Nikhil Patel | Business Intelligence | Best Researcher Award

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Mr. Nikhil Patel | Business Intelligence | Best Researcher Award

Consultant at Deloitte Consulting LLP, United States.

Nikhil Patel is a dedicated consultant at Deloitte Consulting LLP, based in Houston, Texas. With over a decade of experience in technology consulting, HR systems, and business solutions, he has established himself as an expert in SAP SuccessFactors and PeopleSoft. His career spans multiple countries, including India, Singapore, and the United States, where he has contributed significantly to HR technology transformations. Beyond his consulting work, Nikhil has an extensive research background in artificial intelligence, business optimization, and machine learning, with numerous publications in reputed journals. His commitment to innovation and excellence in technology-driven HR solutions makes him a valuable asset in the field of business consulting.

Profile

Google Scholar

Education

Nikhil Patel holds a Master of Business Administration (MBA) from the University of Dubuque, USA, completed between 2020 and 2022. His technical foundation was built with a Bachelor of Engineering (B.E.) in Computer Engineering from Mumbai University, India, which he earned in June 2010. Prior to his bachelor’s degree, he completed a Diploma in Electronics Engineering from the Maharashtra State Board of Technical Education, India, in April 2007. These academic qualifications have provided him with a strong understanding of both technical and business aspects, enabling him to bridge the gap between IT and business processes effectively.

Professional Experience

Currently, Nikhil Patel serves as a consultant at Deloitte Consulting LLP in Houston, Texas, where he assists clients in addressing HR function challenges and implementing advanced payroll and HR solutions. Before joining Deloitte, he worked as an SAP Consultant at Delta System and Software, where he played a key role in designing and implementing SAP SuccessFactors enhancements. His previous roles include Senior Systems Manager at Singapore Zoological Gardens, HRIS Manager at NTUC Fairprice Co-operative Ltd, and PeopleSoft Consultant at Toss-Ex Pte Ltd and MInSysT Consulting Pvt. Ltd. Over the years, he has gained expertise in global HR strategies, system implementations, and software consulting, making significant contributions to HR technology transformations in various industries.

Research Interests

Nikhil Patel’s research interests lie in artificial intelligence, business process optimization, machine learning applications, and digital transformation in human resources. He has extensively explored AI-driven predictive modeling, deep learning applications, and optimization algorithms. His work delves into how AI can enhance business efficiency, improve customer loyalty, and optimize workforce management. Additionally, he is passionate about studying the impact of AI in healthcare, finance, and education, particularly in improving decision-making processes and automating key functions. His research contributions have been recognized internationally, making him a respected voice in AI and business technology.

Awards

Nikhil Patel has received multiple recognitions for his contributions to AI and business technology. His work has been acknowledged in various international conferences, where he has served as an invited speaker and technical chair. He has also been nominated for prestigious awards in AI research and business consulting. His commitment to innovation and excellence has led to his involvement in multiple peer-review committees and editorial boards for reputed journals and conferences, further establishing his influence in the field.

Publications

Nikhil Patel has authored and co-authored multiple research papers published in reputed journals. Some of his notable publications include:

“Choosing Optimal Locations for Temporary Health Care Facilities During Health Crisis Using Binary Integer Programming” – Published in Sage Science Review of Applied Machine Learning.

“Combatting COVID-19: Artificial Intelligence Technologies & Challenges” – Published in Science Open Preprints.

“Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty” – Published in Empirical Quests for Management Essences.

“Inverted U-shape Trajectories of Student Engagement and Teacher Satisfaction in Online Classes: Nonlinear Impact of Zoom Fatigue” – Published in Sage Science Review of Educational Technology.

“Gen-Optimizer: A Generative AI Framework for Strategic Business Cost Optimization” – Published in MDPI/Computers (Submitted).

“The Role of Automation and Artificial Intelligence in Increasing the Sales Volume: Evidence from M, S, and MM Regressions” – Published in the International Journal of Contemporary Financial Issues.

“Mining Public Opinion about Hybrid Working with Roberta” – Published in Empirical Quests for Management Essences.

Conclusion

Nikhil Patel is a distinguished consultant and researcher whose expertise bridges business and technology. His extensive experience in SAP SuccessFactors, AI-driven business optimization, and HR digital transformation has made a significant impact in the consulting and research fields. Through his work at Deloitte and previous roles, he has contributed to global HR strategies, system implementations, and AI research. His numerous publications and conference contributions highlight his dedication to advancing AI applications in business. With a career that continues to evolve, Nikhil remains a prominent figure in HR technology and AI research, shaping the future of digital transformation in business consulting.

Mustapha Mohammed Suraj | Open Source Tools in Data Science | Best Researcher Award

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Mustapha Mohammed Suraj | Open Source Tools in Data Science | Best Researcher Award

 

Research assistant at CSIR-Savanna Agricultural Research Institute, Ghana

Mustapha Mohammed Suraj is a skilled Research Assistant in the Socio-economics section of CSIR-Savanna Agricultural Research Institute (CSIR-SARI) in Tamale, Ghana. With over eight years of professional experience, Suraj specializes in agribusiness and socio-economic research, focusing on sustainable agricultural practices, food security, and farmers’ welfare. His work primarily revolves around data collection, monitoring, evaluation, and designing research instruments for agricultural studies. Suraj’s passion for development is underscored by his proficiency in various data management and analysis tools, such as SPSS, Stata, and Kobo Collect, which enable him to conduct detailed research to enhance the livelihoods of farmers.

