Zeeshan Rasheed | Machine Learning | Research Excellence Award

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Mr. Zeeshan Rasheed | Machine Learning | Research Excellence Award

Mir Chakar Khan Rind University Sibi | Pakistan

Mr. Zeeshan Rasheed is an academic researcher in computer science with a focus on wireless communication systems, artificial intelligence, machine learning, and IoT-enabled network optimization. His research addresses sustainable wireless resource modeling, radio network cooperation, intelligent dataflow strategies for heterogeneous IoT environments, and predictive analytics applied to healthcare and telecommunications. He has published in multidisciplinary international journals such as Data Intelligence, MDPI Smart Cities, and the African Journal of Biomedical Research, highlighting an applied and problem-oriented research approach. With 2 Scopus-indexed publications, 5 citations, and an h-index of 1, his work reflects an emerging research trajectory that integrates AI-driven models with real-world technological and societal challenges, demonstrating growing interdisciplinary research potential as an early-career researcher.

Citation Metrics (Scopus)

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6

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Citations 5

Documents 2

h-index 1


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Featured Publications

Kachi Anvesh | Machine Learning | Best Researcher Award

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Mr. Kachi Anvesh | Machine Learning | Best Researcher Award

Vardhaman College of Engineering | India

Mr. Kachi Anvesh is an Assistant Professor in the Department of Information Technology at Vardhaman College of Engineering, Hyderabad, with over 12 years of teaching and research experience. He is currently pursuing a Ph.D. in Computer Science at Visvesvaraya Technological University, Belagavi, and holds an M.Tech in Software Engineering with distinction and a B.Tech in Information Technology. His research focuses on medical image processing, deep learning, machine learning, and intelligent systems, with notable contributions including the detection of tessellated retinal disease, hypertensive retinopathy, glaucoma, cataract, and wheat head detection using advanced AI models. He has published in reputed journals and conferences such as JIKM, TSP-CMES, and Journal of Autonomous Intelligence, accumulating 13 citations and an h-index of 2. Mr. Anvesh has led innovative projects including bone age detection from X-ray images, facial expression recognition, emotion detection, foreign object debris detection, and predictive analytics systems, and holds certifications in AI and deep learning from IIT Ropar and other platforms, reflecting his strong contribution to engineering and AI research.

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Featured Publications

Anvesh, K., Prasad, S., Laxman, V. V. S. R., & Narayana, B. S. (2019). Automatic student analysis and placement prediction using advanced machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering, 8, 9.

Suma, K., Sunitha, G., Karnati, R., Aruna, E. R., Anvesh, K., Kale, N., & Kishore, P. K. (2024). CETR: CenterNet-Vision transformer model for wheat head detection. Journal of Autonomous Intelligence, 7(3), 6.

Venkatesh, M., Dhanalakshmi, C., Adapa, A., Manzoor, M., & Anvesh, K. (2023). Criminal face detection system.

Anvesh, K., Srilatha, M., Raghunadha Reddy, T., Gopi Chand, M., & Jyothi, G. D. (2018). Improving student academic performance using an attribute selection algorithm. Proceedings of the First International Conference on Artificial Intelligence and Cognitive…, 3.

Rajendar, B., Bhavana, K., Divya, C., Swarna, M., & Anvesh, K. (2017). Evaluation of cardiac tonic activity of methanolic leaf extract of Moringa oleifera. International Journal of Pharma Sciences and Research, 8(6), 152–156.

Lingxin Jin | Computer Science | Best Researcher Award

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Dr. Lingxin Jin | Computer Science | Best Researcher Award

Dr. Lingxin Jin, University of Electronic Science and Technology of China

Dr. Lingxin Jin, based in Chengdu, China, is a Ph.D. candidate in Software Engineering at the University of Electronic Science and Technology of China, where he also completed his Bachelor’s degree with a GPA of 3.8/4.0. His academic focus includes artificial intelligence, machine learning, network security, and software systems. Dr. Jin has gained international experience through an exchange program at the International Technological University in Silicon Valley and held internships involving front-end development and research on backdoor attacks against deep neural networks. His research contributions include publications in high-impact journals such as IEEE Transactions on Computers and the Journal of Circuits, Systems, and Computers, with additional submissions to ACM and IJCAI. Dr. Jin has worked on projects ranging from Linux shell simulations to public opinion analysis systems. He has received several scholarships and honors, including direct Ph.D. program recommendation, and is recognized for his promising research in AI security and adversarial attacks.

