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Facial emotion recognition (FER) is an emerging and significant research area in the pattern recognition domain. In daily life, the role of non-verbal communication is significant, and in overall communication, its involvement is around 55% to 93%. Facial emotion analysis is efficiently used in surveillance videos, expression analysis, gesture recognition, smart homes, computer games, depression treatment, patient monitoring, anxiety, detecting lies, psychoanalysis, paralinguistic communication, detecting operator fatigue and robotics. In this paper, we present a detailed review on FER. The literature is collected from different reputable research published during the current decade. This review is based on conventional machine learning (ML) and various deep learning (DL) approaches. Further, different FER datasets for evaluation metrics that are publicly available are discussed and compared with benchmark results. This paper provides a holistic review of FER using traditional ML and DL methods to highlight the future gap in this domain for new researchers. Finally, this review work is a guidebook and very helpful for young researchers in the FER area, providing a general understating and basic knowledge of the current state-of-the-art methods, and to experienced researchers looking for productive directions for future work.

Drought stress seriously affects crop growth, development, and grain production. Existing machine learning methods have achieved great progress in drought stress detection and diagnosis. However, such methods are based on a hand-crafted feature extraction process, and the accuracy has much room to improve. In this paper, we propose the use of a deep convolutional neural network (DCNN) to identify and classify maize drought stress. Field drought stress experiments were conducted in 2014. The experiment was divided into three treatments: optimum moisture, light drought, and moderate drought stress. Maize images were obtained every two hours throughout the whole day by digital cameras. In order to compare the accuracy of DCNN, a comparative experiment was conducted using traditional machine learning on the same dataset. The experimental results demonstrated an impressive performance of the proposed method. For the total dataset, the accuracy of the identification and classification of drought stress was 98.14% and 95.95%, respectively. High accuracy was also achieved on the sub-datasets of the seedling and jointing stages. The identification and classification accuracy levels of the color images were higher than those of the gray images. Furthermore, the comparison experiments on the same dataset demonstrated that DCNN achieved a better performance than the traditional machine learning method (Gradient Boosting Decision Tree GBDT). Overall, our proposed deep learning-based approach is a very promising method for field maize drought identification and classification based on digital images.

Named Entity Recognition (NER) plays a crucial role in extracting important information such as treatment methods, symptoms, and herbal prescriptions from Traditional Chinese Medicine (TCM) electronic medical records. However, existing NER methods often struggle with the complexity and variability of TCM language, especially when dealing with overlapping or nested entities. To address these issues, we propose DG-SpanTCM, a novel framework that enhances character-level text understanding using a pre-trained language model and improves entity recognition through lexical-semantic features and robust training strategies. Our method also incorporates techniques to handle label imbalance and better identify complex entity structures. Experiments on a real-world TCM dataset show that DG-SpanTCM achieves superior performance, improving the F1-score over strong baseline models. These findings highlight the potential of DG-SpanTCM in advancing automated information extraction for TCM texts.

Evidence-based treatment is the basis of traditional Chinese medicine (TCM), and the accurate differentiation of syndromes is important for treatment in this context. The automatic differentiation of syndromes of unstructured medical records requires two important steps: Chinese word segmentation and text classification. Due to the ambiguity of the Chinese language and the peculiarities of syndrome differentiation, these tasks pose a daunting challenge. We use text classification to model syndrome differentiation for TCM, and use multi-task learning (MTL) and deep learning to accomplish the two challenging tasks of Chinese word segmentation and syndrome differentiation. Two classic deep neural networks—bidirectional long short-term memory (Bi-LSTM) and text-based convolutional neural networks (TextCNN)—are fused into MTL to simultaneously carry out these two tasks. We used our proposed method to conduct a large number of comparative experiments. The experimental comparisons showed that it was superior to other methods on both tasks. Our model yielded values of accuracy, specificity, and sensitivity of 0.93, 0.94, and 0.90, and 0.80, 0.82, and 0.78 on the Chinese word segmentation task and the syndrome differentiation task, respectively. Moreover, statistical analyses showed that the accuracies of the non-joint and joint models were both within the 95% confidence interval, with pvalue < 0.05. The experimental comparison showed that our method is superior to prevalent methods on both tasks. The work here can help modernize TCM through intelligent differentiation.

Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose leukemia by looking at a person’s blood sample under a microscope. They identify and categorize leukemia by counting various blood cells and morphological features. This technique is time-consuming for the prediction of leukemia. The pathologist’s professional skills and experiences may be affecting this procedure, too. In computer vision, traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images such as microscopic blood cells. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells. First, we have divided the previous works into six categories based on the output of the models. Then, we describe various steps of detection and classification of acute leukemia and WBCs, including Data Augmentation, Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction), Classification, and focus on classification step in the methods. Finally, we divide automated detection and classification of acute leukemia and WBCs into three categories, including traditional, Deep Neural Network (DNN), and mixture (traditional and DNN) methods based on the type of classifier in the classification step and analyze them. The results of this study show that in the diagnosis and classification of acute leukemia and WBCs, the Support Vector Machine (SVM) classifier in traditional machine learning models and Convolutional Neural Network (CNN) classifier in deep learning models have widely employed. The performance metrics of the models that use these classifiers compared to the others model are higher. We propose providing models in detecting and classify acute leukemia and WBCs that use a combination of SVM and CNN classifiers in their classification step to achieve optimum performance metrics.

