Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface

Malibari, Areej; Al‐Wesabi, Fahd N.; Obayya, Marwa; Alkhonaini, Mimouna Abdullah; Hamza, Manar Ahmed; Motwakel, Abdelwahed; Yaseen, Ishfaq; Zamani, Abu Sarwar

doi:10.1155/2022/3987494

Cited by 16 publications

(6 citation statements)

References 24 publications

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“…To make sure the higher performance of the ISSADTL-EEGMIC technique, a comparative analysis with recent models (Leeb et al, 2007;Lu et al, 2016;Sharma et al, 2022) in terms of accu y is made in Table 3 and Figure (Kant et al, 2020;Lu et al, 2016;Malibari et al, 2022). data, the ISSADTL-EEGMIC methodology has an accessible average accu y of 95.80%.…”

Section: Experimental Validationmentioning

confidence: 99%

Empowering EEG motor imagery classification with deep transfer learning approach

Mishra,

Gupta,

Srivastava

et al. 2024

Expert Systems

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The brain‐computer interface (BCI) enables individuals with impairments to interact with the real world without relying on the neuromuscular pathway. BCI leverages artificial intelligence (AI) models for control. It can capture brain activity patterns associated with mental processes and convert them into commands for actuators. One promising application of BCI is in rehabilitation centres. When compared to traditional methods integrated with machine learning (ML) approaches that were initially explored in the past decade, there is a relatively limited number of studies utilizing deep learning (DL) approaches in BCI applications. A novel approach has been developed for the automatic recognition of motor imagery (MI) tasks. This article introduces the improved sparrow search algorithm with deep transfer learning‐based EEG motor imagery classification (ISSADTL‐EEGMIC) algorithm for BCIs. The presented ISSADTL‐EEGMIC algorithm primarily focuses on the automated classification of motor imagery tasks. To achieve this, the ISSADTL‐EEGMIC method first preprocesses EEG signals using the wavelet packet decomposition (WPD) technique. Additionally, it employs the neural architecture search network (NASNet) technique for feature extraction. Moreover, the ISSADTL‐EEGMIC model performs the classification process using the stacked sparse autoencoder (SSAE) model. Furthermore, hyperparameter optimization of the SSAE model is performed using the ISSA technique, which helps achieve maximum classification performance. The results from the simulation of the ISSADTL‐EEGMIC algorithm have been examined using two benchmark datasets. The experimental findings suggest that it outperforms recent approaches in terms of performance.

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Section: Experimental Validationmentioning

confidence: 99%

Empowering EEG motor imagery classification with deep transfer learning approach

Mishra,

Gupta,

Srivastava

et al. 2024

Expert Systems

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show abstract

“…In case of no security for these images, it can be easily tampered with and attacked through illicit access. Thus, it is essential to develop highly effective health care image encoding techniques [5,6]. The demand for efficient cryptographic solutions for healthcare images need highly advanced system and its seamless application.…”

Section: Introductionmentioning

confidence: 99%

Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

Duhayyim¹,

Maray²,

Qahmash³

et al. 2023

Computers, Materials &Amp; Continua

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Nowadays, security plays an important role in Internet of Things (IoT) environment especially in medical services' domains like disease prediction and medical data storage. In healthcare sector, huge volumes of data are generated on a daily basis, owing to the involvement of advanced health care devices. In general terms, health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis. At the same time, it is also significant to maintain the delicate contents of health care images during reconstruction stage. Therefore, an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive data. The current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security (IMLOSIE-MIS) technique for IoT environment. The aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them effectively. To do so, the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical image. Besides, shadow image encryption process takes place with the help of Multileader Optimization (MLO) with Homomorphic Encryption (IMLO-HE) technique, where the optimal keys are generated with the help of MLO algorithm. On the receiver side, decryption process is initially carried out and shadow image reconstruction process is conducted. The experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other models.

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“…Moreover, an important drawback found in established ECG recordings is that it tend to create wrong alarms frequently. Hence, remote monitoring gadgets are designed to handle more delicate to abnormal signals from the ECG so as to avert missing any major cardiac actions [4,5]. Thus, there exists an increasing requirement in guiding the doctors to interpret ECG records.…”

Section: Introductionmentioning

confidence: 99%

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

Obayya¹,

Nemri²,

Alharbi³

et al. 2023

Computers, Materials &Amp; Continua

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With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Biomedical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals. The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECG Signal Classification (IBADL-BECGC) approach. To accomplish this, the proposed IBADL-BECGC model initially pre-processes the input signals. Besides, IBADL-BECGC model applies NasNet model to derive the features from test ECG signals. In addition, Improved Bat Algorithm (IBA) is employed to optimally fine-tune the hyperparameters related to NasNet approach. Finally, Extreme Learning Machine (ELM) classification algorithm is executed to perform ECG classification method. The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset. The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

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Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface

Cited by 16 publications

References 24 publications

Empowering EEG motor imagery classification with deep transfer learning approach

Empowering EEG motor imagery classification with deep transfer learning approach

Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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