Dr. David Hua | Artificial Intelligence | Best Researcher Award

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Dr. David Hua | Artificial Intelligence | Best Researcher Award

Ball State University, United States.

Dr. David M. Hua is an Associate Professor at the Center for Information and Communication Sciences, Ball State University. With a rich academic background and over two decades of teaching, Dr. Hua has become a pivotal figure in the intersection of technology education, cybersecurity, and higher education. He is recognized for mentoring student-led innovation and his contribution to emerging tech curricula including offensive security, private cloud infrastructure, and sustainability in IT.

Profile

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🎓 Education

Dr. Hua earned his Ed.D. in Higher Education in 2010 from Ball State University, where he also completed an MBA in Information Systems (2000) and a B.S. in Psychological Science (1991). This diverse academic foundation reflects his commitment to both technical expertise and educational leadership.

💼 Experience

Since July 20, 1998, Dr. Hua has served at Ball State University, advancing to the role of Associate Professor. He began as an Assistant Professor in 2000. His teaching spans undergraduate and graduate levels with courses ranging from cybersecurity and network configuration to cloud technologies. Beyond Ball State, his engagements with other institutions and organizations have broadened his interdisciplinary impact on both students and faculty.

🔬 Research Interests

Dr. Hua’s research interests lie at the crossroads of cybersecurity, AI in mental health surveillance, sustainable IT practices, and technology integration in higher education. He is especially passionate about leveraging machine learning to support mental health outcomes and empower student innovation through data-driven methodologies.

🏆 Awards & Mentorship

Dr. Hua has been an active mentor in various student projects, honors theses, and national competitions like the National Cyber League. He’s also served on several doctoral committees, contributing to dissertations in educational leadership and adult learning. His efforts have earned him recognition as a dedicated mentor, innovator, and academic leader.

📚 Publication

AI-Driven Mental Health Surveillance: Identifying Suicidal Ideation Through Machine Learning Techniques
📅 2025 | Big Data and Cognitive Computing
🧾 Cited by: 3 articles (as of early 2025)
👉 DOI: 10.3390/bdcc9010016

Prof. Dr. Saleh Albahli | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Saleh Albahli | Artificial Intelligence | Best Researcher Award

Qassim University, Saudi Arabia.

Dr. Saleh Albahli is a highly accomplished academic and researcher specializing in Digital Transformation, Data Science, and Artificial Intelligence. Currently an Associate Professor and Vice-Dean of Information Technology Deanship at Qassim University, he is known for spearheading transformative digital initiatives, leading enterprise architecture projects, and contributing to cutting-edge research in machine learning and deep learning. His work is globally recognized, ranking him among the top 2% of scientists in AI research worldwide.

Profile

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🎓 Education

Dr. Saleh Albahli holds a Ph.D. in Computer Science with distinction from Kent State University, USA (2016), showcasing his expertise in advanced computational methodologies and research excellence. He earned a Master’s degree in Information Technology with distinction from The University of Newcastle, Australia (2010), highlighting his dedication to mastering cutting-edge IT solutions. His academic journey began with a Bachelor’s degree in Computer Science from King Saud University, Saudi Arabia (2004), laying a strong foundation for his accomplished career in technology and innovation.

💼 Experience

Dr. Saleh Albahli has built an illustrious career, currently serving as an Associate Professor in the Department of IT at Qassim University since 2020, where he contributes to advancing education and research. Concurrently, he holds dual leadership roles as Vice-Dean of IT Deanship and Director of Enterprise Architecture & Digital Transformation at Qassim University, spearheading transformative initiatives to enhance technological frameworks and drive digital innovation.

Previously, Dr. Albahli gained international experience as a Senior System Analyst at Cleveland Clinic, USA (2015–2016), where he developed cutting-edge systems to optimize healthcare operations. He also served as a Lecturer at Kent State University, USA (2015–2016), imparting knowledge and fostering academic growth. Earlier in his career, he worked as an Oracle Developer and Apps DBA at Riyadh Bank and Integrated Telecom Company in Saudi Arabia (2005–2007), honing his technical expertise in database systems and enterprise applications.

