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Abstract Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

Phalaenopsis orchids are one of the most important exporting commodities for Taiwan. Most orchids are planted and grown in greenhouses. Early detection of orchid diseases is crucially valuable to orchid farmers during orchid cultivation. At present, orchid viral diseases are generally identified with manual observation and the judgment of the grower’s experience. The most commonly used assays for virus identification are nucleic acid amplification and serology. However, it is neither time nor cost efficient. Therefore, this study aimed to create a system for automatically identifying the common viral diseases in orchids using the orchid image. Our methods include the following steps: the image preprocessing by color space transformation and gamma correction, detection of leaves by a U-net model, removal of non-leaf fragment areas by connected component labeling, feature acquisition of leaf texture, and disease identification by the two-stage model with the integration of a random forest model and an inception network (deep learning) model. Thereby, the proposed system achieved the excellent accuracy of 0.9707 and 0.9180 for the image segmentation of orchid leaves and disease identification, respectively. Furthermore, this system outperformed the naked-eye identification for the easily misidentified categories [cymbidium mosaic virus (CymMV) and odontoglossum ringspot virus (ORSV)] with the accuracy of 0.842 using two-stage model and 0.667 by naked-eye identification. This system would benefit the orchid disease recognition for Phalaenopsis cultivation.

On 19 April 2022, in order to overcome a succession of recent global food health and agriculture safety (FHAS) incidents, the Taiwanese government instituted and announced 20 comprehensive Articles under the Taiwanese Food and Agricultural Education Law (TFAEL) for regulating the Taiwanese FHAS in order to completely construct the international strategies under the current global food security challenges of the FAHS. As a result, this research study firstly employs the learning community model (LCM) of the learning theory to measure the implementing depth of the international sustainability practice of the 20 Articles of the Taiwanese FHASE from a learning performance analytical perspective. Then, the social learning theory (SLT) of the educational theory is applied to consolidate the individual behaviors of the relative stakeholders, the organizational management of the relative industries and the socialized consent of the multilateral organization performance assessment network (MOPAN) of the Food and Agriculture Organization (FAO) of the United Nations (UN). After completing quantitative and qualitative analyses, the two most valuable conclusions and findings were as follows: (1) Not only Article 12, but also Articles 3, 4 and 5 of the new law can empirically facilitate strategic management for supporting and promoting the Taiwanese FHAS educational policy dialogue at a global, regional and country scale in order to effectively advance the international sustainable compatibility of the TFAEL. (2) Articles 6, 11 and 16 of the new law can completely construct operational management for building normative and standard-setting Taiwanese FHAS educational works in order to efficiently advance the international sustainable compatibility of the TFAEL. Therefore, beyond the analytical results of this research, the international sustainable compatibility of the TFAEL provides a direction for the development of sustainable food systems, food policies, agricultural food markets and food chains in order to overcome the various contemporary global food security challenges.

This paper proposes a multi–convolutional neural network (CNN)-based system for the detection, tracking, and recognition of the emotions of dogs in surveillance videos. This system detects dogs in each frame of a video, tracks the dogs in the video, and recognizes the dogs’ emotions. The system uses a YOLOv3 model for dog detection. The dogs are tracked in real time with a deep association metric model (DeepDogTrack), which uses a Kalman filter combined with a CNN for processing. Thereafter, the dogs’ emotional behaviors are categorized into three types—angry (or aggressive), happy (or excited), and neutral (or general) behaviors—on the basis of manual judgments made by veterinary experts and custom dog breeders. The system extracts sub-images from videos of dogs, determines whether the images are sufficient to recognize the dogs’ emotions, and uses the long short-term deep features of dog memory networks model (LDFDMN) to identify the dog’s emotions. The dog detection experiments were conducted using two image datasets to verify the model’s effectiveness, and the detection accuracy rates were 97.59% and 94.62%, respectively. Detection errors occurred when the dog’s facial features were obscured, when the dog was of a special breed, when the dog’s body was covered, or when the dog region was incomplete. The dog-tracking experiments were conducted using three video datasets, each containing one or more dogs. The highest tracking accuracy rate (93.02%) was achieved when only one dog was in the video, and the highest tracking rate achieved for a video containing multiple dogs was 86.45%. Tracking errors occurred when the region covered by a dog’s body increased as the dog entered or left the screen, resulting in tracking loss. The dog emotion recognition experiments were conducted using two video datasets. The emotion recognition accuracy rates were 81.73% and 76.02%, respectively. Recognition errors occurred when the background of the image was removed, resulting in the dog region being unclear and the incorrect emotion being recognized. Of the three emotions, anger was the most prominently represented; therefore, the recognition rates for angry emotions were higher than those for happy or neutral emotions. Emotion recognition errors occurred when the dog’s movements were too subtle or too fast, the image was blurred, the shooting angle was suboptimal, or the video resolution was too low. Nevertheless, the current experiments revealed that the proposed system can correctly recognize the emotions of dogs in videos. The accuracy of the proposed system can be dramatically increased by using more images and videos for training the detection, tracking, and emotional recognition models. The system can then be applied in real-world situations to assist in the early identification of dogs that may exhibit aggressive behavior.

