Busra Buran | Decision Making | Best Researcher Award

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Dr. Busra Buran | Decision Making | Best Researcher Award

Research Scholar| Istanbul Technical University, Turkey

Dr. Büşra Buran, born on August 13, 1988, in Kocaeli, Turkey, is a prominent expert in management engineering with a career deeply rooted in public transportation strategy and optimization. She currently serves as the Head of Strategy Development at IETT, Istanbul’s public transport authority. With over a decade of professional experience, Dr. Buran has successfully led numerous projects involving strategic planning, quality management, service improvement, and international collaboration. Her academic achievements include a Ph.D. in Management Engineering from Istanbul Technical University, an M.Sc. in Industrial Engineering from Galatasaray University, and a B.Eng. in Industrial Engineering from Yıldız Technical University. Fluent in English, she combines technical expertise with leadership capabilities, supported by various certifications in project management, quality control, and innovation. Her work has been recognized internationally, and she actively contributes to research with several publications focusing on fuzzy logic applications, transportation models, and digital transformation strategies in public transit systems.

Profile

🎓 Education

Dr. Büşra Buran holds a robust academic background in industrial and management engineering. She completed her Ph.D. in Business Management Engineering at Istanbul Technical University (2018–2023), graduating with a GPA of 3.5/4. Her doctoral research focused on advanced decision-making methods and innovation in public transportation. Prior to that, she earned her Master of Science in Industrial Engineering from Galatasaray University (2010–2012), achieving a GPA of 3.5/4. She began her academic journey with a Bachelor’s degree in Industrial Engineering from Yıldız Technical University (2007–2010), graduating with distinction (3.4/4). Her academic excellence is complemented by certifications in project management, innovation, quality systems, and team leadership. Dr. Buran’s education is deeply integrated with applied engineering practices, statistical modeling (SPSS, Minitab), simulation tools (Simul8), and machine learning (R), equipping her with a strong foundation in both theory and practice. Her studies enabled her to apply analytical, technical, and managerial skills to large-scale public service systems.

🧪 Experience

Dr. Büşra Buran has extensive professional experience in the public transportation sector, primarily at IETT, Istanbul’s leading transit authority. Since 2017, she has been serving as the Head of Strategy Development, where she manages strategic planning, performance metrics, quality systems, corporate innovation, and international partnerships. From 2014 to 2017, she was the Manager of Service Improvement, leading the development of a service quality model, auditing standards, and benchmarking global practices. Between 2013 and 2014, she worked as the Manager of Operation Planning, handling performance analysis and optimization of bus networks, including collaborative projects with TÜBİTAK. Her career began in 2010 as a Consultant for Istanbul’s Bus Rapid Transit system, optimizing routes and managing training for international BRT implementations. She is experienced in project management, process auditing, KPI reporting, and digital transformation. Her leadership has significantly enhanced the efficiency and global standing of Istanbul’s public transport network.

🏅 Awards and Honors

Dr. Büşra Buran has consistently demonstrated academic and professional excellence throughout her career. She graduated with honors at every level—achieving GPAs of 3.4/4 in her undergraduate studies, 3.5/4 in her master’s, and 3.5/4 during her Ph.D. She has also been recognized for her contributions to transportation planning and quality improvement at IETT, receiving internal commendations for successful project outcomes and strategic innovations. Her scientific research on fuzzy logic applications in public transport and sentiment analysis during the COVID-19 pandemic has been published in prestigious journals and international conferences by Springer and Elsevier. She has also earned certifications in Project Management, Corporate Quality, Risk & Process Management, and Leadership Innovation, strengthening her credentials as a thought leader in urban mobility systems. Her scholarly achievements, combined with her impactful real-world projects, highlight her as a distinguished figure in engineering management and urban transportation.

🔬 Research Focus

Dr. Buran’s research centers on the intersection of engineering management, public transportation systems, and fuzzy decision-making models. Her Ph.D. work and subsequent publications explore advanced methodologies such as Spherical and Intuitionistic Fuzzy AHP, hybrid fuzzy systems, sentiment analysis, and business model frameworks tailored for urban transport. She has published widely on topics such as bus type selection, public transport quality modeling, and global benchmarking of transit business models. Her studies frequently employ machine learning and simulation tools like R and Simul8, along with statistical software for system optimization. A significant part of her work also delves into digital transformation, strategic innovation, and service efficiency in public bus systems, particularly in mega-cities like Istanbul. Her research not only contributes theoretical advancements in engineering management but also provides practical insights for decision-makers in transit agencies, enhancing transportation planning, passenger satisfaction, and sustainable mobility strategies.

✅ Conclusion

Dr. Büşra Buran exemplifies the synergy between academic research and public sector innovation. With her strong background in industrial and management engineering, she has driven strategic advancements in Istanbul’s public transportation. Her work is a testament to effective leadership, data-driven decision-making, and continuous quality improvement in urban mobility systems.