Stroke has become the leading cause of disability in adults worldwide. Early precise rehabilitation intervention is crucial for the recovery of stroke patients, with the key lying in accurately identifying patients’ physical characteristics during the rehabilitation phase. Compared to diagnostic techniques such as medical neuroimaging, traditional Chinese medicine(TCM) tongue diagnosis offers good accessibility and ease of application. However, conventional TCM tongue diagnosis relies on the experience of doctors, which introduces a degree of subjectivity, especially since stroke patients exhibit unique characteristics in tongue texture, shape, and coating, making accurate diagnosis more challenging. To address this issue, this paper proposes a deep learning-based automatic recognition approach for the tongue images of stroke patients, aiming to improve the accuracy of automatic extraction and recognition of stroke-related tongue features through image processing and machine learning techniques. First, this study performs image cropping and data augmentation on tongue images. Then, considering that tongue color, coating color, and coating texture are interrelated in TCM theory and jointly reflect the body’s physiological and pathological state, a label-guided multi-label recognition model for tongue images is designed. This model extracts features from the tongue images of stroke patients, learns the correlations among the features, and performs classification to automatically identify key characteristics such as tongue shape, color, and coating. Finally, the model’s performance is quantitatively evaluated. Experimental results show that the proposed deep learning model outperforms several advanced deep learning models, such as resnet and densenet, and existing single-task tongue classification models in automatically recognizing stroke patients’ tongue images. This research improves the accuracy of feature extraction and recognition of tongue characteristics in stroke patients during rehabilitation, providing a convenient and feasible technical approach for real-time evaluation and diagnosis in the stroke rehabilitation process. It has significant clinical application value and research significance.

Tongue coating is a crucial diagnostic indicator in traditional Chinese medicine, intuitively reflecting the body’s physiological and pathological conditions. However, traditional visual inspection methods are highly susceptible to subjective bias, often resulting in diagnostic deviations and inconsistencies. To address these limitations, this study proposes an intelligent tongue coating diagnostic model based on an enhanced YOLOv13. The model integrates a hybrid architecture of swin transformer and YOLOv13, effectively capturing global contextual and local textural features for fine-grained recognition and analysis of tongue coating characteristics. Experimental results show that the enhanced model substantially outperforms the original YOLOv13 in fine-grained feature extraction and boundary localization, establishing a reliable foundation for the objectification, standardization, and intelligent advancement of tongue diagnosis in traditional Chinese medicine.

Drug discovery is a challenging and resource-intensive process characterized by high costs, prolonged development timelines, and regulatory hurdles in the pharmaceutical sector. AI-driven recommendation systems have emerged as an effective approach to enhance candidate selection and optimize drug-target interactions. Typical drug discovery methods are expensive, time-consuming, and frequently have a high failure rate. The inability to quickly identify suitable drug candidates is a significant challenge due to the lack of effective predictive models. To address these issues, the Context-Aware Hybrid Ant Colony Optimized Logistic Forest (CA-HACO-LF) model is proposed. This model combines ant colony optimization for feature selection with logistic forest classification, improving drug-target interaction prediction. By incorporating context-aware learning, the model enhances adaptability and accuracy in drug discovery applications. The research utilized a Kaggle dataset containing over 11,000 drug details. During pre-processing, techniques such as text normalization (lowercasing, punctuation removal, and elimination of numbers and spaces) were applied. Stop word removal and tokenization ensured meaningful feature extraction, while lemmatization refined the word representations to enhance model performance. Feature extraction was further improved using N-grams and Cosine Similarity to assess the semantic proximity of drug descriptions, aiding the model in identifying relevant drug-target interactions and evaluating textual relevance in context. In the classification phase, the CA-HACO-LF model integrates a customized Ant Colony Optimization-based Random Forest (RF) with Logistic Regression (LR) to enhance predictive accuracy in identifying drug-target interactions, leveraging the extracted features and cosine similarity for better performance. The implementation is performed using Python for feature extraction, similarity measurement, and classification. The proposed CA-HACO-LF model outperforms existing methods, demonstrating superior performance across various metrics, including accuracy (0.986%), precision, recall, F1 Score, RMSE, AUC-ROC, MSE, MAE, F2 Score, and Cohen’s Kappa.

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due to its non-invasive nature and cost-effectiveness. With the rise of computational psychiatry, the integration of EEG with artificial intelligence has yielded remarkable results in diagnosing depression. This review offers a comparative analysis of two predominant methodologies in research: traditional machine learning and deep learning methods. Furthermore, this review addresses key challenges in current research and suggests potential solutions. These insights aim to enhance diagnostic accuracy for depression and also foster further development in the area of computational psychiatry.

Background Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Therefore, an automatic, efficient and accurate quality control model is of significance to the development of intelligent tongue diagnosis technology for TCM. Methods Machine learning methods, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (Adaboost), Naïve Bayes, Decision Tree (DT), Residual Neural Network (ResNet), Convolution Neural Network developed by Visual Geometry Group at University of Oxford (VGG), and Densely Connected Convolutional Networks (DenseNet), were utilized to identify good-quality and poor-quality tongue images. Their performances were made comparisons by using metrics such as accuracy, precision, recall, and F1-Score. Results The experimental results showed that the accuracy of the three deep learning models was more than 96%, and the accuracy of ResNet-152 and DenseNet-169 was more than 98%. The model ResNet-152 obtained accuracy of 99.04%, precision of 99.05%, recall of 99.04%, and F1-score of 99.05%. The performances were better than performances of other eight models. The eight models are VGG-16, DenseNet-169, SVM, RF, GBDT, Adaboost, Naïve Bayes, and DT. ResNet-152 was selected as quality-screening model for tongue IQA. Conclusions Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and indicate that applying deep learning methods, specifically deep CNNs, to evaluate poor-quality tongue images is feasible.