Javier Alcover | Data Provenance | Best Researcher Award

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Dr. Javier Alcover | Data Provenance | Best Researcher Award

Laboratorios Diater | Spain

Dr. Javier Alcover is a distinguished Spanish biomedical researcher and Director of the Laboratorio de Aplicaciones at Diater (Spain), where he has been serving since 2000. His research primarily focuses on immunotherapy, allergology, and infectious disease prevention, emphasizing the development of innovative therapeutic approaches that integrate clinical and molecular insights. Over the years, Dr. Alcover has contributed extensively to translational research aimed at improving patient outcomes through safer and more effective immunological treatments. His 2023 study in Vaccines provided real-world clinical evidence on the safety and efficacy of an enhanced allergen-specific immunotherapy for bee venom allergy, underscoring his expertise in clinical immunology. His earlier works, such as the Urologia Internationalis (2019) publication, examined bacterial immune prophylaxis in preventing recurrent urinary tract infections, while his Future Microbiology (2017) study highlighted the antimicrobial potential of natural compounds like xyloglucan, hibiscus, and propolis. In Dermatology and Therapy (2018), Dr. Alcover explored novel nonsteroidal formulations targeting inflammatory and pruritic mediators in allergic contact dermatitis, demonstrating his commitment to advancing topical immunotherapies. His work in Allergy, Asthma & Immunology Research (2016) contributed to the understanding of orthologous allergens and the diagnostic relevance of the major allergen Alt a 1, reflecting his impact on diagnostic innovation. Furthermore, his involvement in hepatitis C virus antibody detection research, published in Transfusion (2016), showcases his broader contributions to infectious disease diagnostics. Dr. Alcover’s interdisciplinary research portfolio reflects a strong dedication to bridging laboratory innovation with clinical practice, advancing immune-based therapeutics, and enhancing diagnostic precision in allergy and infection-related diseases.

Profile: Orcid

Featured Publications

  • González Guzmán, L. A., García Robaina, J. C., Barrios Recio, J., Escudero Arias, E., Liñares Mata, T., Cervera Aznar, R., De La Roca Pinzón, F., Miguel Polo, L. del C., Arenas Villarroel, L., López Couso, V. P., et al., & Alcover, J. (2023). Real-world safety and efficacy clinical data of an improved allergen-specific immunotherapy product for the treatment of bee venom allergy. Vaccines, 11(5), 979.

  • López-Martín, L., Alcover-Díaz, J., Charry-Gónima, P., González-López, R., Rodríguez-Gil, D., Palacios-Peláez, R., & González-Enguita, C. (2019). Prospective observational cohort study of the efficacy of bacterial immune prophylaxis in the prevention of uncomplicated, recurrent urinary tract infections. Urologia Internationalis, 103(4), 456–462.*

  • Gordon, W. C., García López, V., Bhattacharjee, S., Rodríguez Gil, D., Alcover Díaz, J., Pineda de la Losa, F., Palacios Peláez, R., Tiana Ferrer, C., Bacchini, G. S., Jun, B., et al. (2018). A nonsteroidal novel formulation targeting inflammatory and pruritus-related mediators modulates experimental allergic contact dermatitis. Dermatology and Therapy, 8(1), 111–126.*

  • Fraile, B., Alcover, J., Royuela, M., Rodríguez, D., Chaves, C., Palacios, R., & Piqué, N. (2017). Xyloglucan, Hibiscus and Propolis for the prevention of urinary tract infections: Results of in vitro studies. Future Microbiology, 12(6), 533–541.*

  • Moreno, A., Pineda, F., Alcover, J., Rodríguez, D., Palacios, R., & Martínez-Naves, E. (2016). Orthologous allergens and diagnostic utility of major allergen Alt a 1. Allergy, Asthma & Immunology Research, 8(5), 428–437.*

Gokalp Oner | Reproductive endocrinology | Best Academic Researcher Award

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Prof. Dr. Gokalp Oner | Reproductive endocrinology | Best Academic Researcher Award

