Cardiovascular diseases are non-communicable diseases that are considered the leading cause of death worldwide accounting for 17.9 million fatalities. Auscultation of heart sounds is the most common and valuable way of diagnosing heart diseases. Normal heart sounds have a special rhythmic pattern as an indicator of heart integrity. Many experts concentrate on diagnosing the heart by automatic digital auscultation systems which find various distinguishable characteristics for heart sound classifications. This can decrease the mortality rate for cardiovascular diseases and enhance the patient’s quality of life. This study aims to propose a real-time heart sound recognition system to classify both normal and abnormal phonocardiograms with the ability to define the abnormality type if existed. Digital signal processing methods, by applying the fast Fourier transform, filtering techniques, and the dual-tree complex wavelet transform, with machine learning classification algorithms are employed to segment the input phonocardiogram signal, extract meaningful features, and find the appropriate class for the input signal. We utilized three datasets, the PhysioNet of 1395, the GitHub of 800, and the PASCAL of 100 files segmented into three cardiac cycles. The proposed solution relies on the support vector machine and the long-short term memory neural network to distinguish between normal and abnormal heartbeat sounds and to recognize the type of abnormality (in the case distinguished) respectively. The results show that the proposed approach for normal/abnormal classification achieves an overall accuracy of 96.0 and 98.1%, sensitivity of 94.4 and 84.2%, and specificity of 64.9 and 98.4% for two and one support vector machines respectively among the state-of-the-art solutions. The long short-term memory model is also a well-known efficient classifier for temporal data, and the results show the accuracy of 99.2, 99.5, 98.6, and 99.4% for four (aortic stenosis (AS), mitral regurgitation (MR), mitral stenosis (MS), and mitral valve prolapse (MVP)), five (AS, MR, MS, MVP, and normal), six (AS, MR, MS, MVP, extrahls, and extrasystole), and seven classes (AS, MR, MS, MVP, extrahls, extrasystole, and normal). Furthermore, we found an efficient automatic segmentation method that was tested with the PASCAL database achieving a total error of 867,525.6 and 23,590.3 ms for datasets A and B respectively, with a computational time of 0.04 s to segment one cardiac cycle.

Although it was first developed and studied in the late 1970s and early 1977s, the Data Encryption Standard (DES) algorithm has grown in popularity. There are two causes for this occurrence. First, the DES algorithm's complex mathematical structure allows it to serve as the theoretical foundation for a wide variety of applications. Second, the encryption technique works quite well in practice for a variety of applications when implemented correctly. In this paper, we undertake a thorough and practical review of the theoretical aspects of this sort of encryption algorithm and demonstrate how they have been implemented by executing multiple encryption configurations.

Due to the growing interconnection and sensitive nature of IoT applications, this article provides a comprehensive overview of cybersecurity in 5G-enabled Internet of Things (IoT) networks. The review highlights the significant potential of 5G technology while also emphasizing the need for ongoing work to mitigate its weaknesses and fully harness its advantages. In this context, prospective research avenues are crucial to enhance the resilience and security of 5G-enabled systems. The future directions outlined in this document are diverse and seek to tackle significant deficiencies in 5G-IoT cybersecurity research. A primary objective is to align theoretical analyses with practical implementations by examining specific assaults in IoT contexts, thus improving the comprehension of real-world vulnerabilities. Furthermore, as the 5G standard progresses through its implementation, continuous research must remain synchronized with improvements, focusing on components and features that are currently in development. Formulating effective protection and mitigation strategies to address sophisticated threats is essential for safeguarding this integrated environment. Furthermore, enhancing standards and regulatory frameworks is essential to ensure uniform security procedures across various businesses and areas, especially considering the global nature of 5G-IoT applications. Ultimately, investigating sophisticated attack scenarios that leverage the interaction between IoT devices and 5G infrastructure would enhance understanding of vulnerabilities and aid in the development of more resilient security measures. These directives underscore the necessity for ongoing, flexible research to guarantee the robustness and safety of 5G-enabled IoT networks.

Pest insects are a danger to both regional and global food security. In Jordan, the most productive crop is tomato. Jordan's agriculture output is threatened by insect infestations. The study intends to use a deep learning model called convolutional neural networks on a dataset that includes eight categories of insect pest images. A dataset was used and a group of images from reliable sources were added to it. The image collection was analyzed, and an image augmentation technique was used to increase the number of images, which reached 5894 after image augmentation. The data was split among 80 % training and 20 % validation. Convolutional Neural Networks trained on the data achieved 90 % training accuracy, 85 % testing accuracy, and 87 % validation accuracy. A high-accuracy deep learning model was developed that may be utilized on mobile applications to detect pests that affected crops to assist farmers. The original database used was small in size. When tested on deep learning and machine learning systems, the accuracy was very low, reaching 50–60 % without image augmentation, despite image enhancement techniques.