Dr. Khaled Alhawiti | Parkinson’s Monitoring | Best Researcher Award

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Dr. Khaled Alhawiti | Parkinson’s Monitoring | Best Researcher Award 

Dr. Khaled Alhawiti | Parkinson’s Monitoring | University of Tabuk | Saudi Arabia

Dr. Khaled M. Alhawiti is an accomplished Associate Professor in the Faculty of Computers and Information Technology at the University of Tabuk, recognized for his scholarly contributions in artificial intelligence, natural language processing, and Arabic language processing. He completed his Ph.D. in Computer Science from the University of Wales, Bangor University, where he focused on computational models and language technologies that support intelligent information processing. His academic path includes a Master of Science in Information Technology from the University of Technology Malaysia and a Bachelor’s degree in Computer Science from the University of Jordan, reflecting strong foundations in computing and higher education across multiple countries. Professionally, Dr. Khaled M. Alhawiti has built extensive experience in teaching, mentoring, research development, and academic leadership, actively contributing to curriculum enhancement and collaborative research initiatives within his institution and beyond. His research interests span artificial intelligence, data science, natural language processing, Arabic text modeling, speech-based systems, and intelligent educational technologies. He possesses strong research skills in machine learning, adaptive modeling, text compression techniques, rule-based systems, language preprocessing, and large-scale corpus analysis. His publications have been widely cited and indexed in Scopus and leading AI venues, demonstrating the impact of his contributions to computational linguistics and AI-driven text analysis. Dr. Khaled M. Alhawiti has collaborated on multiple international research activities, contributing to academic exchanges across Saudi Arabia, Malaysia, the United Kingdom, and Jordan, strengthening global partnerships in computer science. His awards and honors include recognition for high-impact publications, contributions to AI education research, and active participation in academic committees and professional societies. He is also associated with leading research communities such as IEEE and ACM, promoting engagement in emerging technological advancements.

Professional Profiles: ORCID  | Google Scholar

Featured Publications 

  1. Alhawiti, K. M. (2014). Natural language processing and its use in education. 161 citations.

  2. Alhawiti, K. M. (2015). Advances in artificial intelligence using speech recognition. 42 citations.

  3. Alhawiti, K. M. (2014). Adaptive models of Arabic text. 20 citations.

  4. Zerrouki, T., Alhawiti, K., & Balla, A. (2014). Autocorrection of Arabic common errors for large text corpus. 16 citations.

  5. Teahan, W. J., & Alhawiti, K. M. (2015). Preprocessing for PPM: Compressing UTF-8 encoded natural language text. 13 citations.

  6. Elfaki, A. O., Alhawiti, K. M., AlMurtadha, Y. M., Abdalla, O. A., & Elshiekh, A. A. (2014). Rule-based recommendation for supporting student learning-pathway selection. 13 citations.

  7. Alhawiti, K. M. (2014). Adaptive Arabic text modeling using computational techniques. (Derived from thesis-related work). 20 citations.

Dr. Suvendu Mohanty | Health Monitoring | Best Sensor for Health Monitoring Award

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Dr. Suvendu Mohanty | Health Monitoring | Best Sensor for Health Monitoring Award  

Dr. Suvendu Mohanty, Indian Institute of Technology Madras, India

Dr. Suvendu Mohanty is a Postdoctoral Researcher in Mechanical Engineering at the Indian Institute of Technology Madras, specializing in machine health monitoring, predictive maintenance, and remaining useful life (RUL) estimation of mechanical systems. He holds a Ph.D. in Production Engineering from NIT Agartala, with a research focus on failure prediction of CNG-driven engines. With over a decade of academic and research experience, including prior roles as Assistant Professor, Dr. Mohanty has led and contributed to high-impact projects in collaboration with industry giants such as Walmart Inc. and Honeywell International Inc. His interdisciplinary expertise spans wear analysis, tribology, AI-driven diagnostics, and multi-sensor data fusion. A prolific researcher and active contributor to conferences and workshops, he is passionate about translating research into real-world engineering solutions that enhance reliability and sustainability in industrial systems.

