research focuses on Statistical Modeling and Experimental Designs Planning for Highly Reliable Products under the supervision of Prof. Sanjeev Kumar. He has contributed actively to the global research community through publications in reputed Scopus and SCI-indexed journals and has served as a reviewer for distinguished journals such as the International Journal of Quality & Reliability Management and the Asia Pacific Prognostics and Health Management Conference. His research interests include the design of experiments for high-reliability systems, stochastic degradation modeling, and Bayesian hierarchical analysis for predictive maintenance and reliability forecasting.  His published work demonstrates his capacity for innovation and rigor, as seen in his research on thermal damage modeling, accelerated degradation testing, and stochastic EM approaches for reliability prediction. With a clear vision toward academic and research excellence, Mr. Ankush Sharma continues to contribute meaningfully to the statistical sciences community through teaching assistance, peer reviewing, and mentoring junior researchers. His professional trajectory, marked by academic distinction, research innovation, and scientific integrity, positions him as a promising scholar and future academic leader in applied statistics and reliability research.

Profile: Google Scholar | ORCID

Featured Publications

  • Sharma, A. (2025). Determination of Thermal Damage and Failure Time Analysis in Rocks Using Stochastic Models. Quality Reliability Engineering International, 2 citations.

  • Sharma, A., Tomer, S. K., & Panwar, M. S. (2025). Optimal Plans for Accelerated Destructive Degradation Tests with Stress Interaction Effects. Manuscript under review.

  • Sharma, A., & Tomer, S. K. (2025). Modeling Degradation Processes with Covariate-Dependent Random Initiation: A Stochastic EM Approach with Application to Rock Mechanics. Manuscript under review.

  • Sharma, A., & Tomer, S. K. (2025). Survival Adjusted Sequential Bayesian Experimental Designs for Degradation Models. Manuscript under review.

Mr. Iftikhar ud Din | Applied Electromagnetics | Young Researcher Award

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Mr. Iftikhar ud Din | Applied Electromagnetics | Young Researcher Award

Accomplished Researcher, University of Quebec at Trois-Rivieres, Canada

Mr. Iftikhar ud Din is an accomplished researcher and academic specializing in Telecommunication Engineering, with expertise in antenna design, applied electromagnetics, microwave and millimeter-wave systems, and metamaterial-based biosensors. He is currently pursuing his Ph.D. at the University of Quebec at Trois-Rivières (UQTR), Canada, focusing on the design and prototyping of reconfigurable intelligent surface (RIS)-aided communication systems, a cutting-edge area driving advancements in 6G networks. He holds a Master’s in Telecommunication Engineering and a B.Sc. (Hons.) in Telecommunication Engineering from the University of Engineering and Technology (UET), Peshawar, Pakistan, where his theses focused on metasurface-based 5G antennas and ultra-wideband circular monopole antennas. Professionally, Mr. Iftikhar is associated with the Electromagnetic and Antenna Research Group (EMARG) at UET Mardan, contributing to the design and analysis of high-gain antennas for sub-6 GHz and millimeter-wave spectrums. His research interests include reconfigurable intelligent surfaces (RIS), metamaterial-based antenna systems, terahertz nano-biosensors, and electromagnetic sensing for biomedical and communication applications, integrating AI-based simulation and optimization approaches. His research skills encompass electromagnetic simulation, antenna miniaturization, high-frequency modeling, and metamaterial design for next-generation sensors and communication systems. With 26 Scopus-indexed publications and 351 citations by 253 documents (Scopus ID: 57222105830), his work has been featured in prestigious journals such as IEEE Sensors Journal, IEEE Photonics Journal, Journal of Infrared, Millimeter, and Terahertz Waves, and PLOS ONE.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  • Iftikhar, U. D., Abbasi, N. A., Ullah, W., Ullah, S., Ouameur, M. A., & Jayakody, D. N. K. (2024). A novel and compact metamaterial‐based four‐element MIMO antenna system for millimeter‐wave wireless applications with enhanced isolation. International Journal of Antennas and Propagation, 2024(1), 7480655. (7 citations)

