Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.

Fifth-generation (5G) cellular networks are state-of-the-art wireless technologies revolutionizing all wireless systems. The fundamental goals of 5G are to increase network capacity, improve data rates, and reduce end-to-end latency. Therefore, 5G can support many devices connected to the Internet and realize the Internet of Things (IoT) vision. Though 5 G provides significant features for mobile wireless networks, some challenges still need to be addressed. Although 5 G offers valuable capabilities for mobile wireless networks, specific issues still need to be resolved. This article thoroughly introduces 5G technology, detailing its needs, infrastructure, features, and difficulties. In addition, it summarizes all the requirements and specifications of the 5G network based on the 3rd Generation Partnership Project (3GPP) Releases 15–17. Finally, this study discusses the key specifications challenges of 5G wireless networks.

Electroencephalography (EEG) data serve as a reliable method for fatigue detection due to their intuitive representation of drivers’ mental processes. However, existing research on feature generation has overlooked the effective and automated aspects of this process. The challenge of extracting features from unpredictable and complex EEG signals has led to the frequent use of deep learning models for signal classification. Unfortunately, these models often neglect generalizability to novel subjects. To address these concerns, this study proposes the utilization of a modified deep convolutional neural network, specifically the Inception‐dilated ResNet architecture. Trained on spectrograms derived from segmented EEG data, the network undergoes analysis in both temporal and spatial‐frequency dimensions. The primary focus is on accurately detecting and classifying fatigue. The inherent variability of EEG signals between individuals, coupled with limited samples during fatigue states, presents challenges in fatigue detection through brain signals. Therefore, a detailed structural analysis of fatigue episodes is crucial. Experimental results demonstrate the proposed methodology’s ability to distinguish between alertness and sleepiness, achieving average accuracy rates of 98.87% and 82.73% on Figshare and SEED‐VIG datasets, respectively, surpassing contemporary methodologies. Additionally, the study examines frequency bands’ relative significance to further explore participants’ inclinations in states of alertness and fatigue. This research paves the way for deeper exploration into the underlying factors contributing to mental fatigue.

Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault).

The transformer is a Deep Learning (DL) model that revolutionized language processing with its self-attention mechanism, enabling parallel processing and improving model efficiency, which dramatically reshaped the landscape of speech recognition technology, based on the ability to efficiently manage the dynamic and context-rich nature of speech. The proposed systematic review in this article critically examines the impact of transformer models on speech recognition, covering the published research over the past seven years (from January 1, 2017, to May 15, 2024). The goals of this article are to synthesize the current knowledge, pinpoint emerging trends, and identify research gaps that could be beneficial for future investigations. From an initial pool of 2,838 publications sourced from leading digital libraries, a rigorous two-step screening process applied to distill high-quality studies relevant to the review criteria. We concentrated our analysis on seven pivotal areas as following: the environmental conditions (neutral versus noisy) addressed by the studies, methods of feature extraction employed, characteristics of the transformer models used, datasets utilized, variations in model efficiency, the influence of noise on model generalizability, and trends in self-supervised learning, ending up with 37 articles to review in this paper. Our findings underscore the transformative potential of transformers in enhancing the accuracy and robustness of speech recognition systems, especially in challenging acoustic environments. In addition, this review highlights areas where more research is needed to make speech recognition even better by using transformer technology.

The best service mechanism in multimedia wireless sensor networks can be achieved based on the multimedia traffic flow by developing a proper simulation algorithm process model, to be a trustable indication for real implementations, which is proposed in this article, together with the algorithm model outcome analysis. The quality estimation of the proposed mechanism is investigated by simulating real data transmission and obtaining the integral criterion of the processed mechanism, to determine the queue formation and loading control. Accordingly, it was proved that the first-in first-out algorithm is not useful as a flow algorithm; however, it is regarded as suitable to be considered as the benchmark algorithm when compared with the other algorithms such as priority queue, custom queue, fair queuing, and weighted fair queuing algorithms. Finally, each algorithm’s advantages and disadvantages were verified and the best usage conditions according to certain parameters, such as packet loss probability, average time delay, and jitter, were declared.