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226 results on '"Shahin, Mohammad"'

4. Detection and Classification of Skin Cancer Using Custom-Built CNN

Author

Maghanaki, Mazdak, Shahin, Mohammad, Frank Chen, F., Hosseinzadeh, Ali, 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, Daimi, Kevin, editor, and Al Sadoon, Abeer, editor

Published
2024
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5. Improving Early Diagnosis: The Intersection of Lean Healthcare and Computer Vision in Cancer Detection

Author

Maghanaki, Mazdak, Shahin, Mohammad, Chen, F. Frank, Hosseinzadeh, Ali, 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, Daimi, Kevin, editor, and Al Sadoon, Abeer, editor

Published
2024
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6. Integrating Lean Healthcare and Machine Learning for Cancer Risk Prediction

Author

Shahin, Mohammad, Maghanaki, Mazdak, Chen, F. Frank, Hosseinzadeh, Ali, 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, Daimi, Kevin, editor, and Al Sadoon, Abeer, editor

Published
2024
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7. Deploying Optical Character Recognition to Improve Material Handling and Processing

Author

Shahin, Mohammad, Hosseinzadeh, Ali, Chen, F. Frank, Davis, Marvin, Rashidifar, Rasoul, Shahin, Awni, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Silva, Francisco J. G., editor, Ferreira, Luís Pinto, editor, Sá, José Carlos, editor, Pereira, Maria Teresa, editor, and Pinto, Carla M. A., editor

Published
2024
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8. Deploying Computer-Based Vision to Enhance Safety in Industrial Environment

Author

Shahin, Mohammad, Chen, F. Frank, Hosseinzadeh, Ali, Koodiani, Hamid Khodadadi, Bouzary, Hamed, Rashidifar, Rasoul, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Silva, Francisco J. G., editor, Ferreira, Luís Pinto, editor, Sá, José Carlos, editor, Pereira, Maria Teresa, editor, and Pinto, Carla M. A., editor

Published
2024
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9. An AI-Powered Network Intrusion Detection System in Industrial IoT Devices via Deep Learning

Author

Shahin, Mohammad, Chen, F. Frank, Hosseinzadeh, Ali, Lopez, Enrique Contreras, Bouzary, Hamed, Koodiani, Hamid Khodadadi, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Silva, Francisco J. G., editor, Ferreira, Luís Pinto, editor, Sá, José Carlos, editor, Pereira, Maria Teresa, editor, and Pinto, Carla M. A., editor

Published
2024
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10. Reinforcement Learning-Based Model for Optimization of Cloud Manufacturing-Based Multi Objective Resource Scheduling: A Review

Author

Rashidifar, Rasoul, Chen, F. Frank, Shahin, Mohammad, Hosseinzadeh, Ali, Bouzary, Hamed, Shahin, Awni, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Silva, Francisco J. G., editor, Pereira, António B., editor, and Campilho, Raul D. S. G., editor

Published
2024
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12. Machine learning tools to improve nonlinear modeling parameters of RC columns

Author

Koodiani, Hamid Khodadadi, Jafari, Elahe, Majlesi, Arsalan, Shahin, Mohammad, Matamoros, Adolfo, and Alaeddini, Adel

Subjects
Computer Science - Machine Learning
Abstract

Modeling parameters are essential to the fidelity of nonlinear models of concrete structures subjected to earthquake ground motions, especially when simulating seismic events strong enough to cause collapse. This paper addresses two of the most significant barriers to improving nonlinear modeling provisions in seismic evaluation standards using experimental data sets: identifying the most likely mode of failure of structural components, and implementing data fitting techniques capable of recognizing interdependencies between input parameters and nonlinear relationships between input parameters and model outputs. Machine learning tools in the Scikit-learn and Pytorch libraries were used to calibrate equations and black-box numerical models for nonlinear modeling parameters (MP) a and b of reinforced concrete columns defined in the ASCE 41 and ACI 369.1 standards, and to estimate their most likely mode of failure. It was found that machine learning regression models and machine learning black-boxes were more accurate than current provisions in the ACI 369.1/ASCE 41 Standards. Among the regression models, Regularized Linear Regression was the most accurate for estimating MP a, and Polynomial Regression was the most accurate for estimating MP b. The two black-box models evaluated, namely the Gaussian Process Regression and the Neural Network (NN), provided the most accurate estimates of MPs a and b. The NN model was the most accurate machine learning tool of all evaluated. A multi-class classification tool from the Scikit-learn machine learning library correctly identified column mode of failure with 79% accuracy for rectangular columns and with 81% accuracy for circular columns, a substantial improvement over the classification rules in ASCE 41-13.

