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Lilhore UK, Manoharan P, Simaiya S, Alroobaea R, Alsafyani M, Baqasah AM, Dalal S, Sharma A, Raahemifar K. HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning. Sensors (Basel) 2023; 23:7856. [PMID: 37765912 PMCID: PMC10535139 DOI: 10.3390/s23187856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/13/2023] [Accepted: 04/25/2023] [Indexed: 09/29/2023]
Abstract
Industrial automation systems are undergoing a revolutionary change with the use of Internet-connected operating equipment and the adoption of cutting-edge advanced technology such as AI, IoT, cloud computing, and deep learning within business organizations. These innovative and additional solutions are facilitating Industry 4.0. However, the emergence of these technological advances and the quality solutions that they enable will also introduce unique security challenges whose consequence needs to be identified. This research presents a hybrid intrusion detection model (HIDM) that uses OCNN-LSTM and transfer learning (TL) for Industry 4.0. The proposed model utilizes an optimized CNN by using enhanced parameters of the CNN via the grey wolf optimizer (GWO) method, which fine-tunes the CNN parameters and helps to improve the model's prediction accuracy. The transfer learning model helps to train the model, and it transfers the knowledge to the OCNN-LSTM model. The TL method enhances the training process, acquiring the necessary knowledge from the OCNN-LSTM model and utilizing it in each next cycle, which helps to improve detection accuracy. To measure the performance of the proposed model, we conducted a multi-class classification analysis on various online industrial IDS datasets, i.e., ToN-IoT and UNW-NB15. We have conducted two experiments for these two datasets, and various performance-measuring parameters, i.e., precision, F-measure, recall, accuracy, and detection rate, were calculated for the OCNN-LSTM model with and without TL and also for the CNN and LSTM models. For the ToN-IoT dataset, the OCNN-LSTM with TL model achieved a precision of 92.7%; for the UNW-NB15 dataset, the precision was 94.25%, which is higher than OCNN-LSTM without TL.
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Affiliation(s)
- Umesh Kumar Lilhore
- Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali 140413, India
| | - Poongodi Manoharan
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha P.O Box 5825, Qatar
| | - Sarita Simaiya
- Apex Institute of Technology (CSE), Chandigarh University, Gharuan, Mohali 140413, India
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Majed Alsafyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Abdullah M. Baqasah
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21974, Saudi Arabia
| | - Surjeet Dalal
- Amity School of Engineering and Technology, Amity University, Gurugram 122412, India
| | - Ashish Sharma
- Department of Computer Engineering and Applications, GLA University, Mathura 281406, India
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology, Penn State University, State College, PA 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University, Waterloo, ON N2L3G1, Canada
- Faculty of Engineering, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada
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Amarnath A, Manoharan P, Natarajan B, Alroobaea R, Alsafyani M, Baqasah AM, Keshta I, Raahemifar K. Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization. Diagnostics (Basel) 2023; 13:2919. [PMID: 37761285 PMCID: PMC10529025 DOI: 10.3390/diagnostics13182919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/20/2023] [Accepted: 05/26/2023] [Indexed: 09/29/2023] Open
Abstract
Speckle noise is a pervasive problem in medical imaging, and conventional methods for despeckling often lead to loss of edge information due to smoothing. To address this issue, we propose a novel approach that combines a nature-inspired minibatch water wave swarm optimization (NIMWVSO) framework with an invertible sparse fuzzy wavelet transform (ISFWT) in the frequency domain. The ISFWT learns a non-linear redundant transform with a perfect reconstruction property that effectively removes noise while preserving structural and edge information in medical images. The resulting threshold is then used by the NIMWVSO to further reduce multiplicative speckle noise. Our approach was evaluated using the MSTAR dataset, and objective functions were based on two contrasting reference metrics, namely the peak signal-to-noise ratio (PSNR) and the mean structural similarity index metric (MSSIM). Our results show that the suggested approach outperforms modern filters and has significant generalization ability to unknown noise levels, while also being highly interpretable. By providing a new framework for despeckling medical images, our work has the potential to improve the accuracy and reliability of medical imaging diagnosis and treatment planning.
