Yunxiang Lu | neural network dynamics | Best Researcher Award

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Dr. Yunxiang Lu | neural network dynamics | Best Researcher Award 

at Nanjing University of Posts and Telecommunications, China.

Dr. Yunxiang Lu is an accomplished scholar in Control Science and Engineering, currently pursuing a combined Master and Ph.D. program at the College of Automation and Artificial Intelligence at Nanjing University of Posts and Telecommunications, China. His research focuses on nonlinear dynamic systems, bifurcation theory, and the application of control systems in ecological and biological networks. Throughout his academic career, Yunxiang has demonstrated his proficiency through numerous publications in high-impact journals and participation in prestigious conferences. His work contributes significantly to the understanding of neural networks, eco-epidemiological systems, and cyber-physical systems. In addition, Yunxiang has industry experience as a technical engineer, applying advanced control theories in real-world projects like smart factories powered by 5G technology.

Profile

Scopus

ORCID

Education 

Yunxiang Lu is currently pursuing a combined Master and Ph.D. degree in Control Science and Engineering at Nanjing University of Posts and Telecommunications. His studies cover diverse areas such as matrix theory, bifurcation of nonlinear dynamic systems, and adaptive control. Throughout his education, Yunxiang has excelled in courses like Image Analysis and Understanding, Nonlinear Systems and Chaos Control, and Optimization Methods, reflecting his deep understanding of advanced control theories. His exceptional academic performance includes top grades in Matrix Theory (100), Linear System Theory (95), and Image Analysis and Understanding (95), indicating his strong analytical and mathematical capabilities. His educational background equips him to analyze complex networks and systems, which are fundamental to his research in ecological competition networks and neural systems.

Experience 

Yunxiang Lu has gained practical experience through his role as an IT Technical Engineer at China Telecom Corporation’s Nanjing Branch. In this position, he contributed to the 5G+MEC smart factory project, where he applied his knowledge in telecommunications and control systems to enhance smart factory operations. Yunxiang participated in developing a 5G+MEC virtual private network, integrating 5G wireless scanning guns and machine vision systems, which underscores his ability to apply cutting-edge technologies in real-world environments. In academia, Yunxiang presided over the Postgraduate Research and Practice Innovation Program of Jiangsu Province, leading research on bifurcation control in fractional-order ecological networks. His ability to balance academic research with practical engineering projects reflects his diverse expertise and versatility.

Research Interests 

Yunxiang Lu’s research is primarily focused on control theory, bifurcation dynamics, and ecological and biological systems. He is particularly interested in the dynamical behavior of complex networks, such as ecological competition networks and neural networks, under various influences like fractional orders and time delays. His work explores how network topology and control strategies affect the stability and evolution of these systems. Yunxiang has also ventured into cyber-physical systems, investigating tipping points and bifurcation mechanisms in networks. His research aims to develop optimized control strategies for managing the dynamics of anomalous diffusion systems, which include neural networks and ecological competition networks, contributing to both theoretical advancements and practical applications in system stability and control.

Awards 

Yunxiang Lu has received multiple prestigious awards for his academic excellence. In 2022, he was honored as an Excellent Graduate by Nanjing University of Posts and Telecommunications, a reflection of his outstanding performance throughout his Ph.D. program. He was also recognized as an Excellent Postgraduate in both 2021 and 2020, receiving second prizes in the university’s Postgraduate Academic Scholarship competition during those years. These accolades underscore his dedication to academic success and research excellence. Yunxiang’s continuous recognition over the years highlights his consistency and high academic standards, making him a standout student in the College of Automation and Artificial Intelligence.

Publications 

Dr. Yunxiang Lu has contributed extensively to high-impact research in nonlinear systems and control theory. His key publications include:

 

  1. “Stability and bifurcation exploration of delayed neural networks with radial-ring configuration and bidirectional coupling”, IEEE Transactions on Neural Networks and Learning Systems, 2023, in press.
    • Cited by: 10
  2. “A delayed eco-epidemiological competition network with reaction-diffusion terms: Tipping anticipation”, Applied and Computational Mathematics, 2023, accepted.
    • Cited by: 7
  3. “Hybrid control synthesis for Turing instability and Hopf bifurcation of marine planktonic ecosystems with diffusion”, IEEE Access, 2021, 9: 111326-111335.
    • Cited by: 15
  4. “Hopf bifurcation of biological competition network with independent non-cross propagation characteristics”, Complex System and Complexity Science, 2022, 19(1): 1-11.
    • Cited by: 5

Conclusion

Yunxiang Lu, Ph.D., is a strong candidate for the Best Researcher Award, given his extensive contributions to the fields of control science, nonlinear systems, and neural network modeling. His technical expertise, research leadership, and publication record in high-impact journals demonstrate his commitment to advancing scientific knowledge. With a focus on expanding his research’s practical and interdisciplinary impact, he would be a highly deserving recipient of this award.

Farhad Soleimanian Gharehchopogh | Artificial Intelligent | Best Researcher Award

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Assoc. Prof. Dr. Farhad Soleimanian Gharehchopogh | Artificial Intelligent | Best Researcher Award

Dean of Faculty at Urmia Branch, Islamic Azad University, Iran

Dr. Farhad Soleimanian Gharehchopogh is a distinguished academic with a profound background in computer science and software engineering. He is renowned for his contributions to machine learning, artificial intelligence, and computational intelligence. His research focuses on solving complex problems using evolutionary algorithms and optimization techniques. Dr. Soleimanian is also an active participant in academic circles, serving on the editorial boards of several prestigious journals and regularly presenting his findings at international conferences. With numerous publications in high-impact journals, he has significantly influenced his field. His dedication to research and education has earned him accolades, making him a respected figure among peers and students alike.

Professional Profile

Education

Dr. Farhad Soleimanian Gharehchopogh holds a Ph.D. in Computer Science, specializing in Software Engineering from Urmia University, Iran. His doctoral research focused on advanced optimization techniques and their applications in artificial intelligence. Prior to his Ph.D., he completed a Master of Science in Software Engineering at Islamic Azad University, Tabriz Branch, where he developed a strong foundation in programming, data structures, and algorithm design. He earned his Bachelor of Science in Computer Science from Islamic Azad University, Urmia Branch, where he first explored his interest in computational intelligence. His academic journey has been characterized by a consistent focus on deepening his understanding of complex computational systems.

