Assoc. Prof. Dr Ali Hassan Sodhro | Intelligent Sensors Award | Best Researcher Award

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Assoc. Prof. Dr Ali Hassan Sodhro | Intelligent Sensors Award | Best Researcher Award 

Assoc. Prof. Dr Ali Hassan Sodhro, Kristianstad University, SE-29188 Kristianstad, Sweden, Sweden

Ali Hassan Sodhro is an accomplished researcher with dual Swedish and Pakistani nationality, specializing in energy-efficient and battery-friendly algorithms for wireless body sensor networks, wireless sensor networks, physical layer authentication in IoT-5G, wearable devices, and smart healthcare applications. Currently a Senior Lecturer at Kristianstad University in Sweden, Ali has also served as a Postdoctoral Research Fellow in institutions across Sweden, France, and China, including Luleå University of Technology, Linköping University, and the University Lumiere Lyon 2. His research extends to cybersecurity, network security, cryptography, and domains such as AI, machine learning, and big data analytics. Holding a Ph.D. from the University of Chinese Academy of Sciences (UCAS), Ali has supervised numerous bachelor’s and master’s theses and co-supervised Ph.D. students, contributing substantially to both academic research and grant proposals. His teaching experience spans Swedish institutions like Mid Sweden University and Gothenburg University, alongside earlier academic roles at Sukkur IBA University in Pakistan. Ali is actively involved in conferences, workshop organization, and launching special journal issues, with his work published across multiple prestigious platforms.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award:

Ali Hassan Sodhro is a distinguished researcher with significant contributions to the fields of energy-efficient algorithms for wireless sensor networks, smart healthcare applications, and IoT-driven technologies, particularly within the domain of body sensor networks and wearable devices. With a strong interdisciplinary focus that spans AI, IoT, and cloud computing, his work aligns with many of the emerging challenges in technology and healthcare, areas critical for modern innovations and societal impact.

🎓 Education:

  • Ph.D. in Computer Applications Technology (2016)
    University: Chinese Academy of Sciences, China 🇨🇳
    Thesis: Energy-efficient Communication in Wireless Body Sensor Networks
  • M.Engg in Communication Systems and Networks (2010)
    University: Mehran University of Engineering and Technology, Pakistan 🇵🇰
    Thesis: Security Issue/Authentication and Simulation of LEAP in WSN
  • B.Engg in Telecommunication Engineering (2008)
    University: Mehran University of Engineering and Technology, Pakistan 🇵🇰
    Thesis: Wireless Sensor Networks, Simulation of Ad-Hoc Routing Protocols

💼 Professional Experience:

  • Senior Lecturer at Kristianstad University, Sweden 🇸🇪 (2021–Present)
    Teaching, research, and supervision of student projects; actively engaged in scientific publishing and grant proposal writing.
  • Postdoctoral Fellow at Luleå University of Technology, Sweden 🇸🇪 (2020)
    Contributed to supervision, teaching, and coordination of special journal issues and conferences.
  • Assistant Professor at Sukkur IBA University, Pakistan 🇵🇰 (2016–2017)
    Supervised students, taught courses, and organized academic events.

🧠 Research Focus:

Ali Hassan Sodhro is a highly skilled researcher in Energy-efficient & Battery-friendly Algorithms ⚡ for Wireless Body Sensor Networks 💡, Wearable Devices ⌚, and IoT-5G 🔗. His expertise spans AI/ML 🤖, Cybersecurity 🔒, Network Security 🛡️, Big Data Analytics 📊, and Multimedia Transmission 🎥, with an emphasis on Smart Healthcare 🏥 and Physical Layer Authentication in IoT networks.

Publication top Notes:

Artificial intelligence-driven mechanism for edge computing-based industrial applications

CITED:326

A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks

CITED:297

Mobile edge computing based QoS optimization in medical healthcare applications

CITED:208

Towards an optimal resource management for IoT based Green and sustainable smart cities

CITED:197

Quality of service optimization in an IoT-driven intelligent transportation system

CITED:173

Dr. Yue Wang | Sensor development Award | Best Researcher Award

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Dr. Yue Wang | Sensor development Award | Best Researcher Award 

Dr. Yue Wang, University of Science and Technology Liaoning, China

Dr. Yue Wang is an Associate Professor at the School of Chemical Engineering at the University of Science and Technology Liaoning in China. He earned his Bachelor’s degree from the University of Science and Technology Anshan and both his Master’s and Doctorate degrees from the University of Science and Technology Liaoning and Saitama Institute of Technology, Japan, respectively. Since joining the University of Science and Technology Liaoning in 2006, Dr. Wang has focused his research on sensors and biosensors, biofuel cells, supercapacitors, energy harvesting, and artificial muscles. His work has resulted in over 60 published scientific papers, garnering approximately 600 citations, reflecting his significant contributions to the field. Dr. Wang has secured multiple research grants from various institutions, including the Education Department of Liaoning Province and the Natural Science Foundation of Liaoning Province, to advance his projects on conductive sensors, pesticide sensors, electrochemical biosensors, and wearable smart sensing technologies. Additionally, he completed a visiting scholarship at the University of Texas at Dallas in 2019-2020, further enhancing his academic and research expertise.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award: Yue Wang

Yue Wang is an exemplary candidate for the Best Researcher Award, primarily due to his substantial academic qualifications, extensive research contributions, and impactful work in the field of Material Science, specifically within sensor and biosensor technologies.

