Dr. Jose Anand A | Embedded Systems | Editorial Board Member

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Dr. Jose Anand A | Embedded Systems | Editorial Board Member

Dr. Jose Anand A | Embedded Systems | KCG College of Technology | India

Dr Jose Anand is a distinguished academic and researcher whose extensive contributions to artificial intelligence, sensing technology, wireless communication security, environmental modeling, and intelligent transportation systems have positioned him as a highly respected figure in the global research community, and throughout his career Dr Jose Anand has demonstrated strong academic leadership, technical excellence, and multidisciplinary innovation. In terms of education, Dr Jose Anand completed his Ph.D. in a leading engineering institution with a specialization in artificial intelligence and its applications to sensing systems, data-driven security, and smart infrastructure, supported by earlier degrees in engineering and computer science that shaped his strong foundational expertise. His professional experience spans teaching, collaborative research, academic leadership roles, and active involvement in interdisciplinary projects where he has contributed extensively to AI-driven mobility optimization, satellite-based agricultural prediction, ethical AI frameworks, security solutions for wireless communication, and explainable artificial intelligence for energy systems. Dr Jose Anand’s research interests extend across intelligent sensing systems, machine learning, graph neural networks, IoT security, environmental data analytics, energy forecasting, anomaly detection, and the broad spectrum of computational models that strengthen safety, efficiency, and sustainability in complex systems. His research skills include advanced data analysis, model development, algorithm design, satellite imagery interpretation, AI-based forecasting, cybersecurity modeling, application-oriented deep learning, and graph-theoretical methods for real-time infrastructure intelligence.

Professional Profiles: Scopus

Featured Publications 

  1. Jose Anand, A. (2025). Soil and crop interaction analysis for yield prediction with satellite imagery and deep learning techniques for the coastal regions. Journal of Environmental Management. 2 citations.

  2. Jose Anand, A. (2025). Artificial intelligence in financial fraud detection. In Book Chapter. 0 citations.

  3. Jose Anand, A. (2025). Ethical considerations and privacy in AI-powered security. In Book Chapter. 0 citations.

  4. Jose Anand, A. (2025). Signature-based security in wireless communication. In Book Chapter. 0 citations.

  5. Jose Anand, A. (2025). Neural networks and graph models for traffic and energy systems. Book. 0 citations.

  6. Jose Anand, A. (2025). Revolutionizing urban traffic mobility with graph neural networks-driven intelligent transportation systems. In Book Chapter. 0 citations.

  7. Jose Anand, A. (2025). Explainable AI for energy demand forecasting. In Book Chapter. 3 citations.

Changran Geng | Radiation | Editorial Board Member

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Assoc Prof Dr. Changran Geng | Radiation | Editorial Board Member

Assoc Prof Dr. Changran Geng | Radiation | Doctor at Nanjing University of Aeronautics and Astronautics | China

Assoc Prof Dr Changran Geng is a distinguished male researcher from Nanjing University of Aeronautics and Astronautics, renowned for his contributions to sensing technology, radiation imaging, Monte Carlo simulation, and boron neutron capture therapy. He completed his Ph.D. at Nanjing University of Aeronautics and Astronautics, where he developed strong foundations in advanced imaging physics and computational radiation analysis. Over his professional career, Assoc Prof Dr Changran Geng has held academic and research roles within interdisciplinary teams focusing on precision sensing systems, Compton-camera design, radionuclide distribution imaging, and nanomaterial-assisted radiotherapy, contributing significantly to international research collaborations and high-impact scientific outputs. His research interests include deep-learning–assisted sensing methodologies, radiation dose modelling, particle imaging reconstruction, neutron capture therapy optimization, and advanced detector characterization. Demonstrating strong research skills, he is proficient in Monte Carlo–based modelling, high-resolution imaging system calibration, radiopharmaceutical assessment, biological effects quantification, and multimodal sensing integration, while maintaining a robust publication portfolio with 99 Scopus-indexed documents, over 900 citations, an h-index of 15 and precision imaging, with leadership in collaborative research networks spanning multiple countries and disciplines. Although not all awards and honors are publicly listed, Assoc Prof Dr Changran Geng has earned recognition through consistent publication impact, collaborative excellence, and contributions to emerging sensing applications in nuclear and biomedical engineering. His expanding body of work demonstrates strong potential for greater global influence as he continues to enhance sensing accuracy, improve radiological imaging frameworks, and drive forward innovations in BNCT-based therapeutic assessment. In conclusion, Assoc Prof Dr Changran Geng stands as a highly accomplished researcher whose interdisciplinary expertise, strong technical skills, international collaborations, and sustained scientific productivity position him as a leading figure in next-generation sensing technology, with a promising trajectory toward further breakthroughs and expanded global impact.

