Breast cancer is one of the most prevalent cancers among women worldwide, and early detection of the disease can be lifesaving. Detecting breast cancer early allows for treatment to begin faster, increasing the chances of a successful outcome. Machine learning helps in the early detection of breast cancer even in places where there is no access to a specialist doctor. The rapid advancement of machine learning, and particularly deep learning, leads to an increase in the medical imaging community’s interest in applying these techniques to improve the accuracy of cancer screening. Most of the data related to diseases is scarce. On the other hand, deep-learning models need much data to learn well. For this reason, the existing deep-learning models on medical images cannot work as well as other images. To overcome this limitation and improve breast cancer classification detection, inspired by two state-of-the-art deep networks, GoogLeNet and residual block, and developing several new features, this paper proposes a new deep model to classify breast cancer. Utilizing adopted granular computing, shortcut connection, two learnable activation functions instead of traditional activation functions, and an attention mechanism is expected to improve the accuracy of diagnosis and consequently decrease the load on doctors. Granular computing can improve diagnosis accuracy by capturing more detailed and fine-grained information about cancer images. The proposed model’s superiority is demonstrated by comparing it to several state-of-the-art deep models and existing works using two case studies. The proposed model achieved an accuracy of 93% and 95% on ultrasound images and breast histopathology images, respectively.

In recent years, implementing reinforcement learning in autonomous mobile robots (AMRs) has become challenging. Traditional methods face complex trials, long convergence times, and high computational requirements. This paper introduces an innovative strategy using a customized spiking neural network (SNN) for autonomous learning and control of mobile robots (AMR) in unknown environments. The model combines spike-timing-dependent plasticity (STDP) with dopamine modulation for learning. It utilizes the Izhikevich neuron model, leading to biologically inspired and computationally efficient control systems that adapt to changing environments. The performance of the model is evaluated in a simulated environment, replicating real-world scenarios with obstacles. In the initial training phase, the model faces significant challenges. Integrating brain-inspired learning, dopamine, and the Izhikevich neuron model adds complexity. The model achieves an accuracy rate of 33% in reaching its target during this phase. Collisions with obstacles occur 67% of the time, indicating the struggle of the model to adapt to complex obstacles. However, the model’s performance improves as the study progresses to the testing phase after the robot has learned. Its accuracy surges to 94% when reaching the target, and collisions with obstacles reduce it to 6%. This shift demonstrates the adaptability and problem-solving capabilities of the model in the simulated environment, making it more competent for real-world applications.

Preserving the confidentiality of information is a growing concern in software development. Secure information flow is intended to maintain the confidentiality of sensitive information by preventing them from flowing to attackers. This paper discusses how to ensure confidentiality for multi-threaded programs through a property called observational determinism. Operational semantics of multi-threaded programs are modeled using Kripke structures. Observational determinism is formalized in terms of divergence weak low-bisimulation. Bisimulation is an equivalence relation associating executions that simulate each other. The new property is called bisimulation-based observational determinism. Furthermore, a model checking method is proposed to verify the new property and ensure that secure information flow holds in a multi-threaded program. The model checking method successively refines the Kripke model of the program until the quotient of the model with respect to divergence weak low-bisimulation is reached. Then, bisimulation-based observational determinism is checked on the quotient, which is a minimized model of the concrete Kripke model. The time complexity of the proposed method is polynomial in the size of the Kripke model. The proposed approach has been implemented on top of PRISM, a probabilistic model checking tool. Finally, a case study is discussed to show the applicability of the proposed approach.

As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource accessibility, and security. In this work, we describe a new load-balancing strategy based on the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for distributing work among fog nodes in a large-scale health monitoring system. We ran comprehensive simulations in the iFogSim toolkit to verify the efficacy of the proposed method, comparing the results to the cloud-only implementation, the Fog Node Placement Algorithm (FNPA), the Load Balancing (LAB) scheme, and the Load Balancing Scheme (LBS) in terms of latency and network utilization. The proposed health monitoring system deployment drastically decreases latency and network use in comparison to cloud-only, FNPA, LAB, and LBS schemes.