Sajad Rezvani | Computer vision | Excellence in Research

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Mr Sajad Rezvani | Computer vision | Excellence in Research

Shahrood University of Technology , Iran

Sadjad Rezvani is a highly qualified candidate for the Research for Excellence in Research award. His impressive academic achievements, impactful research contributions, technical expertise, and leadership in mentoring make him a strong contender. His work in masked face recognition, medical image analysis, and image segmentation reflects both the depth and relevance of his research in today’s rapidly evolving tech landscape.

Publication Profile
scopus

Education :

Sadjad Rezvani holds a Master of Science in Computer Engineering with a specialization in Artificial Intelligence from Shahrood University of Technology, Iran. He completed his master’s degree between September 2020 and September 2022, graduating with a GPA of 4/4 (18.59/20). His thesis was titled “Masked Face Recognition Using Deep Learning,” under the guidance of Professor Mansoor Fateh. Prior to this, Sadjad earned his Bachelor of Science in Computer Engineering, specializing in Software Engineering, from Shahrood University of Technology, completing his degree between September 2015 and September 2019 with a GPA of 3.53/4 (16.92/20). His undergraduate thesis was titled “Profiling Web Applications to Improve Intrusion Detection,” supervised by Professor Mohsen Rezvani.

Professional Experience:

Sadjad has practical experience as a Computer Vision Software Engineer in several industries. He worked at Hookan Salt Factory in Shiraz, Iran, from November 2020 to September 2021, where he contributed to the development of a Salt Crack Sorting Machine. In this role, he employed advanced image processing techniques to detect salt impurities in real-time, utilizing tools such as OpenCV, Python, C#, and C++. Additionally, he worked at Shahaab, CO from June 2019 to December 2023 on a Plate Recognition Software project, where he contributed to a system that recognized license plates using CCTV camera data. His work involved maintaining and improving the software using C#, SQL, and other related technologies.

Research Skills:

Sadjad is highly skilled in programming languages such as Python, C++, and C#, and has a strong background in Machine Learning frameworks including PyTorch, TensorFlow, and Scikit-Learn. He is proficient in Computer Vision tools like OpenCV and has experience with databases such as Microsoft SQL Server and MySQL. His technical expertise also extends to advanced image processing, AI for medical diagnosis, and deep learning-based solutions for real-world applications.

Research Focus :

Sadjad’s research interests include Machine Learning (ML), Deep Learning (DL), Generative AI (GenAI), Medical Image Analysis, Limited Data Solutions, and Domain Adaptation. He has contributed to several journal publications, such as the development of ABANet: Attention Boundary-Aware Network for Image Segmentation (2024) and a paper on Single Image Denoising via a New Lightweight Learning-Based Model (2024), among others. His academic research also includes the application of deep learning models for lung CT image segmentation and innovations in masked face recognition using deep learning.

 

Awards :

Sadjad has received recognition for his achievements, including being a member of Iran’s National Elites Foundation in 2023 and being the third-ranked student in his Master of Science program. His certifications include AI for Medical Diagnosis from DeepLearning.AI (Coursera, 2023), Python Project for Data Science from IBM (Coursera, 2022), and specialization courses in Generative Adversarial Networks (GANs) and Machine Learning from Stanford University.

Honours and Awards

  • Member of Iran’s National Elites Foundation, 2023

  • Third-ranked student in the Master of Science in Computer Science program, 2022

 

Publication : 

 

    • Rezvani, S., Fateh, M., & Khosravi, H. (2024). ABANet: Attention Boundary-Aware Network for Image Segmentation. Expert Systems, e13625. [Published May 2024]

    • Rezvani, S., Soleymani Siahkar, F., Rezvani, Y., Alavi Gharahbagh, A., & Abolghasemi, V. (2024). Single Image Denoising via a New Lightweight Learning-Based Model. IEEE Access, August 2024.

    • Rezvani, S., Fateh, M., Fateh, A., & Jalali, Y. (2024). FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation. Biomedical Signal Processing and Control, Revised Sep 2024.

conclusion:

  • Sadjad’s overall profile is well-rounded with strengths across research, academia, technical skills, and professional experience.

  • Continued focus on expanding publication reach, collaboration, and public speaking could further elevate his visibility and impact in the research community.

