Tho T. Quan

• Ho Chi Minh City University of Technology

Lithium‐ion batteries are widely used in moving devices due to their many advantages compared to other battery types. The prevalence of Lithium‐ion batteries is evident, playing its clear role in the operation of small devices as well as large systems such as electric vehicles, flying devices, mobile devices, and more. Monitoring lithium‐ion batter...

This paper focuses on multimodal alignment within the realm of Artificial Intelligence, particularly in text and image modalities. The semantic gap between the textual and visual modality poses a discrepancy problem towards the effectiveness of multi-modalities fusion. Therefore, we introduce Text-Image Joint Embedding Predictive Architecture (TI-J...

Objective: Electronic Health Records (EHRs) provide information to explore those at risk of various diseases, though studying entire populations is limited by data availability, potentially introducing biases. We compared different samples, varied by type of hospital contact, to assess the impact on missing data and model results. Materials and Met...

With the rapid advancement of Artificial Intelligence, particularly in Natural Language Processing, Large Language Models (LLMs) have become pivotal in educational question-answering systems, especially university admission chatbots. Concepts such as Retrieval-Augmented Generation (RAG) and other advanced techniques have been developed to enhance t...

Retrieving events from videos using text queries has become increasingly challenging due to the rapid growth of multimedia content. Existing methods for text-based video event retrieval often focus heavily on object-level descriptions, overlooking the crucial role of contextual information. This limitation is especially apparent when queries lack s...

The diagnosis of knee osteoarthritis is challenging due to its complex nature and various contributing factors. With the advancement of artificial intelligence (AI) technology, some computer vision-based methods have been developed to address this task. However, when applied in practice, these methods encounter numerous challenges. Training a power...

Text-based VQA is a challenging task that requires machines to use scene texts in given images to yield the most appropriate answer for the given question. The main challenge of text-based VQA is exploiting the meaning and information from scene texts. Recent studies tackled this challenge by considering the spatial information of scene texts in im...

Large Language Models (LLMs) have recently advanced many applications on software engineering tasks, particularly the potential for code generation. Among contemporary challenges, code generated by LLMs often suffers from inaccuracies and hallucinations, requiring external inputs to correct. One recent strategy to fix these issues is to refine the...

This research introduces XGA-Osteo, an innovative approach that leverages Explainable Artificial Intelligence (XAI) to enhance the accuracy and interpretability of knee osteoarthritis diagnosis. Recent studies have utilized AI approaches to automate the diagnosis using knee joint X-ray images. However, these studies have primarily focused on predic...

Vulnerable code continues to have a significant impact to software quality, leading to serious consequences such as economic loss, privacy breaches, and threats to national security. Traditional methods of detecting and addressing software security issues are often time-consuming and resource-intensive. This research aims to examine the effectivene...

Bài báo này trình bày về việc phát triển một Trợ lý Ảo Thông minh cho lĩnh vực giáo dục. Trợ lý ảo này được phát triển dựa trên các kỹ thuật AI tiên tiến nhất hiện nay bao gồm các Mô hình Ngôn ngữ Lớn (LLMs), Đồ thị Tri thức (Knowledge Graph) và các kỹ thuật RAG (Retrieval Augmented Generation). Trước tiên, chúng tôi thảo luận việc xây dựng KG từ d...

Paraphrases are texts that convey the same meaning while using different words or sentence structures. It can be used as an automatic data augmentation tool for many Natural Language Processing tasks, especially when dealing with low-resource languages, where data shortage is a significant problem. To generate a paraphrase in multilingual settings,...

The Bahnar, a minority ethnic group in Vietnam with ancient roots, hold a language of deep cultural and historical significance. The government is prioritizing the preservation and dissemination of Bahnar language through online availability and cross-generational communication. Recent AI advances, including Neural Machine Translation (NMT), have t...

Legal Statute Identification (LSI) is a critical task within the realm of law, involving the identification of relevant statutory laws based on the natural language descriptions found in legal documents. Traditionally, this challenge has been approached as a single-class text classification problem. However, due to the inherent complexity of legal...

