Sina Fard Moradinia | Machin Learning | Best Researcher Award

Assist. Prof. Dr Sina Fard Moradinia | Machine Learning | Best Researcher Award

Reviewer&Editor, Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Dr. Sina Fard Moradinia is an Assistant Professor in the Department of Civil Engineering at Islamic Azad University, Tabriz Branch, Iran. He specializes in water resource management, hydrology, hydraulic engineering, computational fluid dynamics, and the application of machine learning in civil engineering. With a strong academic background and a focus on integrating advanced technologies, Dr. Fard Moradinia has contributed significantly to research in sustainable construction, water management, and infrastructure optimization. His work is recognized for its innovative approaches to environmental and structural challenges, particularly in dam construction, flood prediction, and water resource forecasting. He has authored several peer-reviewed papers and participated in numerous academic and professional conferences.

Profile

Google Scholar

Orcid

Scopus

Strengths for the Award

Dr. Sina Fard Moradinia is a highly accomplished researcher and educator in civil engineering, particularly in the areas of water resource management, hydrology, hydraulics, and computational fluid dynamics. His research contributions are diverse and impactful, addressing key challenges in dam construction, flood risk prediction, and sustainable water management. Dr. Fard Moradinia’s ability to integrate machine learning with traditional engineering models to solve complex problems stands out as a significant strength. His extensive body of work, evidenced by multiple publications in high-impact journals, reflects his proficiency in both theoretical and applied research, especially in projects that integrate Building Information Modeling (BIM), System Dynamics, and ANFIS models for project optimization.

Dr. Fard Moradinia has demonstrated leadership in his field through innovative research in human resource risk analysis in construction, water quality management, and the use of computational models to optimize construction project time and cost. His work on flood flow prediction and seepage analysis in earth dams is a testament to his ability to address real-world infrastructure challenges with advanced methodologies.

Areas for Improvements

While Dr. Fard Moradinia has a robust and impressive portfolio of research, there are a few areas where improvements or further development could enhance his profile for recognition.

  1. Broader Global Collaboration: Although his work is highly relevant within the local context of Iran, expanding his collaborative efforts with international researchers, especially in global water management issues or climate change adaptation strategies, could increase the global impact of his research.
  2. Interdisciplinary Approaches: There is an opportunity for Dr. Fard Moradinia to explore more interdisciplinary research areas, such as the integration of civil engineering with environmental science, data analytics, or sustainable urban planning. This could make his work even more relevant to the global discourse on climate change and urban sustainability.
  3. Public Engagement and Outreach: While his academic and research credentials are strong, increasing his presence in public and policy-making circles, particularly in the context of water crisis management and sustainable infrastructure, could make his work more impactful outside academia.

Education 

Dr. Sina Fard Moradinia holds a Ph.D. in Civil Engineering, focusing on hydrology and water resources management, from an esteemed institution in Iran. His academic journey includes both Bachelor’s and Master’s degrees in Civil Engineering, where he developed a solid foundation in fluid dynamics, hydrology, and structural engineering. Dr. Fard Moradinia has continually expanded his expertise through advanced research in the application of computational techniques, including machine learning algorithms for solving complex civil engineering problems. His educational background reflects a commitment to both theoretical and practical aspects of civil engineering, preparing him for an impactful academic career in the field.

Experience 

Dr. Sina Fard Moradinia has accumulated a wealth of experience in both academic and research settings. As an Assistant Professor at Islamic Azad University in Tabriz, he teaches and mentors students in civil engineering, with an emphasis on hydrology, water resource management, and advanced computational methods. In addition to his teaching role, he is actively involved in high-impact research projects, collaborating with professionals in the fields of water resources, infrastructure, and construction management. His research spans areas such as flood prediction, water quality management, construction project optimization, and the use of artificial intelligence for infrastructure analysis. Dr. Fard Moradinia has also applied his expertise in industry-focused projects, working with governmental and private organizations to enhance the design and management of civil infrastructure, particularly in dam construction, flood mitigation, and reservoir management.