Profile

Scopus

Education

Mustapha completed his MPhil in Agribusiness from the University of Ghana in 2023 and obtained his BSc. in Agribusiness from the University for Development Studies, Tamale, in 2017. His academic training has been complemented by additional qualifications in various fields, including Gender and Agriculture, Qualitative Research, and Quantitative Impact Assessment of Agricultural Research and Innovation, further broadening his expertise and approach to research.

Experience

Since 2018, Mustapha has held a prominent role as a Research Assistant at CSIR-SARI, where his main duties include implementing socio-economic studies, conducting qualitative research, and managing data collection and reporting activities. Before this role, he worked as a Service Personnel at CSIR-SARI, assisting in data analysis and literature reviews. Additionally, Mustapha has served as a Field Assistant with BUSAKA Agribusiness Company, where he trained farmers on Good Agricultural Practices (GAPs), land preparation, seed selection, and post-harvest management. His diverse experience spans working with international organizations and research teams, including USAID, CIMMYT, and the World Food Program (WFP), on projects aimed at improving agricultural extension, irrigation, and food security.

Research Interests

Mustapha’s primary research interests focus on post-harvest management, agricultural trade, sustainable agricultural practices (SAPs), climate change, food security, and the welfare of farmers. His work emphasizes understanding the socio-economic dynamics of agricultural communities, particularly in Ghana, to help develop policies and practices that support sustainable development and food security. He is particularly interested in the intersection of climate change and agriculture, exploring how farmers can adapt to new challenges and improve their productivity.

Awards

Mustapha has received notable recognition for his academic excellence and commitment to research. In March 2023, he was awarded the WACCI Scholarship for Postgraduate Students under the Ace Impact Project. He also received the A.G. Leventis Foundation Scholarship for Graduate Students in Agriculture and Veterinary Medicine in March 2023. These scholarships reflect his dedication to advancing agricultural research and development.

Publication and Research Experience

Mustapha has authored and co-authored several impactful publications that address critical agricultural issues. His recent publications include:

Martey, E., Etwire, P. M., Suraj, M. M., & Goldsmith, P. (2023). “PICS or poly sack: Traders’ willingness to invest in storage protection technologies.” Journal of Agriculture and Food Research, 14, 100691.

Martey, E., Etwire, P. M., Kuwornu, J. K., & Suraj, M. M. (2024). “Micro-level welfare effects of integrated soil fertility management in northern Ghana.” Journal of Cleaner Production, 144224.

Martey, E., Etwire, P. M., Asante-Addo, C., Darko, F. A., & Suraj, M. M. (2025). “Examining the risk mitigation strategies of farm households in Ghana: The role of community water resources.” Journal of Environmental Management, 373, 123838.

Suraj, M. M., Martey, E., Kuwornu, J. K., Apiors, E. K., Kemeze, F. H., & Etwire, P. M. (2025). “Membership of Water User Association and Implications for Food Security.” Journal of Agriculture and Food Research, 101739.
These publications explore key agricultural issues in Ghana and the broader African context, contributing valuable insights to the field of agricultural economics and food security.

Conclusion

Mustapha Mohammed Suraj is a dedicated agricultural and agribusiness researcher whose work has significantly contributed to the advancement of sustainable agriculture, climate change adaptation, and food security in Ghana. With a strong academic background and extensive field experience, he remains committed to developing innovative solutions to improve agricultural practices and the livelihoods of farmers. His active participation in research projects and his recognition through awards and fellowships further attest to his potential to shape the future of agriculture in Africa. His diverse expertise in data management, analysis, and field research continues to make a substantial impact on agricultural development in Ghana and beyond.

Nadeem Iqbal | Cryptography | Best Researcher Award

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Dr. Nadeem Iqbal | Cryptography | Best Researcher Award

Associate Professor at The University of Lahore, Pakistan

Nadeem Iqbal is an accomplished academic and researcher, currently serving as an Associate Professor at The University of Lahore, Pakistan. He completed his M.S. from the prestigious National University of Sciences and Technology (NUST), Islamabad, specializing in theorem proving, and later obtained his Ph.D. from NCBA&E. His academic journey is marked by an exceptional GRE quantitative score of 800/800 and an IBM certification in data science. Iqbal is renowned for his research contributions in the fields of cryptography, cybersecurity, and data protection, advancing digital security with innovative encryption algorithms and cryptographic frameworks.

Profile

Google Scholar

Education

Dr. Iqbal’s academic qualifications include an M.S. from NUST, Islamabad, where he specialized in theorem proving, and a Ph.D. from NCBA&E, Pakistan. His strong educational background laid the foundation for his deep research interests in cybersecurity and cryptography. Throughout his educational journey, he exhibited exceptional aptitude, demonstrated by his perfect GRE quantitative score, setting the stage for a career in academia and research. Additionally, his IBM certification in data science reflects his broad expertise in both theoretical and applied aspects of digital security.

Experience

As an Associate Professor at The University of Lahore, Dr. Iqbal has significantly contributed to the academic community through teaching and research. His professional experience includes involvement in over 40 completed and ongoing research projects, with a notable focus on cybersecurity and cryptography. His expertise has not only shaped the academic direction at his institution but also in the wider research community. Dr. Iqbal’s role as a consultant on seven industry projects further showcases his ability to bridge the gap between academia and industry, addressing real-world cybersecurity challenges.

Research Interests

Dr. Iqbal’s primary research interests revolve around cryptography, cybersecurity, and multimedia encryption. He focuses on developing new encryption algorithms, advancing S-box designs, and exploring chaotic maps, chess pieces, and DNA cryptography. His work has enhanced the resilience of digital infrastructures and secured systems against modern cybersecurity threats. Additionally, his research has implications in IoT security, smart cities, and AI-driven cryptographic methods, making significant strides in improving the overall security of digital systems worldwide.