🔬 Research Interests

Digital Transformation and its integration with enterprise architecture

Machine Learning and Deep Learning Pipelines

Big Data Analytics, Data Governance, and Predictive Analytics

Artificial Intelligence Applications in healthcare and business

Process Optimization in technology-driven environments

🏆 Awards & Recognitions

Ranked among the top 2% of scientists globally in AI research (2022)

First Place in Digital Transformation (Qiyas) – Qassim University (2022, 2023)

ISO certifications in 22301, 20000, and 27001 for excellence in IT management

📚 Selected Publications 

Efficient Hyperparameter Tuning for Predicting Student Performance with Bayesian Optimization
Albahli, S.
Multimedia Tools and Applications, 2024, 83(17), pp. 52711–52735.
This study introduces a Bayesian optimization approach to enhance hyperparameter tuning for predictive models in educational datasets, achieving improved accuracy and efficiency. (Citations: 4)

MedNet: Medical Deepfakes Detection Using an Improved Deep Learning Approach
Albahli, S., Nawaz, M.
Multimedia Tools and Applications, 2024, 83(16), pp. 48357–48375.
This paper presents MedNet, a novel deep learning framework tailored to detect medical deepfakes, ensuring the integrity of critical healthcare data. (Citations: 4)

Opinion Mining for Stock Trend Prediction Using Deep Learning
Albahli, S., Nazir, T.
Multimedia Tools and Applications, 2024.
Leveraging deep learning techniques, this research focuses on sentiment analysis to predict stock trends, demonstrating robust performance metrics. (Citations: 0)

An Improved DenseNet Model for Prediction of Stock Market Using Stock Technical Indicators
Albahli, S., Nazir, T., Nawaz, M., Irtaza, A.
Expert Systems with Applications, 2023, 232, 120903.
This work proposes enhancements to DenseNet architectures for stock market predictions based on technical indicators, achieving notable predictive accuracy. (Citations: 10)

A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images
Albahli, S., Nazir, T.
Bioengineering, 2023, 10(10), 1147.
This open-access study develops a circular box-based deep learning model for the accurate detection of signet ring cells in histopathological images, aiding cancer diagnosis.

 

 

 

Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

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Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

University of Saskatchewan, Canada.

Golshid Ranjbaran is a PhD Candidate in Computer Science at the University of Saskatchewan (USASK), specializing in Artificial Intelligence, Machine Learning, and Interpretability. With a Bachelor's degree in Software Engineering and a Master's in Artificial Intelligence from the Science and Research Branch in Iran, he has accumulated several awards, including the Best Paper Award at the IKT Conference in 2021 and Best Researcher at ITRC in 2022. Golshid's research is aimed at advancing AI methodologies and improving machine learning models for real-world applications. He was also a research associate at the Data Science & Big Data Lab in Seville, Spain, in 2023. 🌐

Profile

Google Scholar

Education 🎓

Golshid holds a Bachelor's degree in Software Engineering and a Master's degree in Artificial Intelligence from the Science and Research Branch in Iran. He is currently pursuing a Ph.D. in Computer Science at the University of Saskatchewan (USASK), Canada, where he focuses on AI, machine learning, and interpretability, aiming to bridge the gap between theoretical advancements and practical applications.

Experience 🏢

Golshid has been awarded several prestigious positions and accolades, including a research position at the Data Science & Big Data Lab in Seville, Spain (2023), and was recognized as the Best Researcher at ITRC (2022). He has also contributed to various consultancy projects and industry collaborations, such as working on AI systems at ITRC, smart meters algorithms, and data governance in Iran.

Research Interests 🔍

Enhancing model interpretability through methods like SHAP.

Exploring sentiment analysis for stock market prediction.

Developing augmented techniques for unbalanced tasks in the financial domain.

Improving network security through Moving Target Defense technology.

Investigating Federated Learning for wearable health devices and ontology-based text summarization for efficient information processing.

Awards 🏆

Best Paper Award at the IKT Conference (2021)

Best Researcher Award at the Iran Telecommunication Research Center (ITRC) (2022)

Research Position at the Data Science & Big Data Lab in Seville, Spain (2023)

Nomination for the Gala GSA Award at the University of Saskatchewan (2025).

Selected Publications 📚

C-SHAP: A Hybrid Method for Fast and Efficient Interpretability – Applied Sciences (Q2 Journal), Published 2025.

Leveraging Augmentation Techniques for Tasks with Unbalancedness within the Financial Domain – EPJ Data Science (Q1 Journal), Published 2023.