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🎓 Educational Background

Dr. Lingxin Jin pursued his academic journey in Software Engineering at the University of Electronic Science and Technology of China. He completed his Bachelor’s degree from September 2018 to June 2022, achieving an impressive GPA of 3.8/4.0. During his undergraduate studies, he built a strong foundation through comprehensive coursework in Software Engineering, Computer Networks, Operating Systems, and Artificial Intelligence. Driven by academic excellence, Dr. Jin was recommended for direct entry into the Ph.D. program, which he began in September 2022. Currently, he is a Ph.D. candidate in Software Engineering at the same university, maintaining a GPA of 3.71/4.0. His advanced studies focus on cutting-edge topics such as Information Security Fundamentals and Frontiers, Network Security Theory and Technology, Machine Learning Theory and Algorithms, and Statistical Machine Learning. This academic background highlights his commitment to research and innovation in secure intelligent systems and computational technologies.

💼 Professional Experience

Dr. Lingxin Jin has gained diverse and valuable professional experience that complements his academic pursuits in software engineering and artificial intelligence. In July 2019, he participated in an exchange program at the International Technological University in Silicon Valley, where he engaged in programming robot motion manipulation using Raspberry Pi and Arduino, as well as composing songs using MATLAB—demonstrating his multidisciplinary creativity. From January to June 2021, Dr. Jin interned at Xi’an Deta Information Technology Co., where he focused on front-end development and contributed to building an opinion analysis system. This role honed his skills in UI/UX and real-time data interpretation. He later served as a Software Engineer Intern at Sichuan Meiliankai Science and Technology Co. from September 2021 to June 2022, where he conducted advanced research on backdoor attacks against deep neural networks. These experiences collectively reflect Dr. Jin’s technical versatility and growing expertise in cybersecurity and intelligent systems.

🏅 Additional Experience and Awards

Dr. Lingxin Jin has consistently demonstrated academic excellence throughout his educational journey, earning multiple awards and recognitions. During his undergraduate studies from 2018 to 2022 at the University of Electronic Science and Technology of China, he was honored with first and second-class scholarships for outstanding academic performance. His dedication and scholarly achievements earned him a prestigious recommendation for direct admission into the Ph.D. program, a distinction reserved for top-performing students. As a postgraduate student from 2022 onward, Dr. Jin continued to excel, receiving scholarships for new students as well as second-class scholarships for academic distinction. These accolades not only highlight his strong academic capabilities but also reflect his commitment to advancing in the field of software engineering and artificial intelligence. Dr. Jin’s consistent recognition at both undergraduate and postgraduate levels underscores his potential as a future leader in cutting-edge technological research and innovation.

🧠 Research Focus

Dr. Lingxin Jin’s research primarily focuses on adversarial machine learning, with a particular emphasis on Trojan attacks and security vulnerabilities in deep neural networks (DNNs). His scholarly work explores the life-cycle threats faced by DNNs, covering both attack strategies and defensive countermeasures. His publication in ACM Computing Surveys titled “Trojan Attacks and Countermeasures on Deep Neural Networks from Life-Cycle Perspective” provides a comprehensive overview of attack surfaces throughout a model’s development and deployment phases. Additionally, his work in IEEE Transactions on Computers, “Highly Evasive Targeted Bit-Trojan on Deep Neural Networks”, introduces novel methods of crafting stealthy, highly targeted Trojans that evade standard detection techniques. Through these contributions, Dr. Jin is advancing the field of AI security, focusing on the resilience and trustworthiness of neural networks in critical applications. His research is vital for developing robust defense frameworks and ensuring safe deployment of AI systems in real-world scenarios.

Publication Top Notes

  • 📄 2024: “Highly Evasive Targeted Bit‑Trojan on Deep Neural Networks” (IEEE Trans. on Computers) – DOI:10.1109/TC.2024.3416705; introduces stealthy bit-level Trojans; cited 2 times

  • 📄 2023: “Iterative Training Attack: A Black‑Box Adversarial Attack via Perturbation Generative Network” (J. of Circuits, Systems and Computers) – DOI:10.1142/S0218126623503140; black-box generative adversarial method;

  • 📄 2023: “A Survey of Trojan Attacks and Defense to Neural Networks” (under review at ACM Computing Surveys); comprehensive lifecycle review of Trojan threats

  • 📄 2024: “Data Poisoning‑based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks” (submitted to IJCAI ’25); extends backdoor threats to spiking neural models

 