  • Hamza, M. N., Islam, M. T., Lavadiya, S., Iftikhar, U. D., Sanches, B., Koziel, S., & Naqvi, S. I. (2025). Design and validation of ultra-compact metamaterial-based biosensor for non-invasive cervical cancer diagnosis in terahertz regime. PLOS ONE, 20(2), e0311431. (6 citations)

  • Hamza, M. N., Islam, M. T., Lavadiya, S., Iftikhar, U. D., Sanches, B., Koziel, S., & Naqvi, S. I. (2025). Ultra-compact quintuple-band terahertz metamaterial biosensor for enhanced blood cancer diagnostics. PLOS ONE, 20(1), e0313874. (20 citations)

  • Abbasi, N. A., Virdee, B., Iftikhar, U. D., Ullah, S., Althuwayb, A. A., Rashid, N., & Soruri, M. (2025). High-isolation array antenna design for 5G mm-wave MIMO applications. Journal of Infrared, Millimeter, and Terahertz Waves, 46(1), 12. (12 citations)

Dr. Prashant Kapil | AI and Natural Language Processing | Best Researcher Award – 2243

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Dr. Prashant Kapil | AI and Natural Language Processing | Best Researcher Award 

Distinguished Academic and Researcher, Bennett University, India

Dr. Prashant Kapil is a dedicated researcher and academic recognized for his expertise in Artificial Intelligence (AI) and Natural Language Processing (NLP), focusing on hate speech detection, cross-lingual learning, and ethical AI for social media safety. He earned his Ph.D. in Computer Science and Engineering from the Indian Institute of Technology Patna,  Dr. Kapil also holds an M.E. in Software Engineering from Jadavpur University and a B.Tech in Information Technology from the West Bengal University of Technology, forming a strong academic foundation in computational systems and intelligent algorithms. Professionally, he serves as an Assistant Professor at Bennett University (The Times Group), where he teaches and supervises research in machine learning, NLP, and AI ethics. His Scopus ID is 57219354937, with 157 citations from 154 documents, 4 indexed publications, and an h-index of 3, underscoring his growing research influence. His interests span multimodal and multilingual NLP, transformer-based deep learning, sentiment and emotion analysis, and AI fairness in communication technologies. Dr. Kapil possesses advanced research and analytical skills in Python programming, deep learning frameworks, and computational model development, complemented by his expertise in academic writing and data-driven experimentation. His honors include the Graduate Aptitude Test in Engineering (GATE) Scholarship, the UGC JRF-SRF Fellowship, and the Institute Ph.D. Fellowship from IIT Patna, recognizing his outstanding academic achievements.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

  • Kapil, P., & Ekbal, A. (2025). A transformer-based multi-task learning approach to multimodal hate detection. Natural Language Processing Journal, 2025. (12 citations)

  • Kapil, P., & Ekbal, A. (2023). HHSD: Hindi hate speech detection leveraging multi-task learning. IEEE Access, 2023. (45 citations)

  • Kapil, P., & Ekbal, A. (2020). A deep neural network-based multi-task learning approach to hate speech detection. Knowledge-Based Systems, 2020. (68 citations)

  • Kapil, P., & Ekbal, A. (2024). Cross-lingual zero-shot and few-shot learning for hate speech detection. SSRN Working Paper, 2024. (32 citations)

Dr. Manisha Nene | Artificial Intelligence | Best Researcher Award

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Dr. Manisha Nene | Artificial Intelligence | Best Researcher Award 

Seasoned Leader, Defence Institute of Advanced Technology, India

Dr. Manisha Nene, a seasoned leader at the intersection of research, academia, and industry, holds a Ph.D. in Computer Science and has devoted over two decades to advancing artificial intelligence and cybersecurity. Throughout her career she has held key leadership roles, including Director of the School of Mathematical Sciences and Computer Engineering and Head of the Department of Computer Science & Engineering at DIAT-DRDO. Her professional experience spans guiding doctoral and master’s scholars, leading national-level research projects, and founding MAJINE Systems Pvt. Ltd., which develops cybersecurity and AI-based solutions rooted in her patented innovations. Dr. Nene’s research interests lie in secure AI, trustworthy computing, digital transformation, and responsible infrastructure. She is proficient in advanced research skills such as machine learning, adversarial defense, threat modeling, deep neural networks, cryptographic protocols, and data analytics. Over her career she has received numerous awards, including IETE’s Smt. Triveni Devi Award for her contributions to ICT for society, the Future Crime Research Foundation’s Award of Excellence for PAN-India cyber security training, institute-level Researcher of the Year awards, and multiple Best Paper Awards at international conferences. Her Scopus profile reflects 129 documents, over 716 citations, and an h-index of 13 (Scopus ID: 35488434700).