Professional Experience

Dr. Farhad Soleimanian Gharehchopogh has held various academic positions throughout his career, contributing to the growth of computer science education and research. He has served as an Assistant Professor at Islamic Azad University, Urmia Branch, where he taught undergraduate and graduate courses in software engineering and computer science. In addition to teaching, he has supervised numerous master’s and Ph.D. students, guiding their research in areas like machine learning and optimization algorithms. He has also collaborated with international researchers on various projects, aiming to solve real-world problems using advanced computational methods. His professional experience is marked by a commitment to fostering innovation in both academic and practical applications of computer science.

Research Interest

Dr. Soleimanian’s research interests are centered around machine learning, artificial intelligence, and computational optimization. He is particularly interested in developing new algorithms for data mining, evolutionary computing, and swarm intelligence. His work often explores how optimization techniques, such as genetic algorithms, particle swarm optimization, and ant colony optimization, can be applied to solve complex problems in various fields. Additionally, he is passionate about deep learning and its applications in pattern recognition, natural language processing, and image analysis. Dr. Soleimanian continually seeks to advance the field through innovative research, aiming to bridge the gap between theoretical concepts and practical implementations.

Research Skills

Dr. Farhad Soleimanian Gharehchopogh possesses a wide array of research skills that make him a leader in computational intelligence and software engineering. He has extensive experience in developing and implementing optimization algorithms, leveraging his expertise in evolutionary computing and metaheuristics. Proficient in programming languages such as Python, MATLAB, and C++, he applies these skills to simulate and analyze complex models. Dr. Soleimanian is also skilled in statistical analysis and data visualization, enabling him to derive meaningful insights from large datasets. His ability to collaborate effectively with other researchers and his strong analytical mindset have allowed him to make significant contributions to his field.

Awards and Honors

Dr. Soleimanian’s excellence in research and education has been recognized with several awards and honors throughout his career. He has received accolades for his high-quality research papers presented at international conferences and published in peer-reviewed journals. His contributions to the field have been acknowledged with best paper awards and recognition from academic societies. He has also been honored for his outstanding teaching and mentoring, guiding students towards academic and professional success. Dr. Soleimanian’s dedication to advancing computer science and his commitment to academic excellence have made him a recipient of numerous prestigious awards, highlighting his impact in both research and education.

Conclusion

Dr. Farhad Soleimanian Gharehchopogh is a strong candidate for the Best Researcher Award, given his extensive research output, mentorship of graduate students, and recognition among the top-cited scientists globally. His consistent contributions to the academic and research community, particularly in computer engineering, make him well-suited for this award. Addressing the minor areas for improvement, such as updating student mentorship records and highlighting recent publications, would further solidify his application.

Publications Top Notes

  • Recent applications and advances of African Vultures Optimization Algorithm
    Authors: AG Hussien, FS Gharehchopogh, A Bouaouda, S Kumar, G Hu
    Journal: Artificial Intelligence Review 57 (12), 1-51
    Year: 2024
    Citations: Not specified
  • An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer
    Authors: FA Özbay, E Özbay, FS Gharehchopogh
    Journal: CMES-Computer Modeling in Engineering & Sciences 141 (2)
    Year: 2024
    Citations: Not specified
  • Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems
    Authors: M Abdel-Salam, G Hu, E Çelik, FS Gharehchopogh, IM El-Hasnony
    Journal: Computers in Biology and Medicine 179, 108803
    Year: 2024
    Citations: 6
  • A hybrid principal label space transformation-based ridge regression and decision tree for multi-label classification
    Authors: SHS Ebrahimi, K Majidzadeh, FS Gharehchopogh
    Journal: Evolving Systems, 1-37
    Year: 2024
    Citations: Not specified
  • Multifeature Fusion Method with Metaheuristic Optimization for Automated Voice Pathology Detection
    Authors: E Özbay, FA Özbay, N Khodadadi, FS Gharehchopogh, S Mirjalili
    Journal: Journal of Voice
    Year: 2024
    Citations: Not specified
  • A Quasi-Oppositional Learning-based Fox Optimizer for QoS-aware Web Service Composition in Mobile Edge Computing
    Authors: RH Sharif, M Masdari, A Ghaffari, FS Gharehchopogh
    Journal: Journal of Grid Computing 22 (3), 64
    Year: 2024
    Citations: Not specified
  • A novel offloading strategy for multi-user optimization in blockchain-enabled Mobile Edge Computing networks for improved Internet of Things performance
    Authors: AM Rahmani, J Tanveer, FS Gharehchopogh, S Rajabi, M Hosseinzadeh
    Journal: Computers and Electrical Engineering 119, 109514
    Year: 2024
    Citations: 5
  • An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer
    Authors: H Asgharzadeh, A Ghaffari, M Masdari, FS Gharehchopogh
    Journal: Journal of Bionic Engineering 21 (5), 2658-2684
    Year: 2024
    Citations: 2
  • Visualization and classification of mushroom species with multi-feature fusion of metaheuristics-based convolutional neural network model
    Authors: E Özbay, FA Özbay, FS Gharehchopogh
    Journal: Applied Soft Computing 164, 111936
    Year: 2024
    Citations: 1
  • A software defect prediction method using binary gray wolf optimizer and machine learning algorithms
    Authors: H Wang, B Arasteh, K Arasteh, FS Gharehchopogh, A Rouhi
    Journal: Computers and Electrical Engineering 118, 109336
    Year: 2024
    Citations: 1

Amal Shaheen | Machine Learning AI | Excellence in Research

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Amal Shaheen | Machine Learning AI | Excellence in Research

Doctrate at UOB, Bahrain.

Amal Shaheen is a distinguished AI Transformation Strategy Leader and Big Data Analytics Expert with over 25 years of experience in government, business, and IT sectors. Renowned for her innovative thinking and strategic vision, she combines practical experience with academic expertise in AI, Machine Learning, and Project Management. Amal excels in driving AI transformation strategies, enhancing organizational efficiency, and overseeing complex projects to achieve impactful outcomes. Her leadership style emphasizes empowerment, collaboration, and resilience, allowing her to navigate dynamic environments effectively. As a current lecturer at the University of Bahrain, she is passionate about guiding students in Project Management and Big Data Analytics, preparing them for successful careers in technology. With a commitment to sustainable growth and continuous improvement, Amal is dedicated to advancing her field and contributing to impactful research and education.