Published
2023

18. Waste reduction via image classification algorithms: beyond the human eye with an AI-based vision.

Author

Shahin, Mohammad, Chen, F. Frank, Hosseinzadeh, Ali, Bouzary, Hamed, and Shahin, Awni

Subjects
IMAGE recognition (Computer vision), WASTE minimization, ARTIFICIAL intelligence, CLASSIFICATION algorithms, CUSTOMER satisfaction
Abstract

Modern manufacturing is the world's largest and most automated industrial sector. The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning has enabled a better connection with machines and factory systems. Data harvesting allowed for a more seamless and comprehensive implementation of the knowledge-based decision-making process. New models that provide a competitive edge must be created by combining the Lean paradigm with the new technologies of Industry 4.0. This paper presents novel computer-based vision models for automated detection and classification of damaged packages from intact packages. In high-volume production environments, the package manual inspection process through the human eye consumes inordinate amounts of time poring over physical packages. Our proposed three different computer-based vision approaches detect damaged packages to prevent them from moving to shipping operations that would otherwise incur waste in the form of wasted operating hours, wasted resources and lost customer satisfaction. The proposed approaches were carried out on a data set consisting of package images and achieved high precision, accuracy, and recall values during the training and validation stage, with the resultant trained YOLO v7 model. [ABSTRACT FROM AUTHOR]

Published
2024
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23. A Mathematical Model for Cloud-Based Scheduling Using Heavy Traffic Limit Theorem in Queuing Process

Author

Rashidifar, Rasoul, Chen, F. Frank, Bouzary, Hamed, Shahin, Mohammad, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Kim, Kyoung-Yun, editor, Monplaisir, Leslie, editor, and Rickli, Jeremy, editor

Published
2023
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24. Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning

Author

Shahin, Mohammad, Chen, F, Bouzary, Hamed, Hosseinzadeh, Ali, Rashidifar, Rasoul, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Kim, Kyoung-Yun, editor, Monplaisir, Leslie, editor, and Rickli, Jeremy, editor

Published
2023
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25. Implementation of a Novel Fully Convolutional Network Approach to Detect and Classify Cyber-Attacks on IoT Devices in Smart Manufacturing Systems

Author

Shahin, Mohammad, Chen, FFrank, Bouzary, Hamed, Hosseinzadeh, Ali, Rashidifar, Rasoul, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Kim, Kyoung-Yun, editor, Monplaisir, Leslie, editor, and Rickli, Jeremy, editor

Published
2023
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31. Effectiveness of calcium channel blockade for organophosphorus and carbamate pesticide poisoning – study protocol for an open label, pragmatic, 3-arm RCT repurposing two widely available medicines

Author

Fazle Rabbi Chowdhury, Suvodip Shaw, Abdullah Abu Sayeed, Soumitra Roy, Abu Shahin Mohammad Mahbubur Rahman, Nahida Zafrin, Pritish Tarafder, Muhammad Halimur Rashid, Aniruddha Ghose, Shishir Ranjan Chakraborty, Muhammad Khalilur Rahman, Muhammad Sayedur Rahman, Richard Parker, Muhammad Mahib Ullah, Zakir Hassan, Abdullah Al Mamun Sohag, Muhammad Robed Amin, Muhammad Shafiqul Bari, John Norrie, M. A. Faiz, and Michael Eddleston

Subjects
Organophosphate, carbamate, randomized control trial, magnesium sulphate, nimodipine, Toxicology. Poisons, RA1190-1270
Abstract

AbstractPesticide self-poisoning is one of the three most important global means of suicide, killing an estimated 110–168,000 people each year, mostly in poor rural Asian communities. Organophosphorus (OP) and carbamate anticholinesterase insecticides are responsible for about two-thirds of these deaths. Calcium channel blocking medicines (CCB) may reduce the effect of pesticides and prevent deaths. Two preclinical rodents’ studies and eight clinical studies utilising nimodipine and magnesium sulphate (MgSO4), respectively, showed mixed results. We have established a multi-centre randomised controlled trial (RCT) of patients with OP or carbamate self-poisoning admitted to at least six major hospitals in Bangladesh. The study aims to recruit maximum 3,243 patients over four years. One-third of the patients selected at random will receive standard treatment, while one-third will be treated with additional nimodipine and one-third with additional MgSO4. The additional treatments will be given for 48 h. We will check mortality (currently an estimated 11% die with standard treatment) and need for intensive care for mechanical ventilation across the three groups. This could lead to development of the first novel treatment for anticholinesterase poisoning in 50 years and its introduction into routine hospital practice worldwide.

Published
2023
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50. Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm.

Author

Shahin, Mohammad, Chen, F. Frank, Maghanaki, Mazdak, Hosseinzadeh, Ali, Zand, Neda, and Khodadadi Koodiani, Hamid

Subjects
*CRACKING of concrete, *CONVOLUTIONAL neural networks, *DEEP learning, *IMAGE recognition (Computer vision), *BRIDGE inspection, *IMAGE converters
Abstract

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management. [ABSTRACT FROM AUTHOR]

Published
2024
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