Rab Nawaz Bashir | Machine Learning | Best Researcher Award

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Dr. Rab Nawaz Bashir | Machine Learning | Best Researcher Award

Assistant Professor, COMSATS University, Pakistan

Dr. Rab Nawaz Bashir 🎓 is a distinguished computer scientist and Assistant Professor at COMSATS University Islamabad, Vehari Campus. He holds a Ph.D. in Computer Science from Islamia University Bahawalpur and has been instrumental in shaping research and education in artificial intelligence, machine learning, and the Internet of Things (IoT). With a passion for academic excellence, he has mentored numerous undergraduate, graduate, and Ph.D. students, guiding them toward impactful research and industry readiness. His dedication to scholarly contributions and innovative teaching has made him a respected figure in the field of computer science.

Publication Profile

Education 🎓

Dr. Bashir has a strong academic foundation, earning his Ph.D. in Computer Science (2021) from Islamia University Bahawalpur. Prior to that, he completed an MS in Computer Science (2015) from the same institution. His educational journey began with a Master in Computer Science (MCS) from Pir Mehr Ali Shah University of Arid Agriculture (2008). To further enhance his expertise, he is currently pursuing a Fellowship in Computer Science at Prince Sultan University, Saudi Arabia.

Experience 👨‍🏫

With over 15 years of experience, Dr. Bashir has significantly contributed to academia and research. Since 2022, he has served as an Assistant Professor at COMSATS University Islamabad, Vehari Campus, where he leads curriculum development, organizes seminars, and supervises undergraduate and Ph.D. research. Previously, he was a Lecturer at COMSATS University (2015–2022), University of Agriculture Faisalabad (2014–2015), and Institute of Southern Punjab, Multan (2010–2014). Before transitioning fully into academia, he worked in software development at the University of Agriculture Faisalabad (2008–2010), specializing in secure and scalable web applications using ASP.NET and SQL Server.

Awards and Honors 🏆

Dr. Bashir has been recognized multiple times for his outstanding contributions to research and academic excellence. He has received several Research Awards (2021, 2022, 2023, 2024) and Annual Performance Awards (2022, 2023, 2024) from his institution. Additionally, his leadership in securing National Standard for Education Accreditation Council (NSEAC) accreditation for the university in 2021 and 2024 highlights his commitment to academic quality and institutional development.

Research Focus 🔬

Dr. Bashir’s research spans various cutting-edge domains, including machine learning, IoT, and computer programming. His work emphasizes real-world applications, such as IoT-enabled smart agriculture, machine learning-based fraud detection, and deep learning for natural image processing. His interdisciplinary collaborations have resulted in high-impact research, contributing to advancements in federated learning, fog computing, and agricultural technology.

Conclusion 🌟

Dr. Rab Nawaz Bashir is a leading academic and researcher in computer science, with a strong focus on machine learning, IoT, and programming. His dedication to mentoring students, publishing impactful research, and advancing academic excellence has earned him numerous awards and recognitions. With a vision for future innovations, he continues to push the boundaries of computer science, contributing to both academia and industry through groundbreaking research and leadership.

Publications 📚

A Novel 1-Dimensional Cosine Chaotic Equation and Digital Image Encryption Technique (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3447889

Federated Learning (FL) Model of Wind Power Prediction (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3415781

IoT-Enabled Firmness Grades of Tomato in Cold Supply Chain Using Fusion of Whale Optimization Algorithm and Extreme Learning Machine (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3379327

Mushroom Species Classification in Natural Habitats Using Convolutional Neural Networks (CNN) (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3502543

Machine Learning and Fog Computing-Enabled Sensor Drift Management in Precision Agriculture (IEEE Sensors Journal, 2024) – DOI: 10.1109/JSEN.2024.3451662

Principal Component Analysis (PCA) and Feature Importance-Based Dimension Reduction for Reference Evapotranspiration (ET0) Predictions of Taif, Saudi Arabia (Computers and Electronics in Agriculture, 2024) – DOI: 10.1016/j.compag.2024.109036

Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transactions (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3310576

Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network  (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3301504

Stacked Ensemble Model for Tropical Cyclone Path Prediction (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3292907

Smart Reference Evapotranspiration Using Internet of Things and Hybrid Ensemble Machine Learning Approach (Internet of Things, 2023) – DOI: 10.1016/j.iot.2023.100962

Prof. Dr. Hamid Arabnia | Data Science | Best Researcher Award

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Prof. Dr. Hamid Arabnia | Data Science | Best Researcher Award