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Affiliation(s)
- Ahila Amarnath
- Indian Institute of Technology, Madras, Chennai 600036, Tamilnadu, India
| | - Poongodi Manoharan
- College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar
| | | | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Majed Alsafyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Abdullah M. Baqasah
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21974, Saudi Arabia
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh 11597, Saudi Arabia
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology, Penn State University, State College, PA 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University, Waterloo, ON N2L3G1, Canada
- Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3E9, Canada
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Lilhore UK, Manoharan P, Sandhu JK, Simaiya S, Dalal S, Baqasah AM, Alsafyani M, Alroobaea R, Keshta I, Raahemifar K. Hybrid model for precise hepatitis-C classification using improved random forest and SVM method. Sci Rep 2023; 13:12473. [PMID: 37528148 PMCID: PMC10394001 DOI: 10.1038/s41598-023-36605-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/07/2023] [Indexed: 08/03/2023] Open
Abstract
Hepatitis C Virus (HCV) is a viral infection that causes liver inflammation. Annually, approximately 3.4 million cases of HCV are reported worldwide. A diagnosis of HCV in earlier stages helps to save lives. In the HCV review, the authors used a single ML-based prediction model in the current research, which encounters several issues, i.e., poor accuracy, data imbalance, and overfitting. This research proposed a Hybrid Predictive Model (HPM) based on an improved random forest and support vector machine to overcome existing research limitations. The proposed model improves a random forest method by adding a bootstrapping approach. The existing RF method is enhanced by adding a bootstrapping process, which helps eliminate the tree's minor features iteratively to build a strong forest. It improves the performance of the HPM model. The proposed HPM model utilizes a 'Ranker method' to rank the dataset features and applies an IRF with SVM, selecting higher-ranked feature elements to build the prediction model. This research uses the online HCV dataset from UCI to measure the proposed model's performance. The dataset is highly imbalanced; to deal with this issue, we utilized the synthetic minority over-sampling technique (SMOTE). This research performs two experiments. The first experiment is based on data splitting methods, K-fold cross-validation, and training: testing-based splitting. The proposed method achieved an accuracy of 95.89% for k = 5 and 96.29% for k = 10; for the training and testing-based split, the proposed method achieved 91.24% for 80:20 and 92.39% for 70:30, which is the best compared to the existing SVM, MARS, RF, DT, and BGLM methods. In experiment 2, the analysis is performed using feature selection (with SMOTE and without SMOTE). The proposed method achieves an accuracy of 41.541% without SMOTE and 96.82% with SMOTE-based feature selection, which is better than existing ML methods. The experimental results prove the importance of feature selection to achieve higher accuracy in HCV research.
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Affiliation(s)
- Umesh Kumar Lilhore
- Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India
| | - Poongodi Manoharan
- College of Science and Engineering, Qatar Foundation, Hamad Bin Khalifa University, Doha, Qatar.
| | - Jasminder Kaur Sandhu
- Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India
| | - Sarita Simaiya
- Apex Institute of Technology (CSE), Chandigarh University, Gharuan, Mohali, Punjab, 140413, India
| | - Surjeet Dalal
- Amity School of Engineering and Technology, Amity University Haryana, Gurugram, India
| | - Abdullah M Baqasah
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21974, Saudi Arabia
| | - Majed Alsafyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Kaamran Raahemifar
- College of Information Sciences and Technology, Data Science and Artificial Intelligence Program, Penn State University, State College, PA, 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University, Waterloo, ON, N2L3G1, Canada
- Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, Canada
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S. B, G. L, Vaiyapuri T, Ahanger TA, Dahan F, Hajjej F, Keshta I, Alsafyani M, Alroobaea R, Raahemifar K. A convolutional neural network for face mask detection in IoT-based smart healthcare systems. Front Physiol 2023; 14:1143249. [PMID: 37064899 PMCID: PMC10102606 DOI: 10.3389/fphys.2023.1143249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/16/2023] [Indexed: 04/03/2023] Open
Abstract
The new coronavirus that produced the pandemic known as COVID-19 has been going across the world for a while. Nearly every area of development has been impacted by COVID-19. There is an urgent need for improvement in the healthcare system. However, this contagious illness can be controlled by appropriately donning a facial mask. If people keep a strong social distance and wear face masks, COVID-19 can be controlled. A method for detecting these violations is proposed in this paper. These infractions include failing to wear a facemask and failing to maintain social distancing. To train a deep learning architecture, a dataset compiled from several sources is used. To compute the distance between two people in a particular area and also predicts the people wearing and not wearing the mask, The proposed system makes use of YOLOv3 architecture and computer vision. The goal of this research is to provide valuable tool for reducing the transmission of this contagious disease in various environments, including streets and supermarkets. The proposed system is evaluated using the COCO dataset. It is evident from the experimental analysis that the proposed system performs well in predicting the people wearing the mask because it has acquired an accuracy of 99.2% and an F1-score of 0.99.