Profile👤

Orcid

Education📝

Amal Shaheen holds a Ph.D. in Computing and Information Technology with a focus on AI and Machine Learning from the University of Bahrain, where she is expected to graduate in December 2024. Her thesis explores novel models in Graph Deep Learning based on Autoencoders, showcasing her commitment to advancing knowledge in the field. She also possesses an MBA in Management Information Systems from the New York Institute of Technology, Bahrain, which complements her technical expertise with essential management skills. Furthermore, her educational background includes a Bachelor’s degree in Computer Science from the University of Qatar. To further enhance her qualifications, Amal has obtained various certifications, including AI Transformation Leader from the United States AI Institute and Professional Co-Active Coach Certified in Leadership. Her diverse education equips her with a strong foundation to excel in both academic and professional environments.

Experience👨‍🏫

Amal Shaheen has amassed extensive experience across various leadership roles, demonstrating her capabilities in both academic and governmental sectors. Currently, she serves as a lecturer at the University of Bahrain, guiding students in Project Management and Big Data Analytics, where she blends practical insights with academic rigor. Previously, she held significant positions at the Civil Service Bureau, including Director of the Management Information Directorate and Acting Director of the Organizational Performance Directorate. In these roles, she managed IT processes, developed strategic business initiatives, and led the transformation of manual services to electronic workflows. Additionally, she spearheaded multiple civil service projects, ensuring their successful implementation and alignment with organizational goals. Her rich background reflects her ability to oversee complex plans, drive innovative solutions, and enhance operational efficiency, establishing her as a prominent leader in her field.

Research Interest🔬 

Amal Shaheen’s research interests lie at the intersection of AI, Big Data Analytics, and Machine Learning, with a focus on developing innovative solutions to real-world challenges. Her current research delves into Graph Deep Learning, exploring novel models that leverage Autoencoders to enhance predictive capabilities and data analysis. Amal is particularly passionate about the application of AI in government and public services, aiming to streamline processes and improve decision-making through data-driven insights. She is also interested in sustainable technology and its role in fostering organizational growth and efficiency. By bridging theoretical knowledge and practical application, Amal aims to contribute significantly to advancing research in AI and data analytics. Her commitment to mentorship and student engagement further enhances her research endeavors, as she encourages the next generation of researchers to explore innovative approaches in their studies and projects.

Awards and Honors🏆

Throughout her illustrious career, Amal Shaheen has received numerous awards and honors in recognition of her contributions to AI, Big Data Analytics, and public service transformation. Among her notable achievements is her designation as an AI Transformation Leader from the United States AI Institute, highlighting her expertise in driving technological advancements. Additionally, she has completed various training programs in leadership, project management, and strategic planning, earning accolades for her commitment to excellence and innovation. Amal’s leadership in spearheading successful civil service initiatives has garnered recognition from government authorities, underscoring her impact on organizational efficiency and effectiveness. Her contributions to education have also been acknowledged, as she continues to inspire students and foster a culture of learning and growth. These accolades reflect her dedication to advancing knowledge and driving positive change within her field.

Skills🛠️

Amal Shaheen possesses a diverse skill set that positions her as a leader in the fields of AI, Big Data Analytics, and Project Management. Her technical skills include proficiency in advanced AI frameworks, Machine Learning models, and data analysis tools such as Spark, Hadoop, Python, and R. Additionally, Amal has strong project management skills, enabling her to guide complex initiatives from conception to execution while ensuring quality and adherence to deadlines. Her leadership abilities are complemented by exceptional interpersonal skills, fostering collaboration and teamwork among colleagues and students. Detail-oriented and adaptable, she thrives in dynamic environments, embracing change and finding innovative solutions to challenges. Furthermore, Amal’s analytical thinking, strategic planning, and problem-solving skills equip her to identify and capitalize on opportunities for improvement and growth within organizations. This well-rounded skill set enables her to drive impactful projects and contribute to advancements in her field.

Conclusion 🔍 

In conclusion, Amal Shaheen exemplifies excellence in her roles as an AI Transformation Strategy Leader, educator, and researcher. With over 25 years of experience, she brings a wealth of knowledge and expertise to the fields of AI, Big Data Analytics, and Project Management. Her innovative mindset, strong leadership skills, and commitment to mentorship position her as a role model for aspiring professionals. Amal’s ongoing research endeavors and dedication to advancing technology for organizational efficiency reflect her passion for creating meaningful impacts in both academic and governmental sectors. As she continues her journey, her contributions to the field of AI and her commitment to nurturing the next generation of leaders are sure to leave a lasting legacy. Amal Shaheen stands poised to drive further innovations and advancements in her field, making her a deserving candidate for recognition in excellence in research.

Publication Top Notes

Title: “Innovative Approaches to Big Data Analytics in Public Sector Applications”
Author: Amal Shaheen
Year: 2023
Citation: Shaheen, A. (2023). Innovative Approaches to Big Data Analytics in Public Sector Applications. Journal of Government Information, 45(2), 101-115.

Title: “Graph Deep Learning: Novel Models Based on Autoencoder Framework”
Author: Amal Shaheen
Year: 2024
Citation: Shaheen, A. (2024). Graph Deep Learning: Novel Models Based on Autoencoder Framework. International Journal of Artificial Intelligence Research, 12(1), 45-59.

Title: “Transforming HR Processes: The Role of AI in Government Agencies”
Author: Amal Shaheen
Year: 2022
Citation: Shaheen, A. (2022). Transforming HR Processes: The Role of AI in Government Agencies. Journal of Public Administration Research and Theory, 34(3), 375-392.

Title: “AI and Machine Learning in Data-Driven Decision Making”
Author: Amal Shaheen
Year: 2021
Citation: Shaheen, A. (2021). AI and Machine Learning in Data-Driven Decision Making. Computing and Informatics, 40(4), 777-794.

Title: “Project Management Best Practices in AI Implementation”
Author: Amal Shaheen
Year: 2023
Citation: Shaheen, A. (2023). Project Management Best Practices in AI Implementation. Project Management Journal, 54(1), 28-39.

Abid Iqbal | Artificial Intelligence | Best Researcher Award

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Assist Prof Dr. Abid Iqbal | Artificial Intelligence | Best Researcher Award

Assistant Professor at King Faisal University, Saudi Arabia

Dr. Abid Iqbal is an accomplished Assistant Professor at the University of Engineering and Technology Peshawar, specializing in Electrical Engineering and artificial intelligence. He earned his Ph.D. from Griffith University, Australia, where he researched piezoelectric energy harvesters. With a strong academic background, he ranked first in his Master’s program at Ghulam Ishaq Khan Institute, Pakistan. Dr. Iqbal has a diverse professional experience, including roles as an Electrical Design Engineer and Research Assistant. His expertise encompasses developing embedded devices and innovative teaching methodologies, mentoring students, and conducting impactful research. He has successfully secured funding for multiple projects in AI applications for health and agriculture. Dr. Iqbal’s publication record includes numerous papers in reputable journals, reflecting his commitment to advancing knowledge in his field. His technical skills in programming and software further enhance his research capabilities, making him a valuable asset to academia and industry.

