Cancer classification utilizing voting classifier with ensemble feature selection method and transcriptomic data
- Khatun, Rabea, Akter, Maksuda, Islam, Md Manowarul, Uddin, Md Ashraf, Talukder, Md Alamin, Kamruzzaman, Joarder, Azad, Akm, Paul, Bikash, Almoyad, Muhammad, Aryal, Sunil, Moni, Mohammad
- Authors: Khatun, Rabea , Akter, Maksuda , Islam, Md Manowarul , Uddin, Md Ashraf , Talukder, Md Alamin , Kamruzzaman, Joarder , Azad, Akm , Paul, Bikash , Almoyad, Muhammad , Aryal, Sunil , Moni, Mohammad
- Date: 2023
- Type: Text , Journal article
- Relation: Genes Vol. 14, no. 9 (2023), p.
- Full Text:
- Reviewed:
- Description: Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods. © 2023 by the authors.
- Authors: Khatun, Rabea , Akter, Maksuda , Islam, Md Manowarul , Uddin, Md Ashraf , Talukder, Md Alamin , Kamruzzaman, Joarder , Azad, Akm , Paul, Bikash , Almoyad, Muhammad , Aryal, Sunil , Moni, Mohammad
- Date: 2023
- Type: Text , Journal article
- Relation: Genes Vol. 14, no. 9 (2023), p.
- Full Text:
- Reviewed:
- Description: Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods. © 2023 by the authors.
Automatic driver distraction detection using deep convolutional neural networks
- Hossain, Md Uzzol, Rahman, Md Ataur, Islam, Md Manowarul, Akhter, Arnisha, Uddin, Md Ashraf, Paul, Bikash
- Authors: Hossain, Md Uzzol , Rahman, Md Ataur , Islam, Md Manowarul , Akhter, Arnisha , Uddin, Md Ashraf , Paul, Bikash
- Date: 2022
- Type: Text , Journal article
- Relation: Intelligent Systems with Applications Vol. 14, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Recently, the number of road accidents has been increased worldwide due to the distraction of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of the people. Therefore, it is essential to monitor and analyze the driver's behavior during the driving time to detect the distraction and mitigate the number of road accident. To detect various kinds of behavior like- using cell phone, talking to others, eating, sleeping or lack of concentration during driving; machine learning/deep learning can play significant role. However, this process may need high computational capacity to train the model by huge number of training dataset. In this paper, we made an effort to develop CNN based method to detect distracted driver and identify the cause of distractions like talking, sleeping or eating by means of face and hand localization. Four architectures namely CNN, VGG-16, ResNet50 and MobileNetV2 have been adopted for transfer learning. To verify the effectiveness, the proposed model is trained with thousands of images from a publicly available dataset containing ten different postures or conditions of a distracted driver and analyzed the results using various performance metrics. The performance results showed that the pre-trained MobileNetV2 model has the best classification efficiency. © 2022 The Author(s)
- Authors: Hossain, Md Uzzol , Rahman, Md Ataur , Islam, Md Manowarul , Akhter, Arnisha , Uddin, Md Ashraf , Paul, Bikash
- Date: 2022
- Type: Text , Journal article
- Relation: Intelligent Systems with Applications Vol. 14, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Recently, the number of road accidents has been increased worldwide due to the distraction of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of the people. Therefore, it is essential to monitor and analyze the driver's behavior during the driving time to detect the distraction and mitigate the number of road accident. To detect various kinds of behavior like- using cell phone, talking to others, eating, sleeping or lack of concentration during driving; machine learning/deep learning can play significant role. However, this process may need high computational capacity to train the model by huge number of training dataset. In this paper, we made an effort to develop CNN based method to detect distracted driver and identify the cause of distractions like talking, sleeping or eating by means of face and hand localization. Four architectures namely CNN, VGG-16, ResNet50 and MobileNetV2 have been adopted for transfer learning. To verify the effectiveness, the proposed model is trained with thousands of images from a publicly available dataset containing ten different postures or conditions of a distracted driver and analyzed the results using various performance metrics. The performance results showed that the pre-trained MobileNetV2 model has the best classification efficiency. © 2022 The Author(s)
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