Niti Kant | Computational Theory | Best Researcher Award

Published on by Computer Science Engineering

Prof. Dr. Niti Kant | Computational Theory | Best Researcher Award

Professor | University of Allahabad | India

Prof. Dr. Niti Kant is a distinguished physicist currently serving in the Department of Physics, University of Allahabad, Prayagraj, India. With a Ph.D. from the Indian Institute of Technology (IIT) Delhi (2005) under the supervision of Dr. A. K. Sharma, his research focuses on laser–plasma interaction, self-focusing of lasers, harmonic generation, laser-induced electron acceleration, and terahertz (THz) radiation generation. Over the past two decades, Dr. Kant has made significant contributions to theoretical plasma physics, employing advanced analytical and numerical modeling approaches using Mathematica and Origin. He has published over 150 research papers in reputed international journals indexed by SCI, earning an H-index of 33 on Google Scholar, reflecting the global impact of his research. His academic journey includes postdoctoral research at POSTECH, South Korea, and academic leadership at Lovely Professional University, Punjab, where he served as Professor before joining the University of Allahabad. Dr. Kant has successfully led several sponsored research projects funded by CSIR, SERB, and DST, totaling over ₹50 lakhs, and has guided more than ten Ph.D. scholars in cutting-edge areas such as THz generation, nonlinear optics, and high-power laser–matter interaction. A life member of several prestigious scientific societies, including the Indian Science Congress Association, Optical Society of India, and Plasma Science Society of India, he also serves on editorial and review boards of international journals and as a peer reviewer for top publishers like Elsevier, IOP, and AIP. His work has been recognized with multiple honors, including the Merit Award (2024) by the University of Allahabad, Research Excellence Awards (2020, 2021), and the Outstanding Scientist Award (2020). With active international collaborations across the UK, Czech Republic, South Korea, and the USA, Dr. Kant’s research continues to advance the frontiers of laser–plasma physics, contributing to innovations in photonics, clean energy, and applied plasma technologies with profound implications for scientific and technological progress.

Featured Publication

Kamboj, O., Azad, T., Rajput, J., & Kant, N. (2025). The effect of density ramp on self-focusing of q-Gaussian laser beam in magnetized plasma. Journal of Optics (India). Citations: 2

Azad, T., Kant, N., & Kamboj, O. (2025). Efficient THz generation by Hermite–cosh–Gaussian lasers in plasma with slanting density modulation. Journal of Optics (India). Citations: 23

Singh, J., Kumar, S., Kant, N., & Rajput, J. (2025). Effect of frequency-chirped ionization laser on accelerated electron beam characteristics in plasma wakefield acceleration. European Physical Journal Plus. Citations: 1

Anshal, L., Kant, N., Azad, T., Rajput, J., & Kamboj, O. (2025). Propagation of Hermite–cosh–Gaussian laser beam in free-electron laser device under upward plasma density ramp. Laser Physics Letters. Citations: 1

Azad, T., Kant, N., & Kamboj, O. (2025). Enhanced third harmonic generation and SRS suppression in magnetized rippled plasma using Hermite cosh–Gaussian laser beam. Journal of Optics (India). Citations: 2

Prof. Dr. Niti Kant’s pioneering research in laser–plasma interaction, nonlinear optics, and terahertz generation has advanced the understanding of high-power laser applications, enabling innovations in photonics, clean energy, and next-generation communication technologies. His work bridges fundamental physics with practical technologies, fostering global scientific collaboration and contributing to sustainable technological progress.

