The recognition of human movements is a crucial aspect of AI-related research fields. Although methods using vision and sensors provide more valuable data, they come at the expense of inconvenience to users and social limitations including privacy issues. WiFi-based sensing methods are increasingly being used to collect data on human activity due to their ubiquity, versatility, and high performance. Channel state information (CSI), a characteristic of WiFi signals, can be employed to identify various human activities. Traditional machine learning approaches depend on manually designed features, so recent studies propose leveraging deep learning capabilities to automatically extract features from raw CSI data. This research introduces a versatile framework for recognizing human activities by utilizing CSI data and evaluates its effectiveness on different deep learning networks. A hybrid deep learning network called CNN-GRU-AttNet is proposed to automatically extract informative spatial-temporal features from raw CSI data and efficiently classify activities. The effectiveness of a hybrid model is assessed by comparing it with five conventional deep learning models (CNN, LSTM, BiLSTM, GRU, and BiGRU) on two widely recognized benchmark datasets (CSI-HAR and StanWiFi). The experimental results demonstrate that the CNN-GRU-AttNet model surpasses previous state-of-the-art techniques, leading to an average accuracy improvement of up to 4.62%. Therefore, the proposed hybrid model is suitable for identifying human actions using CSI data.

Theoretical properties of a new survival distribution based upon the renowned distribution has been proposed, such as the probability density function, the cumulative distribution function, the \(r^{th}\) ordinary moment, the moment generating function, the incomplete moments, the survival function, the hazard rate function, the mode, and the asymptotic behavior. This new distribution is called the new mixture of generalized gamma distribution. In addition, the maximum likelihood estimator of an unknown parameter has been studied. Finally, to illustrate usefulness of the proposed distribution, the model has been applied in describing actual data sets.

The demand for diverse traffic condition data has never been greater, driven by the widespread adoption of intelligent transportation systems (ITS), including autonomous vehicles and advanced driver-assistance systems (ADAS). A key element in ITS is the classification of road surface types (RST), which is essential for various ITS applications. Effective RST classification models must deliver robust and consistent performance across different vehicles, driving styles, and terrains. Recent research has shown the benefits of deep learning techniques for classifying sensor-based RST data, as deep learning methods enable automated feature extraction, removing the need for manual processes. Efficient feature extraction is vital for improving RST classification accuracy. This study examines advancements in deep learning techniques for sensor-based RST categorization and assesses suitable models for feature extraction. To understand their functional structures, we first explored various convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We then developed a sophisticated deep learning model, the residual bidirectional gated recurrent unit with squeeze-and-excitation mechanism (ResBiGRU-SE). This model integrates residual connections and squeeze-and-excitation modules to enhance classification accuracy. Our primary objective was to classify road surfaces into dirt, cobblestone, or asphalt categories. Comparative experiments using the publicly available passive vehicular sensors (PVS) dataset demonstrated that the ResBiGRU-SE model outperforms other state-of-the-art models. The ResBiGRU-SE model achieved an accuracy of 98.41\(\%\) and an F1-score of 98.21\(\%\) in accurately categorizing road surfaces, showcasing its superior performance.

The inverse Gaussian distribution, known for its flexible shape, is widely used across various applications. Existing confidence intervals for the mean parameter, such as profile likelihood, reparametrized profile likelihood, and Wald-type reparametrized profile likelihood with observed Fisher information intervals, are generally effective. However, our simulation study identifies scenarios where the coverage probability falls below the nominal confidence level. Wald-type intervals are widely used in statistics and have a symmetry property. We mathematically derive the Wald-type profile likelihood (WPL) interval and the Wald-type reparametrized profile likelihood with expected Fisher information (WRPLE) interval and compare their performance to existing methods. Our results indicate that the WRPLE interval outperforms others in terms of coverage probability, while the WPL typically yields the shortest interval. Additionally, we apply these proposed intervals to a real dataset, demonstrating their potential applicability to other datasets that follow the IG distribution.

The premium calculation considered in the classical bonus-malus system in automobile insurances is dependent only on a number of claims. When a policyholder declared a small claim size, he or she was penalized in the same way as those who made a large claim size. Hence, it is unfair to penalize all policyholders in like manner. In this article, we propose a model for computing the premium based on the bonus-malus system by distinguishing multiple types of claims as follows: small, middle, large, and sever claim sizes. The number of claims is considered based on discrete distributions: Poisson and binomial distributions. Besides, we introduce prior distributions, gamma, and beta distributions, conjugated with these discrete distributions. The Bayesian method is applied for updating the parameters of the model as this approach yields bonus-malus premiums. We use a real example data to demonstrate the model. The randomized neighborhood search (RNS) technique is introduced for estimating the parameters to minimize the statistical value for Akaike’s Information Criterion (AIC). This methodology allows us to obtain a good fit to the data set considered.

Applied Science and Engineering Progress