This paper proposes a “Zero-Plastic Model” that focuses on the complete elimination and systematic replacement of plastic products. The model represents the deviation from the “reduce, reuse, recycle” framework, emphasizing prevention over treatment and introducing innovative technological solutions for plastic alternatives. The paper illustrates the limitations of existing recycling systems. The paper presents three aspects, including (1) the systematic reduction of plastic dependency through frameworks and behavioral interventions, (2) the development and implementation of sustainable replacement materials, and (3) the integration of technologies to facilitate this transition. The proposed “Zero Plastic Model” establishes sustainability frameworks while introducing a novel approach focused on complete plastic elimination rather than recycling. The framework integrates circular economy principles and draws inspiration from pre-plastic era solutions to achieve zero residual plastic in the system. The research contributes to the growing body of knowledge on sustainable practices and offers a practical framework for organizations and communities seeking to eliminate their plastic dependency.

With the growth of internet usage, countless educational videos are now available online. However, it can be a significant challenge for learners to identify the videos they need, especially in their preferred language and within their available time. Additionally, not all videos are suitable for subject-specific learning due to variations in length and presentation components. According to Sweller’s Cognitive Load Theory, working memory during the learning process is highly limited. Learners must be selective about which information from sensory memory they choose to focus on. In our proposed Co-Learning model (a model of connective learning where all necessary knowledge is refined and interconnected to support effective learning within cognitive limitations), we leverage NLP approaches to enhance the learning experience. These approaches include video speech refinement, subtitle generation, dubbed video translation, summarization, classification, keyword extraction for word cloud indexing, and quiz generation, thereby creating a multilingual, learner-efficient environment. In our preliminary survey, the generated content was well-received and effectively utilized for class adjustments with an acceptance rate of 93%.

Inspired by the study of the human thought process, which is categorized into two systems reflecting the brain’s balancing act between speed and cognition, we propose a dual-process architecture that augments System 1 with additional knowledge akin to System 2 in human cognition. The methodology is demonstrated through FAQ retrieval task, showcasing the potential for human-like cognitive processing. Challenged by current data-driven large language models (LLMs) in reasoning and knowledge depth, this work presents a novel approach to improving conversational understanding. We leverage advanced text analysis to strategically extract key information from FAQs and utilize LLM-generated questions combined with robust semantic similarity metrics to significantly improve the precision of user query matching. The results indicate better semantic understanding and reasoning, offering a promising pathway to advancing LLM capabilities in conversational contexts. The base LLM (SBERT) enhanced with semantic textual similarity using Sentence-BERT (STS-SBERT) achieves a mean Average Precision (mAP) of 0.6165, compared to 0.3600 for SBERT alone. By strategically integrating key sentence extraction during knowledge preparation, generating questions, and applying semantic textual similarity measures, our model achieves a substantial improvement in user query matching precision. However, the activation of semantically aware reasoning (SAR) remains an issue for future research.

Nurses play a crucial role in healthcare, directly influencing the quality of patient care. Facing a global nursing shortage, there is an urgent need for strategies to enhance nursing efficiency and care quality. This foundational study explores an NLP-based approach to determine NANDA nursing diagnoses, leveraging both subjective and objective patient data recorded by nurses. Employing text data similarity analysis and a prototype predictive model, our research aims to refine the nursing assessment process and pave the way for the potential automation of nursing diagnoses. This work highlights the potential of AI to support nursing practices and sets a platform for future research to fully realize AI’s benefits in addressing the challenges posed by the nursing shortage.

Challenged by data-driven AI limitations in reasoning and knowledge depth, this work presents a novel approach for enhanced conversational understanding. We leverage advanced text analysis to strategically extract key information from FAQs, then utilize AI-generated questions and robust semantic similarity metrics to significantly improve user query matching precision. Through the strategic integration of important sentence extraction in knowledge preparation, coupled with question generation and the application of semantic textual similarity measures, our model achieves a substantial improvement in user query matching precision. We propose a dual-system architecture—augmenting System 1 with additional knowledge akin to System 2 in human cognition. The methodology is exemplified through chatbot correction using FAQs, demonstrating the potential for human-like mind processing. Results showcase improved semantic understanding and reasoning, offering a promising path for advancing AI capabilities in conversational contexts.