Awards

Muhammad Nadeem Iqbal’s research achievements have not gone unnoticed. He has garnered recognition for his impactful contributions to digital security and cryptography. His innovative research in developing encryption algorithms and advancing cryptographic techniques has been widely cited, establishing him as an authority in the field. Dr. Iqbal’s outstanding academic and professional contributions position him as a strong candidate for the Best Researcher Award. His influence on both academia and industry exemplifies his commitment to advancing digital security, making him a deserving nominee for such honors.

Publications

Dr. Iqbal’s body of work includes 28 journal articles published in prestigious indexed journals. These articles address key aspects of cryptography and cybersecurity, such as the development of novel encryption algorithms and cryptographic frameworks. Some of his key publications include:

Iqbal, M. N., et al. (2020). “A New Approach to S-Box Design for AES Cryptography.” Journal of Cryptographic Engineering.

Iqbal, M. N., & Ahmad, R. (2019). “Chaotic Maps for Secure Communication in IoT Networks.” Cybersecurity and Privacy.

Iqbal, M. N., et al. (2018). “DNA-based Cryptography for Advanced Security Protocols.” International Journal of Cryptology.

Iqbal, M. N., & Shah, T. (2021). “AI-Driven Cryptographic Systems for Next-Generation Security.” Journal of Artificial Intelligence and Cybersecurity.

Iqbal, M. N., et al. (2017). “Multimedia Encryption for Secure Data Transmission in Smart Cities.” Journal of Cybersecurity Research.

Iqbal, M. N., & Khan, M. A. (2022). “Next-Gen Cryptographic Algorithms for Blockchain Security.” Blockchain Technology and Cryptography Journal.

Iqbal, M. N., & Farooq, M. (2019). “Advances in Public Key Cryptography for Secure Cloud Computing.” International Journal of Cloud Computing and Security.

His publications have been widely cited by other researchers in the field, contributing to further advancements in the domain of cybersecurity.

Conclusion

Dr. Muhammad Nadeem Iqbal’s exceptional contributions to cybersecurity and cryptography have established him as a leading figure in the field. His research in encryption algorithms, cryptographic systems, and digital security has had significant implications for enhancing the security of data and digital infrastructures. With a strong academic background, an impressive record of publications, and considerable industry experience, he continues to shape the future of cybersecurity. Dr. Iqbal’s expertise and innovative approach to solving complex security challenges make him a highly deserving candidate for the Best Researcher Award, recognizing his dedication to advancing digital security in the modern era.

Tushar Kafare | Artificial Intelligence | Best Researcher Award

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Dr. Tushar Kafare | Artificial Intelligence | Best Researcher Award

Assistant Professor at Sinhgad College of Engineering, India

Dr. Tushar Vaman Kafare is an Assistant Professor in the Department of Electronics and Telecommunication (E&TC) at the Sinhgad Technical Education Society (STES). With over 14 years of experience in teaching, he has made a significant impact in the field of Electronics and Telecommunication. His research and expertise span across machine learning, deep learning, computer vision, embedded systems, and various programming languages like Python, MATLAB, C, and Embedded C. Dr. Kafare is known for his dedication to teaching and research, having guided numerous student projects and published research work, focusing particularly on machine learning applications in plant disease analysis.

Profile

Google Scholar

Education

Dr. Kafare holds an M.E. degree in Electronics and Telecommunication, as well as a B.E. in Electronics. His strong academic background has been further reinforced by his ranking 6th in his graduation. His academic qualifications, combined with extensive practical and theoretical knowledge, make him a highly skilled educator and researcher. His ongoing Ph.D. research focuses on plant disease analysis using machine learning models, showcasing his commitment to advancing technological applications in agriculture.

Experience

Having joined STES on September 7, 2022, Dr. Kafare brings with him a wealth of experience in academia and industry. His teaching career spans over 14 years, during which he has mentored undergraduate and postgraduate students. He has contributed significantly to course development and the enhancement of educational experiences for students, incorporating advanced techniques in machine learning and embedded systems. Additionally, Dr. Kafare has served as a resource person for numerous workshops and faculty development programs, further demonstrating his expertise and commitment to professional growth.

Research Interests

Dr. Kafare’s primary research interest lies in the application of machine learning and image processing for agricultural advancements. His Ph.D. research focuses on using machine learning models to analyze plant diseases, particularly in grape and apple plants, through advanced image processing techniques. He is also interested in deep learning, computer vision, and embedded systems, areas that allow for the development of innovative solutions for real-world problems. Through his research, he aims to contribute to the growing field of agri-tech by leveraging modern computational techniques to assist in plant disease diagnostics and management.

Awards

Dr. Kafare has been recognized for his outstanding contributions in teaching and research. He received the prestigious Digital Teacher Award from ICT Academy, highlighting his exceptional use of technology in education. Additionally, his academic excellence is reflected in his university ranking, securing 6th place in his graduation. In 2024, he was honored with the Best Paper Award at the International Conference on Machine Learning in Jaipur, India, acknowledging the high impact and relevance of his research in the machine learning community.

Publications

Dr. Kafare has made significant contributions to the field of machine learning and telecommunication through his publications. His work has been widely cited, demonstrating the importance of his research. Below is a list of selected publications:

Kafare, T.V. et al., “Analysis on Plant Disease Diagnosis Using Convolution Neural Networks,” International Journal of Machine Learning, 2023, Scopus/SCI.

Kafare, T.V. et al., “Segmentation Techniques for Plant Disease Detection,” Journal of Image Processing, 2022, Scopus.

Kafare, T.V., “Double Convolution in CNN for Improved Plant Disease Classification,” International Conference on Machine Learning, 2024, Conference paper.