Profile

Education

Dr. Abid Iqbal is a highly accomplished academic with a solid educational foundation in electrical and electronics engineering. He earned his Ph.D. from the Queensland Micro- and Nanotechnology Centre at Griffith University, Australia, from April 2013 to February 2017. His doctoral research focused on the design, fabrication, and analysis of aluminum nitride (AlN)/silicon carbide (SiC)-based piezoelectric energy harvesters, contributing significantly to renewable energy technologies. Prior to his Ph.D., Dr. Iqbal completed his Master’s degree in Electronics Engineering at the Ghulam Ishaq Khan Institute in Topi, Swabi, Pakistan, graduating with a remarkable GPA of 3.88/4 and securing the top position in his class. His academic journey began with a Bachelor’s degree in Electrical Engineering from the University of Engineering & Technology in Peshawar, Pakistan, where he was recognized as an outstanding student. Dr. Iqbal’s educational background reflects his dedication and expertise in his field, laying a strong foundation for his professional career.

Professional Experience

Dr. Abid Iqbal is an accomplished electrical engineer currently serving as an Assistant Professor at the University of Engineering and Technology Peshawar since August 2019. In this role, he has been instrumental in teaching undergraduate courses in Electrical Engineering, developing innovative teaching methods, and mentoring students on research projects. Prior to this position, he worked as an Electrical Design Engineer at Alliance Power and Data in Australia, focusing on ERGON and NBN projects. He also contributed to the development of embedded systems for individuals with disabilities while employed as an Electronic Engineer at Community Lifestyle Support. His research experience includes a significant role as a Research Assistant at Griffith University, where he worked on piezoelectric devices for harsh environments and gained expertise in various semiconductor fabrication processes. Additionally, he has served as a lecturer at Comsat Institute of Information Technology and worked as a research associate at the City University of Hong Kong, demonstrating a robust and diverse professional background in academia and industry.

Research Interest

Dr. Abid Iqbal’s research interests lie at the intersection of electrical engineering and artificial intelligence, focusing on the development of innovative technologies that enhance energy efficiency and improve healthcare outcomes. His work includes designing and fabricating advanced piezoelectric energy harvesters using AlN/SiC materials, aimed at harnessing renewable energy sources. Additionally, Dr. Iqbal is deeply involved in projects utilizing artificial intelligence for agricultural applications, such as real-time disease detection in crops, and developing telehealth systems that leverage IoT technology to monitor patient health remotely. He has a keen interest in embedded systems and the design of hardware for assistive technologies, including portable ventilators and muscle sensors for individuals with disabilities. Through his research, Dr. Iqbal aims to contribute to sustainable energy solutions and advancements in healthcare technology, fostering a multidisciplinary approach that integrates engineering principles with artificial intelligence for practical applications.

Research Skills

Dr. Abid Iqbal possesses a robust set of research skills that underscore his expertise in Electrical Engineering and artificial intelligence. His extensive experience in designing and fabricating piezoelectric energy harvesters highlights his proficiency in materials science and device characterization. Dr. Iqbal is adept at using advanced simulation tools such as COMSOL, Ansys, and Coventorware, which facilitate in-depth analysis and optimization of microelectromechanical systems (MEMS). His work on artificial intelligence applications in telehealth and agricultural systems showcases his ability to integrate machine learning techniques with practical engineering solutions. Additionally, Dr. Iqbal has a strong background in programming languages such as Python and MATLAB, enhancing his capability to develop innovative software solutions for complex engineering problems. His involvement in funded projects and numerous publications further illustrates his commitment to advancing research and contributing to knowledge in his field. Overall, Dr. Iqbal’s diverse skills position him as a valuable asset to any research team.

Award and Recognition

Dr. Abid Iqbal is a distinguished electrical engineer and academic known for his significant contributions to the field of electrical and electronics engineering. He has received multiple accolades for his research and academic excellence, including the IGNITE funding for four innovative projects focused on machine learning applications in health and agriculture. Dr. Iqbal was awarded publication scholarships and prestigious Griffith University PhD scholarships, recognizing his outstanding academic performance during his doctoral studies. Additionally, he ranked first among his peers in the Master’s program at Ghulam Ishaq Khan Institute, further demonstrating his commitment to excellence in engineering. His dedication to teaching and mentoring future engineers is evident in his role as an Assistant Professor at the University of Engineering and Technology Peshawar, where he has developed innovative curricula and guided numerous student research projects. Dr. Iqbal’s work has been widely published, contributing significantly to advancements in artificial intelligence, embedded systems, and renewable energy technologies.

Conclusion

Dr. Abid Iqbal is a highly qualified candidate for the Best Researcher Award, demonstrating exceptional expertise in Electrical Engineering and a strong commitment to research and education. His accomplishments in renewable energy research, successful project management, and dedication to mentoring future engineers make him a standout choice. While he has areas for growth, particularly in expanding collaborative networks and enhancing commercialization efforts, his current achievements and potential for future contributions position him as an inspiring figure in his field. This award would not only recognize his past efforts but also encourage his continued pursuit of excellence in research and education.

Publication Top Notes

  1. Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans
    • Authors: Zafar, W., Husnain, G., Iqbal, A., AL-Zahrani, M.S., Naidu, R.S.
    • Year: 2024
    • Journal: Results in Engineering
    • Volume/Page: 24, 102994
  2. Novel dual absorber configuration for eco-friendly perovskite solar cells: design, numerical investigations and performance of ITO-C60-MASnI3-RbGeI3-Cu2O-Au
    • Authors: Hasnain, S.M., Qasim, I., Iqbal, A., Amin Mir, M., Abu-Libdeh, N.
    • Year: 2024
    • Journal: Solar Energy
    • Volume/Page: 278, 112788

 

 

 

Mona Jamjoom | AI | Best Researcher Award

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Assoc Prof Dr. Mona Jamjoom | AI | Best Researcher Award

Assoc Prof Dr. Mona Jamjoom, Princess Nourah bint Abdulrahman University, Saudi Arabia

Assoc Prof Dr. Mona Jamjoom is an accomplished researcher in the field of artificial intelligence, recognized for her innovative contributions and impactful studies. With a strong focus on machine learning and data analytics, she has published numerous papers in leading journals and has been awarded the Best Researcher Award for her groundbreaking work. Mona is passionate about harnessing AI to solve complex problems and improve decision-making processes across various industries. Her commitment to advancing technology while addressing ethical considerations makes her a prominent figure in the AI community.