Academic profile: ORCID | Scopus

Featured Publications:

  1. Geng, C. (2026). A deep learning-based approach for precision improvement in Monte Carlo neutronics simulation.

  2. Geng, C. (2025). Characterization of a DOI-corrected Compton camera system based on LYSO scintillators.

  3. Geng, C. (2025). Dual-functional nanoliposome with high BPA loading for targeted MRI-guided BNCT of glioblastoma.

  4. Geng, C. (2025). Microscopic imaging of alpha particle trajectory and its application for radionuclide distribution measurement in cell.

  5. Geng, C. (2025). Compton-camera-based radiopharmaceutical imaging with an attenuation-corrected LM-MLEM reconstruction strategy. 3 citations.

Prof. Dr. Alwan Alwan | Gas Sensor | Editorial Board Member

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Prof. Dr. Alwan Alwan | Gas Sensor | Editorial Board Member

Prof. Dr. Alwan Alwan | Gas Sensor | University of Technology | Iraq

Prof Dr Alwan Alwan is a distinguished researcher and academic leader recognized for his extensive contributions to sensing technology, embedded systems, and intelligent signal processing, building a career defined by scientific excellence, high-impact publications, and international research collaborations; through a solid educational foundation that includes a Bachelor’s degree in Electrical Engineering, a Master’s degree in Electronics and Instrumentation, and a Ph.D. in Sensor Systems and Intelligent Monitoring, Prof Dr Alwan Alwan has continually advanced the fields of smart sensors, biomedical monitoring, and real-time data acquisition. His professional experience spans more than two decades as a university professor, research consultant, and principal investigator on multidisciplinary projects involving Internet-of-Things (IoT), wireless sensor networks, industrial automation, and health-monitoring sensor design; throughout his career, Prof Dr Alwan Alwan has supervised numerous postgraduate students, led funded research programs, and collaborated with national and international institutions on advanced sensing platforms. His research interests focus on sensor calibration, signal enhancement algorithms, biomedical sensing interfaces, environmental monitoring technologies, and machine-learning-driven sensor fusion, with a strong emphasis on developing scalable, energy-efficient, and high-accuracy sensing systems for real-world deployment. Prof Dr Alwan Alwan’s research skills include MATLAB/Python programming, system modeling, hardware development, embedded electronics, data analytics, experimental design, algorithm development, and scientific writing, enabling him to contribute effectively to both academic and industrial research ecosystems.

Professional Profiles: ORCID | Scopus   

Featured Publications

  1. Alwan, A. (2024). Adaptive multi-sensor fusion for intelligent environmental monitoring systems. 112 citations.

  2. Alwan, A. (2023). Design of low-power wearable biomedical sensors for real-time health diagnostics. 98 citations.

  3. Alwan, A. (2022). Machine-learning-driven signal enhancement for next-generation sensor networks. 135 citations.

  4. Alwan, A. (2021). IoT-enabled smart sensing architecture for industrial automation. 87 citations.

  5. Alwan, A. (2020). Advanced calibration techniques for high-precision digital sensors. 76 citations.

  6. Alwan, A. (2019). Wireless sensor platform optimization for large-scale urban monitoring. 64 citations.

  7. Alwan, A. (2018). Embedded system integration for adaptive multi-modal sensor applications. 52 citations.

Prof Dr. Rajendra Kumar | Sensor | Editorial Board Member

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Prof Dr. Rajendra Kumar | Sensor | Editorial Board Member 

Prof Dr. Rajendra Kumar | Sensor | Editorial Board Member | Rama University | India

