Accurate detection and classification of breast cancer is a critical task in medical imaging due to the complexity of breast tissues. Due to automatic feature extraction ability, deep learning methods have been successfully applied in different areas, especially in the field of medical imaging. In this study, a novel patch-based deep learning method called Pa-DBN-BC is proposed to detect and classify breast cancer on histopathology images using the Deep Belief Network (DBN). Features are extracted through an unsupervised pre-training and supervised fine-tuning phase. The network automatically extracts features from image patches. Logistic regression is used to classify the patches from histopathology images. The features extracted from the patches are fed to the model as input and the model presents the result as a probability matrix as either a positive sample (cancer) or a negative sample (background). The proposed model is trained and tested on the whole slide histopathology image dataset having images from four different data cohorts and achieved an accuracy of 86%. Consequently, the proposed method is better than the traditional ones, as it automatically learns the best possible features and experimental results show that the model outperformed the previously proposed deep learning methods.

In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

This paper presents a hybrid method for the construction of cryptographically strong bijective substitution-boxes by utilizing the merits of chaotic map and algebraic groups. The hybrid method first generates the key-dependent dynamic S-boxes using chaotic heuristic search strategy and then the S-boxes are evolved with the help of potent proposed algebraic group structures. This paper proposes a new improved combination chaotic map to operate initial search strategy. To augment the strength of generated S-boxes, the algebraic group structures are discovered which have the power to improve their cryptographic strength. The performance assessments using standard criterions are rendered to quantify the strengths of proposed bijective S-boxes. The experimental results and comparisons with recent S-box research findings justify the effectiveness and competence of the proposed bijective S-boxes and anticipated hybrid generation method.

Cryptography is commonly used to secure communication and data transmission over insecure networks through the use of cryptosystems. A cryptosystem is a set of cryptographic algorithms offering security facilities for maintaining more cover-ups. A substitution-box (S-box) is the lone component in a cryptosystem that gives rise to a nonlinear mapping between inputs and outputs, thus providing confusion in data. An S-box that possesses high nonlinearity and low linear and differential probability is considered cryptographically secure. In this study, a new technique is presented to construct cryptographically strong 8×8 S-boxes by applying an adjacency matrix on the Galois fieldGF(2⁸). The adjacency matrix is obtained corresponding to the coset diagram for the action of modular group PSL(2,Z) on a projective linePL(F₇) over a finite fieldF₇. The strength of the proposed S-boxes is examined by common S-box tests, which validate their cryptographic strength. Moreover, we use the majority logic criterion to establish an image encryption application for the proposed S-boxes. The encryption results reveal the robustness and effectiveness of the proposed S-box design in image encryption applications.

Virtual reality (VR) is considered to be one of the main use cases of the fifth-generation cellular system (5G). In addition, it has been categorized as one of the ultra-low latency applications in which VR applications require an end-to-end latency of 5 ms. However, the limited battery capacity and computing resources of mobile devices restrict the execution of VR applications on these devices. As a result, mobile edge-cloud computing is considered as a new paradigm to mitigate resource limitations of these devices through computation offloading process with low latency. To this end, this paper introduces an efficient multi-player with multi-task computation offloading model with guaranteed performance in network latency and energy consumption for VR applications based on mobile edge-cloud computing. In addition, this model has been formulated as an integer optimization problem whose objective is to minimize the sum cost of the entire system in terms of network latency and energy consumption. Afterwards, a low-complexity algorithm has been designed which provides comprehensive processes for deriving the optimal computation offloading decision in an efficient manner. Furthermore, we provide a prototype and real implementation for the proposed system using OpenAirInterface software. Finally, simulations have been conducted to validate our proposed model and prove that the network latency and energy consumption can be reduced by up to 26.2%, 27.2% and 10.9%, 12.2% in comparison with edge and cloud execution, respectively.