Publications

Pritpal Singh | Ambiguous set theory | Best Researcher Award

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Dr. Pritpal Singh | Ambiguous set theory | Best Researcher Award

Pritpal Singh is an Assistant Professor at the Department of Data Science and Analytics, Central University of Rajasthan, India. He earned his Ph.D. in Computer Science and Engineering from Tezpur (Central) University in 2015 and has held various academic and research positions in India, Taiwan, and Poland. His expertise includes soft computing, optimization algorithms, time series forecasting, image analysis, and machine learning. He has published extensively in high-impact journals like IEEE Transactions, Elsevier, and Springer. His research focuses on advanced computational techniques, including quantum-based optimization and fMRI data analysis. Dr. Singh has received prestigious research fellowships, including a Postdoctoral Fellowship from Taiwan’s Ministry of Science and Technology and an International Visiting Research Fellowship from Poland’s Foundation for Polish Science. His work significantly contributes to artificial intelligence, data science, and computational modeling, making him a key figure in these fields. 🚀📊📚

Profile

Education 🎓

Dr. Pritpal Singh obtained his Ph.D. in Computer Science and Engineering from Tezpur (Central) University, Assam, India, in 2015, specializing in soft computing applications for time series forecasting. He completed his Master in Computer Applications (MCA) from Dibrugarh University, Assam, in 2008, following a B.Sc. in Physics, Chemistry, and Mathematics from the same university in 2005. His academic journey began with Higher Secondary (2002) from the Assam Higher Secondary Education Council and HSLC (1999) from the Secondary Education Board of Assam. His doctoral dissertation focused on improving fuzzy time series forecasting models through hybridization with neural networks and optimization techniques like particle swarm optimization. His strong foundation in computational sciences, mathematics, and engineering has shaped his research in AI-driven predictive modeling, optimization, and data analytics. 🎓📚🔬

Experience 👨‍🏫

Dr. Singh has extensive academic and research experience. He is currently an Assistant Professor at the Central University of Rajasthan (since June 2022). Previously, he was an Assistant Professor at CHARUSAT University, Gujarat (2015-2019), and a Lecturer at Thapar University, Punjab (2013-2015). His research experience includes serving as an Adjunct Professor (Research) at Jagiellonian University, Poland (2020-2022) and a Postdoctoral Research Fellow at National Taipei University of Technology, Taiwan (2019-2020). Throughout his career, he has mentored students, led research projects, and contributed significantly to data science, artificial intelligence, and computational modeling. His global exposure has enriched his expertise in optimization, machine learning, and interdisciplinary AI applications. 🌍📊

Research Interests 🔬

Dr. Singh’s research revolves around ambiguous set theory, optimization algorithms, time series forecasting, image analysis, and machine learning. He specializes in hybrid computational techniques, particularly quantum-based optimization and soft computing applications. His work extends to fMRI data analysis, mathematical modeling, and simulation. His research has been published in leading journals such as IEEE Transactions on Systems, Elsevier’s Information Sciences, and Artificial Intelligence in Medicine. His focus on interdisciplinary AI applications, particularly in healthcare and data science, has positioned him as a key contributor to advancing machine learning methodologies. 🧠📊🤖Awards & Recognitions 🏅

Dr. Singh has received multiple prestigious fellowships and recognitions. In 2019, he was awarded a Postdoctoral Research Fellowship by the Ministry of Science and Technology, Taiwan. In 2020, he received the International Visiting Research Fellowship from the Foundation for Polish Science, Poland. His contributions to artificial intelligence, optimization, and data science have been recognized globally through research grants, invited talks, and publications in top-tier journals. His work in soft computing and AI-driven predictive modeling continues to impact both academic and industrial research. 🏅🎖️📜

Publications 📚

  • Scopus 1-2023: P. Singh, An investigation of ambiguous sets and their application to
    decision-making from partial order to lattice ambiguous sets. Decision Analytics
    Journal (Elsevier), 08, 100286, 2023.
  • Scopus 2-2023: P. Singh, A general model of ambiguous sets to a single-valued ambiguous numberswith aggregation operators. Decision Analytics Journal (Elsevier), 08,
    100260, 2023.
  • Scopus 3-2023: P. Singh, Ambiguous set theory: A new approach to deal with unconsciousness and ambiguousness of human perception. Journal of Neutrosophic and
    Fuzzy Systems (American Scientific Publishing Group), 05(01), 52–58, 2023.
  • Scopus 4-2022: P. Singh, Marcin W ˛atorek, Anna Ceglarek, Magdalena F ˛afrowicz, and
    Paweł O´swi˛ecimka, Analysis of fMRI Time Series: Neutrosophic-Entropy Based
    Clustering Algorithm. Journal of Advances in Information Technology, 13(3), 224–
    229, 2022.