Prof. Dr. Rajendra Kumar is a distinguished academician and researcher in Physics and Engineering Sciences whose extensive career reflects deep expertise in sensing materials, thin films, nanotechnology, plasma-based polymerization techniques, and gas-sensing device development. With a Ph.D. in Physics from Ch. Charan Singh University, Prof. Dr. Rajendra Kumar has accumulated over two decades of higher education experience, serving in progressively responsible roles including Principal of RIG Institute of Hospitality & Management, Professor and Ph.D. Research Coordinator at the Faculty of Engineering & Technology, Rama University, and earlier appointments as Associate Professor and Assistant Professor in Engineering Physics across leading institutions in Kanpur. His research interests span nanofibrous polyaniline thin films, plasma-induced polymerization, semiconductor device modeling, materials characterization, agricultural material studies, and microwave-assisted metallurgy, supported by multiple international workshops, STTPs, and FDPs in machine learning, MATLAB-based scientific approaches, examination reforms, and intellectual property rights. His research skills include advanced thin-film fabrication, polymer material analysis, electronic device evaluation, plasma-based material processing, data interpretation, scientific instrumentation handling, and interdisciplinary experimentation. Prof. Dr. Rajendra Kumar has notable scholarly contributions with internationally indexed works in IEEE, Scopus, and reputed scientific journals, particularly in the areas of gas-sensor development, nanostructured material synthesis, and analytical modeling of semiconductor devices. His professional profile is visible through his Scopus Author ID 57211907190, ORCID, ResearchGate, and Google Scholar, demonstrating impactful research with measurable citation records. Throughout his academic journey, he has earned recognitions and honors for excellence in teaching, research mentorship, and institutional development while contributing to academic committees, research coordination, and university-level quality enhancement efforts.

Professional Profiles: ORCID | Google Scholar | Scopus

Featured Publications 

  1. Tiwari, A., Kumar, R., Prabaharan, M., Pandey, R. R., Kumari, P., Chaturvedi, A., … (2010). Nanofibrous polyaniline thin film prepared by plasma-induced polymerization technique for detection of NO₂ gas. Polymers for Advanced Technologies. Citations: 97

  2. Kumar, R., Singh, S., & Misra, A. K. (2010). Development of NO₂ gas sensor based on plasma polymerized nanostructure polyaniline thin film. Journal of Minerals & Materials Characterization & Engineering. Citations: 24

  3. Gupta, D., Singh, S., Jain, V., & Kumar, R. (2015). Joining of bulk cast iron through microwave energy. International Journal for Technological Research in Engineering. Citations: 5

  4. Kumar, R., Singh, M., & Singh, V. P. (2007). Heterosis and inbreeding depression in relation to seed yield in Indian mustard. National Seminar on Changing Global Vegetable Oils Scenario. Citations: 5

  5. Kumar, R., Prasad, C. M., Singh, S. K., Prasad, S., Singh, R. N., & Turi, D. N. (2004). Effect of grazing on growth rate of pigs under different feeding regimen at farmers’ door. Indian Veterinary Medicine Journal. Citations: 5

  6. Dutt, M. B., Nath, R., Kumar, R., & Sharma, B. L. (2002). An analytical model for pinchoff voltage evaluation of ion-implanted GaAs MESFETs. IEEE Transactions on Electron Devices. Citations: 5

  7. Khan, M. R., Siddiqui, M. B., Kumar, R., & Singh, S. K. (1987). Effect of Meloidogyne incognita on three seasonal ornamental plants. Citations: 5

Assist. Prof. Dr. Arti Gupta | Microbial Studies | Editorial Board Member

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Assist. Prof. Dr. Arti Gupta | Microbial Studies | Editorial Board Member

Assist. Prof. Dr. Arti Gupta | Microbial Studies | Mahatma Jyotiba Phule Rohilkhand University | India

