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Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon's formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system's efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50 % and in received image quality with an image coding gain of up to 17 dB. [ABSTRACT FROM AUTHOR]

The introduction of quantum communications, enabled by advancements in quantum computing, is expected to play a significant role in the field of communications. Inherent properties of quantum objects, such as superposition and entanglement, have the potential to provide novel solutions to overcome the challenges encountered by classical communication systems in bandwidth‐intensive applications such as media transmission. This research explores the performance of a quantum communication system in image transmission using quantum superposition and investigates its performance using a simple quantum channel model. With an increase in channel noise, there are significant gains in the rate distortion performance of images transmitted over the quantum channel compared to an ideal classical channel. This novel attempt at constructing a quantum communication‐based image transmission system indicates the potential of the approach to be applied to satisfy the ever‐increasing demands of high‐quality media transmission applications. [ABSTRACT FROM AUTHOR]

One of the significant issues the insurance industry faces is its ability to predict future claims related to individual policyholders. As risk varies from one policyholder to another, the industry has faced the challenge of using various risk factors to accurately predict the likelihood of claims by policyholders using historical data. Traditional machine-learning models that use neural networks are recognized as exceptional algorithms with predictive capabilities. This study aims to develop a deep learning model using sequential deep regression techniques for insurance claim prediction using historical data obtained from Kaggle with 1339 cases and eight variables. This study adopted a sequential model in Keras and compared the model with ReLU and Swish as activation functions. The performance metrics used during the training to evaluate the model performance are R2 score, mean square error and mean percentage error with values of 0.5%, 1.17% and 23.5%, respectively obtained using ReLU, while 0.7%, 0.82%, and 21.3%, obtained using Swish function. Although the results of both models using ReLU and Swish were fairly tolerable, the performance metrics obtained of the model using Swish, as the activation function tends to perform more satisfactorily than that of ReLU. However, to investigate the model performance deeply, therefore, this study recommends that more interest be channeled to the interpretability and explainability of the proposed model and the provisions of AI technologies by insurance industries to enhance accurate claim prediction and minimize losses. [ABSTRACT FROM AUTHOR]

Traditional machine learning metrics (TMLMs) are quite useful for the current research work precision, recall, accuracy, MSE and RMSE. Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%-92%. We included in the prediction process, machine learning models (MLMs) with greater than 99% accuracy with a sensitivity of 95%-98% and specifically in the database. We need to explain the model to domain specialists through the MLMs. Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model. This is achieved by creating a model-independent, locally accurate explanation set that makes it better than the primary model. As we know that human interaction with machine learning systems on this model's interpretability is more crucial. For supporting set validations in model selection insurance premium prediction. In this study, we proposed the use of the (LIME and SHAP) approach to understand research properly and explain a model developed using random forest regression to predict insurance premiums. The SHAP algorithm's drawback, as seen in our experiments, is its lengthy computing time--to produce the findings, it must compute every possible combination. In addition, the experiments conducted were intended to focus on the model's interpretability and explain its ability using LIME and SHAP, not the insurance premium charge prediction. Three experiments were conducted through experiment, one was to interpret the random forest regression model using LIME techniques. In experiment 2, we used the SHAP technique to interpret the model insurance premium prediction (IPP). [ABSTRACT FROM AUTHOR]