  • With his dedication and achievements, Sadjad is well-positioned for recognition in research excellence.

In conclusion, Sadjad is a strong candidate for the award, and with a few adjustments in outreach and collaboration, he could continue to make significant strides in the research world.

 

Francisco Javier Lima Florido | Artificial Intelligence | Best Researcher Award

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Mr Francisco Javier Lima Florido | Artificial Intelligence | Best Researcher Award

Researcher in training , University of Malaga , Spain

Francisco Javier Lima Florido is an accomplished researcher whose work in Machine Learning, Deep Learning, and Natural Language Processing has significant practical and academic merit. His focus on multilingual dialogue systems, health applications, and automatic interpretation solutions speaks to his expertise and potential to impact society through technology. As a PhD student, he is still developing his academic career but has already made noteworthy contributions to the field through participation in significant projects.

Publication Profile
scopus

Education :

Francisco Javier Lima Florido holds a Bachelor’s degree in Software Engineering from the University of Málaga (2016). He also earned a Master’s degree in Software Engineering and Artificial Intelligence from the same institution in 2019. Currently, Francisco is pursuing a PhD in the Translation and Interpreting Department at the University of Málaga, where his research is primarily focused on the intersection of technology and language.

Experience:

Francisco has actively participated in various research projects throughout his academic career. Notably, he was involved in the VIP: Integrated Voice-Text System for Interpreters project. This project explored the integration of voice and text systems for interpreters. Presently, he is contributing to cutting-edge projects like the Neural-based multilingual dialogue systems for the development of health apps (focusing on triage in Spanish, English, and Arabic) and the MI4ALL – Automatic Interpretation for All Using a Deep Learning-based API transfer project. These initiatives demonstrate his extensive experience in developing machine learning models for natural language processing (NLP).

Research Focus:

His primary research interests lie in the application of Machine Learning and Deep Learning techniques to Natural Language Processing (NLP). He is particularly focused on the development of multilingual dialogue systems and automatic interpretation technologies. His work aims to enhance the functionality and accessibility of tools for interpreters and healthcare applications, with a special interest in bridging communication gaps in multilingual settings.

Skills:

Francisco is highly skilled in several areas within Software Engineering and Artificial Intelligence, with a strong emphasis on Machine Learning and Deep Learning. His technical expertise spans:

    • Natural Language Processing (NLP)
    • Multilingual Dialogue Systems
    • Deep Learning Algorithms
    • Machine Learning Model Development
    • Speech-to-Text Technologies
    • Python Programming and related frameworks (e.g., TensorFlow, PyTorch)

 

Publication :

Francisco Javier Lima Florido has contributed to several research projects and publications in the fields of Machine Learning, Deep Learning, and Natural Language Processing. Notably:​

  1. “Mapping tillage direction and contour farming by object-based analysis of UAV images” (2021): This study, co-authored by Francisco J. Lima-Cueto, Rafael Blanco-Sepúlveda, María L. Gómez-Moreno, José Dorado, and José M. Peña, was published in Computers and Electronics in Agriculture. ​

  2. “Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain” (2019): Authored by Francisco J. Lima-Cueto, Rafael Blanco-Sepúlveda, María L. Gómez-Moreno, and Federico B. Galacho-Jiménez, this paper appeared in Remote Sensing. ​

Additionally, Francisco Javier Lima Florido has been involved in research projects such as “VIP: Integrated Voice-Text System for Interpreters” and is currently participating in “Neural-based multilingual dialogue systems for the development of health apps: triage (Spanish – English/Arabic)” and the transfer project “MI4ALL – Automatic Interpretation For All Using a Deep Learning-based API”.

conclusion:

Francisco is highly deserving of consideration for the “Best Researcher Award.” His expertise in cutting-edge AI technologies, especially in the context of language translation and interpretation, holds immense potential for positive social impact. While there are areas for improvement, such as enhancing his publication record and broadening his collaborative network, his current research trajectory shows great promise. His ongoing contributions to AI research and application indicate that he is on a path to becoming a leading figure in the field.

Saba Inam | machine learning | Women Researcher Award

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Dr.Saba Inam |machine learning| Women Researcher Award

Dr Saba InamFatima Jinnah women university, The Mall, Rawalpindi, Pakistan.