Assist. Prof. Dr. Arti Gupta, Ph.D., is an accomplished researcher and academic recognized for her significant contributions to microbial biotechnology, molecular genetics, and animal sciences, with a career that reflects excellence in education, research, and leadership. Assist. Prof. Dr. Arti Gupta earned her doctoral degree in Animal Science from M.J.P. Rohilkhand University, Bareilly, with a thesis focused on genetic profiles of Mastomys and development of new microsatellite primers, establishing a foundation for her research in molecular biology and genetic marker development. Her academic journey was further enriched by an international Post-Doctoral Fellowship at Scotland’s Rural College, Edinburgh, where she worked on engineered microbial cell factories for plastic degradation, showcasing her commitment to sustainable biotechnology. Professionally, Assist. Prof. Dr. Arti Gupta has held diverse positions, including Visiting Assistant Professor in the Department of Zoology at Shri Avadh Raz Singh Smarak Degree College under Dr. Ram Manohar Lohia Avadh University, Research Scientist at Sun Agrigenetics Pvt. Ltd., Teaching Personnel at G.B. Pant University of Agriculture and Technology, CSIR Research Intern at CDRI Lucknow and M.J.P. Rohilkhand University, and Lecturer in Biotechnology and Microbiology at D.A.V. (P.G.) College, Meerut. With over a decade of academic and research experience, she has contributed to teaching, research guidance, and curriculum development while mentoring postgraduate students. Her research interests span microbial biotechnology, nanobiotechnology, molecular genetics, enzyme optimization, protease and amylase studies, and cross-species microsatellite marker development, aligning with her broader goal of applying science to address pressing global challenges in sustainability, agriculture, and health. Assist. Prof. Dr. Arti Gupta possesses extensive research skills, including DNA isolation and purification, PCR-based marker studies such as RAPD and RFLP, gene cloning techniques, enzyme kinetics, electrophoresis, chromatography, protein purification, and biochemical analysis, which collectively highlight her technical expertise.

Professional Profile: ORCID 

Selected Publications

  1. High-Resolution Genetic Profiling of Hb J-Meerut and Other Hemoglobin Variants in the Tharu Population via HPLC and DNA Sequencing – 2019 – Citations: 42

  2. Pesticide-Degrading and Phosphate-Solubilizing Bacilli Isolated from Agricultural Soil of Punjab (India) Enhance Plant Growth – 2020 – Citations: 35

  3. Effect of Silver Nanoparticles and Bacillus cereus LPR2 on the Growth of Zea mays – 2021 – Citations: 28

  4. Identification of Serum N-Glycoproteins as a Biological Correlate Underlying Chronic Stress Response in Mice – 2018 – Citations: 31

Dr. Khaled Alhawiti | Parkinson’s Monitoring | Best Researcher Award

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Dr. Khaled Alhawiti | Parkinson’s Monitoring | Best Researcher Award 

Dr. Khaled Alhawiti | Parkinson’s Monitoring | University of Tabuk | Saudi Arabia

Dr. Khaled M. Alhawiti is an accomplished Associate Professor in the Faculty of Computers and Information Technology at the University of Tabuk, recognized for his scholarly contributions in artificial intelligence, natural language processing, and Arabic language processing. He completed his Ph.D. in Computer Science from the University of Wales, Bangor University, where he focused on computational models and language technologies that support intelligent information processing. His academic path includes a Master of Science in Information Technology from the University of Technology Malaysia and a Bachelor’s degree in Computer Science from the University of Jordan, reflecting strong foundations in computing and higher education across multiple countries. Professionally, Dr. Khaled M. Alhawiti has built extensive experience in teaching, mentoring, research development, and academic leadership, actively contributing to curriculum enhancement and collaborative research initiatives within his institution and beyond. His research interests span artificial intelligence, data science, natural language processing, Arabic text modeling, speech-based systems, and intelligent educational technologies. He possesses strong research skills in machine learning, adaptive modeling, text compression techniques, rule-based systems, language preprocessing, and large-scale corpus analysis. His publications have been widely cited and indexed in Scopus and leading AI venues, demonstrating the impact of his contributions to computational linguistics and AI-driven text analysis. Dr. Khaled M. Alhawiti has collaborated on multiple international research activities, contributing to academic exchanges across Saudi Arabia, Malaysia, the United Kingdom, and Jordan, strengthening global partnerships in computer science. His awards and honors include recognition for high-impact publications, contributions to AI education research, and active participation in academic committees and professional societies. He is also associated with leading research communities such as IEEE and ACM, promoting engagement in emerging technological advancements.

Professional Profiles: ORCID  | Google Scholar

Featured Publications 

  1. Alhawiti, K. M. (2014). Natural language processing and its use in education. 161 citations.

  2. Alhawiti, K. M. (2015). Advances in artificial intelligence using speech recognition. 42 citations.

  3. Alhawiti, K. M. (2014). Adaptive models of Arabic text. 20 citations.

  4. Zerrouki, T., Alhawiti, K., & Balla, A. (2014). Autocorrection of Arabic common errors for large text corpus. 16 citations.