Professor Emeritus, University of Georgia, United States

Dr. Hamid R. Arabnia is a distinguished Professor Emeritus of Computer Science at the University of Georgia, USA 🎓. With a Ph.D. in Computer Science from the University of Kent, England (1987), he has made substantial contributions to the fields of Artificial Intelligence, Data Science, Machine Learning, HPC, and STEM education 🤖📊. Over his career, he has mentored 23 Ph.D. students and played a vital role in advancing computational science and intelligence. He has been an active advocate against cyber-harassment and cyberbullying, winning a landmark lawsuit in 2017–2018, securing a $3 million ruling ⚖️. Prof. Arabnia has an extensive publication record with 300+ peer-reviewed papers and 200+ edited research books, establishing himself among the top 2% most impactful scientists, as recognized by Stanford University 🌍📚.

Publication Profile

🎓 Education

Dr. Arabnia earned his Ph.D. in Computer Science from the University of Kent, England (1987) 🏛️. His research during his doctoral studies laid the foundation for his pioneering contributions in supercomputing and artificial intelligence 🤖💡.

💼 Experience

Dr. Arabnia has been with the University of Georgia since 1987, contributing as a Professor, Graduate Coordinator, and Research Director 🏫. He has served as Editor-in-Chief of The Journal of Supercomputing (Springer) and is the book series editor for Transactions of Computational Science and Computational Intelligence (Springer) 📖. His leadership has also extended to roles as a senior adviser for global corporations and National Science Foundation (NSF) committees for over 10 years 🏆.

🏅 Awards and Honors

Prof. Arabnia has received numerous prestigious awards, including recognitions from IEEE BIBE, ACM SIGAPP, and IMCOM 🏅. His legal victory against cyber-harassment was a landmark case, setting an important precedent in the U.S. legal system ⚖️. His contributions to STEM education and securing $12 million in funding for graduate research at UGA have also been widely recognized 💰📚.

🔬 Research Focus

Dr. Arabnia’s research spans Data Science, AI, HPC, Machine Learning, Imaging Science, and Compute-Intensive Problems 🤖📊. He has been actively involved in cybersecurity legislation advocacy, focusing on cyberstalking and online harassment 🔒. His latest work integrates deep learning, upsampling techniques, and AI-driven smart city applications 🌍.

🔚 Conclusion

Dr. Hamid R. Arabnia is a highly influential researcher, educator, and advocate for ethical AI and cybersecurity 🏆. With over 500 publications and millions in research funding, his contributions have shaped modern supercomputing, artificial intelligence, and digital security 🔬. Recognized among the top 2% impactful scientists globally, his work continues to inspire the next generation of AI and computer science researchers 🚀.

📚 Publications

Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Upsampling under Varying Imbalance Levels (2025) – Preprint

A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection (2025) – Technologies | DOI 📑

SWAG: A Novel Neural Network Architecture Leveraging Polynomial Activation Functions for Enhanced Deep Learning Efficiency (2024) – IEEE Access | DOI 📖

Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis (2023) – Technologies | DOI 📜

A Review of Deep Transfer Learning and Recent Advancements (2023) – Technologies | DOI 📘

Embodied AI-Driven Operation of Smart Cities: A Concise Review (2021) – TechRxiv | DOI 🌍

Mr. Ali Beikmohammadi | Machine Learning | Best Researcher Award

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Mr. Ali Beikmohammadi | Machine Learning | Best Researcher Award

PhD Researcher, Stockholm University, Sweden

👨‍💻 Ali Beikmohammadi is a dedicated researcher in Reinforcement Learning, Deep Learning, and Federated Learning. Currently pursuing his Ph.D. in Computer and Systems Sciences at Stockholm University, Sweden, he has made remarkable contributions to AI research, publishing 15+ papers in top-tier conferences and journals. With a strong foundation in stochastic optimization, telecommunications, and cyber-physical systems, Ali has worked on various industry projects and supervised 30+ Master’s students. His expertise extends to high-performance computing, AI applications in healthcare, and distributed learning, making him a highly influential figure in AI research. 🚀

Publication Profile

Education

🎓 Ali holds a Ph.D. in Computer and Systems Sciences (2021–Present) from Stockholm University, Sweden, where he focuses on sample-efficient reinforcement learning and AI-driven optimization. He earned an M.Sc. in Electrical Engineering (Digital Electronic Systems) (2017–2019) from Amirkabir University of Technology, Iran, specializing in deep learning for plant classification. His B.Sc. in Electrical Engineering (Electronics) (2013–2017) from Bu-Ali Sina University, Iran, involved research on license plate recognition using computer vision. 📚