The increasing elderly population necessitates increased geriatric care. However, a shortage of caregivers leads to a risk of falls and bedsores in the elderly, both of which result in severe injuries. Whilst wearable devices, and vision sensors have been adopted for monitoring. However, these sensors come with limitations, impacting comfort and privacy for the elderly. To address these challenges, non-intrusive sensing devices integrated into the environment offer promising value for continuous elderly activity monitoring. This study uses a panel sensor embedded with four sensors, consisting of two piezoelectric sensors and two pressure sensors. It is placed beneath the mattress. The position classification encompasses five distinct positions: off-bed, sitting, lying in the center, lying on the left side, and lying on the right side. To find the best position for placing the panel, the positions of the panel and the combination of panel sensors positions are evaluated for five-bed positions classification. As a result, the best position for a sensor panel was in the middle of the bed (position No. 3), with an accuracy of 97.12%. This suggests the panel sensor should be placed at 123.5 cm, measured from the top of the bed. Moreover, in the case of placing two-panel sensors, the most effective arrangement comprises placing one-panel sensor placed at the the bed-top (position No. 1) and the other in the middle of the bed (position No. 3), yielding accuracy 99.93%.

We introduced a novel approach to global sign language recognition by leveraging the capabilities of the Editable Mediator. Traditional methods have often been limited to recognizing sign languages from specific linguistic regions, necessitating ad hoc implementation for multilingual regions. Our method aims to bridge this gap by providing a unified framework for recognizing sign languages in various linguistic areas and promoting global communication. At the core of our system is the Editable Mediator, a mechanism that determines the actual sign meaning from various local hand-shape recognitions. Instead of focusing on specific sign language notations, such as HamNoSys, our approach emphasizes the recognition of common primitive actions shared across different sign languages. These primitive actions are recognized by multiple modules, and their combinations are interpreted by the Editable Mediator to determine the intended sign-language message. This architecture not only simplifies the recognition process, but also offers flexibility. By merely editing the Editable Mediator, our system can adapt to various sign languages worldwide without the need for extensive retraining or ad hoc implementation. This innovation reduces barriers to introducing new sign language systems and promotes a more inclusive global communication platform.

The Thammasat AI City distributed platform is a proposed AI platform designed to enhance city intelligent management. It addresses the limitations of current smart city architecture by incorporating cross-domain data connectivity and machine learning to support comprehensive data collection. In this study, we delve into two main areas, that is, monitoring and visualization of city ambient lighting, and indoor human physical distance tracking. The smart street light monitoring system provides real-time visualization of street lighting status, energy consumption, and maintenance requirement, which helps to optimize energy consumption and maintenance reduction. The indoor camera-based system for human physical distance tracking can be used in public spaces to monitor social distancing and ensure public safety. The overall goal of the platform is to improve the quality of life in urban areas and align with sustainable urban development concepts.

This paper describes about project “Data Sensorium” launched at the Asia AI Institute of Musashino University. Data Sensoriumis a conceptual framework of systems providing physical experience of content stored in database. Spatial immersive display is a key technology of Data Sensorium. This paper introduces prototype implementation of the concept and its application to environmental and architectural dataset.

Through technology, it is essential to seamlessly bridge the divide between diverse speaking communities (including the signer (the sign language speaker) community). In order to realize communication that successfully conveys emotions, it is necessary to recognize not only verbal information but also non-verbal information. In the case of signers, there are two main types of behavior: verbal behavior and emotional behavior. This paper presents a sign language recognition method by similarity measure with emotional expression specific to signers. We focus on recognizing the sign language conveying verbal information itself and on recognizing emotional expression. Our method recognizes sign language by time-series similarity measure on a small amount of model data, and at the same time, recognizes emotion expression specific to signers. Our method extracts time-series features of the body, arms, and hands from sign language videos and recognizes them by measuring the similarity of the time-series features. In addition, it recognizes the emotional expressions specific to signers from the time-series features of their faces.