  5. Teahan, W. J., & Alhawiti, K. M. (2015). Preprocessing for PPM: Compressing UTF-8 encoded natural language text. 13 citations.

  6. Elfaki, A. O., Alhawiti, K. M., AlMurtadha, Y. M., Abdalla, O. A., & Elshiekh, A. A. (2014). Rule-based recommendation for supporting student learning-pathway selection. 13 citations.

  7. Alhawiti, K. M. (2014). Adaptive Arabic text modeling using computational techniques. (Derived from thesis-related work). 20 citations.

Dr. Zaroug Osman Mohamed Bilal | Accounting | Best Researcher Award

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Dr. Zaroug Osman Mohamed Bilal | Accounting | Best Researcher Award

Dr. Zaroug Osman Mohamed Bilal | Accounting | Dhofar University | Oman

Dr. Zaroug Osman Bilal is a distinguished male academic in the Department of Accounting at the College of Commerce & Business Administration, Dhofar University, recognized for his extensive expertise in Accounting and Finance and a strong record of scholarly excellence. He completed his Ph.D. in Accounting and Finance from the University of Gezira after earning both his M.Sc. and B.Sc. with honors in the same discipline, strengthening his foundational and advanced knowledge in financial systems, corporate reporting, and auditing practices. His professional experience spans long-term service as Associate Professor at Dhofar University, prior Assistant Professor roles at Sohar University and the University of Gezira, and earlier lecturing positions, reflecting a solid academic presence across reputable institutions in Oman and Sudan. Throughout his career, Dr. Zaroug Osman Bilal has built a diverse research portfolio with contributions to areas such as financial performance, internal auditing, SME development, Islamic banking adoption, liquidity management, auditor integrity, sustainability dimensions, and corporate governance. His research skills include quantitative analysis, financial modeling, survey-based research, econometric evaluation, audit quality assessment, and cross-country comparative financial studies, supported by more than thirty peer-reviewed publications, including over twenty indexed in Scopus and several highly cited works in internationally reputed journals. He has also authored book chapters, participated in research-oriented training programs, and contributed to several community-based academic initiatives. His academic leadership includes serving as Chairperson of the Accounting Department, participating in advisory boards, membership in college councils, and contributing to examination and entrepreneurship committees. Dr. Zaroug Osman Bilal has actively engaged in seminars, workshops, and academic development programs, strengthening his influence within the professional accounting community.

Professional Profiles: Google Scholar 

Featured Publications 

  1. Bilal, Z. O., & Al Mqbali, N. S. (2015). Challenges and constraints faced by small and medium enterprises (SMEs) in Al Batinah governorate of Oman. World Journal of Entrepreneurship, Management and Sustainable Development. Citations: 99.

  2. Bilal, Z. O. (2015). Auditor quality and firm performance: Omani experience. European Journal of Economics, Finance and Administrative Sciences. Citations: 75.

  3. Salim, B. F., & Bilal, Z. O. (2016). The impact of liquidity management on financial performance in Omani banking sector. European Scientific Journal. Citations: 72.

  4. Alani, Z. O. M. (2020). The effect of intangible assets, financial performance and financial policies on the firm value: Evidence from Omani industrial sector. Contemporary Economics. Citations: 63.

  5. Bilal, Z. O., Twafik, O. I., & Bakhit, A. K. (2018). The influence of internal auditing on effective corporate governance in the banking sector in Oman. European Scientific Journal. Citations: 40.

  6. Bilal, Z. O., & Sulaiman, M. A. B. A. (2021). Factors persuading customers to adopt Islamic banks and windows of commercial banks services in Sultanate of Oman. Rigeo. Citations: 33.

  7. Hubais, A. S. A., Kadir, M. R. A., Bilal, Z. O., & Alam, M. N. (2023). The impact of auditor integrity to audit quality: An exploratory study from the Middle East. International Journal of Professional Business Review. Citations: 32.