Investigating Sentiment Analysis of News in Stock Market Prediction – International Journal of Information and Communication Technology Research, Published 2024.

Unsupervised Learning Ontology-Based Text Summarization Approach with Cellular Learning Automata – Journal of Theoretical and Applied Information Technology, Published 2023.

Analyzing the Effect of News Polarity on Stock Market Prediction – Proceedings of the 12th International Conference on Information and Knowledge Technology (IKT), Published 2021.

 

 

Manisha Kasar | Artificial Intelligence | Best Researcher Award

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Dr. Manisha Kasar | Artificial Intelligence | Best Researcher Award

Assistant Professor, Bharati Vidyapeeth Deemed to be University College of engineering, Pune, India.

Dr. Manisha M. Kasar is an accomplished researcher and educator in the field of computer engineering, with over 11 years of experience. Her expertise spans facial recognition systems, artificial intelligence, and machine learning. She currently serves as an Assistant Professor at Bharti Vidyapeeth College of Engineering, Pune. Dr. Kasar has made significant contributions to the research community through her innovative work on emotion recognition, AI-based systems, and security applications. She is also the holder of several patents and has published numerous papers in prestigious journals.

Profile

Scopus

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Education 🎓

Dr. Kasar holds a Ph.D. in Information Technology from Bharti Vidyapeeth University, Pune, completed under the VISVESVARAYA Ph.D. Scheme in 2021. She also earned her M.Tech in Computer Engineering from NMIMS in 2014, and a B.E. in Computer Engineering from NMU in 2009. Her strong academic foundation has been pivotal in her research achievements.

Experience 💼

With over 11 years of experience, Dr. Kasar is currently an Assistant Professor at Bharti Vidyapeeth College of Engineering, Pune. She has previously worked at Vishwakarma Institute of Information Technology and as a visiting faculty member at Bharti Vidyapeeth. Her teaching and administrative skills have been recognized through her roles in various academic institutions, and she has contributed to mentoring and guiding students in advanced technology research.

Research Interest 🔍

Dr. Kasar’s research interests include artificial intelligence, machine learning, computer vision, and security systems. Her work primarily focuses on the development of AI-based applications such as facial emotion recognition, gesture-controlled systems, and fraud detection. She is particularly interested in exploring how machine learning models can optimize real-world applications like security systems and video surveillance.

Awards & Patents 🏆

Dr. Kasar is the holder of two significant patents:

Smart Mirror System with Infrared Blaster.

A Method to Identify Suspicious Financial Transactions and Prevent Fraud.

Her innovative work in these areas showcases her commitment to practical problem-solving through technology.

Publications  📚

Kasar, M., “EmoSense: Pioneering Facial Emotion Recognition with Precision Through Model Optimization,” International Journal of Engineering, April 2024. Cited by 1 article. link

Kasar, M., “AI-based Real-time Hand Gesture-Controlled Virtual Mouse,” Australian Journal of Electrical and Electronics Engineering, 2024. Cited by 0 articles. link

Kasar, M., “Use of Convolutional Neural Network and SVM Classifiers for Traffic Signals Detection,” International Journal on Recent and Innovation Trends in Computing and Communication, 2023. Cited by 3 articles. link

 

 

 

 

 

 

Firozeh solimani | Artificial intelligence | Best Researcher Award

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🌟Dr. Firozeh solimani, Artificial intelligence, Best Researcher Award🏆

Doctorate at Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Italy

Firozeh Solimani is a highly motivated researcher specializing in the intersection of agricultural engineering, computer vision, and artificial intelligence. With a PhD in Industry 4.0 from the University Politecnico di Bari, Italy, she has a strong background in mechanical engineering of biosystems and rural development and management engineering. Currently affiliated with the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, she focuses on innovative methodologies in agriculture for high-throughput plant phenomics using computer vision and AI.

Author Metrics:

Scopus Profile

Firozeh Solimani has established herself as a prolific author in the field of agricultural engineering and plant phenotyping. Her publications have garnered significant attention, as evidenced by citations and journal impact factors. With a consistent track record of high-quality research output, she has become a respected figure in academia and industry.

Citations: Firozeh Solimani’s work has received a total of 49 citations across 48 documents.