Subgraph matching is a challenging problem with a wide range of applications in drug discovery, social network analysis, biochemistry, and cognitive science. It involves determining whether a given query graph is present within a larger target graph. Traditional graph-matching algorithms provide precise results but face challenges in large graph in...

Presents corrections to the paper, (Corrections to “An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network”).

High-Utility Itemset Mining (HUIM) is designed to discover sets of itemsets that can bring high profits from the database. However, HUIM encounters several challenges in picking a suitable minimum utility threshold for each database. A class of algorithms that select the top-k itemsets based on their utility has been proposed to address this issue....

This paper introduces Legar, an innovative Legal Statute Identification system designed to cater to Vietnamese users seeking legal guidance on land law matters. Legar focuses on practical efficiency by harnessing the digital capabilities of the Vietnam Landlaw 2013. This is achieved through the utilization of a specialized legal-masked language mod...

Context Generative Artificial Intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAI's ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Objective. Although many recent publications have explore...

Knee osteoarthritis is one of the most common joint diseases. Many studies have explored automated diagnosis using artificial intelligence, but their results are unsatisfactory when tested on real-world data for Vietnamese patients. Therefore, in this article, we propose a high-performing model based on our datasets, namely DIKO. Unlike other metho...

The problem of predicting key performance indicators in mobile networks has had impacts on improving resource utilization with powerful applications of machine learning and deep learning. Based on these forecasts, telecommunications network operators can be proactive in allocating resources or preventing incidents that affect key performance. Howev...

Lithium-ion batteries are rechargeable and have been used widely in mobile phones and even autonomous vehicles because of their lightweight and high energy density. Therefore, predicting the State-of-Health (SoH) of a battery becomes significant and challenging because of its capacity regeneration. Artificial intelligent methods, especially long sh...

Work orders in the maintenance field and in general resource-constrained problems have integrated multi-objective scheduling in order to produce an efficient job routine. One of the current drawbacks of today’s scheduling apparatuses often introduces conflicting schema and illogical arrangements. In this research, non-dominated sorting genetic algo...

Transforming data into appropriate formats is crucial because it can speed up the training process and enhance the performance of classification algorithms. It is, however, challenging due to the complicated process, resource-intensive and preserved meaning of the data. This study proposes new approaches to building knowledge representation models...

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing approaches for identifying maximal subgraphs typically rely on traversing a graph lattice. How...

Ozone depletion has always been a hot crisis around the globe. Its consequence is the increase in ultraviolet radiation at the surface in many regions and countries, which then causes danger to the human immune system, eyes, and especially skin - the part that is directly exposed most to the sunlight. According to the World Health Organization, the...

5G is the fifth generation of cellular networks and has been used in a lot of different areas. 5G often requires sudden rises in power consumption. To stabilize the power supply, a 5G system requires a lithium-ion battery (LIB) or a mechanism called AC main modernization to provide energy support during the power peak periods. The LIB approach is t...

Abstract—The importance of online social networks (OSNs) has been fueled by the human need for digital communication and broadcasting, as well as the improved state of internet connections and electronic devices. Meanwhile, social bots have been designed to automatically replicate the behavior of legitimate users in order to manipulate these OSNs....

Natural Language Processing (NLP) is one of the major branches in the emerging field of Artificial Intelligence (AI). Classical approaches in this area were mostly based on parsing and information extraction techniques, which suffered from great difficulty when dealing with very large textual datasets available in practical applications. This issue...

In recent years, the application of chatbots evolved rapidly in numerous fields and received increasing attention in the academic and industrial communities. In this paper, we present a novel chatbot framework based on machine learning and deep learning approaches. Our framework not only answers the domain questions but also consists of three prima...

For humans, the COVID-19 pandemic and Coron- avirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interac...

Nowadays, with the undeniable development of 5G technologies, Advanced Artificial Intelligence (AAI) systems using IoT sensors have been widely deployed in many different applications. The emerging requirement on these platforms is the identification of real situations (or contexts) based on time-series information given by sensors, which are ideal...

