Victor Agughasi |Computer Aided Design In Mechanical Engineering|Best Researcher Award

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Assist. Prof. Dr.Victor Agughasi |Computer Aided Design In Mechanical Engineering|Best Researcher Award

Assistant Professor, Maharaja Institute of Technology Mysore, India

Summary:

Assistant Professor, Maharaja Institute of Technology, Mysore, India,Dr. Victor Agughasi is an Assistant Professor at the Maharaja Institute of Technology, Mysore, India. With extensive expertise in his field, he contributes to academic excellence through teaching, research, and mentoring students. His work reflects a commitment to advancing knowledge and fostering innovation in higher education.

 

Professional Profiles:

Scopus

Google Scholar

Orcid

šŸŽ“ Education :

Ph.D. in Computer Science,University of Mysore, Karnataka, India (Sept. 2018 ā€“ Dec. 2023),Thesis: Machine Learning Algorithm for the Diagnosis of Chronic Obstructive Pulmonary Diseases from Chest X-ray Images.,Postgraduate Diploma in Business Administration (PGDBA),Bangalore University, Bangalore, India (Dec. 2016 ā€“ Jan. 2018),M.Sc. in Computer Science,Bangalore University, Bangalore, India (Apr. 2014 ā€“ Mar. 2016),B.Sc. in Computer Science,Michael Okpara University, Abia State, Nigeria (Nov. 2006 ā€“ Oct. 2010),West African Senior School Certificate Examination (WASSCE),Community Secondary School, Okigwe, Imo State, Nigeria (May ā€“ June 2004)

 

šŸ¢Ā Experience:

Assistant Professor,Department of Computer Science and Engineering (Computer Aided Design In Mechanical Engineering), Maharaja Institute of Technology, Mysore, India (Oct. 2023 ā€“ Present),Teaches subjects such as Machine Learning, Computer Vision, Big Data Analytics, Digital Image Processing, Database Management Systems, Python for Data Visualization, and Research Methodology.,Research Associate,Maharaja Institute of Technology, Mysore, India (June 2019 ā€“ Oct. 2023),Focused on Machine Learning, Big Data Analytics, and Mobile App Development in Java. Supervised research projects in Machine Learning.,Visiting Faculty (Voluntary),Dr. Ambedkar Institute for Management Science, Bangalore, India (Aug. 2018 ā€“ May 2019),Conducted courses in Information System & Science and Database Management Systems.,Visiting Faculty (Voluntary),St. Aloysius Degree College, Bangalore, India (Jul. 2017 ā€“ Feb. 2018),Taught sessions on Information System & Science and Database Management Systems.,Teaching Assistant (Voluntary),St. Josephā€™s College, Bangalore, India (Oct. 2014 ā€“ Mar. 2016),Taught Computer Fundamentals and Web Design using PHP.,Java Instructor (Intern),APTECH Computer Education, Bangalore, India (Oct. ā€“ Dec. 2014),Provided training in Computer Fundamentals and Web Design using PHP.,Web Developer,Max-Out Resources Pvt. Ltd, Abuja, Nigeria (Jul. 2011 ā€“ Jun. 2012),High School Teacher,Community Secondary School, Okigwe, Nigeria (Feb. ā€“ Nov. 2009)

Skills:

Proficient in programming languages such as Java, JavaScript, Python, and PHP. Experienced in database systems including MySQL, PostgreSQL, and Oracle. Fluent in English with basic knowledge of Kannada.

 

Research Focus :

Specializes in Medical Imaging, Explainable AI Models, Data Science, Machine Learning, Deep Learning, and Computer Vision. Research emphasizes creating innovative machine learning algorithms for diagnosing chronic diseases from medical imaging data.

 

šŸ”¬Awards:

Received Best Paper Awards at multiple international conferences including ADCIS-2024, ERCICAM-2024, and ICCSA-2021. Recognized as the Best Outgoing Student in PG Science at St. Josephā€™s College, Bangalore, and awarded gold medals in web application competitions organized by APTECH. Secured a Management Scholarship and Certificates of Merit for outstanding academic performance.