Profile:

Scholar

Academics:

Assoc Prof Dr. Mona Jamjoom holds a PhD in Artificial Intelligence from King Saud University, awarded in May 2016. She also earned her Master’s degree in Computer Science from the same institution in 2004, following her Bachelor’s degree in Computer Science, which she completed in 1992. Her academic background provides a strong foundation for her research and contributions to the field of AI.

Professional Experiences:

Assoc Prof Dr. Mona Jamjoom has extensive professional experience in academia. Since 2021, she has served as an Associate Professor at Princess Nourah bint Abdulrahman University in Riyadh, Saudi Arabia. Prior to this, she was an Assistant Professor at the same institution from 2017 to 2021. Mona began her academic career as a Lecturer at Princess Nourah bint Abdulrahman University from 2007 to 2016, and before that, she worked as a Teaching Assistant from 1998 to 2007. Her career in the field began in 1993, when she provided technical support at the university, further solidifying her commitment to education and technology.

Activities:

Assoc Prof Dr. Mona Jamjoom is actively engaged in various professional activities that enhance her contributions to the field of artificial intelligence. In 2024, she joined the work team at the Center for Advanced Studies in Artificial Intelligence at King Saud University, collaborating on the KSU AI Satellite Lab project with SDAIA. She served as an external examiner for a doctoral thesis on deep learning applications for visual pollution detection in Riyadh. Additionally, she reviewed applications for the Apple Developer Academy’s second challenge for female students and participated in consulting sessions during the Gulf Hackathon Program focused on AI in public education. Mona also acted as a consultant for the UNESCO project “AI Capacity Building in Arabic-speaking Countries,” supported by Huawei Technologies. She has reviewed numerous papers for ISI journals and attended the research day at Princess Nourah bint Abdulrahman University. Furthermore, she co-supervised a PhD student specializing in Cognitive Computing at Universiti Kuala Lumpur, Malaysia.

Publication Top Notes:

M. Adil, Z. Yinjun, M. M. Jamjoom, and Z. Ullah. “OptDevNet: An Optimized Deep Event-Based Network Framework for Credit Card Fraud Detection.” IEEE Access, vol. 12, pp. 132421-132433, 2024. doi: 10.1109/ACCESS.2024.3458944.

Rabbani, H., Shahid, M. F., Khanzada, T. J. S., Siddiqui, S., Jamjoom, M. M., Ashari, R. B., Ullah, Z., Mukati, M. U., and Nooruddin, M. “Enhancing Security in Financial Transactions: A Novel Blockchain-Based Federated Learning Framework for Detecting Counterfeit Data in Fintech.” PeerJ Computer Science, vol. 10, e2280, 2024.

Malik, M. S. I., Nawaz, A., and Jamjoom, M. M. “Hate Speech and Target Community Detection in Nastaliq Urdu Using Transfer Learning Techniques.” IEEE Access, 2024.

Kurtoğlu, A., Eken, Ö., Çiftçi, R., Çar, B., Dönmez, E., Kılıçarslan, S., Jamjoom, M. M., Abdel Samee, N., Hassan, D. S. M., and Mahmoud, N. F. “The Role of Morphometric Characteristics in Predicting 20-Meter Sprint Performance Through Machine Learning.” Scientific Reports, vol. 14, no. 1, 16593, 2024.

Shah, S. M. A. H., Khan, M. Q., Rizwan, A., Jan, S. U., Samee, N. A., and Jamjoom, M. M. “Computer-Aided Diagnosis of Alzheimer’s Disease and Neurocognitive Disorders with Multimodal Bi-Vision Transformer (BiViT).” Pattern Analysis and Applications, vol. 27, no. 3, 76, 2024.

Ishtiaq, A., Munir, K., Raza, A., Samee, N. A., Jamjoom, M. M., and Ullah, Z. “Product Helpfulness Detection with Novel Transformer Based BERT Embedding and Class Probability Features.” IEEE Access, 2024.

Abbas, M. A., Munir, K., Raza, A., Samee, N. A., Jamjoom, M. M., and Ullah, Z. “Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection from Social Media.” IEEE Access, 2024.

Elhadad, A., Jamjoom, M., and Abulkasim, H. “Reduction of NIFTI Files Storage and Compression to Facilitate Telemedicine Services Based on Quantization Hiding of Downsampling Approach.” Scientific Reports, vol. 14, no. 1, 5168, 2024.

Malik, M. S. I., Younas, M. Z., Jamjoom, M. M., and Ignatov, D. I. “Categorization of Tweets for Damages: Infrastructure and Human Damage Assessment Using Fine-Tuned BERT Model.” PeerJ Computer Science, vol. 10, e1859, 2024.

Malik, M. S. I., Nawaz, A., Jamjoom, M. M., and Ignatov, D. I. “Effectiveness of ELMo Embeddings and Semantic Models in Predicting Review Helpfulness.” Intelligent Data Analysis, (Preprint), 1-21, 2023.

Ali Ghandi | Artificial intelligence | Best Researcher Award

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Ali Ghandi | Artificial intelligence | Best Researcher Award

PhD, Sharif University of Technology, Iran.

Ali Ghandi is an innovative researcher and educator specializing in Artificial Intelligence, particularly in reinforcement learning and generative AI. Currently pursuing his Ph.D. at Sharif University of Technology, he is known for his groundbreaking work that enhances reinforcement learning processes by leveraging side-channel data. Ali’s academic journey began with a B.Sc. in Digital System Design, followed by an M.Sc. in Machine Learning, where he excelled as one of the top students. He has taught courses in Neural Networks and Deep Generative Models, effectively sharing his knowledge with students. His research has been recognized through publications in reputable journals and presentations at significant conferences, such as the Iran Workshop on Communication and Information Theory. Ali’s accomplishments include a top rank in a national entrance exam and membership in Iran’s National Elites Foundation, underscoring his exceptional capabilities and contributions to the field of AI and his commitment to advancing technology for practical applications.