Rice is a staple food in many Asian countries, but its production requires a high water demand. Moreover, more attention should be paid to the water management of rice due to global climate change and frequent droughts. To address this problem, we propose a rice water stress identification system. Since water irrigation usually affects the opening and closing of rice leaf stomata which directly affects leaf temperature, rice leaf temperature is a suitable index for evaluating rice water stress. The proposed rice water stress identification system uses a CNN (convolutional neural network) to identify water stress in thermal images of rice fields and to classify the irrigation situation into three classes: 100%, 90%, and 80% irrigation. The CNN was applied to extract the temperature level score from each thermal image based on the degree of difference between the three irrigation situations, then these scores were used to further classify the water-stress situation. In the experiments in this study, we compare CNN classification results without considering the degree between each class. The proposed method considerably improves water stress identification. Since rice leaf temperature is relative to air temperature and is not an absolute value, the background temperature is also important reference information. We combine two different methods for background processing to extract more features and achieve more accurate identification.

Bone age is a common indicator of children’s growth. However, traditional bone age assessment methods usually take a long time and are jeopardized by human error. To address the aforementioned problem, we propose an automatic bone age assessment system based on the convolutional neural network (CNN) framework. Generally, bone age assessment is utilized amongst 0–18-year-old children. In order to reduce its variation in terms of regression model building, our system consists of two steps. First, we build a maturity stage classifier to identify the maturity stage, and then build regression models for each maturity stage. In this way, assessing bone age through the use of several independent regression models will reduce the variation and make the assessment of bone age more accurate. Some bone sections are particularly useful for distinguishing certain maturity stages, but may not be effective for other stages, and thus we first perform a rough classification to generally distinguish the maturity stage, and then undertake fine classification. Because the skeleton is constantly growing during bone development, it is not easy to obtain a clear decision boundary between the various stages of maturation. Therefore, we propose a cross-stage class strategy for this problem. In addition, because fewer children undergo X-rays in the early and late stages, this causes an imbalance in the data. Under the cross-stage class strategy, this problem can also be alleviated. In our proposed framework, we utilize an MSCS-CNN (Multi-Step and Cross-Stage CNN). We experiment on our dataset, and the accuracy of the MSCS-CNN in identifying both female and male maturity stages is above 0.96. After determining maturity stage during bone age assessment, we obtain a 0.532 and 0.56 MAE (mean absolute error) for females and males, respectively.

