Two-Stream Mixed Convolutional Neural Network for American Sign Language Recognition.

The Convolutional Neural Network (CNN) has demonstrated excellent performance in image recognition and has brought new opportunities for sign language recognition. However, the features undergo many nonlinear transformations while performing the convolutional operation and the traditional CNN models are insufficient in dealing with the correlation between images. In American Sign Language (ASL) recognition, J and Z with moving gestures bring recognition challenges. This paper proposes a novel Two-Stream Mixed (TSM) method with feature extraction and fusion operation to improve the correlation of feature expression between two time-consecutive images for the dynamic gestures. The proposed TSM-CNN system is composed of preprocessing, the TSM block, and CNN classifiers. Two consecutive images in the dynamic gesture are used as inputs of streams, and resizing, transformation, and augmentation are carried out in the preprocessing stage. The fusion feature map obtained by addition and concatenation in the TSM block is used as inputs of the classifiers. Finally, a classifier classifies images. The TSM-CNN model with the highest performance scores depending on three concatenation methods is selected as the definitive recognition model for ASL recognition. We design 4 CNN models with TSM: TSM-LeNet, TSM-AlexNet, TSM-ResNet18, and TSM-ResNet50. The experimental results show that the CNN models with the TSM are better than models without TSM. The TSM-ResNet50 has the best accuracy of 97.57% for MNIST and ASL datasets and is able to be applied to a RGB image sensing system for hearing-impaired people.

Novel embedding model predicting the credit card's default using neural network optimized by harmony search algorithm and vortex search algorithm.

In today's banking and financial system, using a credit card has become indispensable. The credit card industry has existed due to a shift in consumer preferences and a rise in national economic growth. The number of issuing banks, card issuers, and transaction volumes has increased significantly. Nevertheless, owing to the growth in the number of transactions made with credit cards, both the total amount due and the rate of defaults on credit card loans have become issues that cannot be neglected. This issue must be resolved to ensure the continued and prosperous growth of the banking industry in the years to come. Currently, a few optimization algorithms-Whale optimization algorithm (WOA), Harmony Search (HS), Multi-verse optimization (MVO), and Vortex Search (VS)-have been used to achieve this purpose. However, because credit card default data is volatile and unequal, it is challenging for typical optimization algorithms to offer steady approaches with optimal performance. Studies have indicated that optimizing algorithms with suitable properties can significantly improve performance. To improve performance, some tuning was applied to the ANN. This study will assess twenty-three parameters, and the efficacy of all four approaches will be compared using ROC and AUC evaluations. The suggested model's performance is contrasted with a scenario where the classifiers were trained using original data. In contrast, the AUC values for VS-MLP were 0.7407 and 0.7271, while those for HS-MLP were 0.7074 and 0.6997. In the training and testing phases, AUC values of 0.7469 and 0.7329 from MVO-MLP and 0.72 and 0.7185 from WOA-MLP, respectively. The results show that the training accuracy of HS, VSA, MVO, and WOA are similar; MVO has the highest training accuracy. The credit card industry can benefit significantly from this methodology, which may help resolve default probabilities.

Mass-Produced Skin-Inspired Piezoresistive Sensing Array with Interlocking Interface for Object Recognition.

E-skins, capable of responding to mechanical stimuli, hold significant potential in the field of robot haptics. However, it is a challenge to obtain e-skins with both high sensitivity and mechanical stability. Here, we present a bioinspired piezoresistive sensor with hierarchical structures based on polyaniline/polystyrene core-shell nanoparticles polymerized on air-laid paper. The combination of laser-etched reusable templates and sensitive materials that can be rapidly synthesized enables large-scale production. Benefiting from the substantially enlarged deformation of the hierarchical structure, the developed piezoresistive electronics exhibit a decent sensitivity of 21.67 kPa-1 and a subtle detection limit of 3.4 Pa. Moreover, an isolation layer is introduced to enhance the interface stability of the e-skin, with a fracture limit of 66.34 N/m. Furthermore, the e-skin can be seamlessly integrated onto gloves without any detachment issues. With the assistance of deep learning, it achieves a 98% accuracy rate in object recognition. We anticipate that this strategy will render e-skin with more robust interfaces and heightened sensing capabilities, offering a favorable pathway for large-scale production.

Hierarchical Piezoelectric Composites for Noninvasive Continuous Cardiovascular Monitoring.

Continuous monitoring of blood pressure (BP) and multiparametric analysis of cardiac functions are crucial for the early diagnosis and therapy of cardiovascular diseases. However, existing monitoring approaches often suffer from bulky and intrusive apparatus, cumbersome testing procedures, and challenging data processing, hampering their applications in continuous monitoring. Here, a heterogeneously hierarchical piezoelectric composite is introduced for wearable continuous BP and cardiac function monitoring, overcoming the rigidity of ceramic and the insensitivity of polymer. By optimizing the hierarchical structure and components of the composite, the developed piezoelectric sensor delivers impressive performances, ensuring continuous and accurate monitoring of BP at Grade A level. Furthermore, the hemodynamic parameters are extracted from the detected signals, such as local pulse wave velocity, cardiac output, and stroke volume, all of which are in alignment with clinical results. Finally, the all-day tracking of cardiac function parameters validates the reliability and stability of the developed sensor, highlighting its potential for personalized healthcare systems, particularly in early diagnosis and timely intervention of cardiovascular disease.