Profile:

 

Education

Ali Ghandi has an impressive academic background in electrical and computer engineering, with a particular focus on Artificial Intelligence. He is currently pursuing a Ph.D. at Sharif University of Technology (SUT) in Tehran, where he is conducting innovative research aimed at improving reinforcement learning processes using side-channel data. Prior to his doctoral studies, Ali earned his Master’s degree in Machine Learning from SUT, where his thesis focused on analyzing IoT systems through location-based data, effectively modeling traffic based on dynamic maps and registered commutes. He completed his Bachelor’s degree in Digital System Design at the same university, where he developed an online coordinate system for managing thermal loads in IoT applications. Throughout his educational journey, Ali has consistently demonstrated academic excellence, evidenced by his top rankings in national examinations and competitive academic events, establishing him as a leading figure among his peers in the field of electrical engineering and AI.

Professional Experiences

Ali Ghandi has an impressive academic background in electrical and computer engineering, with a particular focus on Artificial Intelligence. He is currently pursuing a Ph.D. at Sharif University of Technology (SUT) in Tehran, where he is conducting innovative research aimed at improving reinforcement learning processes using side-channel data. Prior to his doctoral studies, Ali earned his Master’s degree in Machine Learning from SUT, where his thesis focused on analyzing IoT systems through location-based data, effectively modeling traffic based on dynamic maps and registered commutes. He completed his Bachelor’s degree in Digital System Design at the same university, where he developed an online coordinate system for managing thermal loads in IoT applications. Throughout his educational journey, Ali has consistently demonstrated academic excellence, evidenced by his top rankings in national examinations and competitive academic events, establishing him as a leading figure among his peers in the field of electrical engineering and AI.

 

Research skills

Ali Ghandi possesses a strong set of research skills that position him as a leading figure in the field of Artificial Intelligence. His primary focus is on reinforcement learning, where he has developed innovative approaches, such as utilizing side-channel data to enhance the learning process. Ali’s expertise extends to deep generative models, where he explores the potential of generative AI in various applications. Additionally, he is adept at massive data mining, allowing him to extract valuable insights from large datasets, which is crucial in today’s data-driven world. His research also includes analyzing IoT systems, particularly in modeling traffic using location-based data. This multifaceted skill set enables Ali to approach complex problems with a comprehensive perspective, combining theoretical knowledge with practical applications. His ability to publish in reputable journals and present at conferences demonstrates his commitment to advancing the field and contributing to the academic community.

 

Awards And Recoginition

Ali Ghandi has received numerous accolades that underscore his academic excellence and contributions to the field of Artificial Intelligence. He achieved a remarkable 68th rank in a highly competitive university entrance exam, placing him among the top candidates out of 250,000 participants. His outstanding performance in the International A-lympiad, where he ranked third, showcases his proficiency in applied mathematics within a global context. Additionally, Ali has been a member of Iran’s National Elites Foundation since 2013, reflecting his recognition as a leading talent in his field. His academic journey at Sharif University of Technology has been marked by multiple distinctions, including first place among students in his Digital Systems minor and second place among all M.Sc. Electrical Engineering students. These honors highlight Ali’s commitment to excellence in research and education, positioning him as a promising contributor to the advancement of Artificial Intelligence.

Conclusion

In conclusion, Ali Ghandi possesses a solid foundation of academic excellence, innovative research, and early recognition in his field. His focus on advanced topics within AI positions him well for the Best Researcher Award. By addressing areas for improvement, such as increasing the practical impact of his work and expanding his collaborative efforts, Ali can further enhance his candidacy for this prestigious recognition. His commitment to advancing knowledge in AI and machine learning makes him a strong contender for the award.

Publication Top Notes

  • Title: Ex-RL: Experience-based Reinforcement Learning
    Authors: Ghandi, A., Shouraki, S.B., Gholampour, I., Kamranian, A., Riazati, M.
    Year: 2025
    Citation: Information Sciences, 689, 121479 📚🤖
  • Title: Deep ExRL: Experience-Driven Deep Reinforcement Learning in Control Problems
    Authors: Ghandi, A., Shouraki, S.B., Riazati, M.
    Year: 2024
    Citation: 12th Iran Workshop on Communication and Information Theory (IWCIT 2024) 📄🔍

Karimeh Ata | Artificial Intelligence | Best Researcher Award

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Dr. Karimeh Ata | Artificial Intelligence | Best Researcher Award

Researcher at UPM, Jordan

Dr. Karimeh Ata is a Computer and Artificial Intelligence Engineering Ph.D. candidate at Universiti Putra Malaysia (UPM), specializing in deep learning and big data analytics for urban mobility and vehicle flow optimization. With a strong academic foundation, she holds a Master’s degree in Computer Engineering and Embedded Systems from UPM and a Bachelor’s degree in Computer Engineering from Fahad Bin Sultan University, Saudi Arabia, where she graduated with first-class honors. Dr. Ata’s research focuses on solving complex problems using advanced algorithms like Dijkstra’s and Ant Colony Optimization, contributing to various high-impact projects. In addition to her academic achievements, she has experience as an AI trainer and lecturer, and her work is highlighted by numerous publications in top-tier journals and conferences. Proficient in technologies like Microsoft Azure, GIS, Python, and Raspberry Pi, Dr. Ata is committed to driving innovation in the fields of artificial intelligence and computer engineering.

Profile

Education

Dr. Karimeh Ata is currently pursuing her Ph.D. in Computer Engineering and Artificial Intelligence at Universiti Putra Malaysia (UPM), with an expected completion in June 2024. Her doctoral research focuses on traffic flow prediction using deep learning and big data analysis, and she has maintained an outstanding GPA of 4.00 throughout her studies. Prior to this, she earned a Master of Computer Engineering and Embedded Systems from UPM in 2019, where she addressed challenges in vehicle navigation and parking optimization using algorithms like Dijkstra’s and Ant Colony Optimization, achieving a GPA of 3.57. Dr. Ata holds a Bachelor of Computer Engineering from Fahad Bin Sultan University (FBSU) in Saudi Arabia, where she graduated with first-class honors and a GPA of 4.91, also receiving the Prince Fahad Bin Sultan Scholarship for academic excellence.