Lisandra Díaz de la Paz | Data Science | Best Researcher Award

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Assoc. Prof. Dr. Lisandra Díaz de la Paz | Data Science | Best Researcher Award

Assoc. Prof. Dr. Lisandra Díaz de la Paz, Central University “Marta Abreu” of Las Villas, Cuba

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a Cuban computer scientist and academic with a Ph.D. in Technical Sciences (2023), a Master’s (2011), and a Bachelor’s (2008) in Computer Science from the Central University “Marta Abreu” of Las Villas (UCLV). She serves as an Associate Professor and researcher specializing in databases, decision-support systems, data integration, metadata management, and artificial intelligence. With over 15 years of teaching experience, she has instructed various undergraduate and postgraduate courses in computer science and related fields. Dr. Díaz de la Paz has completed extensive postgraduate training in areas such as software engineering, machine learning, and data science. She currently leads the Information Systems discipline and serves as Vice Dean of Research and Postgraduate Studies at the MFC Faculty, UCLV. Her research focuses on data quality models, big data, Python programming, semantic web, LLMs, and generative AI. She is an active contributor to Cuba’s technological advancement.

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Academic Background

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a distinguished academic in the field of Computer Science with a robust educational foundation acquired from the Central University “Marta Abreu” of Las Villas (UCLV), Cuba. She earned her Bachelor’s degree in Computer Science in July 2008, followed by a Master’s degree in the same field in December 2011. Demonstrating a continuous commitment to academic excellence and research, she completed her Doctorate in Technical Sciences in November 2023. This progression reflects her deepening expertise and scholarly dedication within computing and technical disciplines. Her academic journey at UCLV has equipped her with strong theoretical and practical knowledge, forming the basis for her professional contributions as a university professor, researcher, and academic leader. Dr. Díaz de la Paz’s qualifications underpin her role in advancing research in artificial intelligence, databases, and data systems while mentoring the next generation of computing professionals in Cuba and beyond.

Professional Role and Academic Specialization

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a dedicated professor and researcher with a strong focus on the field of Computer Science. Currently holding the academic rank of Associate Professor, she plays a vital role in higher education by teaching, mentoring, and guiding students across multiple levels of university instruction. Her primary specialization lies in computing, where she has developed expertise in areas such as databases, data quality, artificial intelligence, decision-support systems, and big data technologies. As both an educator and researcher, she combines theoretical knowledge with practical applications, contributing to academic excellence and technological advancement. Her position as a faculty member enables her to engage in curriculum development, academic leadership, and innovative research initiatives. Dr. Díaz de la Paz’s dual role as a professor and researcher allows her to bridge the gap between knowledge creation and dissemination, making her an influential figure in the Cuban academic and scientific community.

Awards and Recognitions

Assoc. Prof. Dr. Lisandra Díaz de la Paz has received multiple prestigious awards in recognition of her contributions to computing and educational technologies. She was a co-author of the project “Algorithms and Tools for the Library Management System,” which earned the 2024 Provincial CITMA Award in Villa Clara. In 2021, she received the Provincial CITMA Award for her work on improving the accuracy and completeness of bibliographic records in MARC 21 format. In 2019, she received the Annual Award from the Minister of Higher Education for her research in database systems and computing. Her 2018 work on the ABCD Library Management System implementation across Cuban higher education institutions was recognized for its scientific and educational impact. She also received CITMA awards in 2016 and 2012 for her innovative contributions to active database rule maintenance and business rule implementation in relational databases, respectively—highlighting her sustained excellence in research and technical innovation.

Research Focus

Assoc. Prof. Dr. Lisandra Díaz de la Paz focuses her research primarily on data quality, metadata management, bibliographic systems, and decision support through data-driven computing. Her work encompasses key areas such as the completeness and accuracy of bibliographic records in MARC 21 format, ETL process optimization, metadata profiling, and author name disambiguation using ontologies and deep learning. She has also explored big data integration with NoSQL systems, MapReduce techniques for anomaly detection, and frameworks for metadata quality evaluation in the context of open science. Her contributions have practical applications in library science, digital repositories, and institutional decision-making, particularly within educational and academic information systems. Additionally, her interdisciplinary approach blends artificial intelligence, machine learning, semantic web technologies, and business intelligence, supporting national and international collaboration for improving data infrastructure. These efforts position her as a leading researcher in data-centric computing, database technologies, and intelligent information systems.