srividhya chandran | climate finance | Best Researcher Award

Published on by Computer Science Engineering

Ms. srividhya chandran | climate finance | Best Researcher Award

Research Scholar | Bharathiar University | India

C. Srividhya is a Research Scholar in the Department of Commerce at Bharathiar University, Coimbatore, specializing in climate change economics, financial inclusion, and fintech innovation. Her research integrates sustainability, artificial intelligence, and finance to address global economic and environmental challenges. She has published several articles in reputed international journals, including the International Journal of Engineering Development and Research, International Journal of Advanced Research in Science, Communication and Technology, and International Journal of Progressive Research in Engineering Management and Science. Her notable works such as “AI-Driven Decarbonization: A Machine Learning Framework for Optimising Climate Mitigation Strategies” and “Climate Change Risk and Firm Risk: A Bibliometric Analysis Using Biblioshiny” reflect her interdisciplinary approach and contribution to climate and financial research. Srividhya has participated in more than seventeen national and international conferences organized by prominent institutions like Bharathiar University, Rathinam College, Bishop Heber College, and IIM Jammu, presenting and publishing impactful papers in collaboration with Dr. M. Nirmala on topics such as fintech adoption, women entrepreneurship, and economic transformation. Holding an M.Com in Finance and Accounting with distinction from Bharathiar University and a B.Com (Professional Accounting) from Karunya Institute of Technology and Sciences, she has also completed advanced research methodology and entrepreneurship development programs sponsored by ICSSR and DST. Her research aims to harness the potential of digital finance and artificial intelligence to promote sustainability, inclusive growth, and economic resilience, establishing her as a promising young scholar contributing meaningfully to the intersection of finance, technology, and climate policy.

Featured Publication

Srividhya, C. (2025). Understanding the integration of climate change risk and corporate financial risk: A scientometric analysis. International Journal of Advanced Research in Science, Communication and Technology, 5(1), 45–56.

Srividhya’s research bridges the gap between finance, technology, and sustainability by integrating artificial intelligence into climate risk assessment and financial decision-making. Her work supports evidence-based policies for sustainable economic growth, promoting resilience, inclusivity, and innovation in global financial systems.

Muhammad Asif Munir | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Mr. Muhammad Asif Munir | Machine Learning | Best Researcher Award

Assistant Professor| Swedish College of Engineering and Technology | Pakistan

Dr. Muhammad Asif Munir is an accomplished researcher and academic in the field of Electrical Engineering, currently serving as an Assistant Professor at the Swedish College of Engineering and Technology, District Rahim Yar Khan, Punjab, Pakistan, and pursuing his Ph.D. at The Islamia University of Bahawalpur. His research primarily focuses on machine learning and deep learning applications in biomedical image analysis, with a particular emphasis on addressing the challenges of small and imbalanced radiomics datasets. With six peer-reviewed publications indexed in SCI and Scopus journals, including IEEE Access and Future Internet (MDPI), and a growing citation record of 56 citations (h-index: 4, i10-index: 2), Dr. Munir has demonstrated consistent academic excellence and research innovation. His notable contribution, the GSRA-KL framework, introduces a novel sparse regularized autoencoder–based methodology that significantly enhances synthetic data generation and improves the predictive accuracy of gene mutation analysis in lung cancer radiomics. This work not only contributes to the evolution of precision oncology but also exemplifies the integration of AI-driven data synthesis with clinical applications. His ongoing research explores the incorporation of explainable artificial intelligence (XAI) into radiomics for more interpretable, transparent, and reliable predictive modeling, fostering clinically explainable AI systems in healthcare. Dr. Munir’s interdisciplinary approach bridges data science, medical imaging, and clinical decision support, aiming to make AI tools both scientifically robust and ethically transparent. A member of professional organizations such as IEEE and IAENG, he remains actively engaged in promoting research collaboration and advancing the global discourse on intelligent healthcare systems. Through his scholarly contributions, Dr. Munir is significantly impacting the development of data-efficient, interpretable, and patient-centered AI frameworks, reinforcing the global transition toward smart healthcare technologies and next-generation precision medicine. His commitment to research excellence and translational impact continues to position him as a promising figure in the convergence of engineering and medical AI research.

Featured Publication

Aslam, M. A., Munir, M. A., & Cui, D. (2020). Noise removal from medical images using hybrid filters of technique. Journal of Physics: Conference Series, 1518(1), 012061.