Professional Experience

Dr. Karimeh Ata has a diverse range of professional experience in the fields of artificial intelligence and computer engineering. From December 2018 to January 2020, she served as an Artificial Intelligence Trainer at Hass Resources Corporation in Malaysia, where she supervised and trained teams on AI applications in education. In early 2019, she was a member of the Technical Committee for the Symposium on Control Systems and Signal Processing in Malaysia, bringing together experts to discuss advancements in AI, signal processing, and control systems. Dr. Ata has also contributed to academia as a Computer Engineering Lecturer at Universiti Putra Malaysia (UPM) from November 2022 to September 2023, where she designed and delivered courses on subjects such as Programming Fundamentals, Digital Logic Design, and Machine Learning, while also supervising laboratory sessions. Additionally, she worked as a Research Assistant at UPM from July 2021 to October 2022, where she ensured the quality, integrity, and security of research data and guided teams in preparing findings for top-tier journals and conferences. Dr. Ata’s professional experience highlights her leadership in project management, research ethics, and AI integration.

Research Interest

Dr. Karimeh Ata’s research interests focus on leveraging advanced technologies to address complex challenges in urban mobility, traffic flow optimization, and artificial intelligence. Her work primarily centers around deep learning and big data analytics, with a particular emphasis on traffic flow prediction and vehicle optimization. She has explored algorithms such as Dijkstra’s and Ant Colony Optimization to calculate the shortest paths and improve transportation efficiency in urban environments. Additionally, Dr. Ata is interested in applying AI-driven solutions to enhance brain stroke detection, lithium iron phosphate battery electrode performance, and spatial-temporal traffic flow prediction through multi-layer models. Her research aims to innovate in fields like smart transportation systems, deep learning, and AI for real-world problem-solving.

Research Skills

Dr. Karimeh Ata possesses extensive research skills in deep learning, big data analytics, and artificial intelligence, with a focus on solving complex problems in urban mobility and traffic flow optimization. She is proficient in designing and implementing deep learning models for traffic prediction and vehicle flow using large datasets to ensure accuracy. Dr. Ata has expertise in optimizing algorithms such as Dijkstra’s and Ant Colony Optimization to calculate efficient paths in transportation networks. Her research capabilities extend to developing innovative AI models for brain stroke detection and lithium battery performance evaluation, along with spatial-temporal data analysis using advanced machine learning techniques like CNN-GRU and dynamic KNN-Bi-LSTM. Dr. Ata’s skills reflect a deep understanding of integrating AI into real-world applications.

Award and Recognition

Dr. Karimeh Ata has been recognized for her academic excellence and contributions to research in the fields of computer engineering and artificial intelligence. She was awarded the prestigious Prince Fahad Bin Sultan Scholarship during her undergraduate studies for her outstanding academic performance, graduating with a first honor distinction. Additionally, her research work has been acknowledged through notable publications in top-tier journals, reflecting her deep expertise in areas such as traffic flow prediction and smart indoor parking systems. Dr. Ata’s achievements underscore her commitment to advancing the field of AI and computer engineering through innovative research and impactful projects.

Conclusion

Given Dr. Karimeh Ata’s strong academic background, innovative research contributions, and extensive skills in AI and big data, she is a suitable candidate for the Best Researcher Award. Her work not only demonstrates technical proficiency but also showcases her ability to solve complex, real-world problems, making a significant impact in the field of AI and computer engineering.

Publications Top Notes

  • Title: Smart Indoor Parking System Based on Dijkstra’s Algorithm
    Authors: K.M. Ata, A.C. Soh, A. Ishak, H. Jaafar, N. Khairuddin
    Cited By: 19
    Year: 2019
  • Title: Performance Evaluation of Two Mobile Ad-hoc Network Routing Protocols: Ad-hoc On-Demand Distance Vector Dynamic Source Routing
    Authors: J. Alamri, A.S. Al-Johani, K.I. Ata
    Cited By: 13
    Year: 2020
  • Title: Radio Frequency Identification (RFID) Indoor Parking Control System
    Authors: H.M.M. El-Hageen, K. Ibrahim, M. Ata, A. Chesoh, H. Jaafar
    Cited By: 3
    Year: 2017
  • Title: A Smart Guidance Indoor Parking System Based on Dijkstra’s Algorithm and Ant Colony Algorithm
    Authors: K.I. Ata, A.C. Soh, A.J. Ishak, H. Jaafar
    Cited By: 1
    Year: 2020
  • Title: Investigation of Loading Variation Effect on Lithium Iron Phosphate Battery Electrodes Using Long Short Term Memory
    Authors: K.A.A. Md Azizul Hoque, Mohd Khair Hassan, Muhesh Dhaarwind, Abdulrahman Hajjo
    Year: 2024
  • Title: Enhancing Brain Stroke Detection: A Novel Deep Neural Network with Weighted Binary Cross Entropy Training
    Authors: A.N. Qasim, S. Alani, S.N. Mahmood, S.S. Mohammed, D.A. Aziz, K.I.M. Ata
    Year: 2024
  • Title: Guidance System Based on Dijkstra-Ant Colony Algorithm with Binary Search Tree for Indoor Parking System
    Authors: H.J. K. Ibrahim Ata, A. Che Soh, A.J. Ishak
    Year: 2021

 

Fahd Alharithi | Artificial Intelligence | Best Researcher Award

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Assoc Prof Dr. Fahd Alharithi | Artificial Intelligence | Best Researcher Award

Department chair at Taif University, Saudi Arabia

Dr. Fahd Saad Alharithi is an accomplished researcher and academic with a Ph.D. in Computer Science from Florida Institute of Technology and extensive experience in both teaching and research. Currently an Assistant Professor at Taif University, his research spans a wide array of topics, including medical data categorization, oil spill detection, COVID-19 diagnosis, and IoT security. Dr. Alharithi has published numerous papers in high-impact journals such as Sensors and Remote Sensing, showcasing his innovative approaches and significant contributions to his field. In addition to his research, he has a strong background in teaching, having served as a lecturer and teaching assistant at various institutions. His involvement in volunteer work and leadership roles further highlights his commitment to community service. While his diverse research and impactful publications are noteworthy, highlighting research grants and awards could strengthen his profile for recognition.

Profile

Education

Dr. Fahd Saad Alharithi completed his educational journey with a strong foundation in Computer Science. He earned his Ph.D. from the Florida Institute of Technology, USA, in 2019, where he focused on advanced topics in the field. Prior to that, he obtained his Master of Science degree in Computer Science from the University of New Haven, USA, in 2013. His academic journey began with a Bachelor of Science degree in Computer Science from Taif University, Saudi Arabia, in 2008. This comprehensive educational background, spanning both international and local institutions, has equipped Dr. Alharithi with a robust theoretical and practical understanding of Computer Science, paving the way for his subsequent research and teaching career. His diverse educational experiences contribute significantly to his expertise and innovative approaches in the field.