Kafare, T.V., et al., “Fungal Disease Detection in Grapes Using Machine Learning,” Journal of Agricultural Technology, 2021, Scopus.

Conclusion

Dr. Tushar Vaman Kafare’s career is marked by his dedication to both teaching and research, with a clear focus on applying machine learning and image processing to solve practical problems in agriculture. With over 14 years of teaching experience, he has proven himself as a skilled educator and researcher. His ongoing Ph.D. research, along with his numerous publications and awards, highlights his expertise in his field. As an active participant in academic and professional activities, he continues to contribute to the development of students and the academic community at large, particularly in the domains of machine learning and embedded systems.

Eric Nizeyimana | AI in Healthcare | Best Researcher Award

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Dr. Eric Nizeyimana | AI in Healthcare | Best Researcher Award

Lecturer at University of Rwanda, Rwanda.

Eric Nizeyimana is a highly accomplished researcher, educator, and IT professional with a Ph.D. in Internet of Things (IoT) with a specialization in Embedded Systems from the University of Rwanda. His expertise encompasses a broad spectrum of advanced technologies such as IoT, Machine Learning, Blockchain, Security, and Embedded Systems. Nizeyimana’s research journey has led him to international academic exchange programs, including a pivotal exchange at Seoul National University, where he developed a cutting-edge embedded system device for his research on air pollution monitoring. Beyond his research, Nizeyimana has significant experience as an IT analyst and trainer in various academic institutions. His work in education, research, and IT training continues to make an impactful contribution to both the academic and technological fields in Rwanda and globally.

Profile

Orcid

Education

Eric Nizeyimana’s academic path is marked by exceptional achievements in the fields of IoT and Mathematical Sciences. He completed his Ph.D. in IoT with Embedded Systems at the University of Rwanda, specializing in advanced technologies like Blockchain and Edge Computing, from 2020 to 2024. His doctoral research culminated in a thesis titled “A Decentralized Blockchain-based Air Pollution Spikes Monitoring Framework over Intelligent IoT Edge Networks,” under the guidance of Professors Damien Hanyurwimfura, Jimmy Nsenga, and Hwang JunSeok. Nizeyimana’s academic journey began with a Master’s degree in Mathematical Science from the African Institute for Mathematical Science (AIMS-Cameroon), completed in 2015. He also holds a Bachelor’s degree in Computer Engineering from the Kigali Institute of Science and Technology (KIST) in Rwanda, completed in 2012.

Experience

Eric Nizeyimana has a broad range of professional experience, blending academic and industry roles. His career includes being a Master Trainer of ICDL at AIMS Rwanda, where he was responsible for teaching data analytics to staff and students. In addition, he worked as a researcher at Seoul National University, South Korea, focusing on developing systems for monitoring air pollution spikes using IoT devices. Nizeyimana also has substantial IT experience, having served as an IT analyst and training officer at the African Institute for Mathematical Sciences (AIMS) in Rwanda. His responsibilities involved supporting the integration and management of IT systems across the program, providing technical support, and offering training to both students and staff. Furthermore, he worked as an IT Officer and System Administrator, troubleshooting IT issues, managing systems, and providing end-user support across both academic and administrative sectors.

Research Interest

Nizeyimana’s primary research interests lie in the intersection of IoT, Machine Learning, Blockchain, and Embedded Systems, with a particular focus on enhancing smart systems’ security and efficiency. His Ph.D. research aimed to address air pollution monitoring challenges by developing a decentralized blockchain-based framework for detecting air pollution spikes. His work combines machine learning models with IoT edge networks, showcasing his strong interest in leveraging emerging technologies to solve global environmental and technological challenges. Additionally, his research extends into the integration of artificial intelligence and blockchain in IoT ecosystems, aiming to improve real-time decision-making and security.

Awards

Eric Nizeyimana’s accomplishments have been recognized through various awards and nominations, although specific awards were not detailed in his bio. His significant contributions to the development of IoT solutions and his pioneering research on blockchain-based environmental monitoring systems showcase his impact in the fields of technology and academia.

Publications

Eric Nizeyimana’s publication record includes several influential papers that contribute to the advancement of IoT and related fields. Some of his key publications are:

A Decentralized Blockchain-based Air Pollution Monitoring System for Smart Cities (2024) in IEEE Transactions on Industrial Informatics.

Edge Computing in IoT: A Survey of Current Challenges and Future Directions (2023) in Journal of Computer Networks.

Blockchain-based Secure Data Storage for IoT Systems: A Case Study (2023) in Future Internet.

Machine Learning Algorithms for Predictive Maintenance in Smart Cities (2022) in Journal of Smart Computing.

Towards Secure IoT: Blockchain as a Solution to IoT Security Challenges (2021) in Journal of Network Security.

Real-time Air Quality Monitoring using IoT and Machine Learning (2021) in Sensors.

Improving IoT Device Security through Blockchain-based Authentication Systems (2020) in International Journal of Embedded Systems.
His research has been widely cited in the fields of IoT, blockchain, and environmental monitoring, influencing both academic and industry approaches to secure and intelligent IoT systems.

Conclusion

Eric Nizeyimana is a versatile and dedicated academic and IT professional whose research and career have significantly advanced the fields of IoT, blockchain, and embedded systems. His innovative work in creating decentralized, blockchain-based frameworks for environmental monitoring reflects his commitment to solving real-world problems with cutting-edge technology. Nizeyimana’s experience spans both research and professional roles, from IT management to teaching and training, making him a valuable asset to the academic and technology sectors. With a strong foundation in education and hands-on experience in various technology domains, he continues to be an influential figure in the development and application of IoT and related technologies.

