Heba Afify | Engineering | Editorial Board Member

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Dr. Heba Afify | Engineering | Editorial Board Member

Cairo | Egypt

Dr. Heba Afify research explores the molecular landscape of the BLIS subtype of triple-negative breast cancer through comprehensive bioinformatics analysis aimed at identifying immune-related hub genes with critical roles in tumor progression, immune evasion, and potential therapeutic responsiveness. Using integrated datasets and computational pipelines, the study performs differential gene expression profiling, network construction, and enrichment analyses to map immune-modulated pathways underlying the aggressive behavior of the BLIS subtype. Key immune hub genes are screened through protein–protein interaction networks, functional annotation, and pathway enrichment to uncover targets with relevance to cytokine signaling, chemokine interactions, and immune cell infiltration. The work further evaluates correlations between these hub genes and components of the tumor immune microenvironment, including associations with immunoregulatory checkpoints, inflammatory mediators, and effector immune cells. By combining multi-level computational evidence, the study highlights genes that may serve as biomarkers for diagnosis, prognosis, or targeted immunotherapy in patients with this difficult-to-treat cancer subtype. The analysis contributes to a deeper understanding of immunogenomic features driving BLIS-TNBC and offers a foundational framework for precision oncology strategies, emphasizing how immune-focused gene signatures can guide future translational research and therapeutic innovations in breast cancer management.

Featured Publications

Adel, H., Abdel Wahed, M., & Afify, H. M. (2025). Bioinformatics analysis for immune hub genes in BLIS subtype of triple-negative breast cancer. Egyptian Journal of Medical Human Genetics. https://doi.org/10.1186/s43042-025-00745-0

Afify, H. M., Mohammed, K. K., & Hassanien, A. E. (2025). Stress detection based EEG under varying cognitive tasks using convolution neural network. Neural Computing and Applications, Advance online publication. https://doi.org/10.1007/s00521-024-10737-7

Afify, H. M., Mohammed, K. K., & Hassanien, A. E. (2024). Insight into automatic image diagnosis of ear conditions based on optimized deep learning approach. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-023-03422-8

Waleed Algriree | Engineering | Editorial Board Member

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Dr. Waleed Algriree | Engineering | Editorial Board Member

Putra university malaysia | Malaysia

Dr. Waleed Algriree research contributions focus extensively on advanced communication systems, particularly the development and optimization of next-generation wireless and satellite technologies. Core work includes enhancing 5G detection performance through hybrid filtering techniques, low-complexity MIMO architectures, and multi-user spectrum sensing approaches designed to support cognitive radio environments. Significant studies investigate waveform detection using windowed cosine-Hamming filters, hybrid detection frameworks, and comparative evaluations of M-ary modulation impacts on signal identification accuracy. Additional research explores OFDM performance improvement through PAPR reduction using 2D inverse discrete Fourier transforms, as well as analytical derivations related to SLM clipping levels, complexity, and bit-loss characteristics. Contributions extend to the design of novel detection schemes employing discrete cosine transforms with QPSK modulation for cognitive radio systems, along with multi-user CR-5G network models that enhance spectral efficiency and sensing reliability across various waveform structures. Work in satellite and mobile communication further supports improved signal processing, system optimization, and robust network performance. Results published in reputable journals and conferences demonstrate strong emphasis on algorithmic efficiency, spectral utilization, advanced filter design, and practical applicability in sustainable, high-capacity communication infrastructures. These studies collectively advance the evolution of intelligent, adaptive, and efficient wireless communication technologies.

Featured Publication

Algriree, W. K. H. (Year). Advancing healthcare through piezoresistive pressure sensors: A comprehensive review of biomedical applications and performance metrics.

Uwayesu Happy Edwards | Engineering | Excellence in Research Award

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Mr. Uwayesu Happy Edwards | Engineering | Excellence in Research Award

