This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth. The proposed AI model has the capacity to make highly accurate predictions and detect faults in DC motor drives, thus helping to ensure timely maintenance and reduce operational breakdowns. As a result, comparative analysis reveals that the proposed framework can achieve higher accuracy than the current existing method of combining CNN with Long Short-Term Memory networks (CNN-LSTM) as well as other CNNs, LSTMs, and traditional methods. The proposed CNN-RNN model can provide early fault detection for motor drives of industrial robots with a simpler architecture and lower complexity of the model compared to CNN-LSTM methods, which can enable the model to process faster than CNN-LSTM. It effectively extracts dynamic features and processes sequential data, achieving superior accuracy and precision in fault diagnosis, which can make it a practical and efficient solution for real-time fault detection in motor drive control systems of industrial robots.

To build intelligent model learning in conventional architecture, the local data are required to be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage of personalization, and insufficient use of network resources. To address these issues, federated learning (FL) is introduced by offering a systematical framework that converges the distributed modeling process between local participants and the parameter server. However, the challenging issues of insufficient participant scheduling, aggregation policies, model offloading, and resource management still remain within conventional FL architecture. In this survey article, the state-of-the-art solutions for optimizing the orchestration in FL communications are presented, primarily querying the deep reinforcement learning (DRL)-based autonomy approaches. The correlations between the DRL and FL mechanisms are described within the optimized system architectures of selected literature approaches. The observable states, configurable actions, and target rewards are inquired into to illustrate the applicability of DRL-assisted control toward self-organizing FL systems. Various deployment strategies for Internet of Things applications are discussed. Furthermore, this article offers a review of the challenges and future research perspectives for advancing practical performances. Advanced solutions in these aspects will drive the applicability of converged DRL and FL for future autonomous communication-efficient and privacy-aware learning.

This paper proposes a new robust model for text classification on the Stanford Sentiment Treebank v2 (SST-2) dataset in terms of model accuracy. We developed a Recurrent Neural Network Bert based (RNN_Bert_based) model designed to improve classification accuracy on the SST-2 dataset. This dataset consists of movie review sentences, each labeled with either positive or negative sentiment, making it a binary classification task. Recurrent Neural Networks (RNNs) are effective for text classification because they capture the sequential nature of language, which is crucial for understanding context and meaning. Bert excels in text classification by providing bidirectional context, generating contextual embeddings, and leveraging pre-training on large corpora. This allows Bert to capture nuanced meanings and relationships within the text effectively. Combining Bert with RNNs can be highly effective for text classification. Bert’s bidirectional context and rich embeddings provide a deep understanding of the text, while RNNs capture sequential patterns and long-range dependencies. Together, they leverage the strengths of both architectures, leading to improved performance on complex classification tasks. Next, we also developed an integration of the Bert model and a K-Nearest Neighbor based (KNN_Bert_based) method as a comparative scheme for our proposed work. Based on the results of experimentation, our proposed model outperforms traditional text classification models as well as existing models in terms of accuracy.

Internet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to be completed quickly within the time constraints of IoT devices. To address this challenge, researchers have proposed computation offloading, where computing tasks are sent to edge servers instead of being executed locally on user devices. This approach involves using edge servers located near users in cellular network base stations, and also known as Mobile Edge Computing (MEC). The goal is to offload tasks to edge servers, optimizing both latency and energy consumption. The main objective of this paper mentioned in the summary is to design an algorithm for time- and energy-optimized task offloading decision-making in MEC environments. Therefore, we developed a Lagrange Duality Resource Optimization Algorithm (LDROA) to optimize for both decision offloading and resource allocation for tasks, whether to locally execute or offload to an edge server. The LDROA technique produces superior simulation outcomes in terms of task offloading, with improved performance in computation latency and cost usage compared to conventional methods like Random Offloading, Load Balancing, and the Greedy Latency Offloading scheme.

This paper examines the critical role of indoor positioning for robots, with a particular focus on small and confined spaces such as homes, warehouses, and similar environments. We develop an algorithm by integrating deep neural networks (DNNs) with the extended Kalman filter (EKF) method, which is known as DNN-EKF, to obtain an accurate indoor localization for ensuring precise and reliable robot movements within the use of Ultra-Wideband (UWB) technology. The study introduces a novel methodology that combines advanced technology, including DNN, filtering techniques, specifically the EKF and UWB technology, with the objective of enhancing the accuracy of indoor localization systems. The objective of integrating these technologies is to develop a more robust and dependable solution for robot navigation in challenging indoor environments. The proposed approach combines a DNN with the EKF to significantly improve indoor localization accuracy for mobile robots. The results clearly show that the proposed model outperforms existing methods, including NN-EKF, LPF-EKF, and other traditional approaches. In particular, the DNN-EKF method achieves optimal performance with the least distance loss compared to NN-EKF and LPF-EKF. These results highlight the superior effectiveness of the DNN-EKF method in providing precise localization in indoor environments, especially when utilizing UWB technology. This makes the model highly suitable for real-time robotic applications, particularly in dynamic and noisy environments.