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·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
• Optimization Sciences
• Control Theory
• Operational Research
• Decision Sciences
• Information Theory
• Information Technology
• Computer Networks and Communications
• Mathematical Programming
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Issues up to 2022 co-published with and available at:
Accurate analysis of ECG data is necessary for effective heart attack detection. The InRes-106 model, a hybrid fine-tuned optimizer that improves ECG data security and detection, is presented in this study. It blends deep learning models like ResNet50 and InceptionV3 with sophisticated image processing techniques like artifact removal and Histogram Equalization (HE). The study assesses five pre-trained models: VGG19, DenseNet201, MobileNetV2, and ResNet50 after pre-processing ECG images to enhance quality and remove artifacts before putting out the innovative InRes-106 model. With a remarkable 98.34% accuracy, this model establishes a new standard for heart attack detection and demonstrates improvements in cardiac care.
[1] S. Mandala, S. S. Amini, A. R. Syaifullah, M. Pramudyo, S. Nurmaini, and A. H. Abdullah, “Enhanced myocardial infarction identification in phonocardiogram signals using segmented feature extraction and transfer learning-based classification,” IEEE Access, vol. 11, pp. 136654–136665 (2023).[2] B. Ramesh and K. Lakshmanna, “A Novel Early Detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy Inference System,” IEEE Access, vol. 12, pp. 26683–26695 (2024), doi: 10.1109/ACCESS.2024.3366537.[3] S. Akter, F. M. J. M. Shamrat, S. Chakraborty, A. Karim, and S. Azam, “Covid-19 detection using deep learning algorithm on chest X-ray images,” Biology (Basel)., vol. 10, no. 11 (2021), doi: 10.3390/biology10111174.[4] S. M. Rezaeijo, M. Ghorvei, R. Abedi-Firouzjah, H. Mojtahedi, and H. Entezari Zarch, “Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms,” Egypt. J. Radiol. Nucl. Med., vol. 52, no. 1, Dec. (2021), doi: 10.1186/s43055-021-00524-y.[5] V. Atanasoski, J. Petrović, L. P. Maneski, M. Miletić, M. Babić, A. Nikolić, and M. D. Ivanović, “A morphology-preserving algorithm for denoising of EMG-contaminated ECG signals,” IEEE Open Journal of Engineering in Medicine and Biology (2024).[6] P. Madan, V. Singh, D. P. Singh, M. Diwakar, B. Pant, and A. Kishor, “A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification,” Bioengineering, vol. 9, no. 4 (2022), doi: 10.3390/bioengineering9040152.[7] M. N. Hasan, M. A. Hossain, and A. S. Akash, “Detection of Cardiovascular Disease from ECG Image Using Optimized Weighted Average Ensemble Machine Learning Technique,” in 2023 26th International Conference on Computer and Information Technology, ICCIT 2023 (2023). doi: 10.1109/ICCIT60459.2023.10441119.[8] T. Mahmood, A. Rehman, T. Saba, T. J. Alahmadi, M. Tufail, S. A. O. Bahaj, and Z. Ahmad, “Enhancing Coronary Artery Disease Prognosis: A Novel Dual-Class Boosted Decision Trees Strategy for Robust Optimization,” IEEE Access (2024).[9] S. Muthumeena and L. P. On, “Deep Learning Framework for Cardio Vascular Disease Prediction Using ECG Images,” 2024 Int. Conf. Smart Syst. Electr. Electron. Commun. Comput. Eng., pp. 244–249 (2024), doi: 10.1109/ICSSEECC61126.2024.10649540.[10] A. A. Aleidan, Q. Abbas, Y. Daadaa, I. Qureshi, G. Perumal, M. E. Ibrahim, and A. E. Ahmed, “Biometric-based human identification using ensemble-based technique and ECG signals,” Applied Sciences, vol. 13, no. 16, p. 9454 (2023).[11] R. Al-Tam, A. Al-Hejri, E. Naji, … F. H.-I., and U. 2024, “A Hybrid Framework of Transformer Encoder and Residential Conventional for Cardiovascular Disease Recognition Using Heart Sounds,” IEEE Access (2024), doi: 10.1109/ACCESS.2024.3451660.[12] R. J. Urbanowicz, M. Meeker, W. La Cava, R. S. Olson, and J. H. Moore, “Relief-based feature selection: Introduction and review,” Journal of Biomedical Informatics, vol. 85. pp. 189–203 (2018). doi: 10.1016/j.jbi.2018.07.014.[13] N. K. Chauhan and N. Kaur, “Predicting Cardiovascular Diseases Using Machine Learning Integrated with Feature Selection Through Blue Whale Optimization,” in 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, Institute of Electrical and Electronics Engineers Inc., pp. 464–469 (2023). doi: 10.1109/UPCON59197.2023.10434597.[14] S. P. Patro, N. Padhy, and R. D. Sah, “An improved ensemble learning approach for the prediction of cardiovascular disease using majority voting prediction,” Int. J. Model. Identif. Control, vol. 41, no. 1–2, pp. 68–86 (2022), doi: 10.1504/ijmic.2022.127098.[15] A. Pramanik, P. Rajput, and S. Aluvala, “Applying Healthcare Analytics to Diagnose and Predict Coronary Artery Disease Using Machine Learning Techniques,” in Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, Institute of Electrical and Electronics Engineers Inc., pp. 610–614 (2023). doi: 10.1109/ICACCTech61146.2023.00104.[16] A. Raj, A. Kumar, S. Kumar, and S. Budhiraja, “Heart Disease Prediction using Machine Learning,” in 2023 9th International Conference on Signal Processing and Communication, ICSC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 113–119 (2023). doi: 10.1109/ICSC60394.2023.10441292.[17] A. K. Berdaly and Z. M. Abdiahmetova, “PREDICTING HEART DISEASE USING MACHINE LEARNING ALGORITHMS,” Kazn. Bull. Math. Mech. Comput. Sci. Ser., vol. 115, no. 3, pp. 101–111 (2022), doi: 10.26577/JMMCS.2022.v115.i3.10.[18] S. N. Ajani, P. Khobragade, M. Dhone, B. Ganguly, N. Shelke, and N. Parati, “Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 7s, pp. 546–559 (2024).[19] E. Irmak, “COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model,” Phys. Eng. Sci. Med., vol. 45, no. 1, pp. 167–179, Mar. (2022), doi: 10.1007/s13246-022-01102-w.[20] T. Anwar and S. Zakir, “Effect of Image Augmentation on ECG Image Classification using Deep Learning,” in 2021 International Conference on Artificial Intelligence, ICAI 2021, 2021, pp. 182–186. doi: 10.1109/ICAI52203.2021.9445258.
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