Abstract—Object sensors are widely used for motion capture, particularly in sport motion analysis for classification of stokes. In this paper, a comparative study was performed to examine the Forehand strokes classification of three Machine Learning (ML) models in Table Tennis. Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel function, Long Short-Term Memory (LSTM) and 2-Dimensional Convolutional Neural Network (2D-CNN) are considered. Tuning the models’ parameters and examining sensitivities of the models to the number of training datasets and type of data are studied. All models were trained and tested on Table Tennis Forehand strokes’ signals, which were collected from professional and novice Table Tennis players’. A BNO055 sensor was attached to the center of the standard paddle as an Object sensor to measure the strokes’ signals. To provide a robustness assessment of all models to type of dataset, the study employs 80% of novice players’ strokes samples for testing classification accuracy. The empirical results suggest that the LSTM and the 2D-CNN classification outperforms with a substantial performance increase of approximately 7% more than the RBF-SVM. In addition, in the case of models sensitivity to the number of training samples it can be said that, the LSTM model performance on the self-collected dataset is not significantly sensitive to the number of training samples. However, the number of the developed RBF-SVM parameters is significantly less than other two deep models and are easy to set. Study shows that the modified LSTM model performed better than other two models.

Alzheimer's disease (AD) is a chronic neurodegenerative disorder. It is the most common type of dementia that has remained as an incurable disease in the world, which destroys the brain cells irreversibly. In this study, a systems biology approach was adopted to discover novel micro-RNA and gene-based biomarkers of the diagnosis of Alzheimer's disease. The gene expression data from three AD stages (Normal, Mild Cognitive Impairment, and Alzheimer) were used to reconstruct co-expression networks. After preprocessing and normalization, Weighted Gene Co-Expression Network Analysis (WGCNA) was used on a total of 329 samples, including 145 samples of Alzheimer stage, 80 samples of Mild Cognitive Impairment (MCI) stage, and 104 samples of the Normal stage. Next, three gene-miRNA bipartite networks were reconstructed by comparing the changes in module groups. Then, the functional enrichment analyses of extracted genes of three bipartite networks and miRNAs were done, respectively. Finally, a detailed analysis of the authentic studies was performed to discuss the obtained biomarkers. The outcomes addressed proposed novel genes, including MBOAT1, ARMC7, RABL2B, HNRNPUL1, LAMTOR1, PLAGL2, CREBRF, LCOR, and MRI1and novel miRNAs comprising miR-615-3p, miR-4722-5p, miR-4768-3p, miR-1827, miR-940 and miR-30b-3p which were related to AD. These biomarkers were proposed to be related to AD for the first time and should be examined in future clinical studies.

Multimodal data can convey user emotions and feelings more effectively and interactively than unimodal content. Thus, multimodal sentiment analysis (MSA) research has recently acquired great significance as a field of study. However, most current approaches either acquire sentimental features independently for each modality or simply combine multiple modal features. Thus, semantic details pertinent to sentiment analysis and the relationship between visual and textual content have been neglected. Furthermore, most available multimodal datasets are sentiment-annotated, although user emotions are usually rich and unlimited. Motivated by these observations, this paper proposes a novel deep multi-view attentive network (DMVAN) for robust multimodal sentiment and emotion classification. The DMVAN model has three phases: feature learning, attentive interaction learning, and cross-modal fusion learning. During the feature learning phase, visual features from a multi-view perspective (region and scene) and textual features from various levels of analysis (word, sentence, and document) are extracted to capture information effectively for accurate classification. In the attentive interaction learning phase, we employed the image-text interaction learning mechanism to enhance visual and textual information interaction by extracting sentimental and discriminative visual features and utilizing the textual information to guide the learning process of image features. Moreover, a cross-modal fusion learning module has been developed to integrate the various features into a comprehensive framework that leverages the complementary nature of multiple modalities, followed by a multi-head attention mechanism that is intended to extract sufficient information from the intermediate features, thereby enabling the creation of a robust joint representation. Finally, a multi-layer perceptron with multiple stacking-fully connected layers is used to deeply fuse the modal features, thereby enhancing sentiment classification performance. An interpretable multimodal sentiment classification model is further developed utilizing the local interpretable model-agnostic explanation model (LIME) to ensure the model’s explainability and strength. To perform a multimodal emotion classification, an image-text emotion dataset named emotion-Getty (EMO-G) was constructed from Getty Images and labeled by distinct emotions. The proposed model was tested on three real-world datasets, attaining 99.801% accuracy on binary_Getty (BG), 96.867% on Twitter, and 96.174% on the EMO-G dataset. These results show that the suggested model outperforms single-model techniques and current state-of-the-art methodologies based on model evaluation criteria.

SMS spam is a pervasive issue that affects millions worldwide, leading to significant inconvenience, time wastage, and potential financial scams. Given the prevalence and potential harm, accurate and real-time detection of SMS spam is crucial. This paper proposes a novel approach to SMS spam detection involving five steps: preprocessing, feature extraction, feature fusion, feature selection, and classification. Our model is designed to simultaneously capture local, temporal, and global text message features using a hybrid deep learning model to enhance feature representation. We evaluated our model using the UCI dataset, comparing it with traditional and deep learning algorithms such as RF and BERT using cross-validation to ensure the robustness of our results. Our proposed method exhibited superior performance, achieving a good accuracy of 99.56%, surpassing other methods. The effectiveness of this method in SMS spam detection proved its potential for real-world implementation, where it could substantially mitigate the prevalence and impact of SMS spam.