Experience

💡 With extensive research and industry collaborations, Ali has supervised 30+ Master’s students at Stockholm University and Karolinska Institutet, applying AI to healthcare, recommendation systems, forecasting, and network optimization. He has also instructed 91 students in Health Informatics courses, focusing on time-series analysis, deep learning, and reinforcement learning. His industry collaborations include Scania CV AB, Hitachi Energy, and the University of California, where he played key roles in algorithm design, pipeline development, and AI-driven performance optimization. 🤖

Awards and Honors

🏆 Ali’s exceptional contributions to AI and engineering have earned him prestigious scholarships such as the Lars Hierta Memorial Foundation Scholarship (2025) and the Rhodins, Elisabeth, and Herman Memory Scholarship (2024). He is a member of the Iran National Elites Foundation and has received the Outstanding Paper Award at the 5th ICSPIS’19 Conference. His academic excellence is further highlighted by ranking 1st in GPA during his B.Sc. and M.Sc. studies. 🌟

Research Focus

🔬 Ali’s research revolves around Reinforcement Learning, Deep Learning, and Federated Learning, with a strong emphasis on stochastic optimization, telecommunications, and cyber-physical systems. His recent work explores teacher-assisted reinforcement learning, federated learning without data similarity constraints, and cost-sensitive AI models for industrial applications. His contributions aim to enhance AI’s efficiency, scalability, and applicability across domains like healthcare, robotics, and automation. ⚙️

Conclusion

🌍 Ali Beikmohammadi is an accomplished AI researcher, educator, and industry collaborator pushing the frontiers of Reinforcement Learning, Deep Learning, and Federated Learning. With multiple high-impact publications, prestigious awards, and hands-on experience in AI-driven solutions, he continues to bridge the gap between academic research and real-world AI applications. His passion for cutting-edge AI innovations positions him as a leading voice in modern AI research. 🚀✨

Publications

Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Upsampling under Varying Imbalance Levels

TA-Explore: Teacher-assisted exploration for facilitating fast reinforcement learning – Published at International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2023).  Paper Link

Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms – Artificial General Intelligence Conference (2023).  Paper Link

Human-inspired framework to accelerate reinforcement learning – arXiv (2023).  Paper Link

Compressed federated reinforcement learning with a generative model – ECML-PKDD (2024).  Paper Link

On the Convergence of Federated Learning Algorithms without Data Similarity – IEEE Transactions on Big Data (2024).  Paper Link

Parallel Momentum Methods Under Biased Gradient Estimations – IEEE Transactions on Control of Network Systems (2025).  Paper Link

A Cost-Sensitive Transformer Model for Prognostics Under Highly Imbalanced Industrial Data – arXiv (2024).  Paper Link

Mrs. Edna Rocio Bernal Monroy | Machine Learning | Best Researcher Award

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Mrs. Edna Rocio Bernal Monroy | Machine Learning | Best Researcher Award

UNAD, Colombia

Dr. Edna Rocío Bernal Monroy is an accomplished computer scientist and researcher specializing in informatics, machine learning, and healthcare technologies. With a strong academic background and diverse international experience, she has contributed significantly to health informatics, wearable sensors, and intelligent systems. Dr. Bernal Monroy has worked across multiple institutions in Colombia, France, and Spain, engaging in teaching, research, and project management. Her work in artificial intelligence (AI) for healthcare has earned her prestigious awards and recognition in the global scientific community.

Publication Profile

🎓 Education

Dr. Bernal Monroy holds a Ph.D. in Information & Communication Technology from the University of Jaén, Spain (2017–2021), focusing on informatics and AI applications in healthcare. She completed a Master of Engineering in Information Systems and Networks at Claude Bernard Lyon 1 University, France (2010–2012). Additionally, she pursued a Specialization in Management of Innovative Health Projects at INCAE Business School, Nicaragua (2016–2017) and earned a Bachelor of Engineering in Computer Science & Technology from the Pedagogical and Technological University of Colombia (2005–2010).

💼 Experience

Dr. Bernal Monroy has held teaching and research roles in various universities. She served as a Full-Time Teacher at the National Open and Distance University, Bogotá (2014–2020) and worked at the San Gil University Foundation (2013–2014) as a Systems Engineering Lecturer. She was also a faculty member at the Pedagogical and Technological University of Colombia (2014–2015). Additionally, she gained international experience as a Project Manager in Informatics at CALYDIAL, France (2011–2012).