Istanbul Aydin University | Turkey

Prof. Dr. Gökalp Öner is a distinguished Turkish obstetrician, gynecologist, and reproductive endocrinologist recognized for his pioneering contributions to assisted reproductive technologies, artificial intelligence in medicine, and women’s health research. Born in Çorum in 1981, he demonstrated academic excellence from an early age, ranking among the top students nationally and graduating at the top of his class from Hacettepe Faculty of Medicine in 2005. He specialized in Obstetrics and Gynecology at Erciyes University, where his research began focusing on reproductive endocrinology, polycystic ovary syndrome (PCOS), endometriosis, and in vitro fertilization (IVF). Prof. Öner has significantly advanced the integration of artificial intelligence into reproductive medicine, being the first in Turkey—and among the first globally—to develop AI-assisted embryo selection and uterine evaluation technologies, revolutionizing IVF success prediction and clinical decision-making. With over 1,089 citations, an h-index of 17, and an i10-index of 28, Prof. Öner’s scholarly impact is widely recognized. His prolific academic output includes over 100 publications and multiple high-impact studies on topics such as the efficacy of omega-3 in PCOS, the comparative effects of metformin and letrozole on endometriosis, and hormonal influences on ovarian reserve and fertility outcomes. He has received seven national scientific awards, authored two books, and serves as editor for 11 medical journals while directing the IVF Center at Kayseri System Hospital and serving as a professor at Istanbul Aydın University Faculty of Medicine. Prof. Öner’s interdisciplinary expertise bridges reproductive endocrinology, clinical gynecology, and AI-driven diagnostics, positioning him at the forefront of innovation in fertility science. His work continues to shape modern reproductive medicine through the application of intelligent technologies to enhance personalized treatment, improve pregnancy outcomes, and expand scientific understanding of female reproductive health.

Profile: Google Scholar | Orcid | Scopus

Featured Publications

  • Öner, G., & Müderris, İ. İ. (2013). Efficacy of omega-3 in the treatment of polycystic ovary syndrome. Journal of Obstetrics and Gynaecology, 33(3), 289–291.

  • Öner, G., Özçelik, B., Özgun, M. T., Serin, İ. S., Öztürk, F., & Başbuğ, M. (2010). The effects of metformin and letrozole on endometriosis and comparison of the two treatment agents in a rat model. Human Reproduction, 25(4), 932–937.

  • Müderris, İ. İ., Boztosun, A., Öner, G., & Bayram, F. (2011). Effect of thyroid hormone replacement therapy on ovarian volume and androgen hormones in patients with untreated primary hypothyroidism. Annals of Saudi Medicine, 31(2), 145–151.

  • Öner, G., & Müderris, İ. İ. (2011). Clinical, endocrine and metabolic effects of metformin vs N-acetyl-cysteine in women with polycystic ovary syndrome. European Journal of Obstetrics & Gynecology and Reproductive Biology, 159(1), 127–131.

  • Cabıoğlu, N., Karanlık, H., Kangal, D., Özkurt, E., Öner, G., Sezen, F., Yılmaz, R., et al. (2018). Improved false-negative rates with intraoperative identification of clipped nodes in patients undergoing sentinel lymph node biopsy after neoadjuvant chemotherapy. Annals of Surgical Oncology, 25(10), 3030–3036.

Arman Gheysari | Consensus Algorithms | Best Researcher Award

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Mr. Arman Gheysari | Consensus Algorithms | Best Researcher Award

