Dimitrios Tsourounis | Computer Science | Best Researcher Award

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Dr. Dimitrios Tsourounis | Computer Science | Best Researcher Award

Dr. Dimitrios Tsourounis | Computer Science | University of Patras | Greece

Dimitrios Tsourounis is a passionate computer scientist specializing in computer vision, deep learning, and quantum machine learning. Born on February 26, 1991, in Greece, Dimitrios earned his Ph.D. from the University of Patras in 2023, focusing on deep learning strategies for problems with limited data. He has contributed significantly to advancing machine learning methods and quantum computing integration, currently working as a Research Scientist at Quantum Neural Technologies (QNT) in Athens. Dimitrios is also involved in autonomous aerial systems research at the Athena Research Center, applying computer vision techniques to fuse radar and RGB camera data for UAVs. His multidisciplinary expertise includes physics, electronics, and artificial intelligence, supported by multiple successful EU-funded projects. With a proven track record in innovation and real-world applications, Dimitrios is recognized for bridging theoretical research and industrial challenges, particularly in quantum-enhanced machine learning and biometric security.

Author Profile

Scopus | Orcid | Google Scholar

Education 

Dimitrios completed his Ph.D. in Computer Vision at the University of Patras, Greece (2017-2023), specializing in deep learning, neural networks, and AI strategies for limited data scenarios under Prof. George Economou’s supervision. His doctoral thesis explored novel transfer learning and knowledge distillation techniques. Prior to this, Dimitrios earned an M.Sc. in Electronics, Engineering and Computer Science (2015-2017) from the University of Patras, graduating summa cum laude with a thesis on deep sparse coding. His academic foundation was built on a B.Sc. in Physics (2010-2015) from the same university, graduating magna cum laude, with research focused on sparse representation for offline handwritten signature recognition. Dimitrios also briefly studied medicine before shifting to physics and computing, showcasing a diverse academic background. Throughout his studies, he demonstrated academic excellence, receiving top grades and honors in rigorous technical fields that combine physical sciences with computer engineering.

Experience

Dimitrios currently works as a Research Scientist in Quantum Machine Learning at Quantum Neural Technologies (QNT) in Athens, designing quantum algorithms and integrating machine learning with quantum computing for industrial applications such as pharmaceuticals, cryptography, and finance. Since July 2025, he has been a Computer Vision Scientist at the Athena Research Center, focusing on UAV systems that fuse radar and camera data for autonomous aerial navigation. His Ph.D. research (2017-2023) involved deep learning for limited data, emphasizing convolutional neural networks and biometric applications. Dimitrios contributed to the DeepSky project on cloud type estimation using multi-sensor data and worked on Greek lip reading datasets employing deep sequential models. He also participated in RoadEye, developing AI solutions for road condition monitoring, pothole, and speed bump detection. Throughout his career, Dimitrios has utilized tools like Python, PyTorch, TensorFlow, Qiskit, and Matlab, continuously merging theoretical innovation with practical applications in computer vision, AI, and quantum technologies.

Awards and Honors

Dimitrios Tsourounis has received notable recognition for his academic and research excellence. He was awarded a prestigious scholarship from the Greek State Scholarships Foundation (IKY) to support his Ph.D. studies, reflecting his outstanding merit. Throughout his academic career, Dimitrios graduated summa cum laude for his M.Sc. and magna cum laude for his B.Sc., highlighting consistent academic distinction. His research contributions have been supported by competitive European Union and Greek national funding programs, including co-funding for projects such as DeepSky and RoadEye. Dimitrios has also been acknowledged within the quantum computing and AI research communities for pioneering integration of machine learning with quantum frameworks. His work has earned invitations to collaborate with leading academic and industry partners, reinforcing his reputation as an innovative scientist. While yet to accumulate traditional prize awards, his growing publication record and project leadership positions underscore his impact and future promise in computer science and quantum technologies.

Research Focus 

Dimitrios Tsourounis’s research centers on computer vision, deep learning, and quantum machine learning, with a particular focus on addressing challenges of limited data availability in neural network training. His Ph.D. work pioneered transfer learning and knowledge distillation methods tailored to biometric security and pattern recognition. Currently, Dimitrios explores quantum-enhanced machine learning algorithms leveraging variational quantum circuits to improve performance on complex scientific and industrial problems. His expertise also spans multimodal data fusion, combining radar and visual data in autonomous aerial systems to enhance object detection accuracy. Additionally, he investigates sequential deep learning architectures for tasks such as lip reading in the Greek language and environmental sensing through cloud type recognition using thermal and all-sky cameras. Dimitrios integrates classical machine learning frameworks like PyTorch with quantum programming tools such as Qiskit and Pennylane, pushing the frontier of hybrid classical-quantum AI. His work aims to bridge theoretical advances and practical applications across fields including cryptography, healthcare, and autonomous vehicles.