🏆 Awards and Honors

Dr. Bernal Monroy has received several prestigious distinctions for her research contributions. She was awarded the Google LARA 2018 Google Research Award for Latin America for her doctoral project on innovation. She also served as a European Project Researcher for REMIND – H2020 – MSCA-RISE-2016 under the European Union’s research initiative. Additionally, she received the CAHI Research Fellowship from the Central American Healthcare Initiative (CAHI) in 2016 for her contributions to healthcare technology and informatics.

🔬 Research Focus

Dr. Bernal Monroy’s research interests lie at the intersection of AI, machine learning, healthcare informatics, and wearable technologies. She specializes in intelligent monitoring systems for healthcare applications, particularly in preventing pressure ulcers through wearable inertial sensors and using AI-driven analytics for healthcare improvements. Her work also extends to human activity recognition, telemedicine, and IoT solutions for health applications.

🏁 Conclusion

Dr. Edna Rocío Bernal Monroy is a leading researcher in AI-driven healthcare solutions with extensive experience in informatics, machine learning, and wearable technologies. Her pioneering research has contributed significantly to intelligent monitoring systems, earning her global recognition and prestigious awards. Through her academic contributions, research projects, and international collaborations, she continues to drive innovation in healthcare informatics and AI applications. 🚀

📚 Publications

Implementation of Machine Learning Techniques to Identify Patterns that Affect the Social Determinants of the Municipality of Tumaco – Nariño (2024) – Published in Encuentro Internacional de Educación en Ingeniería, this paper focuses on using AI to analyze social determinants of health.

Fuzzy Monitoring of In-Bed Postural Changes for the Prevention of Pressure Ulcers Using Inertial Sensors Attached to Clothing (2020) – Published in the Journal of Biomedical Informatics, this research has been cited 31 times and explores AI-driven healthcare monitoring solutions.

Intelligent System for the Prevention of Pressure Ulcers by Monitoring Postural Changes with Wearable Inertial Sensors (2019) – Published in Proceedings, this work highlights wearable sensor-based intelligent systems for healthcare and has been cited 11 times.

UJA Human Activity Recognition Multi-Occupancy Dataset (2021) – A dataset publication in collaboration with other researchers, cited 3 times.

Finite Element Method for Characterizing Microstrip Antennas with Different Substrates for High-Temperature Sensors (2017) – Explores sensor technologies for high-temperature environments.

Estudio de Apoyo para la Implementación de un Sistema de Telemedicina en Lyon, Francia (2013) – Discusses telemedicine systems and their applications in France.

Mr. Ahmad Faraz Hussain | Machine learning | Best Scholar Award

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Mr. Ahmad Faraz Hussain | Machine learning | Best Scholar Award

PhD student, Zhejiang university, China

Ahmad Faraz Hussain is an accomplished researcher and engineer specializing in audio signal processing, speaker recognition, and wireless sensor networks. With a strong academic background and extensive technical experience, he has contributed significantly to the field of electronics and information engineering. His work spans research, teaching, and industry, reflecting his passion for innovation and education.

Publication Profile

Scopus

🎓 Education:

Ahmad Faraz Hussain earned his Master of Science in Electronics & Information Engineering from the South China University of Technology, China (2017–2019), achieving an impressive 90%. His thesis focused on “Speaker Recognition with Emotional Speech,” showcasing his expertise in audio processing. He completed his Bachelor of Science in Electrical Engineering from the University of Engineering and Technology, Peshawar, Pakistan (2009–2014), with a thesis on “ZigBee-Based Wireless Sensor Network for Building Safety Monitoring.”

💼 Professional Experience:

Ahmad has a diverse professional journey, beginning as a Research Assistant at the South China University of Technology (2017–2019), where he worked on cutting-edge projects in speech recognition. Before that, he served as a Lecturer at Polytechnical College Kohat (2016–2017), imparting knowledge to aspiring engineers. His technical expertise was further honed during his two-year tenure as a Technical Engineer at PTCL, Pakistan, where he worked on telecommunications and networking solutions.

🏆 Awards and Honors:

Ahmad was a recipient of the prestigious CSC Scholarship, which enabled him to pursue his master’s degree in China. His academic excellence and dedication to research have earned him recognition in both academic and professional circles.

🔬 Research Focus:

Ahmad’s research interests lie in audio signal processing, speaker recognition, speech recognition, and wireless sensor networks. His work focuses on developing advanced methodologies for improving speech-based systems and enhancing security through smart sensor networks. His contributions to these fields are evident in his multiple publications and research projects.