Amirkabir University of Technology | Iran

Mr. Arman Gheysari is a researcher in Computer Engineering at Amirkabir University of Technology, Tehran, Iran, with expertise in blockchain technology, distributed systems, fault tolerance, dependability, and reinforcement learning. His research is centered on improving the reliability, performance, and security of computing architectures and networked systems through advanced optimization and intelligent algorithms. Mr. Gheysari’s notable contribution includes the security-aware optimization of Proof-of-Work (PoW) blockchain performance using a Genetic Algorithm, published in Sustainable Computing: Informatics and Systems (2025), which introduces a systematic method to enhance blockchain efficiency without compromising its resilience to attacks. He has also worked on simultaneous optimization of network-on-chip (NoC) architectures using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to achieve optimal trade-offs between latency, reliability, and buffer size constraints, significantly advancing scalable on-chip communication networks. In addition to his academic research, Mr. Gheysari holds patents on fault-tolerant and Byzantine-resilient system-on-chip (SoC) designs, where he integrates blockchain-based consensus mechanisms and Merkle tree analysis to detect and isolate faulty processing elements, ensuring robust and energy-efficient system performance. His interdisciplinary work effectively bridges blockchain systems, artificial intelligence, and hardware reliability engineering, contributing to the development of secure, adaptive, and high-performance distributed computing infrastructures. Through his innovative research, Mr. Gheysari continues to advance the fields of dependable computing and blockchain-based fault-tolerant systems.

Profile: Google Scholar

Featured Publications

Gheysari, A., & Zarandi, H. R. (2025). Security-aware optimization of PoW-based blockchain performance using a genetic algorithm approach. Sustainable Computing: Informatics and Systems, 101232.

Abdolhosseini, H., Zarandi, H. R., & Gheysari, A. (2025). Simultaneous optimization of network-on-chip to improve reliability and reduce average packet latency considering buffer size constraints. The Journal of Supercomputing, 81(13), 1260.

Gheysari, A., Almahdawi, M., & Zarandi, H. R. (2025, March). Optimizing Proof-of-Work blockchain network communications using a genetic algorithm. In Proceedings of the 29th International Computer Conference, Computer Society of Iran (CSICC 2025).

Gang Qin | Sports Science | Best Researcher Award

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Dr. Gang Qin | Sports Science | Best Researcher Award

Hanyang University | China

Dr. Gang Qin is an emerging researcher in the interdisciplinary field of Sports Science and Artificial Intelligence, currently pursuing his Ph.D. in Sport Science at Hanyang University, Seoul, South Korea (2021–2026). His academic and research pursuits bridge Sports Training, Exercise Physiology, Sports Statistics, and Sports Medicine with cutting-edge AI-driven network technologies, particularly focusing on enhancing athletic performance through digital and intelligent systems. Dr. Qin’s recent work, featured in the IEEE Transactions on Consumer Electronics (2025), presents a groundbreaking study titled “AI-driven 6G network slicing for Distance Collaborative Sports Training: Edge Cloud Resource Allocation Strategy.” This research explores the integration of 6G communication networks and deep learning algorithms to optimize remote and collaborative sports training environments, especially in basketball. By introducing an Adaptive Pelican Optimized–Elman Spike Neural Network (APO-ESNN), his study demonstrates an innovative resource allocation framework achieving 92% efficiency and 85% session productivity, marking a significant advancement in real-time sports analytics and network utilization. Dr. Qin’s model effectively leverages edge cloud computing to deliver ultra-low latency, high connectivity, and personalized coaching through AI-powered data processing, enabling interactive and efficient athlete–coach engagement. His research not only contributes to the future of intelligent sports ecosystems but also establishes a technological foundation for 6G-enabled virtual coaching systems that can transform training methodologies across disciplines. Through his pioneering approach, Dr. Gang Qin exemplifies the potential of interdisciplinary innovation—merging sports science with emerging AI and communication technologies to redefine how performance optimization, data-driven coaching, and immersive training are achieved in the era of intelligent sports networks.

Profile: Orcid

Featured Publication

Hou, Y., Wang, Z., Qin, G., & Zhong, H. (2025). AI-driven 6G network slicing for distance collaborative sports training: Edge cloud resource allocation strategy. IEEE Transactions on Consumer Electronics.

