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We introduce a hybrid deep learning model for recognizing hand gestures from electromyography (EMG) signals in subacute stroke patients: the one-dimensional convolutional long short-term memory neural network (CNN-LSTM). The proposed network was trained, tested, and cross-validated on seven hand gesture movements, collected via EMG from 25 patients exhibiting clinical features of paresis. EMG data from these patients were collected twice post-stroke, at least one week apart, and divided into datasets A and B to assess performance over time while balancing subject-specific content and minimizing training bias. Dataset A had a median post-stroke time of 16.0 ± 8.6 days, while dataset B had a median of 19.2 ± 13.7 days. In classification tests based on the number of gesture classes (ranging from two to seven), the hybrid model achieved accuracies ranging from 85.66% to 82.27% in dataset A and from 88.36% to 81.69% in dataset B. To address the limitations of deep learning with small datasets, we developed a novel bilateral data fusion approach that incorporates EMG signals from the non-paretic limb during training. This approach significantly enhanced model performance across both datasets, as evidenced by improvements in sensitivity, specificity, accuracy, and F1-score metrics. The most substantial gains were observed in the three-gesture subset, where classification accuracy increased from 73.01% to 78.42% in dataset A, and from 77.95% to 85.69% in dataset B. In conclusion, although these results may be slightly lower than those of traditional supervised learning algorithms, the combination of bilateral data fusion and the absence of feature engineering offers a novel perspective for neurorehabilitation, where every data segment is critically significant.

Recognition of Bengali handwritten digits has several unique challenges, including the variation in writing styles, the different shapes and sizes of digits, the varying levels of noise, and the distortion in the images. Despite significant improvements, there is still room for further improvement in the recognition rate. By building datasets and developing models, researchers can advance state-of-the-art support, which can have important implications for various domains. In this paper, we introduce a new dataset of 5440 handwritten Bengali digit images acquired from a Bangladeshi University that is now publicly available. Both conventional machine learning and CNN models were used to evaluate the task. To begin, we scrutinized the results of the ML model used after integrating three image feature descriptors, namely Binary Pattern (LBP), Complete Local Binary Pattern (CLBP), and Histogram of Oriented Gradients (HOG), using principal component analysis (PCA), which explained 95% of the variation in these descriptors. Then, via a fine-tuning approach, we designed three customized CNN models and their stack to recognize Bengali handwritten digits. On handcrafted image features, the XGBoost classifier achieved the best accuracy at 85.29%, an ROC AUC score of 98.67%, and precision, recall, and F1 scores ranging from 85.08% to 85.18%, indicating that there was still room for improvement. On our own data, the proposed customized CNN models and their stack model surpassed all other models, reaching a 99.66% training accuracy and a 97.57% testing accuracy. In addition, to robustify our proposed CNN model, we used another dataset of Bengali handwritten digits obtained from the Kaggle repository. Our stack CNN model provided remarkable performance. It obtained a training accuracy of 99.26% and an almost equally remarkable testing accuracy of 96.14%. Without any rigorous image preprocessing, fewer epochs, and less computation time, our proposed CNN model performed the best and proved the most resilient throughout all of the datasets, which solidified its position at the forefront of the field.

Background Human activity recognition is a dynamic and challenging task. It is a large field of research and development. It involves predicting the movement of a person from the raw sensor data using a machine learning model. To accurately detect human activities for e-health systems, several research attempts have been carried out using data mining and machine learning techniques, but there still is room to improve the performance. To this aim, human activities such as walking, standing, laying, sitting, walking upstairs, walking downstairs are predicted using prominent machine learning models. The aim of human activity recognition is examining actions from photos or video clips. This serves as the driving force behind human activity identification systems' aim to accurately classify input data into the relevant activity category. Methods Six machine learning techniques, including decision tree, random forest, linear regression, Naïve bayes, k-nearest neighbour, and neural networks algorithms, were used for human activity recognition. Results The performance of decision tree, random forest, linear regression, Naïve bayes, k-nearest neighbor, and neural network algorithms was assessed with a human activity recognition dataset. From the results, the random forest classifier and neural network gave good results, whereas the Naïve bayes result was not satisfying. Conclusions We classified the SITTING, STANDING, LAYING, WALKING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS activities with machine learning techniques with 98% of accuracy

