Tag: Artificial Intelligence Research Award

Jerry (Zeyu) Gao | Computer Science | Innovative Research Award

Published on by World Science Awards

Innovative Research Award

Jerry Zeyu Gao
Professor, Department of Computer Engineering, San Jose State University, United States

Jerry Zeyu Gao
Affiliation San Jose State University
Country United States
Scopus ID 7404475003
Documents 248
Citations 4,460
h-index 35
Subject Area Computer Science, Software Engineering, Artificial Intelligence
Event World Science Awards

Jerry Zeyu Gao is a distinguished computer scientist, software engineering researcher, educator, and innovator recognized for his extensive contributions to software testing, quality assurance, mobile computing, cloud engineering, artificial intelligence testing, and smart city technologies. As a Professor at San Jose State University, he has developed an influential body of scholarly work spanning software validation, cloud services, mobile application engineering, machine learning applications, intelligent systems, and data-driven urban technologies. His research achievements have contributed substantially to both academic advancement and industrial practice, making him a strong candidate for recognition through an Outstanding Researcher Award.[1]

Abstract

Professor Jerry Zeyu Gao has established an internationally recognized research profile through decades of contributions to software engineering, software testing, cloud computing, artificial intelligence systems, mobile applications, and smart city technologies. His research has produced influential methodologies, testing frameworks, cloud-based architectures, AI validation models, and intelligent urban service solutions. With more than 240 indexed publications, thousands of citations, and an h-index of 35, his scholarly output demonstrates both academic significance and practical relevance. His work continues to influence research communities across software engineering, AI quality assurance, and data-driven computing.[1]

Keywords

Software Engineering, Software Testing, Artificial Intelligence, AI Testing, Cloud Computing, Mobile Computing, Smart Cities, Machine Learning, Quality Assurance, Big Data Analytics, Intelligent Systems, Test Automation.

Introduction

The increasing complexity of software-intensive systems requires advanced methodologies for validation, quality assurance, automation, and intelligent decision support. Professor Jerry Zeyu Gao has dedicated his academic career to addressing these challenges through pioneering research in software testing, mobile systems, cloud services, and artificial intelligence. His work bridges theoretical foundations with industrial implementation, contributing significantly to modern software engineering practices and emerging AI-enabled technologies.[2]

Research Profile

Professor Gao serves in the Department of Computer Engineering at San Jose State University. His academic career encompasses software engineering, cloud systems, mobile applications, AI testing, smart city infrastructure, autonomous systems, environmental analytics, agricultural intelligence, and machine learning applications. He has supervised numerous research projects and collaborated extensively with academic institutions and industry partners worldwide. His interdisciplinary approach has enabled the development of innovative solutions across multiple technological domains.[3]

Research Contributions

Professor Gao’s research contributions span several influential areas of computer science and engineering. His work on object-oriented software testing helped establish foundational methodologies for quality assurance in modern software systems. He later expanded his research into component-based software testing, cloud testing services, mobile application validation, AI software testing, and intelligent automation. More recently, his research has focused on smart cities, renewable energy intelligence, environmental monitoring, agricultural AI platforms, and computer vision-based analytics.[4]

  • Software testing and quality assurance methodologies.
  • Cloud-based testing infrastructure and Testing-as-a-Service (TaaS).
  • Artificial intelligence testing frameworks.
  • Smart city analytics and intelligent transportation systems.
  • Machine learning applications in agriculture and sustainability.
  • Autonomous systems and computer vision validation.

Publications

Professor Gao has authored and co-authored more than 240 scholarly publications, including journal articles, conference papers, book chapters, edited proceedings, and technical books. His publications appear in respected venues such as IEEE Access, IEEE Computer, IEEE Software, World Wide Web, Smart Cities, Agriculture, Remote Sensing, Energies, Sustainability, and numerous IEEE international conferences.[5]

  • Object-Oriented Software Testing (IEEE Computer Society Press).
  • Testing and Quality Assurance for Component-Based Software.
  • Engineering Wireless-Based Software Systems.
  • Mobile Application Testing – A Tutorial (IEEE Computer).
  • AI Testing for Intelligent Chatbots – A Case Study.
  • Integration of UAV and Remote Sensing Data for Early Diagnosis of Crop Diseases.

Research Impact

The impact of Professor Gao’s work is reflected through extensive citation activity, international collaborations, editorial contributions, and practical adoption of research outcomes. His publications have accumulated more than four thousand citations, while several of his studies have become highly referenced resources within software engineering and cloud computing communities. His contributions have influenced academic curricula, industrial testing frameworks, AI validation approaches, and smart infrastructure development projects worldwide.[4]

Award Suitability

Professor Jerry Zeyu Gao demonstrates the qualities expected of an Outstanding Researcher Award recipient through sustained scholarly excellence, innovation, interdisciplinary leadership, and measurable scientific impact. His long-standing commitment to advancing software engineering, cloud services, AI quality assurance, and smart city technologies has produced a substantial body of influential research. The breadth of his contributions, combined with his educational leadership and international collaborations, highlights his significance within the global research community.[5]

Conclusion

Jerry Zeyu Gao has made substantial and sustained contributions to software engineering, testing methodologies, cloud computing, artificial intelligence systems, and intelligent urban technologies. His extensive publication record, strong citation performance, educational leadership, and innovative research portfolio collectively support recognition through an Outstanding Researcher Award. His work continues to shape emerging technological disciplines while providing practical solutions to complex real-world challenges.

