In the realm of facial expression recognition (FER), the identification and classification of seven universal emotional states, surprise, disgust, fear, happiness, neutrality, anger, and contempt, are of paramount importance. This research focuses on the application of convolutional neural networks (CNNs) for the extraction and categorization of these expressions. Over the past decade, CNNs have emerged as a significant area of research in human-computer interaction, surpassing previous methodologies with their superior feature learning capabilities. While current models demonstrate exceptional accuracy in recognizing facial expressions within controlled laboratory datasets, their performance significantly diminishes when applied to real-time, uncontrolled datasets. Challenges such as degraded image quality, occlusions, variable lighting, and alterations in head pose are commonly encountered in images sourced from unstructured environments like the internet. This study aims to enhance the recognition accuracy of FER by employing deep learning techniques to process images captured in real-time, particularly those of lower resolution. The objective is to augment the accuracy of FER in real-world datasets, which are inherently more complex and collected under less controlled conditions, compared to laboratory-collected data. The effectiveness of a deep learning-based approach to emotion detection in photographs is rigorously evaluated in this work. The proposed method is exhaustively compared with manual techniques and other existing approaches to assess its efficacy. This comparison forms the foundation for a subjective evaluation methodology, focusing on validation and end-user satisfaction. The findings conclusively demonstrate the method's proficiency in accurately recognizing emotions in both laboratory and real-world scenarios, thereby underscoring the potential of deep learning in the domain of facial emotion identification.

In modern industrial environments, the security and sustainability of Industrial Control Systems (ICS) have become crucial. This comprehensive review examines the transformative potential of Artificial Intelligence (AI) in ICS, focusing on technologies like Machine Learning (ML), Deep Learning (DL), Large Language Models (LLMs), and cloud computing. Moreover, this research explores integrating existing and proposed sustainable practices within the ICS framework, with a particular emphasis on energy efficiency and carbon footprint reduction, to enhance the overall sustainability of ICS. This review employed a systematic approach to select relevant articles from multiple reputable databases, such as Scopus, IEEE Explore, Science Direct, ACM digital library, Web of Science, and IET digital library, including 250 articles that provide valuable insights into the intersection of AI, security, and sustainability in ICS. This review examines vulnerabilities in ICS, such as data breaches, insider threats, and malware, emphasizing the need for effective anomaly detection. It highlights how AI technologies like anomaly detection and predictive analytics can enhance threat detection and response in ICS by improving accuracy and efficiency. The review offers insights to researchers and professionals on the future of secure, sustainable ICS, supporting a resilient industrial landscape that meets cybersecurity, compliance, and sustainability goals.

5G (Fifth generation) is comparatively more advanced telecommunication technology in cellular networks that has higher speed and wide range. 5G technology will be implemented in 2020, which could provide 100 times faster speed rate than the already existing 4G or LTE technologies. Currently Hybrid model is used in 5G architecture. In this study we have involved some critical components of 5G hybrid model for SDN, which are created by virtualization on currently deployed physical SDN networks. Findings from the study of hybrid 5G model would require further development in media transmission and innovation for making more improvements in data rate speed. The Proposed model is based on a scheme, which supports, and is integrated, with both optical and wireless domains. It would be a unified management system which could have the optical components with 5G wireless components.

The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent‐based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady‐state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large‐scale sensing delay in the CR network.

The Internet of Things (IoT) is a rapidly expanding field, with billions of devices now online. This expansion has increased demand for efficient and dependable wireless sensor connectivity. A new method for reducing wireless sensor connectivity time in IoT systems using the Improved Hybrid Grey Wolf Optimization (IHGWO) strategy is proposed. Grey Wolf Optimization (GWO) is a population-based metaheuristic algorithm based on wolf hunting behaviour. It has been demonstrated to be effective in solving a wide range of optimization problems, including wireless sensor network issues. We have used IHGWO in this paper to optimize the placement and connectivity time of wireless sensors in an IoT system. Our method is evaluated in three scenarios: (1) without optimization, (2) with IHGWO and 5G critical and hybrid additions, and (3) with IHGWO and optimized sensor positions determined by the defined algorithm. These results demonstrate the potential of IHGWO as a method for reducing the amount of time wireless sensors require to connect in Internet of Things (IoT) systems. Using IHGWO, we were able to reduce wireless sensor connectivity time significantly.