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

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/

Ezekiel Olatunji | Built Environment | Best Researcher Award

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Mr. Ezekiel Olatunji | Built Environment
| Best Researcher Award

University of Wolverhampton | United Kingdom

Mr. Ezekiel Olatunji Doctoral Researcher at the University of Wolverhampton, Mr. Ezekiel Olatunji focuses on developing innovative frameworks for assessing and enhancing flood resilience within socially diverse communities. His research explores the intersection of community engagement, infrastructure planning, and risk management, with the goal of improving adaptive capacity and awareness in flood-prone regions. By integrating qualitative and quantitative research methodologies, including structured interviews, surveys, and participatory workshops, he investigates how social, economic, and cultural factors influence community preparedness and recovery. His work also incorporates the use of analytical tools such as SPSS and NVIVO to interpret complex data and extract meaningful insights that inform policy and practice. The outcomes of his research aim to guide local authorities, urban planners, and policymakers in designing more inclusive and resilient flood management strategies. Through his doctoral work, Mr. Olatunji contributes to the growing body of knowledge on disaster risk reduction and environmental resilience, aligning his efforts with the United Nations Sustainable Development Goals (SDGs), particularly those related to sustainable cities and climate action. His research underscores the importance of a community-centered approach to resilience, combining academic rigor with practical solutions for sustainable development.

Profile: Google Scholar | Orcid

Featured Publications

Olatunji, E. O., Adebimpe, O. A., & Oladokun, V. O. (2023). A fuzzy logic approach for measuring flood resilience at community level in Nigeria. International Journal of Disaster Resilience in the Built Environment, 14(4), [Article details pending].

Olatunji, E., Proverbs, D., Pathirage, C., Suresh, S., Cooper, J., & Capewell, L. (2024). A community-scale framework for evaluating flood resilience across socially diverse communities. Leeds Beckett University.

Olatunji, E., Proverbs, D., Pathirage, C., Suresh, S., & Ekundayo, O. (2025). Towards a participatory assessment of community flood resilience. Purdue University.

Olatunji, E., Ekundayo, O., Proverbs, D., Pathirage, C., Suresh, S., Emonson, P., & others. (2025). The role of stakeholder partnerships in building flood resilient communities: Case study of the FAIR project in the UK. Taylor & Francis.

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.

 

Pedro Pitrez | Engineering | Best Researcher Award

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Assist. Prof. Dr. Pedro Pitrez | Engineering | Best Researcher Award

Assist. Prof. Dr. Pedro Pitrez  at Assistente Convidado – FEUP , Portugal

Pedro Pitrez is a Mechanical Engineer specializing in thermal energy and internal combustion engine systems. He holds a Master’s degree and is currently completing his Ph.D. at FEUP, focusing on energy systems and mechanical engineering. Pitrez has extensive experience in both academia and industry, with a passion for research, development, and teaching. He has worked as a lecturer at UTAD and FEUP, teaching subjects such as Applied Thermodynamics and Mechanics. His professional journey includes working at INEGI, where he developed a machine for cork painting, and at EDP Geração, managing operations for hydroelectric power plants. Known for his technical expertise, Pitrez combines his engineering knowledge with a drive for innovation, contributing to various research projects, academic articles, and conferences.

Publication Profile

Scopus

🎓 Education

Pedro Pitrez’s educational background includes a Bachelor’s degree in Mechanical Engineering from UTAD, followed by a Master’s degree in Mechanical Engineering from FEUP. His Ph.D. work at FEUP focuses on the areas of energy systems and combustion. Pitrez has excelled in his academic career, achieving strong results with a Master’s thesis on preparing a Porsche 911 internal combustion engine for competition. His academic training also includes specialized research in thermal energy, reflected in his current work and studies. Furthermore, he has contributed significantly to educational platforms, having taught courses such as Applied Thermodynamics II and Mechanics III. His current research at FEUP and INEGI is an embodiment of his continuous pursuit of knowledge and advancement in the field of mechanical and energy engineering.

