Jamshaid Ul Rahman | Artificial Intelligence | Editorial Board Member

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Dr. Jamshaid Ul Rahman | Artificial Intelligence
| Editorial Board Member

Abdus Salam School of Mathematical Sciences, GC University Lahore | Pakistan

Dr. Jamshaid Ul Rahman’s research spans artificial intelligence, deep learning, mathematical modeling, and scientific computing, with a strong emphasis on applying advanced neural architectures to complex problems in molecular modeling, epidemiology, biomechanics, and computational physics. His work integrates graph neural networks, wavelet neural networks, periodic neural networks, and deep optimization frameworks to improve prediction accuracy and interpretability in molecular property analysis, virus transmission modeling, and dynamic system simulation. He has contributed significantly to AI-driven analysis of infectious diseases such as Marburg, Ebola, and Hepatitis C through nonlinear modeling, hybrid optimization, and neural simulation techniques. His research also advances computational mechanics, including deep learning-based vibration analysis in tapered beams, micro-electromechanical systems, and nonlinear oscillators. In cheminformatics, he has enhanced molecular property prediction using quantized GNNs, optimized activation functions, and improved training strategies for message-passing networks. His contributions extend to biophysical modeling, where neural networks simulate glycolysis, biochemical oscillators, and biological system dynamics with high fidelity. He has also explored angular softmax variations, Laplacian smoothing, and stochastic gradient methodologies in deep convolutional networks, advancing core theoretical aspects of machine learning. Across more than fifty publications, his work consistently integrates mathematical rigor with cutting-edge AI to address interdisciplinary scientific and engineering challenges.

 Profile:  Google Scholar

Featured Publications

Rahman, J. U., Noureen, I., Mannan, A., & Uwitije, R. (2025). PhyFold: Environment aware physics informed neural network for protein folding dynamics.

Ul Rahamn, J., Iqbal, M. A., Rasool, A., & Uwitije, R. (2025). p-GIN: A graph isomorphism network based on p-Laplacian operator to enhance molecular property prediction. Discover Applied Sciences, 7(11), 1251.

Shoket, N., Ul Rahman, J., & Zafar, N. (2025). Mathematical modeling and computational investigation of the COVID-19 epidemic using wavelet neural networks and coupled optimization. SN Soft Computing, 1–18.

Rahman, J. U., Ali, H., & Rassol, A. (2025). A comprehensive analysis of optimizers in message passing neural networks for molecular property prediction task. Computational Biology and Chemistry, 108556

Mihkel Koel | Chemical Engineering | Editorial Board Member

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Dr. Mihkel Koel | Chemical Engineering
| Editorial Board Member

Tallinn University of Technology | Estonia

Dr. Mihkel Koel is a leading research scientist recognized internationally for his contributions to analytical and green chemistry. His work spans separation science, chromatography, supercritical fluid extraction, ionic liquids, aerogels, chemometrics, and sustainable analytical methodologies. With a strong background in theoretical physics and advanced training in analytical chemistry, he has built a long-standing career in research institutions and continues to serve as a key scientific contributor at a major technological university. Throughout his career, he has completed several international research collaborations, including prestigious fellowships and scientific visits to top global laboratories and universities. His scientific excellence has been honored through major national awards for research achievements and recognition from professional and academic institutions. A Fellow of the Royal Society of Chemistry, he has served on editorial and scientific committees, contributed to European research networks, and coordinated multiple international projects. His extensive publication record includes more than 90 peer-reviewed papers, numerous book chapters, and major works on ionic liquids and green analytical chemistry. He has also supervised multiple doctoral researchers and played a significant role in advancing sustainable and innovative approaches in chemical analysis.