Dr. Saba Inam is a lecturer in the Department of Mathematical Sciences at Fatima Jinnah Women University in Rawalpindi, Pakistan. She earned her PhD in Algebraic Cryptography from the Capital University of Science and Technology, completing her studies between February 2014 and January 2019. Her research interests include Algebraic Number Theory, Algebraic Cryptography, Applied Cryptography, image encryption, Cloud Computing, Machine Learning, and Deep Learning. Dr. Inam has contributed to over 20 publications, accumulating 247 citations, and her work has garnered more than 3,367 reads. Notably, she co-authored the article “An efficient image encryption algorithm using 3D-cyclic Chebyshev map and elliptic curve,” published in November 2024.

Publication Profile

Google Scholar

Orcid

Education :

Dr. Saba Inam holds a PhD in Mathematics from Capital University of Science and Technology (CUST), Islamabad (2019). She completed her MS in Mathematics from COMSATS Institute of Information Technology, Islamabad, in 2007 with a CGPA of 3.6/4, achieving 1st Division. She earned her M.Sc. in Mathematics from Quaid-i-Azam University, Islamabad (2005), and her B.Sc. in Mathematics (Maths A, Maths B, Stats) from the University of the Punjab (2003), both with 1st Division.

Experience :

Dr. Inam has extensive academic and research experience. Since September 2007, she has been serving as a Lecturer in Mathematics at Fatima Jinnah Women University, Rawalpindi. She also held the position of Incharge, Department of Mathematical Sciences from August 2016 to January 2018. Before that, she worked as a Research Associate at COMSATS Institute of Information Technology, Islamabad, from March to August 2007.

Research Focus :

Dr. Inam’s research interests span across multiple domains, including:

Cryptography & Security: Algebraic Cryptography, Cryptology, CryptanalysisAI & Data Security: Image Encryption, Blockchain, IoT, Deep Learning, Machine LearningMathematical Sciences: Fluid Mechanics, Geometric Function Theory.

 

Awards:

Dr. Inam’s academic excellence has been recognized through various awards and honors:

Scholarship – Capital University of Science and Technology (CUST), Islamabad (2013-2018)Dean’s Roll of Honor – Received twice during PhD courseworkDiploma in Academic Excellence in Discrete Mathematics – Abdul Salam School of Mathematical Sciences, GC University, Lahore (2012)Scholarship – COMSATS Institute of Information Technology, Islamabad (2005-2007)

Skills:

Dr. Inam possesses expertise in:Programming & Computational Tools: Matlab, Python, Mathematica, APCoCoA, Scientific Workplace, LaTeXOffice & Documentation: Proficient in Microsoft Office Suite,Dr. Saba Inam continues to contribute significantly to the fields of cryptography, image encryption, and mathematical security frameworks, with a strong focus on deep learning and blockchain applications.

Publication :

 

Three dimensional boundary layer flow of a viscoelastic nanofluid with Soret and Dufour effects

Numerical simulation of Darcy–Forchheimer 3D unsteady nanofluid flow comprising carbon nanotubes with Cattaneo–Christov heat flux and velocity and thermal slip conditions

An effective color image encryption based on Henon map, tent chaotic map, and orthogonal matrices

A new ElGamal-like cryptosystem based on matrices over groupring

Analytic study of a novel color image encryption method based on the chaos system and color codes

Blockchain based medical image encryption using Arnold’s cat map in a cloud environment

A numerical study of rotating Bödewadt flow of micropolar fluid over porous disk

A Novel Public Key Cryptosystem and Digital Signatures

A new method of image encryption using advanced encryption Standard (AES) for network security

Research on color image encryption approach based on chaotic Duffing map

Conclusion

Dr. Inam’s strengths, including her academic excellence, research impact, and leadership in academia, position her as a strong candidate for the “Research for Women Researcher Award.” While there are areas to enhance collaboration and public outreach, her work is already shaping critical developments in data security and encryption. Recognizing her would not only celebrate her achievements but also inspire more women to pursue careers in research and technology, contributing significantly to both academic and societal progress.

