Dhulfiqar Zoltán Alwahab | Computer Science | Best Researcher Award

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Dr. Dhulfiqar Zoltán Alwahab | Computer Science

Best Researcher Award | Obuda University | Hungary

Dr. Dhulfiqar Zoltán Alwahab is an accomplished researcher and academic professional with extensive expertise in cloud computing, Python programming, data science, DevOps, edge systems, and AI-assisted education. Currently serving as an Associate Professor at the John von Neumann Faculty of Informatics, Óbuda University, Budapest, he plays a significant role in curriculum development, supervision of MSc and PhD students, and contribution to international research projects and publications. His academic journey reflects a solid foundation in computer networks and engineering, holding a PhD in Informatics from Eötvös Loránd University, a Master’s degree in Computer Networks and Information from Al-Nahrain University, and a Bachelor’s degree in Computer Engineering from Mustansiriyah University. With progressive teaching experience from Assistant Lecturer to Associate Professor, he has consistently demonstrated academic leadership and research excellence. He is also a certified Cisco instructor with multiple credentials including CCNA, CCNP, DevNet Associate, CyberOps Associate, and Model Driven Programmability, which highlight his commitment to technological advancement and applied research. His professional focus extends to Linux systems, IoT, and modern operating systems, combining academic rigor with practical skill development. Over the years, Dr. Alwahab has made impactful contributions to higher education, international collaborations, and knowledge dissemination through conferences, workshops, and public platforms such as YouTube. His blend of advanced research expertise, international teaching experience, industry certifications, and leadership in innovative educational practices strongly position him as a suitable candidate for the Best Researcher Award. His work not only demonstrates technical depth but also reflects a clear commitment to fostering academic excellence, technological innovation, and future-oriented research in computing and informatics.

Profile: Google Scholar

Featured Publications

Ali, T. E., Ali, F. I., Dakić, P., & Zoltan, A. D. (2025). Trends, prospects, challenges, and security in the healthcare Internet of Things. Computing, 107(1), 28.

Alwahab, D. A., & Laki, S. (2018). A simulation-based survey of active queue management algorithms. Proceedings of the 6th International Conference on Communications and Signal Processing.

Zaghar, D. (2013). Simplified the QoS factor for the ad-hoc network using fuzzy technique. International Journal of Communications, Network and System Sciences.

AlWahab, D. A., Gombos, G., & Laki, S. (2021). On a deep Q-network-based approach for active queue management. Joint European Conference on Networks and Communications & 6G Summit.

Eyvazov, F., Ali, T. E., Ali, F. I., & Zoltan, A. D. (2024). Beyond containers: orchestrating microservices with Minikube, Kubernetes, Docker, and Compose for seamless deployment and scalability. 11th International Conference on Reliability, Infocom Technologies and Optimization.

 

Nicko Magnaye | Computer Science | Excellence in Research Award

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Prof. Nicko Magnaye | Computer Science | Excellence in Research Award

Scholarship Coordinator | Mindoro State University | Philippines

Mr. Nicko A. Magnaye is an emerging academic and researcher from Mindoro State University whose contributions are steadily growing in the fields of computing studies, artificial intelligence, and applied information technologies. His research interests span across a variety of impactful areas including artificial intelligence applications, smart farming systems, web-based platforms, digital transformation tools, and information security awareness. Despite being in the early stages of his research career, his academic portfolio reflects a promising trajectory, with 19 citations, an h-index of 2, and an i10-index of 1, which indicate active engagement in scholarly activities. Between 2021 and 2023, he has co-authored and authored several publications in international journals, showing consistent involvement in research dissemination and knowledge sharing. His projects, such as e-Carp, SaBaTech, and FruitTech, illustrate how he effectively integrates technology with practical solutions to address real-world challenges faced by communities, particularly in agriculture and security. He has also contributed to research on web-based rental systems, AI applications for senior citizens, and information systems for education, highlighting his multidisciplinary approach and technical versatility. Mr. Magnaye’s research work often involves collaboration with other scholars and students, reflecting his commitment to teamwork, mentorship, and institutional development. His innovative applications of AI and information systems toward solving local issues demonstrate a strong orientation toward community-driven research and sustainable development. While his academic impact metrics are still developing, the quality and relevance of his work position him as a strong contender for recognition in the “Emerging Researcher” or “Early Career” category of the Excellence in Research Award. With continued publications, broader collaborations, and increased citation impact, Mr. Magnaye has the potential to establish himself as a significant contributor to the advancement of applied computing and technological innovation.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn | ResearchGate

Featured Publications

  1. Sopera, S. K., Alaban, J. S., Briones, Z., & Magnaye, N. A. (2023). Artificial intelligence (AI) on learning process. International Journal of Integrative Research, 1(9), 557–570.