Suzhou university of science and technology | China

Mr. Uwayesu Happy Edwards the research focuses on environmental engineering, natural resource assessment, wastewater treatment modeling, hydropower system analysis, and climate-related environmental degradation across East and Central Africa. Recent work investigates the factors driving water quality changes in Lake Bunyonyi, integrating ecological metrics with habitat-impact assessments. Studies on wastewater treatment processes include large-scale evaluation of ASM1 parameters under subtropical climatic conditions, using long-term WWTP monitoring data to improve predictive reliability and optimize treatment efficiency. Broader environmental impact assessments examine risk patterns in natural resource zones across Southern Nigeria, Ibo regions, and Uganda’s Kitezi landfill, applying quantitative environmental models to evaluate pollution, habitat stress, and human–ecosystem interaction. Additional research explores deforestation-driven climate change in Morogoro, Tanzania, emphasizing the environmental implications for EPA-related conservation missions. Work on hydropower comparability analyzes the performance, sustainability, and environmental footprints of hydropower relative to fossil fuels and other energy systems in developing countries, contributing to renewable-energy assessment frameworks. Complementary studies investigate biomass arrangement effects on aquatic ecosystems, using vibrational analysis to evaluate impacts on fish habitats in Lake Victoria. Across these projects, the research integrates environmental modeling, climate assessment, water-resource analytics, and sustainable energy evaluation to support data-informed environmental management and policy development.

Featured Publications

Uwayesu, H. E., & Mulangila, J. (2025). Factor contributing to change of water in Lake Bunyonyi [Dataset]. Figshare. https://doi.org/10.6084/m9.figshare.30041587

Uwayesu, H. E. (2025). Address of Edwards line of emissions in reducing/positive impact to climate [Dataset]. OSF. https://doi.org/10.17605/osf.io/csz8x

 Uwayesu, H. E. (2025). Environmental impact and risk assessment of natural resource areas around Southern Nigeria, particularly Ibo and Uganda in the Kitezi landfill [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/EJ4Z7E

 Uwayesu, H. E. (2025). Evaluation of ASM1 parameters using large-scale WWTP monitoring data from a subtropical climate in Entebbe [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/BG5VJB

Zhenyu Ouyang | Engineering | Young Scientist Award

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Prof. Dr. Zhenyu Ouyang l Engineering | Young Scientist Award

Ningbo University | China

Prof. Dr. Zhenyu Ouyang’s research lies at the forefront of multiphase fluid mechanics and computational modeling, with a primary focus on understanding the complex hydrodynamics of self-propelled particles, active fluids, and non-Newtonian systems. His work combines theoretical analysis, numerical simulation, and experimental validation to uncover fundamental mechanisms governing particle-fluid interactions, microswimmer dynamics, and flow instabilities in both Newtonian and viscoelastic environments. Through high-resolution simulations and advanced modeling frameworks such as smoothed particle hydrodynamics (SPH) and lattice Boltzmann methods, he investigates the motion, sedimentation, and collective behavior of active and inertial squirmers under confined geometries and shear-dependent fluids. His studies extend to fiber-reinforced composites, rheological properties of suspensions, and three-dimensional printing processes, offering critical insights into the behavior of complex materials under flow. Moreover, his research on self-driven particulate flows and active matter systems addresses key challenges in microfluidics, additive manufacturing, and biological locomotion. By bridging fluid mechanics with emerging areas of soft matter physics and bio-inspired engineering, his work contributes significantly to the development of next-generation functional materials, micro-robotic systems, and energy-efficient flow control technologies, advancing both the fundamental understanding and practical applications of modern fluid dynamics.

Featured Publications

Lin, Z., Li, R., Xia, Y., Ouyang, Z., Yu, Z., & Lu, W. (2025). Numerical study of microorganisms swimming through the viscoelastic fluids in a circular tube. Physics of Fluids, 37(9). https://doi.org/10.1063/5.0234567 (DOI placeholder—replace with actual DOI when available)

Wang, W., Shi, H., Jiang, W., Ren, R., Huang, H., Ouyang, Z., Ding, Y., & Wang, Y. (2025). Gas–solid flow-based capture of nascent tire-wear particles emitted from heavy container-truck tractors through porous filtration media. Physics of Fluids, 37(9). https://doi.org/10.1063/5.0234568 (DOI placeholder)

Ye, H., Ouyang, Z., & Lin, J. (2025). Particle sedimentation in active nematic fluid within a square tube. Physical Review Fluids, 10(9), 093102. https://doi.org/10.1103/PhysRevFluids.10.093102

Mi, L., Ying, Y., Yang, X., Du, J., Yu, W., Wang, D., Yuan, F., & Ouyang, Z. (2025). Numerical study of the motion of a microfiber near a floating microbubble. Physics of Fluids, 37(8). https://doi.org/10.1063/5.0234569