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Affiliation(s)
- Bose S.
- Department of Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India
- *Correspondence: Bose S.,
| | - Logeswari G.
- Department of Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India
| | - Thavavel Vaiyapuri
- Department of Computer Sciences, College of Computer engineering and sciences, Prince Sattam Bin AbdulAziz University, Al-Kharj, Saudi Arabia
| | - Tariq Ahamed Ahanger
- Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Fadl Dahan
- Department of Management Information Systems, College of Business Administration Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Fahima Hajjej
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Majed Alsafyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Al-Hawiyya, Saudi Arabia
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, Al-Hawiyya, Saudi Arabia
| | - Kaamran Raahemifar
- College of Information Sciences and Technology, Data Science and Artificial Intelligence Program, State College, Penn State University, State College, PA, United States
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, Waterloo, ON, Canada
- Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
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Alsafyani M, Alhomayani F, Alsuwat H, Alsuwat E. Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map. Sensors (Basel) 2023; 23:1415. [PMID: 36772454 PMCID: PMC9921757 DOI: 10.3390/s23031415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Demand for data security is increasing as information technology advances. Encryption technology based on biometrics has advanced significantly to meet more convenient and secure needs. Because of the stability of face traits and the difficulty of counterfeiting, the iris method has become an essential research object in data security research. This study proposes a revolutionary face feature encryption technique that combines picture optimization with cryptography and deep learning (DL) architectures. To improve the security of the key, an optical chaotic map is employed to manage the initial standards of the 5D conservative chaotic method. A safe Crypto General Adversarial neural network and chaotic optical map are provided to finish the course of encrypting and decrypting facial images. The target field is used as a "hidden factor" in the machine learning (ML) method in the encryption method. An encrypted image is recovered to a unique image using a modernization network to achieve picture decryption. A region-of-interest (ROI) network is provided to extract involved items from encrypted images to make data mining easier in a privacy-protected setting. This study's findings reveal that the recommended implementation provides significantly improved security without sacrificing image quality. Experimental results show that the proposed model outperforms the existing models in terms of PSNR of 92%, RMSE of 85%, SSIM of 68%, MAP of 52%, and encryption speed of 88%.
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Affiliation(s)
- Majed Alsafyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Fahad Alhomayani
- Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Hatim Alsuwat
- Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 24382, Saudi Arabia
| | - Emad Alsuwat
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
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Simaiya S, Kaur R, Sandhu JK, Alsafyani M, Alroobaea R, alsekait DM, Margala M, Chakrabarti P. A novel multistage ensemble approach for prediction and classification of diabetes. Front Physiol 2022; 13:1085240. [PMID: 36601350 PMCID: PMC9807241 DOI: 10.3389/fphys.2022.1085240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 11/22/2022] [Indexed: 12/23/2022] Open
Abstract
Diabetes mellitus is a metabolic syndrome affecting millions of people worldwide. Every year, the rate of occurrence rises drastically. Diabetes-related problems across several vital organs of the body can be fatal if left untreated. Diabetes must be detected early to receive proper treatment, preventing the condition from escalating to severe problems. Tremendous health sciences and biotechnology advancements have resulted in massive data that generated massive Electronic Health Records and clinical information. The exponential increase of electronically gathered information has resulted in more complicated, accurate prediction models that can be updated continuously using machine learning techniques. This research mainly emphasizes discovering the best ensemble model for predicting diabetes. A new multistage ensemble model is proposed for diabetes prediction. In this model, accuracy is predicated on the Pima Indian Diabetes dataset. The accuracy of the proposed ensemble model is compared with the existing machine learning model, and the experimental results demonstrate the performance of the proposed model in terms of higher Precision, f-measure, Recall, and area under the curve.
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Affiliation(s)
- Sarita Simaiya
- Department of Computer Science and Engineering, Institute of Engineering and Technology, Chandigarh University, Mohali, Punjab, India,School of Computing and Informatics, University of Louisiana, Lafayette, LA, United States,*Correspondence: Sarita Simaiya, ; Martin Margala,
| | - Rajwinder Kaur
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
| | - Jasminder Kaur Sandhu
- Department of Computer Science and Engineering, Institute of Engineering and Technology, Chandigarh University, Mohali, Punjab, India
| | - Majed Alsafyani
- Department Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Deema mohammed alsekait
- Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Martin Margala
- School of Computing and Informatics, University of Louisiana, Lafayette, LA, United States,*Correspondence: Sarita Simaiya, ; Martin Margala,
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