Breast cancer constitutes a significant threat to women’s health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better, and treating various diseases. For achieving the early detection of breast cancer, this paper proposes a Machine Learning-based two-level top-down hierarchical approach for breast cancer detection and classification into three classes: normal, benign, and malignant, using the Mammographic Image Analysis Society (MIAS) mammography dataset. Different data preprocessing techniques are applied before using feature extraction techniques and machine learning algorithms for classification. The first classification stage which distinguishes between normal and abnormal cases is comprised of Gray Level Co-occurrence Matrix (GLCM) as a feature extraction technique and random forest as a classifier, followed by the second classification stage which classifies the abnormal cases into benign or malignant cases and is comprised of Local Binary Patterns (LBP) as a feature extraction technique and random forest as a classifier. The classification accuracy for the first stage is 97% and an F1-score of 0.98 and 0.97 for normal and abnormal classes. While for the second stage, the classification accuracy is 75% and an F1-score of 0.76 and 0.74 for benign and malignant classes. The overall hierarchical classification system achieves a classification accuracy of 85%, Matthews correlation coefficient (MCC) of 0.76, and F1-score of 0.98, 0.7, and 0.74 for normal, benign, and malignant test cases.

Current research in interfacing clusters within Hierarchical Networks-on-Chip (HNoC) as well as interfacing NoC-based systems adopts a centralized approach. In this approach, a specific Processing Element (PE) acts as a gateway between interfacing peripherals and the rest of NoC elements. This paper evaluates this approach and show that it is not optimal for handling the inter-NoC communication. Routing inter-NoC traffic through a system to its gateway PE deteriorates the network performance. Results show that both the throughput and latency of the centralized approach degrade with the increase in the inter-NoC traffic bandwidth. To alleviate this, we propose a novel distributed approach, which separates the inter-NoC traffic from the intra-NoC one. Our approach relies on distributed buffers to allow PEs to efficiently communicate with the interfacing peripheral. We evaluate our approach against other interfacing ones using synthetic traffic as well as real benchmark applications. Our evaluation covers the whole system performance as well as its inter-and intra-NoC parts. Results prove that the proposed approach outperforms previous interfacing ones in terms of throughput and latency. The proposed approach significantly enhances the inter-NoC performance without any deterioration in the intra-NoC one. Considering the inter-NoC performance, we achieve a throughput that is close to the maximum possibly attainable one. Other approaches show major performance degradation, reaching as low as 10% of this maximum attainable throughput.

This paper presents new stability conditions for two‐dimensional (2‐D) systems in state‐space description. Both discrete and continuous systems are studied. These results are based on the criteria first presented by Huang, De Carlo, Strintzis, Murray, Delsarte, et al. and on the discrete Lyapunov equation with complex elements for 2‐D systems. The stability properties of the Mansour matrix are also used for stability testing in state‐space. Criteria for the VSHP property of 2‐D polynomials are further presented using the continuous Lyapunov equation with complex elements and the stability properties of the Schwarz matrix form. The stability properties of the Schwarz matrix are also used for testing the VSHP property of 2‐D polynomials in state‐space. The proposed new criteria are non‐conservative for the stability analysis of 2‐D discrete and continuous systems and achieve the aim of reducing the original 2‐D problem as much as possible to a set of 1‐D stability tests. Numerical examples are given to illustrate the utility of the proposed conditions.

The advanced technology of vehicles makes them vulnerable to external exploitation. The current trend of research is to impose security measures to protect vehicles from different aspects. One of the main problems that counter Intrusion Detection Systems (IDSs) is the necessity to have a low false acceptance rate (FA) with high detection accuracy without major changes in the vehicle network infrastructure. Furthermore, the location of IDSs can be controversial due to the limitations and concerns of Electronic Control Units (ECUs). Thus, we propose a novel framework of multistage to detect abnormality in vehicle diagnostic data based on specifications of diagnostics and stacking ensemble for various machine learning models. The proposed framework is verified against the KIA SOUL and Seat Leon 2018 datasets. Our IDS is evaluated against point anomaly attacks and period anomaly attacks that have not been used in its training. The results show the superiority of the framework and its robustness with high accuracy of 99.21%, a low false acceptance rate of 0.003%, and a good detection rate (DR) of 99.63% for Seat Leon 2018, and an accuracy of 99.22%, a low false acceptance rate of 0.005%, and good detection rate of 98.59% for KIA SOUL.

Acne Vulgaris is a common type of skin disease that affects more than 85% of teenagers and frequently continues even in adulthood. While it is not a dangerous skin disease, it can significantly impact the quality of life. Hyperspectral imaging (HSI), which captures a wide spectrum of light, has emerged as a tool for the detection and diagnosis of various skin conditions. However, due to the high cost of specialised HS cameras, it is limited in its use in clinical settings. In this research, a novel acne detection system that will utilise reconstructed hyperspectral (HS) images from RGB images is proposed. A dataset of reconstructed HS images is created using the best-performing HS reconstruction model from our previous research. A new acne detection algorithm that is based on reconstructed HS images and RetinaNet algorithm is introduced. The results indicate that the proposed algorithm surpasses other techniques based on RGB images. Additionally, reconstructed HS images offer a promising and cost-effective alternative to using expensive HSI equipment for detecting conditions like acne or other medical issues.

This article presents an indoor assistive system that addresses the challenges faced by visually impaired individuals. The proposed system helps the visually impaired individuals to move indoor and make them independent of any external assistance. The proposed system consists of a camera with a processing unit and an accompanying Time-of-Flight sensor providing an efficient, convenient and cost-effective solution. The proposed system achieves average object detection accuracy of 73.34% and a 5% error margin in detecting the distance and length of detected objects. The performance comparison with two existing systems shows that the proposed system provides a very close performance to the benchmarks with advantages of portability easy-to-use and no requirement for cloud services.