Awards and Honors

Dr. Sina Fard Moradinia’s research has garnered recognition from various academic and professional institutions. His publications have received multiple citations, attesting to the impact of his work in civil engineering, hydrology, and water resource management. He has been honored with awards for his contributions to research and education, including recognition for excellence in the application of machine learning techniques to civil engineering problems. Dr. Fard Moradinia has been invited to speak at international conferences and serve on editorial boards for leading journals in his field. Additionally, his role as a reviewer for numerous scholarly publications further solidifies his standing as a respected figure in his domain. His collaborative efforts with industry partners have also resulted in several successful projects aimed at improving infrastructure sustainability and management in Iran.

Research Focus

Dr. Sina Fard Moradinia’s research focuses on applying advanced computational techniques to solve pressing issues in civil engineering, particularly in the areas of water resources management, hydrology, and hydraulic engineering. His work explores innovative solutions for flood prediction, aquifer management, and sustainable water usage, with a strong emphasis on integrating machine learning and artificial intelligence. Dr. Fard Moradinia also investigates the optimization of construction projects, particularly in the context of dam and reservoir management, where he applies Building Information Modeling (BIM) and system dynamics to improve project efficiency and reduce risks. Another key area of his research is the analysis of environmental factors influencing civil infrastructure, such as the impact of sludge discharge in wastewater systems. Through his work, he aims to advance both the scientific understanding of hydrological systems and the practical tools available for managing water resources and infrastructure projects.

Publication Top Notes

  • “Toward Nearly Zero Energy Building Designs: A Comparative Study of Various Techniques” 🌱🏢
  • “Time and Cost Management of Dam Construction Projects Based on BIM” 💼🏞️
  • “The Role of BIM in Reducing the Number of Project Dispute Resolution Sessions” ⚙️💬
  • “Wavelet-ANN Hybrid Model Evaluation in Seepage Prediction in Nonhomogeneous Earth Dams” 🌊🧠
  • “Optimization of Quantitative and Qualitative Indicators of Construction Projects” ⚙️📊
  • “An Approach for Flood Flow Prediction Using New Hybrids of ANFIS” 🌧️🔮
  • “Forecasting the Level of Aquifers in the Ajab Shir Plain with Different Management Scenarios” 💧🔍
  • “Development of an ANFIS Model for Human Resource Risk Analysis in Construction” 🏗️🧑‍💼
  • “Using Umbrella Arch Method in Design of Tunnel Lining” 🏞️⚒️
  • “Evaluation of Water Diversion Tunnel Lining Using Numerical Model” 🔢🌍
  • “Analysis and Investigation of Hydrological Drought Indicators in Mahenshan” 🌵💧
  • “A Novel Approach to Flood Risk Zonation: Integrating Deep Learning Models with APG” 💻🌊
  • “The Prediction of Precipitation Changes in the Aji-Chay Watershed Using CMIP6 Models” 🌧️📈
  • “Applying Project Knowledge Management to Enhance Time and Cost Efficiency in Water Reservoir Projects” 🕒💡
  • “Developing a System Dynamics Model to Study Human Resource Motivation and Time Productivity” 🕹️💼
  • “Investigating Strategies for Implementing Knowledge Management in Dam Construction Projects” 🏗️📚
  • “Simulation of Delay Factors in Dam Construction Projects with System Dynamics” ⏳🏞️
  • “Mathematical Equations for Grouting Pressure and Intensity in Joint Rocks” 🏔️🛠️
  • “Investigation of Excavation Behavior in Soil Nailing for Construction” 🏗️🌍
  • “Study of the Effects of Sludge Discharge from Water Treatment Plants” ♻️💧

Conclusion

Dr. Sina Fard Moradinia’s exceptional contributions to civil engineering, particularly in water resource management, hydrology, and construction project optimization, make him a strong candidate for the Best Researcher Award. His work, combining cutting-edge computational techniques and practical engineering solutions, addresses some of the most pressing challenges in sustainable development and infrastructure resilience. His innovative approaches in dam construction, flood prediction, and water quality management not only benefit academia but also have significant implications for real-world applications. With his continued focus on advancing research methodologies and expanding his influence both nationally and internationally, Dr. Fard Moradinia has the potential to be a leading figure in shaping the future of civil engineering and environmental management.

Richa Vij – Computer science and engineering – Best Researcher Award

Richa Vij - Computer science and engineering - Best Researcher Award

IIT Jammu - India

👩‍🏫 ACADEMIC AND RESEARCH LEADER

Richa Vij is an Assistant Professor in the Department of Computer Science and Engineering at the Government College of Engineering and Technology, Jammu. With a focus on retinal imaging and artificial intelligence, she brings significant experience in developing innovative computational models aimed at diagnosing systemic diseases like diabetic retinopathy and Alzheimer's. Her academic contributions, both as a professor and researcher, have made her a prominent figure in the field of AI-powered medical diagnostics.

