Accurate and efficient recognition of cardiac arrhythmias is crucial for fast diagnosis and accurate prevention of cardiovascular diseases. This paper investigates the use of an electrocardiogram (ECG) signal analysis based on hybrid form of orthogonal polynomials, namely, Squared Krawtchouk-Tchebichef Polynomials (SKTP) to obtain accurate recognition of cardiac arrhythmias. The proposed method exploits the combined properties representation power of Krawtchouk and Tchebichef moments to capture discriminative characteristics of ECG waveforms. The SKTP has the ability to enhance the feature robustness against noise and baseline wander. ECG signals from the compiled MIT-BIH arrhythmia dataset are used for performance evaluation of the proposed method. SKTP-based features are then computed from each segment and fed into Support Vector Machine (SVM) for arrhythmia classification. Experimental results demonstrate that the proposed method achieves high recognition performance compared to the existing works, attaining an overall accuracy of 91.33%. The findings indicate that SKTP-based features provide a compact, and discriminative representation of ECG signals, making them suitable for real-time arrhythmia monitoring in wearable and telemedicine applications.

Speech enhancement algorithms and machine learning can play a fundamental role in signal processing to improve speech quality. These techniques can be used to reduce noise and distortions in speech signals, hence ensuring clearer and more intelligible speech. By leveraging advanced machine learning, speech enhancement algorithms not only improve the listener’s auditory system, but also increase the efficacy of speech recognition systems. In particular, deep learning is a class of machine learning techniques, which have recently been used in speech enhancement. This paper proposes the use of Discrete Hahn polynomials (DHPs) o extract spectral features from noisy signals using fully connected neural networks and convolutional neural network. Deep learning can efficiently capture the contextual information of speech signals, resulting in superior improvements in speech quality and intelligibility properties. The results are evaluated based on the well-known TIMIT database. The results show that the presented model is able to enhance the speech signal for different conditions.

Cloud storage capacity for surveillance videos is restricted by the massive amounts of bandwidth needed to upload them and the substantial storage space they consume. As a result, researchers are actively exploring methods to reduce file sizes while maintaining critical visual details. The goal is to achieve a balance between minimizing the storage size required and preserving video quality, all while keeping costs as low as possible and achieving the best possible results. This paper proposes a developed approach to optimize surveillance video compression. Specifically, each frame, after being acquired, is applied to extract regions of interest (ROI) where motion has occurred, ensuring that the entire desired area is captured. Next, the extracted areas with no changes are zeroed out, reducing unnecessary data. The resulting area is then encoded and transmitted. When the encoded video is received, the decoding process is carried out to restore the video to its original content, i.e., video frames. Three datasets are used to evaluate the performance of the developed algorithm. In addition, the obtained results are compared to the basic video, which shows that the developed algorithm produced good results, outperforming the MJPEG algorithm and existing algorithms.

Speech is considered the most important way for communication between humans. However, various types of noise degrade speech signals and reduce speech clarity. Usually, speech should be clear as much as possible to be used in the application at hand such as mobile telephony, Tele-communication and hearing aids systems, smart phone applications. Speech enhancement techniques aim to enhance the clarity and quality of speech, thereby improving its overall intelligibility. This is performed using various speech enhancement algorithms (SEA) like filtering, spectral subtraction, and deep learning techniques. This paper provides a brief review on different SEA, and it contains the study of several enhancement models with a discussion of their properties. An investigation of the enhancement process in time and transform domain is performed. Some basic types of background noise are explained with providing a brief description of the challenges and opportunities of speech enhancement processes. The presented paper relates to several research studies in the field of speech enhancement, observed up to 2024. This paper presented the use of enhancement techniques for suppression process through judging several enhancement algorithms for getting the best performance.

To date, cardiovascular disease (CVD) is responsible for a considerable number of deaths each year. Hence, developing an effective CVD prediction model is essential to reducing mortality rates. To this end, this paper makes use of different machine learning (ML) classifiers such as logistic regression (LG), K-nearest neighbor (KNN), support vector machine (SVM), gradient boosting (GB), and adaptive boosting (AdaB) to improve CVD prediction performance. In addition, this paper investigates the use of ML based on the ANOVA feature selection method and voting ensemble model by aggregating different ML classifiers to improve CVD prediction. The CVD performance is evaluated using key metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The results demonstrate that the proposed approach achieves the highest CVD prediction accuracy compared to state-of-the-art methods recording 93.44%. The findings obtained in this paper suggest that the SVM ANOVA feature selection and ensemble approaches can be considered practical strategies for improving the prediction accuracy of CVD.