Publication Top Notes

  • 📘 Algorithm to correct instance-level anomalies in large data using MapReduce – Cited by 7 – 2016

  • 📗 Data quality analysis in ABCD suite sources – Cited by 7 – 2015

  • 📕 Techniques to capture changes and maintain updated data warehouse – Cited by 5 – 2015

  • 📙 Data market for decision-making on teaching/research staff at UCLV – Cited by 5 – 2013

  • 📒 Techniques to capture data changes (extended version) – Cited by 4 – 2015

  • 📓 Automation of data loading processes in HR data market at UCLV – Cited by 4 – 2014

  • 📘 Weights estimation in completeness measurement of bibliographic metadata – Cited by 3 – 2021

  • 🧠 Author name disambiguation using ontology & deep learning – Cited by 1 – 2022

  • 📊 CompMARC tool for measuring completeness in MARC 21 – Cited by 1 – 2016

  • 📚 Model for metadata quality evaluation: Proposal for open science – Published – 2024

  • 📝 Accuracy measurement of author names in MARC 21 records – Published – 2018

  • 📈 Optimal weight estimation for completeness in MARC 21 metadata – Published – 2017

  • 🔍 Metadata profiling tool in MARC 21 PMMarc v2.0 – Published – 2017

  • 💾 Method for selecting data model and NoSQL system in big data – Published – 2017

  • 🛠 Procedure to improve completeness in MARC 21 records – Published – 2017

Hong Wang | Artificial Intelligence | Best Researcher Award

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Prof. Hong Wang | Artificial Intelligence | Best Researcher Award

Prof. Hong Wang, Shandong Normal University, China

Prof. Wang earned his Ph.D. in Computer Science from the Chinese Academy of Sciences. His research focuses on Artificial Intelligence, Machine Learning, Healthcare Big Data, and Bioinformatics. 🧠 He has extensive teaching experience, with roles from Lecturer to Doctoral Supervisor. He has received multiple honors, including the Outstanding Graduate Tutor award and Shandong Province Science and Technology Progress prizes. 🏆 Prof. Wang has published widely, including papers on molecular property prediction and drug interactions. His current research includes cutting-edge AI applications in health. 💻

 

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Education Background 🎓

Prof. Hong Wang completed his PhD in Computer Science from the Chinese Academy of Sciences in Beijing, China, from 1999 to 2002. Prior to that, he earned a Master of Science in Computer Science from Tianjin University in Tianjin, China, between 1988 and 1991. His academic journey began at Tianjin University, where he obtained his Bachelor of Science in Computer Science in 1988. His strong educational foundation has supported his exceptional career in AI, machine learning, and bioinformatics. 📚💻

 

Working Experience 👨‍🏫

Prof. Hong Wang has had a distinguished academic career at Shandong Normal University, starting as a Teaching Assistant from 1991 to 1995. He then served as a Lecturer from 1995 to 2000 and quickly advanced to the position of Associate Professor from 2000 to 2006. Since 2006, he has held the prestigious title of Professor, contributing significantly to the university’s academic growth. In 2009, Prof. Wang also became a Doctoral Supervisor, guiding the next generation of scholars and researchers. His career spans over three decades, focusing on teaching, research, and mentorship. 🎓📚👨‍🔬

 

Honors and Awards 🏅

Prof. Hong Wang has received numerous prestigious honors throughout his career, reflecting his dedication and contributions to academia. In March 2021, he was recognized as a March 8th Red Banner Holder. He was named Outstanding Graduate Tutor in September 2021 for his exceptional mentoring. In March 2019, he received the award for Outstanding Contribution to Achievement. His excellence in teaching was acknowledged with the University-Level Distinguished Teacher award in December 2014, followed by the Individual with Excellence in Teacher Ethics award in September 2014. Additionally, he was honored as a Good Teacher and Friend to College Students in January 2003. 🌟🎓👨‍🏫

 

Research Experience and Achievements 🔬

Prof. Hong Wang has led impactful research projects, including funding from the National Natural Science Foundation of China, with programs spanning from 2021 to 2024 (62072290) and 2017 to 2020 (61672329). He is also part of the Jinan City Science and Technology Bureau project from 2023 to 2024 (202228110). His outstanding contributions have earned him several prestigious awards, such as the Shandong Computer Society Science and Technology Progress Second Prize (First Place) in July 2024. Additionally, he received the Shandong Province Science and Technology Progress First Prize (7th place) in December 2022 and the Shandong Province Higher Education Outstanding Research Achievements Second Prize (First Place) in both 2020 and 2018. 🏆📚

 