 

Conclusion:

Based on the information provided:,Suitability: Dr. Victor Agughasi appears to be a strong candidate for the award, provided his accomplishments align with the specific goals of the awarding body.,Recommendations: A detailed application highlighting research impact, innovation, and leadership, complemented by addressing areas for improvement, would enhance his candidacy.

Ā Publications:

  • ResNet-50 vs VGG-19 vs Training from Scratch: A Comparative Analysis of the Segmentation and Classification of Pneumonia from Chest X-Ray Images
    Authors: Agughasi Victor Ikechukwu, Murali S, Deepu R, RC Shivamurthy
    Publication: Global Transitions Proceedings
    Year: 2021
    Citations: 5

 

  • CX-Net: An Efficient Ensemble Semantic Deep Neural Network for ROI Identification from Chest X-Ray Images for COPD Diagnosis
    Authors: AV Ikechukwu, S Murali
    Publication: Machine Learning: Science and Technology
    Year: 2023
    Citations: 21

 

  • i-Net: A Deep CNN Model for White Blood Cancer Segmentation and Classification
    Authors: AV Ikechukwu, S Murali
    Publication: International Journal of Advanced Technology and Engineering Exploration
    Year: 2022
    Citations: 19

 

  • Semi-Supervised Labelling of Chest X-Ray Images Using Unsupervised Clustering for Ground-Truth Generation
    Authors: Agughasi Victor Ikechukwu, S Murali
    Publication: Applied Engineering and Technology
    Year: 2023
    Citations: 13

 

  • Explainable Deep Learning Model for Covid-19 Diagnosis
    Authors: AV Ikechukwu, P Sreyas, A Sena, H Preetham, K Raksha
    Publication: IRJMETS
    Year: 2022
    Citations: 10

 

  • Energy-Efficient Deep Q-Network: Reinforcement Learning for Efficient Routing Protocol in Wireless Internet of Things
    Authors: AV Ikechukwu, S Bhimshetty
    Publication: Indonesian Journal of Electrical Engineering and Computer Science
    Year: 2024
    Citations: 8

 

  • xAI: An Explainable AI Model for the Diagnosis of COPD from CXR Images
    Authors: Agughasi Victor Ikechukwu, S Murali
    Publication: 2023 IEEE 2nd International Conference on Data, Decision, and Systems (ICDDS)
    Year: 2023
    Citations: 6

 

  • COPDNet: An Explainable ResNet50 Model for the Diagnosis of COPD from CXR Images
    Authors: AV Ikechukwu, S Murali, B Honnaraju
    Publication: 2023 IEEE 4th Annual Flagship India Council International Subsections Conference
    Year: 2023
    Citations: 6

 

  • The Superiority of Fine-Tuning Over Full-Training for the Efficient Diagnosis of COPD from CXR Images
    Authors: Agughasi Victor Ikechukwu
    Publication: Inteligencia Artificial
    Year: 2024
    Citations: 3

 

  • Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques
    Authors: Agughasi Victor Ikechukwu
    Publication: Inteligencia Artificial
    Year: 2024
    Citations: 2

 

  • Diagnosis of Chronic Kidney Disease Using NaĆÆve Bayes Algorithm Supported by Stage Prediction Using eGFR
    Authors: Agughasi Victor Ikechukwu, Nivedha K, Prakruthi NM, Fathima Farheen, Harini K
    Publication: Not specified in detail
    Year: 2020
    Citations: 1

 

  • Advances in Thermal Imaging: A Convolutional Neural Network Approach for Improved Breast Cancer Diagnosis
    Authors: Agughasi Victor Ikechukwu, Sampoorna Bhimshetty, Deepu R, M.V Mala
    Publication: IEEE Xplore
    Year: 2024
    Citations: Not provided

 

