In general, patients who are unwell do not know with which outpatient department they should register, and can only get advice after they are diagnosed by a family doctor. This may cause a waste of time and medical resources. In this paper, we propose an attention-based bidirectional long short-term memory (Att-BiLSTM) model for service robots, which has the ability to classify outpatient categories according to textual content. With the outpatient text classification system, users can talk about their situation to a service robot and the robot can tell them which clinic they should register with. In the implementation of the proposed method, dialog text of users in the Taiwan E Hospital were collected as the training data set. Through natural language processing (NLP), the information in the dialog text was extracted, sorted, and converted to train the long-short term memory (LSTM) deep learning model. Experimental results verify the ability of the robot to respond to questions autonomously through acquired casual knowledge.

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. This study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 key points using an open-source tool called DeepLabCut, which accurately helped mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the long short-term memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior exceeded 90%.

Traditional animal behavior recognition models require extensive labeled datasets and frequent retraining, limiting their adaptability across species and environments. Additionally, existing systems rely on predefined behavior categories, making it difficult for researchers to customize recognition models to specific behavioral patterns relevant to their studies. Different research fields, such as animal welfare monitoring, conservation, and behavioral ecology, often require distinct behavior classifications, yet current systems lack the flexibility to accommodate these varying needs. This study aims to develop an expandable and user-driven animal behavior recognition system utilizing DeepLabCut for pose estimation and a BiLSTM-based classification model. By integrating Dynamic Adaptive Elastic Weight Consolidation (DA-EWC), the system enables incremental learning, allowing new behaviors to be added with minimal annotation while preserving previously learned behaviors. The proposed system is trained on dolphin behavior datasets using DeepLabCut for keypoint extraction and a BiLSTM model for sequence classification. Additionally, a user-friendly interface is implemented to facilitate behavior annotation and efficient model updates. The proposed system achieves 96.5% accuracy in behavior classification, surpassing conventional models such as Faster R-CNN. Compared to standard EWC, DA-EWC maintains an average of 8.3% higher accuracy when incorporating new behaviors. Furthermore, the system reduces annotation efforts by 9.3%, enabling users to expand behavior categories efficiently. This expandable behavior recognition system significantly enhances adaptability and efficiency in animal behavior monitoring. By supporting user-driven incremental learning, it provides a scalable solution for behavior analysis across different research domains, addressing the need for customizable and evolving behavior classification.

The rapid developments in hardware, software, and communication technologies have facilitated the spread of interconnected sensors, actuators, and heterogeneous devices such as single board computers, which collect and exchange a large amount of data to offer a new class of advanced services characterized by being available anywhere, at any time, and for anyone. This ecosystem is widely referred to as the Internet of Things (IoT). In the past years, the number of deployments both for sensor networks and the IoT grew significantly. This continuous and exponential growth is facilitated by investments and research activities originating from industry, academia, and governments while the penetration of these technologies is also driven by the high technology acceptance rates of both consumers and technologists across disciplines. Such networks collect, store, and exchange a large volume of heterogeneous data. Nevertheless, their rapid and widespread deployment, along with their participation in the provisioning of potentially critical services (e.g., safety applications, healthcare, and manufacturing) raise numerous issues related to the security, data analysis, and energy awareness of the performed operations and provided services.

Advances in artificial intelligence-based autonomous applications have led to the advent of domestic robots for smart elderly care; the preliminary critical step for such robots involves increasing the comprehension of robotic visualizing of human activity recognition. In this paper, discrete hidden Markov models (D-HMMs) are used to investigate human activity recognition. Eleven daily home activities are recorded using a video camera with an RGB-D sensor to collect a dataset composed of 25 skeleton joints in a frame, wherein only 10 skeleton joints are utilized to efficiently perform human activity recognition. Features of the chosen ten skeleton joints are sequentially extracted in terms of pose sequences for a specific human activity, and then, processed through coordination transformation and vectorization into a codebook prior to the D-HMM for estimating the maximal posterior probability to predict the target. In the experiments, the confusion matrix is evaluated based on eleven human activities; furthermore, the extension criterion of the confusion matrix is also examined to verify the robustness of the proposed work. The novelty indicated D-HMM theory is not only promising in terms of speech signal processing but also is applicable to visual signal processing and applications.