Publications 

  • “Deep Sparse Coding for Signal Representation”

  • “Neural Networks for Biometric Applications with Limited Data”

  • “Quantum Variational Circuits in Machine Learning”

  • “Fusion of Radar and RGB Data in UAV Object Detection”

  • “Lip Reading Greek Words Using Sequential Deep Learning”

  • “Cloud Type Estimation with All-Sky and Thermal Cameras”

  • “Real-Time Road Condition Monitoring via Computer Vision”

  • “Knowledge Distillation Techniques in Convolutional Neural Networks”

Conclusion

Dimitrios Tsourounis exemplifies a forward-thinking computer scientist, seamlessly integrating deep learning and quantum computing to tackle real-world challenges. His academic excellence, coupled with his innovative research in limited-data neural networks and quantum-enhanced AI, positions him as a leading researcher in computer vision and machine learning. Dimitrios’s contributions advance both theoretical knowledge and practical solutions across diverse sectors, from autonomous systems to pharmaceuticals. His dedication and interdisciplinary approach promise significant future impact in computer science and emerging quantum technologies.

 

WEI-CHENG LIEN | Artificial Intelligence | Best Researcher Award

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Dr. WEI-CHENG LIEN | Artificial Intelligence | Best Researcher Award

👤 Dr. WEI-CHENG LIEN, Hyson Technology Inc, Taiwan

Wei-Cheng Lien is the CEO and Co-Founder of HysonTech Inc., based in Taiwan, where he leads the company’s AIoT solutions for various industries. With a Ph.D. in Electrical Engineering from National Cheng Kung University, Lien specializes in AI, AIoT hardware/software integration, and digital transformation. He has held significant leadership roles across diverse organizations, including his work as a director for the Taiwan Artificial Intelligence Association and as an industry technology advisor for the National Applied Research Laboratories. Lien’s contributions to fields such as medical diagnostics, smart traffic enforcement, and manufacturing automation have earned him recognition globally. His work has influenced both academia and industry, with numerous awards and patents to his name. He continues to innovate at the intersection of AI and IoT, driving digital transformation and leading key projects like Taiwan Taoyuan International Airport Terminal 3.

Professional Profile

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🌟 Research for Best Researcher Award: Wei-Cheng Lien

Suitability for the Award:

Wei-Cheng Lien is exceptionally suited for the Research for Best Researcher Award due to his remarkable achievements in both the academic and entrepreneurial realms, coupled with his extensive experience in the field of electrical engineering and artificial intelligence (AI). With a Ph.D. in Electrical Engineering (EE) from National Cheng Kung University and a track record of over 50 technical papers and 9 patents, Dr. Lien has made significant contributions to various cutting-edge areas, such as AI, AIoT (Artificial Intelligence of Things) hardware/software integration, and digital transformation.

As the CEO of HysonTech Inc., he has been instrumental in driving innovative AIoT solutions across diverse industries, from aquaculture to medical diagnostics, emphasizing his capability to translate research into impactful real-world applications. His leadership extends beyond his company, as he holds prominent advisory roles with leading institutions like the Taiwan National Science and Technology Council and the Institute for Information Industry. His leadership in AI and technology is further demonstrated by his contributions to the Taiwan Artificial Intelligence Association and several high-profile projects such as the Taiwan Taoyuan International Airport Terminal 3 development.

🎓 Education 

Wei-Cheng Lien earned his Ph.D. in Electrical Engineering (EE) from National Cheng Kung University (NCKU), Taiwan, where he achieved a remarkable GPA of 4.0. His educational journey also includes a Bachelor’s degree in EE from NCKU, where he earned a GPA of 3.4. During his academic tenure, Lien demonstrated a strong focus on Artificial Intelligence, AIoT, and digital transformation, with his research laying the groundwork for his subsequent professional achievements. Lien’s time at Duke University as a visiting scholar further broadened his perspective, and his contributions to AI and electronics research gained significant recognition. His education, coupled with extensive hands-on experience in AI and hardware/software integration, has shaped him into a leader in the industry. Lien’s academic excellence and innovation are the bedrock upon which his diverse professional experiences and entrepreneurial ventures are built.

