The Ministry of Health and Welfare's Statistics reports present the incidence of domestic care violence becomes higher annually. However, there is no efficient method to get rid of physical abuse. After being ill-treated of violence, someone is assessed injure by official organization. Then, the victims take legal actions to damage after the events. The deep learning motion recognized violence to family care in advance. To analyst that the images from complex data sets on the internet is important. The key part of images that are recognized as physical abuse is ambiguous and distorted in many pictures. The solution of ambiguity is to label joint points of human skeleton by OpenPose, and to train the marked joint point features in Bi-directional Long Short-Term Memory (BiLSTM). The accuracy is about to 96%, that can effectively detect physical abuse in time in the experimental results.

Automatic gear defect detection equipment is relatively expensive, so small and medium-sized enterprises cannot afford the cost of such equipment. Therefore, most companies still use manual inspection methods for gear defect detection. Manual inspection methods not only take a long time but also has uneven detection quality. This paper proposes to use AI technology to build a cheap and fast gear defect detection method. And this method is used to complete the detection of gear tooth profile defects, tooth pitch defects and central hole defects. The method proposed in this paper is divided into four steps. In the first step, the ResNet model [1] is used to classify whether the gear image is complete or not. In the second step, the YOLOv4 model [2] is used to find the rectangular area of the tooth shape and tooth pitch in the image and cut it out. The third step is to use the UNet model [3] to segment the tooth profile and pitch profile, and calculate the area occupied by the profile. Finally, whether the difference from the average area is too large is used as the basis for judging whether the gear is defective. In the experiment result, 186 gear images are used for detection, and the obtained accuracy is about 91%. This result in addition to verifying the feasibility of the proposed method, it is also found that the proposed method can quickly and accurately detect gear defects that are difficult to judge by human eyes.

Many people believe that when drowning occurs, there will be calls for help. In fact, people who are drowning do not get too many splashes or cry for help. They only try to get themselves out of the water by treading on the water. The drowning condition may cause serious brain damage, so it is extremely important to shorten the time it takes to detect the occurrence of drowning and rescue.This paper proposes using computer image processing technology to introduce artificial intelligence motion technology, mounting the camera on the bottom of the swimming pool, and use OpenPose to mark the image joint point features, and input the captured joint point features into the recursive neural network to determine whether the swimmer is drowning. The final training result is about 89.4% accurate, so it can be used to assist on-site lifeguards to detect swimmers who may be drowning, and to reduce incidents that cannot be detected immediately

Stock market has a considerable impact in the whole financial market. Among researches on prediction, stock price movements prediction is a quite hot topic. In this paper, stock price movements were predicted by utilizing various stock information by technical means of deep learning. The architecture based on LSTM using Attention proposed in this paper was proven through experiment to be able to effectively improve prediction accuracy.

This study proposes a new unsupervised approach for targets detection and classification in multispectral Magnetic Resonance (MR) images. The proposed method comprises two processes, namely Target Generation Process (TGP) and Constrained Energy Minimization (CEM). TGP is a fuzzy-set process that generates a set of potential targets from unknown information, and applies these targets to be desired targets in CEM Finally, the real MR images are used in the experiments to evaluate the effectiveness of proposed method. Experiment results reveal that the proposed method segments a multispectral MR image much more effectively than either FMRIB's Automated Segmentation Tool (FAST) or Fuzzy C-means (FC).

The objective of this paper is to establish an "averaged learning subspace method" (ALSM) applicable for classification of multi-spectral MR images. By using the ALSM to process the massive amounts of information in multi-spectral MR images, and classification tissues of brain. The classification result of each tissue has shown by binary image, respectively. The classification results would assist doctor to diagnose more efficiently and more accurately and thus to gain more time for necessary action. In order to further evaluate the performance of ALSM, the high order statistics is adopted assessment and compare with perceptron neural network.

Presently, radiologists used to rely on contrast-injection to acquire the contrast-enhanced breast magnetic resonance imaging (MRI), in order to improve the accuracy of breast cancer screening. Instead of contrast-injection, this paper proposed a spectral signature detection technology, constrained energy minimization (CEM), which could successfully classify breast MRIs into four major tissues (fatty tissue, glandular tissue, tumor and muscle) and show the classified results in high contrast images. After compared with a specific subspace projection operator called orthogonal subspace projection (OSP), the commonly used C-means (CM) algorithm and real contrast-injected breast MRIs, the results show that the high contrast images generated by CEM have superior quality.

Magnetic resonance imaging (MRI) has become a useful modality since it provides unparallel capability of revealing soft tissue contrast as well as 3D visualization. One potential application of MRI in clinical practice is the parenchyma classification and segmentation of normal and pathological tissue. It is the first step to address a wide range of clinical problems. This paper presents a new spectral signature detection approach to magnetic resonance (MR) image classification. It is called the extension (extenics, extension theory), which can separate the blocks efficiently so as to reduce the noise effect upon tissues. This paper has demonstrated satisfactory noise-proof features of extension. A series of experiments is conducted and compared with the commonly used c-means method for performance evaluation. The results show that the Extensions method is a promising and effective technique for MR image classification.

This paper presents a new spectral signature detection approach to magnetic resonance (MR) image classification. It is called constrained energy minimization (CEM) method, which is derived from the minimum variance distortionless response in passive sensor array processing. It considers a bank of spectral channels as an array of sensors where each spectral channel represents a sensor and object spectral signature in multispectral MR images are viewed as signals impinging upon the array. The strength of the CEM lies on its ability in detection of spectral signatures of interest without knowing image background. The detected spectral signatures are then used for classification. The CEM makes use of a finite impulse response (FIR) filter to linearly constrain a desired object while minimizing interfering effects caused by other unknown signal sources. Unlike most spatial-based classification techniques, the proposed CEM takes advantage of spectral characteristics to achieve object detection and classification. A series of experiments is conducted and compared with the commonly used c-means method for performance evaluation. The results show that the CEM method is a promising and effective spectral technique for MR image classification.