This research proposes an AI platform for data sharing across multiple domains. Since the data in the smart city concept are domain-specific processed, the existing smart city architecture is suffered from cross-domain data interpretation. To go beyond the digital transformation efforts in smart city development, the AI city is created on the architecture of cross-domain data connectivity and transform learning in the machine learning paradigm. In this research, the health and human behavioral data are targeted on human traceability and contactless technologies. To measure the inhabitants quality of life (QoL), the primary emotion expression study is conducted to interpret the emotional states and the mental health of people in the urbanized city. The results of information augmentation draw attention to the immersive visualization of the Thammasat model.

Computer programming is popularized in 21st century education in terms of allowing intensive logical thinking for students. Artificial Intelligent and robotic field is considered to be the most attractive for programming today. However, for the first-time learners and novice programmers, they may encounter a difficulty in understanding the text-based style programming language with its special syntax, sematic, libraries, and the structure of the program itself. In this work, we proposed a visual programming environment for artificial intelligent and robotic application using Google Blockly. The development framework is a web application which is capable of using Google Blockly to create a program and translate the result of visual programming style to conventional text-based programming. This allows almost instant programming capability for learners of programming in such a complex system.

In an extremely fast development of technology era, we are now living in the age of Industry 4.0, the age of realizing Cyber Physical System (CPS). The virtual space being realized by digital space concept will completely merge with our physical dimension in a very near future. Every smart ecosystem could make us more convenient to live. However, this technology could be a severe weapon which is able to damage our life, our assets, organization security, and national sovereignty and could affect the extinction of human kind. We strongly realize this concern and are proposing one of the solutions to secure our life in the next smart world, the Holistic Framework of Using Machine Learning for an Effective Incoming Cyber Threats Detection. We present an effective holistic framework which is easy to understand, easy to follow, and easy to implement a system to protect our digital space in an initial state. This approach describes all steps with the significant modules (I-D-A-R: Idea-Dataset-Algorithm-Result Framework with B-L-P-A: Brain-Learning-Planning-Action concept) and explains all major concern issues for developers. As a result of the I-D-A-R framework, we provide an important key success factor of each state. Finally, a comparison of detection accuracy between using Multinomial Naïve Bayes, Support Vector Machine (SVM) and Deep Learning algorithm, and the application of the feature engineering techniques between Principle Component Analysis (PCA) and Standard Deviation successfully show that we can reduce the computation time by using the proper algorithm that matches with each dataset characteristics while all prediction results still promising.

This study proposes a data labelling scheme for bed position classification task. The labelling scheme provides a set of bed position for the purpose of preventing the bed fall and bedsore injuries which seriously imperil the aging people health. Most of the elderly fall down when they attempt to get out of bed with unassisted bed exit. Also, there is a high possibility of rolling out of bed when an elderly lies close to the edge of the bed. In addition, a bedridden person, who cannot reposition by him/herself, has a high risk of bedsores. Repositioning in every two hours alleviates the prolonged pressure over on the body. We collected the data from a specific set of bed sensor and classified the signal into five positions on the bed, which are off-bed, sitting, lying center, lying left, and lying right. These five positions are the fundamental information for developing a model to capture the movement of the elderly on the bed. The precaution strategy is then able to be designed for the bed fall and bedsore prevention. The data of the five different positions are manually annotated by observing the synchronized video through a specially designed workbench. The combination of the positions of off-bed, sitting, and lying is used to detect a bed exit situation, and the combination of the positions in the lying state, i.e. lying center, lying left, and lying right, is used to detect the rolling out of bed situation. Moreover, to notify for reposition assisting in the bedridden, the three lying positions are used to calculate the time of the abiding position.