  • Effective Approach for Fine-Tuning Pre-Trained Models for the Extraction of Texts from Source Codes
    Authors: D Shruthi, HK Chethan, VI Agughasi
    Publication: ITM Web of Conferences
    Year: 2024
    Citations: Not provided

Mahmoud Marhamati |Artificial | Best Researcher Award

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Mr.Mahmoud Marhamati |Artificial | Best Researcher Award

Mr. Mahmoud Marhamati ,PhD candidate in Tehran University of Medical Science, Iran

Mr. Mahmoud Marhamati is a PhD candidate at Tehran University of Medical Sciences in Iran. His research focuses on advancing medical science through innovative studies in his field. He is dedicated to contributing to healthcare improvements and academic excellence, and is actively involved in both research and academic pursuits at the university.

Summary:

Strengths: Innovation in noisy data augmentation, high-impact publications, interdisciplinary collaboration, and significant contributions to COVID-19 research.,Areas for Improvement: Diversifying AI research topics and enhancing recent paper visibility.

Professional Profiles:

Google Scholar

šŸŽ“ Education :

M. Marhamati holds an advanced degree in medical sciences, specifically focusing on computational medicine and biomedical research. This background includes a strong foundation in medical imaging, computational biology, and clinical research, preparing them to contribute significantly to the application of artificial intelligence in healthcare. The educational path also demonstrates a blend of medical knowledge with a deep understanding of technological advancements in disease detection, management, and clinical trials.

šŸ¢Ā Experience:

M. Marhamati has a rich experience in the intersection of healthcare and technology, particularly in the development and enhancement of machine learning and deep learning algorithms for medical image analysis. Their work spans from the detection of COVID-19 using X-ray and CT images to research in chronic disease management, leveraging the Internet of Things (IoT). They have also contributed to clinical research, including trials related to the efficacy of various medical interventions, such as intravenous catheter patency, airway monitoring during CPR, and pain management in medical procedures.

šŸ› ļøSkills:

Deep Learning & Machine Learning: Extensive experience in applying deep convolutional neural networks (CNNs) for medical image analysis, including the detection of COVID-19 and tuberculosis.,Medical Imaging Analysis: Expertise in X-ray and CT image processing, particularly for respiratory diseases like COVID-19.,Clinical Research & Trials: Proven track record in designing and conducting clinical trials, focusing on novel therapeutic interventions and medical devices.,Biomedical Research: Ability to bridge clinical practice with cutting-edge research, with publications in both medical and technical fields.,Data Augmentation & Noise Handling: Experience in developing noise-robust deep learning models and augmentation strategies to improve model generalization.,Internet of Things (IoT) in Healthcare: Knowledge in integrating IoT technologies for chronic disease management, particularly during the COVID-19 pandemic.

šŸ”¬Awards:

Throughout their career, M. Marhamati has received recognition for their innovative work in applying deep learning to medical image analysis. Their research has been published in top-tier journals, and they have been acknowledged for their contributions to improving the detection of diseases like COVID-19 and tuberculosis through AI-based models. Additionally, their clinical research has garnered attention for improving patient care practices.

Research Focus:

M. Marhamatiā€™s research is primarily focused on the application of deep learning and AI in medical imaging and healthcare. One key area is the development of noise-robust deep convolutional neural networks (CNNs) for the detection of COVID-19 and tuberculosis from X-ray and CT images. They have also pioneered strategies for learning-to-augment methods to enhance the generalizability of CNN models in noisy environments. Moreover, their work extends to the integration of IoT in healthcare, exploring its role in managing chronic diseases, especially during pandemics.