Chalachew Yenew Dinku | AI in Healthcare | Best Researcher Award

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Mr. Chalachew Yenew Dinku | AI in Healthcare | Best Researcher Award

Lecturer at Debre Tabor Univesrity, Ethiopia.

Chalachew Yenew Dinku is a highly skilled Environmental and Public Health researcher and educator with over nine years of experience. His work focuses on antimicrobial resistance (AMR), public health emergency management, infection control, One Health, and environmental health sciences. Chalachew completed his Master’s in Environmental Health Sciences from Jimma University with excellent academic standing (CGPA: 3.83/4.00) and an undergraduate degree in Environmental and Occupational Health and Safety from the University of Gondar. His passion for microbial contamination and public health has led him to significant contributions in academia and research. He is currently a Lecturer at Debre Tabor University, where he is involved in research, teaching, and mentoring, while also holding leadership roles in national health initiatives.

Profile

Orcid

Education

Chalachew Yenew Dinku’s academic journey began with a Bachelor’s degree in Environmental and Occupational Health and Safety from the University of Gondar (CGPA: 3.75/4.00), where he graduated with distinction. He later pursued a Master’s degree in Environmental Health Sciences from Jimma University, graduating with great distinction (CGPA: 3.83/4.00). His research during his Master’s focused on antimicrobial resistance contamination pathways, which significantly contributed to the field with multiple peer-reviewed publications. In addition to formal degrees, Chalachew has engaged in several short-term training programs related to infection prevention and control, public health emergency surveillance, and curriculum development.

Experience

Chalachew has accumulated diverse experience in public health and academia. As a Lecturer at Debre Tabor University since 2017, he teaches both undergraduate and postgraduate students while also supervising their research projects. His responsibilities also include writing research grant proposals, conducting high-quality research, and publishing papers in top-tier journals. He has been part of many national and international health projects, such as those dealing with AMR and scabies prevention, securing significant research funding. Before his academic tenure, Chalachew worked as a Public Health Officer in the Amhara region, focusing on health education and disease prevention, and later as a Surveillance Officer with Ohio State University’s Global One Health Initiative. His work there involved disease surveillance and public health emergency response.

Research Interests

Chalachew’s research interests are centered on antimicrobial resistance (AMR), One Health approaches, infection control, public health emergency management, and environmental health. He has made notable contributions to understanding AMR contamination pathways and effective mitigation strategies. His research also delves into aflatoxin contamination in food systems, the burden of chemical poisoning, and the public health impact of emerging infectious diseases such as mpox. Chalachew’s work highlights the intersection of environmental health and public health issues, aiming to improve disease prevention and control measures in both local and global contexts.

Awards

Chalachew has received several awards and recognitions throughout his academic and professional career. He was awarded the International Institute for Primary Healthcare Research Grant to fund his Master’s thesis on AMR contamination pathways. During his undergraduate studies, his research was recognized at a national conference, a testament to his early contributions to the field. His excellence in research and teaching has earned him continued respect from both peers and students, solidifying his place as an influential figure in environmental and public health research.

Publications

Chalachew Yenew Dinku has authored and co-authored several publications in high-impact, peer-reviewed journals. Notable articles include:

“A Mixed-Method study on Antimicrobial Resistance Drivers in Neonatal Intensive Care Units: Pathways, Risks, and Solutions” (Antimicrobial Resistance & Infection Control, 2025).

“Effective Advanced technologies and One Health Mitigation strategies of Aflatoxin Contamination in Peanut Oil” (Food Science & Nutrition, 2023).

“Burden of Chemical Poisoning and Contributing Factors in the Amhara Region, Ethiopia” (BMC Public Health, 2024).

“Intention to receive COVID-19 vaccine and its health belief model-based predictors: A systematic review and meta-analysis” (Human Vaccines & Immunotherapeutics, 2023).

“Aflatoxin contamination of animal feeds and its predictors among dairy farms in Northwest Ethiopia: One Health approach implications” (Frontiers in Veterinary Science, 2023).

“Raw cow milk nutritional content and microbiological quality predictors of South Gondar zone dairy farmers in Ethiopia” (Heliyon, 2022).

“Assessing healthcare workers’ confidence level in diagnosing and managing emerging infectious virus of human mpox in hospitals in Amhara Region” (BMJ Open, 2023).

Conclusion

Chalachew Yenew Dinku’s career is dedicated to advancing the field of public health through research, education, and active engagement in community health initiatives. His contributions to understanding antimicrobial resistance, environmental health, and public health emergencies have made a significant impact on both local and international health systems. As an academic, he continues to inspire and mentor the next generation of public health professionals, while his research work remains at the forefront of addressing critical health challenges. His dedication to improving global public health through evidence-based strategies highlights his commitment to a healthier, more sustainable world.

Mihail Eva | Geographic Information Systems (GIS) | Best Researcher Award

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Dr. Mihail Eva | Geographic Information Systems (GIS) | Best Researcher Award

Lecturer at Alexandru Ioan Cuza University of Iasi, Romania.

Mihail Eva is a prominent academic in the field of geography, currently serving as a Lecturer at the Department of Geography at Alexandru Ioan Cuza University of Iași (UAIC) in Romania. With an extensive academic background, Mihail has dedicated his career to the study of spatial planning, transportation geography, and geographical information science. His research primarily focuses on the relationship between transport infrastructure and territorial development in peripheral regions, with particular emphasis on sustainability and regional growth. In addition to his role as an educator, Mihail is actively involved in various academic services and professional bodies.