Speech synthesis, which aims to generate natural and comprehensible speech from input text, is a popular research topic with a wide range of industrial applications. However, it appears to be a difficult problem due to its strong dependency on data, particularly for accent-sensitive and multi-dialect languages, e.g. Vietnamese. Perhaps the most com...

Covid-19 is a global disaster that needs computing power to analyze, predict and interpret. So far, there have been several models doing the job. With a huge amount of daily data, deep learning models can be trained to achieve highly accurate forecasts but their mechanism lacks explainability. Epidemiological models, e.g. SIR, on the other hand, ca...

Hydrological drought forecasting is a key component in water resources modeling as it relates directly to water availability. It is crucial in managing and operating dams, which are constructed in rivers. In this study, multiple extreme learning machines (ELMs) are utilized to forecast hydrological drought. For this purpose, the standardized hydrol...

In the current era, the amount of information from the Internet in general and the electronic press in particular has increased rapidly and has extremely useful information value in all aspects of life, many popular users have posted several high-quality writings as casual blogs, notes or reviews. Some of them are even selected by editors to be pub...

From the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people’s...

With the rapid development of social media channels, many popular users have posted several high-quality writings as casual blogs, notes or reviews. Some of them are even selected by editors to be published in professional venues. However, the original posts often come without titles, which are needed to be manually added by the editing teams. This...

In recent years, lithium-ion batteries (LIB) have been used widely in portable electronic devices because of their advantages of durability, stability, high-capacity, low-cost, light-weight and smallscale. It makes LIB also deployed in various complex systems, in which efficient prediction of battery data, especially state-of-health (SoH), becomes...

State-of-Health (SOH) prediction of a Lithium-ion battery is essential for preventing malfunction and maintaining efficient working behaviors for the battery. In practice, this task is difficult due to the high level of noise and complexity. There are many machine learning methods, especially deep learning approaches, that have been proposed to add...

Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we attempt to address this issue by...

This study examines rainfall forecasting for the Perfume (Huong) River basin using the machine learning method. To be precise, statistical measurement indicators are deployed to evaluate the reliability of the actual accumulated data. At the same time, this study applied and compared two popular models of multi-layer perceptron and the k -nearest n...

Wireless sensor networks (WSNs) are important tools to monitor various events happening in specific environments. In WSNs, regarding the network size and number of sensor nodes, several mobile sink nodes can be used to collect the monitored data. In such cases, having a clustered WSN helps to get the sensed data more efficiently to the sink nodes....

Non-dominated sorting genetic algorithm II (NSGA-II) is introduced as a powerful variant of genetic algorithm because it alleviates computational complexity and removes sharing parameter in comparing to other multiobjective evolutionary algorithms (MOEAs). Master-slave, island model and diffusion model are three approaches to parallel MOEAs. Howeve...

From the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people’s...

The increasing number of security attacks have inspired researchers to employ various classifiers, such as support vector machines (SVMs), to deal with them in Intrusion detection systems (IDSs). This paper presents a comprehensive study and investigation of the SVM-based intrusion detection and feature selection systems proposed in the literature....

Object coreference resolution is used in sentiment analysis to identify sentiment words referring to an aspect of an object in a document. However, this poses a challenge in natural language processing and is consequently an area of ongoing research. Further, to the best of our knowledge, object coreference resolution with more than one object has...

The Unified Medical Language System, or UMLS, is a repository of medical terminology developed by the U.S. National Library of Medicine for improving the computer system’s ability of understanding the biomedical and health languages. The UMLS Metathesaurus is one of the three UMLS knowledge sources, containing medical terms and their relationships....

Identification of license plates on intermodal containers (or containers) while entering and departing from the yard provides a wide range of practical benefits, such as organizing automatic opening of the rising arm barrier at the entrance and exit to and from the site. In addition, automatic container code recognition can also assist in thwarting...

This paper discusses detection of brand crisis on online social media, i.e. when a brand is being suffered from unexpectedly high frequency of negative comments on online channels such as social networks, electronic news, blog and forum. In order to do so, we combined the usage of probabilistic model for burst detection with ontology-based aspect-l...