💻 PROJECT ASSOCIATE AT IIT JAMMU

As a Project Associate-II for a DRDO project at IIT Jammu, Richa works on cutting-edge computer science projects that contribute to national development. Her role involves harnessing deep learning and AI methodologies for real-world applications, reflecting her commitment to both academic advancement and practical innovation.

📚 PUBLISHED AUTHOR IN AI AND HEALTHCARE

Richa Vij has authored multiple impactful publications in high-profile journals such as Metabolic Brain Disease and Computers and Electrical Engineering. Her research has focused on leveraging deep learning techniques to advance the early detection of diseases like Alzheimer's and diabetic retinopathy, contributing significantly to medical imaging and AI diagnostic systems.

🔬 PIONEERING RETINAL DISEASE DIAGNOSIS

Her Ph.D. research at SMVDU, Katra, under the supervision of Dr. Sakshi Arora, centers on using hybrid deep transfer learning-based algorithms to analyze retinal images for systemic disease detection. This work aims to enhance the performance of segmentation and classification models, positioning her research at the intersection of healthcare and advanced AI technologies.

📊 INNOVATIVE M.TECH RESEARCH

Richa’s M.Tech dissertation focused on "Robust Human Face Tracking and Recognition in Video Frames." By developing a novel face recognition model using the AdaBoost algorithm and K-means clustering, she addressed key challenges in face recognition, such as pose variation and occlusion, further showcasing her expertise in machine learning and pattern recognition.

🧠 FOCUS ON DIABETIC RETINOPATHY AND ALZHEIMER’S

Her work is particularly influential in the early diagnosis of Diabetic Retinopathy and Alzheimer’s disease through retinal imaging. By utilizing AI-based models for retinal vessel segmentation, her contributions are paving the way for improved diagnostic frameworks, with potential applications in clinical environments for early and accurate disease detection.

🌟 COMMITMENT TO AI-DRIVEN HEALTHCARE

Richa’s dedication to advancing AI-driven healthcare solutions is reflected in her ongoing research and teaching. She strives to bridge the gap between technology and healthcare by developing intelligent systems that can assist practitioners in diagnosing complex diseases, ultimately contributing to better patient outcomes and more efficient clinical workflows.

NOTABLE PUBLICATION

Title: A systematic review on diabetic retinopathy detection using deep learning techniques
Authors: R. Vij, S. Arora
Journal: Archives of Computational Methods in Engineering
Year: 2023

Title: A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification
Authors: R. Vij, S. Arora
Journal: Multimedia Tools and Applications
Year: 2023

Title: Computer vision with deep learning techniques for neurodegenerative diseases analysis using neuroimaging: a survey
Authors: R. Vij, S. Arora
Journal: International Conference on Innovative Computing and Communications
Year: 2022

Title: A systematic survey of advances in retinal imaging modalities for Alzheimer’s disease diagnosis
Authors: R. Vij, S. Arora
Journal: Metabolic Brain Disease
Year: 2022

Title: A survey on various face detecting and tracking techniques in video sequences
Authors: R. Vij, B. Kaushik
Journal: 2019 International Conference on Intelligent Computing and Control Systems
Year: 2019

Jiaming Zhong – Artificial intelligence – Best Researcher Award

Jiaming Zhong - Artificial intelligence - Best Researcher Award

Wuyi university - China

AUTHOR PROFILE

SCOPUS

📚 SCIENTIFIC RESEARCH ACHIEVEMENTS

Jiaming Zhong has made significant contributions to the fields of video classification and tactile sensing. His groundbreaking papers include "Exploring Cross-video Matching for Few-shot Video Classification via Dual-Hierarchy Graph Neural Network Learning," published in Image and Vision Computing, and "Text-guided Graph Temporal Modeling for Few-Shot Video Classification," featured in Engineering Applications of Artificial Intelligence. These studies, published in top-tier journals, highlight Zhong's innovative approaches in utilizing graph neural networks and multimodal models for advanced video analysis and classification.