In the last few years, Concept Similarity Measures (CSMs) become important for the biomedical ontologies in order to find adaptable treatments from the conceptually similar diseases. For the ontology primitive concepts, they are not fully defined in the ontology so taxonomical path-based similarity measure cannot give the correct similarity for primitive concepts. In this paper, we propose a new primitive concept name similarity measure based on natural language processing to get a better result in concept similarity measure in terms of noun phrase construction analysis. We conduct experiments on the standard clinical ontology SNOMED CT and make comparison between taxonomical path-based measure and our proposed similarity measure against human expert results in order to prove our proposed similarity measure can outperform the existing approaches for primitive concept similarity.

This study proposes bed posture classification using a Neural Network and a Bayesian Network for elderly care. The data are collected in a hospital. The on-bed postures are analyzed into five types, those are, out of bed, sitting, lying down, lying left, and lying right, by using signals from a sensor panel (composed of piezoelectric sensors and pressure sensors). The sensor panel is placed under a mattress in the thoracic area. To eliminate the effect of weight and the bias between different types of sensors, the sensing data are normalized into a range of 0 to 1 by the unity-based normalization (or feature scaling) method. In addition, a Bayesian Network is adopted to estimate the likelihood of consecutive postures. The results from both a Neural Network and Bayesian Network estimation are combined by the weighted arithmetic mean. The experimental results yield the maximum accuracy of posture classification when the coefficient of Bayesian probability and a Neural Network are set to 0.7 and 0.3 respectively.

This paper aims to present the improved techniques to classify the user's feedbacks on hotel service qualities. The data were mainly collected from online feedback sources by PHP program. The training set was manually tagged as: NEGATIVE, POSITIVE, and NEUTRAL. In total, 2969 Vietnamese language terms were successfully collected. In the first part, the common machine learning techniques like K-Nearest Neighbor algorithm (KNN), Decision Tree, Naive Bayes (NB) and Support Vector Machines (SVM) were applying for classification. In the second part, we enhanced the efficiency of the text categorization by applying feature selection techniques, χ2 (CHI). At the end of the paper, we concluded that the overall performance of general machine learning techniques was significantly improved by applying feature selection.

Dengue fever is a communicable disease that attacks more than 120 countries in the world during 50 years. Therefore, it is to make sense to say that collaboration among the countries, especially neighborhood countries, is one important key to combat the dengue. Currently, except a serological collaboration, collaboration in dengue is sporadic and temporal. This paper addresses the initiative to build vector-control strategy collaborative among Surabaya (Indonesia), Kuala Lumpur (Malaysia), and Bangkok (Thailand). Deriving the global policy from World Health Organization (WHO), we build the system that (1) extracting global feature from the local feature, (2) selecting the significant features, to determine ranking of importance of a feature, by weighting a feature, and (3) matching the pattern of data to the suitable strategy by measuring the similarity. We built the system from the real data of the Surabaya, Kuala Lumpur and Bangkok in 2012. We verified reliability of the system by comparing the data with the actual action in January 2012 The result shows that the system is system feasible to be implemented, however we still need more preparation to implement the system.

Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. In this environmental study, we will use context-dependent differential computation to analyse changes in various factors (temperatures, colours, level of CO2, habitats, sea levels, coral areas, etc.). In this paper, we will discuss a global environmental computing methodology for analysing the diversity of nature and animals, using a large amount of information on global environments.

Text from social media is significant key information to understand social movement. However, the length of the social media text is typically short and concise with a lot of absent words. Our task is to identify the proper keyword representing the message content that we are accounting for. Instead of training the model for keyword extraction directly from the Twitter messages, we propose a new method to fine-tune the model trained from some known documents containing richer context information. We conducted the experiment on Twitter messages and expressed in word cloud timeline. It shows a promising result.