💼 Experience

Pedro Pitrez has an extensive professional background in both academia and industry. From March 2020 to August 2022, he worked as a researcher at INEGI, where he was involved in the development of industrial machines, including a machine for cork painting. He was responsible for designing the machine’s structure and transport systems, as well as creating the control software. Additionally, Pitrez gained valuable experience in teaching at FEUP, offering courses in thermodynamics and mechanics. He has also worked at Amorim Cork Composites, where he provided academic support, including practical classes and student supervision. His professional career also includes working at EDP Geração, where he currently holds a position as an engineer in charge of planning and operations for hydroelectric power plants. Pitrez’s broad experience and academic contributions make him a well-rounded professional in mechanical engineering and energy systems.

🏆 Awards & Honors

Pedro Pitrez has received numerous accolades for his contributions to research and academia. Notably, he won the Best Paper Award at the International Conference on Technologies and Materials for Renewable Energy, Environment, and Sustainability (TMREES23) in 2023. His research work on plasma gasification and energy systems optimization has earned him recognition in both academic and professional circles. Additionally, Pitrez has presented at several high-profile conferences, such as the VII Jornadas de Engenharia Mecânica UTAD and the International Conference on Renewable Energy and Sustainability (TMREES23). His academic journey has been marked by consistent excellence, having also contributed to published articles in reputable journals such as Energy Reports and ENCIT 2020. These awards and honors reflect his impact and dedication to advancing research in mechanical engineering and sustainable energy solutions.

🔍 Research Focus

Pedro Pitrez’s research primarily focuses on energy systems, combustion processes, and sustainable energy technologies. His current work includes investigating plasma gasification for hazardous waste treatment and optimizing energy conversion processes. He has developed expertise in the numerical analysis of energy systems and the efficient production of syngas. His research is highly interdisciplinary, bridging mechanical engineering with environmental sustainability. Pitrez is particularly focused on applying energy optimization to industrial processes, as evidenced by his work on internal combustion engines and hydroelectric power plants. Additionally, his ongoing Ph.D. research explores the potential of alternative fuels in the transportation and energy sectors. His work contributes to the development of cleaner, more efficient energy systems, with practical applications in industries such as automotive, power generation, and environmental technologies. Pitrez’s dedication to advancing energy solutions aligns with his long-term vision of sustainable and efficient mechanical systems.

Publication Top Notes

  • Energy Recovery from Infectious Hospital Waste and Its Safe Neutralization

    • Authors: P. Pitrez, Pedro; E.L.M. Monteiro, Eliseu L.M.; A.I. Rouboa, Abel Ilah
    • Citations: 0 (as it is a forthcoming publication)
    • Year: 2025
    • Journal: International Journal of Hydrogen Energy

  • Numerical Analysis of Plasma Gasification of Hazardous Waste Using Aspen Plus

    • Authors: Not provided in the reference.
    • Citations: 0
    • Year: 2023
    • Journal: Energy Reports
    • Volume: 9, Pages 418-426

  • Optimization of Plasma Gasification System for Treatment of COVID-19 Hazardous Waste for Valorization of LHV and H2 Composition

    • Authors: Not provided in the reference.
    • Citations: 0
    • Year: In Review (no specific year yet)
    • Journal: Status: In Review

 

Yogesh Thakare | Engineering | Best Researcher Award

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Yogesh Thakare | Engineering | Best Researcher Award

Dr Yogesh Thakare, Ramdeobaba University, Nagpur, India

Dr. Yogesh Thakare 🎓 is an accomplished researcher and educator in Electronics and Communication Engineering. He earned his Ph.D. (2020) from SGB Amravati University, specializing in DRAM design using submicron technology 💾. Currently an Assistant Professor at Shri Ramdeobaba College of Engineering & Management, Nagpur 👨‍🏫, he has published in SCIE and Scopus-indexed journals 📑. His research spans FPGA architectures, AI, IoT, and biomedical systems 🤖. A GATE qualifier (94.92%), he has led government-funded projects 💰 and organized AI & IoT workshops 🏗️. Passionate about innovation, he contributes to cutting-edge electronics and computing technologies ⚡.