Aslam, M. A., Xue, C., Wang, K., Chen, Y., Zhang, A., Cai, W., Ma, L., Yang, Y., Sun, X., & Munir, M. A. (2020). SVM based classification and prediction system for gastric cancer using dominant features of saliva. Nano Biomedicine and Engineering, 12(1), 1–13.

Munir, M. A., Aslam, M. A., Shafique, M., Ahmed, R., & Mehmood, Z. (2022). Deep stacked sparse autoencoders – A breast cancer classifier. Mehran University Research Journal of Engineering and Technology, 41(1), 41–52.

Aslam, M. A., Munir, M. A., Ahmad, R., Samiullah, M., Hassan, N. M., & Mahnoor, S. (2022). Deep neural networks for prediction of cardiovascular diseases. Nano Biomedicine and Engineering, 14(1).

Munir, M. A., Shah, R. A., Ali, M., Laghari, A. A., Almadhor, A., & Gadekallu, T. R. (2024). Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis. IEEE Access.

Dr. Muhammad Asif Munir’s research advances intelligent healthcare by integrating machine learning and explainable AI to enhance diagnostic accuracy and transparency in medical imaging. His innovations in radiomics and synthetic data generation foster data-efficient, interpretable, and globally applicable solutions that strengthen precision oncology and next-generation healthcare systems.

Bincy Baburaj Kaluvilla | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Dr. Bincy Baburaj Kaluvilla | Machine Learning | Best Researcher Award

Head of Academics | Learners University College | United Arab Emirates

Dr. Bincy B. Kaluvilla is an accomplished academic and researcher specializing in sustainable finance, investment management, and hospitality education, with a particular emphasis on integrating environmental, social, and governance (ESG) principles into financial and hospitality frameworks. She currently serves as Head of Academics and Partnerships at Learners University College, UAE, and previously worked as Assistant Professor and Undergraduate Program Manager at the Emirates Academy of Hospitality Management, where she played a central role in program leadership, faculty coordination, and industry collaboration. Holding a Ph.D. in Accounting from the University of Leicester, an M.Res in Accounting and Finance from the University of Glasgow, and professional recognition as a Fellow of the Higher Education Academy (UK) and CPA Australia, Dr. Kaluvilla combines strong academic foundations with practical insight. Her research encompasses real estate finance, green finance, ESG reporting, and digital transformation in hospitality, contributing over fifteen peer-reviewed publications and book chapters in leading journals such as Frontiers in Computer Science, Asia Pacific Journal of Tourism Research, and Library Hi Tech News, with growing citation impact across Scopus and Web of Science databases. She has authored chapters for major publishers including Springer Nature, Emerald, IGI Global, and Apple Academic Press, addressing emerging issues in sustainable investment, digital currencies, and responsible finance. Her academic influence extends globally through conference presentations at EuroCHRIE in Vienna, GHLS in Dubai, and IPoE in the UAE. Beyond research, she has led significant corporate training initiatives with the Jumeirah Group, Omran Group, and the UAE Ministry of Foreign Affairs, advancing professional development and gender empowerment within the hospitality industry. Through her research, teaching, and leadership, Dr. Kaluvilla continues to advance global understanding of sustainable finance and investment practices, fostering stronger links between academia, industry, and community development.

Featured Publication

Fahad, Z., Kaluvilla, B. B., & Mulla, T. (2024). Embracing the new era: Artificial intelligence and its multifaceted impact on the hospitality industry. Journal of Open Innovation: Technology, Market, and Complexity, 10(4), 100390.

Ghazanfar, U., Kaluvilla, B. B., & Zahidi, F. (2023). The post-COVID emergence of dark kitchens: A qualitative analysis of acceptance and the advantages and challenges. Research in Hospitality Management, 13(1), 23–30.

Kaluvilla, B. B. (2024). Cultural preservation through technology in UAE libraries. Library Hi Tech News, 41(8), 6–9.

Kalarikkal, S. A., Thamilvannan, G., & Kaluvilla, B. B. (2024). Enhancing access to missionary archives: The role of digital libraries and online repositories. Library Hi Tech News.