Jian Nong | Computer Vision and Image Processing | Best Researcher Award

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Prof. Dr. Jian Nong | Computer Vision and Image Processing | Best Researcher Award

Associate Professor at Wuzhou University | China

Prof. Dr. Jian Nong is a distinguished Associate Professor at the School of Artificial Intelligence, Wuzhou University, with expertise spanning computer vision, deep learning, and high-performance computing. He earned his Ph.D. in Computer Technology and Its Applications from the Macau University of Science and Technology, where he cultivated a strong research foundation in intelligent computing and visual information analysis. His academic pursuits center on visual object tracking, multi-modal data fusion, sentiment analysis, and GPU-based parallel processing. Prof. Dr. Nong has authored several influential papers in reputed international journals and conferences, including “Robust Tracking via Rethinking Prediction Head” (Image and Vision Computing, 2025), “Dual-stream Multi-modal Interactive Vision-language Tracking” (ACM, 2024), “SentiRank: A Novel Approach to Sentiment Leader Identification in Social Networks Based on the D-TFRank Model” (Electronics, 2025), and “Efficient Parallel Processing of R-Tree on GPUs” (Mathematics, 2024). His research outcomes contribute substantially to advancing intelligent vision systems, data-driven decision-making, and high-efficiency computing architectures. As the head of a research and teaching team supporting the Digital Xijiang River Project, he integrates academic research with applied innovation to address regional and industrial digitalization challenges. His ongoing research projects include the development of deep reinforcement learning-based recommendation methods for multi-objective optimization in complex shipping environments and object tracking algorithms leveraging multi-cue information. With over a decade of experience and more than ten scholarly publications, Prof. Dr. Jian Nong continues to play a pivotal role in bridging artificial intelligence theory and practical application, fostering the next generation of intelligent computing systems and contributing to the growth of AI-driven technologies on a global scale.

Profile: Orcid

Featured Publications

  1. Nong, J., Qi, Y., Mo, Z., Wang, J., & Liang, Y. (2025). Robust tracking via rethinking prediction head. Image and Vision Computing, 152, 105780.

  2. Huang, J., Lan, B., Nong, J., Pang, G., & Hao, F. (2025). SentiRank: A novel approach to sentiment leader identification in social networks based on the D-TFRank model. Electronics, 14(14), 2751.

  3. Mo, Z., Zhang, G., Nong, J., Zhong, B., & Li, Z. (2024, December 3). Dual-stream multi-modal interactive vision-language tracking. In Proceedings of the ACM Conference.

  4. Nong, J., He, X., Chen, J., & Liang, Y. (2024). Efficient parallel processing of R-Tree on GPUs. Mathematics, 12(13), 2115.

Ranko Romanić | Food Technology | Best Researcher Award

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Assoc. Prof. Dr. Ranko Romanić | Food Technology | Best Researcher Award

University of Novi Sad | Serbia

Assoc. Prof. Dr. Ranko Romanić is an accomplished researcher and academic in the field of Food Engineering, currently serving as an Associate Professor at the Department of Food Preservation Engineering, Faculty of Technology, University of Novi Sad, Serbia. His research primarily focuses on the technology of vegetable oils and fats, with special expertise in the production and optimization of cold-pressed oils, chemometric modeling, and improving the oxidative stability and nutritional quality of edible oils. His Ph.D. research, titled “Hemometric approach to the optimization of technological parameters for the production of cold-pressed oil of high-oleic sunflower seeds,” laid the foundation for his continuing work on process optimization and quality enhancement in oil production. Assoc. Prof. Dr. Ranko Romanić has contributed to more than seven national and provincial research projects and has published extensively in high-impact international journals such as Foods, Processes, Food Chemistry, and Journal of the Science of Food and Agriculture. His recent studies explore the development of omega-3-enriched oil blends, sustainable winterization processes, and biopolymer film applications in oil packaging. As Editor-in-Chief of the journal “Uljarstvo – Journal of Edible Oils Industry”, he plays a pivotal role in advancing scientific communication within his field. Additionally, Assoc. Prof. Dr. Ranko Romanić leads the accredited Laboratory for Food Testing (ISO/IEC 17025), ensuring high standards in food quality assessment and compliance. A member of the Serbian Chemical Society and the Institute for Standardization of Serbia, he actively collaborates with industry to enhance oil production technologies and promote sustainable food processing practices.