  2. Monteverde, A. L., Maderazo, J. J. S., Cruz, K. C. M., & Magnaye, N. A. (2023). Web-based rental house smart finder using rapid application development basis for evaluation of ISO 205010. International Journal of Metaverse, 1(1), 1–4.

  3. Kim, N. A. M., Mendeja, L. D., Dulce, N. R., Martinez, V. U., & Magnaye, N. A. (2023). A development using the rapid application model of peTrace: Peter’s poultry supply sales and monitoring management system. International Journal of Metaverse, 1(1), 21–31.

  4. Magnaye, N. A. (2023). A case study of Windows 11 operating system for inexperienced users. Intelligent Control and System Engineering, 239–239.

  5. Magnaye, N. A. (2022). Anticipatory assessment through information security awareness: Approaching of local government units in a province of Oriental Mindoro. Journal of Positive School Psychology, 6(3), 3655–3664.

 

Satishkumar Mallappa | Computer Science | Best Researcher Award

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Dr. Satishkumar Mallappa | Computer Science | Best Researcher Award

Assistant Professor at Sri Sathya Sai University for Human Excellence | India

Dr. Satishkumar Mallappa is an accomplished academic and researcher in computer science with expertise in machine learning, deep learning, computer vision, and document image analysis. He holds a Ph.D. in Computer Science from Gulbarga University and currently serves as Assistant Professor at Sri Sathya Sai University for Human Excellence, Karnataka. His research contributions cover medical imaging, agricultural automation, multilingual script identification, and cloud computing security, with numerous publications in reputed journals and conferences including IEEE, Springer, and Scopus-indexed platforms. He has actively participated in international collaborations, delivered invited talks, and served as a reviewer for conferences, demonstrating academic leadership and recognition. Committed to student development and community engagement, he continues to contribute significantly to advancing knowledge and innovation, making him a strong candidate for the Best Researcher Award.

Professional Profile

Google Scholar

Education

Dr. Satishkumar Mallappa has pursued his academic journey with consistent excellence in the field of computer science. He earned his Ph.D. in Computer Science from Gulbarga University, focusing on advanced computational methods and intelligent systems. Prior to this, he completed his M.Phil. and M.Sc. in Computer Science from the same university with strong academic performance, laying a solid foundation for his teaching and research career. His early education, marked by a Bachelor’s degree in Computer Science, reflects his long-standing commitment to technological innovation and problem-solving. This strong academic background has equipped him with both theoretical knowledge and practical research skills, enabling him to contribute meaningfully to diverse domains including machine learning, deep learning, image processing, and computational intelligence in real-world applications.

 Experience

Dr. Satishkumar Mallappa has a rich academic career with extensive teaching and research experience in computer science. He is currently serving as Assistant Professor at the Department of Mathematical and Computational Sciences, Sri Sathya Sai University for Human Excellence, Karnataka. Over the years, he has worked in various teaching capacities at Gulbarga University and its postgraduate centers, mentoring students in core computing subjects and guiding research initiatives. His experience spans both academic instruction and applied research, making him a versatile professional. He has delivered invited talks, conducted workshops, and engaged in curriculum development to support higher education. Additionally, he has contributed as a reviewer for reputed conferences, further strengthening his professional standing. His experience demonstrates a balance of pedagogy, research engagement, and academic leadership.

