Dr. Annarosa Scalcione | Machine Learning | Research Excellence Award

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Dr. Annarosa Scalcione | Machine Learning | Research Excellence Award 

Dr. Annarosa Scalcione | Machine Learning | Polytechnic University of Turin | Italy

Dr. Annarosa Scalcione is a female biomedical engineer with a strong interdisciplinary background in biomedical instrumentation, sensor-based health monitoring, medical imaging, and digital healthcare solutions, combining engineering rigor with clinical relevance. She completed advanced academic training in biomedical engineering at Politecnico di Torino, with specialization in biomedical instrumentation and sensor systems, supported by foundational education in biomedical engineering from the same institution, where her academic work focused on sustainable biomaterials and applied medical technologies. Her professional experience includes roles as a Junior Application Consultant contributing to the digitalization of hospital clinical and administrative processes, operating room specialist engagement within medical institutions, and academic teaching collaboration supporting undergraduate engineering education. Dr. Annarosa Scalcione has led and contributed to multiple applied and experimental research projects, including the design of a web-based neonatal monitoring platform integrating sensor-derived growth data, dynamic visualization, personalized alerts aligned with international health standards, and telemedicine functionalities. Her research portfolio also includes experimental biomechanics studies using mobile sensors to evaluate neuromuscular performance, automated classification of spinal lesions from medical imaging using machine learning and radiomics, and advanced image segmentation methodologies applied to neurological datasets.

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Prof. Dr. Cornelia Aurora Gyorod | Machine Learning | Research Excellence Award

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Prof. Dr. Cornelia Aurora Gyorod | Machine Learning | Research Excellence Award 

Prof. Dr. Cornelia Aurora Gyorod | Machine Learning | University of Oradea | Romania

Prof. Dr. Cornelia Aurora Gyorod is a senior academic and internationally recognized researcher in Computer Science and Information Technology, specializing in database systems, data mining, expert systems, and large-scale data-driven computing architectures that underpin modern intelligent and sensing-based systems. She holds a Ph.D. in Computer Science from the University of Oradea and currently serves as a Professor in the Faculty of Electrical Engineering and Information Technology, Department of Computers and Information Technology, where she has demonstrated long-standing excellence in teaching, research, and academic leadership. Her educational background is complemented by advanced professional certifications in project management, project evaluation, and enterprise database technologies, reflecting her strong methodological and organizational competence. Her professional experience spans progressive academic roles including junior assistant, assistant professor, lecturer, associate professor, and full professor, during which she has been responsible for delivering core and advanced courses such as Databases, Expert Systems, Computer Programming, Advanced Database Systems, and Data Warehousing, alongside supervising undergraduate, master’s, and doctoral research. Her strengths for this award include a strong international research profile, with 70+ peer-reviewed publications, primarily indexed in Scopus and IEEE-affiliated venues, accumulating 800+ citations and an established Scopus Author ID and ORCID record.

Citation Metrics (Google Scholar)

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Mr. Osheyor Gidiagba | Machine Learning | Best Researcher Award

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Mr. Osheyor Gidiagba | Machine Learning | Best Researcher Award 

Mr. Osheyor Gidiagba | Machine Learning | University of Johannesburg | South Africa

