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Your search keyword '"Zein-Sabatto, Saleh"' showing total 79 results
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79 results on '"Zein-Sabatto, Saleh"'

1. Advancing Healthcare: Innovative ML Approaches for Improved Medical Imaging in Data-Constrained Environments

Author

Amin, Al, Hasan, Kamrul, Zein-Sabatto, Saleh, Hong, Liang, Shetty, Sachin, Ahmed, Imtiaz, and Islam, Tariqul

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Healthcare industries face challenges when experiencing rare diseases due to limited samples. Artificial Intelligence (AI) communities overcome this situation to create synthetic data which is an ethical and privacy issue in the medical domain. This research introduces the CAT-U-Net framework as a new approach to overcome these limitations, which enhances feature extraction from medical images without the need for large datasets. The proposed framework adds an extra concatenation layer with downsampling parts, thereby improving its ability to learn from limited data while maintaining patient privacy. To validate, the proposed framework's robustness, different medical conditioning datasets were utilized including COVID-19, brain tumors, and wrist fractures. The framework achieved nearly 98% reconstruction accuracy, with a Dice coefficient close to 0.946. The proposed CAT-U-Net has the potential to make a big difference in medical image diagnostics in settings with limited data., Comment: 7 pages, 7 figures

Published
2024

2. Empowering Healthcare through Privacy-Preserving MRI Analysis

Author

Amin, Al, Hasan, Kamrul, Zein-Sabatto, Saleh, Chimba, Deo, Hong, Liang, Ahmed, Imtiaz, and Islam, Tariqul

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

In the healthcare domain, Magnetic Resonance Imaging (MRI) assumes a pivotal role, as it employs Artificial Intelligence (AI) and Machine Learning (ML) methodologies to extract invaluable insights from imaging data. Nonetheless, the imperative need for patient privacy poses significant challenges when collecting data from diverse healthcare sources. Consequently, the Deep Learning (DL) communities occasionally face difficulties detecting rare features. In this research endeavor, we introduce the Ensemble-Based Federated Learning (EBFL) Framework, an innovative solution tailored to address this challenge. The EBFL framework deviates from the conventional approach by emphasizing model features over sharing sensitive patient data. This unique methodology fosters a collaborative and privacy-conscious environment for healthcare institutions, empowering them to harness the capabilities of a centralized server for model refinement while upholding the utmost data privacy standards.Conversely, a robust ensemble architecture boasts potent feature extraction capabilities, distinguishing itself from a single DL model. This quality makes it remarkably dependable for MRI analysis. By harnessing our groundbreaking EBFL methodology, we have achieved remarkable precision in the classification of brain tumors, including glioma, meningioma, pituitary, and non-tumor instances, attaining a precision rate of 94% for the Global model and an impressive 96% for the Ensemble model. Our models underwent rigorous evaluation using conventional performance metrics such as Accuracy, Precision, Recall, and F1 Score. Integrating DL within the Federated Learning (FL) framework has yielded a methodology that offers precise and dependable diagnostics for detecting brain tumors., Comment: 6

Published
2024

3. An Explainable AI Framework for Artificial Intelligence of Medical Things

Author

Amin, Al, Hasan, Kamrul, Zein-Sabatto, Saleh, Chimba, Deo, Ahmed, Imtiaz, and Islam, Tariqul

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The healthcare industry has been revolutionized by the convergence of Artificial Intelligence of Medical Things (AIoMT), allowing advanced data-driven solutions to improve healthcare systems. With the increasing complexity of Artificial Intelligence (AI) models, the need for Explainable Artificial Intelligence (XAI) techniques become paramount, particularly in the medical domain, where transparent and interpretable decision-making becomes crucial. Therefore, in this work, we leverage a custom XAI framework, incorporating techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-Cam), explicitly designed for the domain of AIoMT. The proposed framework enhances the effectiveness of strategic healthcare methods and aims to instill trust and promote understanding in AI-driven medical applications. Moreover, we utilize a majority voting technique that aggregates predictions from multiple convolutional neural networks (CNNs) and leverages their collective intelligence to make robust and accurate decisions in the healthcare system. Building upon this decision-making process, we apply the XAI framework to brain tumor detection as a use case demonstrating accurate and transparent diagnosis. Evaluation results underscore the exceptional performance of the XAI framework, achieving high precision, recall, and F1 scores with a training accuracy of 99% and a validation accuracy of 98%. Combining advanced XAI techniques with ensemble-based deep-learning (DL) methodologies allows for precise and reliable brain tumor diagnoses as an application of AIoMT., Comment: 7 pages, 8 figures

Published
2024

5. Intelligent Self-reliant Cyber-Attacks Detection and Classification System for IoT Communication Using Deep Convolutional Neural Network

Author

Al-Haija, Qasem Abu, McCurry, Charles D., Zein-Sabatto, Saleh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ghita, Bogdan, editor, and Shiaeles, Stavros, editor

Published
2021
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42. Modernization of Engineering Research Institute at Tennessee State University

Author

TENNESSEE STATE UNIV NASHVILLE COLL OFENGINEERING AND TECHNOLOGY, Malkani, Mohan J., Bodruzzaman, Mohammed, Shirkhodaie, Amir, Yuen, Geoffery, Zein-Sabatto, Saleh, TENNESSEE STATE UNIV NASHVILLE COLL OFENGINEERING AND TECHNOLOGY, Malkani, Mohan J., Bodruzzaman, Mohammed, Shirkhodaie, Amir, Yuen, Geoffery, and Zein-Sabatto, Saleh

Abstract

Funding from 1997 DoD HBCU/MI infrastructure support for educational instrumentation provided support for Tennessee State University to modernize its Engineering Research Institute (ERI). State of the art equipment with associated software was purchased to enhance the research capabilities of image processing, concurrent manufacturing, networking and communication and robotics and machine vision laboratories. The impact of this equipment led to completion of 13 undergraduate senior capstone design projects and 14 masters' theses during 1999-2000 In addition 7 new graduate level courses were established to enrich the electrical and mechanical engineering curriculum and enhance the career opportunities of our students. The equipment funds were leveraged to obtain two new grants in robotics from NASA Ames and JPL totaling $631,000, and a contract from BWXT-Yl2 security complex for $198,000 Based on enhanced research capabilities Tennessee Board of Regents approved Ph.D degree program in Computer and Information Systems Engineering (CISE) in the College of engineering, Technology and Computer Science.

Published
2003

45. Distributed sensor concepts for perimeter surveillance and vehicle classification

Author

Rogers, Tamara, primary, Shirkhodaie, Amir, additional, Mitra, Atindra K., additional, Johnson, Fred, additional, Foxx, Chico, additional, Young, Sean, additional, Westbrook, LaMar, additional, Marrs, Tony, additional, Lewis, Tom, additional, Zein-Sabatto, Saleh, additional, Yao, Fenghui, additional, Malkani, Mohan, additional, Rababaah, Haroun, additional, Leonard, James, additional, and Johnson, Cheutaunia, additional

Published
2009
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