Publication Top Notes

  • EDDINet: Enhancing drug-drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning – 2024
  • EMPPNet: Enhancing Molecular Property Prediction via Cross-modal Information Flow and Hierarchical Attention – Cited by 3, 2023
  • GCNs–FSMI: EEG recognition of mental illness based on fine-grained signal features and graph mutual information maximization – Cited by 8, 2023
  • Detecting depression tendency with multimodal features – Cited by 9, 2023
  • A Soft-Attention Guidance Stacked Neural Network for neoadjuvant chemotherapy’s pathological response diagnosis using breast dynamic contrast-enhanced MRI – Cited by 1, 2023
  • Adaptive dual graph contrastive learning based on heterogeneous signed network for predicting adverse drug reactions – Cited by 6, 2023
  • Predicting drug-drug adverse reactions via multi-view graph contrastive representation model – Cited by 11, 2023
  • Explainable knowledge integrated sequence model for detecting fake online reviews – Cited by 9, 2023
  • CasANGCL: Pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction – Cited by 19, 2023
  • Dual network contrastive learning for predicting microbe-disease associations – Cited by 2, 2022
  • Knowledge graph construction for computer networking course group in secondary vocational school based on multi-source heterogeneous data – Cited by 2, 2022
  • Test Paper Generation Based on Improved Genetic Simulated Annealing Algorithm – 2022
  • MS-ADR: Predicting drug–drug adverse reactions based on multi-source heterogeneous convolutional signed network – Cited by 6, 2022
  • Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events – Cited by 1, 2022

Debajyoti Dhar | Computer Science | Best Researcher Award

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Mr. Debajyoti Dhar | Computer Science | Best Researcher Award

Mr. Debajyoti Dhar, Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior, India

Debajyoti Dhar is an ambitious B.Tech student with a CGPA of 7.67/10, specializing in Computer Science. He has honed his skills through impactful internships, including as a Software Development Engineer at Defence Research and Development Establishment and a Full Stack Developer at Edilitics Private Limited. Debajyoti has contributed to projects like a Decentralized FPS Game with NFT Marketplace and a Ticket Management Platform, showcasing his expertise in blockchain, cloud systems, and machine learning. He has co-authored IEEE conference papers and a journal paper, demonstrating his strong research capabilities. 💻📊🔗

 

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Education Background

Debajyoti Dhar is currently pursuing a Bachelor of Technology in Computer Science at the Indian Institute of Information Technology and Management Gwalior. He started his academic journey in December 2021 and is expected to graduate in July 2025. With a CGPA of 7.67/10.00, Debajyoti has demonstrated a strong academic performance, excelling in his coursework. His education has equipped him with a solid foundation in computer science, preparing him for advanced projects and research in areas such as software development, machine learning, and blockchain technology. 📚💻🚀

 

Professional Experience

Debajyoti Dhar has gained valuable experience through multiple internships, showcasing his expertise in software development. At Defence Research and Development Establishment (Dec 2022–Oct 2023), he developed a heavy gas detection model in Java and created a 2D plotter in Python for data visualization. During his time at Edilitics Private Limited (Apr–Jun 2023), he built a robust backend using FastAPI and enhanced development efficiency with CI/CD pipelines and Docker. At Mak Design Private Limited (May–Jul 2024), he created a real-time chat module with Django and ReactJS, ensuring end-to-end encryption. 💻🔧🚀

 

Achievements

Debajyoti Dhar has demonstrated exceptional skills through various achievements. As a freelance developer for Metarootz, he built a full-stack blockchain social media project using NodeJS, ExpressJS, and MongoDB for the backend, and NextJS with TailwindCSS for the frontend. He delivered a comprehensive 5-day training bootcamp on web app deployment automation with Docker, Kubernetes, and Github Actions for industry professionals. Debajyoti has also co-authored two IEEE conference papers on computer vision and deep learning and contributed to a machine learning paper in MDPI Sensors journal. Additionally, he solved 300+ DSA questions on GFG and LeetCode. 📈💻📚

 

Research Focus

Mr. Debajyoti Dhar has contributed significantly to machine learning and optimization techniques, particularly in the context of environmental prediction. His recent work, “Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale”, published in Sensors, demonstrates his expertise in applying advanced algorithms to solve agricultural and environmental challenges. The research focuses on soil organic carbon prediction using machine learning models, emphasizing scalability and efficiency. This aligns with his broader focus on data science, AI-driven predictions, and sustainable technologies to address complex real-world problems in various domains. 🌍🤖📊