Publication Profile

Google Scholar

Academic Excellence

Dr. Yogesh Thakare earned his Ph.D. in Electronics Engineering from SGB Amravati University in 2020, focusing on Dynamic Random Access Memory (DRAM) design using submicron technology ⚡🔬. His academic journey reflects excellence, having completed his M.Tech with Distinction (85.10%) 🎓🏆 and his B.E. with First-Class (72.62%) 📚✨. With a strong foundation in electronics and a passion for advanced semiconductor technologies, Dr. Thakare has made significant contributions to memory design and innovation. His expertise in microelectronics and circuit design continues to drive advancements in the field, shaping the future of high-performance computing and digital storage solutions 💡🔍.

Funded Research & Grants

Dr. Yogesh Thakare has demonstrated exceptional research leadership by securing ₹24.6 Lakhs from CSIR for developing an automated water distribution system 💧🔬. His innovative approach aims to enhance water management efficiency through automation, contributing to sustainable resource utilization 🌱💡. This significant funding underscores his expertise in engineering solutions that address real-world challenges 🏗️⚙️. With a strong commitment to technological advancement, Dr. Thakare continues to drive impactful research that promotes water conservation and smart distribution systems 🌍📊. His work not only fosters scientific progress but also supports community welfare by ensuring efficient and equitable water access 🚰✅.

Experience

Dr. Yogesh Thakare is an experienced educator with over 14 years of teaching in top engineering institutes 🏫, including Shri Ramdeobaba College of Engineering and Management, Nagpur. As an Assistant Professor, he has played a key role in shaping technical education 📚. His passion for emerging technologies has led him to organize numerous workshops on Artificial Intelligence 🤖, the Internet of Things 🌐, and Machine Learning 📊, empowering students with cutting-edge knowledge. Through his dedication to academic excellence and innovation, Dr. Thakare continues to inspire the next generation of engineers and researchers 🚀.

Research Focus

Dr. Yogesh Thakare’s research spans electronics, artificial intelligence, IoT, and machine learning 🤖📡. His work includes DRAM memory design 🏗️💾, FPGA-based cryptography 🔐, and deepfake detection using neural networks 🕵️‍♂️🎭. He has contributed to environmental intelligence systems 🌱📊, weather prediction for agriculture 🌦️🚜, and smart monitoring technologies 📡🏠. Additionally, he has explored cortisol detection for stress monitoring 🧪⚕️ and crime reporting frameworks 🚔📜. His interdisciplinary research integrates hardware and AI-driven solutions, making impactful advancements in computing, security, and human well-being 🔬💡. His innovative approach bridges technology and real-world applications, enhancing automation, safety, and intelligence. 🚀

Publication Top Notes

Intelligent Life Saver System for People Living in Earthquake Zone.

An Effect of Process Variation on 3T-1D DRAM

Analysis of power dissipation in design of capacitorless embedded DRAM

IoT-Enabled Environmental Intelligence: A Smart Monitoring System

Detection of Deepfake Video Using Residual Neural Network and Long Short-Term Memory.

A Read-out Scheme of 1T-1D DRAM Design with Transistor Assisted Decoupled Sensing Amplifier in 7 nm Technology

Enhancing weather prediction and forecasting for agricultural applications using machine learning

FPGA Implementation of Compact Architecture for Lightweight Hash Algorithm for Resource Constrained Devices

Crafting visual art from text: A generative approach

Cortisol Detection Methods for Stress Monitoring: Current Insight and Future Prospect: A Review

An Ensemble Learning with Deep Feature Extraction Approach for Recognition of Traffic Signs in Advanced Driving Assistance Systems

Development and design approach of an sEMG-based Eye movement control system for paralyzed individuals

 

Santos Kumar Das | Engineering | Best Researcher Award

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Dr. Santos Kumar Das | Engineering | Best Researcher Award

