The Matters of Deep Reinforcement Learning Toward Practical AI Applications

[[notice]]補正完

Automatic detection of nostril and key markers in images

In the medical field, one of complex challenges in surgery involving the skin, cartilage, mucosa, and the skeletal platform is the cleft nasal deformity. The causes of deformity are nasal formed from improper fusion of the medial and lateral nasal prominences with the maxillary prominence during embryologic development. Primary rhinoplasty or first time of cleft nose repair is the nasal surgery for repairing the cleft nasal deformity. In post cheiloplasty, the primary rhinoplasty patients must use nasal splint for supporting surgical wound. However, regular nasal splints are expensive and only come with default nasal size. This limits nasal splint from supporting specific patient cases such as children. The goal of this study is to implement a program that can automatically detect the nostril and green marker in patient images for creating custom-made 3D nasal splints. The proposed method is created utilizing the CNN model. YOLO architecture is utilized because it is the one of several CNN architectures that works well with the face recognition task. The YOLOV5, YOLOV8, and YOLO-NAS are attempted and compared in the training phase. The model with a highest performance is selected, and fine-tuned for adjusting to be compatible with a patient dataset. The fine-tuned YOLOV8 reaches a mAP with 99.5%. The predicted images from fine-tuned model are used to perform body part segmentation like Otsu's thresholding and discover contour to locate essential features like the green marker and nostril in ellipse and bounding boxes. The nostril distance is calculated using the bounding box and ellipse. The columella distance or nostril gap is measured from the distance between nostrils in YOLO predicted label. Both distances are converted into centimeter scale and evaluated with ground truth value in volunteer and patient cases from nasal expert for inspecting efficiency between bounding box, ellipse, and size of marker. The result proves that the proposed model can detect crucial features with mean absolute error 0.102. Furthermore, the proposed model indicates that marker size has no effect on detection and distance, according to the findings of the experiments

Selecting Control Menu on Electric Wheelchair Using Eyeball Movement for Difable Person

Each country's number of people with disabilities and strokes increases yearly. Hand defects and stroke make them have limitations in doing activities. It caused their hand has paralyzed. Hence, they find it difficult to do daily activities, such as running a wheelchair, choosing a menu on the screen display, and so on. One solution offered is utilizing eye movement as a navigation tool that can replace the role of the user's hand, so they can run a wheelchair independently or choose a menu selection on display by themselves through the movement of their eyes. Detection of eyeball movements in this study only utilizes a camera as a sensor mounted in front of the user. So that it is more practical and easier to use than if we have to pair an electrooculography sensor in the area around the user's eyes. This research proposed a new approach to detect the five gazes (upward, downward, leftward, rightward, and forward) of the eyeball movements by using Backpropagation Neural Network (BPNN) and Dynamic Line Sector Coordinate (DLSC). Line Sector Coordinate is used to detect the eyeball movement based on the pupil coordinate position. The eyeball movement direction was analyzed from four lengths of a line. Our proposed method can detect five gaze directions that can be used for selecting four menus on the display monitor. The mean accuracy of our proposed method to detect eye movements for each gaze is 88.6%

DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

The explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company???s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day???s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data

MDPrePost-Net: A Spatial-Spectral-Temporal Fully Convolutional Network for Mapping of Mangrove Degradation Affected by Hurricane Irma 2017 Using Sentinel-2 Data

Mangroves are grown in intertidal zones along tropical and subtropical climate areas, which have many benefits for humans and ecosystems. The knowledge of mangrove conditions is essential to know the statuses of mangroves. Recently, satellite imagery has been widely used to generate mangrove and degradation mapping. Sentinel-2 is a volume of free satellite image data that has a temporal resolution of 5 days. When Hurricane Irma hit the southwest Florida coastal zone in 2017, it caused mangrove degradation. The relationship of satellite images between pre and post-hurricane events can provide a deeper understanding of the degraded mangrove areas that were affected by Hurricane Irma. This study proposed an MDPrePost-Net that considers images before and after hurricanes to classify non-mangrove, intact/healthy mangroves, and degraded mangroves classes affected by Hurricane Irma in southwest Florida using Sentinel-2 data. MDPrePost-Net is an end-to-end fully convolutional network (FCN) that consists of two main sub-models. The first sub-model is a pre-post deep feature extractor used to extract the spatial–spectral–temporal relationship between the pre, post, and mangrove conditions after the hurricane from the satellite images and the second sub-model is an FCN classifier as the classification part from extracted spatial–spectral–temporal deep features. Experimental results show that the accuracy and Intersection over Union (IoU) score by the proposed MDPrePost-Net for degraded mangrove are 98.25% and 96.82%, respectively. Based on the experimental results, MDPrePost-Net outperforms the state-of-the-art FCN models (e.g., U-Net, LinkNet, FPN, and FC-DenseNet) in terms of accuracy metrics. In addition, this study found that 26.64% (41,008.66 Ha) of the mangrove area was degraded due to Hurricane Irma along the southwest Florida coastal zone and the other 73.36% (112,924.70 Ha) mangrove area remained intact

Smart Orca: Using Interactive Toys as the English Learning Analytics Tool to Evaluate Learner Behavior

[[notice]]補正完

Novel Spatio-Temporal Continuous Sign Language Recognition Using an Attentive Multi-Feature Network

Given video streams, we aim to correctly detect unsegmented signs related to continuous sign language recognition (CSLR). Despite the increase in proposed deep learning methods in this area, most of them mainly focus on using only an RGB feature, either the full-frame image or details of hands and face. The scarcity of information for the CSLR training process heavily constrains the capability to learn multiple features using the video input frames. Moreover, exploiting all frames in a video for the CSLR task could lead to suboptimal performance since each frame contains a different level of information, including main features in the inferencing of noise. Therefore, we propose novel spatio-temporal continuous sign language recognition using the attentive multi-feature network to enhance CSLR by providing extra keypoint features. In addition, we exploit the attention layer in the spatial and temporal modules to simultaneously emphasize multiple important features. Experimental results from both CSLR datasets demonstrate that the proposed method achieves superior performance in comparison with current state-of-the-art methods by 0.76 and 20.56 for the WER score on CSL and PHOENIX datasets, respectively