 

Publication Top Notes  

  • Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale (2024) 📚

Deepali Bhamare | Deep Learning | Best Researcher Award

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Ms. Deepali Bhamare | Deep Learning | Best Researcher Award

Ms. Deepali Bhamare, S.S.V.P.S.B.D’s COE Dhule, India

Deepali Bhamare is an accomplished educator and engineer with over two decades of experience in electronics and telecommunication. She holds a B.E. in Electronics and Telecommunication from NMU Jalgaon (2002), an M.E. in Digital Communication from R.G.P.V. Bhopal (2012), and is pursuing a PhD in Electronics Engineering. Deepali has worked in industry as a QC and Testing Engineer before transitioning into academia, where she currently serves as Assistant Professor at S.S.V.P.S. College of Engineering Dhule. She has actively contributed to various institutional committees and has attended numerous FDPs and workshops related to AI, machine learning, and research methodologies. 🎓📊📡

 

Publication Profile

Scopus

Educational Qualification

Ms. Deepali Bhamare has a robust educational background in Electronics and Telecommunications. She completed her H.S.C. in Science with 74.08%, followed by a B.E. in Electronics and Telecommunication Engineering from N.M.U. Jalgaon (64%) in 2002. She also holds a master’s degree in Digital Communication from R.G.P.V. Bhopal (75.03%), and is currently pursuing her PhD in Electronics Engineering from N.M.U., which showcases her commitment to advancing her expertise in the field.

Professional Experience

Her extensive work experience spans across both industry and academia. She worked as a Quality Control Engineer at renowned firms like Satronix India Pvt Ltd and Penguin Audio Products Ltd. Since 2008, she has transitioned into academia, holding roles such as Lecturer and Assistant Professor at S.S.V.P.S. College of Engineering, Dhule. Her academic career, coupled with her technical experience, demonstrates her comprehensive understanding of engineering principles and practical applications.

Training and Development

Ms. Bhamare has actively participated in various Faculty Development Programs (FDPs), Short-Term Training Programs (STTPs), and workshops. Notable topics include “Next Generation Artificial Intelligence,” “Python Programming with Django,” “Machine Learning and Deep Learning,” “Neural Networks and Fuzzy Logic,” and “Artificial Intelligence in Healthcare.” These programs indicate her continuous efforts to stay updated with emerging technologies, particularly in AI, machine learning, and data science.

Academic Involvement

In addition to teaching, she has held several key positions in her college, such as Member of the Anti-Ragging Committee, BC Cell, and Extra Curricular Cell, as well as Lab In-Charge. These roles highlight her dedication to both student welfare and the efficient management of college resources.

Conclusion

Ms. Deepali Bhamare’s well-rounded qualifications, research pursuits in electronics, and ongoing professional development through numerous FDPs and workshops position her as a strong candidate for the Best Researcher Award. Her blend of academic knowledge, research focus, and involvement in emerging technologies such as AI and machine learning, make her a notable contributor to the field of electronics engineering.

 

Publication Top Notes  

A Review on Person Identification Using Periocular Biometrics

Person Identification System Using Periocular Biometrics Based on Hybrid Optimal Dense Capsule Network

Noor .A. Rashed | Computer Science Award | Women Researcher Award

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Dr . Noor .A. Rashed | Computer Science Award | Women Researcher Award

Dr. Noor Rashid, Iraq

Dr. Noor Rashid is a Ph.D. candidate at the University of Technology, Baghdad, specializing in Computer Science. She earned her master’s degree from the University of al-Anbar in 2018. Her research covers areas such as Artificial Intelligence, secure data systems, machine learning, data mining, image processing, and project management automation. Her current focus is on optimization algorithms, particularly multi-objective optimization (2022-2023). Dr. Rashid has contributed significantly to the field, including her recent publication on evolutionary and swarm-based algorithms. She continues to advance AI and optimization research in her academic journey.

 

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Employment

Dr. Noor Rashid is currently employed at the University of Technology, Baghdad, Iraq, in the Department of Computer Science. As a dedicated researcher and educator, she contributes to the university’s mission by advancing studies in Artificial Intelligence, secure data systems, and optimization algorithms. Her role involves teaching and mentoring students while conducting innovative research in multi-objective optimization and machine learning. Dr. Rashid’s work continues to impact both the academic community and the broader technological landscape through her involvement in cutting-edge computer science projects.