Profile: Google Scholar | Scopus | Orcid

Featured Publications

  • Lužaić, T., Škrbić, J., Nakov, G., Petrović, J., & Romanić, R. (2025). Deep-frying performance of palm olein and sunflower oil variants: Antioxidant-enriched and high-oleic oil as potential substitutes. Processes, 13(10), 3285.

  • Lužaić, T., Nakov, G., Kravić, S., Jocić, S., & Romanić, R. (2025). Influence of hull and impurity content in high-oleic sunflower seeds on pressing efficiency and cold-pressed oil yield. Applied Sciences, 15(6), 3012.

  • Romanić, R., Lužaić, T., Pezo, L., & Radić, B. (2024). Omega-3 blends of sunflower and flaxseed oil—Modeling chemical quality and sensory acceptability. Foods, 13(23), 3722.

  • Lužaić, T., Nedić Grujin, K., Pezo, L., Nikolovski, B., Maksimović, Z., & Romanić, R. (2024). Implementation of cellulose-based filtration aids in industrial sunflower oil dewaxing (winterization): Process monitoring, prediction, and optimization. Foods, 13(18), 2960.

  • Romanić, R. S., Lužaić, T. Z., & Radić, B. Đ. (2021). Enriched sunflower oil with omega-3 fatty acids from flaxseed oil: Prediction of the nutritive characteristics. LWT, 151, 112064.

Yang Liu | Pattern Recognition | Innovative Research Award

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Assist. Prof. Dr. Yang Liu | Pattern Recognition | Innovative Research Award

Assistant professor at Zhejiang University | China

Assistant Professor Dr. Yang Liu is an emerging scholar specializing in computer vision, machine learning, and remote sensing, with a strong research focus on unsupervised representation learning, facial modeling, and multimodal image translation. His work integrates deep learning and generative modeling to advance intelligent visual understanding systems. Notably, his 2025 paper “Adaptive Sparse Contrastive Learning for Unsupervised Object Re-identification” in Pattern Recognition introduces an innovative sparse contrastive framework for improved feature discrimination in object re-identification. His 2024 studies in Knowledge-Based Systems and Remote Sensing present significant contributions to multi-objective reinforcement learning through dynamic preference inference and to SAR-to-multispectral image translation via S2MS-GAN, enhancing cross-modal synthesis and efficiency. Earlier works in IEEE Signal Processing Letters and IEEE Access showcase his expertise in fine-scale 3D face reconstruction, texture fusion, and photorealistic head modeling. Collaborating with international teams from leading universities such as Zhejiang University and Northwestern Polytechnical University, Assistant Professor Dr. Yang Liu continues to drive innovation at the intersection of computer vision and AI. His ongoing research aims to develop more adaptive, interpretable, and sustainable AI-driven visual intelligence systems that can bridge the gap between human perception and machine understanding in complex, real-world environments.

Profile

Featured Publications

  • Zheng, D., Liu, Y., Zhou, D., Xiao, J., Zhang, B., & Chen, L. (2025). Adaptive sparse contrastive learning for unsupervised object re-identification. Pattern Recognition, 157, 112604.

  • Liu, Y., Zhou, Y., He, Z., Yang, Y., Han, Q., & Li, J. (2024). Dynamic preference inference network: Improving sample efficiency for multi-objective reinforcement learning by preference estimation. Knowledge-Based Systems, 305, 112512.

  • Liu, Y., Han, Q., Yang, H., & Hu, H. (2024). High-resolution SAR-to-multispectral image translation based on S2MS-GAN. Remote Sensing, 16(21), 4045.