Kaluvilla, B. B., Mulla, T., Zahidi, F., & Wondirad, A. (2024). Driving sustainable choices through understanding consumer behaviour and underlying factors that influence the purchasing intention of refurbished furniture. SSRN Electronic Journal.

Abazar Asghari | High Performance Computing | Best Researcher Award

Published on by Computer Science Engineering

Assoc. Prof. Dr . Abazar Asghari | High Performance Computing | Best Researcher Award

SCBFs Structures| University of Tehran |Iran

Dr. Abazar Asghari is an Associate Professor of Structural Engineering at the Faculty of Civil Engineering, Urmia University of Technology, Iran. He received his Ph.D. in Structural Engineering from Tehran University in 2002, focusing on the determination of ultimate loads and possible failure lines for continuous media using adaptive finite element methods. With more than three decades of academic, research, and technical experience, Dr. Asghari has made distinguished contributions to the areas of steel structure design, seismic performance evaluation, and nonlinear structural analysis. He has authored and co-authored over 25 scientific papers published in reputable international and national journals, including the Journal of Constructional Steel Research, Scientia Iranica, Neural Computing and Applications, and Structure and Infrastructure Engineering. His collaborative research with scholars such as Amir H. Gandomi and Saeed Saharkhizan has introduced innovative methodologies in seismic modeling, hybrid computational approaches, and performance-based structural design. Dr. Asghari is also the author of several highly regarded textbooks and design guides, including the multi-volume series Dynamics of Structures and Steel Structures Design, which are widely used in Iranian engineering education and practice. Beyond academia, he has played a major role in developing the Iranian National Building Code, serving as a technical committee member and primary text supplier for Chapter 10 on the design and construction of steel buildings. He currently chairs the Sub-Committee on Loads and Pressures (ISIRI/TC98/SC2) at the National Iranian Standards Organization and serves on the Board of Directors of the Iranian Society of Steel and Structures. Through his extensive research, teaching, and standardization work, Dr. Asghari has significantly contributed to advancing structural safety, seismic resilience, and sustainable engineering practices both in Iran and internationally.

Featured Publication

Gandomi, A. H., Faramarzifar, A., Rezaee, P. G., Asghari, A., & Talatahari, S. (2015). New design equations for elastic modulus of concrete using multi expression programming. Journal of Civil Engineering and Management, 21(6), 761–774. Cited by: 83

Asghari, A., & Saharkhizan, S. (2019). Seismic design and performance evaluation of steel frames with knee-element connections. Journal of Constructional Steel Research, 154, 161–176. Cited by: 49

Asghari, A., & Gandomi, A. H. (2016). Ductility reduction factor and collapse mechanism evaluation of a new steel knee braced frame. Structure and Infrastructure Engineering, 12(2), 239–255. Cited by: 33

Aminian, P., Javid, M. R., Asghari, A., Gandomi, A. H., & Esmaeili, M. A. (2011). A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method. Neural Computing and Applications, 20(8), 1321–1332. Cited by: 32

Jaberi, V., & Asghari, A. (2022). Seismic behavior of linked column system as a steel lateral force resisting system. Journal of Constructional Steel Research, 196, 107428. Cited by: 26

Md. Habibullah Shakib | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Mr. Md. Habibullah Shakib | Machine Learning | Best Researcher Award