 

Education and Qualifications 🎓📜

Dr. Noor Rashid is currently pursuing her Ph.D. in Computer Science at the University of Technology, Baghdad, Iraq, from November 2021 to November 2024. Her doctoral research focuses on advanced areas such as optimization algorithms and Artificial Intelligence, contributing to cutting-edge technological advancements. Prior to this, Dr. Rashid earned her master’s degree from the College of Computer Science and Information Technology at the University of al-Anbar in 2018. Her academic background equips her with a strong foundation in secure data, machine learning, and project management systems, preparing her for continued success in the field.

 

Research Focus 🎯🔬

Dr. Noor Rashid’s research primarily focuses on Artificial Intelligence (AI), particularly in machine learning, optimization algorithms, and data mining. Her studies delve into complex areas such as multi-objective optimization and evolutionary algorithms, aiming to solve real-world computational problems. Additionally, Dr. Rashid has worked extensively on medical image processing, applying AI techniques like ANN and SVM to detect and classify diseases like diabetic retinopathy. Her research bridges the gap between AI and healthcare, making significant contributions to secure data, networks, and advanced algorithmic developments. 🚀🧠

 

Publication Top Notes

  • Diagnosis retinopathy disease using GLCM and ANN – N. Rashed, S. Ali, A. Dawood – J. Theor. Appl. Inf. Technol 96, 6028-6040, 2018 (Cited by: 4) 📖
  • Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems – N.A. Rashed, Y.H. Ali, T.A. Rashid, A. Salih – arXiv preprint, 2024 (Cited by: 2) 🌐
  • Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms –  Rashed, Y.H. Ali, T.A. Rashid – Algorithms 17(9), 416, 2024 📑
  • ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy –  Rashed (Upcoming) 🔍

Deepali Hirolikar | Machine Learning Award | Best Researcher Award

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Dr. Deepali Hirolikar | Machine Learning Award | Best Researcher Award

Dr. Deepali Hirolikar, PDEA,s College of Engineering, Manjari(Bk.), Pune, India

Dr. Deepali S. Hirolikar is the Head of the Department of Information Technology at PDEA’s College of Engineering, Pune, with 18 years of experience in academia. She holds a PhD in Information Technology from Shri JJT University, Rajasthan. Dr. Hirolikar has published numerous papers in national and international journals, focusing on topics such as IoT, cloud computing, and machine learning. She has also published a book on IoT security paradigms. As an active contributor to various workshops and conferences, she has received multiple accolades for her work. 🖥️📚🎓

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Experience 🏆

Prof. Dr. Deepali S. Hirolikar has amassed over 18 years of experience in academia. She currently serves as the Head of the Information Technology Department and Assistant Professor at PDEA’s College of Engineering, Manjari, Pune, a position she has held since September 6, 2005. Before this, she was a Lecturer in the Computer Engineering Department at SRGSIOT, Hadapsar.

Education 📚

She completed her SSC at Keshavraj Vidyalaya, Latur in 1995 with distinction, and her HSC at Dayanand Science Junior College, Latur in 1997 with first class. She earned her Diploma in Computer Science Engineering from PLGP, Latur in 2000 with first class, and her BE in Computer Science and Engineering from Dr. BAMU, Aurangabad in 2004 with distinction. Prof. Dr. Hirolikar obtained her ME in Information Technology from UOP Pune, MIT College of Engineering, Pune in 2011 with first class, and her PhD in Information Technology from Shri JJT University, Rajasthan in 2021.

 

Research Focus

Deepali Hirolikar’s research primarily focuses on using metaheuristic methods and machine learning for efficiently predicting and classifying heart disease data. Her work includes the development and application of advanced algorithms to enhance the accuracy and efficiency of heart disease prediction models. By leveraging mathematical and engineering principles, she contributes to the field of medical data analysis, particularly in identifying patterns and improving diagnostic processes. Her research also spans the integration of machine learning techniques with medical datasets to facilitate better health outcomes.

Publication Top Notes

Metaheuristic Methods for Efficiently Predicting and Classifying Real Life Heart Disease Data Using Machine Learning

Qibin Zhao | Machine Learning Award | Best Researcher Award

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Prof Dr. Qibin Zhao | Machine Learning Award | Best Researcher Award

Prof Dr. Qibin Zhao, RIKEN, Japan

👨‍💼 Dr. Qibin Zhao is a prominent figure in the field of machine learning and deep learning, serving as the Team Leader at RIKEN Center for Advanced Intelligence Project in Tokyo, Japan. With a Ph.D. in Computer Science and Engineering from Shanghai Jiao Tong University, China, his expertise spans across tensor networks, computer vision, and brain imaging/signal processing. Dr. Zhao has received numerous research grants and awards, including the ICASSP Best Student Paper Award in 2019. He actively contributes to academic activities as an area chair and organizer in prestigious conferences like NeurIPS and ICML, while also serving as a reviewer for leading journals.