References

  1. Elsevier. (n.d.). Scopus Author Details: Jerry Zeyu Gao, Author ID 7404475003. https://www.scopus.com/authid/detail.uri?authorId=7404475003
  2. Gao, J. Z., Tsai, W. T., Paul, R., & Uehara, T. Mobile Testing as a Service (MTaaS).
  3. San Jose State University Faculty Profile and Academic Biography.
  4. Gao, J. Z. Research Publications in Software Engineering, AI Testing, and Smart Cities.
  5. Gao, J. Z. Object-Oriented Software Testing, IEEE Computer Society Press.

Muath Alrammal | Computer Science | Innovative Research Award

Published on by World Science Awards

Innovative Research Award

Muath Alrammal
Affiliation University of Wollongong Dubai
Country United Arab Emirates
Scopus ID 35108740800
Documents 25
Citations 207
h-index 6
Subject Area Computer Science, Big Data, Blockchain, Artificial Intelligence
Event World Science Awards
ORCID 0000-0002-3240-6262

Muath Alrammal is a computer scientist, researcher, and academic specializing in big data systems, XML stream processing, machine learning, blockchain technologies, artificial intelligence, and distributed data architectures. He currently serves as Assistant Professor at the University of Wollongong Dubai and has developed a multidisciplinary research portfolio spanning data-intensive computing, cybersecurity, blockchain-enabled sustainability solutions, malware detection, and intelligent information systems. His scholarly contributions encompass journal articles, conference publications, book chapters, funded research projects, and industry-focused innovation initiatives.[1]

With academic training in France and extensive experience across higher education institutions in the United Arab Emirates, Alrammal has contributed to advancing research in scalable data processing, performance modeling, reinforcement learning, blockchain applications, and artificial intelligence-driven software engineering. His work reflects a combination of theoretical research and practical implementation directed toward digital transformation and emerging technologies.[2]

Abstract

This article presents an academic overview of Muath Alrammal, highlighting his educational background, research specialization, scholarly contributions, and impact within the fields of computer science, artificial intelligence, blockchain technologies, cybersecurity, and large-scale data processing. His work combines foundational research in XML stream processing and scalable information systems with contemporary investigations in machine learning, blockchain-enabled applications, malware analysis, and software engineering. Through academic publications, funded projects, industrial certifications, and collaborative research activities, Alrammal has contributed to the advancement of data-driven technologies and digital transformation initiatives across academia and industry.[1]

Keywords

Big Data, XML Stream Processing, Blockchain, Artificial Intelligence, Machine Learning, Cybersecurity, Data Analytics, Distributed Systems, Software Engineering, Web3 Technologies.

Introduction

The evolution of digital ecosystems has increased the demand for scalable computing systems capable of processing massive volumes of structured and unstructured information. Researchers working at the intersection of data science, distributed computing, and intelligent systems play a critical role in addressing these challenges. Muath Alrammal has established a research profile focused on large-scale data processing, stream-based information retrieval, machine learning applications, and blockchain integration. His academic journey includes doctoral research in France, postdoctoral appointments, leadership positions in higher education, and ongoing involvement in emerging technologies and innovation-driven research initiatives.[1]

Research Profile

Alrammal earned a Ph.D. in Computer Science from Université Paris-Est, France, where his doctoral research focused on algorithms for XML stream processing, external memory management, and scalable performance optimization. His graduate studies were preceded by a Master of Science in Information Technology from Télécom SudParis. Following the completion of his doctorate, he undertook postdoctoral research projects involving high-performance computing, artificial intelligence applications in finance, and secure large-scale document processing systems.[3]

His academic appointments include positions at the University of Wollongong Dubai, Higher Colleges of Technology, and Al-Khawarizmi International College. Across these institutions, he has contributed to teaching, curriculum development, research supervision, and academic governance while maintaining an active publication record in computer science and information technology disciplines.[1]

Research Contributions

Alrammal’s contributions span several research domains, including XML stream processing, performance prediction models, XPath selectivity estimation, malware detection frameworks, reinforcement learning systems, blockchain-enabled resource management, and AI-assisted software engineering. His early work contributed methodologies for scalable querying and processing of large XML datasets, while his more recent investigations have explored cybersecurity analytics, blockchain-based sustainability applications, and intelligent decision-support systems.[4]

  • XML stream processing and scalable query optimization.
  • Big data analytics and performance modeling.
  • Blockchain and decentralized information systems.
  • Machine learning and reinforcement learning applications.
  • Cybersecurity and anti-malware intelligence frameworks.
  • AI-driven software requirements engineering.

Publications

Selected scholarly outputs include journal articles, conference proceedings, and book chapters covering machine learning, blockchain technologies, cybersecurity, XML processing, and intelligent computing systems. Representative publications include contributions to sustainable management systems using blockchain, malware detection methodologies, Industry 4.0 frameworks, reinforcement learning models, and scalable XML query processing techniques.[5]

  • Machine Learning with Python (CRC Press, 2022).
  • Blockchain Technology for Sustainable Management of Electricity and Water Consumption (2023).
  • A Blockchain Solution for Water and Electricity Management (2022).
  • A Two-Layered Machine Learning Approach for Anti-Malware Sustainability (2022).
  • Forward XPath Stream Processing: End-to-End Confidentiality and Scalability (2014).
  • Performance Prediction Model for Forward XPath Processing (2012).