Education

  • Bachelor’s Degree in Material Science
    University of Science and Technology Anshan, China
    September 1998 – July 2002
  • Master’s Degree in Material Science
    University of Science and Technology Liaoning, China
    September 2003 – March 2006
  • Ph.D. in Material Science
    Saitama Institute of Technology, Japan
    April 2008 – March 2011

Work Experience

  • Associate Professor
    University of Science and Technology Liaoning, China
    April 2006 – Present
  • Visiting Scholar
    University of Texas at Dallas
    April 2019 – March 2020

Publication top Notes:

A carbon black–doped chalcopyrite–based electrochemical sensor for determination of hydrogen peroxide

Glucose oxidase, horseradish peroxidase and phenothiazine dyes-co-adsorbed carbon felt-based amperometric flow-biosensor for glucose

Crab gill–derived nanorod-like carbons as bifunctional electrochemical sensors for detection of hydrogen peroxide and glucose

Cellulose-derived hierarchical porous carbon based electrochemical sensor for simultaneous detection of catechol and hydroquinone

A triphenylamine based fluorescent probe for Zn2+ detection and its applicability in live cell imaging

1,8-naphthalimide-triphenylamine-based red-emitting fluorescence probes for the detection of hydrazine in real water samples and applications in bioimaging in vivo

Assist Prof Dr. Loknath Sai Ambati | Activity detection Award | Best Researcher Award

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Assist Prof Dr. Loknath Sai Ambati | Activity detection Award | Best Researcher Award 

Assist Prof Dr. Loknath Sai Ambati, Oklahoma City University, United States

Dr. Loknath Sai Ambati is an accomplished academic and researcher specializing in Information Systems and Data Analytics. Currently serving as an Assistant Professor of Data Analytics at Oklahoma City University, Dr. Loknath Sai Ambati holds a Doctor of Philosophy in Information Systems, with a specialization in Artificial Intelligence, from Dakota State University, where they also earned two master’s degrees in Information Systems and Data Analytics. With over five years of teaching experience, they have instructed various courses at both undergraduate and graduate levels, focusing on business analytics, healthcare analytics, and social media mining.The Activity Detection Award celebrates innovations in behavioral recognition technology. Explore eligibility, qualifications, publications, and submission guidelines for this esteemed recognition.

Professional Profile:

SCOPUS

 

Summary of Suitability for Best Researcher Award: Loknath Sai Ambati

Based on Loknath Sai Ambati’s impressive educational background, research contributions, and professional experience, he is a highly suitable candidate for the Best Researcher Award.

Education

Dakota State University, Madison, South Dakota
Doctor of Philosophy in Information Systems (Artificial Intelligence)
Master of Science in Information Systems
Master of Science in Data Analytics
GPA: 4.0/4.0
August 2018 – April 2023 (PhD)
August 2019 – December 2020 (MS in Information Systems)
August 2016 – December 2017 (MS in Data Analytics)

VIT University, Chennai, India
Bachelor of Technology in Electronics and Communication Engineering
GPA: 8.55/10
July 2012 – May 2016

Work Experience

Assistant Professor of Data Analytics
Oklahoma City University
September 2023 – Present

  • Teaching graduate-level Data Analytics courses.
  • Engaging in research activities related to Information Systems and Data Analytics.
  • Participating in service activities, including serving on review committees for various conferences and journals.
  • Serving as the Faculty Advisor for the Indian Student Association at OCU.

Visiting Assistant Professor of Business Analytics
Indiana University
May 2022 – August 2023

  • Teaching various Business Analytics courses at both undergraduate and graduate levels.
  • Conducting research activities in healthcare and social media analytics.
  • Participating in service activities, including serving on review committees for conferences and journals.

Graduate Research Assistant
Dakota State University
August 2018 – May 2022

  • Worked on innovations in wearable technology integrated with Artificial Intelligence for healthcare.
  • Assisted the supervisor with research projects and interacted with students regarding course content.
  • Volunteered as an instructor for certain courses as needed.