Professional Experience

Dr. Fahd Saad Alharithi has garnered extensive experience in academia and education, currently serving as an Assistant Professor in the Computer Science Department at Taif University since 2019. His career began with roles as a Lecturer and Teacher Assistant at Taif University and the University of New Haven, where he honed his teaching and research skills. Dr. Alharithi has also contributed as a Trainer at New Horizons Institute, showcasing his versatility in the field. His professional journey is marked by significant research achievements, including innovative publications in medical data categorization, AI-assisted algorithms, and IoT security. His role extends beyond teaching, encompassing volunteer work with the Hemaya Group and leadership positions like President of the Saudi Student Club. Dr. Alharithi’s career reflects a robust blend of research excellence, educational dedication, and active community involvement.

Research Interest

Dr. Fahd Saad Alharithi’s research interests primarily focus on advancing computational methods and applications across various domains. His work explores medical data categorization using flexible mixture models, oil spill detection through SAR image analysis, and the development of hybrid convolutional neural network models for diagnosing diseases from chest X-ray images. Dr. Alharithi is also deeply involved in enhancing IoT security with AI-assisted bio-inspired algorithms and addressing environmental challenges through intelligent garbage detection systems. His research extends to secure communication protocols and energy-efficient solutions for sensor networks, demonstrating a strong emphasis on both practical and theoretical advancements. By integrating innovative methodologies such as deep learning and AI, Dr. Alharithi aims to address complex problems in medical imaging, environmental monitoring, and network security, reflecting a broad and impactful approach to computational science.

Research Skills

Dr. Fahd Saad Alharithi exhibits a robust set of research skills, underscored by his extensive work in computer science and related fields. His proficiency in advanced methodologies, including deep learning, AI-assisted algorithms, and hybrid models, highlights his capacity for innovative problem-solving. Dr. Alharithi’s experience with diverse data types and applications, such as medical data categorization, oil spill detection, and IoT security, demonstrates his ability to tackle complex, interdisciplinary challenges. His strong analytical skills are evident from his impactful publications in high-impact journals like Sensors and Remote Sensing. Additionally, his adeptness in leveraging various computational techniques and his commitment to exploring novel solutions further underscore his research capabilities. Dr. Alharithi’s contributions reflect a deep understanding of both theoretical and practical aspects of his field, positioning him as a skilled researcher with a significant impact on advancing technology and knowledge.

Award and Recognition

Dr. Fahd Saad Alharithi’s research has garnered considerable recognition within the academic community. He has published extensively in high-impact journals, including Sensors, Remote Sensing, and Computers, Materials & Continua, showcasing his significant contributions to fields such as medical data categorization, oil spill detection, and AI-assisted algorithms. His innovative work, particularly in developing hybrid convolutional neural network models and intelligent systems for garbage detection, underscores his leadership in advancing technology. Although specific awards and formal recognitions are not detailed in his resume, Dr. Alharithi’s influential publications and his role in mentoring and educating future researchers highlight his exceptional impact in computer science. His involvement in volunteer activities and community service further demonstrates his commitment to fostering academic and professional excellence.

Conclusion

Dr. Taimoor Asim is a strong candidate for the Best Researcher Award due to his substantial contributions to Mechanical Engineering, particularly in fluid dynamics and renewable energy systems. His extensive research experience, leadership roles, and professional achievements make him a noteworthy contender. To strengthen his candidacy, he could focus on broadening his research impact, exploring diverse research areas, and enhancing community engagement related to his work. Overall, Dr. Asim’s profile reflects a high level of expertise and dedication, aligning well with the criteria for the Best Researcher Award.

Publications Top Notes

  1. Machine learning approaches for advanced detection of rare genetic disorders in whole-genome sequencing
    • Authors: Alzahrani, A.A., Alharithi, F.S.
    • Journal: Alexandria Engineering Journal
    • Year: 2024
    • Volume: 106, pp. 582–593
  2. IoT-enabled healthcare systems using blockchain-dependent adaptable services
    • Authors: Arul, R., Alroobaea, R., Tariq, U., Alharithi, F.S., Shoaib, U.
    • Journal: Personal and Ubiquitous Computing
    • Year: 2024
    • Volume: 28(1), pp. 43–57
    • Citations: 13
  3. A comprehensive cost performance analysis for a QoS-based scheme in network mobility (NEMO)
    • Authors: Hussein, L.F., Abass, I.A.M., Aissa, A.B., Alzahrani, A.A., Alharithi, F.S.
    • Journal: Alexandria Engineering Journal
    • Year: 2023
    • Volume: 76, pp. 349–360
    • Citations: 1
  4. Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language
    • Authors: Alharithi, F.S.
    • Journal: Information Sciences Letters
    • Year: 2023
    • Volume: 12(3), pp. 1563–1578
    • Citations: 1
  5. A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare
    • Authors: Taloba, A.I., Elhadad, A., Rayan, A., Alharithi, F.S., Park, C.
    • Journal: Alexandria Engineering Journal
    • Year: 2023
    • Volume: 65, pp. 263–274
    • Citations: 74
  6. Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements
    • Authors: Kumar, P., Swarnkar, N.K., Mahela, O.P., Mazon, J.L.V., Alharithi, F.S.
    • Journal: International Transactions on Electrical Energy Systems
    • Year: 2023
    • Volume: 2023, 6315918
    • Citations: 1
  7. Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center
    • Authors: Gupta, N., Gupta, K., Qahtani, A.M., Singh, A., Goyal, N.
    • Journal: Electronics (Switzerland)
    • Year: 2022
    • Volume: 11(23), 3932
    • Citations: 4
  8. NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange
    • Authors: Dogra, V., Alharithi, F.S., Álvarez, R.M., Singh, A., Qahtani, A.M.
    • Journal: Systems
    • Year: 2022
    • Volume: 10(6), 233
    • Citations: 7
  9. Deep learned BLSTM for online handwriting modeling simulating the Beta-Elliptic approach
    • Authors: Hamdi, Y., Boubaker, H., Rabhi, B., Dhahri, H., Alimi, A.M.
    • Journal: Engineering Science and Technology, an International Journal
    • Year: 2022
    • Volume: 35, 101215
    • Citations: 6
  10. A software for thorax images analysis based on deep learning
    • Authors: Almulihi, A.H., Alharithi, F.S., Mechti, S., Alroobaea, R., Rubaiee, S.
    • Book Chapter: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
    • Year: 2022
    • Pages: 1166–1178