Shahaboddin Shamshirband

With the increasing integration of artificial intelligence into consumer healthcare devices, preserving the privacy and integrity of sensitive medical data has become paramount. Traditional deep learning models often overlook the privacy of the input level, leading to a possible exposure of sensitive attributes during training and inference. This paper presents an Embedded Privacy-Aware Transformer with Sparse Attention, specifically designed for real-time health monitoring on consumer healthcare platforms. The proposed model incorporates differential privacy through Laplace noise injection to anonymize sensitive input features, while efficiently processing nonsensitive features using an entropy-guided sparse attention mechanism. This design not only ensures robust privacy protection but also reduces computational overhead, making it suitable for deployment in resource-constrained consumer devices such as wearables and home health monitors. Experimental evaluation in a five-fold cross-validation lung cancer dataset demonstrates the effectiveness of the model, achieving an AUC of 0.982 and an F1 score of 0.95, while maintaining high utility and privacy guarantees in edge-enabled health monitoring scenarios.

This study discusses the new stochastic maximum power point tracking (MPPT) control approach towards the photovoltaic cells (PCs). PC generator is isolated from the grid, resulting in a direct current (DC) microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed a Networked Control Systems (NCSs) beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the Takagi–Sugeno (T– S) fuzzy model is another option. Takagi–Sugeno (T–S) fuzzy model is presented in a unified framework, for which 1) the fuzzy observer–based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer–based controller can be created using this same premises be the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulating in the PC generator model for the realtime climate data obtained in China demonstrates the importance of our method. In addition, by using a new Lyapunov–Krasovskii functional (LKF) for combining to the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the T-P transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators.

The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of the smart cities. Application domains include the essential aspects of the smart cities including the energy, healthcare, transportation, security, and pollution. The research methodology presents a state-of-the-art taxonomy, evaluation and model performance where the ML algorithms are classified into one of the following four categories: decision trees, support vector machines, artificial neural networks, and advanced machine learning methods, i.e., hybrid methods, ensembles, and Deep Learning. The study found that the hybrid models and ensembles have better performance since they exhibit both a high accuracy and low overall cost. On the other hand, the deep learning (DL) techniques had a higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference was negligible, it can be concluded that using either of them is appropriate.

The increasing demand for green Unmanned Aerial Vehicles (UAVs) in civil and military applications has been noticed in the last few decades. As a result, Flying Ad-hoc Networks (FANETs) caught the attention of industries and academia. Due to its exceptional features, it has replaced other traditional networks deployed in all circumstances. However, UAVs have limited resources, and efficient use of these resources is critical for maximizing network lifetime and Quality of Experience (QoE). In this paper, Fisheye State Routing (FSR) protocol is exploited for communication between several UAVs in a topology changing network employing a 3x3 Manhattan grid mobility model. Several network parameters, i.e., packet delivery and drop ratios, end-to-end delay, channel utilization, dropped packet ratio behavior, throughput, jitter, are employed to validate FSR’s effectiveness in FANETs. Results analysis is carried out in two different network scenarios with twenty and thirty nodes, respectively. The proposed routing protocol has effectively utilized UAV resources with 46% and 53% packet delivery ratios with maximum channel utilization and throughput over time in both scenarios. Moreover, FSR has 0.010 and 0.253 seconds delay and minimum jitter in twenty and thirty nodes comparatively influential among other routing protocols in end-to-end delay. The proposed approach provides excellent results compared to several other topology-based routing protocols and efficiently utilizes onboard resources.

This multilevel inverter type summarizes an output voltage of medium voltage based on a series connection of power cells employing standard configurations of low-voltage components. The main problems of cascaded switched-capacitor multilevel inverters (CSCMLIs) are the harmful reverse flowing current of inductive loads, the large number of switches, and the surge current of the capacitors. As the number of switches increases, the reliability of the inverter decreases. To address these issues, a new CSCMLI is proposed using two modules containing asymmetric DC sources to generate 13 levels. The main novelty of the proposed configuration is the reduction of the number of switches while increasing the maximum output voltage. Despite the many similarities, the presented topology differs from similar topologies. Compared to similar structures, the direction of some switches is reversed, leading to a change in the direction of current flow. By incorporating the lowest number of semiconductors, it was demonstrated that the proposed inverter has the lowest cost function among similar inverters. The role of switched-capacitor inrush current in the selection of switch, diode, and DC source for inverter operation in medium and high voltage applications is presented. The inverter performance to supply the inductive loads is clarified. Comparison of the simulation and experimental results validates the effectiveness of the proposed inverter topology, showing promising potentials in photovoltaic, buildings, and domestic applications. A video demonstrating the experimental test, and all manufacturing data are attached.

Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media to disseminate COVID-19 information. Also, the proposed infrastructure can effectively provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.

Road safety, optimized traffic management, and passenger comfort have always been the primary goals of the vehicle networking research community. Advances in computer and communication technologies have made the dream of modern intelligent vehicles a reality through the use of smart sensors, cameras, networking devices, and storage capabilities. Autonomous operation of modern intelligent vehicles requires massive computations where tasks are outsourced. In recent years, various computing paradigms, e.g., mobile cloud computing (MCC), vehicular cloud computing (VCC), multi-access or mobile edge computing (MEC), vehicular edge computing (VEC), vehicular fog computing (VFC), and volunteer computing based VANET (VCBV), have been developed to move computational resources close to the user and handle the delay-sensitive applications of modern intelligent vehicles. Therefore, in this study, we provide a comprehensive overview of all computing paradigms related to vehicular networks. We also present the architectural details, similarities, differences, and key features of each computing paradigm. Finally, we conclude the study with open research challenges in vehicular networks along with future research directions.