💼  Professional Experience

Wei-Cheng Lien is the CEO and Co-Founder of HysonTech Inc., where he leads innovative AIoT solutions across sectors like aquaculture, smart traffic enforcement, and medical diagnostics. Under his leadership, HysonTech integrates AI technologies into hardware and software systems, offering complete solutions to its clients. Prior to this, Lien served as the CTO of Slash Living Culture Company, where he developed reed-based plant straws and building materials, incorporating AIoT monitoring techniques. His career also includes his role as Manager of the Intelligence System Division at AVITONE CO., LTD., where he focused on image system integration for advanced X-ray scanners. Lien’s earlier experience at MediaTek Inc. as Senior Engineer and Fellow Student further honed his expertise in AI and computing technologies. His extensive professional experience spans AI integration, digital transformation, and innovative product development, driving growth and technological advancement in multiple industries.

🏅Awards and Recognition

Wei-Cheng Lien has been honored with over 60 prestigious awards throughout his career. Notably, he was named the 2025 Outstanding Alumni of Taichung Municipal Taichung First Senior High School and received the 2024 Taiwan AI Award at the AI Taiwan x Future Commerce Exhibition. Lien’s achievements in AI and digital transformation were recognized with the 2024 Taiwan InnoTech Expo’s Two Invention Medals, along with Best Paper Awards at various conferences, including the Investigative Technology and Forensic Science Symposium. He was also awarded the 2024 Best Conference Paper Award at the IEEE International Conference on Electronic Communications, Internet of Things, and Big Data. Lien’s entrepreneurial success was acknowledged with the 2024 Best Startup Selection in Beijing and other notable innovation awards. Additionally, he was recognized with the 2023 EE Times Asia Award for Best AI Product of the Year and numerous awards from government and industry organizations for his contributions to AI and digital transformation.

🌍 Research Skills Artificial Intelligence

Wei-Cheng Lien’s research skills lie at the forefront of AI, AIoT integration, and digital transformation. His expertise spans IC design, testing, diagnosis, and software-hardware systems integration, which allows him to create innovative solutions across a range of industries. Lien’s work in AI computing, particularly in CPU DFT architectures and low-cost test techniques for System on Chips (SoCs), has been instrumental in reducing test costs and improving design accuracy. His research on output selection for SOC debug systems has earned global recognition for reducing test response volumes and minimizing area overhead. Lien has authored over 50 technical papers and holds 9 patents, further demonstrating his proficiency in cutting-edge research. His ability to bridge the gap between theoretical knowledge and practical application has been a key factor in his contributions to AI-driven industries such as healthcare, smart cities, and advanced manufacturing. Lien’s ongoing research continues to push the boundaries of AI and IoT technology.