🔚 Conclusion:

Ahmad Faraz Hussain is a dedicated researcher and engineer with a strong foundation in speech and wireless sensor technologies. His academic achievements, professional experience, and research contributions highlight his commitment to innovation and education. With a passion for higher learning and community service, he continues to make impactful contributions to the field of electronics and information engineering. 🚀

📚 Publications:

Three-Dimensional Dynamic Positioning Using a Novel Lyapunov-Based Model Predictive Control for Small Autonomous Surface/Underwater Vehicles

Fish Detection and Classification Based on Improved ViT

ZigBee-Based Wireless Sensor Network for Building Safety Monitoring – Published in the Journal of TWASP. Read here.

Speaker Recognition with Emotional Speech – Published in GSJ. Read here.

Speech Emotion Recognition – Under review.

ZigBee and GSM-Based Security System for Business Places– Accepted for publication.

Internet of Things-Based Information System for Smart Wireless Sensor Healthcare Applications – Submitted for review.

Abdulkareem Alzahrani | Computational Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Abdulkareem Alzahrani | Computational Intelligence | Best Researcher Award

Associate Professor of Artificial Intelligence at CS Dept. and Vice-Dean for Postgraduate Studies, Research, Innovation, and Quality, Saudi Arabia

🎓 Dr. Abdulkareem Aodah Alzahrani is an Associate Professor in Computer Science specializing in Artificial Intelligence at Al-Baha University, Saudi Arabia. He currently serves as the Vice Dean for Postgraduate Studies, Research, Innovation, and Quality at the Faculty of Computing and Information. With a career spanning over 16 years, Dr. Alzahrani has held several leadership roles, including Head of the Computer Information Systems and IT Departments. He is a founding member of multiple research and innovation committees, contributing significantly to the advancement of AI and machine learning applications. 🌟

Publication Profile

Google Scholar

Education

📚 Dr. Alzahrani earned his Ph.D. in Computer Science from the University of Essex, UK, in 2017, specializing in Artificial Intelligence. He also holds an MSc in Advanced Web Engineering from the University of Essex (2011) and a BEd in Computer Science from Abha Teacher College, Saudi Arabia (2007). His academic journey reflects his passion for advancing AI and computational research. 🌍

Experience

💼 Dr. Alzahrani has held pivotal roles at Al-Baha University, including Vice Dean (2023–present), Member of the Standing Committee for Scientific Research and Innovation (2024–present), and Head of the Computer Information Systems Department (2020–2023). He was instrumental in establishing a cooperative computer research lab between Al-Baha University and the Research, Development, and Innovation Authority. With extensive teaching and administrative experience, he has significantly contributed to enhancing the university’s academic and research environment. 🌐

Awards and Honors

🏅 Dr. Alzahrani has received the Reward for Excellence four times during his Ph.D. studies, awarded by the Saudi Arabian Cultural Bureau in London. Additionally, he was honored with the Abha Award of Excellence in IT in 2006, recognizing his contributions to the field. His accolades underscore his commitment to academic and technological excellence. 🏆

Research Focus

🔍 Dr. Alzahrani’s research focuses on Artificial Intelligence, Machine Learning, and their applications in healthcare, tourism, and security. His work includes developing robust machine learning models, sentiment analysis for multimedia, and AI-driven solutions for real-world challenges. He is particularly interested in hybrid frameworks and innovative methodologies for enhancing computational efficiency. 🤖

Conclusion

🌟 Dr. Abdulkareem Aodah Alzahrani is a distinguished academic and researcher dedicated to advancing AI and computing. His extensive experience, impactful research, and leadership roles make him a prominent figure in Saudi Arabia’s academic and technological landscape. 🚀

Publications

AI-Driven Innovations in Tourism: Developing a Hybrid Framework for the Saudi Tourism Sector (2025) – AI, 6.1, DOI:10.3390/ai6010007.
Cited by: 7.

A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Based on Hassanat Distance Metric (2024) – DOI:10.21203/rs.3.rs-4492948/v1.
Cited by: 10.

A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric (2024) – Mathematics, 12.22, DOI:10.3390/math12223623.
Cited by: 15.

Advanced CKD Detection through Optimized Metaheuristic Modeling in Healthcare Informatics (2024) – Scientific Reports, 14.1, DOI:10.1038/s41598-024-63292-5.
Cited by: 20.

DeepSVDNet: A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images (2024) – Computer Systems Science and Engineering, 48.2, DOI:10.32604/csse.2023.039672.
Cited by: 25.