Associate Professor at National Institute of Technology Rourkela, India

Dr. Santos Kumar Das, an Associate Professor at the Department of Electronics and Communication Engineering, National Institute of Technology (NIT) Rourkela, is an accomplished researcher with expertise in AI, IoT, Sensor Networking, and Optical Networking, including LiFi, FSO, and SDN. With a Ph.D. in Communication Networks from NIT Rourkela and an M.S. in Electrical Communication Engineering from IISc Bangalore, Dr. Das has an extensive academic and professional background. He has supervised 12 Ph.D., 53 M.Tech., and 54 B.Tech. theses and managed numerous government-funded research projects totaling over ₹671 lakh. A recipient of multiple awards, including the Best Research Paper Award at IoTCloud’21 and INDICON 2023, he has over 50 journal publications. Dr. Das is actively involved in academia as a reviewer, technical committee member, and conference session chair. His innovative contributions in 6G communication and IoT for societal applications make him a strong contender for the Best Researcher Award.

Professional Profile

Education

Dr. Santos Kumar Das has an impressive educational background, marked by a strong foundation in electronics and communication engineering. He completed his Ph.D. in Communication Networks from the National Institute of Technology (NIT), Rourkela, in 2014. Prior to that, he earned a Master of Science (M.S.) in Electrical Communication Engineering from the prestigious Indian Institute of Science (IISc), Bangalore, in 2002, graduating with first-class honors. Dr. Das also holds a Bachelor of Engineering (B.E.) degree in Electronics and Communication from VSSUT (formerly UCE), Burla, Odisha, completed in 1998, where he again achieved first-class honors. His academic journey began with a strong foundation in science during his higher secondary education at D.D. College, Keonjhar, Odisha, where he secured first-class marks. With this comprehensive educational background, Dr. Das has built a distinguished career in research and teaching, focusing on cutting-edge technologies in communications and networking.

Professional Experience

Dr. Santos Kumar Das has a diverse and extensive professional experience spanning academia, industry, and research. Currently serving as an Associate Professor at the Department of Electronics and Communication Engineering at NIT Rourkela since 2009, he has significantly contributed to the institution’s academic and research initiatives. Prior to his academic career, Dr. Das gained substantial industry experience as a Senior Software Engineer at Palvision and ITXpress in Singapore, where he worked on cutting-edge network systems and software development. He also held roles as a Software Engineer at Actatek and Network Engineer at Netmarks, contributing to advanced technological solutions in various sectors. In addition, Dr. Das worked as a Research Associate at CEMNet Lab, NTU Singapore, and as a Research Engineer at A-Star Singapore, where he was involved in high-impact research in communication networks and sensor technologies. His broad range of roles has enriched his expertise and research focus, particularly in IoT, AI, and Optical Networking.

Research Interest

Dr. Santos Kumar Das has a broad and innovative research focus, primarily centered around emerging technologies in communications and networking. His key research interests include Artificial Intelligence (AI), the Internet of Things (IoT), Sensor Networking, and Optical Networking, particularly focusing on cutting-edge technologies like LiFi, Free-Space Optics (FSO), and Software-Defined Networking (SDN). Dr. Das explores the integration of AI in IoT applications, aiming to enhance the intelligence and efficiency of network systems. His work in optical networking focuses on leveraging advanced communication techniques for high-speed data transmission and next-generation wireless systems, with a special emphasis on 6G communication technologies. He also investigates IoT-based smart city applications, environmental monitoring systems, and industrial IoT for better security, safety, and resource management. By combining AI with sensor networks and optical technologies, Dr. Das contributes to the development of sustainable, intelligent, and high-performance communication systems for both industrial and societal applications.