Documents: She has authored or co-authored 4 documents indexed in Scopus.

h-index: The h-index, which quantifies both the productivity and impact of an author’s publications, is not explicitly stated but can be inferred to be 3 based on the provided information (as there are at least 3 documents with 3 or more citations each).

Education:

Firozeh Solimani holds a PhD in Industry 4.0 from the University Politecnico di Bari, Italy, where she conducted research on high-throughput plant phenomics using computer vision and AI. Prior to her doctoral studies, she earned an MSc in Mechanical Engineering of Biosystems from Khuzestan University of Agricultural Sciences and Natural Resources, Iran, and a BSc in Rural Development and Management Engineering from Payam Noor Poldokhtar University, Iran.

Research Focus:

Firozeh Solimani’s research focuses on leveraging advanced technologies such as computer vision, artificial intelligence, and machine learning to revolutionize agriculture, particularly in the realm of plant phenotyping. Her work aims to develop innovative methodologies for high-throughput data acquisition and analysis, with the goal of improving crop productivity, sustainability, and resilience in the face of environmental challenges.

Professional Journey:

Firozeh Solimani’s professional journey has been characterized by a dedication to interdisciplinary research and collaboration. Starting with her undergraduate studies in rural development and management engineering, she has progressively delved deeper into the intersection of engineering, agriculture, and technology. Her journey has taken her from Iran to Italy, where she pursued her master’s and doctoral degrees, and she is currently engaged in cutting-edge research at the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing.

Honors & Awards:

Throughout her career, Firozeh Solimani has been recognized for her outstanding contributions to the field of agricultural engineering. She has received several honors and awards for her research excellence, innovative methodologies, and academic achievements. These accolades reflect her dedication, passion, and commitment to advancing scientific knowledge and addressing real-world challenges in agriculture.

Publications Noted & Contributions:

Firozeh Solimani’s publications have made significant contributions to the field of agricultural engineering and plant phenotyping. Her research outputs range from peer-reviewed articles in prestigious journals to conference presentations and posters. Notable contributions include the development of novel methodologies for high-throughput plant phenotyping using computer vision and AI, optimization of detection algorithms for plant traits, and advancements in hardware and software systems for 3D plant phenotyping.

Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity

  • Authors: Firozeh Solimani, Cardellicchio, A., Dimauro, G., Cellini, F., Renò, V.
  • Journal: Computers and Electronics in Agriculture, 2024, 218, 108728
  • Abstract: This article explores the optimization of tomato plant phenotyping detection using the YOLOv8 architecture, addressing the challenges posed by data complexity.
  • Citations: 2

A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping

  • Authors: Firozeh Solimani, Cardellicchio, A., Nitti, M., Dimauro, G., Renò, V.
  • Journal: Information (Switzerland), 2023, 14(4), 214
  • Abstract: This systematic review investigates the hardware and software factors that influence high-throughput plant phenotyping.
  • Citations: 3

Detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors

  • Authors: Cardellicchio, A., Firozeh Solimani, Dimauro, G., Cellini, F., Renò, V.
  • Journal: Computers and Electronics in Agriculture, 2023, 207, 107757
  • Abstract: This article presents the detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors.
  • Citations: 44

Influence of some Operational Parameters on Boom Spray Drift

  • Authors: Firozeh Solimani, Rahnama, M., Asoodar, M.A., Raini, M.G.N., Hormozi, M.A.
  • Journal: Agricultural Engineering International: CIGR Journal, 2022, 24(2), pp. 70–82
  • Abstract: This study investigates the influence of operational parameters on boom spray drift in agricultural applications.
  • Citations: 0

Research Timeline:

Firozeh Solimani’s research timeline reflects a progressive trajectory of academic and professional growth. Starting with her undergraduate studies in rural development and management engineering, she pursued graduate studies in mechanical engineering of biosystems before transitioning to her doctoral research in Industry 4.0. Her research journey has been characterized by a focus on leveraging advanced technologies to address key challenges in agriculture, culminating in her current work on high-throughput plant phenomics.

Collaborations and Projects:

Firozeh Solimani has been actively engaged in collaborative research projects aimed at advancing agricultural engineering and technology. Her collaborations span academia, industry, and international partnerships, reflecting a commitment to interdisciplinary teamwork and knowledge exchange. Through her involvement in various projects, she has contributed to the development of innovative methodologies, technologies, and solutions for enhancing crop productivity, sustainability, and resilience.