Conclusion:

Mr. Mahmoud Marhamati is a highly suitable candidate for the Best Researcher Award due to his innovative contributions to AI in medical imaging, particularly in the detection and management of COVID-19. His interdisciplinary approach, impactful publications, and focus on real-world healthcare applications position him as a forward-thinking researcher who exemplifies excellence in combining AI with healthcare innovation. To further strengthen his candidacy, expanding into other AI applications beyond COVID-19 and seeking leadership opportunities would broaden his impact

Publications :

  • Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images
    • Authors: M. Momeny, A.A. Neshat, M.A. Hussain, S. Kia, M. Marhamati, et al.
    • Journal: Computers in Biology and Medicine
    • Year: 2021
    • Citations: 67

 

  • Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images
    • Authors: A. Akbarimajd, N. Hoertel, M.A. Hussain, A.A. Neshat, M. Marhamati, et al.
    • Journal: Journal of Computational Science
    • Year: 2022
    • Citations: 36

 

  • Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy
    • Authors: M. Momeny, A.A. Neshat, A. Gholizadeh, A. Jafarnezhad, E. Rahmanzadeh, et al.
    • Journal: Computers in Biology and Medicine
    • Year: 2022
    • Citations: 32

 

  • Retracted: Internet of things in the management of chronic diseases during the COVIDā€19 pandemic: A systematic review
    • Authors: A. Shamsabadi, Z. Pashaei, A. Karimi, P. Mirzapour, K. Qaderi, M. Marhamati, et al.
    • Journal: Health Science Reports
    • Year: 2022
    • Citations: 27

 

  • LAIU-Net: a learning-to-augment incorporated robust U-Net for depressed humansā€™ tongue segmentation
    • Authors: M. Marhamati, A.A.L. Zadeh, M.M. Fard, M.A. Hussain, K. Jafarnezhad, et al.
    • Journal: Displays
    • Year: 2023
    • Citations: 18

 

  • Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT
    • Authors: M.A. Hussain, Z. Mirikharaji, M. Momeny, M. Marhamati, A.A. Neshat, R. Garbi, et al.
    • Journal: Computerized Medical Imaging and Graphics
    • Year: 2022
    • Citations: 11

 

  • Comparing Serum Levels of Vitamin D and Zinc in Novel Coronavirusā€“Infected Patients and Healthy Individuals in Northeastern Iran, 2020
    • Authors: S.J. Hosseini, B. Moradi, M. Marhamati, A.A. Firouzian, E. Ildarabadi, A. Abedi, et al.
    • Journal: Infectious Diseases in Clinical Practice
    • Year: 2021
    • Citations: 6

 

  • Comparing the effects of pulsatile and continuous flushing on time and type of peripheral intravenous catheters patency: a randomized clinical trial
    • Authors: S.J. Hosseini, F. Eidy, M. Kianmehr, A.A. Firouzian, F. Hajiabadi, M. Marhamati, et al.
    • Journal: Journal of Caring Sciences
    • Year: 2021
    • Citations: 4

 

  • Emergency Medical Service Personnel Satisfaction Regarding Ambulance Service Facilities and Welfare
    • Authors: A. Jesmi, H.M. Ziyarat, M. Marhamati, T. Mollaei, H. Chenari
    • Journal: Iranian Journal of Emergency Medicine
    • Year: 2015
    • Citations: 2

 

  • Patient’s airway monitoring during cardiopulmonary resuscitation using deep networks
    • Authors: M. Marhamati, B. Dorry, S. Imannezhad, M.A. Hussain, A.A. Neshat, et al.
    • Journal: Medical Engineering & Physics
    • Year: 2024
    • Citations: 1

 

  • Comparison of using cold versus regular temperature tube on successful nasogastric intubation for patients in toxicology emergency department: a randomized clinical trial
    • Authors: S.R. Mazlom, A.A. Firouzian, H.M. Norozi, A.G. Toussi, M. Marhamati
    • Journal: Journal of Caring Sciences
    • Year: 2020
    • Citations: 1

 

  • Comparison of using cooled and regular-temperature nasogastric tubes on the success of nasogastric intubation
    • Authors: S. Mazlom, M. Marhamati, H. Norozi, A. Ghasemi Toosi
    • Journal: Evidence Based Care
    • Year: 2015
    • Citations: 1