  • Liu, Y., Fan, Y., Guo, Z., Zaman, A., & Liu, S. (2023). Fine-scale face fitting and texture fusion with inverse renderer. IEEE Signal Processing Letters, 30, 139–143.

  • Fan, Y., Liu, Y., Lv, G., Liu, S., Li, G., & Huang, Y. (2020). Full face-and-head 3D model with photorealistic texture. IEEE Access, 8, 188041–188051.

Dhruv Sharma | Computer Vision | Best Researcher Award

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Dr. Dhruv Sharma | Computer Vision | Best Researcher Award

Amity University | India

Dr. Dhruv Sharma has made extensive contributions to the domains of artificial intelligence, deep learning, and multimodal systems through a wide range of impactful publications. His research encompasses visual data captioning, adaptive attention mechanisms, and transformer-based models that enhance image understanding and description generation. Notable works include Evolution of Visual Data Captioning Methods, Datasets, and Evaluation Metrics: A Comprehensive Survey, Automated Image Caption Generation Framework using Adaptive Attention and Bi-LSTM, and XGL-T Transformer Model for Intelligent Image Captioning, which collectively advance the field of vision-language integration. His studies such as Lightweight Transformer with GRU Integrated Decoder for Image Captioning and Control With Style: Style Embedding-based Variational Autoencoder for Controlled Stylized Caption Generation Framework propose innovative architectures for stylistic and efficient captioning. In addition, he has developed frameworks like FDT–Dr2T: A Unified Dense Radiology Report Generation Transformer Framework for X-ray Images and Unma-Capsumt: Unified and Multi-Head Attention-Driven Caption Summarization Transformer, highlighting his interest in medical AI and caption summarization. His earlier works, including Memory-Based FIR Digital Filter using Modified OMS-LUT Design and Modified Efficient OMS LUT-Design for Memory-Based Multiplication, show his foundational expertise in signal processing and hardware-efficient algorithms. Moreover, his contributions such as Obscenity Detection Transformer and DVRGNet reflect his commitment to developing socially responsible AI for content moderation. Overall, Dr. Sharma’s scholarly output demonstrates a consistent trajectory from traditional signal processing to cutting-edge multimodal AI, bridging research innovation with practical applications in intelligent computing and human-centered artificial intelligence.

Profile: Google Scholar

Featured Publications

  • Sharma, D., Dhiman, C., & Kumar, D. (2023). Evolution of visual data captioning methods, datasets, and evaluation metrics: A comprehensive survey. Expert Systems with Applications, 221, 119773.

  • Sharma, D., Dhiman, C., & Kumar, D. (2024). XGL-T transformer model for intelligent image captioning. Multimedia Tools and Applications, 83(2), 4219–4240.

  • Sharma, D., Dhiman, C., & Kumar, D. (2024). Control with style: Style embedding-based variational autoencoder for controlled stylized caption generation framework. IEEE Transactions on Cognitive and Developmental Systems, 1–11.

  • Sharma, D., Dhiman, C., & Kumar, D. (2024). FDT–Dr2T: A unified dense radiology report generation transformer framework for X-ray images. Machine Vision and Applications, 35, 1–13.

  • Sharma, D., Dhiman, C., & Kumar, D. (2022). Automated image caption generation framework using adaptive attention and Bi-LSTM. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1–5). IEEE.

Youwei Wang | Data Mining | Young Researcher Award

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Mr. Youwei Wang | Data Mining | Young Researcher Award 