📖 Publication Top Notes

  • A rapid household mite detection and classification technology based on artificial intelligence-enhanced scanned images
    • Authors: Lin, L.H.-M., Lien, W.-C., Cheng, C.Y.-T., Lai, Y.-T., Peng, Y.-T.
    • Citation: Internet of Things (The Netherlands), 2025, 29, 101484
  • Image Demoiréing via Multiscale Fusion Networks With Moiré Data Augmentation
    • Authors: Peng, Y.-T., Hou, C.-H., Lee, Y.-C., Lin, Y.-T., Lien, W.-C.
    • Citation: IEEE Sensors Journal, 2024, 24(12), pp. 20114–20127
    • Citations: 3
  • Fully automated learning and predict price of aquatic products in Taiwan wholesale markets using multiple machine learning and deep learning methods
    • Authors: Lai, Y.-T., Peng, Y.-T., Lien, W.-C., Liao, C.-J., Chiu, Y.-S.
    • Citation: Aquaculture, 2024, 586, 740741
    • Citations: 2
  • Traffic Violation Detection via Depth and Gradient Angle Change
    • Authors: Peng, Y.-T., Liu, C.-Y., Liao, H.-H., Lien, W.-C., Hsu, G.-S.J.
    • Citation: 2022 IEEE 7th International Conference on Intelligent Transportation Engineering, ICITE 2022, 2022, pp. 326–330
    • Citations: 1
  • Unveiling of How Image Restoration Contributes to Underwater Object Detection
    • Authors: Peng, W.-Y., Peng, Y.-T., Lien, W.-C., Chen, C.-S.
    • Citation: 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021, 2021
    • Citations: 7
  • Output bit selection methodology for test response compaction
    • Authors: Lien, W.-C., Lee, K.-J.
    • Citation: Proceedings – International Test Conference, 2016, 0, 7805873
  • A Test-per-cycle BIST architecture with low area overhead and no storage requirement
    • Authors: Shiao, C.-M., Lien, W.-C., Lee, K.-J.
    • Citation: 2016 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2016, 2016, 7482556
    • Citations: 11
  • Efficient LFSR Reseeding Based on Internal-Response Feedback
    • Authors: Lien, W.-C., Lee, K.-J., Hsieh, T.-Y., Chakrabarty, K.
    • Citation: Journal of Electronic Testing: Theory and Applications (JETTA), 2014, 30(6), pp. 673–685
    • Citations: 7
  • Output-bit selection with X-avoidance using multiple counters for test-response compaction
    • Authors: Lien, W.-C., Lee, K.-J., Chakrabarty, K., Hsieh, T.-Y.
    • Citation: Proceedings – 2014 19th IEEE European Test Symposium, ETS 2014, 2014, 6847823
    • Citations: 3
  • Output selection for test response compaction based on multiple counters
    • Authors: Lien, W.-C., Lee, K.-J., Chakrabarty, K., Hsieh, T.-Y.
    • Citation: Technical Papers of 2014 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2014, 2014, 6834865
    • Citations: 2

 

NIKOLAOS EPISKOPOS | Computer Science | Best Researcher Award

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Mr. NIKOLAOS EPISKOPOS | Computer Science | Best Researcher Award

👤 Mr. NIKOLAOS EPISKOPOS, IBM, Greece

Nikolaos Episkopos is an accomplished Data Scientist, Software Developer, and Data Science Consultant with over eight years of professional experience. Based in Athens, Greece, Nikolaos specializes in AI, cybersecurity, and predictive medicine, contributing to impactful EU-funded R&D projects and Open Source Software initiatives. His innovative work includes AI solutions for fraud detection, federated learning toolkits for intrusion detection, and optimizing data pipelines. Passionate about the intersection of technological advancements and societal impact, Nikolaos has played pivotal roles in enhancing banking services, securing SCADA systems, and developing blockchain-based video streaming systems. As a professional with a robust academic background and diverse technical skills, he combines creativity and precision to deliver groundbreaking solutions.

Professional Profile

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🌟 Evaluation of Nikolaos Episkopos for the Research for Best Researcher Award

Summary of Suitability

Nikolaos Episkopos demonstrates an exceptional profile as a data scientist and software developer with significant contributions to research and development projects, particularly in the domains of artificial intelligence (AI), cybersecurity, and healthcare. With over eight years of professional experience, he has effectively bridged academia and industry, showcasing the ability to lead and innovate in complex technological domains. His work has resulted in tangible outcomes, including funding acquisitions, novel AI solutions, and impactful publications.

Nikolaos’s recent achievements at IBM and INLECOM highlight his ability to tackle real-world challenges through data-driven innovation. At IBM, he developed advanced AI models for fraud detection, contributing to financial security solutions. His role at INLECOM involved technical project management and significant contributions to EU Horizon projects, where his efforts secured funding and established strategic partnerships. These accomplishments reflect his leadership, technical expertise, and collaborative skills in advancing scientific research.

🎓 Education 

Nikolaos holds a Master’s degree in Data Science & Information Technologies from the National and Kapodistrian University of Athens, where he honed expertise in AI, data analysis, and cybersecurity. Currently pursuing an MSc in Cybersecurity at the University of West Attica, he is expanding his knowledge of threat modeling and advanced cryptographic techniques. His academic journey reflects a commitment to excellence, as he consistently excelled in designing AI models and deploying secure, scalable systems. Nikolaos’ education equips him to navigate the rapidly evolving landscape of AI and cybersecurity, combining rigorous academic training with practical, hands-on experience to solve complex technical challenges effectively.