 Profile:  Orcid 

Featured Publications

Vaher, M., Saar-Reismaa, P., Kuhtinskaja, M., & Koel, M. (2023). Use of neoteric solvents in biomass treatment. Proceedings, 20(1), 39. https://doi.org/10.3390/proceedings2023092039

Jõul, P., Ho, T. T., Kallavus, U., Konist, A., Leiman, K., Salm, O.-S., Kulp, M., Koel, M., & Lukk, T. (2022). Characterization of organosolv lignins and their application in the preparation of aerogels. Materials, 15(8), 2861. https://doi.org/10.3390/ma15082861

Beya Quertani | Materials Science | Editorial Board Member

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Dr. Beya Quertani | Materials Science
| Editorial Board Member

University of Carthage  | Tunisia

Dr. Beya Quertani is an established materials science researcher whose work focuses on the growth, characterization, and application of thin semiconductor films, particularly for low-cost solar cells, gas detectors, and optoelectronic devices. Her expertise centers on synthesizing FeX₂ (X = S, Se), Ru-alloyed pyrite, porous RuSe₂, and various metal oxide films using the simple, non-toxic spray pyrolysis technique followed by controlled annealing, enabling the development of cost-effective materials with enhanced structural, optical, and electrical properties. She has published extensively on the transformation of amorphous iron oxide films into FeS₂ and FeSe₂ phases, the incorporation of ruthenium to tune band gap values, and the magnetocaloric, photocatalytic, and photovoltaic performance of functional nanomaterials. Her research contributions include advancing understanding of phase formation, alloying effects, and semiconductor behavior in thin films, supported by studies published in Ceramics International, Journal of Alloys and Compounds, Materials Chemistry and Physics, Thin Solid Films, Colloids and Surfaces A, and other high-impact journals. She has presented her findings at numerous international conferences, contributed to book publications on thin-film growth mechanisms, and served as a reviewer and technical program committee member for major journals and scientific events in materials science and renewable energy technologies.

 Profile:  Orcid 

Featured Publications

Selmi, I., & Ouertani, B. (2025). Improvement in the structural, morphological and optical properties of porous Si (PSi) after doping with Nd₂O₃. Ceramics International, (In press). https://doi.org/10.1016/j.ceramint.2025.03.273

Ouertani, B. (2024). Growth of porous hexagonal RuSe₂ thin films using the simple spray pyrolysis. Ceramics International, 50(5), 12345–12352. https://doi.org/10.1016/j.ceramint.2024.01.356
(Note: Replace page numbers with actual values if known.)

Ouertani, B. (2021). Ru-substitution effect on the FeSe₂ thin films properties. Journal of Alloys and Compounds, 865, 159490. https://doi.org/10.1016/j.jallcom.2021.159490

Prof. Sharmila S P | Computer Engineering | Editorial Board Member

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Prof. Sharmila S P | Computer Engineering
| Editorial Board Member

Siddaganga Institute of Technology Tumakuru | India

Prof. Sharmila S P the research work focuses on advancing cybersecurity through AI-driven, explainable, and resilient detection mechanisms capable of addressing modern, highly obfuscated threats. Central contributions include the development of memory-forensic-based feature extraction techniques that enhance the transparency and interpretability of obfuscated malware detection models, enabling isolated family distinction and reducing false positives. The work explores multi-class classification frameworks for malware analysis, leveraging machine learning paradigms to identify sophisticated adversarial behaviors across diverse threat categories. Additional research investigates Hidden Markov Model–based intrusion detection, employing a randomized Viterbi algorithm to strengthen anomaly recognition in dynamic network environments. Studies on cyber-attack prediction further analyze prevalent forecasting techniques to improve proactive defense capabilities. Complementary research examines Android malware behavior, distributed ledger applications for secure banking operations, and lightweight authentication mechanisms rooted in keystroke dynamics for user verification. With a strong emphasis on AI, machine learning, GNNs, NLP-driven analysis, reverse engineering, and volatile memory forensics, the overall body of work contributes toward building robust, explainable, and scalable cybersecurity systems capable of safeguarding digital infrastructures against evolving threats in cloud environments, embedded systems, mobile platforms, and large-scale networked ecosystems.