[Purpose/Significance] The development of the Internet has led to the rapid development of social networks, providing users with a convenient channel for the release, dissemination and acceptance of information. However, its low-threshold characteristics have also given rise to a group of the \"Internet water army\"–online social spammers, who are paid to post online comments with particular content and spread false information on purpose. They have become a major problem for the Internet ecology. It is of great significance to detect the Internet water army, prevent their malicious attacks, and combat and eliminate their negative effects on the security of the online public opinion. [Method/Process] First, we analyzed the development process and characteristics of the online social spammers, summarized the algorithms used in previous studies and the characteristics mentioned, and sorted out three research starting points: text features, interaction features and graph structure features. Then, an online social spammer detection method based on deep learning was proposed. Combined with the three aspects of user basic information, historical remarks and interaction behavior, six types of features were extracted from the basic information, recent remarks, social intimacy, interaction behavior, microblog number and membership level. Through feature depth extraction and vector splicing and fusion, the user feature vectors were formed with the same length. Finally, a convolutional neural network was used as a classifier to build an automatic, high-precision and high-efficiency spammer detection model. Two Chinese online spammer datasets collected from the Sina Weibo platform were selected for the experiment. The features of the datasets were spliced and aligned to form the Weibo Spammer 2023 dataset as the model training dataset, which prevented the data features of a single dataset from being too discrete and reducing modle generalization. Considering the overfitting problem in the model training process, we solved the problem by adding abandoned layers. [Results/Conclusions] The online spammer detection model constructed in this experiment has significantly improved in terms of metrics such as precision and accuracy. At the same time, the ablation experiment shows that the six features extracted in this experiment have a positive effect on the detection process. Through empirical analysis, the model constructed in this paper has a high detection accuracy and detection efficiency, which can provide certain technical support and theoretical guidance for online spammer identification. By using machine learning methods to actively identify online social spammer accounts, real-time monitoring and prevention of key spammer accounts can prevent the occurrence of malicious network events more timely and effectively and reduce the risk of illegal forces damaging the public opinion ecology.

Age estimation from human faces is an important yet challenging task in computer vision because of the large differences between physical age and apparent age. Due to the differences including races, genders, and other factors, the performance of a learning method for this task strongly depends on the training data. Although many inspiring works have focused on the age estimation of a single human face through deep learning, the existing methods still have lower performance when dealing with faces in videos because of the differences in head pose between frames, which can lead to greatly different results. In this paper, a combined system of age estimation and head pose estimation is proposed to improve the performance of age estimation from faces in videos. We use deep regression forests (DRFs) to estimate the age of facial images, while a multiloss convolutional neural network is also utilized to estimate the head pose. Accordingly, we estimate the age of faces only for head poses within a set degree threshold to enable value refinement. First, we divided the images in the Cross-Age Celebrity Dataset (CACD) and the Asian Face Age Dataset (AFAD) according to the estimated head pose degrees and generated separate age estimates for images with different poses. The experimental results showed that the accuracy of age estimation from frontal facial images was better than that for faces at different angles, thus demonstrating the effect of head pose on age estimation. Further experiments were conducted on several videos to estimate the age of the same person with his or her face at different angles, and the results show that our proposed combined system can provide more precise and reliable age estimates than a system without head pose estimation.

Adenocarcinoma is a type of cancer that develops in the glands present on the lining of the organs in the human body. It is found that histopathological images, obtained as a result of biopsy, are the most definitive way of diagnosing cancer. The main objective of this work is to use deep learning techniques for the detection and classification of adenocarcinoma using histopathological images of lung and colon tissues with minimal preprocessing. Two approaches have been utilized. The first method entails creating two CNN architectures: CNN with a Softmax classifier (AdenoCanNet) and CNN with an SVM classifier (AdenoCanSVM). The second approach corresponds to training some of the prominent existing architecture such as VGG16, VGG19, LeNet, and ResNet50. The study aims at understanding the performance of various architectures in diagnosing using histopathological images with cases taken separately and taken together, with a full dataset and a subset of the dataset. The LC25000 dataset used consists of 25,000 histopathological images, having both cancerous and normal images from both the lung and colon regions of the human body. The accuracy metric was taken as the defining parameter for determining and comparing the performance of various architectures undertaken during the study. A comparison between the several models used in the study is presented and discussed.

Automated monitoring using deep learning can reduce labor costs and increase efficiency and has been shown to be equally or more accurate than humans at processing data (Torney et al., 2019; Ditria et al., 2020a). [...]the expansion of deep learning techniques in the last few years in marine science call for higher volumes of data for training than traditional machine learning methods. [...]there is a need for accessible, quality annotated datasets for deep learning models to further the progress of applying these techniques in ecology. The contributions of this dataset include: (1) a comprehensive dataset of ecologically important fish species that captures the complexity of backgrounds observed in unconstrained seagrass ecosystems to form a robust and flexible model; (2) a variety of modalities for rapid and flexible testing or comparison of different frameworks, and (3) unaltered imagery for investigation of possible data augmentation and performance enhancement using pre- and post-processing techniques. Dataset To continue the development of automated tools for fish monitoring, we report a dataset “Annotated videos of luderick from estuaries in southeast Queensland, Australia” which was used to train a deep learning algorithm for automated species identification and abundance counts presented in Ditria et al.

To solve the problem of ship recognition in video images, a ship recognition method based on Morphological Watershed image segmentation and Zemike moment is proposed. Firstly, the video frame image is pre-processed by gray algorithm, and then the gray image is filtered by wavelet transform to remove noise. After denoising, the Morphological Watershed algorithm is used to segment the image and extract the ship area in the image. Next, the feature of ship image is extracted based on deep learning convolution neural network (CNN) and Zemike moment method. Finally, the KNN-SVM classifier is trained according to the image features and class labels to realize the automatic recognition of ships. Experimental results show that the method can effectively identify 3 types of ships, with an average detection accuracy of 87%.

It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them.