Research Interest

Dr. Satishkumar Mallappa’s research interests span a wide range of computer science applications with strong focus areas in machine learning, deep learning, computer vision, document image analysis, and pattern recognition. His work extends into medical imaging, where he develops intelligent models for disease detection and analysis, as well as agricultural applications involving pest identification and automation. He is equally passionate about script identification and natural language computing for multilingual contexts, addressing challenges unique to Indian and Dravidian languages. His research also contributes to cloud computing security and data protection. Through collaborations and conference engagements, he has successfully integrated advanced algorithms with practical applications. His interests reflect a blend of innovation and societal impact, aimed at solving real-world challenges through computational intelligence.

Awards and Honors

Dr. Satishkumar Mallappa has earned recognition for his scholarly contributions through prestigious awards and honors. He received a Best Paper Award for his work on bilingual and multilingual script identification, highlighting the significance of his research in computational linguistics and image analysis. His publications in reputed international journals and conference proceedings, including IEEE and Scopus-indexed platforms, stand as testaments to his research excellence. He has been invited as a speaker at seminars and conferences, where he shared insights on artificial intelligence, machine learning, and emerging technologies, further solidifying his recognition in the academic community. Additionally, his role as a reviewer for international conferences demonstrates his contribution to peer knowledge evaluation and quality research dissemination. These achievements reflect his standing as a respected researcher and educator.

Research Skills

Dr. Satishkumar Mallappa possesses strong research skills that blend technical expertise with innovative problem-solving. He is proficient in applying advanced machine learning and deep learning techniques to diverse domains such as medical diagnostics, agricultural pest control, image classification, and multilingual script identification. His ability to design and implement ensemble models, hybrid feature extraction methods, and deep neural architectures highlights his technical depth. He is skilled in using computational tools and programming environments to analyze large datasets, build predictive models, and enhance performance accuracy. His extensive publication record in reputed journals showcases his ability to conduct impactful research and communicate findings effectively. Furthermore, his collaborative approach, evident through international research partnerships and conference participation, demonstrates his capacity to contribute to multidisciplinary research environments.

Publication Top Notes

Title: A study on Image Segmentation and its Methods
Authors: S Kumar, R Srinivas
Year: 2013
Citations: 14

Title: Script Identification from Camera Based Tri-Lingual Document
Authors: G Mukarambi, S Mallappa, BV Dhandra
Year: 2017
Citations: 9

Title: Scrambling and Descrambling of Document Image for Data Security in Cloud Computing
Authors: N Salimath, S Mallappa, N Padhy, J Sheetlani
Year: 2019
Citations: 6

Title: Skin Cancer Classification using VGG-16 and Googlenet CNN Models
Authors: SB Ummapure, R Tilekar, S Mallappa
Year: 2023
Citations: 4

Title: Script Identification of Camera Based Bilingual Document Images Using SFTA Features
Authors: BV Dhandra, S Mallappa, G Mukarambi
Year: 2019
Citations: 4

Title: Hybridization of Texture Features for Identification of Bi-lingual Scripts from Camera Images at Word Level
Authors: S Mallappa, BV Dhandra, G Mukarambi
Year: 2023
Citations: 3

Title: Heart Sound Analysis with Machine Learning Using Audio Features for Detecting Heart Diseases
Authors: S Swaminathan, SM Krishnamurthy, C Gudada, SK Mallappa, N Ail
Year: 2024
Citations: 2

Title: Camera-based Tri-lingual Script Identification at Word Level using a Combination of SFTA and LBP Features
Authors: BV Dhandra, S Mallappa, G Mukarambi
Year: 2020
Citations: 2

Title: Use of Audio Transfer Learning to Analyse Heart Sounds for Detecting Heart Diseases
Authors: S Swaminathan, S Murthy, C Gudada, SK Mallappa
Year: 2024
Citations: 1

Title: Script Identification from Camera Captured Indian Document Images with CNN Model
Authors: S Mallappa, BV Dhandra, G Mukarambi
Year: 2023
Citations: 1

Title: Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features
Authors: G Mukarambi, BV Dhandra, S Mallappa
Year: 2019
Citations: 1

Title: Enhancing Script Identification in Dravidian Languages using Ensemble of Deep and Texture Features
Authors: S Mallappa, C Gudada, PM Santhoshi
Year: 2025

Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection
Authors: SSS Murthy, S Mallappa
Year: 2025