Research Impact

The research activities of Alrammal demonstrate an emphasis on practical impact and technology transfer. His funded projects have addressed malware clustering systems, XML document processing, and intelligent computing frameworks. Ongoing projects involving AI-driven software requirements classification and decentralized credit scoring systems illustrate the application of advanced computational methods to real-world challenges. These efforts contribute to digital transformation, cybersecurity enhancement, sustainable infrastructure management, and intelligent automation initiatives.[2]

Award Suitability

Muath Alrammal’s academic record aligns with the objectives commonly associated with Emerging Research Excellence Award programs. His multidisciplinary research portfolio demonstrates sustained scholarly productivity, innovation in data-intensive computing, contributions to blockchain and artificial intelligence applications, and engagement with industry-oriented research initiatives. The combination of publications, research leadership, funded projects, international collaborations, and technology-focused educational contributions supports recognition within emerging and applied research categories.[1]

Conclusion

Muath Alrammal has developed a diverse and evolving research profile spanning big data systems, blockchain technologies, artificial intelligence, cybersecurity, and distributed computing. Through scholarly publications, academic leadership, interdisciplinary collaborations, and industry-focused innovation projects, he has contributed to the advancement of computational research and digital transformation initiatives. His work reflects an ongoing commitment to bridging theoretical developments with practical technological applications across multiple domains of computer science.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Muath Alrammal, Author ID 35108740800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=35108740800
  2. Alrammal, M. Research projects and academic profile documentation relating to artificial intelligence, blockchain, and software engineering initiatives.
  3. Alrammal, M. Doctoral thesis: Algorithms for XML Stream Processing: Massive Data, External Memory and Scalable Performance. Université Paris-Est, France.
  4. Alrammal, M., & Hains, G. Research contributions in XML stream processing, selectivity estimation, and scalable information systems.
  5. Alrammal, M., Xanthidou, O. K., & Naveed, M. (2022). Machine Learning with Python. Chapman & Hall/CRC.
    https://doi.org/10.1201/9781003139010
  6. Alrammal, M., Abu-Amara, F., Ismail, Z., & Nadeem, M. (2023). Blockchain Technology for Sustainable Management of Electricity and Water Consumption.
    https://doi.org/10.3390/engproc2023059223

Zahraa Ghabriess | Computer Science | Research Excellence Award

Published on by World Science Awards

Research Excellence Award

Zahraa Ghabriess
ENSTA Bretagne, Lebanon

Zahraa Ghabriess
Affiliation ENSTA Bretagne
Country Lebanon
Google Scholar rkoyQ8oAAAAJ&hl
Subject Area Cybersecurity, Artificial Intelligence, Federated Learning, IoT Security
Event World Science Awards
ORCID 0009-0003-5746-9504

Zahraa Ghabriess is a cybersecurity researcher and doctoral candidate whose academic and professional activities focus on intelligent threat detection, access control systems, federated learning architectures, and security solutions for IoT-enabled 5G and beyond networks. Her work integrates artificial intelligence, machine learning, edge computing, and cybersecurity engineering to address contemporary challenges in digital infrastructure protection. Through research internships, software engineering practice, and doctoral investigations, she has contributed to emerging approaches for intrusion detection and access control automation in distributed environments.[1]

Abstract

Zahraa Ghabriess has developed an interdisciplinary research profile that combines cybersecurity, artificial intelligence, machine learning, and distributed computing systems. Her ongoing doctoral research at ENSTA Bretagne investigates federated edge architectures for intrusion detection in IoT-enabled 5G and beyond communication networks. Through conference publications, submitted journal manuscripts, and collaborative research projects, she contributes to the development of scalable, privacy-preserving, and intelligent security frameworks designed for modern networked environments. Her work addresses emerging cybersecurity challenges associated with large-scale connected systems while promoting advanced detection mechanisms based on federated learning and artificial intelligence methodologies.[2]

Keywords

Cybersecurity, Federated Learning, Intrusion Detection Systems, Internet of Things, 5G Networks, Edge Computing, Artificial Intelligence, Machine Learning, Access Control, Process Mining, Threat Detection, Secure Computing.

Introduction

The rapid expansion of interconnected digital systems has increased the need for innovative cybersecurity solutions capable of protecting large-scale networks from sophisticated threats. Researchers working at the intersection of artificial intelligence and cybersecurity play a crucial role in developing adaptive security mechanisms for future communication infrastructures. Zahraa Ghabriess represents a new generation of cybersecurity researchers whose work focuses on integrating machine learning, federated learning, and edge intelligence into practical security frameworks for IoT-enabled environments. Her academic trajectory demonstrates a commitment to addressing complex security challenges through rigorous research and technological innovation.[3]

Research Profile

Following the completion of a Bachelor of Computer Science and a Master of Science in Cybersecurity from the Lebanese University – Faculty of Sciences, Zahraa Ghabriess expanded her expertise through software engineering practice, research internships, and doctoral studies. Her technical competencies encompass cybersecurity, ethical hacking, artificial intelligence, machine learning frameworks, process mining, data mining, secure programming, web development, mobile application development, and database management systems.[1]

Her current doctoral research at ENSTA Bretagne focuses on intelligent intrusion detection frameworks designed for IoT-enabled 5G and beyond networks. The research combines federated learning methodologies with edge computing architectures to enhance detection accuracy while preserving privacy and reducing centralized processing constraints. These investigations contribute to the broader development of resilient and scalable cybersecurity infrastructures.[2]

Research Contributions

  • Development of the FEDGE framework, a federated edge architecture for attack detection in IoT-enabled 5G and beyond networks.
  • Research on semi-decentralized federated learning models aimed at improving intrusion detection performance and scalability.
  • Investigation of automated extraction of Attribute-Based Access Control (ABAC) rules from Object-Centric Event Logs (OCEL).
  • Application of machine learning techniques for detecting unauthorized access attempts through HTTP request and response analysis.
  • Comparative evaluation of emerging technologies for attack detection in advanced wireless communication networks.