Analytics Developer
Baylor Scott and White Health
February 2018 – August 2018

  • Applied machine learning algorithms to denial data, achieving savings of up to $0.5 million on denials.
  • Implemented statistical models to reduce denial claims and enhance revenue efficiency.
  • Analyzed correlations between physician coding behaviors and Medicare Risk Adjustment Factor (RAF) scores.
  • Technologies used: Power BI, R, SAS, Python, SQL, MicroStrategy, Advanced Excel.

Publication top Notes:

Human Body Full-body Motion Gesture Image Feature Capture in Mobile Sensor Networks

Intrusion Detection System: A Comparative Study of Machine Learning-Based IDS

Explosive force acquisition of sprinter lower limb in training based on WSN

Two-phase classification: ANN and A-SVM classifiers on motor imagery BCI

Optimal trained long short-term memory for opinion mining: a hybrid semantic knowledgebase approach

FHE-Blockchain: Enhance the Scheme for Secret Sharing of IoMT Data using Decentralized Techniques

Design of Civil Aviation Security Check Passenger Identification System Based on Residual Convolution Network

 

Mr Anandarup Roy | Internet of Things | Best Researcher Award

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Mr Anandarup Roy| Internet of Things | Best Researcher Award

Mr Anandarup Roy,Senior Research Fellow, Indian Statistical Institute, Kolkata,India

Anandarup Roy is a Ph.D. candidate in Computer Science at the Indian Statistical Institute (ISI), Kolkata, specializing in combinatorial secret sharing. His thesis was submitted on July 19, 2024, and he expects to receive his degree by December 2024. He is advised by Prof. Bimal Kumar Roy and co-supervised by Prof. Mridul Nandi, both from the Applied Statistics Unit at ISI.

Professional Profile:

Summary of Suitability for the Best Researcher Award:

Anandarup Roy, a Ph.D. candidate at the Indian Statistical Institute, has made significant contributions to the field of computer science, particularly in combinatorial secret sharing. His research extends previous work in Bayesian incentive-compatible mechanism design and social learning, demonstrating a robust understanding of complex statistical models and their applications.

Education

He Naifeng is pursuing a PhD at the prestigious Nanjing University of Aeronautics and Astronautics, where he has built a strong foundation in automation and robotics. His academic journey reflects a commitment to advancing technology in mobile robotics, demonstrating a keen interest in both theoretical knowledge and practical applications.

Work Experience

From 2016 to 2018, Anandarup worked as a project-linked person at the Economics Research Unit of ISI, where he contributed to a project on Bayesian incentive-compatible mechanism design under the supervision of Prof. Manipushpak Mitra. This research extended his master’s thesis by examining learning processes in a social choice environment with risk-neutral agents.

Skills

Anandarup is proficient in using Linux OS (Ubuntu) and LaTeX. He possesses basic programming knowledge in C, making him well-equipped for computational tasks related to his research.

Research Focus

His research focuses on autonomous navigation for wheel-legged robots, with particular emphasis on reinforcement learning in control systems and intelligent motion control. He aims to develop practical applications that enhance the performance and adaptability of mobile robots in challenging environments.

Publication top Notes:

  • Combining Dynamic Selection and Data Preprocessing for Imbalance Learning
    Year: 2018
    Journal: Neurocomputing
    Volume/Pages: 286, 179-192
  • SVM-based Hierarchical Architectures for Handwritten Bangla Character Recognition
    Year: 2009
    Journal: International Journal on Document Analysis and Recognition (IJDAR)
    Volume/Pages: 12, 97-108
  • Lecithin and Venom Haemolysis
    Year: 1945
    Journal: Nature
    Volume/Pages: 155 (3945), 696-697
  • A Novel Approach to Skew Detection and Character Segmentation for Handwritten Bangla Words
    Year: 2005
    Journal: Digital Image Computing: Techniques and Applications (DICTA’05)
    Pages: 30-30
  • JCLMM: A Finite Mixture Model for Clustering of Circular-Linear Data and Its Application to Psoriatic Plaque Segmentation
    Year: 2017
    Journal: Pattern Recognition
    Volume/Pages: 66, 160-173
  • An HMM Framework Based on Spherical-Linear Features for Online Cursive Handwriting Recognition
    Year: 2018
    Journal: Information Sciences
    Volume/Pages: 441, 133-151
  • Pair-Copula Based Mixture Models and Their Application in Clustering
    Year: 2014
    Journal: Pattern Recognition
    Volume/Pages: 47 (4), 1689-1697
  • Character Segmentation for Handwritten Bangla Words Using Artificial Neural Network
    Year: 2005
    Journal: Proceedings of the 1st IAPR TC3 NNLDAR
  • SWGMM: A Semi-Wrapped Gaussian Mixture Model for Clustering of Circular–Linear Data
    Year: 2016
    Journal: Pattern Analysis and Applications
    Volume/Pages: 19, 631-645
  • Headline Based Text Extraction from Outdoor Images
    Year: Not specified (conference paper)
    Journal: Pattern Recognition and Machine Intelligence: 4th International Conference