Bone age assessment (BAA) is an important indicator of child maturity. Generally, a person is evaluated for bone age mostly during puberty stage; compared to toddlers and post-puberty stages, the data of bone age at puberty stage are much easier to obtain. As a result, the amount of bone age data collected at the toddler and post-puberty stages are often much fewer than the amount of bone age data collected at the puberty stage. This so-called data imbalance problem affects the prediction accuracy. To deal with this problem, in this paper, a data imbalance immunity bone age assessment (DIIBAA) system is proposed. It consists of two branches, the first branch consists of a CNN-based autoencoder and a CNN-based scoring network. This branch builds three autoencoders for the bone age data of toddlers, puberty, and post-puberty stages, respectively. Since the three types of autoencoders do not interfere with each other, there is no data imbalance problem in the first branch. After that, the outputs of the three autoencoders are input into the scoring network, and the autoencoder which produces the image with the highest score is regarded as the final prediction result. In the experiments, imbalanced training data with a positive and negative sample ratio of 1:2 are used, which has been alleviated compared to the original highly imbalanced data. In addition, since the scoring network converts the classification problem into an image quality scoring problem, it does not use the classification features of the image. Therefore, in the second branch, we also add the classification features to the DIIBAA system. At this time, DIIBAA considers both image quality features and classification features. Finally, the DenseNet169-based autoencoders are employed in the experiments, and the obtained evaluation accuracies are improved compared to the baseline network.

Since the symptoms of the diabetic retinopathy, macular degeneration, and many other eye diseases are often associated with the bright spots, dark spots, and vascular abnormalities in retinal images, this study proposes a retinopathy image segmentation (system) to extract the bright spots, dark spots, and blood vessels from retinal images. Moreover, since a high proportion of diabetic patients suffer from the diabetic retinopathy with the most severe one to be the retinal detachment, the retinal image-based tractional retinal detachment diagnosis system is proposed, as well. Two genetic-based parameter detectors are given to provide the most appropriate values for the parameters used by the retinopathy image segmentation system and the tractional retinal detachment diagnosis system. The experimental results illustrate that the retinopathy image segmentation system and the tractional retinal detachment diagnosis system can provide expressive results in segmenting bright spots, dark spots, blood vessels, and in diagnosing tractional retinal detachment. Both systems are helpful for the ophthalmologists in diagnosing eye diseases.

This paper focuses on color differences among the edges of objects in an image. The variations of colors among the objects in the image can depict the directions and simple geometric shapes of most objects in the image regardless of the influence from the shift variant of objects and scale variant in the image. Based on the feature of color differences among the edges of objects, this paper constructs an image retrieval system. Experimental results show that this system can effectively and quickly deliver the desired database images to the users

Under the hyper-dynamic education situation, this research, in order to comprehensively explore the interplays between Teacher Competence Demands (TCD) and Learning Organization Requests (LOR), cross-employs the data refined method of Descriptive Statistics (DS) method and Analysis of Variance (ANOVA) and Principal Components Analysis (PCA) approaches to cross-analyze the empirical questionnaires of senior high schools in Changhua and Taichung regions in Taiwan from four brief analytical perspectives, including Individual Demand Competency (IDC), Professional Knowledge Competency (PKC), Resource Satisfaction Competency (RSC) and Cooperative Relationship Competency (CRC). Based on the results of evaluated measurements, the four most decisive TCDs for constructing the potential LOR are "reading competency for IDC," "cross-subjects and empirical teaching competencies for PKC," "convincing competency for RSC" and "cooperative competency" which is the most ponderable contribution of this research. Furthermore, in order to survive in the lowest birth-rate and highest democratic-rights education era, Taiwanese senior high schools must concretely put more school's administrative resource and aggressively collect more sponsoring from non-for-profit education organizations and governmental education departments to provide a series of empirical and digital on-job certificate programs and training license courses to assist teachers to nurture these most critical TCDs including reading, cross-subjects and empirical teaching, convincing and cooperative.

Industrial environments frequently encounter complex time-series data such as machine vibration patterns, motor thermal imaging, and sensor pressure metrics. Equipment failure prediction grapples with the temporal nature of the data and the challenge posed by minority failure instances. In this paper, we introduce a refined generative mechanism, building on the foundation of the Wasserstein generative adversarial network (WGAN) and the borderline synthetic minority oversampling technique (Borderline-SMOTE). By utilizing time-series features, the proposed method effectively addresses the intricacies of predictive modeling. To demonstrate its efficacy, we used a complex and multisensor hydraulic system dataset for validation. Experimental results indicate that the proposed method outperforms existing strategies, enhancing the F1 score by at least 2.21% and achieving a recall rate of 95.51%. This suggests a promising direction for enhancing fault prediction in complex industrial settings. [ABSTRACT FROM AUTHOR]