Publications

The publication record of Zahraa Ghabriess reflects active engagement in cybersecurity research, particularly in intelligent attack detection and federated learning applications. Her notable conference publication examines the integration of advanced technologies for attack detection in IoT-enabled 5G and beyond networks and was presented at the International Wireless Communications and Mobile Computing Conference (IWCMC 2025). Additional submitted manuscripts address forward-looking security visions for future networks and introduce novel federated edge frameworks for intrusion detection.[2]

  • Ghabriess, Z., Harb, H., Mansour, A., Yao, K. C., & Osswald, C. (2025). Attacks Detection in IoT-enabled 5G and Beyond Networks: Performance Evaluation of Integrating Cutting-Edge Technologies. IEEE IWCMC 2025.
  • IoT-Enabled 5G and Beyond Networks: A Forward Security Vision (Submitted Survey Paper).
  • FEDGE: A Federated Edge Framework for Attack Detection in IoT-Enabled 5G and Beyond Networks (Submitted Journal Paper).
  • SD-FEDGE: A Semi-Decentralized Federated Edge Framework for Attack Detection in IoT-Enabled 5G Networks (Ongoing Journal Paper).

Research Impact

The significance of Zahraa Ghabriess’s research lies in its practical relevance to next-generation communication networks and critical digital infrastructures. Her investigations into federated edge learning seek to overcome limitations associated with centralized security systems while supporting privacy preservation, scalability, and real-time threat detection. Such contributions are increasingly important as IoT deployments continue to expand across industrial, commercial, and public sectors. Her work also demonstrates the growing convergence of artificial intelligence and cybersecurity as complementary disciplines for addressing emerging security risks.[3]

Award Suitability

Zahraa Ghabriess demonstrates strong suitability for recognition within emerging researcher and cybersecurity innovation award categories. Her academic achievements include advanced graduate education, active doctoral research, conference dissemination, interdisciplinary collaboration, and the development of novel cybersecurity frameworks addressing contemporary technological challenges. The combination of theoretical contributions and practical implementation experience positions her research within areas of growing international importance, particularly those involving intelligent security systems, federated learning architectures, and future communication networks.[2]

Conclusion

Zahraa Ghabriess has established a promising academic profile characterized by research excellence in cybersecurity, artificial intelligence, and federated learning systems. Through doctoral investigations, collaborative research initiatives, and scientific dissemination activities, she contributes to the advancement of secure and intelligent digital infrastructures. Her work reflects contemporary priorities in cybersecurity research and demonstrates the potential for meaningful impact on the protection of future IoT-enabled communication environments.[1]

References

  1. Professional curriculum vitae and academic profile of Zahraa Ghabriess, including educational background, professional experience, technical competencies, certifications, and doctoral research activities.
  2. Ghabriess, Z., Harb, H., Mansour, A., Yao, K. C., & Osswald, C. (2025). Attacks Detection in IoT-enabled 5G and Beyond Networks: Performance Evaluation of Integrating Cutting-Edge Technologies. Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC 2025).
    https://doi.org/10.1109/IWCMC62903.2025
  3. ENSTA Bretagne and Lab-STICC Research Activities. Doctoral research information relating to federated learning, intrusion detection systems, and cybersecurity applications for IoT-enabled communication infrastructures.
    https://www.ensta-bretagne.fr

Masoud Kargar | Artificial Intelligence | Best Researcher Award

Published on by World Science Awards

Assist. Prof. Dr . Masoud Kargar | Artificial Intelligence | Best Researcher Award

Islamic Azad University, Iran

Assist. Prof. Dr. Masoud Kargar is a distinguished researcher and educator in the field of Computer Engineering with expertise spanning artificial intelligence, machine learning, reinforcement learning, and software engineering. He earned his Ph.D. in Software Engineering from Islamic Azad University, Qazvin Branch in 2020, where his doctoral research focused on multi-programming language software system modularization, following a strong academic foundation in computer engineering and programming. Currently serving as an Assistant Professor at the Islamic Azad University, Tabriz Branch, he has over two decades of teaching and supervisory experience across multiple Iranian universities, mentoring numerous master’s and doctoral students in advanced topics such as deep learning, natural language processing, data mining, and intelligent systems. His research interests center on applied AI, big data, optimization algorithms, and their real-world applications in healthcare, finance, and smart cities. Dr. Kargar is highly skilled in programming, software development, data analytics, and advanced modeling, with expertise in Python, C++, MATLAB, and AI frameworks such as TensorFlow and PyTorch. He has authored more than 40 research documents indexed in Scopus, IEEE, IET, and Springer, receiving over 500 citations with an h-index of 11, alongside publishing books and book chapters on programming, deep learning, and generative adversarial networks. His excellence has been recognized with several awards, including the prestigious Professor Kambiz Badie’ Award in Artificial Intelligence (2025) and Best Paper Award at the International Symposium on Telecommunications (2024). In addition, he serves as Associate Editor of the Iran Journal of Computer Science (Springer), an active peer reviewer for leading journals, and has held leadership roles as Director of ICT and head of AI research groups. In conclusion, Dr. Kargar’s blend of academic rigor, innovative research, mentorship, and international recognition underscores his strong contributions to advancing computer science and artificial intelligence.

Profiles: Scopus | ORCID

Featured Publications

  1. Bayani, A., & Kargar, M. (2024). LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network. Physiological Reports.