Researcher| World University of Bangladesh| Bangladesh

Mr. Md. Habibullah Shakib is an emerging researcher and analyst from Bangladesh with over 3.5 years of research experience in artificial intelligence, supervised and deep learning, genetic AI, and foundation models. He holds a Bachelor of Science in Computer Science and Engineering from the World University of Bangladesh and a Diploma in Computer Technology from the National Polytechnic Institute. His research focuses on developing intelligent and secure computing systems, with significant contributions to Android malware detection, federated learning, autonomous systems, and IoT-based smart home automation. Among his key projects are the Active Federated YOLOR Model for enhancing autonomous vehicle safety, deep learning and genetic AI approaches for Android malware detection, and the integration of Conformer, Active Learning, and Federated Learning models for encrypted malware traffic detection. His ongoing work on Autonomous Generative AI for Android malware detection reflects his interest in advancing cutting-edge AI-driven cybersecurity solutions. Recognized for his scholarly engagement, he received a Certificate of Reviewing from the Information Processing and Management journal (Elsevier, 2024). He has built a growing academic presence with profiles on Google Scholar, ORCID, SSRN, GitHub, and the AD Scientific Index. Fluent in Bangla and English, he combines strong analytical and organizational skills with a commitment to innovation and teamwork. Through his dedication to ethical AI development, quantitative data analysis, and research collaboration, Md. Habibullah Shakib aims to contribute globally to the progress of intelligent systems, data-driven decision-making, and digital security for sustainable technological advancement.

Featured Publication

Shakib, M. (2023). Android malware detection approach based on genetic AI, CNN, RNN, LSTM, GRU, and active learning. SSRN. Cited by: 1

Shakib, M. H., Yeasin, M., Rahman, M. H., Rahman, K. M., Hossain, S., & Mahi, F. F. (2025). Active learning model used for Android malware detection. Machine Learning with Applications, 100680. Cited by: 8

Shakib, M. D. H. (2024). Android malware detection using transformer and encoder models. SSRN. Cited by: 5

Shakib, M. H. (2024). Comparing conformer, genetic artificial intelligence conformer, and active learning conformer approaches for encrypted Android malware traffic detection. SSRN. Cited by: 4

Pascal Vollenweider | Software Engineering | Best Researcher Award

Published on by Computer Science Engineering

Mr. Pascal Vollenweider| Software Engineering | Best Researcher Award

Biomechanical Engineer | Straumann Group| Switzerland

Mr. Pascal Vollenweider is a highly skilled biomechanical engineer whose work bridges mechanical engineering and biomedical science, with a strong focus on orthodontic biomechanics and laser-based material processing. He currently serves as a Biomechanical Engineer at Ortho RDI, Institut Straumann AG in Basel, where he specializes in in-vitro testing of clear aligners and the development of advanced experimental methods. He holds a Master of Science in Life Science with a specialization in Biomedical Engineering from FHNW Muttenz (2020–2023), where his thesis on the Biomechanical Investigation of Orthodontic Tooth Movements Induced by Clear Aligners earned the highest distinction, demonstrating his ability to integrate engineering precision with biological understanding to enhance clinical orthodontic applications. His earlier academic training includes a Bachelor of Science in Mechanical Engineering from FHNW Brugg-Windisch, with a focus on Production Engineering, where his research on Laser Machining of PEEK for Security Features on Surfaces reflected his proficiency in high-precision manufacturing and materials science. Professionally, he has contributed to the Institute for Product and Production Engineering at FHNW, engaging in laser-based surface structuring, supervising student research, and supporting industrial collaborations. His multidisciplinary expertise enables him to contribute to the development of next-generation orthodontic devices and biomedical solutions that combine mechanical innovation with patient-centered design. Fluent in German, English, and French, Vollenweider demonstrates strong potential for global collaboration and continued research excellence. His work embodies a commitment to innovation, scientific rigor, and societal benefit through engineering-driven advancements in healthcare technology.

Featured Publication

Vollenweider, P. (2023). Validation of the quantitative case analysis method for measuring orthodontic tooth movement. Journal of Biomedical Engineering and Orthodontic Research, 15(2), 145–156.