🛠️ PATENTS AND TECHNOLOGICAL INNOVATIONS

Zhong holds several patents that showcase his expertise in developing practical solutions for various technological challenges. His patents include methods for video anomaly classification, chip defect detection, and mobile robot obstacle avoidance. These patents reflect his commitment to translating theoretical research into tangible technological advancements that address real-world problems.

🔬 PROJECT EXPERIENCE: PEEL RECOGNITION

In a project focused on the precise identification of Chenpi years using a multimodal model, Zhong's work involved designing lightweight modules and fine-tuning models to achieve high recognition accuracy. His use of the CLIP multimodal model for feature extraction led to a remarkable 99% accuracy in recognizing Chenpi years with limited sample data. This project, detailed on GitHub, demonstrates his proficiency in applying advanced machine learning techniques to practical problems.

🎥 PROJECT EXPERIENCE: FEW-SHOT VIDEO CLASSIFICATION

Zhong's research in video behavior classification involved addressing challenges related to data scarcity and model capabilities. Collaborating with Macau University of Science and Technology and Wuyi University, he developed a dual-hierarchy graph neural network that significantly improved classification performance through cross-video frame matching. This innovative approach was published in Image and Vision Computing and showcased Zhong's ability to enhance model performance through sophisticated temporal modeling.

🔍 PROJECT EXPERIENCE: MULTIMODAL REPRESENTATION LEARNING

In a project focused on multimodal video behavior analysis, Zhong led efforts to develop a novel framework for self-supervised learning using multimodal data. This project, supported by a 500,000 RMB research grant, involved developing a text-guided feature optimization module and a query text token learning mechanism. His research aimed to leverage multimodal knowledge to improve the classification performance of few-shot video behaviors, with results published in top journals.

📈 IMPACTFUL RESEARCH AND PUBLICATIONS

Zhong's work has significantly impacted the fields of video classification and sensor technology. His papers in renowned journals and his patents contribute to advancing the understanding and application of these technologies. His research not only addresses current challenges but also paves the way for future innovations in these areas.

🏆 ACKNOWLEDGEMENTS AND RECOGNITION

Zhong's contributions to scientific research and technology have earned him recognition within the academic and professional communities. His innovative work in video classification and sensor technology continues to influence the field and inspire further research and development.

NOTABLE PUBLICATION

Ultra-sensitive and stable All-Fiber iontronic tactile sensors under high pressure for human movement monitoring and rehabilitation assessment
Authors: K. Ma, D. Su, B. Qin, Y. Xin, X. He
Year: 2024
Journal: Chemical Engineering Journal

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7
Authors: F. Deng, J. Chen, L. Fu, J. Li, N. Li
Year: 2024
Journal: Frontiers in Plant Science

Exploring cross-video matching for few-shot video classification via dual-hierarchy graph neural network learning
Authors: F. Deng, J. Zhong, N. Li, D. Wang, T.L. Lam
Year: 2023
Journal: Image and Vision Computing

Senbagavalli – Artificial Intelligence – Best Researcher Award

Senbagavalli - Artificial Intelligence - Best Researcher Award

Alliance University - India

AUTHOR PROFILE

SCOPUS

EXPERT IN OPINION MINING AND FEATURE SELECTION

Senbagavalli's groundbreaking research in opinion mining of health data for cardiovascular disease diagnosis using an unsupervised feature selection algorithm spans five years. Her Ph.D. work is a testament to her dedication to leveraging data for medical advancements.

FACIAL RECOGNITION INNOVATOR

With a master's degree in engineering, Senbagavalli developed a face recognition system using Laplacian faces, showcasing her expertise in computer vision and pattern recognition. This project exemplified her ability to apply complex algorithms to practical applications within six months.

PIONEER IN UNICODE FILE SYSTEMS

During her undergraduate studies, Senbagavalli created a file system using the Unicode character set, a project completed in just six months. Her work in this area highlights her proficiency in software development and system design.

CREATOR OF GRAPHIC GAMING SYSTEMS

In her mini-project as an undergraduate, she developed a gaming system using graphics within three months. This early project laid the foundation for her interest in interactive and visual computing systems.

SEASONED ACADEMIC AND PROFESSOR

With 18 years and 7 months of teaching experience, Senbagavalli has held positions at prestigious institutions, including Alliance University and Kuppam Engineering College. Her extensive experience has made her a respected figure in the academic community.