  2. Kargar, M. (2020). New internal metric for software clustering algorithms validity. IET Software.

  3. Kargar, M. (2020). Improving the modularization quality of heterogeneous multi-programming software systems by unifying structural and semantic concepts. The Journal of Supercomputing.

  4. Kargar, M., Izadkhah, H., & Isazadeh, A. (2019). Tarimliq: A new internal metric for software clustering analysis. 2019 Iranian Conference on Electrical Engineering (ICEE). IEEE.

  5. Izadkhah, H., Kargar, M., & Isazadeh, A. (2019). Towards comprehension of the multi-programming language software systems. 2019 IEEE Conference on Knowledge Based Engineering and Innovation (KBEI). IEEE

Kah Ong Michael Goh | Computer Science | Best Researcher Award

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Assist. Prof. Dr. Kah Ong Michael Goh | Computer Science | Best Researcher Award

Associate Professor from Multimedia University | Malaysia

Assoc. Prof. Ts. Dr. Goh Kah Ong Michael is a prominent academician and innovator in the field of Artificial Intelligence, particularly known for his contributions to biometrics, computer vision, image processing, and smart city systems. He is currently serving as an Associate Professor at the Faculty of Information Science and Technology (FIST), Multimedia University (MMU), Malaysia. His professional journey spans over two decades, beginning as a tutor and progressively advancing to senior academic roles, including a tenure as Deputy Dean for Student Affairs and Alumni. Dr. Goh’s work focuses on practical, high-impact research that integrates AI into real-world applications such as traffic management, intelligent authentication, and urban system automation. A hands-on technologist, he has built strong industry ties and led collaborative research projects involving government and private sectors. His accomplishments include numerous international awards and publications, reflecting his ability to merge theoretical depth with applied innovation. Dr. Goh’s contributions extend beyond academia through leadership roles, student mentoring, and his involvement in technology exhibitions and innovation showcases. With an ever-evolving research agenda, he continues to be a valuable contributor to Malaysia’s technological advancement and is a role model for aspiring researchers in AI and computer science.

Professional Profile

Scopus Profile | ORCID Profile | Google Scholar

Education

Dr. Michael Goh pursued all his higher education at Multimedia University (MMU), Malaysia, reflecting a strong and continuous academic association with the institution. He earned his Bachelor of Information Technology (Hons.), majoring in Software Engineering. This undergraduate foundation in software development provided him with a firm grounding in computational thinking and programming. He then obtained his Master of Science in Information Technology by Research, where he began to delve into research-oriented activities, focusing on emerging areas in digital systems and human-computer interaction. His academic progression culminated in the completion of his Doctor of Philosophy (Ph.D.) in Information Technology by Research. His doctoral work emphasized advanced topics in biometrics and contactless identity recognition, a theme that would continue to define his professional research identity. Throughout his academic journey, Dr. Goh has demonstrated exceptional scholarly dedication and subject mastery, which laid the groundwork for his teaching, supervision, and innovative research contributions at MMU. His educational background, centered on a research-intensive model, reflects the synthesis of academic theory and practical innovation that characterizes his work today.

Experience

Assoc. Prof. Dr. Goh Kah Ong Michael has a well-established professional history with Multimedia University, Malaysia, spanning over two decades. He began his career as a Tutor at the Faculty of Information Science & Technology (FIST). He was appointed as a Lecturer and elevated to Senior Lecturer. He served as Deputy Dean of Student Affairs and Alumni, where he provided strategic leadership in academic administration and student engagement. He also had an industrial attachment with Heathmetrics Sdn Bhd, fostering industry-academic collaboration and applying academic research to practical applications. This rich blend of academic and industry experience has honed his capabilities in academic governance, curriculum development, student mentorship, and real-world technology deployment. His ongoing role as Associate Professor continues to leverage his expertise in AI, biometrics, and software development. Through his involvement in university committees, innovation competitions, and cross-institutional collaboration, Dr. Goh demonstrates a commitment to excellence in teaching, research, and societal impact, making him a vital contributor to both MMU and Malaysia’s wider research ecosystem.

Research Interest

Dr. Goh’s research interests encompass a wide spectrum of areas within Artificial Intelligence and digital systems engineering. A significant portion of his work is dedicated to contactless biometric technologies, especially those using palm vein, palm print, and finger vein recognition. These technologies are integral to secure authentication systems and form the core of his early and ongoing research. He has also extensively explored video analytics, pattern recognition, image processing, and data classification for security, healthcare, and smart city applications. One of his signature projects, the “Smart Traffic Impact Assessment System”, represents a major advancement in urban AI, combining real-time data analysis with predictive modeling. Another domain of interest is gait recognition and spatiotemporal feature extraction, applied to age-based classification systems using AI algorithms. His interdisciplinary approach blends software engineering with signal processing and machine learning, leading to innovative tools with societal benefits. Additionally, he is actively engaged in research around reinforcement learning for dynamic pricing systems, integrating AI with economics. Dr. Goh’s projects reflect a strong application-driven research philosophy, pushing boundaries in how AI can be embedded into everyday environments for efficiency, safety, and sustainability.

Research Skills

Dr. Goh possesses a diverse and advanced set of research skills that have been instrumental in developing intelligent digital solutions. His core technical proficiencies include AI modeling, deep learning, video analytics, and multimodal data fusion, particularly in biometric systems. He is highly skilled in software and application development, with extensive experience in developing both academic prototypes and deployable commercial systems. His expertise also extends to database design and management, essential for handling large-scale biometric and visual data. He has a strong command over object recognition and pattern classification techniques using AI and machine learning frameworks. Dr. Goh is also experienced in reinforcement learning algorithms, used in his dynamic pricing and smart city projects. On the academic side, he is adept at writing research proposals, publishing in high-impact journals, and presenting findings at international conferences. His collaborative skills are evidenced by successful multi-author book chapters and interdisciplinary project leadership. Moreover, he excels in mentoring postgraduate students and coordinating innovation competitions. With this unique combination of programming, analytical, leadership, and project management skills, Dr. Goh consistently delivers impactful, high-quality research.