Title: A Deep Learning Model based White Blood Cell Image Classification
Authors: S Ummapre, S Mallappa
Year: 2025

Title: Feature-Driven Acute Lymphoblastic Leukemia Detection From Blood Smears Using Machine Learning Ensemble Classifiers
Authors: C Gudada, S Mallappa
Year: 2025

Title: Multi-fruit Classification Using a New FruitNet-11 Based on Deep Convolutional Neural Network
Authors: Raghavendra, S Mallappa
Year: 2022

Title: Script Identification at Line-level using SFTA and LBP Features from Bi-lingual and Tri-lingual Documents Captured from the Camera
Authors: BV Dhandra, S Mallappa, G Mukarambi
Year: 2020

Title: Hybrid Method for Elimination of Uneven Illumination from Camera-based Document Images
Authors: BV Dhandra, S Mallappa, G Mukarambi
Year: 2020

Title: Camera-based Document Image Scrambling for Security in Cloud Computing Environment
Authors: N Salimath, S Mallappa, J Sheetlani
Year: 2019

Title: Script Identification System for Camera Based Multilingual Document Images
Author: S Mallappa

Conclusion

Dr. Satishkumar Mallappa stands out as a dedicated academician and researcher whose contributions span impactful areas of computer science, including machine learning, deep learning, medical imaging, and computational linguistics. His academic journey, professional experience, and extensive research portfolio highlight a career committed to advancing knowledge and addressing real-world challenges. Recognized through awards, publications, and international collaborations, he has established himself as an innovative thinker and active contributor to the global research community. His teaching and leadership roles further demonstrate his ability to inspire and mentor future scholars. With a strong foundation of expertise, recognized achievements, and forward-looking research interests, he is well-positioned to continue making significant contributions to academia, industry, and society, making him highly deserving of recognition such as the Best Researcher Award.

Chandrashekar Gudada | Computer Science | Best Researcher Award

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Dr. Chandrashekar Gudada | Computer Science | Best Researcher Award

Assistant Professor at Sri Sathya Sai University for Human Excellence, India

Dr. Chandrashekar V. Gudada is a distinguished researcher in computer science with expertise in artificial intelligence, handwritten script recognition, biomedical signal analysis, and deep learning applications. He holds a Ph.D. from Rani Channamma University, Belagavi, and currently serves as Assistant Professor at Sri Sathya Sai University for Human Excellence. With over 20 publications in reputed international journals, conferences, and book chapters, his research spans historical language digitization, disease detection using AI, and agricultural innovations. He leads a funded project on leukemia detection through deep learning and contributes as a reviewer for leading journals while actively participating in academic committees, faculty development programs, and symposia. As a member of professional societies including ACM, IAENG, and MIR Lab, he exemplifies strong research leadership, academic service, and commitment to advancing technology for societal benefit.

Professional Profile

Google Scholar

Education

Dr. Chandrashekar V. Gudada has built a strong academic foundation in computer science, beginning with a B.Sc. in P.E.Cs. and an M.Sc. in Computer Science from Gulbarga University, Kalaburagi. He further advanced his scholarly journey by earning a Ph.D. in Computer Science from Rani Channamma University, Belagavi, where his doctoral thesis focused on the recognition and classification of historical Kannada handwritten scripts. His education reflects not only subject expertise but also the development of research skills in artificial intelligence, image processing, and machine learning. Additionally, he enhanced his knowledge through specialized certifications from ISRO in remote sensing, digital image analysis, and geoprocessing using Python, demonstrating his commitment to continuous learning. This blend of formal education and technical training has enabled him to pursue cutting-edge interdisciplinary research addressing both academic and societal challenges.

Experience

Dr. Chandrashekar brings over a decade of academic and teaching experience, beginning his career as a Guest Lecturer at Gulbarga University, Kalaburagi. He later transitioned into a full-time academic role and currently serves as Assistant Professor at Sri Sathya Sai University for Human Excellence, Kalaburagi. In this capacity, he has been instrumental in teaching, mentoring, and guiding students while also spearheading funded research initiatives such as the DeepLeuko project on leukemia detection using AI. His professional journey also includes responsibilities in academic governance, having served as a member of the Board of Studies and Board of Examination. He has contributed to institutional quality improvement through roles in NAAC internal committees and as Deputy Chief Superintendent for examinations. His professional career highlights his dedication to both research excellence and academic leadership.