Improved Support Vector Machine Based on CNN-SVD for Vision-Threatening Diabetic Retinopathy Detection and Classification (2024) – PLOS ONE, 19.1, DOI:10.1371/journal.pone.0295951.
Cited by: 18.

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework (2023) – Computer Systems Science and Engineering, 46.2, DOI:10.32604/csse.2023.035149.
Cited by: 30.

Harnessing Machine Learning for Arabic COVID-19 Omicron News Classification: A Comparative Study (2023) – International Journal of Advances in Soft Computing & Its Applications, 15.2.

A Comparative Study for SDN Security Based on Machine Learning (2023) – International Journal of Interactive Mobile Technologies, 17.11, DOI:10.3991/ijim.v17i11.39065.
Cited by: 12.

Cloud Intrusion Detection System Based on SVM  (2023) – International Journal of Interactive Mobile Technologies, 17.11, DOI:10.3991/ijim.v17i11.39063.
Cited by: 14.

 

Deekshitha Kosaraju | Artificial Intelligence Award | Best Researcher Award

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Ms. Deekshitha Kosaraju | Artificial Intelligence Award | Best Researcher Award

LIMS Junior Developer, ALS Group USA, Corp., United States

Deekshitha Kosaraju is an accomplished Computer Science graduate from The University of Texas at Dallas, with a strong academic foundation and technical expertise in a variety of programming languages, frameworks, and cloud technologies. Her expertise spans Java, Python, JavaScript, and R, among others. Deekshitha is currently working as a Junior Developer at ALS Group USA, where she focuses on improving data integration and system efficiency. She is passionate about cloud computing, machine learning, and AI, and has published several papers on cutting-edge AI techniques, including explainable AI and quantum computing integration. 🎓👩‍💻📚

Publication Profile

Google Scholar

Education

Deekshitha Kosaraju graduated with a Bachelor of Science in Computer Science from The University of Texas at Dallas, maintaining a GPA of 3.6/4.0. During her time at university, she was honored with the Academic Excellence Scholarship. Her coursework included a wide range of subjects such as Data Structures, Machine Learning, Software Engineering, and Operating Systems. 🎓🏆

Experience

Deekshitha has gained invaluable professional experience through internships and full-time roles. Currently, she works as a Junior Developer at ALS Group USA, where she contributes to streamlining workflows, automating processes, and improving data transfer efficiency. She has previously interned at Radiant Digital, where she worked on low-code platforms and developed mobile applications that enhanced field coordination. In addition, her experience at Pearson as a Software Engineer Intern allowed her to improve user engagement and business outcomes through AI-driven applications. 💼💻

Awards and Honors

Deekshitha was awarded the Academic Excellence Scholarship during her time at The University of Texas at Dallas. Her achievements in academic and professional arenas reflect her dedication to excellence and innovation in the field of computer science. 🌟🏅

Research Focus

Deekshitha’s research primarily focuses on Artificial Intelligence, with specific attention to explainable AI, zero-shot learning, meta-learning, reinforcement learning, and AI’s integration with cloud computing and quantum technologies. She is also interested in exploring the applications of AI in various domains, such as healthcare and data analytics. Her research contributions include exploring how AI can enhance big data analytics and cloud computing innovations. 🤖📊

Conclusion

With a diverse set of technical skills and a passion for advancing AI and cloud technologies, Deekshitha Kosaraju continues to make impactful contributions to the field of Computer Science. She remains committed to expanding her knowledge in AI and exploring innovative solutions to real-world problems. 🌐🚀

Publications :

Shedding light on AI: exploring explainable AI techniques
International Journal of Research and Review, 2020
Read Article

Zero-Shot learning: teaching AI to understand the unknown
International Journal of Research and Review, 2021
DOI: 10.52403/ijrr.20211161

How meta learning enhances reinforcement learning in AI
Galore International Journal of Applied Sciences & Humanities, 2021
DOI: 10.52403/gijash.20210706

Crossing domains: the role of transfer learning in rapid AI prototyping and deployment
International Journal of Science & Healthcare Research, 2021
DOI: 10.52403/ijshr.20210464

Artificial intelligence in cloud computing: enhancements and innovations
Galore International Journal of Applied Sciences & Humanities, 2021
DOI: 10.52403/gijash.20211010

Quantum computing and artificial intelligence: a fusion poised to transform technology
International Journal of Research and Review, 2021
DOI: 10.52403/ijrr.20210974

The role of artificial intelligence in enhancing big data analytics
Galore International Journal of Applied Sciences and Humanities, 2021

Prabakaran Raghavendran | Artificial Neural Network | Young Scientist Award

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Mr. Prabakaran Raghavendran | Artificial Neural Network | Young Scientist Award