Awards and Honors

Dr. Goh has received numerous awards and recognitions at national and international levels, affirming his excellence in research and innovation. Most notably, he was awarded the ITEX SPECIAL MINDS Thematic Award 2024 and a Gold Medal for his “Smart Traffic Impact Assessment System” at the International Invention, Innovation, Technology Exhibition (ITEX). He also earned multiple accolades for “CloudPark – The Smart City Parking Solution,” including gold medals and top placements in PROCOM and Infineon competitions. His consistent success in innovation is further illustrated  for biometric systems, video puzzle learning tools, and intelligent scanning devices. he received the Outstanding Research Award from Multimedia University, a testament to his sustained scholarly contribution. Earlier recognitions, including the Silver Medal at ITEX for “Palm’n Go – A Touchless Biometric System”, mark the beginning of his decorated research journey. Dr. Goh’s portfolio of over 18 innovation awards highlights his commitment to creating solutions that are both technically robust and socially impactful. These accolades validate his role as a thought leader in biometric AI and smart systems research.

Publication Top Notes

  • “An automated palmprint recognition system”, Image and Vision Computing, 2005 – Cited 396.

  • “PalmHashing: a novel approach for cancelable biometrics”, Information Processing Letters, 2005 – Cited 255.

  • “Touch-less palm print biometrics: Novel design and implementation”, Image and Vision Computing, 2008 – Cited 245.

  • “Facial expression recognition using a hybrid CNN–SIFT aggregator”, International Workshop on Multi-disciplinary Trends in Artificial Intelligence, 2017 – Cited 198.

  • “Palmprint recognition with PCA and ICA”, Proc. Image and Vision Computing New Zealand, 2003 – Cited 163.

Conclusion

Assoc. Prof. Ts. Dr. Goh Kah Ong Michael stands as a shining example of how academic rigor, technological innovation, and community engagement can converge to make a lasting impact. His career is marked by groundbreaking contributions in AI-driven biometrics and smart city solutions, with practical outputs recognized at the highest levels through international innovation awards. As a mentor, educator, and innovator, he continues to shape the future of information technology and digital systems in Malaysia and beyond. His research not only addresses complex technical challenges but also offers scalable solutions that benefit society, including urban traffic management and secure identification technologies. With his impressive publication record, long-term academic service, and forward-looking research agenda, Dr. Goh is well-positioned to assume future leadership roles in research policy, international collaboration, and higher education development. His contributions exemplify excellence in research translation and academic leadership, making him a deserving candidate for international recognition and continued advancement in the global research landscape.

Sina Ahmadi | Computer Science | Excellence in Research

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Mr. Sina Ahmadi | Computer Science | Excellence in Research

Scholar at National Coalition of Independent Scholars (NCIS), Canada

Sina Ahmadi is an accomplished management professional with significant experience in cloud infrastructure, software engineering, security optimization, and networking. His extensive career has seen him working in prominent positions, managing complex projects and teams. He is recognized for his ability to design and manage Kubernetes clusters, cloud networking, and optimize service meshes such as Istio for global applications. Sina is known for his strategic vision in aligning technical solutions with business goals, consistently delivering results that exceed client expectations. His work spans across multiple global organizations, including Block, ME Bank, and MYOB, where he played key roles in cloud infrastructure, networking, and security solutions. Sina’s deep technical expertise is matched by his leadership abilities, having mentored teams, facilitated technical discussions, and driven innovative projects that have had a measurable impact on business outcomes. His contributions also extend to the academic sphere, where he regularly contributes as a peer reviewer and has published several influential papers on topics such as cloud security, AI in security, and network defense. With numerous awards, accolades, and professional affiliations, Sina continues to be a thought leader in his field.

Professional Profile

Education:

Sina Ahmadi holds a Master’s degree in Information Technology from the University of Melbourne (2015–2017), where he earned a place on the Dean’s Honors List, showcasing his academic excellence. His undergraduate studies in Computer Science (B.Sc.) were completed at the University of Mazandaran (2005–2010), forming the foundation of his technical expertise. During his time at the University of Melbourne, Sina’s academic focus honed his skills in cloud computing, networking, and security, which would later define his professional career. His education provided him with both a theoretical understanding and practical skills, enabling him to address complex technical challenges in the field of IT infrastructure, cloud architecture, and security. Sina has consistently sought to build on his academic credentials through ongoing professional development, as evidenced by his memberships in leading organizations such as IEEE, ACM, and ACS. These affiliations not only reflect his commitment to staying at the forefront of technological advancements but also contribute to his continuous learning and research in the field. Sina’s educational background, coupled with his professional experience, has empowered him to make significant contributions to cloud security and infrastructure engineering.