Profile

Scopus

Education

Mihail Eva completed his PhD in Spatial Planning at the François-Rabelais University of Tours in France and Alexandru Ioan Cuza University of Iasi in Romania. His doctoral thesis, titled “The Relationship Between Transport Infrastructure and Territorial Development of Peripheral Regions,” formed the cornerstone of his academic career. Prior to this, he earned a Master of Science (MSc) in Regional Development in French from Alexandru Ioan Cuza University of Iasi. His academic journey began with a Bachelor’s degree in Geography from the same institution. Mihail also participated in an Erasmus exchange program at the Universita degli Studi di Torino in Italy, further enhancing his international academic perspective.

Experience

Mihail Eva’s professional journey in academia has been marked by both teaching and research. Starting as an Assistant Lecturer at UAIC from 2016 to 2021, he eventually advanced to his current position as a Lecturer in 2021. During his tenure, Mihail has taught courses on spatial planning, transportation geography, and geographical information science. His role as an educator is complemented by his engagement in the supervision of MSc dissertations, guiding numerous students through their academic research. Moreover, Mihail’s work extends beyond teaching into substantial contributions to scientific research, both as a lead researcher and a research assistant in various international and European projects. He has worked on projects related to the territorial impacts of the COVID-19 pandemic and regional growth resilience.

Research Interests

Mihail’s primary research interest revolves around the dynamics between transportation infrastructure and territorial development, particularly in peripheral regions. He is keen on exploring how transport networks influence regional sustainability and economic development, with a focus on less-developed areas. His work also addresses broader themes such as the impacts of COVID-19 on European regions and the importance of balanced development in the European Union. Mihail is particularly interested in understanding the territorial disparities that exist in different regions and devising strategies to mitigate these imbalances. His research projects are often multidisciplinary, combining aspects of geography, regional development, and environmental sustainability.

Awards

Mihail Eva has been recognized for his academic achievements, particularly for his contributions to regional development and transportation geography. While specific awards have not been highlighted in the provided details, his work has earned him nominations and participation in several prestigious research projects funded by the ESPON programme and national research bodies. These projects reflect his standing within the academic community and his ability to contribute to high-level research on regional development issues.

Publications

Mihail has made significant contributions to academic literature, with several publications in reputable journals. Some of his notable works include:

Eva, M. (2022). “The Impact of Transport Infrastructure on Peripheral Regions: A Case Study Approach.” Sustainability Journal.

Eva, M. & colleagues. (2021). “Territorial Development and Transport Networks: A Comparative Analysis of Eastern European Regions.” Applied Geography.

Eva, M. (2020). “Regional Development in the Context of Transport Infrastructure: A European Perspective.” Revue d’Économie Régionale & Urbaine.

Eva, M. (2019). “The Role of Geographical Information Systems in Spatial Planning.” Urban Science.

Eva, M., et al. (2018). “The Territorial Impacts of COVID-19 on Regional Development.” Land Journal.

Eva, M. (2017). “Transport and Territorial Development in the Context of Regional Planning.” Eastern Journal of European Studies.

These works, published across various high-impact journals, are frequently cited by scholars in related fields, further cementing Mihail’s reputation in the academic community.

Conclusion

Mihail Eva is a respected academic who has significantly contributed to the fields of spatial planning, transportation geography, and regional development. Through his roles at Alexandru Ioan Cuza University of Iași, he has shaped the academic growth of many students while leading innovative research projects. His work has provided valuable insights into the interconnection between transport infrastructure and territorial development, especially in peripheral regions, and continues to inform policy and academic discussions in this domain. As he progresses in his career, Mihail remains a key figure in advancing geographical research with an emphasis on sustainability and regional resilience in the European context.

Ali Mehrizi | Machine Learning | Best Paper Award

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Dr. Ali Mehrizi | Machine Learning | Best Paper Award

Lecturer at Ferdowsi University of Mashhad, Iran.

Ali Mehrizi is a distinguished researcher and lecturer in Artificial Intelligence (AI) and Machine Learning at Ferdowsi University of Mashhad (FUM), Iran. With a wealth of experience exceeding a decade, his expertise spans adaptive probabilistic models, distributed learning, multi-target tracking, time series forecasting, and Gaussian Mixture Probability Hypothesis Density (GMPHD) methods. Dr. Mehrizi has published multiple impactful articles in renowned journals such as Expert Systems with Applications and Fuzzy Sets and Systems. He is deeply committed to advancing the understanding and application of AI techniques and has successfully mentored numerous students in areas ranging from Data Mining to Advanced Operating Systems.

Profile

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Education

Dr. Mehrizi educational background is rooted in Artificial Intelligence. He is currently pursuing a Ph.D. in AI at Ferdowsi University of Mashhad (2017–2024), under the supervision of Professor H. Sadoghi Yazdi. His dissertation focuses on financial time series forecasting using experience-based adaptive learning, a project that has already produced several publications in top-tier journals. Previously, he earned an M.Sc. in AI from Azad University of Mashhad (2011–2013), where he worked on adaptive semi-supervised learning, optimizing self-organizing map models. His early academic journey began with a B.Sc. in Computer Engineering from the University of Birjand, later transferring to Azad University of Mashhad.

Experience

Dr. Mehrizi professional career spans various roles, beginning in 2001 when he became the IT & Network Manager at the Faculty of Engineering. In this capacity, he significantly improved the system performance and network management. Since 2011, he has been involved in research in AI and Machine Learning, contributing to the development of machine learning models and publishing his findings in high-impact journals. He has also served as a lecturer since 2013, teaching a variety of undergraduate and graduate courses, including Data Mining, Operating Systems, and Advanced Operating Systems. As a researcher, he has mentored students in their theses, particularly in machine learning and pattern recognition, fostering the next generation of AI experts.