Award and Honor

Dr. Santos Kumar Das has received numerous prestigious awards and honors throughout his career, reflecting his excellence in research, teaching, and professional contributions. He was recognized as a Senior Member of IEEE in 2019, showcasing his standing in the global engineering community. Dr. Das has received multiple Best Paper Awards, including at the International Conference on Next Generation Computing Technologies (NGCT) in 2017, Electronic Systems and Intelligent Computing (ESIC) in 2020, and IoTCloud’21, III T Kottayam in 2021. He was also honored with the Best Faculty Advisor Award at NIT Rourkela in 2022 and 2023 for his outstanding guidance to students. Dr. Das’s leadership and contributions to academic committees have earned him recognition as an editorial board member and reviewer for multiple journals. Additionally, his involvement in technical committees and chairing sessions at international conferences, such as TENCON 2023 and SGCNSP 2023, further exemplify his significant impact in the field of engineering and technology.

Conclusion

Dr. Santos Kumar Das stands out as a highly accomplished researcher with a stellar record in academia, research, and mentorship. His contributions to IoT, AI, and advanced networking, coupled with his leadership in projects and professional service, make him an outstanding candidate for the Best Researcher Award. Addressing minor areas for improvement, such as broadening his research scope and enhancing global collaboration, could further solidify his position as a leader in the field. Overall, he is highly deserving of this recognition.

Publications Top Noted

  • A comprehensive review on deep learning-based methods for video anomaly detection
    Authors: R Nayak, UC Pati, SK Das
    Journal: Image and Vision Computing
    Year: 2021
    Citations: 268
  • Time series based air pollution forecasting using SARIMA and prophet model
    Authors: KKR Samal, KS Babu, SK Das, A Acharaya
    Conference: Proceedings of the 2019 International Conference on Information Technology
    Year: 2019
    Citations: 158
  • Multi-directional temporal convolutional artificial neural network for PM2.5 forecasting with missing values: A deep learning approach
    Authors: KKR Samal, KS Babu, SK Das
    Journal: Urban Climate
    Year: 2021
    Citations: 74
  • An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach
    Authors: KKR Samal, AK Panda, KS Babu, SK Das
    Journal: Sustainable Cities and Society
    Year: 2021
    Citations: 47
  • Temporal convolutional denoising autoencoder network for air pollution prediction with missing values
    Authors: KKR Samal, KS Babu, SK Das
    Journal: Urban Climate
    Year: 2021
    Citations: 44
  • Swin transformer based vehicle detection in undisciplined traffic environment
    Authors: P Deshmukh, GSR Satyanarayana, S Majhi, UK Sahoo, SK Das
    Journal: Expert Systems with Applications
    Year: 2023
    Citations: 42
  • Critical review on slope monitoring systems with a vision of unifying WSN and IoT
    Authors: DK Yadav, S Jayanthu, SK Das, S Chinara, P Mishra
    Journal: IET Wireless Sensor Systems
    Year: 2019
    Citations: 36
  • Multi-output TCN autoencoder for long-term pollution forecasting for multiple sites
    Authors: KKR Samal, AK Panda, KS Babu, SK Das
    Journal: Urban Climate
    Year: 2021
    Citations: 35
  • Video-based real-time intrusion detection system using deep-learning for smart city applications
    Authors: R Nayak, MM Behera, UC Pati, SK Das
    Conference: 2019 IEEE International Conference on Advanced Networks and
    Year: 2019
    Citations: 32
  • A vehicle detection technique using binary images for heterogeneous and lane-less traffic
    Authors: GSR Satyanarayana, S Majhi, SK Das
    Journal: IEEE Transactions on Instrumentation and Measurement
    Year: 2021
    Citations: 31
  • Design of real-time slope monitoring system using time-domain reflectometry with wireless sensor network
    Authors: DK Yadav, G Karthik, S Jayanthu, SK Das
    Journal: IEEE Sensors Letters
    Year: 2019
    Citations: 31
  • Observation of multiphonon transverse wobbling in 133Ba
    Authors: KR Devi, S Kumar, N Kumar, FS Babra, MSR Laskar, S Biswas, S Saha, …
    Journal: Physics Letters B
    Year: 2021
    Citations: 30