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

🎓 Education :

Ph.D. in Computer Science,University of Mysore, Karnataka, India (Sept. 2018 – 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 – Jan. 2018),M.Sc. in Computer Science,Bangalore University, Bangalore, India (Apr. 2014 – Mar. 2016),B.Sc. in Computer Science,Michael Okpara University, Abia State, Nigeria (Nov. 2006 – Oct. 2010),West African Senior School Certificate Examination (WASSCE),Community Secondary School, Okigwe, Imo State, Nigeria (May – June 2004)

 

🏢 Experience:

Assistant Professor,Department of Computer Science and Engineering (Computer Aided Design In Mechanical Engineering), Maharaja Institute of Technology, Mysore, India (Oct. 2023 – 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 – 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 – May 2019),Conducted courses in Information System & Science and Database Management Systems.,Visiting Faculty (Voluntary),St. Aloysius Degree College, Bangalore, India (Jul. 2017 – Feb. 2018),Taught sessions on Information System & Science and Database Management Systems.,Teaching Assistant (Voluntary),St. Joseph’s College, Bangalore, India (Oct. 2014 – Mar. 2016),Taught Computer Fundamentals and Web Design using PHP.,Java Instructor (Intern),APTECH Computer Education, Bangalore, India (Oct. – Dec. 2014),Provided training in Computer Fundamentals and Web Design using PHP.,Web Developer,Max-Out Resources Pvt. Ltd, Abuja, Nigeria (Jul. 2011 – Jun. 2012),High School Teacher,Community Secondary School, Okigwe, Nigeria (Feb. – 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.

 

🔬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. Joseph’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.

 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 Naïve 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

SASANK VVS |machine learning| Best Researcher Award

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Dr. SASANK VVS |machine learning| Best Researcher Award

ASSISTANT PROFESSOR,K L UNIVERSITY,India

Dr. VVS Sasank is an Assistant Professor at K L University, India. With a strong background in his field of expertise, Dr. Sasank is dedicated to advancing research and education. He has made significant contributions to academic literature and is recognized for his commitment to fostering innovative learning methodologies. At K L University, he focuses on mentoring students, conducting impactful research, and contributing to the institution’s mission of excellence in higher education. His areas of interest include [add specific areas of research or specialization if known].

Summary:

Dr. Sasank VVS has exhibited a robust academic and research profile, combining strong academic qualifications, a distinguished teaching career, and significant contributions to research. His efforts in publishing SCI-indexed papers, receiving patents, and securing prestigious certifications reflect his dedication to advancing knowledge in his field. His work is not only academic but also highly relevant in the practical application of IoT and machine learning technologies.

 

Professional Profiles:

Scopus

🎓 Education :

Dr. Sasank has consistently demonstrated academic brilliance, earning a Ph.D. in Computer Science from K.L. University (2019–2023), focusing on cutting-edge research. He completed his M.Tech in Computer Science and Technology at GITAM University (2016), securing a CGPA of 9.11 and achieving the top rank in his department. His B.Tech in Information Technology at GITAM University (2013) was marked by distinction with a CGPA of 8.15. Earlier, he excelled in his intermediate education at Mega Junior College (2009) and 10th-grade studies at Jassver English Medium School (2007), graduating with first-class distinctions.

 

🏢 Experience:

With extensive teaching and administrative experience, Dr. Sasank currently serves as an Assistant Professor and ERP Registration In-charge at K.L. University (December 2021–Present), where he manages course registrations and teaches advanced computer science topics. Previously, he held roles as an Assistant Professor and Placement Officer at Anil Neerukonda Institute of Technology & Sciences (2017–2019) and as an Assistant Professor at Lendi Institute of Engineering & Technology (2016–2017). His teaching journey began as a Teaching Assistant at GITAM University (2015–2016).

🛠️Skills:

Dr. Sasank is proficient in programming languages such as C, C++, Java, and DevOps, and database systems like Oracle-10G, MySQL, and PostgreSQL. His teaching repertoire includes specialized subjects like DBMS, Software Engineering, Computer Architecture, UI/UX Design, and DevOps. Certified as a Google Associate Cloud Engineer (2023) and AWS Cloud Practitioner, he combines technical and academic expertise to advance his professional goals

 

Research Focus :

A committed researcher, Dr. Sasank has published 5 SCI-indexed papers, with 1 under review, alongside 3 Scopus-indexed papers, 1 book chapter, and 3 IEEE conference papers. His research interests span IoT, machine learning, software modeling, and data management systems. Additionally, he has guided 12 B.Tech batches, 2 M.Tech project batches, and 1 ongoing Ph.D. scholar, showcasing his mentorship abilities.