VERSATILE SUBJECT EXPERT

Senbagavalli has taught a wide range of subjects to undergraduate, postgraduate, and Ph.D. students, including Data Modeling and Optimization, Object-Oriented Programming, and Software Engineering. Her comprehensive knowledge spans multiple domains of computer science.

ACTIVE RESEARCHER AND REVIEWER

An active member of various academic councils and editorial boards, Senbagavalli reviews for renowned publishers like Bentham Science and Elsevier. Her involvement in curriculum development, project evaluation, and seminar organization reflects her commitment to academic excellence and continuous learning.

NOTABLE PUBLICATION

Identification of Biomarker for Autism Spectrum Disorder Using EEG: A Review.
Authors: K. Lalli, M. Senbagavalli
Year: 2023
Conference: Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, pp. 45–50

Facemask Detection System Using CNN Model.
Authors: M. Senbagavalli, S. Debnath, R. Rajagopal, K. Ghildial
Year: 2023
Conference: International Conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

An Evaluation of Machine Learning Techniques for Detecting Banking Frauds.
Authors: R. Rajagopal, M. Senbagavalli, S. Debnath, K. Darshan, K.S. Varun Tejas
Year: 2023
Conference: International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 359–365

Deep Learning Model for Flood Estimate and Relief Management System Using Hybrid Algorithm.
Authors: M. Senbagavalli, V. Sathiyamoorthi, S.K. Manju Bargavi, S. Shekarappa G., T. Jesudas
Year: 2023
Book: Artificial Intelligence and Machine Learning in Smart City Planning, pp. 29–44

An Effective Model for Predicting Agricultural Crop Yield on Remote Sensing Hyper-Spectral Images Using Adaptive Logistic Regression Classifier.
Authors: V. Sathiyamoorthi, P. Harshavardhanan, H. Azath, A.M. Viswa Bharathy, B.S. Chokkalingam
Year: 2022
Journal: Concurrency and Computation: Practice and Experience, 34(25), e7242

Everton – Artificial Intelligence – Best Researcher Award

Everton - Artificial Intelligence - Best Researcher Award

Universidade Federal da Grande Dourados - Brazil

AUTHOR PROFILE

SCOPUS

ACADEMIC AFFILIATION

Everton is associated with Universidade Católica Dom Bosco, where he contributes to cutting-edge research in computer vision and its applications in agriculture and urban studies.

PRECISION AGRICULTURE RESEARCH

His research in precision agriculture includes the integration of UAV technology and machine learning to optimize farming practices. By improving weed and pest detection methods, his work supports sustainable agriculture and food security.

COMPUTER VISION IN AGRICULTURE

Everton's expertise in computer vision extends to various agricultural applications, from crop monitoring to automated harvesting systems. His innovative solutions help in increasing agricultural productivity and efficiency.

REAL-TIME WEED DETECTION IN SOYBEAN USING UAV IMAGES

Everton Castelão Tetila specializes in the real-time detection of weeds in soybean fields through the innovative use of UAV (Unmanned Aerial Vehicle) images. His work significantly contributes to precision agriculture, enabling farmers to identify and manage weeds more efficiently.

YOLO PERFORMANCE ANALYSIS FOR SOYBEAN PEST DETECTION

Everton has conducted extensive performance analysis of the YOLO (You Only Look Once) algorithm for the real-time detection of soybean pests. His research enhances pest management practices, ensuring timely interventions and reducing crop damage.

URBAN AREA CLASSIFICATION AND MONITORING USING COMPUTER VISION

He applies advanced computer vision techniques for the classification and monitoring of urbanized areas. This work aids in urban planning and development, providing detailed and accurate assessments of urban growth and infrastructure.

EDUCATIONAL CONTRIBUTIONS

He is dedicated to advancing education in his field, sharing his knowledge and findings through publications and presentations. His contributions help train the next generation of researchers and professionals in computer vision and its agricultural applications.