Vikas Palekar | Machine Leaning | Best Researcher Award

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Mr. Vikas Palekar | Machine Leaning | Best Researcher Award

 

Profile

Education

He is currently pursuing a Ph.D. in Computer Science and Engineering at Vellore Institute of Technology, Bhopal, Madhya Pradesh, since December 2018. His research focuses on developing an Adaptive Optimized Residual Convolutional Image Annotation Model with a Bionic Feature Selection Strategy. He holds a Master of Engineering (M.E.) in Information Technology from Prof. Ram Meghe College of Engineering Technology and Research, Badnera (SGBAU Amravati), which he completed in December 2012 with an impressive 88.00%, securing the first merit position in the university for the summer 2012 examination. Prior to that, he earned a Bachelor of Engineering (B.E.) in Computer Science and Engineering from Shri Guru Gobind Singhji Institute of Engineering Technology and Research, Nanded (SRTMNU, Nanded), in June 2007, achieving a commendable 74.40%.

Work experience

He is currently working as an Assistant Professor in the Department of Computer Engineering at Bajaj Institute of Technology, Wardha, since July 31, 2023. In addition to his teaching responsibilities, he serves as the Academic Coordinator of the department and has worked as a Senior Supervisor for the DBATY Winter-23 Exam at Government College of Engineering, Yavatmal.

Previously, he worked as an Assistant Professor (UGC Approved, RTMNU, Nagpur) in the Department of Computer Science and Engineering at Datta Meghe Institute of Engineering, Technology & Research, Wardha, from June 14, 2011, to June 30, 2023. During this tenure, he held the position of Head of the Department from April 21, 2016, to June 30, 2023. He taught various subjects, including Distributed Operating Systems, TCP/IP, System Programming, Data Warehousing and Mining, Artificial Intelligence, and Computer Architecture and Organization. Additionally, he contributed to university examinations as the Chief Supervisor in the Winter-2015 Examination and a committee member for the Summer-2013, Summer-2015, and Summer-2018 Examinations. He also played a key role in institutional development as a member of the Admission Committee, NBA & NAAC core committees at the department level, and as the convener of the National Level Technical Symposium “POCKET 16” organized by the CSE Department on March 16, 2016.

Earlier in his career, he served as an Assistant Professor in the Department of Computer Engineering at Bapurao Deshmukh College of Engineering, Wardha, from November 26, 2008, to April 30, 2011. He taught subjects such as Unix and Shell Programming, Object-Oriented Programming, and Operating Systems while also serving as a Department Exam Committee Member.

Achievement

He was the first university topper (merit) in M.Tech (Information Technology) and received the Best Paper Award at the 2021 International Conference on Computational Performance Evaluation (ComPE), organized by the Department of Biomedical Engineering, North Eastern Hill University (NEHU), Shillong, Meghalaya, India, from December 1st to 3rd, 2023. He has actively participated in various conferences, including presenting the paper “Label Dependency Classifier using Multi-Feature Graph Convolution Networks for Automatic Image Annotation” at ComPE 2021 in Shillong, India. He also presented his research on “Visual-Based Page Segmentation for Deep Web Data Extraction” at the International Conference on Soft Computing for Problem Solving (SocProS 2011) held from December 20-22, 2011. Additionally, he contributed to the Computer Science & Engineering Department at Sardar Vallabhbhai National Institute of Technology, Surat, by presenting “A Critical Analysis of Learning Approaches for Image Annotation Based on Semantic Correlation” from December 13-15, 2022. His work on “A Survey on Assisting Document Annotation” was featured at the 19th International Conference on Hybrid Intelligent Systems (HIS) at VIT Bhopal University, India, from December 10-12, 2022. Furthermore, he co-authored a study titled “Review on Improving Lifetime of Network Using Energy and Density Control Cluster Algorithm,” which was presented at the 2018 IEEE International Students’ Conference on Electrical, Electronics, and Computer Science (SCEECS) in Bhopal, India.

 

Publication

Dingming Wu | Computer Science | Best Researcher Award

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Dr. Dingming Wu | Computer Science | Best Researcher Award

 

Profile

  • scopus

Education

He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Master’s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelor’s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010.

Work experience

He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback.

Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data.

Publication

  • [1] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Jointly modeling transfer learning of
    industrial chain information and deep learning for stock prediction[J]. Expert Systems with
    Applications, 2022, 191(7):116257.
    [2] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu.A hybrid framework based on extreme
    learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock
    prediction[J]. Expert Systems with Applications, 2022, 207(24):118006.
    [3] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Construction of stock portfolio based on
    k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022,
    252(18):109358.
    [4] Dingming Wu, Xiaolong Wang∗, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling
    method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162.
    [5] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. A hybrid method based on extreme
    learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021,
    23(4):440.
    Papers to be published:
    [6] Wavelet transform in conjunction with temporal convolutional networks for time series
    prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole
    Author.
    [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive
    Neurodynamics; Status: accept; Position: First Author.
    [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor
    Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL
    INTELLIGENCE; Status: under review; Position: First Author.
    [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal
    Processing and Control; Status: Revise; Position: First Corresponding Author.
    [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks
    EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems;
    Status: under review; Position: Co-First Author.
    [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space.
    Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI);
    Status: under review; Position: Second Corresponding Author.
    [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal:
    EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second
    Corresponding Author.
    [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG
    Decoding. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: Second Corresponding Author.
    [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference:
    International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review;
    Position: Second Corresponding Author.
    [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion
    Recognition. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: First Author.