Hawazin Elani | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Dr. Hawazin Elani | Machine Learning | Best Researcher Award

Harvard University | United States

Dr. Hawazin W. Elani, Ph.D., is an accomplished scholar and academic leader whose research integrates dentistry, epidemiology, and health policy to advance oral health equity through data-driven, interdisciplinary approaches. She serves as an Associate Professor in the Department of Health Policy and Management at the Harvard T.H. Chan School of Public Health and in the Department of Oral Health Policy and Epidemiology at the Harvard School of Dental Medicine, with additional affiliations at the Harvard Data Science Initiative and the Kempner Institute for the Study of Natural and Artificial Intelligence. Dr. Elani earned her Ph.D. in Dental Sciences with a concentration in Epidemiology and Population Health and an M.Sc. from McGill University, as well as an MMSc in Oral Biology and a Clinical Certificate in Prosthodontics from Harvard. Her research explores health disparities, oral health policy, and the application of artificial intelligence and machine learning in predicting oral health outcomes. She has authored over 30 peer-reviewed publications in high-impact journals such as Health Services Research, JAMA Network Open, and Journal of Dental Research, with her work cited widely for shaping discussions on healthcare access and reform. As principal investigator on multiple NIH and foundation-funded projects, including R01 and K-series grants, she has led innovative studies assessing the effects of Medicaid expansion and socioeconomic factors on dental care utilization. Recognized with Harvard’s Young Mentor Award and Distinguished Junior Faculty Award in 2024, Dr. Elani also contributes to national and international committees, including the NIH, the National Academies of Sciences, and the Medicaid Policy Research Advisory Group. Through her leadership, global collaborations, and dedication to mentoring, she continues to advance the intersection of artificial intelligence, population health, and oral health policy, driving forward equitable and sustainable improvements in healthcare delivery worldwide.

Profiles: Scopus | ORCID
Featured Publication

lani, H. W., Kawachi, I., & Sommers, B. D. (2020). Changes in emergency department dental visits after Medicaid expansion. Health Services Research, 55(1), 76–84.

Elani, H. W., Simon, L., Ticku, S., Bain, P. A., Barrow, J., & Riedy, C. A. (2018). Does providing dental services reduce overall health care costs? A systematic review of the literature. Journal of the American Dental Association (1939), 149(6), 430–438.e10.

Elani, H. W., Starr, J. R., Da Silva, J. D., & Gallucci, G. O. (2018). Trends in dental implant use in the U.S., 1999–2016, and projections to 2026. Journal of Dental Research, 97(13), 1424–1430.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., Cleber, S., & Bittner, N. (2019). Comparison of the color appearance of peri-implant soft tissue with natural gingiva using anodized pink-neck implants and pink abutments: A prospective clinical trial. The International Journal of Oral & Maxillofacial Implants, 34(1), 168–175.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., & Bittner, N. (2017). A prospective clinical trial to assess the optical efficacy of pink neck implants and pink abutments on soft tissue esthetics. Journal of Esthetic and Restorative Dentistry, 29(3), 213–219.

Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Published on by Computer Science Engineering

Dr. Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Libero professionista | Consiglio Nazionale delle RicercheThis link is disabled | Italy

Dr. Christian Peluso is a researcher specializing in artificial intelligence with expertise in federated learning, deep learning, and cybersecurity, focusing on privacy-preserving systems for mobile and distributed environments. His research aims to develop intelligent models capable of processing complex and varied data while safeguarding user privacy and ensuring compliance with data protection regulations. Christian earned his Master’s degree in Artificial Intelligence from the University of Pisa with the highest distinction, presenting a thesis titled PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection, which introduced an innovative federated learning approach for mobile malware analysis using convolutional neural networks optimized for image-based data. He has actively collaborated with the Consiglio Nazionale delle Ricerche (CNR) and several academic and research institutions, contributing to projects that merge AI, cybersecurity, and data privacy. His publications, including “PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection” and “An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning,” reflect his deep involvement in advancing secure and decentralized AI solutions. He has also contributed to research on explainability-driven malware analysis using deep learning, aimed at improving model interpretability and aiding analysts in identifying malicious software components efficiently. Christian’s technical proficiency covers Python, machine learning frameworks, and reverse engineering methodologies, enabling him to design intelligent systems with strong analytical and practical impact. His academic achievements and professional experiences in software engineering, mobile application security, and AI-driven analysis demonstrate a consistent pursuit of excellence and innovation. His work not only strengthens theoretical understanding in federated machine learning but also delivers practical tools for protecting digital ecosystems. Through his commitment to research, collaboration, and ethical AI development, he continues to contribute meaningfully to the evolving landscape of artificial intelligence and data security. 17 Citations 3 Documents 2 h-index View h-index

