This paper presents an online educational platform that leverages facial expression recognition technology to monitor students’ progress within the classroom. Periodically, a camera captures images of students in the classroom, processes these images, and extracts facial data through detection methods. Subsequently, students’ learning statuses are assessed using expression recognition techniques. The developed approach then dynamically refines and enhances teaching strategies using the acquired learning status data. In the course of the experiment, we enhance facial expression recognition accuracy through the utilization of ResNet-50 for effective feature extraction. Additionally, by adjusting the residual down-sampling module, we bolster the correlation among input features, thus mitigating the loss of feature information. Simultaneously, a convolutional attention mechanism module is incorporated to reduce the influence of irrelevant areas within the feature map. The proposed method achieves an accuracy of 87.62% and 88.13 % on the RAF-DB and FER2013 expression datasets, respectively. In comparison with the original ResNet-50 network and the expression recognition outcomes found in existing literature, the suggested approach demonstrates enhanced accuracy and improved detection of students’ learning states and expression variations. Consequently, the application of facial expression recognition technology in online learning, along with the optimization of online teaching resources and strategies grounded in the results of recognition, holds tangible value for augmenting the quality of online learning experiences. We have benchmarked the proposed model against state-of-the-art techniques and conducted evaluations using the FER-2013, CK+, and KDEF datasets. The significance of these results lies in their potential application within educational institutions.

The increased utilization of digital instruments like smartphones, Internet of Things (IoT) sensors, cameras, and microphones has resulted in extensive amounts of big data. Inherent challenges associated with big data include significant data dimensionality, redundancy, and irrelevant information. The main objective of feature selection is eliminating unnecessary features, thereby minimizing time and space requirements. This paper proposes a new Binary Crayfish Optimization Algorithm (BinCOA) for feature selection. The Crayfish Optimization Algorithm (COA) is a new metaheuristic algorithm inspired by the simulation of Crayfish’s search for food, summer resorts, and competitive habits. The original COA has been augmented with two primary enhancements to improve its performance. The refracted opposition-based learning strategy is a novel enhancement incorporated into the initialization step of the COA algorithm to strengthen the algorithm’s capability for exploitation. The crisscross strategy is added to the original COA, increasing the COA’s convergence accuracy. The algorithm’s performance is assessed by evaluating a set of 30 benchmark datasets. The proposed BinCOA is evaluated in comparison to seven contemporary wrapper feature selection methods. The experimental findings indicate that BinCOA consistently outperforms existing algorithms in classification accuracy, average fitness value, and the number of selected features. Furthermore, the statistical significance of the results is verified by calculating the Wilcoxon rank-sum test.

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems are still confronted with a problem that reduces their efficiency, such as energy consumption in wearable devices because they are battery-powered and have limited storage. This paper presents a novel algorithm for the compression of ECG signals to reduce energy consumption in RHMs. The proposed algorithm uses discrete Krawtchouk moments as a feature extractor to obtain features from the ECG signal. Then the accelerated Ant Lion Optimizer (AALO) selects the optimum features that achieve the best-reconstructed signal. Our proposed algorithm is extensively validated using two benchmark datasets: MIT-BIH arrhythmia and ECG-ID. The proposed algorithm provides the average values of compression ratio (CR), percent root mean square difference (PRD), signal to noise ratio (SNR), Peak Signal to noise ratio (PSNR), and quality score (QS) are 15.56, 0.69, 44.52, 49.04 and 23.92, respectively. The comparison demonstrates the advantages of the proposed compression algorithm on recent algorithms concerning the mentioned performance metrics. It also tested and compared against other existing algorithms concerning Processing Time, compression speed and computational efficiency. The obtained results show that the proposed algorithm extremely outperforms in terms of (Processing Time = 6.89 s), (compression speed = 4640.19 bps) and (computational efficiency = 2.95). The results also indicate that the proposed compression algorithm reduces energy consumption in a wearable device by decreasing the wake-up time by 3600 ms.

Remote Healthcare Monitoring Systems (RHMs) that employ fetal phonocardiography (fPCG) signals are highly efficient technologies for monitoring continuous and long-term fetal heart rate. Wearable devices used in RHMs still face a challenge that decreases their efficacy in terms of energy consumption because these devices have limited storage and are powered by batteries. This paper proposes an effective fPCG compression algorithm to reduce RHM energy consumption. In the proposed algorithm, the Discrete Orthogonal Charlier Moment (DOCMs) is used to extract features of the signal. The householder orthonormalization method (HOM) is used with the Charlier Moment to overcome the propagation of numerical errors that occur when computing high-order Charlier polynomials. The proposed algorithm’s performance is evaluated in terms of CR, PRD, SNR, PSNR, and QS and provides the average values 18.33, 0.21, 48.85, 68.86, and 90.88, respectively. The results of the comparison demonstrate the proposed compression algorithm’s superiority over other algorithms. It also tested in terms of compression speed and computational efficiency. The results indicate that the proposed algorithm has a high Compression speed (218.672 bps) and high computational efficiency (21.33). Additionally, the results reveal that the proposed algorithm decreases the energy consumption of a wearable device due to the transmission time decreasing for data by 3.68 s.

Bio-signals are extensively used in diagnosing many diseases in wearable devices. In signal processing, signal reconstruction is one of the essential applications. Discrete orthogonal moments (DOMs) are effective analysis tools for signals that can extract digital information without redundancy. The propagation of numerical errors is a significant challenge for the computation of DOMs at high orders. This problem damages the orthogonality property of these moments, which restricts the ability to recover the signal's distinct and unique components with no redundant information. This paper proposes a stable computation of DOMs based on QR decomposition methods: the Gram–Schmidt, Householder, and Given Rotations methods. It also presents a comparative study on the performance of the types of moments: Tchebichef, Krawtchouk, Charlier, Hahn, and Meixner moments. The proposed algorithm's evaluation is done using the MIT-BIH arrhythmia dataset in terms of mean square error and peak signal to noise ratio. The results demonstrate the superiority of the proposed method in computing DOMs, especially at high moment orders. Moreover, the results indicate that the Householder method outperforms Gram–Schmidt and Given Rotations methods in execution time and reconstruction quality. The comparative results show that Tchebichef, Krawtchouk, and Charlier moments have superior reconstruction quality than Hahn and Meixner moments, and Tchebichef generally has the highest performance in signal reconstruction.

The development of Internet of Healthcare Things (IoHT) technology leads to high upgrades in monitor signal systems concerning healthcare. Battery life-saving is one of the biggest issues facing healthcare monitoring signal systems. The most important approach to achieve this goal is to decrease the signal size (i.e., data or signal compression); thus, we need to make data compression. In this study, we introduced a novel approach to decrease the size of the transmitted Phonocardiography (FPCG) bio-medical signal by a set of Fractional Order Chebychev Discrete Orthogonal Moments with the Modified Gram-Schmidt Method (FrCMs- MGSM). The efficiency of the proposed approach has been estimated over a benchmark dataset called Fetal PCG, using many sound metrics: QS, CR, SNR, PRD, and PSNR. The efficiency of the proposed approach was evident when compared to recently published approaches. The empirical findings show that the suggested strategy is highly effective regarding compression time (0.99) and computing efficiency (37.64). The outcomes also demonstrate a (4388 ms) reduction in wearable device wake-up time. Thanks to the suggested compression algorithm, which significantly reduces energy consumption.