Search

A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient’s health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.

To provide accessible, intelligent, and efficient remote access such as the internet of things, rehabilitation, autonomous driving, virtual games, and healthcare, human action recognition (HAR) has gained much attention among computer vision researchers. Several methods have already been addressed to ensure effective and efficient action recognition based on different perspectives including data modalities, feature design, network configuration, and application domains. In this article, we design a new deep learning model by integrating criss-cross attention and edge convolution to extract discriminative features from the skeleton sequence for action recognition. The attention mechanism is applied in spatial and temporal directions to pursue the intra- and inter-frame relationships. Then, several edge convolutional layers are conducted to explore the geometric relationships among the neighboring joints in the human body. The proposed model is dynamically updated after each layer by recomputing the graph on the basis of k-nearest joints for learning local and global information in action sequences. We used publicly available benchmark skeleton datasets such as UTD-MHAD (University of Texas at Dallas multimodal human action dataset) and MSR-Action3D (Microsoft action 3D) to evaluate the proposed method. We also investigated the proposed method with different configurations of network architectures to assure effectiveness and robustness. The proposed method achieved average accuracies of 99.53% and 95.64% on the UTD-MHAD and MSR-Action3D datasets, respectively, outperforming state-of-the-art methods.

Nowadays, the demand for human–machine or object interaction is growing tremendously owing to its diverse applications. The massive advancement in modern technology has greatly influenced researchers to adopt deep learning models in the fields of computer vision and image-processing, particularly human action recognition. Many methods have been developed to recognize human activity, which is limited to effectiveness, efficiency, and use of data modalities. Very few methods have used depth sequences in which they have introduced different encoding techniques to represent an action sequence into the spatial format called dynamic image. Then, they have used a 2D convolutional neural network (CNN) or traditional machine learning algorithms for action recognition. These methods are completely dependent on the effectiveness of the spatial representation. In this article, we propose a novel ranking-based approach to select key frames and adopt a 3D-CNN model for action classification. We directly use the raw sequence instead of generating the dynamic image. We investigate the recognition results with various levels of sampling to show the competency and robustness of the proposed system. We also examine the universality of the proposed method on three benchmark human action datasets: DHA (depth-included human action), MSR-Action3D (Microsoft Action 3D), and UTD-MHAD (University of Texas at Dallas Multimodal Human Action Dataset). The proposed method secures better performance than state-of-the-art techniques using depth sequences.

Human activity recognition has become a significant research trend in the fields of computer vision, image processing, and human–machine or human–object interaction due to cost-effectiveness, time management, rehabilitation, and the pandemic of diseases. Over the past years, several methods published for human action recognition using RGB (red, green, and blue), depth, and skeleton datasets. Most of the methods introduced for action classification using skeleton datasets are constrained in some perspectives including features representation, complexity, and performance. However, there is still a challenging problem of providing an effective and efficient method for human action discrimination using a 3D skeleton dataset. There is a lot of room to map the 3D skeleton joint coordinates into spatio-temporal formats to reduce the complexity of the system, to provide a more accurate system to recognize human behaviors, and to improve the overall performance. In this paper, we suggest a spatio-temporal image formation (STIF) technique of 3D skeleton joints by capturing spatial information and temporal changes for action discrimination. We conduct transfer learning (pretrained models- MobileNetV2, DenseNet121, and ResNet18 trained with ImageNet dataset) to extract discriminative features and evaluate the proposed method with several fusion techniques. We mainly investigate the effect of three fusion methods such as element-wise average, multiplication, and maximization on the performance variation to human action recognition. Our deep learning-based method outperforms prior works using UTD-MHAD (University of Texas at Dallas multi-modal human action dataset) and MSR-Action3D (Microsoft action 3D), publicly available benchmark 3D skeleton datasets with STIF representation. We attain accuracies of approximately 98.93%, 99.65%, and 98.80% for UTD-MHAD and 96.00%, 98.75%, and 97.08% for MSR-Action3D skeleton datasets using MobileNetV2, DenseNet121, and ResNet18, respectively.

Human action recognition has turned into one of the most attractive and demanding fields of research in computer vision and pattern recognition for facilitating easy, smart, and comfortable ways of human-machine interaction. With the witnessing of massive improvements to research in recent years, several methods have been suggested for the discrimination of different types of human actions using color, depth, inertial, and skeleton information. Despite having several action identification methods using different modalities, classifying human actions using skeleton joints information in 3-dimensional space is still a challenging problem. In this paper, we conceive an efficacious method for action recognition using 3D skeleton data. First, large-scale 3D skeleton joints information was analyzed and accomplished some meaningful pre-processing. Then, a simple straight-forward deep convolutional neural network (DCNN) was designed for the classification of the desired actions in order to evaluate the effectiveness and embonpoint of the proposed system. We also conducted prior DCNN models such as ResNet18 and MobileNetV2, which outperform existing systems using human skeleton joints information.

Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.