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247 results on '"Chen, F. Frank"'

4. 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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5. 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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6. 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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7. 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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8. 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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9. 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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15. 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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18. 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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31. 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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47. Editorial

Author

Garza-Reyes, Jose Arturo, Kumar, Vikas, Chen, F. Frank, and Wang, Yi-Chi

Published
2017
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