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Summary: Node localization in wireless sensor networks (WSNs) ensures that the collected data is contextually accurate, enabling effective monitoring and management of various applications. Recently, there has been a surge in research focused on addressing node localization within WSNs. Emerging trends in this field involve the application of metaheuristic optimization techniques to refine node location determination accuracy. However, existing techniques often struggle with balancing accuracy, energy consumption, network lifetime, and computational efficiency, particularly in challenging WSN environments. Therefore, this research introduces a novel approach called efficient hybrid bat sand cat swarm optimization (EHBSCSO) to address node localization within WSNs. The hybrid method leverages the exploration capabilities of the bat optimization algorithm and the exploitation strengths of the sand cat swarm optimization algorithm. This combination allows for efficient determination of node positions, significantly improving localization accuracy while minimizing energy consumption. The EHBSCSO utilizes the received signal strength indicator (RSSI) and time of flight (ToF) approaches to assess distances among nodes accurately. Accurate node localization directly improves data quality by ensuring spatially precise data collection, reducing communication overhead, and enhancing the overall reliability of the collected data. Compared to conventional methods, the proposed EHBSCSO algorithm demonstrates superior performance, with a mean localization error of 0.18%, energy consumption of 7.2 J, computational time of 8.9 s, and localization time of 0.19 s. These metrics underscore its efficiency and precision. The research indicates that EHBSCSO not only optimizes localization accuracy but also contributes to energy efficiency and faster computational times, addressing key challenges in WSN node localization. [ABSTRACT FROM AUTHOR]

Deep learning has received increasing attention in all fields and has made considerable progress in facial expression recognition (FER). Mainly, the conventional FER methods are trained for constrained datasets, which may not operate well for real‐time images. Such real‐time image sequences limit the accuracy and efficacy of the traditional system. In this work, the authors present a novel deep learning framework which combines convolutional neural network (CNN) with long short‐term memory (LSTM) cell for real‐time FER. The novel framework has three main aspects: (i) two different pre‐processing techniques are employed to handle illumination variances and to preserve subtle edge information of each image; (ii) correspondingly, the pre‐processed images are inputted to the two individual CNN architecture which extracts the spatial features very effectively; (iii) spatial feature maps from two individual CNN layers are fused and integrated with an LSTM layer which extracts temporal relations between the successive frames. They experimented the authors' proposed method on three publically available FER databases and also with self‐created database. With pre‐processing, their proposed model achieves comparable and better results to the state‐of‐the‐art. [ABSTRACT FROM AUTHOR]

Over the past decades, facial expression recognition (FER) has become an interesting research area and achieved substantial progress in computer vision. FER is to detect human emotional state related to biometric traits. Developing a machine based human FER system is a quite challenging task. Various FER systems are developed by analysing facial muscle motion and skin deformation based algorithms. In conventional FER system, the developed algorithms work on the constrained database. In the unconstrained environment, the efficacy of existing algorithms is limited due to certain issues during image acquisition. This study presents a detailed study on FER techniques, classifiers and datasets used for analysing the efficacy of the recognition techniques. Moreover, this survey will assist researchers in understanding the strategies and innovative methods that address the issues in a real‐time application. Finally, the review presents the challenges encountered by FER system along with the future direction. [ABSTRACT FROM AUTHOR]