Mr. Osheyor Joachim Gidiagba is an accomplished researcher and engineer whose expertise lies in Mechanical and Industrial Engineering, currently pursuing his Ph.D. at the University of Johannesburg, South Africa, where his research focuses on developing a hybrid model combining Machine Learning and Multi-Criteria Decision-Making (MCDM) to enhance sustainable supplier selection and performance optimization in industrial systems. His academic foundation includes a Master’s in Applied Science Mechanics (Cum Laude) and a Bachelor’s degree in Mechanical Engineering (First Class Honors), underscoring his consistent academic excellence and technical depth. Professionally, Mr. Gidiagba has worked as an Asset Management Engineer at the Ministry of Power and Domestic Water Development, Awka, Nigeria, where he successfully supervised and implemented multiple infrastructure projects, including the installation of electrical transformers and overhead water tanks across several communities. His work emphasized system reliability, supplier evaluation, and maintenance optimization, demonstrating his ability to translate research into impactful real-world engineering applications. His research interests encompass machine learning applications in decision-making, sustainable engineering systems, reliability-centered maintenance, industrial data analytics, and asset integrity management. His technical skills include data modeling, predictive maintenance, statistical analysis, multi-criteria decision-making, and system reliability evaluation, supported by proficiency in computational tools and industrial analytics. Mr. Gidiagba has published 7 Scopus-indexed research papers, accumulating 30 citations with an h-index of 3, reflecting his growing scholarly influence. His key contributions, such as applying fuzzy logic, TOPSIS, and hybrid decision models in sustainable industrial practices, highlight his innovative approach to bridging the gap between artificial intelligence and engineering sustainability. He has also engaged in international research collaborations that focus on improving decision-support systems and operational efficiency in industrial and mining sectors.

Professional Profiles: Scopus

Featured Publications 

  1. Gidiagba, O. J. (2025). Multi-Criteria Decision Support for Sustainable Supplier Evaluation in Mining SMEs: A Fuzzy Logic and TOPSIS Approach. Logistics.

Assist. Prof. Dr. Ali Alfayly | Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Ali Alfayly | Machine Learning | Best Researcher Award 

Assist. Prof. Dr. Ali Alfayly | Machine Learning | Public Authority of Applied Education and Training | Kuwait

Assist. Prof. Dr. Ali Hussain Alfayly, SMIEEE, is a highly accomplished Kuwaiti academic and researcher serving as an Assistant Professor in the Department of Computer Science at the College of Basic Education, Public Authority for Applied Education and Training (PAAET), Kuwait, where he has established himself as a prominent contributor to the fields of computer science, artificial intelligence, cybersecurity, robotics, and educational technologies. He earned his Ph.D. in Computer Science from the University of Plymouth in the United Kingdom, building on his earlier M.Sc. in Advanced Computer Science from the University of Manchester, an M.Sc. in Computer and Network Technology, and a B.Sc. in Computer and Network Technology, both from Northumbria University. His professional career includes serving as Lecturer and Lab Demonstrator at the University of Plymouth in the United Kingdom and as a Network Engineer at Kuwait International Bank, experiences that equipped him with both academic and industry perspectives. Dr. Ali Hussain Alfayly’s research interests encompass Explainable Artificial Intelligence, Machine Learning, UAV systems, cybersecurity and network management, robotics, intelligent systems, and educational technology, reflecting a multidisciplinary approach aimed at solving real-world challenges.

Professional Profile: ORCID | Scopus

Selected Publications

  1. Detection of Fault Events in Software Tools Integrated with Human–Computer Interface Using Machine Learning, 2025 – Citations: 5

  2. Citizens’ Satisfaction Factors in E-Government Services: An Empirical Study from Kuwait, 2024 – Citations: 8

  3. Extended Technology Acceptance Model for Multimedia-Based Learning in Higher Education, 2022 – Citations: 12

  4. Challenges of Applying Semantic Web Approaches on E-Government Web Services: Survey, 2021 – Citations: 15

Dr. Sampath Dakshina Murthy Achanta | Machine Learning | Best Innovation Award

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Dr. Sampath Dakshina Murthy Achanta | Machine Learning | Best Innovation Award 

Dr. Sampath Dakshina Murthy Achanta, Vignan’s Institute of Information Technology, India