 

🔬Awards:

He was recognized for his academic excellence by securing 1st rank in M.Tech (CSE) at GITAM University (2016) and is celebrated for his innovative project guidance and research accomplishments.

Conclusion:

Dr. Sasank VVS is an excellent candidate for the Best Researcher Award due to his substantial contributions to both the academic and research domains. His accomplishments in research, teaching, and innovation underscore his potential for long-term impact in the field of computer science. With a few strategic improvements, such as broadening his research areas and fostering more international collaborations, Dr. Sasank can continue to make significant contributions to the global scientific community.

 Publications:

  • Prostate cancer classification using adaptive swarm intelligence-based deep attention neural network
    • Authors: Sowmya, D., Bhavani, S.A., Sasank, V.V.S., Rao, T.S.
    • Year: 2024
    • Citations: 0

 

  • Real-time face detection and recognition
    • Authors: Lekhaz, A., Sasank, V.V.S., Rao, Y.K.
    • Year: 2024
    • Citations: 0

 

  • Effective Segmentation and Brain Tumor Classification Using Sparse Bayesian ELM in MRI Images
    • Authors: Sasank, V.V.S., Venkateswarlu, S.
    • Year: 2023
    • Citations: 1

 

  • Monitor and Control Electricity for Home Appliance
    • Authors: Sharma, P.K., Lakshmi, B., Prakash, A., Sasank, V.V.S., Ashokkumar, N.
    • Year: 2023
    • Citations: 0

 

  • An Analysis and Study of Brain Cancer with RNN Algorithm based AI Technique
    • Authors: Vallathan, G., Rao Yanamadni, V., Vidhya, R.G., Ambhika, C., Sasank, V.V.S.
    • Year: 2023
    • Citations: 7

 

  • Machine Learning Model to Reduce the Various Defects on Die Casting Process
    • Authors: Jagadeesan, S., Janardhan, M., Singh, B., Sasank, V.V.S., Kapila, D.
    • Year: 2023
    • Citations: 0

 

  • Smart Home Messenger Notifications System using IoT
    • Authors: Sai, M.R., Teja, K.K., Sasank, V.P., Kavitha, M., Aravinth, S.S.
    • Year: 2023
    • Citations: 10

 

  • Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images
    • Authors: Sasank, V.V.S., Venkateswarlu, S.
    • Year: 2022
    • Citations: 15

 

  • HATE SPEECH & OFFENSIVE LANGUAGE DETECTION USING ML & NLP
    • Authors: Panchala, G.H., Sasank, V.V.S., Adidela, D.R.H., Ashesh, K., Prasad, C.
    • Year: 2022
    • Citations: 6

 

  • Security and Privacy in Associated Self-Controlled Cars
    • Authors: Sasank, V.V.S., Lokesh, B., Mahesh, J., Kumar, T.S., Prasad, C.
    • Year: 2022
    • Citations: 0

 

Zubair Akhtar Mohd | Computer Aided Design In Mechanical Engineering | Best Researcher Award

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Mr. Zubair Akhtar Mohd | Computer Aided Design In Mechanical Engineering | Best Researcher Award

 Mr. Zubair Akhtar Mohd, Technische Hochschule Ingolstadt, Germany

Mr. Zubair Akhtar Mohd is a Research Associate at Technische Hochschule Ingolstadt, Germany, specializing in automotive engineering and artificial intelligence applications in predictive modeling and manufacturing optimization. He holds a Master’s degree in Automotive Engineering from THI and a Bachelor’s in Mechanical Engineering from Aligarh Muslim University, India. His research focuses on integrating Finite Element Analysis (FEA) with AI, using advanced machine learning algorithms like CNNs and RNNs to forecast the lifespan of electronic components. Mr. Mohd is also involved in scientific projects, data generation for materials testing, and academic teaching in CAD and simulation.

 

Professional Profiles:

 

🎓 Education :

Holds a Master’s degree in Automotive Engineering from Technische Hochschule Ingolstadt, Germany, with a GPA of 1.9, focusing on production optimization and AI in automotive systems. Bachelor’s degree in Mechanical Engineering from Aligarh Muslim University, India, with a GPA of 1.6, specializing in vehicle technology and CAD/CAE programming.

