Anomaly detection is of great significance for intelligent surveillance videos. Current works typically struggle with object detection and localization problems due to crowded and complex scenes. Hence, we propose a Deep Spatiotemporal Translation Network (DSTN), novel unsupervised anomaly detection and localization method based on Generative Adversarial Network (GAN) and Edge Wrapping (EW). In training, we use only the frames of normal events in order to generate their corresponding dense optical flow as temporal features. During testing, since all the video sequences are input into the system, unknown events are considered as anomalous events due to the fact that the model knows only the normal patterns. To benefit from the information provided by both appearance and motion features, we introduce (i) a novel fusion of background removal and real optical flow frames with (ii) a concatenation of the original and background removal frames. We improve the performance of anomaly localization in the pixel-level evaluation by proposing (iii) the Edge Wrapping to reduce the noise and suppress non-related edges of abnormal objects. Our DSTN has been tested on publicly available anomaly datasets, including UCSD pedestrian, UMN, and CUHK Avenue. The results show that it outperforms other state-of-the-art algorithms with respect to the frame-level evaluation, the pixel-level evaluation, and the time complexity for abnormal object detection and localization tasks.

Regular screening and timely treatment play a crucial role in addressing the progression and visual impairment caused by cataracts, the leading cause of blindness in Thailand and many other countries. Despite the potential for prevention and successful treatment, patients often delay seeking medical attention due to the gradual and relatively asymptomatic nature of cataracts. To address this challenge, this research focuses on the identification of cataract abnormalities using image processing techniques and machine learning for preliminary assessment. The LeNet-convolutional neural network (LeNet-CNN) model is employed to train a dataset of digital camera images, and its performance is compared to the support vector machine (SVM) model in categorizing cataract abnormalities. The evaluation demonstrates that the LeNet-CNN model achieves impressive results in the testing phase. It attains an accuracy rate of 96%, exhibiting a sensitivity of 95% for detecting positive cases and a specificity of 96% for accurately identifying negative cases. These outcomes surpass the performance of previous studies in this field. This highlights the accuracy and effectiveness of the proposed approach, particularly the superior performance of LeNet-CNN. By utilizing image processing technology and convolutional neural networks, this research provides an effective tool for initial cataract screening. Patients can independently assess their eye health by capturing self-images, facilitating early intervention and medical consultations. The proposed method holds promise in enhancing the preliminary assessment of cataracts, enabling early detection and timely access to appropriate care.

This work aims to develop an elderly assistance system using voice commands by applying speech recognition technology. The system is designed to enhance convenience for elderly individuals and also reduce labor costs associated with hiring caregivers. It leverages the principles of the Internet of Things (IoT) to control various household electrical devices through voice commands. Through Natural Language Processing (NLP), the system processes natural language to interpret voice commands, allowing users to control lighting, open applications, make phone calls, and more. The researchers utilized a Confusion Matrix model to measure the accuracy of the voice command recognition system. It assessed whether the system correctly interpreted the voice commands issued by users. The testing, conducted with a group of individuals aged 55-60, revealed an accuracy rate of 85%. This demonstrates the system’s effectiveness and potential to augment the independence of elderly individuals. In essence, this work presents a voice-command assistance tool that combines speech recognition and IoT, promising enhanced convenience for the elderly and potential savings in caregiving expenses.

Currently, waste management is one of the most critical issues in In this research, factors influencing the acceptance of digital technology in the digital herbal platform and innovative health data management for healthcare in Thailand were thoroughly investigated. The focus was on individuals interested in herbal treatment and alternative healthcare, including the younger generation unfamiliar with herbal usage. The main objective was to develop a central platform that encompasses herbal distribution, supports community participation, and promotes online medical tourism. The analysis of data from users interested in herbal remedies and alternative healthcare enabled us to apply Inbound Marketing technology in developing the "Digital Herbal Hub." This platform emphasizes convenience and inclusivity for all genders and ages, aiming to promote online medical tourism in Thailand.

In the contemporary era, individuals are actively seeking spaces or platforms aligned with their interests and hobbies. Presently, there is a notable interest in pet-related activities, and we have observed a growing trend in this regard. Recognizing this opportunity, our group is cognizant of the potential to create a centralized platform for the community interested in pets. The PetHub application originated from the collaborative ideas of group members who have developed it further based on their shared passion and interest in pets. The primary objective of the application is to establish a space for discussion and communication, offering a plethora of features for individuals who love and are interested in pets. PetHub aims to go beyond the conventional role of a platform solely focused on pets. It aspires to create a diverse and high-quality community, regardless of whether users are experienced pet owners or general pet enthusiasts. The application seeks to create a space where everyone can connect, share, and learn about pets in detail. With a focus on promoting love for pets collectively, PetHub aims to be a platform for building a valuable community that encompasses people's affection for animals.

This research aims to design a system that assists entrepreneurs in the herbal medicine industry to optimize resource utilization during the production process, both in terms of cost and time. The proposed system aggregates essential data in the production process, aiding decision-making for entrepreneurs. Additionally, it aims to contribute to income distribution among herb cultivators and provide valuable information to the public interested in various herbal medicines. A questionnaire survey, involving 400 participants, was conducted to gather opinions and insights. The Unified Theory of Acceptance and Use of Technology (UTAUT) was employed to enhance the efficiency of measurement. The majority of respondents were female (66.5%) and aged between 21--30 years (56.75%). The collected data were analyzed using SmartPLS V.4 software with Consistent PLS-SEM bootstrapping.