Professional Profile:

SCOPUS

ORCID

GOOGLE SCHOLAR

🏅 Summary of Suitability for Best Sensor for Health Monitoring Award

Nominee: Dr. Suvendu Mohanty
Designation: Postdoctoral Researcher, Mechanical Engineering
Institution: Indian Institute of Technology Madras, India

Dr. Suvendu Mohanty is an exceptional candidate for the Best Sensor for Health Monitoring Award, recognized for his impactful research in multi-sensor data fusion, predictive diagnostics, and machine health monitoring systems. His work lies at the critical intersection of mechanical engineering, artificial intelligence, and sensor-based prognostics, directly advancing the field of health monitoring technologies for both machines and potential extensions to biomedical systems

🎓 Education

  • Ph.D. in Production Engineering
    🏫 National Institute of Technology (NIT) Agartala, India | 📅 2024
    📚 Thesis: Failure Prediction of Engine Driven by CNG Through Prognostic Approach
    📊 CGPA: 8.93/10

  • M.Tech. in Thermal Engineering
    🏫 NIT Patna, India | 📅 2013
    📚 Thesis: Analysis of Exhaust Emission of Internal Combustion Engine Using Biodiesel Blend
    📊 CGPA: 7.73/10

  • B.Tech. in Mechanical Engineering
    🏫 Bhadrak Institute of Engineering & Technology (BIET), Odisha, India | 📅 2011
    📚 Thesis: Turbulent Fluid Flow & Heat Transfer in Mixing Junction Using Gambit and Fluent
    📊 CGPA: 7.32/10

🛠️ Work Experience

  • 🔬 Postdoctoral Researcher
    🏢 Engineering Asset Management Group, Mechanical Engineering, IIT Madras
    📅 Aug 2024 – Present
    ✅ Focus: Predictive maintenance, multi-sensor data integration, AI-based diagnostics
    🤝 Industrial Collaborations: Walmart Inc., Honeywell International Inc.

  • 👨‍🏫 Assistant Professor
    🏫 Hi-Tech Institute of Technology, Bhubaneswar
    📅 June 2013 – July 2015
    🧪 Courses: IC Engine, Thermodynamics, Heat Transfer

  • 👨‍🏫 Assistant Professor
    🏫 Gandhi Institute for Education and Technology, Bhubaneswar
    📅 Aug 2015 – Dec 2016
    🧪 Courses: Mechanical Measurements, Heat Transfer, Labs

🏆 Achievements

  • 🔧 Successfully executed multiple research collaborations with Honeywell and Walmart Inc. on predictive maintenance and diagnostics.

  • 📊 Developed AI-integrated health monitoring systems for rotating machinery and induction motors.

  • 📝 Published and presented several research papers in national seminars and workshops.

  • 🧪 Led experimental diagnostics on bearing systems using the Honeywell Versatile Transmitter (HVT) system.

🥇 Awards & Honors

  • 🧭 Postdoctoral Research Fellowship, IIT Madras (2024–Present)

  • 🏅 Organising Committee Member – International Conference on Next Generation Technologies: Design and Manufacturing (ICNGT), IIT Madras, Nov 2024

  • 🏅 Organising Member, FFMA-2012, NIT Patna

  • 🧠 National Cyber Olympiad Participant – Science Olympiad Foundation

  • 🎤 Seminar Presenter – RTMERAF at the Institution of Engineers, Tripura

  • 🎓 Workshop Participation – CTSR and ECED programs at NIT Agartala

Publication Top Notes:

Maintenance analytics for achieving sustainability using CNG as alternative fuel

A frame work for comparative wear based failure analysis of CNG and diesel operated engines

Application of Artificial Intelligence for Failure Prediction of Engine Through Condition Monitoring Technique

Fractal mathematics applications for wear image analysis of engines using biofuels

Artificial Neural Network coupled Condition Monitoring for advanced Fault Diagnosis of Engine

Experimental Investigation of Tribo-Corrosive Nature of Biodiesel and its Effect on Lubricating System

Intelligent prediction of engine failure through computational image analysis of wear particle

Importance of Tribological study for Internal Combustion Engines using Biofuel

Body Area Network

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Introduction of Body Area Network

Body Area Networks (BANs) are a frontier in wireless sensor technology, enabling the monitoring of physiological data, vital signs, and physical activity within or around the human body. These networks have profound implications for healthcare, sports, and wearable technology.

Medical BANs for Health Monitoring:

Investigating the development of BANs for continuous monitoring of vital signs, such as heart rate, blood pressure, and glucose levels, in clinical and home settings.

Wearable BAN Devices:

Focusing on wearable BAN devices, including smartwatches and fitness trackers, that integrate seamlessly with the human body to track activity, sleep, and health metrics.

Sensor Integration and Miniaturization:

Addressing challenges in sensor miniaturization and integration within BAN devices, enabling unobtrusive and comfortable long-term wear.

BAN Security and Privacy:

Analyzing security measures and privacy safeguards in BANs to protect sensitive medical and personal data from unauthorized access and breaches.

Wireless Communication Technologies for BANs:

Exploring wireless communication protocols, such as Bluetooth Low Energy (BLE) and Zigbee, optimized for BANs to ensure reliable and energy-efficient data transmission.