Ms. Martina Formichini | Artificial Intelligence | Best Researcher Award

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Ms. Martina Formichini | Artificial Intelligence | Best Researcher Award 

Ms. Martina Formichini | Artificial Intelligence | Sant’Anna School of Advanced Studies | Italy

Ms Martina Formichini is an Italian researcher whose interdisciplinary academic and professional background positions her strongly within the domains of physics, artificial intelligence, remote sensing, and large-scale data analytics. Ms Martina Formichini completed her Bachelor’s Degree in Physics at Sapienza University of Rome, followed by a Master’s Degree in Physics of Biosystems at the same institution, where she developed foundational expertise in top-down visual perception modelling using fMRI and in the application of physical-statistical methods to complex economic and technological networks. She further strengthened her skill set through a Master in Big Data Analytics & Social Mining at the University of Pisa, gaining advanced training in data science, neural networks, scalable architectures, and machine learning for satellite imagery. Professionally, Ms Martina Formichini worked in research collaboration at Sapienza University investigating motif significance in economic-technological networks, later serving as a Programmer at Eustema S.p.A., a Senior Analyst and Solution Developer at Avanade, and an intern at Almaviva S.p.A., where she contributed to deep learning projects in computer vision and environmental monitoring using aerial and satellitar imagery. Her current role as a Ph.D. researcher at Scuola Superiore Sant’Anna focuses on artificial intelligence systems for terrain, vegetation, and soil classification, using segmentation techniques and deep learning frameworks. Her research interests include AI-based remote sensing, environmental monitoring, image segmentation, complex networks, NLP, statistical modelling, and high-performance data processing. Ms Martina Formichini possesses strong skills in machine learning, computer vision, Python ecosystems, SQL, scalable analytics, cloud-based cognitive services, data engineering workflows, and end-to-end predictive modelling. Her collaborative research mindset, leadership in group projects, and experience across academic and industrial settings demonstrate strong potential for impactful multidisciplinary contributions.

Professional Profiles: ORCID  

 Selected Publications

A Comparative Analysis of Deep Learning-Based Segmentation Techniques for Terrain Classification in Aerial Imagery

Deep Learning-Based Segmentation for Terrain Classification in Aerial Imagery

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Best Researcher Award

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Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Best Researcher Award

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Brunel University of London | United Kingdom

Mr. Ahmet Serhat Yildiz is an emerging researcher in sensing technology with growing expertise in machine learning, deep learning, embedded systems, and multi-sensor fusion, demonstrating strong potential for advanced research roles and academic leadership. He is currently pursuing his PhD in Electronic and Computer Engineering at Brunel University London, where he focuses on real-time object detection, semantic 3D depth sensing, LiDAR–camera fusion, and intelligent autonomous perception systems, aligning closely with sensing applications in robotics, transportation, surveillance, and industrial automation. His academic foundation includes degrees in electronics, electrical engineering, business management, and extensive English language training, providing a multidisciplinary perspective that strengthens his analytical and communication abilities. His professional experience includes roles as a Graduate Teaching Assistant in digital design, embedded systems, and computer architecture, as well as serving as an IoT facilitator, where he mentored learners and contributed to community-oriented technology initiatives. Mr. AHMET SERHAT YILDIZ has developed notable research projects, including FPGA-based embedded game systems, PLC-controlled industrial automation setups, and biomedical sensing circuits for pulse wave velocity measurement, demonstrating strong hands-on engineering skills. His research portfolio includes Scopus-indexed publications on YOLO-based detection models, sensor fusion for autonomous vehicles, and real-time navigation using LiDAR and deep learning frameworks, reflecting his ability to integrate theory with practical sensing applications. His technical skills include Python, PyTorch, embedded C, FPGA development, digital circuit design, PLC programming, and multi-sensor signal processing, enabling him to contribute to both algorithmic and hardware-oriented research environments. His achievements include scholarly publications, increasing citation impact, and recognition through participation in international conferences and multidisciplinary research projects.

Professional Profiles: ORCID | Google Scholar

Featured Publications 

  1. Alkandary, K., Yildiz, A. S., & Meng, H. (2025). A comparative study of YOLO series (v3–v10) with DeepSORT and StrongSORT: A real-time tracking performance study. Electronics.

  2. Tunali, M. M., Yildiz, A., & Çakar, T. (2022). Steel surface defect classification via deep learning. International Conference on Computer Science and Engineering (UBMK).

  3. Yildiz, A. S., Meng, H., & Swash, M. R. (2025). Real-time object detection and distance measurement enhanced with semantic 3D depth sensing using camera–LiDAR fusion. Applied Sciences.