 

Publication Profile:

Scopus

Education

📚 Dr. Qibin Zhao’s academic journey is marked by excellence and dedication. He earned his Ph.D. in Computer Science and Engineering from Shanghai Jiao Tong University, China, from 2004 to 2009, laying the foundation for his future contributions to the field. Prior to this, he obtained his M.S. in Computer Science at Guangxi University, China, from 2001 to 2004, and his B.S. in Computer Science at Henan University of Science and Technology, China, from 1996 to 2000. This comprehensive educational background equipped him with the necessary skills and knowledge to excel in his career in research and academia. 🎓

 

Working Experience

👨‍💼 Dr. Qibin Zhao’s professional journey reflects a commitment to advancing the fields of artificial intelligence and computer science. Since 2020, he has held the position of Team Leader at the Tensor Learning Team within the RIKEN Center for Advanced Intelligence Project in Tokyo, Japan, guiding cutting-edge research initiatives. Concurrently, he serves as a Visiting Professor at Tokyo University of Agriculture and Technology and was a Part-time Lecturer at Waseda University, both in Tokyo. His leadership roles include being the Unit Leader of the Tensor Learning Unit at RIKEN from 2017 to 2020. Dr. Zhao’s international influence extends to his visiting professorships in China and Japan, alongside his impactful research scientist roles at RIKEN. 🌐

 

Awards and Honors:

🏆 Dr. Qibin Zhao’s contributions to signal processing and artificial intelligence have garnered significant recognition. Notable among his accolades is the 2019 ICASSP Best Student Paper Award for groundbreaking work presented by L. Yuan. His research excellence was further acknowledged with the 2018 IEEE Signal Processing Magazine Best Paper Award, authored by A. Cichocki and team. Dr. Zhao’s impact extends to Japan, where he received the 3rd IEEE Signal Processing Society Japan Best Paper Award in 2018. Additionally, he has been honored with the 5th Research Incentive Award by the RIKEN President in 2014, among other prestigious recognitions for his pioneering research in brain signal decoding and affective brain-computer interfaces. 🌟

 

Research Focus:

🔬 Dr. Qibin Zhao’s research primarily focuses on advanced techniques in tensor decomposition and multiway data analysis, leveraging the power of tensor networks in various applications. His work encompasses areas such as semi-supervised multi-view concept decomposition, robust kernel PCA for multidimensional data, Bayesian tensor factorization for scalable analysis, and noisy tensor completion methods. With expertise in tensor ring factorization, he explores innovative approaches for image completion, fusion, and analysis in hyperspectral and multispectral domains. Dr. Zhao’s contributions extend to exclusive and consistent NMF for multi-view representation learning, deep matrix factorization with hypergraph regularization, and novel tensorized transformer networks for medical image segmentation. 🧠

 

Publication Top Notes:

  1. Semi-supervised multi-view concept decomposition – Jiang, Q., Zhou, G., Zhao, Q. (2024) Expert Systems with Applications 📝
    • Citations: 0
  2. Noisy Tensor Completion via Low-Rank Tensor Ring – Qiu, Y., Zhou, G., Zhao, Q., Xie, S. (2024) IEEE Transactions on Neural Networks and Learning Systems 📝
    • Citations: 8, Cited by: Unknown
  3. Exclusivity and consistency induced NMF for multi-view representation learning – Huang, H., Zhou, G., Zheng, Y., Yang, Z., Zhao, Q. (2023) Knowledge-Based Systems 📝
    • Citations: 0, Cited by: Unknown
  4. Diverse Deep Matrix Factorization with Hypergraph Regularization for Multi-View Data Representation – Huang, H., Zhou, G., Liang, N., Zhao, Q., Xie, S. (2023) IEEE/CAA Journal of Automatica Sinica 📝
    • Citations: 3, Cited by: Unknown
  5. TT-Net: Tensorized Transformer Network for 3D medical image segmentation – Wang, J., Qu, A., Wang, Q., Liu, J., Wu, Q. (2023) Computerized Medical Imaging and Graphics 📝
    • Citations: Unknown, Cited by: Unknown