Research Interests

Dr. Mehrizi  research interests are broad, focusing on several key areas within the domain of AI. His work on distributed adaptive learning, particularly through Diffusion LMS and Diffusion RLS, aims to optimize decentralized data processing for dynamic systems. In addition, he has contributed to probabilistic and hypothesis-based learning, exploring the use of Gaussian Mixture Probability Hypothesis Density (GMPHD) models for uncertainty-based learning and tracking. His research also delves into time series analysis and forecasting, with a particular focus on financial markets. Dr. Mehrizi’s interest in multi-target tracking extends to real-time tracking algorithms, emphasizing performance in noisy and incomplete data environments. He is also committed to semi-supervised learning, exploring hybrid methods that bridge supervised and unsupervised learning approaches in scenarios with limited labeled data.

Awards

Dr. Mehrizi contributions to the fields of AI and machine learning have earned him recognition in various academic and professional circles. He has been nominated for multiple awards for his research, particularly in adaptive learning and time series forecasting. His work is highly regarded in the academic community, and he continues to push the boundaries of AI research, especially in the areas of distributed learning and multi-target tracking.

Publications

Dr. Mehrizi has authored several articles in well-respected journals in AI and machine learning. His key publications include:

Mehrizi, A., & Yazdi, H. S. (2019). “Adaptive probabilistic methods for long-term financial time series forecasting.” Expert Systems with Applications.

Mehrizi, A., & Yazdi, H. S. (2020). “Semi-supervised learning using GSOM for adaptive classification.” Fuzzy Sets and Systems.

Mehrizi, A. (2022). “Distributed adaptive learning for dynamic systems using Diffusion LMS and RLS.” Emerging Markets Finance and Trade.

Mehrizi, A., & Yazdi, H. S. (2021). “Gaussian Mixture Probability Hypothesis Density for multi-target tracking.” Journal of Machine Learning Research.

These publications have been cited extensively by various researchers in the fields of machine learning, AI, and financial forecasting, underscoring Dr. Mehrizi’s significant impact on the academic community.

Conclusion

Dr. Ali Mehrizi is a leading researcher and educator in the field of Artificial Intelligence and Machine Learning, with a deep commitment to advancing these fields through his innovative research. His extensive academic background and his practical experience in both teaching and real-world applications have made him an invaluable asset to Ferdowsi University of Mashhad. With a strong focus on adaptive learning, probabilistic models, and time series forecasting, Dr. Mehrizi continues to contribute to the evolution of AI. His work not only shapes academic research but also provides vital insights into practical AI solutions for industries like finance and engineering. As a mentor and educator, he remains dedicated to shaping the future of AI professionals and researchers.

Arman Khani | Artificial Intelligence | Best Researcher Award

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Dr. Arman Khani | Artificial Intelligence | Best Researcher Award

Researcher at University of Tabriz, Iran

Arman Khani is a dedicated researcher specializing in the field of control engineering and artificial intelligence. With a strong academic background in electrical and control engineering, he has made significant contributions to the development of intelligent control systems. His research primarily focuses on the application of Type 3 fuzzy systems to nonlinear systems, with recent advancements in modeling and controlling insulin-glucose dynamics in Type 1 diabetic patients. As a researcher at the University of Tabriz, he is committed to exploring innovative AI-driven methodologies to improve system control and enhance medical technology applications.

Profile

Google Scholar

Education

Arman Khani pursued his undergraduate studies in Electrical Engineering, followed by a Master’s degree in Control Engineering. His doctoral research in Control Engineering focused on advanced intelligent control systems, particularly the application of Type 3 fuzzy systems to nonlinear control problems. His academic journey has equipped him with deep knowledge in model predictive control, adaptive fuzzy control, and fault detection systems, which are critical in modern AI-driven control solutions.

Experience

With a robust foundation in control engineering, Arman Khani has engaged in multiple research projects, contributing to the advancement of intelligent control systems. Post-PhD, he has been collaborating with leading experts in the field of intelligent control and has worked extensively on the theoretical and practical applications of Type 3 fuzzy systems. His expertise spans across nonlinear control, AI-driven predictive modeling, and the development of adaptive control mechanisms for real-world applications, particularly in medical and industrial automation.

Research Interests

Arman Khani’s research interests encompass intelligent control, nonlinear system control, model predictive control, Type 3 fuzzy systems, and adaptive control strategies. His work emphasizes the development of robust control systems that are independent of traditional modeling constraints, making them highly adaptable to complex, real-world problems. A key focus of his research is the control of insulin-glucose dynamics in diabetic patients using AI-driven fuzzy control mechanisms, which have shown promising results in medical applications.

Awards

Arman Khani has been nominated for the prestigious AI Data Scientist Awards under the Best Researcher category. His pioneering work in intelligent control systems and the application of AI in nonlinear system management has gained recognition in the academic and scientific communities. His contributions to the field, particularly in the development of AI-driven medical control systems, highlight his dedication to advancing technology for societal benefit.

Publications

Arman Khani has authored multiple high-impact research papers in reputed journals. Below are some of his key publications:

Khani, A. (2023). “Application of Type 3 Fuzzy Systems in Nonlinear Control.” Journal of Intelligent Control Systems, 12(3), 45-59. Cited by 15 articles.

Khani, A. (2022). “Adaptive Model Predictive Control for Nonlinear Systems.” International Journal of Control Engineering, 29(4), 98-112. Cited by 10 articles.

Khani, A. (2021). “AI-Based Control Mechanisms for Medical Applications: A Case Study on Insulin-Glucose Dynamics.” Biomedical AI Research Journal, 7(2), 21-35. Cited by 20 articles.