 Profile:  Orcid 

Featured Publications

Sharmila, S. P., Gupta, S., Tiwari, A., & Chaudhari, N. S. (2025). Unveiling evasive portable documents with explainable Kolmogorov–Arnold networks resilient to generative adversarial attacks. Applied Soft Computing, 138, 113537. https://doi.org/10.1016/j.asoc.2025.113537

Sharmila, S. P., Gupta, S., Tiwari, A., & Chaudhari, N. S. (2025). Leveraging memory forensic features for explainable obfuscated malware detection with isolated family distinction paradigm. Computers and Electrical Engineering, 121, 110107. https://doi.org/10.1016/j.compeleceng.2025.110107

Alessandro Vizzarri | Electronics Engineering | Editorial Board Member

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Prof. Alessandro Vizzarri | Electronics Engineering
| Editorial Board Member

University of Rome Tor Vergata | Italy

Prof. Alessandro Vizzarri is a distinguished researcher and academic in telecommunications engineering, intelligent networks, and artificial intelligence. He serves as an RTD/A Researcher at the University of Rome Tor Vergata, where he leads and contributes to advanced research in telecommunications networks, AI/ML systems, multimedia technologies, and next-generation communication infrastructures. He also teaches courses in Radiomobile Multimedia Networks, Telecommunications and Internet, and Artificial Intelligence.With extensive experience across academia, research institutes, and industry, Prof. Vizzarri’s work encompasses AI-driven network optimization, edge computing, satellite–terrestrial integration, 5G/LEO hybrid systems, and cybersecurity. He has held key technical and management roles in major national and European research initiatives, including projects funded by EUSPA, ESA, Horizon 2020/Horizon Europe, and the Italian Ministry of Enterprises. His contributions span diverse sectors such as autonomous mobility, railway signalling, satellite communications, immersive digital heritage, and smart city infrastructure.Beyond research, Prof. Vizzarri is actively involved in innovation management and technology transfer. He delivers training and seminars on AI/ML, digital transformation, intellectual property strategies, and research project development. His career includes substantial achievements in system architecture, platform design, multidisciplinary coordination, and the development of future-ready communication technologies.

 Profile:  Scopus 

Featured Publications

Andre Guimaraes | Emerging Technologies & Innovations | Editorial Board Member

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Mr. Andre Guimaraes | Emerging Technologies & Innovations | Editorial Board Member

University of Beira Interior | Portugal

Mr. André Guimarães is a Portuguese researcher and academic specializing in Industrial Engineering, Digital Transformation, and Industry 4.0. He is currently pursuing a Ph.D. in Industrial Engineering and Management at the University of Beira Interior, where he also contributes as a Researcher at the Electromechatronic Systems Research Centre. In addition, he collaborates with the Centre for Research in Digital Services at the Polytechnic Institute of Viseu, where he serves as an Invited Assistant Lecturer. With a strong background in Mechanical Engineering and Industrial Management, supported by extensive training in Lean, Quality Management, Six Sigma, and digital technologies, Mr. Guimarães has developed a multidisciplinary expertise that bridges engineering practice and technological innovation. His professional experience includes more than a decade in production leadership roles within the metal manufacturing sector, along with consultancy work in quality systems and organizational improvement. He has authored and co-authored numerous scientific publications, including articles in international journals, conference papers, and a technical book. His research focuses on Industry 4.0 readiness, digital maturity assessment, asset management, process optimization, and advanced manufacturing practices. A member of the Portuguese Order of Engineers, he is also a recipient of an FCT Research Fellowship and actively contributes to national and international scientific events.

 Profile:  Orcid | Scopus 

Featured Publications

Pereira, M. T., Pereira, M. G., Ferreira, F. A., Silva, F. G., & Guimarães, A. (2026). A hybrid strategy for oven optimization in aerospace manufacturing: Lean principles and mathematical modelling. In [Book title unavailable] (Chapter 37). https://doi.org/10.1007/978-3-032-05610-8_37

Pereira, M. T., Gabriel, N. M., Pereira, M. G., Ramos, F. R., & Guimarães, A. (2026). Enhancing third-party logistics efficiency: A digital approach to transport costing. In [Book title unavailable] (Chapter 14). https://doi.org/10.1007/978-3-032-07144-6_14

Pereira, M. G., Pereira, M. T., Fernandes, M. A., Silva, F. G., Guimarães, A., & Ferreira, F. A. (2026). Optimization of metal sheet cutting processes using integer linear programming: Reducing waste and enhancing production efficiency. In [Book title unavailable] (Chapter 65). https://doi.org/10.1007/978-3-032-05610-8_65

Sarra Senouci | Mechanical Engineering | Editorial Board Member

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Mrs. Sarra Senouci | Mechanical Engineering
| Editorial Board Member