profile: GOOGLE SCHOLAR | SCOPUS | ORCID 

Featured Publications

  • Nene, M. A secure AI framework for adversarial attack mitigation in critical infrastructures. (202, 45 citations)

  • Nene, M. Trustworthy deep learning in cyber-physical systems: techniques and challenges. (2022, 55 citations)

  • Nene, M. Privacy-preserving machine learning with homomorphic encryption in cloud environments. (2020, 38 citations)

  • Nene, M. Blockchain-enabled authentication protocols for Internet of Things security. (2019, 29 citations)

Ms. Kaiser Sun | Natural Language Processing | Young Scientist Award

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Ms. Kaiser Sun | Natural Language Processing | Young Scientist Award

Emerging Leader in AI, Johns Hopkins University, United States

Ms. Kaiser Sun is an emerging leader in artificial intelligence and computational linguistics whose work bridges fundamental research and practical impact. She is currently pursuing a Ph.D. in Computer Science at Johns Hopkins University under Professor Mark Dredze, building on her M.S. in Computer Science and Engineering from the University of Washington and dual B.S./B.A. degrees in Computer Science & Engineering and Mathematics from the same institution. Ms. Kaiser Sun has accumulated a rich portfolio of professional experience, including roles as Applied Scientist Intern at Amazon Web Services AI Labs, AI Resident at Meta AI – FAIR Labs, Software Development Engineer Intern at Amazon, Data Science Intern at Noonum, undergraduate researcher at the Washington Experimental Mathematics Lab, and intern at NOAA. Across these positions she has collaborated with leading mentors such as Peng Qi, Yuhao Zhang, Adina Williams, and Dieuwke Hupkes. Her primary research interests focus on natural language processing, large language models, interpretability, multilingual assessment of stereotypes, and the intersection of optimization and model evaluation. Ms. Kaiser Sun’s research skills span deep learning architectures, empirical foundations of machine learning, convex optimization, multilingual NLP, and large-scale model analysis; she is proficient in Python, Java, TypeScript, SQL, JavaScript, C++, R, and MATLAB, and experienced with PyTorch, AllenNLP, Spark, AWS, Microsoft Azure, and React. Her work has appeared in respected venues such as Nature Machine Intelligence, Findings of ACL, Findings of EMNLP, and NAACL, and she has contributed to influential community efforts like Queer in AI and Google Research’s CSRMP mentorship program. On Scopus, Ms. Kaiser Sun holds ID 57224529767 with 70 total citations indexed across 68 documents, 5 primary authored documents, and an h-index of 2 — impressive indicators for a researcher at her career stage.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

  • Sun, K., Marasović, A. (2021). Effective attention sheds light on interpretability. Findings of ACL. 23 citations.

  • Sun, K., Qi, P., Zhang, Y., Liu, L., Wang, W. Y., Huang, Z. (2023). Tokenization consistency matters for generative models on extractive NLP tasks. Findings of EMNLP. 17 citations.

  • Mitchell, M., Attanasio, G., Baldini, I., Clinciu, M., Clive, J., Delobelle, P., … Sun, K. (2025). SHADES: Towards a multilingual assessment of stereotypes in large language models. Proceedings of NAACL. 12 citations.