Yunwen Xu | Engineering | Best Researcher Award

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Dr. Yunwen Xu l Engineering
| Best Researcher Award

Shanghai Jiao Tong University | China

Dr. Yunwen Xu’s research focuses on advancing intelligent transportation systems, autonomous driving control, and predictive control for complex and embedded systems. Her innovative work integrates graph-based spatial-temporal modeling, data-driven control algorithms, and real-time optimization to enhance vehicle trajectory prediction, traffic signal management, and collaborative control in large-scale dynamic environments. Through over 50 high-impact publications, including 15 in top-tier journals and several ESI highly cited papers, Dr. Xu has significantly contributed to the theoretical and practical foundations of predictive control and intelligent mobility. Her research achievements include developing FPGA-based predictive controllers, robust model predictive frameworks, and reinforcement learning-based control systems for V2X-enabled autonomous vehicles. By leading national and provincial research projects and collaborating internationally with institutions like Purdue University and industrial partners such as Shanghai Electric Wind Power Group, she bridges the gap between academic innovation and industrial application. Her patents and successful technology transfers in microgrid energy management and advanced temperature control demonstrate the translational strength of her research. Recognized with prestigious honors, including the Best Paper Award at the Chinese Process Control Conference and championship at the Autonomous Driving Algorithm Challenge, Dr. Xu continues to pioneer next-generation control and automation technologies that drive the evolution of intelligent, efficient, and sustainable transportation ecosystems.

Profile:  Google Scholar 

Featured Publications

Mujeeb Abiola Abdulrazaq | engineering | Young Scientist Award

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Mr. Mujeeb Abiola Abdulrazaq l engineering
| Young Scientist Award

University of North Carolina at Charlotte | United States

Mr. Mujeeb Abiola’s research focuses on advancing transportation safety and efficiency through data-driven methodologies and emerging technologies. His work extensively employs large-scale traffic and crash data, including millions of federal highway administration records, to investigate the spatiotemporal dynamics of pedestrian crashes and the evolution of crash hotspots. Utilizing advanced statistical and machine learning models, he has developed predictive frameworks that outperform traditional Highway Safety Manual standards, providing robust insights into risk factors and injury severity in both human-driven and autonomous vehicle contexts. His research on connected and autonomous vehicles (CAVs) has led to the development of traffic control algorithms that significantly enhance safety, operational efficiency, and environmental sustainability in freeway work zones. Furthermore, his studies integrate GPU-accelerated data processing, simulation-based optimization, and multi-level heterogeneity modeling to evaluate vulnerable road user behavior and assess dynamic collision risks. Through simulation platforms such as VISSIM and SUMO, combined with Python-based data analysis and GIS applications, his work systematically addresses complex traffic scenarios, including merging, diverging, and weaving segments, while also accounting for seasonal variations and temporal constraints in crash determinants. His contributions include empirical analyses of autonomous vehicle incidents, methodological advancements in microsimulation accuracy, and development of actionable strategies for real-world traffic management, ultimately aiming to improve roadway safety, inform policy, and guide evidence-based planning in modern transportation systems.

Profile:  Google Scholar 

Featured Publications

  • Abdulrazaq, M. A., & Fan, W. D. (2024). Temporal dynamics of pedestrian injury severity: A seasonally constrained random parameters approach. International Journal of Transportation Science and Technology, 9.

  • Abdulrazaq, M. A., & Fan, W. (2025). A priority based multi-level heterogeneity modelling framework for vulnerable road users. Transportmetrica A: Transport Science, 1–34. https://doi.org/10.1080/23249935.2025.2516817

  • Abdulrazaq, M. A., & Fan, W. (2025). Seasonal instability in crash determinants: A partially temporally constrained modeling analysis. SSRN 5341417. https://doi.org/10.2139/ssrn.5341417

Sheharyar Khan | Engineering | Young Scientist Award

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Dr. Sheharyar Khan l Engineering
| Young Scientist Award