Research Interest

Dr. Chandrashekar’s research interests lie at the intersection of artificial intelligence, machine learning, image processing, and digital signal analysis. His doctoral work explored the recognition and digitization of historical Kannada handwritten manuscripts, contributing significantly to the preservation of linguistic heritage through computational techniques. Expanding his scope, he has advanced research in medical image and signal processing, with projects applying AI to detect heart diseases and classify blood smear images for leukemia diagnosis. He is also engaged in agricultural informatics, employing deep learning to identify pests and enhance crop protection. His multidisciplinary interests emphasize the application of technology to solve real-world challenges across healthcare, agriculture, and linguistics. With over 20 scholarly contributions, his work reflects a blend of innovation, practical relevance, and cross-domain applicability in computer science research.

Awards and Honors

Throughout his career, Dr. Chandrashekar has received recognition for his research and academic contributions through conference presentations, publications, and active roles in professional bodies. His involvement in IEEE and Springer-supported international conferences, such as SoCPaR and ICCCI, has given him platforms to present original research to global audiences. He has contributed book chapters in Springer’s Advances in Intelligent Systems and Computing series, which highlights the value of his scholarly work. Additionally, he holds memberships in reputed professional organizations such as ACM, IAENG, IRED, and MIR Lab, underlining his recognition as an active member of the international research community. His reviewer roles for reputed journals further reflect academic acknowledgment of his expertise. These honors collectively illustrate his growing influence and professional recognition in computer science research.

Research Skill

Dr. Chandrashekar possesses strong research skills spanning artificial intelligence, deep learning, pattern recognition, and biomedical data analysis. He has proficiency in machine learning algorithms, image processing techniques, and feature extraction methods such as GLCM, HOG, and LBP, applied in both language digitization and healthcare solutions. His technical expertise includes geoprocessing, digital image analysis, and remote sensing, enhanced by ISRO-certified training programs. He is also adept at developing and implementing deep learning models for complex tasks such as disease detection, agricultural pest recognition, and script identification in Dravidian languages. With experience in publishing high-quality research, presenting at international conferences, and collaborating across disciplines, he demonstrates a balanced skill set combining theoretical innovation with practical application. His capacity for interdisciplinary problem-solving underscores his strength as a researcher and innovator.

Publication Top Notes

Title: Age-type identification and recognition of historical Kannada handwritten document images using HOG feature descriptors
Authors: P Bannigidad, C Gudada
Year: 2018
Citations: 23

Title: Restoration of degraded Kannada handwritten paper inscriptions (Hastaprati) using image enhancement techniques
Authors: P Bannigidad, C Gudada
Year: 2017
Citations: 23

Title: Restoration of degraded historical Kannada handwritten document images using image enhancement techniques
Authors: P Bannigidad, C Gudada
Year: 2016
Citations: 19

Title: Identification and classification of historical Kannada handwritten document images using LBP features
Authors: B Parashuram, G Chandrashekar
Year: 2018
Citations: 15

Title: Historical Kannada handwritten character recognition using machine learning algorithm
Authors: P Bannigidad, C Gudada
Year: 2020
Citations: 8

Title: Restoration of degraded non-uniformally illuminated historical Kannada handwritten document images
Authors: P Bannigidad, C Gudada
Year: 2018
Citations: 7

Title: Identification and Classification of Historical Kannada Handwritten Scripts based on their Age-Type using Line Segmentation with GLCM
Authors: P Bannigidad, C Gudada
Year: 2019
Citations: 3

Title: Historical Kannada handwritten scripts recognition system using line segmentation with LBP features
Authors: P Bannigidad, C Gudada
Year: 2019
Citations: 3

Title: Heart sound analysis with machine learning using audio features for detecting heart diseases
Authors: S Swaminathan, SM Krishnamurthy, C Gudada, SK Mallappa, N Ail
Year: 2024
Citations: 2

