The increasing popularity of mobile phones has led to an abundance of online reviews, making it challenging for consumers to make well-informed purchasing decisions. This study proposes a novel recommendation system-based mobile phone rating classification approach using federated learning and Term Frequency-Inverse Document Frequency (TF-IDF) features. We created a novel dataset by scraping over 13,000 mobile phone reviews from Flipkart’s website. The proposed approach involves the development of a federated deep neural network (FDNN) to classify the newly created Flipkart dataset. This approach includes data cleaning, balancing, TF-IDF feature extraction, and prediction using federated learning. We employ two clients and one server and conduct three rounds of experiments. The experimental results demonstrate that the proposed approach achieved an accuracy of 96.68% on the aggregated server side while maintaining the security of customer data on their local devices. The proposed approach has the potential to assist consumers in making well-informed purchasing decisions and can be extended to other e-commerce platforms with large datasets of online reviews.

High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision making.

This research article presents an enhanced YOLOv8 model with an additional feature extraction layer integrated into the traditional YOLOv8 architecture to improve fault detection performance in smart additive manufacturing, specifically for FDM 3D printers. Hyperparameter optimization techniques are employed to ensure the model is trained with optimal input and batch size configurations. The findings demonstrate that the additional module successfully enhances the model’s performance in detecting faults during the FDM 3D printing process. The best results are achieved using the YOLOv8s model with an image input size of 640 and a batch size of 16, achieving a mAP _val (50-95) of 89.7%. Despite the increased complexity from additional layers, there is a favorable trade-off between performance and complexity. Furthermore, a testbed implementation is conducted to validate the model’s performance in a real-world setting, showing that the fault detection latency remains insignificant even with multiple Raspberry Pi clients. Overall, this research provides insights into improving fault detection in smart additive manufacturing and highlights the effectiveness of the proposed YOLOv8 model with additional extraction layers.

Autonomous decision-making is considered an intercommunication use case that needs to be addressed when integrating open radio access networks with mobile-based 5G communication. The robustness of innovations is diminished by the conventional method of designing an end-to-end radio access network solution. Through an analysis of these possibilities, this paper presents a machine learning-based intelligent system whose primary goal is load balancing using Artificial Neural Networks with Particle Swam Optimization-enabled metaheuristic optimization mechanisms for telecommunication industry requests, like product compatibility. We increase the proposed system’s reliability by using third-generation partnership project standards to automate the distribution of transactional load among various connected units. This intelligent system encloses the hierarchy of automation enabled by artificial intelligence. Conversely, AI-enabled open radio access control explores the barriers to next-generation intercommunication, including those after 5G. It covers deterministic latency and capabilities, physical layer-based dynamic controls, privacy and security, and testing applications for AI-based controller designs.

The advancement of the communications system has resulted in the rise of the Internet of Things (IoT), which has increased the importance of cybersecurity research. IoT, which incorporates a range of gadgets into networks to offer complex and intelligent services, must maintain user privacy and deal with attacks such as spoofing, denial of service (DoS), jamming, and eavesdropping. Attacks change with time, and new ones develop every day. Numerous researchers look into IoT system attack models and evaluate machine learning, deep learning, and federated learning-based IoT security solutions. However, present methods usually require to produce reliable and encouraging results. Therefore, this study proposes a methodology for leveraging federated learning to identify large attacks on IoT devices using the novel CIC_IoT 2023 dataset. The method uses a federated deep neural network to achieve precise categorization. Before model training, the data was preprocessed using various data preparation techniques to guarantee the creation of a trustworthy dataset for categorization. The suggested method involves feature normalization, data balancing, and model prediction utilizing federated learning. The experimental findings show that the proposed method attained an exceptional accuracy of 99.00%, endorsing it for attack detection.

In recent years, driven by the continuous development of mobile Internet technology and artificial intelligence technology, the improvement of the manufacturing level of 6G Internet of Everything (IoE) products and the increase in residents’ income level, the 6G IoE industry has shown a sustained and stable development trend. However, 6G IoE has great security risks. Network anomaly detection is very important for 6G IoE. The anomaly detection method based on traditional deep auto-encoder uses the reconstruction error to determine whether the sample to be measured is normal data or abnormal data. However, the reconstruction errors generated by the above method on normal data and abnormal data are very close, which leads to some abnormal data being easily misclassified as normal data. Therefore, an anomaly detection method based on deep auto-encoder and capsule graph convolution via sparrow search algorithm in 6G IoE is proposed. First, the capsule graph network uses the bottleneck feature of the input sample to generate the bottleneck feature of the pseudo-abnormal data, so as to increase the abnormal data information in the training set. The capsule dynamic fusion strategy aggregates different factors to obtain new item embedding. Second, deep auto-encoder reconstructs the bottleneck characteristics with abnormal data information into normal data as much as possible, and increases the difference of reconstruction error between abnormal data and normal data. In the process of network classification, we use the sparrow search algorithm to find the optimal value of the function. And at the same time, it prevents the algorithm from prematurity and improves the classification effect. Finally, we conduct experiments on public data sets to compare with other advanced methods. Experimental results show that the proposed method can effectively enlarge the difference between normal data and abnormal data in reconstruction error.