Professional Experience:

Sina Ahmadi’s professional journey spans a diverse range of roles in the tech industry, showcasing his ability to lead teams and deliver innovative solutions across various domains such as cloud infrastructure, networking, and security. Currently, as a Senior Staff Engineer at Block, he oversees the global platform and networking infrastructure on AWS, setting the platform’s vision and roadmap to align with business goals. He has played pivotal roles in managing cloud platforms for global companies like Afterpay and Square, where he was responsible for ensuring the seamless operation of network infrastructure and traffic management. Prior to this, as Platform Lead for Infra & Edge Networking at Block, Sina successfully delivered solutions for global app connectivity and edge networking. His experience at ME Bank further solidified his leadership abilities, where he designed and implemented security and network solutions while managing cloud teams. In his earlier roles at MYOB and Rundl, Sina honed his expertise in Kubernetes management, security, and cloud architecture, consistently optimizing system performance and security. His diverse career has allowed him to manage large-scale projects and lead teams that have shaped the digital transformation of major organizations.

Research Interests:

Sina Ahmadi’s research interests primarily lie in the intersection of cloud computing, network security, and artificial intelligence. His focus is on optimizing security measures in cloud environments, particularly in multi-cloud and hybrid cloud infrastructures. He is deeply engaged in exploring innovative solutions for Distributed Denial of Service (DDoS) attack prevention, network intrusion detection, and the application of zero-trust architectures in cloud networks. Sina is also interested in the role of AI and machine learning in enhancing cloud security, specifically in developing next-generation firewalls and intrusion detection systems. His work delves into edge computing security, examining how emerging technologies like edge networks impact the overall security and privacy of cloud infrastructures. In addition to his interest in security, Sina is also passionate about cloud networking, including the implementation of complex service meshes like Istio and Envoy to improve scalability, reliability, and performance in cloud-based applications. His research interests aim to solve critical challenges faced by organizations in securing their cloud and network environments while ensuring seamless and efficient connectivity across distributed platforms.

Research Skills:

Sina Ahmadi possesses a comprehensive set of research skills, with a strong foundation in both theoretical and applied aspects of cloud computing, networking, and security. His proficiency in cloud platforms like AWS, combined with his expertise in Kubernetes and Istio, allows him to tackle complex research challenges in infrastructure optimization and network security. Sina has honed his ability to conduct in-depth research on cloud security, from designing secure cloud architectures to investigating novel solutions for mitigating security threats in cloud environments. He excels in analyzing large datasets, drawing meaningful insights, and applying these insights to solve practical industry problems. His extensive experience as a peer reviewer for journals like IEEE Access and SN Computer Science highlights his analytical skills and ability to assess and critique cutting-edge research in his field. Sina’s research skills are complemented by his hands-on experience in managing multi-region cloud infrastructures, implementing security controls, and developing automation processes for enhanced productivity. His expertise in AI-based security systems and network intrusion detection algorithms further reinforces his capabilities in advanced research areas within cloud and network security.

Awards and Honors:

Sina Ahmadi has received numerous awards and accolades for his exceptional contributions to cloud computing and security. One of his notable recognitions is the “Keep ME Secure” award from ME Bank, acknowledging his outstanding achievement in security. His academic excellence at the University of Melbourne earned him a place on the Dean’s Honors List, further demonstrating his commitment to high standards in both education and professional practice. Additionally, Sina’s role as a reviewer for prestigious journals like IEEE Access and SN Computer Science highlights his standing as a respected thought leader in his field. His continuous contributions to the advancement of cloud security and infrastructure engineering have been instrumental in shaping industry standards, and his work has been widely recognized by both academic and professional communities. These accolades not only reflect his technical expertise but also his leadership in driving innovation in cloud infrastructure, networking, and security.

Conclusion:

Sina Ahmadi is an exemplary professional and researcher whose contributions to the fields of cloud infrastructure, networking, and security have had a significant impact on the industry. His leadership in managing global platforms for major organizations such as Block and ME Bank, combined with his research on cloud security and network defense strategies, showcases his ability to bridge the gap between theory and practice. Sina’s academic background, coupled with his extensive professional experience, positions him as a thought leader in the tech community. His work in optimizing cloud and Kubernetes infrastructures, along with his research on AI-based security systems, contributes to the evolving landscape of cloud technologies. His dedication to continuous learning, mentoring, and collaboration has earned him numerous awards and professional recognitions, affirming his status as an influential figure in cloud computing and network security. As he continues to expand his research and professional contributions, Sina is poised to further shape the future of secure and scalable cloud environments.

Publications Top Notes

  1. Title: A Comprehensive Study on Integration of Big Data and AI in Financial Industry and its Effect on Present and Future Opportunities
    Author: S Ahmadi
    Year: 2024
    Citations: 70
    Journal: International Journal of Current Science Research and Review 7 (1), 66-74
  2. Title: Open AI and its Impact on Fraud Detection in Financial Industry
    Author: S Ahmadi
    Year: 2023
    Citations: 63
    Journal: Journal of Knowledge Learning and Science Technology ISSN, 2959-6386
  3. Title: Optimizing Data Warehousing Performance Through Machine Learning Algorithms in the Cloud
    Author: S Ahmadi
    Year: 2023
    Citations: 48
    Journal: International Journal of Science and Research (IJSR) 12 (12), 1859-1867
  4. Title: Elastic Data Warehousing: Adapting To Fluctuating Workloads With Cloud-Native Technologies
    Author: S Ahmadi
    Year: 2023
    Citations: 40
    Journal: Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)
  5. Title: Next Generation AI-Based Firewalls: A Comparative Study
    Author: S Ahmadi
    Year: 2023
    Citations: 37
    Journal: International Journal of Computer (IJC) 49 (1), 245-262
  6. Title: Zero trust architecture in cloud networks: application, challenges and future opportunities
    Author: S Ahmadi
    Year: 2024
    Citations: 27
    Journal: Journal of Engineering Research and Reports 26 (2), 215-228
  7. Title: Challenges and Solutions in Network Security for Serverless Computing
    Author: S Ahmadi
    Year: 2024
    Citations: 26
    Journal: International Journal of Current Science Research and Review 7 (1), 218-229
  8. Title: Security Implications of Edge Computing in Cloud Networks
    Author: S Ahmadi
    Year: 2024
    Citations: 19
    Journal: Journal of Computer and Communications 12, 26-46
  9. Title: Security And Privacy Challenges in Cloud-Based Data Warehousing: A Comprehensive Review
    Author: S Ahmadi
    Year: 2023
    Citations: 18
    Journal: Journal of Computer Science Trends and Technology 11 (6), 17-27
  10. Title: Cloud Security Metrics and Measurement
    Author: S Ahmadi
    Year: 2023
    Citations: 15
    Journal: Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)