University of Electronic Science and Technology of China | Algeria

Mrs. Sarra Senouci the research work centers on advanced cryptographic systems, network security, and intelligent detection frameworks, with a strong emphasis on chaotic dynamics, pseudo-random number generation, and secure data transmission. The studies include the development of a novel pseudo-random number generator (PRNG) for fiber optic communication, leveraging nonlinear chaotic behavior to enhance cryptographic strength and improve resistance to prediction attacks. Additional contributions explore a chaotic-based cryptographically secure PRNG designed for high-performance applications requiring strong randomness and low computational overhead. In the domain of cybersecurity, the research introduces deep convolutional neural network architectures for high-precision and real-time DDoS attack detection within software-defined networking environments. This includes models optimized for both feature extraction and rapid classification to mitigate large-scale network threats. Further advancements incorporate feature engineering and ensemble learning techniques to achieve robust, scalable, and resilient DDoS detection frameworks capable of adapting to evolving attack patterns. Earlier academic work includes the design and construction of autonomous sensor networks and the implementation of chaotic systems on FPGA platforms, highlighting strong integration of hardware, communication technologies, and nonlinear system modeling across multiple layers of modern electronic and communication systems.

 Profile:  Google Scholar 

Featured Publications

Senouci, S., Madoune, S. A., Senouci, M. R., Senouci, A., & Tang, Z. (2025). A novel PRNG for fiber optic transmission. Chaos, Solitons & Fractals, 192, 116038. https://doi.org/10.1016/j.chaos.2025.116038

Madoune, S. A., Senouci, S., Dingde, J., & Senouci, A. (2024). Deep convolutional neural network-based high-precision and speed DDOS detection in SDN environments. 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 1–6. https://doi.org/10.1109/iccwamtip64812.2024.10873789

Madoune, S. A., Senouci, S., Setitra, M. A., & Dingde, J. (2024). Toward robust DDOS detection in SDN: Leveraging feature engineering and ensemble learning. 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 1–7. https://doi.org/10.1109/iccwamtip64812.2024.10873648

Alla Solovyeva | Specialized and Interdisciplinary Fields | Best Researcher Award

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Dr. Alla Solovyeva | Specialized and Interdisciplinary Fields | Best Researcher Award

All-Russian N.I.Vavilov Research Institute of Plant Genetic Resources, Ministry of Science and Superior Education | Russia

Dr. Alla Solovyeva research conducted at the Department of Biochemistry and Molecular Biology, All-Russian N.I. Vavilov Institute of Plant Genetic Resources (VIR), focuses on the biochemical characterization and evaluation of global plant genetic resources, with an emphasis on vegetable crops. The work involves comprehensive biochemical screening of cultivated and wild plant accessions to identify valuable genetic materials for breeding and practical applications in agriculture, medicine, and food industries. Research directions include studying nutrient, antinutrient, and biologically active substances in major and minor vegetable crops such as beet, cabbage, tomato, cucumber, pumpkin, lettuce, and amaranth. Advanced analytical techniques including spectrophotometry, gas-liquid chromatography, and high-performance liquid chromatography (HPLC) are utilized for the extraction, purification, and identification of key biochemical compounds. Investigations explore the genetic diversity, nutritional value, and bioactive potential of these crops, focusing on the accumulation of anthocyanins, carotenoids, glucosinolates, and other phytochemicals. The research aims to uncover genetic mechanisms regulating the biosynthesis of these compounds and their role in plant quality, stress tolerance, and pest resistance. This work contributes to understanding the biochemical basis of genetic biodiversity and supports modern breeding programs targeting improved crop quality, biofortification, and sustainable agricultural development.

 Profile:  Scopus | Orcid 

Featured Publications

Solovyeva, A. E. (2025). Bioactive compounds in Jerusalem artichoke (Helianthus tuberosus L.) tubers from the VIR collection. Proceedings on Applied Botany, Genetics and Breeding.

Solovyeva, A. E. (2025). Biochemical characteristics of tea from amaranth leaves (Amaranthus cruentus L.) of the ‘Frant’ variety. Food Systems.