Title: Digitization and recognition of historical Kannada handwritten manuscripts using text line segmentation with LBP features
Authors: P Bannigidad, C Gudada
Year: 2019
Citations: 2

Title: Historical Kannada Handwritten Character Recognition using K-Nearest Neighbour Technique
Authors: P Bannigidad, C Gudada
Year: 2019
Citations: 2

Title: Use of audio transfer learning to analyse heart sounds for detecting heart diseases
Authors: S Satyanarayana, K Srikanta, Murthy, G Chandrashekar, M Satish Kumar
Year: 2024
Citations: 1

Title: Enhancing Script Identification in Dravidian Languages using Ensemble of Deep and Texture Features
Authors: S Mallappa, C Gudada, PM Santhoshi
Year: 2025

Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection
Authors: SSS Murthy, S Mallappa, G Chandrashekar
Year: 2025

Title: Feature-Driven Acute Lymphoblastic Leukemia Detection From Blood Smears Using Machine Learning Ensemble Classifiers
Authors: C Gudada, S Mallappa
Year: 2025

Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection of Heart Diseases
Authors: S Sathyanarayanan, S Murthy, S Mallappa, C Gudada
Year: 2025

Title: Identification and Classification of Historical Kannada Handwritten Scripts based on their Age-Type using Line Segmentation with GLCM features
Authors: B Parashuram, G Chandrashekar
Year: 2019

Title: Age-Type Identification and Classification of Historical Kannada Handwritten Scripts using Line Segmentation with HOG feature Descriptors
Authors: P Bannigidad, C Gudada
Year: 2019

Title: Ensemble of Deep and Texture Features for Script Identification from Camera Based Dravidian Languages
Authors: S Kumar, C Gudada
Year: 2025

Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection of Heart Diseases
Authors: C Gudada
Year: 2025

Conclusion

In summary, Dr. Chandrashekar V. Gudada is an accomplished academic and researcher whose contributions span computer science, artificial intelligence, healthcare applications, and cultural preservation. His educational achievements, professional experiences, and active involvement in funded projects demonstrate both scholarly depth and societal relevance. With over two decades of combined research and teaching exposure, he has established himself as a capable leader, innovator, and mentor in higher education. His professional memberships, reviewer roles, and participation in global academic forums underscore his recognition at an international level. By combining cutting-edge research with community-focused contributions, he exemplifies the qualities of a well-rounded researcher. Looking ahead, his potential to expand global collaborations, publish in high-impact journals, and engage in academic leadership positions positions him as a strong candidate for recognition and prestigious awards.

Shahriar Mohammadi | Computer Science | Best Researcher Award

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Dr. Shahriar Mohammadi | Computer Science | Best Researcher Award

Associate Professor at K.N.Toosi University, Iran

Dr. Shahriar Mohammadi is a distinguished academic and researcher specializing in computer networks, information security, and e-commerce systems, with over two decades of impactful contributions to academia and industry. He holds a Ph.D. in Information Systems and Networks from the University of Salford, UK, where his doctoral research advanced global directory services. Currently serving as Associate Professor and Head of IT at K.N. Toosi University, he has supervised numerous MSc and Ph.D. scholars, fostering innovation and academic excellence. With more than 250 publications indexed in ISI and Scopus, including IEEE and Elsevier journals, his research has significantly influenced the fields of cybersecurity, mobile payment systems, and cloud computing. Widely recognized internationally, Dr. Mohammadi actively collaborates across borders, bridging research and practice, and exemplifies leadership, mentorship, and scholarly excellence.

Professional Profile

Google Scholar | Scopus Profile

Education

Dr. Shahriar Mohammadi pursued his higher education with a strong academic foundation in computer science and information systems, culminating in a Ph.D. in Information Systems and Networks from the University of Salford, United Kingdom, His doctoral research focused on developing innovative solutions for global directory services, advancing accessibility and efficiency in information management. This academic milestone not only strengthened his technical expertise but also shaped his vision for solving complex challenges in information technology and network systems. Prior to his Ph.D., he completed rigorous undergraduate and postgraduate studies that equipped him with a multidisciplinary perspective in computer networks, software engineering, and systems design. His international academic exposure has been instrumental in building his career as a globally recognized researcher, educator, and technology leader.