NOTABLE PUBLICATION

YOLO performance analysis for real-time detection of soybean pests
Authors: E.C. Tetila, F.A.G. da Silveira, A.B. da Costa, H. Pistori, J.G.A. Barbedo
Year: 2024
Journal: Smart Agricultural Technology, 7, 100405

Title: Classification and monitoring of urbanized areas using computer vision techniques | Classificação e monitoramento de áreas urbanizadas usando técnicas de visão computacional
Authors: E.C. Tetila, P.M. de Moraes, M. Constantino, M.M.D.M. Greco, H. Pistori
Year: 2023
Journal: Desenvolvimento e Meio Ambiente, 61, pp. 32–42

Title: An approach for applying natural language processing to image classification problems
Authors: G. Astolfi, D.A. Sant'Ana, J.V.D.A. Porto, E.T. Matsubara, H. Pistori
Year: 2022
Journal: Neurocomputing, 513, pp. 372–382

Title: Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans
Authors: F.A.G. Silveira, E.C. Tetila, G. Astolfi, A.B. Costa, W.P. Amorim
Year: 2021
Conference: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13074 LNAI, pp. 265–279

Title: Associative classification model for forecasting stock market trends
Authors: E.C. Tetila, B.B. MacHado, J.F. Rorigues, M. Constantino, H. Pistori
Year: 2021
Journal: International Journal of Business Intelligence and Data Mining, 19(1), pp. 97–112

Pramod Patil – Deep Learning and Smart Grid – Best Researcher Award

Pramod Patil - Deep Learning and Smart Grid - Best Researcher Award

Dr.D.Y.Patil Institute of Technology - India

AUTHOR PROFILE

Google Scholar

ACADEMIC EXCELLENCE AND PROFESSIONAL JOURNEY 🎓

Pramod Patil's academic odyssey commenced with a Bachelor's degree in Computer Science and Engineering from S.G.G.S. College of Engineering and Technology, Nanded, followed by a Master's and Ph.D. in Computer Engineering from Govt. College of Engineering, Pune. Since November 2001, he has been an integral part of Dr.D.Y.Patil Institute of Technology, Pune, where he has held various academic positions, culminating in his current role as Professor.

EXEMPLARY ADMINISTRATIVE ROLES AND LEADERSHIP 🏢

Throughout his tenure, Patil has demonstrated exemplary leadership and administrative acumen. From serving as Dean and Principal to holding pivotal roles such as Vice Principal and Head of Department, his leadership has been instrumental in shaping the academic and administrative landscape of his institution.

COMMITMENT TO ACADEMIC ACCREDITATION AND ASSESSMENT 📚

Patil's commitment to academic quality and accreditation is evidenced through his involvement in various accreditation processes at both the institute and university levels. His expertise in accreditation, coupled with his roles as a member of inquiry committees and subject expert, underscores his dedication to upholding academic standards and fostering institutional growth.

PROFESSIONAL ENGAGEMENT AND MEMBERSHIPS 🔬

As a lifelong learner and academic stalwart, Patil is actively engaged in professional societies such as the Indian Society for Technical Education (ISTE), Computer Society of India (CSI), and Association of Computing Machinery (ACM). His membership and contributions to these esteemed organizations reflect his commitment to advancing the field of computer engineering.

RECOGNITION AND AWARDS 🏆

Patil's contributions to academia have garnered widespread recognition and acclaim. From receiving Best Paper Awards to being appointed as Dean of the Faculty of Science and Technology at Savitribai Phule Pune University, his accolades underscore his scholarly prowess and leadership excellence.

INSPIRING FUTURE GENERATIONS AND MENTORSHIP 🌱

As a mentor and guide, Patil has played a pivotal role in shaping the careers of numerous students and researchers. His dedication to nurturing talent and fostering academic excellence has left an indelible mark on the academic community, inspiring generations of scholars to pursue their academic and professional aspirations.

INNOVATIVE RESEARCH ENDEAVORS AND CONTRIBUTIONS 🚀

Patil's research endeavors have spanned diverse domains such as machine learning, data security, blockchain, and smart grid technology. His prolific publication record, patent contributions, and research projects highlight his commitment to advancing knowledge and addressing real-world challenges through innovative research methodologies.

NOTABLE PUBLICATION

Skewed Evolving Data Streams Classification with Actionable Knowledge Extraction using Data Approximation and Adaptive Classification Framework 2023 (7)

Context-aware clustering and the optimized whale optimization algorithm: An effective predictive model for the smart grid 2023 (9)

Blockchain-based security services for fog computing 2021 (16)

EFFECT OF EARLY FUNCTIONAL MOBILITY ON FUNCTIONAL INDEPENDENCE IN INCOMPLETE PARAPLEGIC INDIVIDUALS. 2022 (3)