Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment.

The potential degradation of the Global Positioning System and other Global Navigation Satellite Systems under several circumstances gives rise to the development of alternative position, navigation, and timing (PNT) technologies aiming at maintaining efficient and safe operations. In this article, we exploit the use of reconfigurable intelligent surfaces (RISs) as enablers for the design of an alternative PNT solution, with improved accuracy and efficiency. The specific problem of RISs' orchestration and configuration is treated via the adoption of game theory and reinforcement learning (RL). Initially, a satisfaction game is formulated and solved among the targets, enabling them to autonomously determine the optimal number of RISs that will contribute to their PNT service, while the specific set of RISs to be used is determined by a novel RL algorithm. In order to further maximize the received signal strength at each target of the reflected signals from the specific set of RISs, the phase-shift optimization of the latter is performed. Based on the above, an iterative least squares algorithm is adopted, following the multilateration technique, in order for each target to estimate its position and timing. The performance evaluation of the proposed approach is achieved via modeling and simulation.

This paper introduces the INFLUTRUST framework that is designed to address challenges in trust-based influencer marketing campaigns on Online Social Networks (OSNs). The INFLUTRUST framework enables the influencers to autonomously select products across the OSN platforms for advertisement by employing a reinforcement learning algorithm. The Stochastic Learning Automata reinforcement algorithm considers the OSN platforms’ provided monetary rewards, the influencers’ advertising profit, and the influencers’ trust levels towards the OSN platforms to enable the influencers to autonomously select an OSN platform. The trust model for the influencers incorporates direct and indirect trust, which are derived from past interactions and social ties among the influencers and the OSN platforms, respectively. The OSN platforms allocate rewards through a multilateral bargaining model that supports competition among the influencers. Simulation-based results validate the INFLUTRUST framework’s effectiveness across diverse scenarios, with the scalability analysis demonstrating its robustness. Comparative evaluations highlight the INFLUTRUST framework’s superiority in considering trust levels and reward allocation fairness, benefiting both the influencers and the OSN platforms.

Multi-access edge computing (MEC) has attracted the interest of the research and industrial community to support Internet of things (IoT) applications by enabling efficient data processing and minimizing latency. This paper presents significant contributions toward optimizing the resource allocation and enhancing the decision-making process in edge computing environments. Specifically, the TRUST-ME model is introduced, which consists of multiple edge servers and IoT devices, i.e., users, with varied computing tasks offloaded to the MEC servers. A utility function was designed to quantify the benefits in terms of latency and cost for the IoT device while utilizing the MEC servers’ computing capacities. The core innovation of our work is a novel trust model that was designed to evaluate the IoT devices’ confidence in MEC servers. This model integrates both direct and indirect trust and reflects the trustworthiness of the servers based on the direct interactions and social feedback from other devices using the same servers. This dual trust approach helps with accurately gauging the reliability of MEC services and ensuring more informed decision making. A reinforcement learning framework based on the optimistic Q-learning with an upper confidence bounds action selection algorithm enables the IoT devices to autonomously select a MEC server to process their computing tasks. Also, a multilateral bargaining model is proposed for fair resource allocation of the MEC servers’ computing resources to the users while accounting for their computing demands. Numerical simulations demonstrated the operational effectiveness, convergence, and scalability of the TRUST-ME model, which was validated through real-world scenarios and comprehensive comparative evaluations against existing approaches.

This article introduces the concept of wireless controls for heliostat-based concentrating solar-thermal power (CSP) systems, highlighting the cost advantages over traditional wired connections. Initially, the article reviews the current state of the art for wireless control development in CSP systems, emphasizing their early stage in terms of adopting wireless communication technology. The article introduces the novel HELIOCOMM system, which leverages the advanced wireless networking technology of integrated access and backhaul to support wireless closed-loop controls in the CSP field, while additionally employing artificial intelligence or artificial intelligence-based clustering, entropy-based routing, and resource allocation. These elements enhance the energy efficiency of the photovoltaic and battery operating heliostats, reduce the latency to guarantee the closed-loop control of the heliostats in a real-time manner, and optimize bandwidth allocation in wireless links. This article provides a comprehensive overview of the HELIOCOMM system’s architecture and its components, highlighting its potential to transform CSP systems.

The control of heliostats in existing Concentrated Solar Power (CSP) fields is performed based on wired communications, resulting in high installation, maintenance, and operation cost. This paper introduces a wireless communication system for CSP fields based on the Integrated Access and Backhaul (IAB) technology, a distributed resource management mechanism, and an artificial intelligent or AI-based clustering mechanism for the heliostats. The proposed system ensures efficient communication between the heliostats and their cluster-heads as well as among the cluster-heads themselves in order to communicate with the Central Station (CS) residing in the CSP field. The clustering is optimized using the Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, accounting for the CSP fields’ topology and channel gain characteristics. Furthermore, a game-theoretic approach is employed to optimize the transmission power and bandwidth allocation, aiming to enhance the energy efficiency while meeting the stringent latency demands of the heliostat autocalibration. Experiments and numerical results from a CSP field with 7,683 heliostats validate the system’s efficacy in maintaining robust wireless communication and energy efficiency and highlight the proposed model’s capability to support the wireless communication needs of CSP fields.