 

🏢 Experience:

Currently working as a Research Associate at the Institute of Innovative Mobility, Technische Hochschule Ingolstadt, focusing on method development for predicting electronics component lifespan using deep learning. Previously employed as a working student at CADS Engineering GmbH, contributing to vehicle design and occupant protection research, and as an Industrial Engineer in India, implementing safety and efficiency improvements in manufacturing processes.

🛠️Skills:

Proficient in Python, TensorFlow, PyTorch, and Linux, with additional expertise in tools such as Git, JavaScript, and MS Office applications. Experience with HTML, CSS, and Carla, and extensive knowledge in engineering software like NX CAD, Ansys, and Tableau. Fluent in English and German at B2 level, alongside native proficiency in Hindi.

 

Research Focus :

Specialized in the integration of Finite Element Analysis (FEA) simulation data with deep learning for predictive modeling. Research includes advanced deep learning models, such as CNNs and RNNs, and generative forecasting with VQ-VAE. Emphasis on machine learning algorithms for materials inspection and automated data collection.

 

🔬Awards:

Awarded various certifications, including specialization in self-driving car technologies from the University of Toronto and advanced machine learning courses from DeepLearning.AI. Actively involved in technical leadership roles, such as Technical Coordinator for AMU’s national college fest and team leader for the American Society of Mechanical Engineers (ASME). Published work in Springer Publications on ergonomics for productivity improvement.

Conclusion:

Mr. Zubair Akhtar Mohd’s interdisciplinary skills, innovative research focus, and dedication to academia make him a deserving candidate for the Best Researcher Award. With potential to expand his publication record and increase his collaborative efforts, Mr. Mohd’s career trajectory reflects both current excellence and promise for further significant contributions to engineering and AI research.

 Publications:

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

🎓 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.

🏢 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.

🛠️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.

🔬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. Marhamati’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 COVID‐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 humans’ 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 Coronavirus–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

 

 

Shankar Patil | Deep Learning | Best Researcher Award

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Prof Dr. Shankar Patil | Deep Learning | Best Researcher Award

Prof Dr, Shankar Patil,Smt. Indira Gandhi College of Engineering, Ghansoli, India

Prof. Dr. Shankar Patil is a distinguished academician and researcher affiliated with Smt. Indira Gandhi College of Engineering in Ghansoli, India. With a robust background in engineering education and research, Dr. Patil has made significant contributions to the field, particularly in the areas of [please specify his key areas if known]. He holds [degrees or qualifications], and his expertise spans [mention specific areas of expertise or research interests]. Dr. Patil is actively involved in [mention any significant roles, committees, or academic initiatives he’s part of]. His dedication to advancing knowledge and fostering academic excellence underscores his commitment to the field of engineering education and research

Professional Profiles:

Scopus

Objective:

To procure a challenging position in an organization where I can promote my ideas and knowledge with the best engineering qualities for the benefit of the organization.

Education :

  • Ph.D. in Computer Science & Engineering, Singhania University, Pacheri, September 2018.
  • Master of Computer Engineering, Bharati Vidyapeeth Deemed University, Pune, 2005.
  • Bachelor of Computer Engineering, Walchand College of Engineering, Sangli, Shivaji University, Kolhapur, 1998.

Membership:

  • Life Member, Indian Society of Technical Education (ISTE), Membership Number LM41153.
  • Computer Society of India (CSI), Membership Number N1158683.
  • Recognized as PhD Guide in Computer Engineering at University of Mumbai.
  • Recognized as Post-Graduate Teacher at University of Mumbai.

Publications :

  • Object Identification and Alerting Method for Pattern Analysis,” Inderscience Journal of Computational Vision and Robotics, February 2024.
  • “Yolo V4-Based Hybrid Feature Enhancement Network with Robust Object Detection under Adverse Weather Conditions,” Springer Nature journal Signal, Image and Video Processing, March 2024.
  • “Caritas- ‘Serving Smiles’,” 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), India, 2023.
  • “Online Exam Proctoring System Based on Computer Aided Design In Mechanical Engineering,” 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), India, 2023.
  • “Melanoma Skin Cancer Disease Detection Using Convolutional Neural Network,” 3rd International Conference of Emerging Technologies 2022 (INCET2022), India, May 2022.