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Iadarola, G., Casolare, R., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2021). A semi-automated explainability-driven approach for malware analysis through deep learning. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. Cited by: 19

Ciaramella, G., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2024). An approach for privacy-preserving mobile malware detection through federated machine learning. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024). SciTePress.
Cited by: 5

Peluso, C., Ciaramella, G., Mercaldo, F., Santone, A., & Martinelli, F. (2024). A federated learning-based Android malware detector through differential privacy. In International Conference on Computer Aided Systems Theory (EUROCAST 2024) (pp. 307–319).

Daniel Atnafu Chekole | Computational Theory | Best Researcher Award

Published on by Computer Science Engineering

Mr. Daniel Atnafu Chekole | Computational Theory | Best Researcher Award

Researcher | Space Science and Geospatial Institute | Ethiopia

Mr. Daniel Chekole specializes in atmospheric and space physics, with focused expertise in ionospheric modeling, space weather forecasting, and heliospheric studies. His research integrates ground-based and satellite data to investigate ionospheric dynamics, magnetospheric processes, and their coupling with solar-terrestrial interactions. His scientific contributions emphasize the development and validation of regional ionospheric and atmospheric models using advanced computational methods and machine learning algorithms. Daniel has played a leading role in projects such as the development of regional HF propagation and ionospheric models, prediction of solar energetic particle flux using artificial intelligence, and the establishment of monitoring systems like the Mini-Neutron Monitor. His scholarly work explores low-frequency plasma waves, magnetohydrodynamic instabilities, and the effects of rotation and self-gravity in plasma environments, contributing to the understanding of astrophysical and geophysical plasma systems. Through publications in reputed journals, he has analyzed the performance of ionospheric models such as NeQuick-2 and IRI-Plas over East Africa, evaluated solar and geomagnetic activity indices, and examined storm-time ionospheric irregularities. His technical proficiency spans MATLAB, Python, and MHD simulation tools, which he applies in the modeling and forecasting of space weather phenomena relevant to communication and navigation systems. Daniel’s participation in international workshops and collaborations with institutions such as NASA, UCAR/CPAESS, and DLR reflects a strong engagement in the global heliophysics and space science community. His ongoing work continues to contribute to regional and international initiatives aimed at enhancing predictive capabilities for solar-terrestrial disturbances and improving understanding of ionospheric variability over equatorial regions. Daniel Chekole’s research contributions are reflected in 17 citations, 5 documents, and an h-index of 2 (View h-index).

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Chekole, D. A., Giday, N. M., & Nigussie, M. (2019). Performance of NeQuick-2, IRI-Plas 2017 and GIM models over Ethiopia during varying solar activity periods. Journal of Atmospheric and Solar-Terrestrial Physics, 195, 105117. Cited by 14.

Moges, S. T., Giday, N. M., Chekole, D. A., Ulich, T., & Sherstyukov, R. O. (2022). Storm-time observations of traveling ionospheric disturbances and ionospheric irregularities in East Africa. Radio Science, e2022RS007426. Cited by 7.

Strauss, R. D., Giday, N. M., Seba, E. B., Chekole, D. A., Garuma, G. F., Kassa, B. H., & others. (2023). First results from the ENTOTO neutron monitor: Quantifying the waiting time distribution. Advances in Space Research, 72(3), 805–815. Cited by 5.

Garuma, G. F., Tessema, S. B., Tiky, A. Y., Addis, Z. W., Adde, Y. A., Giday, N. M., & others. (2022). First Ethiopian Remote Sensing Satellite (ETRSS-1): Mission information and overview. Authorea Preprints. Cited by 5.

Chekole, D. A., & Giday, N. M. (2020). Evaluation of ionospheric and solar proxy indices for IRI-Plas 2017 model over the East African equatorial region during solar cycle 24. Advances in Space Research, 66(3), 604–611. Cited by 3.