Dr. Achanta Sampath Dakshina Murthy is a distinguished academic and innovator currently serving as a Senior Associate Professor in the Department of Electronics and Communication Engineering at Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam. He is also the Head of the Vignan Centre for Innovations and Start-ups. With a Ph.D. in Image Processing from KL University, Andhra Pradesh, Dr. Murthy brings a strong foundation in digital electronics, communication systems, and biomedical signal analysis. He has over nine years of teaching experience and one year of industry exposure. His contributions to academia include more than 70 research publications, 20 research awards, six granted patents, ten design patents, and multiple funded projects. His research interests encompass human motion analysis, biomedical image and video processing, and IoT-enabled assistive technologies. Notably, his doctoral work focused on intelligent gait analysis using AI-driven IoT sensors for fall prediction in physically challenged individuals. He actively mentors students, coordinates institutional quality assurance (IQAC), and fosters innovation through successful incubation projects like ScitiSense and Illuminisense. Recognized for his academic leadership and innovation ecosystem contributions, Dr. Murthy is also an editor of the “Futuristic Trends in IoT” book series and a reviewer on multiple editorial boards.

Professional Profile:

ORCID

SCOPUS

GOOGLE SCHOLAR

🏆 Summary of Suitability: Best Innovation Award

Dr. Achanta Sampath Dakshina Murthy stands out as a trailblazer in academic innovation, applied research, and translational technology development, making him an ideal candidate for the Best Innovation Award. With a strong foundation in Image Processing, Biomedical Engineering, and IoT-enabled healthcare, his contributions are both technologically advanced and socially impactful.

🎓 Education

  • Ph.D. in Image Processing – 2023, KL University, Andhra Pradesh

  • M.Tech in Digital Electronics and Communication Systems – 2015, JNTU Kakinada

  • B.Tech in Electronics and Communication Engineering – 2013, JNTU Kakinada

  • Intermediate (M.P.C) – 2009, Board of Intermediate, A.P

  • X (CBSE) – 2007, Central Board of Secondary Education, Delhi

💼 Work Experience

  • 👨‍🏫 Senior Associate Professor (From April 1, 2025 – Present)
    Vignan’s Institute of Information Technology (A), Visakhapatnam

  • 👨‍🏫 Associate Professor (Dec 27, 2023 – March 31, 2025)
    Same institute

  • 👨‍🏫 Assistant Professor (June 15, 2016 – Dec 26, 2023)

  • 🛠️ Technician, Nirmala Industries, Auto Nagar (June 1, 2015 – June 13, 2016)

Current Roles:

  • 🚀 Head, Vignan’s Centre for Innovations & Start-ups (Aug 19, 2024 – Present)

  • 🧪 Associate Dean, R&D (Feb 19, 2024 – Aug 19, 2024)

  • 🏛️ IQAC Coordinator (Oct 26, 2021 – May 31, 2025)

🏆 Achievements

  • 📜 Ph.D. Awarded in presence of Hon’ble Sri Ramnath Kovind & Governor of A.P (Nov 30, 2024)

  • 📩 Letter of Appreciation as ARIIA Coordinator by IIC for innovation & entrepreneurship efforts (2022–23)

  • 📘 Editor of “Futuristic Trends in IoT” – IIP Series, 2023

  • 🙌 Appreciation for Research & Innovation by Chairman Dr. Lavu Rathaiah Garu (May 2023)

  • 🧠 SYRF Membership, Scholars Academic & Scientific Society (2021)

  • 📜 Ratified as Assistant Professor by JNTU Kakinada (2016)

  • 🏅 Outstanding Academic Performance in B.Tech, Vizag Institute of Technology (2011)

🧬 Research, Awards & Innovations

  • 📈 Scopus h-index: 15

  • 📝 Research Papers Published: 70

  • 🏅 Research Awards: 20

  • 🧠 Patents: 4 Published | 6 Granted

  • 🧩 Design Patents: 10

  • ©️ Copyrights: 4

  • 📚 Books Published: 7

  • 📋 Editorial Board/Reviewer: 15

  • 🏵️ Certificates of Appreciation: 100+

💡 Project Grants

  1. 🥾 Smart Shoe project under STPI Chunauti 2.0 – ₹90,000 Grant (2022–2023)

  2. 🧬 ScitiSense startup incubated at MedTech CoE, STPI Lucknow (2024)