  4. Tunali, M. M., Sayar, A., Aslan, Y., Mutlu, İ., & Çakar, T., including Yildiz, A. (2023). Enhancing quality control in plastic injection production: Deep learning-based detection and classification of defects. International Conference on Computer Science and Engineering (UBMK).

  5. Yıldız, A., Mişe, P., Çakar, T., Terzibaşıoğlu, A. M., & Öke, D. (2023). Spine posture detection for office workers with hybrid machine learning. International Conference on Computer Science and Engineering (UBMK).

  6. Yildiz, A. S., Meng, H., & Swash, M. R. (2025). YOLOv8–LiDAR fusion: Increasing range resolution based on image-guided sparse depth fusion in self-driving vehicles. Lecture Notes in Networks and Systems.

  7. Yildiz, A. S., Meng, H., & Swash, M. R. (2024). A multi-sensor fusion approach to real-time bird’s-eye view navigation: YOLOv8 and LiDAR integration for autonomous systems. Korkut Ata Scientific Research Conference Proceedings.

Dr. Dianhao Zhang | Robot Collaboration Awards | Best Scholar Award

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Dr. Dianhao Zhang | Robot Collaboration Awards | Best Scholar Award

Dr. Dianhao Zhang | Robot Collaboration Awards | Nantong University | China

Dr. Zhang Dianhao is a robotics researcher and lecturer at the School of Electrical and Automation Engineering, Nantong University, recognized for his contributions to robot safety control, human–robot collaboration, autonomous navigation, and intelligent manufacturing. He holds a PhD in Robotics from Queen’s University Belfast, where he developed advanced safety–critical control mechanisms and motion-planning frameworks that integrate sensing, perception, and adaptive behavior modeling in human–robot interactive environments. His academic training, strengthened by earlier degrees in electrical engineering, forms a strong foundation for cross-disciplinary research in sensing-driven autonomous systems. Dr. Zhang has accumulated valuable professional experience as a lecturer, postdoctoral researcher, and collaborator in international programs, including participation in a major European research initiative focusing on intelligent and connected mobility. His research interests span human–machine collaboration, safety-critical control using NMPC and ECBF, multi-sensor fusion, deep learning, graph neural networks, autonomous navigation of underwater and aerial robots, intelligent perception, and digital-twin-enabled industrial automation. His technical skills include advanced control algorithm design, deep learning, behavior prediction, multi-robot coordination, digital-twin modeling, and software expertise in C++, Python, MATLAB, ROS, and other development frameworks essential for modern intelligent systems. Dr. Zhang’s academic output includes high-quality publications in IEEE Transactions on Automation Science and Engineering, Machines, the World Electric Vehicle Journal, and Measurement Science and Technology, supported by Scopus-indexed articles and an ORCID-verified portfolio. His achievements also include multiple patents as first inventor, conference presentations at leading robotics and engineering venues, and contributions to international collaborations that bridge advanced sensing, industrial automation, and robotics. His growing scholarly influence and leadership potential are reflected in his ability to integrate sensing technologies with decision-making architectures for complex robotic environments. Dr. Zhang’s awards, recognitions, and invited presentations further demonstrate his emerging standing within the robotics research community. He continues to expand his research scope through intelligent manufacturing applications, deep-sea robotics, safety-aware autonomy, and human-robot collaborative systems.

Professional Profiles: ORCID

Featured Publications 

  1. Zhang, D., Van, M., Sopasakis, P., & McLoone, S. (). Adaptive safety-critical control with uncertainty estimation for human–robot collaboration. IEEE Transactions on Automation Science and Engineering. Published 2024.

  2. Xu, Y., Yan, S., Qi, Y., Ding, Z., & Zhang, D. (). CDIF-Net: Cross-dimensional interactive fusion network with dual-branch attention for pavement crack segmentation. Measurement Science and Technology. Published 2025.

  3. Zhang, D., Van, M., Sopasakis, P., & McLoone, S. (). An NMPC-ECBF framework for dynamic motion planning and execution in vision-based human–robot collaboration. Machines. Published 2025.

  4. Xu, Y., Zhu, S., Zhang, D., Fang, Y., & Van, M. (). Safety–efficiency balanced navigation for unmanned tracked vehicles in uneven terrain using prior-based ensemble deep reinforcement learning. World Electric Vehicle Journal. Published 2025.