Prof. Yankun Peng | Smart Monitoring Award | Best Researcher Award

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Prof. Yankun Peng | Smart Monitoring Award | Best Researcher Award 

Prof. Yankun Peng, China Agricultural University, China

Dr. Peng is a distinguished researcher and professor in the field of Agricultural Engineering with a focus on intelligent detection systems and automated devices for evaluating agricultural product quality and safety. He holds a Ph.D. in Biological and Agricultural Engineering from Tokyo University of Agriculture and Technology, Japan, and has extensive academic and professional experience in both China and the United States. Since 2007, Dr. Peng has served as a Professor and PhD supervisor at the College of Engineering, China Agricultural University (CAU), where he also holds key leadership roles including Director of the National R&D Center for Agro-Processing Technology and Equipment and the National Technical Center for Nondestructive Evaluation, Identification, Instrument, and Equipment of Famous Agro-foods.

Professional Profile:

 

Summary of Suitability for Best Researcher Award 

Dr. Peng has authored 293 peer-reviewed journal articles and 257 conference proceedings, showcasing his prolific research output.He holds 107 patents (including a US patent), with 22 patents industrialized, reflecting his significant contributions to applied science and technology. Additionally, he has developed 18 series of equipment for agro-food quality inspection and grading. Dr. Peng has established 14 standards and authored 4 books and 17 book chapters, demonstrating his leadership in setting benchmarks and contributing to scientific literature.

Education

  • Ph.D. in Biological and Agricultural Engineering
    Tokyo University of Agriculture and Technology, Tokyo, Japan
    Apr. 1993 – Mar. 1996
    Major: Agricultural Engineering, Specialty in Biological Production Science
    Dissertation Title: Active Noise Control on Agricultural/Biological Production Machinery

    • Developed and designed a new type of Active Noise Control (ANC) system/equipment.
    • Proposed a Recurrent Least Squares (RLS) algorithm for noise reduction.
    • Conducted computer simulations of noise reduction effects using C/C++ programming language.
    • Constructed an Adaptive Digital Filter (ADF) system with digital signal processors (DSP) and C/C++ programming.
    • Evaluated the control system on actual machinery and simplified the control algorithm using matrix theory.
  • M.S. in Engineering in Agricultural Electrification & Automation
    Graduate School of Northeast Agricultural University, Harbin, China
    Sep. 1985 – Dec. 1988
    Major: Agricultural Electrification & Automation
    Thesis Title: A Microcomputer Control System for Livestock Granulated Feed Processing

    • Developed a PID feedback control system using a microcomputer.
    • Proposed a new control method for the rotation speed of a servomechanism.
    • Designed a controller using a microcomputer and assembly programming language.
    • Invented a grain flow sensor and applied the control system to livestock feed production.
    • Proposed a method for judging the stability of linear time-invariant systems.

Professional Experience

  • Professor and Ph.D. Supervisor
    Department of Agricultural Engineering, College of Engineering, China Agricultural University (CAU)
    Beijing, China
    Mar. 2007 – Present

    • Research in nondestructive measurement and instrumentation for agricultural product quality and safety.
    • Development of hyperspectral/multispectral and Raman spectral imaging methods for meat microbial contamination detection.
    • Development of rapid real-time inspection/detection systems and NIR optical instruments for agricultural product contaminants.
    • Teaching courses on nondestructive measurement technology and hyperspectral imaging techniques for agro-food quality attributes.
    • Supervised over 60 graduate students in agricultural engineering research.
  • Director, National R&D Center for Agro-Processing Technology and Equipment
    Ministry of Agriculture and Rural Affairs, China
    Nov. 2009 – Present

    • Oversight of national research and development projects related to agro-processing technology and equipment.
  • Director, National Technical Center for Nondestructive Evaluation, Identification, Instrument and Equipment of Famous, Special, Excellent and New Agro-foods
    Ministry of Agriculture and Rural Affairs, China
    Dec. 2019 – Present

    • Leadership in the development and evaluation of nondestructive techniques and equipment for agro-food quality assessment.

Publication top Notes:

Real-time lettuce-weed localization and weed severity classification based on lightweight YOLO convolutional neural networks for intelligent intra-row weed control

Tailored Au@Ag NPs for rapid ractopamine detection in pork: Optimizing size for enhanced SERS signals

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion

SERS characterization and concentration prediction of Salmonella in pork

Rapid Quantitative detection of Ractopamine using Raman scattering features combining with Deep Learning

Non-destructive detection of TVC in pork by machine learning techniques based on spectral information