💼  Professional Experience

Nikolaos Episkopos has held roles at leading organizations like IBM, INLECOM, and MetaMind Innovations. At IBM, he spearheaded the development of AI solutions for card fraud detection and enhanced banking services. At INLECOM, he managed Horizon projects, securing significant funding and partnerships while optimizing algorithms for AI tools. During his tenure at MetaMind Innovations, he developed Federated Learning toolkits for intrusion detection, authored academic papers, and secured SCADA systems. At Fogus Innovations, Nikolaos implemented blockchain-enabled video streaming optimization and co-authored publications on advanced AI platforms. His ability to lead technical projects, develop AI models, and foster innovation underscores his exceptional contribution to the tech industry.

🏅 Awards and Recognition 

Nikolaos has been recognized for his contributions to EU-funded Horizon projects, which have brought substantial funding and technological advancements to his organizations. He co-authored papers published in prestigious journals like IEEE Transactions on Mobile Computing and Computer Science Review, highlighting his expertise in AI and cybersecurity. Additionally, his innovative solutions in fraud detection and SCADA security have been acknowledged within the tech community. Nikolaos’ commitment to open-source projects on GitHub further demonstrates his dedication to knowledge sharing and continuous improvement. His achievements reflect a career driven by excellence and societal impact.

🌍 Research Skills On Computer Science

Nikolaos excels in designing and deploying AI-driven solutions across domains such as cybersecurity, predictive medicine, and fraud detection. His expertise encompasses Federated Learning, blockchain integration, and data analysis using tools like TensorFlow, PyTorch, and Spark. Skilled in optimizing algorithms and building scalable data pipelines, Nikolaos has delivered solutions that reduce execution time and enhance efficiency. His academic research, coupled with industry application, positions him as a thought leader in leveraging AI for societal impact.

📖 Publication Top Notes

1. A comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions
  • Author(s): Ioannis Makris, Aikaterini Karampasi, Panagiotis Radoglou-Grammatikis, Nikolaos Episkopos, Eider Iturbe, Erkuden Rios, Nikos Piperigkos, Aris Lalos, Christos Xenakis, Thomas Lagkas, et al.
  • Citation: Computer Science Review, 2025-05
2. To DASH, or Not to DASH? Optimal Video Bitrate Selection and Edge Network Caching in MEC-Empowered Slice-Enabled Networks
  • Author(s): Dionysis Xenakis, Nikolaos Episkopos
  • Citation: IEEE Transactions on Vehicular Technology, 2024-04
3. PEER-TO-PEER VIDEO CONTENT DELIVERY OPTIMIZATION SERVICE IN A DISTRIBUTED NETWORK
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2022-10-24
4. Cache-Aware Adaptive Video Streaming in 5G networks
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2021-05-04
5. SECONDO: A Platform for Cybersecurity Investments and Cyber Insurance Decisions
  • Author(s): Aristeidis Farao, Sakshyam Panda, Sofia Anna Menesidou, Entso Veliou, Nikolaos Episkopos, George Kalatzantonakis, Farnaz Mohammadi, Nikolaos Georgopoulos, Michael Sirivianos, Nikos Salamanos, et al.
  • Citation: Trust, Privacy and Security in Digital Business (TrustBus), 2020-09-14
6. On-device caching of popular video content on Android-powered devices
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2018-08-14

Sheeja Rani S | Computer Science Award | Best Researcher Award

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Dr. Sheeja Rani S | Computer Science Award | Best Researcher Award

👤 Dr. Sheeja Rani S, American University of Sharjah, United Arab Emirates

Dr. Sheeja Rani S is a visionary researcher and academician specializing in Computer Science and Engineering, with a strong focus on Wireless Sensor Networks, IoT, and Smart Grids. She earned her Ph.D. from Noorul Islam Centre for Higher Education in 2023, where her thesis emphasized energy-efficient clustering algorithms for wireless sensor networks. Her academic journey is complemented by over a decade of teaching and research experience, where she worked on innovative solutions in cybersecurity, cloud computing, and machine learning. Currently serving as a Postdoctoral Research Assistant at the American University of Sharjah, Dr. Sheeja collaborates with leading experts on cutting-edge projects. With over 20 journal papers, numerous conference contributions, and a passion for impactful research, she strives to advance technology and foster intellectual growth. Her mission is to combine her expertise and mentorship skills to inspire future innovators while contributing to meaningful explorations in academia and beyond.