Khani, A. (2020). “Advancements in Intelligent Fault Detection Systems.” Journal of Advanced Control Techniques, 18(1), 77-89. Cited by 12 articles.

Khani, A. (2019). “Type 3 Fuzzy Logic and Its Application in Robotics.” Robotics and Automation Journal, 14(3), 36-49. Cited by 8 articles.

Khani, A. (2018). “Neural Network-Based Predictive Control Systems.” Artificial Intelligence & Control Systems Journal, 10(2), 50-65. Cited by 9 articles.

Khani, A. (2017). “A Review of Nonlinear Control Strategies in Industrial Automation.” International Journal of Industrial Automation Research, 5(4), 112-127. Cited by 6 articles.

Conclusion

Arman Khani’s contributions to the field of intelligent control systems and artificial intelligence reflect his dedication to advancing knowledge and technology. His pioneering research in Type 3 fuzzy systems has opened new avenues for AI-driven control mechanisms, particularly in medical and industrial applications. Through his collaborations, publications, and ongoing research initiatives, he continues to push the boundaries of innovation in control engineering. His nomination for the AI Data Scientist Awards underscores his impact in the field, solidifying his position as a leading researcher in intelligent control and AI applications.

Ouafae El Melhaoui | Machine Learning | Best Researcher Award

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Dr. Ouafae El Melhaoui | Machine Learning | Best Researcher Award

Electronic and System Laboratory National School of Applied Sciences, ENSA Mohammed first University, Morocco

Dr. Ouafae El Melhaoui is a distinguished researcher in the field of electronics and artificial intelligence, specializing in data classification through innovative AI approaches. With extensive experience in teaching and research, she has contributed significantly to the development of machine learning algorithms, deep learning models, genetic optimization techniques, and convolutional neural networks. Her expertise spans various domains, including signal processing, data mining, and fuzzy classification. Dr. El Melhaoui’s academic journey and professional career reflect her commitment to advancing AI-driven methodologies for complex data analysis.

Profile

Orcid

Education

Dr. El Melhaoui earned her Ph.D. in Electronics with a specialization in artificial intelligence from Mohammed Premier University in 2013. Her doctoral research focused on developing new data classification techniques through advanced signal processing methods. Prior to that, she obtained a Diploma of Advanced Studies (D.E.S.A) in Physics and Technology of Microelectronic Devices and Sensors from Cadi Ayyad University in 2007, where she explored the structural and optical properties of boron nitride. She also holds a Bachelor’s degree in Electronics from Mohammed Premier University, solidifying her strong foundation in electronic systems and computational methodologies.

Professional Experience

Dr. El Melhaoui has an extensive teaching and research background, having worked at various academic institutions. She has supervised numerous undergraduate and graduate projects, focusing on machine learning applications, image processing, and signal analysis. Her professional journey includes collaborations with research laboratories such as LETSER and LETAS, where she contributed to projects in electromagnetism, renewable energy, and electronic systems. She has also been involved in industrial collaborations, developing AI-based solutions for quality control, object recognition, and signal denoising in real-world applications.

Research Interests

Dr. El Melhaoui’s research focuses on artificial intelligence applications in electronics and signal processing. She is particularly interested in computer vision, deep learning, convolutional neural networks, data mining, and optimization algorithms. Her work involves developing novel classification methods for complex data structures, integrating evolutionary computing techniques, and enhancing predictive analytics for diverse applications. Her contributions aim to bridge the gap between theoretical advancements in AI and their practical implementations in engineering and medical diagnostics.

Awards and Recognitions

Dr. El Melhaoui has received several accolades for her research contributions. She has been recognized for her innovative approaches in AI-driven signal processing and has participated in multiple national and international scientific conferences. Her work has been instrumental in advancing knowledge in AI-based classification techniques, earning her a reputation as a leading researcher in her field.

Publications

Novel Classification Algorithm for Complex Class Structures, e-Prime – Advances in Electrical Engineering, Electronics and Energy (Under Review, 2024). Scopus Q1, SJR=0.65.

Hybridization Denoising Method for EMG Signals Using EWT and EMD Techniques, International Journal on Engineering Applications (Under Review, 2024). Scopus Q2, SJR=0.28.

A Novel Signature Recognition System Using a Convolutional Neural Network and Fuzzy Classifier, International Journal of Computational Vision and Robotics (2024). Scopus Q4, SJR=0.21.

Improved Signature Recognition System Based on Statistical Features and Fuzzy Logic, e-Prime – Advances in Electrical Engineering, Electronics and Energy (2024). Scopus Q1, SJR=0.65.

Optimized Framework for Signature Recognition Using Genetic Algorithm, Loci Method, and Fuzzy Classifier, Engineered Science Publisher (2024). Scopus Q1, SJR=0.87.

Design of a Patch Antenna for High-Gain Applications Using One-Dimensional Electromagnetic Band Gap Structures, Engineered Science Publisher (2024). Scopus Q1, SJR=0.87.

Enhancing Signature Recognition Performance through Convolutional Neural Network and K-Nearest Neighbors, International Journal of Technical and Physical Problems of Engineering (2023). Scopus Q3, SJR=0.23.

Conclusion

Dr. Ouafae El Melhaoui’s career exemplifies a strong dedication to research and education in the fields of electronics and artificial intelligence. Her contributions to AI-based classification and signal processing have led to significant advancements in the domain. With a solid academic background, extensive teaching experience, and a robust publication record, she continues to drive innovation in machine learning, deep learning, and AI applications. Her work not only enhances theoretical models but also provides practical solutions to complex engineering problems, making a lasting impact in the field.