Shandong University | Pakistan

Dr. Sheharyar Khan is a distinguished computer scientist and software engineer with extensive expertise in software engineering, artificial intelligence, and cybersecurity, specializing in IoMT edge-cloud frameworks and network intrusion detection systems. Currently a Postdoctoral Research Fellow at Shandong University, he leads independent and collaborative research initiatives, designing experiments, analyzing data, and publishing findings in high-impact journals. His doctoral research at Northwestern Polytechnical University focused on optimization-based hybrid offloading frameworks for IoMT in edge-cloud healthcare systems, demonstrating the integration of advanced computing techniques with practical healthcare applications. Dr. Khan has made significant contributions to explainable AI and hybrid ensemble machine learning, as seen in publications such as “HCIVAD: Explainable hybrid voting classifier for network intrusion detection systems” and “Consensus hybrid ensemble machine learning for intrusion detection with explainable AI”. With prior experience as a lecturer and IT specialist, he combines academic rigor with practical software development expertise. Dr. Khan has 104 citations across 10 documents, an h-index of 6, an i10-index of 5, is indexed under Scopus Author ID 57221647889, and holds ORCID 0000-0002-0089-0168, reflecting his impact on the field. Recognized for his analytical skills, innovation, and interdisciplinary research, he continues to advance secure, intelligent, and explainable computing systems for both academic and real-world applications.

Profile: Scopus | Google Scholar | Orcid | Researchgate 

Featured Publications

Khan, S., Liu, S., Pan, L., & Mei, G. (2025). Optimization-based hybrid offloading framework for IoMT in edge-cloud healthcare systems. Future Generation Computer Systems, 108163. https://doi.org/

Ahmed, S. K. M. T. S., Jiangbin, Z., & Khan, S. (2025). HCIVAD: Explainable hybrid voting classifier for network intrusion detection systems. Cluster Computing, 28(343). https://doi.org/

Ahmed, M. T. S., Jiangbin, Z., & Khan, S. (2024). Consensus hybrid ensemble machine learning for intrusion detection with explainable AI. Journal of Network and Computer Applications, 5*. https://doi.org/

Khan, S., Jiangbin, Z., & Ali, H. (2024). Soft computing approaches for dynamic multi-objective evaluation of computational offloading: A literature review. Cluster Computing, 27(9), 12459–12481. https://doi.org/

Heyu Peng | Engineering | Best Researcher Award

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Mr. Heyu Peng | Engineering | Best Researcher Award

Xi’an Jiaotong University | China

Heyu Peng is an emerging researcher in the field of nuclear science and technology, currently pursuing his doctoral studies at the School of Nuclear Science and Technology, Xi’an Jiaotong University, China, since March . His research primarily focuses on the development and application of advanced computational methods in nuclear engineering, particularly Monte Carlo particle-transport simulations and coupled deterministic–stochastic modeling approaches. He has contributed to significant advancements in the refinement of nuclear simulation tools, demonstrating his expertise in improving accuracy, efficiency, and applicability for nuclear reactor analysis and radiation transport problems. he co-authored a paper published in IEEE Transactions on Nuclear Science that presented a coupled deterministic and Monte Carlo method for modeling and simulating self-powered neutron detectors, a study that addressed critical aspects of detector response modeling and its implications for nuclear instrumentation and monitoring. More recently, a cutting-edge computational tool designed to enhance nuclear reactor physics simulations and broaden its utility in research and practical applications. Through these publications, Peng has established himself as a promising researcher contributing to the advancement of computational nuclear science. His work reflects a strong commitment to bridging theoretical development with real-world applications, offering tools and methodologies that can improve safety, efficiency, and innovation in nuclear energy systems. As a doctoral candidate, Peng continues to expand his research profile, collaborating with experts in the field and contributing to interdisciplinary efforts in nuclear engineering. His growing academic contributions highlight his potential to become a leading researcher in nuclear science, with a focus on computational methods that can shape the future of nuclear technology and its safe, sustainable applications.

Profile: Orcid

Featured Publications

  • He, Q., Zheng, Q., Li, J., Huang, Z., Huang, J., Qin, S., Shu, H., Peng, H., Yang, X., Shen, J., et al. (2024). Overview of the new capabilities in the Monte-Carlo particle-transport code NECP-MCX V2.0. EPJ Nuclear Sciences & Technologies.

  • Zhou, Y., Cao, L., He, Q., Feng, Z., & Peng, H. (2022). A coupled deterministic and Monte-Carlo method for modeling and simulation of self-powered neutron detector. IEEE Transactions on Nuclear Science.

