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Summary: In this manuscript, optimized wireless communication for 6G signals processing using memory‐augmented deep unfolding network (WC‐6G‐SP‐MADUN) is proposed. The aim of this work is to give a memory‐augmented deep unfolding network approach with a focus on belief propagation decoding of error correction codes and multiple‐input multiple‐output (MIMO) wireless systems. In contrast, compares and tabulates the performance of memory‐augmented deep unfolding methodologies with classic principled procedures, which could potentially enable 6G. Here, the wireless communication for 6G signal processing methods was used for a variety of receiver functions, including self‐interference cancelation, signal estimation and detection using memory‐augmented deep unfolding Network (MADUN), and advanced error correction by clouded leopard optimization algorithm (CLOA). Furthermore, outline the general framework of memory‐augmented deep unfolding method for any kind of signal processing application and talk about cutting edge research paths that will make it possible to build communication networks in the future. The proposed approach is implemented in MATLAB; the performance is analyzed with performance metrics such as reliability, complexity, SNR gain, convergence rate, hardware efficiency, and quantization bit width. The proposed approach attains higher SNR gain 17.96%, 24.75%, and 30.09%; lower complexity 17.92%, 23.41%, and 30.13%; and higher convergence 18.01%, 25.41%, and 31.39% when analyzed with existing techniques such as redefined wireless communication for 6G: signal processing meets deep learning and deep unfolding (RWC‐6G‐SP‐DU), lower complication deep unfolded neural network receiver for MIMO under probability data association detector (LC‐DUNN‐MIMO‐PDAD), and deep learning in physical layer communications: evolution with prospects on 5G and 6G networks (DL‐PLC‐6G), respectively. [ABSTRACT FROM AUTHOR]

Companies use performance appraisals to figure out how well their employees are doing. This involves setting goals, deciding what's important to measure, and then checking if employees meet those goals. Employees get regular feedback on how they're doing, and there are training programs to help them get better. A good performance appraisal system is fair and objective, making sure everyone is treated the same way. For insurance companies, it's especially important to have a strong system for evaluating employee performance. This is because the insurance industry has its own unique challenges and is always changing. The study highlighted in this summary talks about why it's so crucial for insurance companies to regularly evaluate how their employees are doing. By doing this, companies can improve how well their employees work, help them grow in their careers, and make sure everyone is working towards the company's goals. The summary aims to explain the specific needs and benefits of performance appraisals designed specifically for the insurance industry in an easy-to-understand way. [ABSTRACT FROM AUTHOR]

Cognitive science and neuroscience use human facial expressions of emotion. Every single facial expression can be seen at different passions in a face space. Nowadays, facial expression recognition and analysis is vital due to the demand of introducing advanced biometric applications in every domain space. The imperative task in facial expressions of emotion classification is precise feature extraction, which helps to get detailed description of facial marks. Existing feature descriptors are suffering from various problems such as intensity variations, discrimination, vulnerability etc. In this paper, propose a new feature descriptor method called LDMEP (Local Directional Maximum Edge Patterns) for facial expression analysis to overcome the hindrance. We calculated the gradients in four directions of reference pixel to elicit the more feature for better recognition instead of calculating the local differences among neighboring pixels. We also access the orientations of the pixels then thresholded based on the dynamic threshold to avoid the featureless area calculation. Furthermore, we considered only dominant magnitude and orientation directions instead of all eight directions to generate feature. Thus, imperative and efficient features are covered in dominant positions to detect the strong edges. The paper confers that how the subsequent model can be used for the recognition of facial expression of emotion. [ABSTRACT FROM AUTHOR]

The purpose of the study is to develop a comprehensive model on consumers' acceptance of online channel for financial products by integrating variables from existing models and by adding relevant new variables. A survey was conducted to collect data from respondents in India, aged 25 years and above, employed/self-employed, who have bought at least one financial product online and have at least one insurance product online or offline. The conceptual model was tested through PLS-SEM, and it was inferred from the analysis that the marketers can play a robust role in making the online channel complementary to the offline channel by maintaining the website quality through information and system quality, which in turn will have a favorable influence over behavioral intention towards online channel via the consumers' perceived usefulness, perceived ease of use and perceived benefits. [ABSTRACT FROM AUTHOR]