Research Scholar, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (Deemed to be University), India

Prabakaran Raghavendran is a dynamic researcher and Ph.D. candidate at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, specializing in Fractional Differential Equations, Integral Transforms, Functional Differential Equations, and Control Theory. With a strong academic foundation in Mathematics, he earned an M.Sc. in Mathematics with an impressive CGPA of 9.79 from the same institution in 2022. He is currently pursuing his Ph.D., contributing significantly to the field with several research publications, patents, and international conference presentations. 🌟

Publication Profile

Education:

Prabakaran completed his B.Sc. in Mathematics at Loyola College, Chennai, in 2020 with a CGPA of 9.25. He further advanced his academic career by obtaining an M.Sc. in Mathematics from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology in 2022, where he excelled with a CGPA of 9.79. Currently, he is pursuing his Ph.D. at the same institution, expected to complete in 202X. 🎓📚

Experience:

Prabakaran has been actively engaged in the research and development of advanced mathematical models and algorithms. His experience spans across fractional differential equations, fuzzy analysis, cryptography, and artificial neural networks. Additionally, he has contributed to the development of innovative technologies, holding multiple patents in signal analysis, optimization, and medical applications. His work is widely recognized in the academic and research communities. 💼🔬

Awards and Honors:

Prabakaran’s academic excellence and dedication to research have earned him several prestigious awards, including the Best Paper Presentation Award for his work on Fractional Integro Differential Equations at the 7th International Conference on Mathematical Modelling, Applied Analysis, and Computation (ICMMAAC-24) in Beirut, Lebanon. He is also a life member of both the International Association of Engineers (IAENG) and the International Organization for Academic and Scientific Development (IOASD). 🏅🌍

Research Focus:

Prabakaran’s research focuses on Fractional Differential Equations, Integral Transforms, and Control Theory, with particular attention to their applications in various fields such as cryptography, artificial neural networks, and fuzzy analysis. He has developed new methodologies for solving complex mathematical models and is deeply involved in finding practical solutions for issues such as Parkinson’s disease prognosis, noise reduction in signals, and optimization in robotics. 🔍🔢

Conclusion:

Prabakaran Raghavendran is a passionate and dedicated researcher in the field of Mathematics, with a strong focus on fractional differential equations and control theory. His groundbreaking work in both theoretical and applied mathematics has earned him recognition through publications and patents. With his ongoing research contributions, he continues to push the boundaries of mathematical modeling and its applications in real-world problems. 🌐💡

Publications:

A Study on the Existence, Uniqueness, and Stability of Fractional Neutral Volterra-Fredholm Integro-Differential Equations with State-Dependent Delay. Fractal Fractional, 9 (1), 1-23. (2024) (SCIE-WoS & Scopus) (Q1).

Analytical Study of Existence, Uniqueness, and Stability in Impulsive Neutral Fractional Volterra-Fredholm Equations.  Journal of Mathematics and Computer Science, 38 (3), 313-329. (2024) (WoS & Scopus) (Q1).

Application of Artificial Neural Networks for Existence and Controllability in Impulsive Fractional Volterra-Fredholm Integro-Differential Equations. Applied Mathematics in Science and Engineering, 32 (1), 1-21. (2024) (SCIE-WoS-Scopus).

Existence and Controllability for Second-Order Functional Differential Equations With Infinite Delay and Random Effects.  International Journal of Differential Equations, 5541644, 2024, 1-9. (2024) (WoS & Scopus).

Solving the Chemical Reaction Models with the Upadhyaya Transform. Orient J Chem, 2024; 40(3). (WoS) (WoS).

Md. Emran Biswas | Data science | Best Researcher Award

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Mr. Md. Emran Biswas | Data science | Best Researcher Award

Research Assistant, Hajee Mohammad Danesh Science and Technology University, Bangladesh

🌟 Md. Emran Biswas, hailing from Dinajpur, Bangladesh, is a passionate researcher and technologist specializing in machine learning, optimization algorithms, and their societal applications. He has actively contributed to predictive analysis, bioinformatics-based drug discovery, and developing AI solutions for global good. As a skilled programmer and researcher, Emran’s work has earned recognition through multiple publications, accolades, and groundbreaking projects in his field.

Publication Profile

Scopus

Education

🎓 Md. Emran Biswas completed his B.Sc. in Electronics and Communication Engineering at Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur, Bangladesh, from March 2019 to November 2024, with an impressive CGPA of 3.412/4.00. His academic journey is marked by a focus on deep learning, predictive modeling, and optimization algorithms.