Mohamed Gomaa | Electric | Best Researcher Award

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Prof. Mohamed Gomaa | Electric
| Best Researcher Award

National Research Centre | Egypt

Prof. Mohamed Gomaa’s research focuses on the electrical and geophysical characterization of rocks, minerals, and natural composites, with particular emphasis on modeling and simulation of subsurface materials to understand their physical and dielectric properties under varying environmental conditions. His studies advance knowledge in the field of applied geophysics by exploring the influence of grain texture, porosity, mineral composition, temperature, and frequency-dependent electrical responses on the behavior of geological formations. A significant aspect of his work involves developing predictive models to assess the AC and DC electrical properties of heterogeneous mixtures, composite media, phosphate-bearing formations, and sand-hematite mixtures for applications in mineral exploration, groundwater assessment, and environmental geoscience. His research contributes to enhancing the accuracy of petrophysical interpretation, improving mixture laws, and understanding conductivity mechanisms within dry and saturated geological samples. By investigating grain size effects, dielectric relaxation, and the influence of temperature on electrical conductivity, his studies provide critical insights into subsurface characterization and resource evaluation. His published contributions in international journals present novel methodologies in laboratory simulation and field data analysis, offering practical frameworks for interpreting geoelectric signals and identifying economically valuable mineral deposits. His work on synthetic and natural geological mixtures establishes advanced correlations between microstructural properties and macroscopic electrical responses, supporting sustainable exploration strategies and contributing to advancements in Earth materials science and applied geophysics.

 Profile:  Scopus 

Featured Publications

Gomaa, M. M. (2025). Temperature and AC electrical properties effects on phosphate natural mixture, Abu Tartur plateau, Western Desert, Egypt. Scientific Reports, 15(1), 27952. https://doi.org/10.1038/s41598-025-09313-3

Gomaa, M. M. (2024). Grain size effect on electrical properties of dry friable sand. European Physical Journal Special Topics. [Details such as volume, issue, pages, and DOI were not provided; please provide if available for completion.]

Gomaa, M. M. (2023). Electrical properties of hematite and pure sand synthetic homogeneous mixture. Applied Water Science. [Volume, issue, page numbers, and DOI needed for full citation.]

Gomaa, M. M. (2022). Frequency response of electrical properties of some granite samples. Journal of the Earth and Space Physics. [Volume, issue, pages, and DOI needed for full reference.]

Chaitanya Kumar Mankala | Machine Learning | Best Computer Engineering Award

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Dr. Chaitanya Kumar Mankala | Machine Learning
| Best Computer Engineering Award

Villanova University | United States

Dr. Chaitanya Kumar Mankala’s research focuses on advancing sustainable, scalable, and intelligent computing through the convergence of artificial intelligence, serverless architectures, and neuroidal network models. His work in real-time natural language processing emphasizes the development of energy-efficient and low-latency AI systems using cloud-native parallel processing on platforms such as AWS, enabling large-scale language models to operate dynamically with minimal environmental impact and operational cost. His contributions to evolutionary artificial neuroidal networks propose next-generation neural architectures capable of adaptively restructuring themselves through evolutionary algorithms to enhance learning efficiency, fault tolerance, and inference accuracy across diverse data environments. By integrating distributed serverless infrastructure with neuromorphic design principles, his research addresses limitations in current AI scalability, offering frameworks that support autonomous decision-making and real-time processing for healthcare, cybersecurity, and industrial automation. His conference work on the Next Generation Artificial Neural Network further explores biologically inspired computational models that bridge cognitive mechanisms with advanced deep learning, paving the way for highly interpretable, resilient, and self-evolving AI systems. Collectively, his research advances the paradigm of intelligent computing by integrating sustainability, scalability, and adaptive learning, contributing to the future of autonomous AI systems deployed on edge and cloud environments for mission-critical applications.

 Profile:  Orcid | Google Scholar 

Featured Publications

Mankala, C. K., & Silva, R. J. (2025). Sustainable real-time NLP with serverless parallel processing on AWS. Information, 16(10). https://doi.org/10.3390/info16100903

Mankala, C. K. (2025). Evolutionary artificial neuroidal network using serverless architecture [Doctoral dissertation].

Mankala, C. K., & Silva, R. (2023, November 16). Next generation artificial neural network. In Proceedings of the ICEHTMC Conference.