  • Sun, K., Dredze, M. (2024). Amuro & Char: Analyzing the relationship between pre-training and fine-tuning of large language models. Proceedings of the 10th Workshop on Representation Learning for NLP. 10 citations

Prof. Dr. Salem Alkhalaf | Artificial Intelligence | Best Academic Researcher Award

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Prof. Dr. Salem Alkhalaf | Artificial Intelligence | Best Academic Researcher Award

Distinguished Researcher, Qassim University, Saudi Arabia

Prof. Dr. Salem Alkhalaf is a dynamic and accomplished researcher whose work spans information and communication technology, e-learning systems, and digital transformation. He holds a Ph.D. in Information and Communication Technology from Griffith University, supported by prior degrees in ICT and Computer Education. Prof. Dr. Salem Alkhalaf currently serves in senior academic and leadership roles at Qassim University, where he has steered initiatives in enterprise architecture, digital content management, and e-learning strategy. His research interests include collaborative learning environments, information quality in learning management systems, usability evaluation, and culturally adaptive educational technologies. He excels in research skills such as mixed methods design, structural equation modeling, system evaluation, cross-cultural adaptation, and large-scale empirical studies. He maintains an outstanding scholarly footprint: Scopus ID: 41661143900, with 2,021 citations across 1,885 documents, 179 published works, and an h-index of 23. His professional engagements include membership in IEEE, ACM, ACS, contributions as a reviewer and editorial board member, and leadership in national e-government and audit teams. Recognized through institutional awards, research grants, and best paper honors, he is committed to advancing scholarship, mentoring emerging researchers, and expanding global collaborations. Prof. Dr. Salem Alkhalaf combines visionary leadership with rigorous scholarship, making him a prominent figure positioned to drive future breakthroughs in AI, educational technology, and ICT research.

Profile: GOOGLE SCHOLAR | SCOPUS  | ORCID

Mr. Yishak Beyene | Soil Science | Academic Brilliance Recognition Award

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Mr. Yishak Beyene | Soil Science | Academic Brilliance Recognition Award

Researcher, Wachemo University, Ethiopia

Mr. Yishak Beyene is a dedicated Soil Science lecturer, researcher, and advisor at Wachemo University, Ethiopia, with a distinguished academic and professional trajectory in soil chemistry, fertility, land use, and geospatial analysis. He completed his B.Sc. in Natural Resource Management and M.Sc. in Soil Science from Wolaita Sodo University, building a solid foundation for research in sustainable land and soil management. His professional experience encompasses lecturing undergraduate soil science courses, supervising student research and seminars, conducting soil laboratory and field analyses, and providing academic consultation. He has served as a reviewer for international journals including Taylor & Francis, Hindawi, and Heliyon. Mr. Beyene’s research interests focus on phosphorus sorption characteristics, land-use impacts on soil properties, soil fertility mapping, crop suitability analysis, conservation tillage, agroforestry, and hydro-climatic variability, employing advanced analytical techniques such as GIS, GPS, and statistical software including R and SAS. His key research skills include experimental soil science, field and laboratory methodologies, geospatial data analysis, project coordination, and academic mentorship. His professional achievements are highlighted by publications in reputable journals, ongoing high-impact research projects, and active participation in student mentorship, voluntary academic initiatives, and community-oriented research activities, reflecting strong leadership and collaboration capabilities. His awards and honors include recognition for academic excellence during his higher education and multiple acknowledgments for his active engagement in research and professional development programs.

Profile: ORCID

Featured Publications

  • Beyene, Y., Laekamariam, F., Alemayehu, K., Gifole, G., Lakew, G., & Alemu, A. (2022). Phosphorus sorption characteristics of acidic Luvisols and Nitisols under varying lime rates, and response validation using wheat. Communications in Soil Science and Plant Analysis. https://doi.org/10.1080/00103624.2022.2070637

  • Beyene, Y., Mulatu, C., & Tamrat, S. (2022). Effect of land use on selected soil properties in different soil types at Harar Gita Watershed, Southern Ethiopia. Uttar Pradesh Journal of Zoology, 43(13), 1–8.

  • Beyene, Y. (2022). Evaluation of adsorption kinetics and isotherm models for phosphorus in Luvisols and Vertisols under different land uses in Central Ethiopia. Advances in Environmental and Soil Science.

  • Beyene, Y., Gebre, B., Michael, M. W., & Taye, K. (Ongoing). Selected soil physicochemical characteristics under different land use and soil depth in Handosha Sub-Watershed, Hadiya Zone, Southern Ethiopia.