 

Farhad Soleimanian Gharehchopogh | Artificial Intelligent | Best Researcher Award

Published on by World Science Awards

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

Praveen Naik | Artificial Intelligence Award | Best Researcher Award

Published on by World Science Awards

Mr. Praveen Naik | Artificial Intelligence Award | Best Researcher Award

Researcher at Meru University of Science and Technology, Kenya

Mr. Erick Mutwiri Kirimi is a dedicated and accomplished individual with a strong background in mathematics. With a Bachelor’s degree in Education Science and ongoing PhD studies in Computational and Applied Mathematics, he has developed a deep understanding of mathematical concepts and their practical applications. Mr. Kirimi’s academic journey includes serving as a part-time lecturer at several universities, where he imparts his knowledge to students. He has also gained valuable teaching experience as a mathematics and chemistry teacher, including serving as the Head of the Mathematics Department. His academic achievements are further highlighted by scholarships, including a full scholarship for his PhD studies in Computational Mathematics and a partial scholarship for his PhD studies in Applied Mathematics. These scholarships reflect his commitment to academic excellence and his potential to make significant contributions to the field of mathematics. Mr. Kirimi’s research skills, teaching abilities, leadership qualities, computer proficiency, and strong communication and interpersonal skills make him a well-rounded individual poised for success in his academic and professional endeavors.

Professional Profiles:

Professional Experience:

Praveen Naik has been a Research Fellow at the National Institute of Technology Karnataka, Surathkal since 2020. In this role, he has conducted research on “Investigation of Arecanut Images for Grading through Non-Destructive Methods.” His contributions to the project include dataset curation, the development of a lightweight and efficient model, implementation of an Adaptive Genetic-Based Model Optimization, introduction of a non-destructive methodology, and successful resolution of Arecanut grading challenges. Prior to his current position, Praveen Naik served as a Senior Assistant Professor at Shri Madhwa Vadiraja Institute of Technology and Management from 2013 to 2020. During this time, he managed a variety of subjects, crafted compelling curricula, and conducted impactful lectures. He also provided mentorship to students, collaborated seamlessly with peers, and efficiently managed administrative responsibilities within the academic setting. From 2010 to 2011, Praveen Naik worked as a Software Programmer at SouthCan Software, where he played a pivotal role in the Milk Dairy Project. His responsibilities included supervising day-to-day dairy operations, overseeing tasks such as data entry, manipulation, and transactions. He also contributed to report customization using SQL-Server Reporting Service, thereby enhancing reporting functionalities for the organization.

Academic:

Since 2020, Praveen Naik has been pursuing a Ph.D. in Information Technology at the National Institute of Technology Karnataka, Surathkal. Prior to this, he completed his M.Tech in Computer Science and Engineering from Atria Institute of Technology, Bengaluru, from 2011 to 2013. Praveen’s academic journey began with a Bachelor’s degree in Information Science and Engineering from Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, which he completed from 2006 to 2010.

Areas of Specialization:

Praveen Naik has specialized expertise in Object Detection, Model Optimization, and Deep Learning, with a focus on YOLOv5. His experience includes extensive work in Dataset Curation, ensuring high-quality data inputs for machine learning models.

Achievements:

Praveen Naik has demonstrated his academic prowess by qualifying in several prestigious examinations. He cleared the GATE (Graduate Aptitude Test in Engineering) in 2020, showcasing his proficiency in engineering concepts. Additionally, he achieved qualification in the UGC-NET (University Grants Commission – National Eligibility Test) in 2020, indicating his in-depth knowledge and understanding of his field. Furthermore, he passed the K-SET (Karnataka State Eligibility Test) in 2019, demonstrating his expertise and competence in the field of education.

Publications:

  1. Flower Phenotype Recognition and Analysis using YoloV5 Models
    • Authors: PM Naik, B Rudra
    • Year: 2022
    • Journal: Grenze International Journal of Engineering & Technology (GIJET)
    • Volume: 8
    • Issue: 2
    • Citations: 3
  2. Deep learning-based arecanut detection for X-ray radiography: improving performance and efficiency for automated classification and quality control
    • Authors: PM Naik, B Rudra
    • Year: 2024
    • Journal: Nondestructive Testing and Evaluation
    • Pages: 1-21
  3. Classification of Arecanut X-Ray Images for Quality Assessment Using Adaptive Genetic Algorithm and Deep Learning
    • Authors: PM Naik, B Rudra
    • Year: 2023
    • Journal: IEEE Access
    • Volume: 11
    • Pages: 127619-127636
  4. Prevention of Webshell Attack using Machine Learning Techniques
    • Authors: S YC, PM Naik, B Rudra
    • Year: 2021
    • Journal: Grenze International Journal of Engineering & Technology (GIJET)
    • Volume: 7
    • Issue: 1