  3. 🌐 Illuminisense incubated at AIC STPI, Bengaluru – OCP 6.0 (2024)

Publication Top Notes:

Numerical Investigation Using Machine Learning Process Combination of Bio PCM and Solar Salt for Thermal Energy Storage Applications

A wireless IOT system towards gait detection technique using FSR sensor and wearable IOT devices

Machine Learning Algorithms for Anomaly Detection in IoT Networks

Prediction and Prevention of Malicious URL Using ML and LR Techniques for Network Security

An IoT-based agriculture maintenance using pervasive computing with machine learning technique

An automated detection of heart arrhythmias using machine learning technique: SVM

Executing CNN-LSTM Algorithm for Recognizable Proof of Cervical Spondylosis Infection on Spinal Cord MRI Image

Implementation of online and offline product selection system using FCNN deep learning: Product analysis

Assist. Prof. Dr. Hossein Bagherpour | Machine Learning Awards | Best Researcher Award

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Assist. Prof. Dr. Hossein Bagherpour | Machine Learning Awards | Best Researcher Award

Assist. Prof. Dr. Hossein Bagherpour, Department of Biosystems Engineering, Bu-Ali Sina Universit, Iran

Dr. Hossein Bagherpour is an accomplished Assistant Professor in the Department of Biosystems Engineering at Bu-Ali Sina University, where he has served since 2013. Holding a Ph.D. and M.Sc. in Biosystems and Agricultural Machinery Engineering from Tarbiat Modares University and a B.Sc. in Mechanical Engineering from the University of Tehran, his interdisciplinary expertise bridges advanced engineering with agricultural innovation. Dr. Bagherpour is a leading researcher in the application of artificial intelligence and machine vision in precision agriculture, with a focus on plant disease detection, crop quality assessment, and robotic harvesting. He has supervised multiple Ph.D. and M.Sc. theses on deep learning, image processing, and AI-driven diagnostics for crops like rose, wheat, hazelnut, and quince. His contributions significantly advance smart farming technologies, offering solutions for enhanced productivity and sustainable agriculture in small and large-scale systems.

Professional Profile:

GOOGLE SCHOLAR

ORCID

Summary of Suitability for Best Researcher Award – Dr. Hossein Bagherpour

Dr. Hossein Bagherpour is an exemplary candidate for the Best Researcher Award, recognized for his pioneering work at the intersection of biosystems engineering, artificial intelligence, and precision agriculture. As an Assistant Professor at Bu-Ali Sina University since 2013, Dr. Bagherpour has made significant contributions to the development and application of intelligent systems in agricultural automation and food quality assessment.

🎓 Education

  • 🧪 Ph.D. in Biosystems Engineering – Tarbiat Modares University, Tehran, Iran

  • 🚜 M.Sc. in Agricultural Machinery Engineering – Tarbiat Modares University, Tehran, Iran

  • ⚙️ B.Sc. in Mechanical Engineering (Design of Machinery) – University of Tehran, Tehran, Iran

🏢 Work Experience

  • 👨‍🏫 Assistant Professor, Department of Biosystems Engineering, Bu-Ali Sina University (2013–Present)

    • 📍 Faculty of New Agriculture, Room 207

    • 📍 Business Incubator Center No. 2, Room 7

🏆 Achievements & Contributions

  • 📊 Supervised numerous Ph.D. and M.Sc. theses focusing on AI, deep learning, and smart agricultural systems

  • 🤖 Developed algorithms for robotic harvesting, crop disease detection, and quality inspection using machine learning and computer vision

  • 📚 Published multiple research papers (see Google Scholar) in areas such as AI-based phenotyping, intelligent sensors, and agricultural robotics

🎖 Awards & Honors

  • 🌟 Recognized for advancing smart agriculture through AI integration

  • 🧠 Leader in AI-driven research in agricultural biosystems

Publication Top Notes:

Hyperparameter Optimization of ANN, SVM, and KNN Models for Classification of Hazelnuts Images Based on Shell Cracks and Feature Selection Method

Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field

Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases

Detection of different adulteration in cinnamon powder using hyperspectral imaging and artificial neural network method

Design, Construction, and Evaluation of a Precision Vegetable Reaper to Use in Small Plots

A New Method to Optimize Deep CNN Model for Classification of Regular Cucumber Based on Global Average Pooling

Dr. Yunfei Feng | Machine learning | Best Researcher Award

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Dr. Yunfei Feng | Machine learning | Best Researcher Award 

Dr. Yunfei Feng, Department of Computer Science, United States

Dr. Yunfei Philip Feng is an accomplished professional in the field of computer science, currently serving as a Staff Machine Learning Engineer at Walmart Inc.’s Global Tech division. With a Ph.D. in Computer Science from Iowa State University, where his dissertation focused on the recognition of Activities of Daily Living, Dr. Feng has a robust academic background complemented by visiting scholar positions at prestigious institutions such as Peking University, Northeastern University, National Central University, and Nihon University. His research interests include system simulation, robotics, edge computing, computer vision, sensor fusion, machine learning, and wireless communication.Dr. Feng has significantly contributed to Walmart’s technology advancements, notably developing and optimizing systems for job application processing, mentor match recommendations, and internal chatbot functionalities. His expertise extends to building CI/CD pipelines, deploying machine learning models, and enhancing real-time streaming APIs’ performance. Prior to his tenure at Walmart, he held key roles in digital experience and analytics at Sam’s Club Technology, where he led innovative projects in indoor localization, inventory management with AGVs, and mobile app development. Dr. Feng’s early career at China Electronics Corporation involved designing central control rooms for smart buildings and integrating various systems for complex environments. His extensive experience and innovative contributions position him as a leading expert in leveraging technology to improve productivity and user experiences in diverse settings.

Professional Profile:

SCOPUS

Education

Iowa State University, Ames, IA, USA
Ph.D., Computer Science
August 2012 – May 2018

  • Dissertation: Recognition of Activities of Daily Living
  • Committee members: Carl K. Chang, Johnny S. Wong, Peter Martin, Jin Tian, Simanta Mitra

Communication University of China, Beijing, China
Master of Engineering, Communication and Information System
September 2007 – June 2009

  • Overall Ranking: 2/140
  • Focus: Wireless Communication and 3G/4G Cellular Communication, Error Correction Code, Digital Audio Broadcasting
  • Solo PI, 10,000 CNY. Coded Modulation Scheme with CPPC Codes for Digital Television Broadcasting, Beijing, China 2008-2009

Shenyang University of Technology, Shenyang, China
Bachelor’s Degree, Major in Communications Engineering
September 2003 – July 2007

  • Overall Ranking: 3/130
  • Minor in Computer Science

Academic Work

Peking University, Beijing, China
Visiting Scholar, Department of Computer Science
July 2017 – July 2017

Northeastern University, Shenyang, China
Visiting Scholar, Department of Computer Science
June 2017 – June 2017

National Central University, Taoyuan, Taiwan
Visiting Scholar, Department of Computer Science & Information Engineering
June 2016 – July 2016

Nihon University, Koriyama, Fukushima, Japan
Visiting Scholar, Department of Computer Science
June 2016 – June 2016

Research Interests

  • System Simulation
  • Robotics
  • Edge Computing
  • Computer Vision
  • Computer Audition
  • Sensor Fusion on Smart Devices and Smart Systems
  • Machine Learning
  • Deep Learning
  • Wireless Communication
  • Indoor Localization

Publication top Notes:

Sound of Daily Living Identification Based on Hierarchical Situation Audition

LiLo: ADL Localization with Conventional Luminaries and Ambient Light Sensor

A multi-objective decomposition-based ant colony optimisation algorithm with negative pheromone

Overview of cashier-free stores and a virtual simulator

A computer-aided detection system for the detection of lung nodules based on 3D-ResNet