Experience

💼 Emran served as a Research Assistant at Petarhub and DIOT Lab, HSTU, contributing to machine learning, predictive modeling, and optimization projects. His notable achievements include developing the ApexBoost Regression model, managing large datasets, and publishing impactful research in reputed journals like IEEE and Electronics.

Research Interests

🔍 Emran’s research focuses on machine learning, optimization algorithms, and their transformative applications in areas like bioinformatics-based drug discovery, predictive analysis, and societal challenges. His work aligns with the vision of ‘AI for Good,’ driving impactful innovation.

Awards

🏆 Emran has earned recognition for his innovative projects, including First Runner-Up at the Project Exhibition 2022 for his “Face Detection-Based Attendance System” and Second Runner-Up in 2023 for his “AI-Based Health Checking System.” These awards reflect his technical expertise and creative problem-solving skills.

Publications

Machine Learning Approach to Estimate Requirements for Target Productivity of Garments Employees. IEEE ICEEICT 2024 (Cited by: 5)

An Effective Data-Driven Approach to Predict Bike Rental Demand. Google Scholar (Cited by: 12)

Spatio-Temporal Feature Engineering and Selection-Based Flight Arrival Delay Prediction Using Deep Feedforward Regression Network. Electronics, 13(24), p.4910 (Cited by: 9)

 

Sara Tehsin | Deep learning | Best Researcher Award

Published on by Computer Scientists Awards

Ms. Sara Tehsin | Deep learning | Best Researcher Award

PhD Student, National University of Sciences and Technology, Islamabad, Pakistan

Sara Tehsin is a motivated and results-driven professional with over ten years of experience in Image Processing and Machine Learning. As an Engineering Lecturer at HITEC University in Taxila, Pakistan, she excels in delivering high-quality educational experiences and has a proven track record of producing outstanding results through her strong work ethic, adaptability, and effective communication skills. She is passionate about academic development and seeks opportunities to contribute her expertise while furthering her professional growth. 📚💻

Publication Profile

Google Scholar

Education

Sara Tehsin is currently pursuing a PhD in Computer Engineering at the National University of Sciences and Technology (NUST), Islamabad, where she has achieved a remarkable GPA of 3.83/4.00. Her research focuses on Digital Forensics, Deep Learning, and Digital Image Processing. She holds a Master’s degree in Computer Engineering from NUST, where she graduated with a GPA of 3.7/4.0, and a Bachelor’s degree from The Islamia University of Bahawalpur, with a GPA of 3.36/4.00. 🎓🌟

Experience

Sara has extensive teaching experience, currently serving as an Engineering Lecturer at HITEC University since September 2019, where she develops engaging curriculum and delivers lectures aligned with international standards. Previously, she was a Computer Science Lecturer at Sharif College of Engineering and Technology, and she also served as a Teaching Assistant at NUST and a Lab Engineer at Foundation University. Her roles have encompassed curriculum development, practical instruction, and student support in various computer science subjects. 👩‍🏫🔧

Research Interests

Sara’s research interests encompass Digital Forensics, Deep Learning, Digital Image Processing, and Machine Learning. She focuses on developing innovative solutions for image recognition and forgery detection, contributing significantly to the fields of computer vision and machine learning. Her work aims to enhance the accuracy and efficiency of image processing systems. 🧠🔍

Publications

Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition
S. Tehsin, S. Rehman, M.O.B. Saeed, F. Riaz, A. Hassan, M. Abbas, R. Young, …
IEEE Access, 5, 24495-24502 (2017)
Cited by: 21

Improved maximum average correlation height filter with adaptive log base selection for object recognition
S. Tehsin, S. Rehman, A.B. Awan, Q. Chaudry, M. Abbas, R. Young, A. Asif
Optical Pattern Recognition XXVII, 9845, 29-41 (2016)
Cited by: 18

Fully invariant wavelet enhanced minimum average correlation energy filter for object recognition in cluttered and occluded environments
S. Tehsin, S. Rehman, F. Riaz, O. Saeed, A. Hassan, M. Khan, M.S. Alam
Pattern Recognition and Tracking XXVIII, 10203, 28-39 (2017)
Cited by: 12

Comparative analysis of zero aliasing logarithmic mapped optimal trade-off correlation filter
S. Tehsin, S. Rehman, A. Bilal, Q. Chaudry, O. Saeed, M. Abbas, R. Young
Pattern Recognition and Tracking XXVIII, 10203, 22-37 (2017)
Cited by: N/A