Professional Profile

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🌟 Evaluation of Dr. Sheeja Rani S for the Research for Best Researcher Award

Summary of Suitability

Dr. Sheeja Rani S stands out as a highly qualified candidate for the “Research for Best Researcher Award,” showcasing an exceptional academic trajectory, prolific research output, and impactful contributions to multiple interdisciplinary domains. With a Ph.D. in Computer Science and Engineering focusing on improving energy efficiency in wireless sensor networks (WSNs), her research has addressed critical challenges in IoT, cloud computing, and smart grid technologies. These fields are not only contemporary but also pivotal for sustainable and secure technological advancements.

🎓 Education 

  • Ph.D. in Computer Science and Engineering (2023)
    Noorul Islam Centre for Higher Education
    Thesis: Improving Energy Efficiency Based on Clustering Algorithms for Wireless Sensor Networks.
  • M.E. in Computer Science and Engineering (2012)
    Noorul Islam Centre for Higher Education
  • M.Sc. Integrated Software Engineering (2009)
    Anna University, Chennai

Dr. Sheeja’s academic pursuits are rooted in innovation, particularly in optimizing computational techniques for energy efficiency and data security. Her Ph.D. research laid a foundation for creating advanced clustering mechanisms in wireless sensor networks, while her postgraduate and undergraduate studies focused on mastering computer science fundamentals and software engineering. She remains committed to lifelong learning and applying her knowledge to address emerging technological challenges.

💼  Professional Experience 

  • Postdoctoral Research Assistant (2023-Present)
    American University of Sharjah

    • Research on cybersecurity, smart grids, and cloud computing.
    • Published 12 journal papers in high-impact areas like IoT and machine learning.
  • Research Assistant (2022-2023)
    University of Sharjah

    • Focused on IoT, WSNs, and cloud computing.
    • Published 11 journal papers on financial distress prediction and IoT advancements.
  • Assistant Professor (2012-2021)
    John Cox Memorial CSI Institute of Technology

    • Taught advanced programming and database systems.
    • Managed academic coordination and examination processes.

Dr. Sheeja’s professional journey showcases a blend of teaching, research, and academic leadership, reflecting her dedication to advancing the field of computer science.

🏅 Awards and Recognitions 

  • Best Researcher Award (2023) – Recognized for impactful research in IoT and WSN.
  • Academic Excellence Award (2021) – Awarded for outstanding teaching and mentorship.
  • Research Grant Award (2022) – Funded for innovative studies on machine learning and cybersecurity.
  • Publication Excellence Award (2023) – Honored for prolific contributions to reputed journals.

Dr. Sheeja has consistently received accolades for her exceptional academic and research contributions. Her achievements reflect her dedication to excellence and her ability to produce innovative solutions that address global challenges.

🌍  Research Skills On Computer Science Award 

Dr. Sheeja’s research expertise spans:

  • Wireless Sensor Networks (WSN): Energy-efficient routing and clustering.
  • IoT: Developing secure and scalable architectures for smart environments.
  • Machine Learning: Applying predictive models for financial and cybersecurity domains.
  • Smart Grids: Integration of AI for optimal energy distribution.
  • Cloud Computing: Enhancing reliability and fault tolerance in virtualized environments.

📖 Publication Top Notes

Improved buffalo optimized deep feed forward neural learning based multipath routing for energy-efficient data aggregation in WSN
    • Authors: SS Rani, KS Sankar
    • Citation: Measurement: Sensors 27, 100662
    • Cited by: 8
    • Year: 2023
Optimized deep learning for Congestion-Aware continuous target tracking and boundary detection in IoT-Assisted WSN
    • Authors: AM Khedr, SS Rani, M Saad
    • Citation: IEEE Sensors Journal 23 (7), 7938-7948
    • Cited by: 8
    • Year: 2023
Enhancing Supply Chain Management with Deep Learning and Machine Learning Techniques: A Review
    • Authors: SSR Khedr, Ahmed M
    • Citation: Journal of Open Innovation: Technology, Market, and Complexity, 100379
    • Cited by: 5
    • Year: 2024
Hybridized Dragonfly and Jaya algorithm for optimal sensor node location identification in mobile wireless sensor networks
    • Authors: AM Khedr, SS Rani, M Saad
    • Citation: The Journal of Supercomputing 79 (15), 16940-16962
    • Cited by: 4
    • Year: 2023
Enhancing financial distress prediction through integrated Chinese Whisper clustering and federated learning
    • Authors: AI Al Ali, AM S S Rani Khedr
    • Citation: Journal of Open Innovation: Technology, Market, and Complexity 10 (3), 100344
    • Cited by: 2
    • Year: 2024