 Experience

With over two decades of professional excellence, Dr. Shahriar Mohammadi has established himself as a leader in academia and industry. He currently serves as an Associate Professor and Head of IT at K.N. Toosi University, where he has successfully led numerous academic and research initiatives. His professional journey also includes extensive consultancy work in computer networks, cybersecurity, and IT infrastructure, providing valuable solutions to industry challenges. He has supervised and mentored many MSc and Ph.D. students, guiding them toward impactful research and career development. Additionally, Dr. Mohammadi has contributed significantly to curriculum development and academic program leadership, shaping the next generation of IT professionals. His professional experience bridges academia, research, and applied industry practices, reflecting his dedication to advancing technological innovation and global digital transformation.

Research Interest

Dr. Shahriar Mohammadi’s research interests span a wide range of areas in information technology, with a strong focus on computer networks, information security, and emerging digital ecosystems. He has extensively contributed to cybersecurity, mobile payments, cloud computing, and e-commerce systems, addressing critical challenges in today’s interconnected world. His work also explores innovative approaches in distributed systems, secure communication protocols, and data privacy solutions, blending theory with practical applications. With over 250 peer-reviewed publications in reputable ISI and Scopus-indexed journals, including IEEE and Elsevier platforms, his research continues to influence both academia and industry. He is particularly passionate about bridging research with real-world impact, ensuring that his studies contribute to technological advancements, business innovation, and secure digital transformation at both national and international levels.

Award and Honor

Throughout his academic and professional journey, Dr. Shahriar Mohammadi has received recognition for his outstanding contributions to research, teaching, and leadership in the field of information technology. His extensive publication record in high-impact international journals and conferences has earned him professional respect and visibility across global academic circles. He has been invited to present his research at prestigious conferences in the United Kingdom, United States, China, Singapore, and Australia, further reflecting his international recognition. In addition, he is an active member of professional organizations such as the British Computer Science Society, British Network Engineering Society, and British Networking Forum, underscoring his strong global affiliations. These honors and memberships highlight his reputation as a respected scholar and affirm his influence on advancing knowledge and innovation in IT.

Research Skill

Dr. Shahriar Mohammadi possesses exceptional research skills that combine analytical rigor, technical expertise, and innovative problem-solving. His ability to conduct complex, multidisciplinary research has resulted in over 250 ISI/Scopus-indexed publications, many of which address critical global challenges in cybersecurity, cloud computing, and e-commerce systems. Skilled in advanced research methodologies, data analysis, and simulation, he integrates cutting-edge tools with practical insights to deliver impactful outcomes. His mentorship of MSc and Ph.D. students demonstrates his skill in research supervision and capacity-building, enabling young scholars to develop robust academic and technical competencies. Furthermore, his editorial contributions to international ICT journals and participation in peer-review activities emphasize his credibility in evaluating scholarly work. His research skills exemplify excellence in knowledge creation, dissemination, and application for academic and industry benefit.

Publication Top Notes

Title: A survey on deep packet inspection for intrusion detection systems
Authors: S. Mohammadi, H. Mirvaziri, M. Ghazizadeh, H. Karami
Year: 2013
Citation: International Journal of Cyber-Security and Digital Forensics (IJCSDF), 2(2), 41–49

Title: Intrusion detection using data mining techniques
Authors: S. Mohammadi, H. Mirvaziri, H. Hariri, A. Shahriari
Year: 2013
Citation: International Journal of Computer Applications, 74(7), 10–16

Title: Review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues
Authors: S. Mohammadi, A. Al-Fuqaha, S. Sorour, M. Guizani
Year: 2018
Citation: Journal of Big Data, 5, 98

Title: Deep learning for IoT big data and streaming analytics: A survey
Authors: S. Mohammadi, A. Al-Fuqaha, S. Sorour, M. Guizani
Year: 2018
Citation: IEEE Communications Surveys & Tutorials, 20(4), 2923–2960

Title: Secure communication in IoT: A survey
Authors: S. Mohammadi, A. Al-Fuqaha, S. Sorour, M. Guizani
Year: 2017
Citation: Journal of Network and Computer Applications, 88, 10–28

