Next-generation wireless network applications that combine the Internet of Things (IoT), intelligent edges, and connectivity technologies will benefit every area, including e-health and medical Internet of Things (M-IoT). The fifth generation (5G) mobile network technology cannot match the requirements of emerging mobile apps, which require extreme communication speed, network intelligence, ultra-low latency, comprehensive connectivity, and the capacity to manage diversely related usages. The sixth generation (6G) mobile network technology establishes new performance standards that the fifth generation (5G) mobile network technology could not satisfy. For incredibly immersive applications such as 3D communications and enormous virtual reality (VR)/ extended reality (XR) applications to be economically viable, 6G capabilities must be delivered at a large scale. Deploying several tiny cells to construct ultra-dense networks (UDN) is one option for tackling the extraordinary rise in capacity and coverage needs. The proposed work estimates that only the future 6G networks can deliver a high-performance connection with many connected devices, particularly in challenging situations such as diverse mobility, energetic environments, and extreme density. Accordingly, this article discusses the most current and forthcoming 6G network-compatible advancements to comprehensively review 6G mobile communication technologies in single survey research. At the outset, we thoroughly overview UDN and the 6G system’s goals, motivations, requirements, architecture, and conceptual parts.

Networked cameras that continuously capture video data have generated a high demand for hybrid edge-to-cloud servers that can process live videos in real time. The environment of art museums is rarely studied, but visual analysis is an important factor in categorizing and distinguishing individuals and crowds through smart surveillance systems. This paper demonstrates how video surveillance data from art museums can be analyzed to identify abnormal behavior using an innovative deep learning framework. To enhance the extracted features, a spatial feature extraction method based on the U-Net architecture is applied, along with the encoder component of the proposed approach, MobileNetV2. Additionally, we propose an improved Long-Short-Term Memory (LSTM) algorithm for extracting temporal features. Optical flow enhances surveillance in art museums by tracking individuals and crowds. Our approach yields an average accuracy of 97.67±1.23% when applied to a collection of video datasets. Using U-Net, MobileNetV2, and optimized LSTM algorithms, the model recognizes patterns in video data, such as crowd motion in museums. Consequently, this methodology generates reliable results as well as being computationally efficient. Compared to the state-of-the-art, the proposed method is more comprehensive and generalizable for analyzing atypical museum visitor behavior.

In neuropathological diseases such as Alzheimer’s Disease (AD), neuroimaging and Magnetic Resonance Imaging (MRI) play crucial roles in the realm of Artificial Intelligence of Medical Things (AIoMT) by leveraging edge intelligence resources. However, accurately classifying MRI scans based on neurodegenerative diseases faces challenges due to significant variability across classes and limited intra-class differences. To address this challenge, we propose a novel approach aimed at improving the early detection of AD through MRI imaging. This method integrates a Convolutional Neural Network (CNN) with a Cascade Attention Model (CAM-CNN). The CAM-CNN model outperforms traditional CNNs in AD classification accuracy and processing complexity. In this architecture, the attention mechanism is effectively implemented by utilizing two constraint cost functions and a cross-network with diverse pre-trained parameters for a two-stream architecture. Additionally, two new cost functions, Satisfied Rank Loss (SRL) and Cross-Network Similarity Loss (CNSL), are introduced to enhance collaboration and overall network performance. Finally, a unique entropy addition method is employed in the attention module for network integration, converting intermediate outcomes into the final prediction. These components are designed to work collaboratively and can be sequentially trained for optimal performance, thereby enhancing the effectiveness of AD stage classification and robustness to interference from MR images. Validation using the Kaggle dataset demonstrates the model’s accuracy of 99.07% in multiclass classification, ensuring precise classification and early detection of all AD subtypes. Further validation across three feature categories with varying numbers confirms the robustness of the proposed approach, with deviations from the standard criteria of less than 1%. Applied in Alzheimer’s patient care, this capability holds promise for enhancing value-based therapy and clinical decision-making. It aids in differentiating Alzheimer’s patients from healthy individuals, thereby improving patient care and enabling more targeted therapies.

So far, the communication standard development requires specific parameters to achieve the requests of the desired application, most frequently, the connection speed rate. On the other hand, the term Beyond the Fifth Generation (B5G) symbolizes certain specifications required to succeed the future-proof of the Fifth Generation (5G), i.e., the predicted high-level parameters, such as ultra-reliable low-latency communications, massive machine-type communications, and improved mobile broadband, which are essential for the expected high-level future applications; consequently, 5G wireless (cellular) networks must be reconsidered precisely and in-depth to cope with the applications’ required high-level standard parameters in B5G. Therefore, it is crucial to develop novel wireless access configurations and technologies that utilize additional spectrum. However, this alone is not sufficient for now. Incorporating technologies such as software-defined networking, cloud computing, machine learning, 3D networking, and network function virtualization into B5G networks is imperative due to raised concerns regarding decentralization, transparency, interoperability, privacy, and security. This page provides a comprehensive overview of B5G’s design, functionality, and security, as well as its relationship to cloud computing. Furthermore, the proposed study examines the techniques employed for data transmission in B5G applications, such as Vehicle-to-Vehicle (V2V), Device-to-Device (D2D), and Machine to Machine (M2M) transmissions. Lastly, the proposed study focuses on essential technology based software services, such as healthcare, smart grid, tourism, and agricultural services. These services use the advantages of B5G communication networks and cloud computing. So, the proposed work collects all the necessary information for researches and developers in one article, supported by the most up-to-date references.