This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

Firearm violence is one of the leading causes of death in many countries around the world, including Thailand. This work proposes a fast and accurate automated method to classify firearm brands from bullet markings. Specifically, a panoramic image of a bullet collected from a crime scene was captured using a developed mobile phone application and custom-built portable hardware. The top three state-of-the-art CNNs pretrained on ImageNet-DenseNet121, ResNet50, and Xception-were further trained on the same training set, which was composed of 718 bullets collected from eight different firearm brands-Beretta, Browning, CZ, Glock, Norinco, Ruger, Sig Sauer, and Smith & Wesson-using a five-fold cross validation technique. DenseNet121 provided the highest AUC of 0.99 for CZ classification (the most common registered firearm brand in Thailand) and the highest average AUC for the eight firearm brands (0.9780 ± 0.0130 SD), which was significantly higher than those of ResNet50 and Xception. In addition, there were no interaction effects between the CNN model and firearm brand on AUC. DenseNet121, which had the highest AUC, was evaluated on the test set (72 bullets), and the results showed that the Beretta and CZ classifications had the lowest accuracy (91.18%), followed by the Browning and Norinco classifications (96.88%), whereas the Glock, Ruger, Sig Sauer, and Smith & Wesson classifications had the highest accuracy (98.41%). These results suggest that the developed mobile phone application based on a deep learning algorithm and the custom-built portable hardware have promising potential for use at crime scenes to classify firearms from bullet markings. By narrowing down the list of suspects, this convenient approach can potentially accelerate bullet identification processes for many forensic science examiners. INDEX TERMS Forensic science, automated firearm classification, 9 mm bullet marking, densely connected convolutional network.

This work studies the features of a proposed automated stroke self-screening application that utilizes the gyroscope and accelerometer devices in smartphones to determine the possible onset of a stroke by assessing arm muscle weakness. The application requires users to perform two arm movements to evaluate arm weakness and pronation: Curl-up and Raise-up. For the purpose of the study, 68 subjects, consisting of 36 stroke patients with symptoms of arm weakness and 32 healthy subjects, consented to participate. A total of 78 handcrafted features were proposed, 26 of which were extracted from Curl-up and Raise-up for each arm. Then, the differences between corresponding features for each arm were calculated. These features were then tested on 63 combinations of three classical feature selection methods, three feature sets (i.e., Curl-up-only features, Raise-up-only features, and both-exercises combined features) and seven well-known classification methods. The results from ten runs of 10-fold cross-validation showed that Curlup-only features achieved an average sensitivity of 83.3%, significantly higher than those of the Raise-uponly features or both-exercises features. From all possible combinations, the random forest classification based on information gain feature selection from Curl-up-only features achieved the most efficient results for arm-weakness-stroke screening. It achieved an average sensitivity of 94.8%, an average specificity of 75.2%, an average accuracy of 84.1%, and an average area under the receiver operating characteristic curve of 85.0%. Our work proposes a novel accessible method to screen symptoms of arm weakness that may indicate the onset of a stroke using a single mobile device. In the future, we can combine this method with other methods of evaluating facial drooping and slurred speech to create a complete Face, Arm, Speech, Time (FAST) assessment application.

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Sexual violence is a severe and chronic occurrence around the world that has not been resolved. The stigmatized nature of sexual violence has forced victims and survivors to accept prejudiced accusations cultivated from discriminatory norms when they are never at fault nor responsible for such violations against their sexuality. LAW-U is an Artificial Intelligence (AI) chatbot that gives legal guidance to survivors of sexual violence by recommending the most relevant Supreme Court decisions to the survivors' situations. In Thai, "LAW-U" − pronounced similarly to "รออยู ่ " − means "I will wait for you", which signifies the chatbot's unconditional support to the user. 182 Thai Supreme Court cases of sexual violence, relating to Sections 276, 277, 278, and 279 of the Criminal Code, were used to develop Natural Language Processing (NLP) pipelines for LAW-U. Legal experts then generated mock-up dialogs from Supreme Court decisions which became the conversations used to train LAW-U. The computation of the similarity scores and the calculation of combined percentages of common keywords and keywords' synonyms were completed to increase the model's accuracy. When applying the model to the hold-out testing dataset, the accuracy was 88.89% for an exact match between the user's input and the Supreme Court case − this confirmed that LAW-U was ready for real-life application. LAW-U's unique design hopes to act as a precedent for other works at home and abroad to perpetuate awareness of sexual violence and eliminate any tolerance against these crimes by empowering sexual violence victims and survivors to reaffirm their inherent rights.