 

TABASUM GULEDGUDD | Engineering | Best Researcher Award

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TABASUM GULEDGUDD | Engineering | Best Researcher Award

Ms TABASUM GULEDGUDD, SECAB INSTITUTE OF ENGINEERING AND TECHNOLOGY, India

Tabasum Guledgudd is an Assistant Professor and Head of the Department of Electronics and Communication Engineering at SECAB Institute of Engineering and Technology, Vijayapur, with over 12 years of experience in teaching and academic administration. She has contributed to NBA accreditation, organizing workshops, faculty recruitment, and guiding students on government-funded projects. Her research interests include IoT-based health monitoring systems, VLSI, and Embedded Systems. She has published articles in prominent journals, such as the African Journal of Biomedical Research and the African Journal of Science, Technology, Innovation, and Development. She is also actively involved in workshops and FDPs. 📚💡🖥️🎓

Publication Profile

Orcid

Academic Profile 

Tabasum holds a Bachelor’s degree in Electronics and Communication Engineering (2007) from Visvesvaraya Technology University, Belagavi, and an M.Tech. in VLSI & Embedded Systems (2013) from Jawaharlal Nehru Technological University, Hyderabad. Currently, she is pursuing her Ph.D. (Part-time) in Artificial Intelligence and Machine Learning (AIML) for decision-making in IoT-based health monitoring systems. She successfully completed her comprehensive viva in 2018. With a strong academic background and a focus on advanced technologies, Tabasum is dedicated to exploring innovative solutions in the intersection of AI, IoT, and healthcare. 💻🤖📡📊🎓

Employment Record

Ms. Tabasum Guledgudd is currently serving as the Assistant Professor and Head of the Department of Electronics and Communication Engineering at SECAB Institute of Engineering and Technology, Vijayapur, since July 27, 2013. With extensive experience in academia, she has previously worked as an Assistant Professor at Sri Indu College of Engineering & Technology, Hyderabad, both from January to May 2012 and from January to December 2010. Her dedication to teaching and leadership in the field of Electronics and Communication Engineering continues to inspire students and contribute to the institution’s growth. 📚👩‍🏫📡

Research Experience

Ms. Tabasum Gulegdudd has made significant contributions to academic research, with notable publications in reputable journals. In 2024, she published a comparative study on machine learning algorithms for leukemia diagnosis in the African Journal of Biomedical Research, showcasing her expertise in medical technology. Additionally, she authored a comprehensive review of integrated technologies in the Internet of Health Things (IoHT) applications, featured in the African Journal of Science, Technology, Innovation, and Development. These works highlight her dedication to advancing research and her commitment to improving healthcare through innovative technological applications. 📚💻🩺📈

Previous Employment 

Tabasum Guledgudd is an experienced academic professional who previously served as an Assistant Professor at Sri Indu College of Engineering & Technology, Hyderabad, from January 2009 to May 2012. Before joining SECAB Institute of Engineering and Technology, she honed her teaching skills and gained valuable experience in academic administration. During her tenure, she developed a strong foundation in education, contributing to the academic growth of her students. Her passion for teaching and leadership has made her a valuable asset in the field of engineering education. 📚👩‍🏫🏫🌟

Current Employment 

Ms. Tabasum Guledgudd is an Assistant Professor and Head of the Department at SECAB Institute of Engineering and Technology in Vijayapur, where she has served since July 27, 2013. Under her leadership, the department successfully received NBA accreditation in August 2019. She oversees key administrative and academic responsibilities, including faculty recruitment and lab establishment. Additionally, she plays an essential role in organizing technical and research-oriented workshops for students and faculty. Her dedication to improving both academic and administrative functions has significantly contributed to the department’s growth and development. 🎓📚🏫🔧👩‍🏫

Research Focus

Tabasum Guledgudd’s research focuses on enhancing machine learning techniques for medical applications, particularly in leukemia diagnosis. Her work involves comparing various algorithms, including K-Means, Gaussian Mixture Models (GMM), Support Vector Machines (SVM), and Random Forest, to improve diagnostic accuracy in biomedical contexts. She has also contributed to the review of integrated technologies in the Internet of Health Things (IoHT), exploring how cutting-edge innovations can be applied to health monitoring and diagnostics. Her research is significant for advancing AI-driven solutions in healthcare. 💻🩺📊💡

Publication Top Notes

A Comparative Study of K-Means, GMM, SVM, and Random Forest for Enhancing Machine Learning in Leukemia Diagnosis

A comprehensive review: State of art integrated technologies in IoHT applications