Central University of Finance and Economics | China

Mr. Youwei Wang is an Associate Professor at the School of Information, Central University of Finance and Economics, Beijing, China. He holds a Ph.D. in Computational Bioinformatics, a Master’s in Computer Application Technology, and a Bachelor’s in Computer Science and Technology, all from Jilin University. His research primarily focuses on data mining, deep learning, and social networks, with significant contributions to misinformation detection, fraud analysis, and blockchain security. Dr. Wang has published over 60 research papers, including SCI and EI-indexed works, and authored one book with an ISBN. He also holds two patents and has led multiple funded projects from the National Natural Science Foundation of China, the Ministry of Education, and the Beijing Natural Science Foundation. A member of the China Computer Federation (CCF) and the China Cyberspace Security Association, Dr. Wang collaborates actively with institutions such as Tianjin University of Finance and Economics. His research employs advanced techniques in graph modeling, deep learning, and knowledge distillation to improve fake content recognition, sentiment analysis, and smart contract anomaly detection, thereby contributing to digital governance and financial technology. He teaches courses on Network Content Security Analysis, C++ Programming, and AI Programming, mentoring graduate students in applied artificial intelligence. Mr. Youwei Wang commitment to innovation and interdisciplinary exploration continues to advance the fields of information security, machine learning, and financial data analytics.

Profile: Scopus

Featured Publications

  • Wang, Y., Feng, L., Xie, J., & Feng, Q. (2023). Fast multi-channel adaptive learning-enriched learning algorithm for text classification. Multimedia Tools and Applications. (SCI, CCF C)

  • Wang, Y., Feng, L., Zhu, Y., Li, Y., & Chen, F. (2022). Improved AdaBoost algorithm using multisatisfied samples oriented feature selection and weighted non-negative matrix factorization. Neurocomputing, 506, 133–149. (SCI, CAS District 2, CCF C)

  • Wang, Y., & Feng, L. (2021). An adaptive boosting algorithm based on weighted feature selection and category classification confidence. Applied Intelligence, 51(6), 6879–6888. (SCI, CAS District 2, CCF C)

  • Wang, Y., Feng, L., & others. (2024). Dual EROU-CON-based sentiment classification method combining global and local attention. The Journal of Supercomputing, 89, 2799–2817. (SCI, CCF C)

  • Wang, Y., Lu, K., & Feng, L. (2024). Sentiment classification method based on user personality and semantic-motivation features. Journal of Electronics.

Naveed Anjum | Security | Best Researcher Award

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Mr. Naveed Anjum | Security | Best Researcher Award

University of Science and Technology Beijing | China

Mr. Naveed Anjum Mian is a dedicated PhD researcher and Research Fellow with a strong passion for innovative, cost-effective, and realistic approaches in computer science. His research focuses on large language models (LLMs) for social network cyberbullying detection and graph-based network security, leveraging expertise in machine learning, deep learning, and graph neural networks (GNNs). Mr. Mian has extensive teaching experience as a lecturer in computer science, delivering engaging lectures and hands-on lab sessions in programming, database systems, network communication, and object-oriented programming while promoting research and development in network security and AI-driven solutions. His professional experience also includes roles as a software engineer, contributing to web development, user requirement analysis, and the maintenance of large-scale portals like Zameen.com. Academically, he holds an MS in Computer Science with a strong CGPA and a BS in Computer Science, currently pursuing a PhD at the University of Science and Technology Beijing. Mr. Mian is proficient in Python and its libraries, including Transformers, PyTorch, TensorFlow, Keras, Pandas, NumPy, Scikit-Learn, Matplotlib, and Deep Graph, and is skilled in Linux and Hadoop for large-scale data processing. His research contributions include publications on the security and privacy of industrial big data and multi-source data fusion schemes for intrusion detection in networks, accumulating 35 citations with an h-index of 2 and an i10-index of 1. Through his work, Mr. Mian combines analytical rigor with practical applications, contributing to advancements in AI, cybersecurity, and data-driven solutions while fostering knowledge sharing and innovative practices in academia and industry.

Profile: Google Scholar | Oricid

Featured Publications

Anjum, N., Latif, Z., & Chen, H. (2025). Security and privacy of industrial big data: Motivation, opportunities, and challenges. Journal of Network and Computer Applications.

Anjum, N., Latif, Z., Lee, C., Shoukat, I. A., & Iqbal, U. (2021). MIND: A multi-source data fusion scheme for intrusion detection in networks. Sensors, 21(144941).