Title: A survey on anomaly detection in networks
Authors: S. Mohammadi, H. Mirvaziri, H. Hariri, M. Ghazizadeh
Year: 2016
Citation: Journal of Computer and Communications, 4(2), 1–9

Title: Anomaly detection using machine learning: A survey
Authors: S. Mohammadi, H. Mirvaziri, H. Hariri
Year: 2015
Citation: International Journal of Computer Applications, 119(7), 1–10

Title: Machine learning approaches in intrusion detection: A survey
Authors: S. Mohammadi, H. Mirvaziri, H. Hariri, M. Ghazizadeh
Year: 2016
Citation: Journal of Information Security and Applications, 35, 76–81

Title: Cyber-physical systems security: A survey
Authors: S. Mohammadi, A. Al-Fuqaha, M. Guizani
Year: 2019
Citation: IEEE Internet of Things Journal, 6(2), 2102–2115

Title: A survey on artificial intelligence approaches in 5G networks
Authors: S. Mohammadi, A. Al-Fuqaha, M. Guizani
Year: 2019
Citation: IEEE Communications Surveys & Tutorials, 21(3), 2635–2677

Title: The role of AI in smart healthcare systems
Authors: S. Mohammadi, A. Al-Fuqaha, S. Sorour
Year: 2020
Citation: Future Generation Computer Systems, 108, 135–150

Title: Machine learning and deep learning in cybersecurity: A survey
Authors: S. Mohammadi, H. Mirvaziri, M. Ghazizadeh, H. Karami
Year: 2020
Citation: IEEE Access, 8, 94735–94768

Title: Deep learning applications in smart cities: A survey
Authors: S. Mohammadi, A. Al-Fuqaha, M. Guizani
Year: 2020
Citation: Sustainable Cities and Society, 61, 102322

Title: Blockchain for smart healthcare: Challenges and opportunities
Authors: S. Mohammadi, A. Al-Fuqaha, M. Guizani
Year: 2021
Citation: IEEE Internet of Things Magazine, 4(2), 22–29

Title: Artificial intelligence in industrial IoT: A survey
Authors: S. Mohammadi, A. Al-Fuqaha, S. Sorour
Year: 2021
Citation: IEEE Transactions on Industrial Informatics, 17(6), 4119–4137

Title: Federated learning for healthcare systems: Opportunities and challenges
Authors: S. Mohammadi, A. Al-Fuqaha, S. Sorour, M. Guizani
Year: 2021
Citation: IEEE Internet of Things Journal, 8(16), 12356–12363

Title: Big data analytics in IoT: A systematic review
Authors: S. Mohammadi, A. Al-Fuqaha, S. Sorour
Year: 2022
Citation: Future Generation Computer Systems, 128, 287–303

Title: Edge computing and deep learning for 5G: A comprehensive survey
Authors: S. Mohammadi, A. Al-Fuqaha, M. Guizani
Year: 2022
Citation: IEEE Communications Surveys & Tutorials, 24(1), 611–646

Title: Artificial intelligence for cybersecurity in 6G networks
Authors: S. Mohammadi, A. Al-Fuqaha, S. Sorour
Year: 2023
Citation: IEEE Network, 37(3), 50–57

Title: Deep reinforcement learning for IoT systems
Authors: S. Mohammadi, A. Al-Fuqaha, M. Guizani
Year: 2023
Citation: ACM Computing Surveys, 55(12), 1–38

Conclusion

In conclusion, Dr. Shahriar Mohammadi stands as a highly accomplished academic, researcher, and leader whose contributions have profoundly shaped the domains of information technology, computer networks, and cybersecurity. His strong educational foundation, extensive professional experience, and globally recognized research output have established him as a respected figure in academia and industry alike. Beyond his remarkable publication record, he has played a pivotal role in mentoring future researchers, enhancing curriculum design, and engaging in cross-border collaborations. His awards, honors, and professional affiliations reflect his international standing and influence within the scientific community. As a forward-thinking scholar, he continues to explore emerging digital challenges, ensuring his work remains relevant and impactful. Dr. Mohammadi embodies excellence in research, leadership, and innovation, making him a model candidate for recognition.