A prosthesis is an equipment provided to people who lost one or some parts of their limbs to help them having almost normal behaviors in daily or hard activities. The convenience and intelligence of devices should create easiness and flexibility for users. Artificial devices require inter-disciplinary collaboration from neurosurgeons, surgical surg...

Most of the information on the Internet is represented in the form of microtexts, which are short text snippets such as news headlines or tweets. These sources of information are abundant, and mining these data could uncover meaningful insights. Topic modelling is one of the popular methods to extract knowledge from a collection of documents; howev...

Network alignment, the problem of identifying similar nodes across networks, is an emerging research topic due to its ubiquitous applications in many data domains such as social-network reconciliation and protein-network analysis. While traditional alignment methods struggle to scale to large graphs, the state-of-the-art representation-based method...

Network alignment, the problem of identifying similar nodes across networks, is an emerging research topic due to its ubiquitous applications in many data domains such as social-network reconciliation and protein-network analysis. While traditional alignment methods struggle to scale to large graphs, the state-of-the-art representation-based method...

Cloud-edge computing is a hybrid model of computing where resources and services provided via the Internet of Things (IoT) between large-scale and long-term data informs of the cloud layer and small-scale and short-term data as edge layer. The main challenge of the cloud service providers is to select the optimal candidate services that are doing t...

Purpose This paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge. Design/methodology/approach We deployed the emerging deep lea...

Class-association Rules (CARs) mining is a knowledge discovery technique with many practical applications. One of the extensions of mining CARs algorithm is to combine information about data classes to derive rules between item and class. However, in the class-imbalance field, it is difficult to mine the rules related to minor classes. One of the s...

Author gender detection (AGD) is a serious and crucial issue in Internet security applications, in particular in email, messenger, and social network communications. Detecting the gender of communication partner helps preventing massive fraud and abuses happening through social media such as email, blogs, forums. Text and writings of people on the...

Battery module safety is a major concern for the commercial success of electric vehicles (EVs). Concurrently, its also important to have a mechanically sound and ergonomically fit battery pack design. To solve this problem, a Hybrid Multi‐Output‐Predictive Modelling based NSGA II approach is proposed. In this approach, the multiple predictive model...

Recommendation systems are powerful tools that can alleviate system overload problems by recommending the most relevant items (contents) to users. Recommendation systems allow users to find useful, interesting items from a significantly large space and also enhance the user’s browsing experience. Relevant items are determined by predicting user’s r...

Automatic classification of virus instances into a concept hierarchy has been attracting much attention from malware research community. However, it is definitely not a trivial work, because malwares usually come in binary forms whose actions are complicated and obfuscated. Therefore, the typical data mining approaches based on feature extraction a...

During the last two decades, we easilly see that the World Wide Web's link structure is modeled as the directed graph. In this paper, we will model the World Wide Web's link structure as the directed hypergraph. Moreover, we will develop the PageRank algorithm for this directed hypergraph. Due to the lack of the World Wide Web directed hypergraph d...

A prosthesis is equipment provided to people who lost one or some parts of their limbs to help them having almost normal behaviors in daily or hard activities. Convenience and intelligence of devices should create easiness and flexibility for users. Artificial devices require interdisciplinary collaboration from neurosurgeons, surgical surgeons, ph...

Aspect-based Opinion Summary (AOS), consisting of aspect discovery and sentiment classification steps, has recently been emerging as one of the most crucial data mining tasks in e-commerce systems. Along this direction, the LDA-based model is considered as a notably suitable approach, since this model offers both topic modeling and sentiment classi...

Most of the information on the Internet is represented in the form of microtexts, which are short text snippets like news headlines or tweets. These source of information is abundant and mining this data could uncover meaningful insights. Topic modeling is one of the popular methods to extract knowledge from a collection of documents, nevertheless...

Entity co-reference resolution and sentiment analysis are independent problems and popular research topics in the community of natural language processing. However, the combination of those two problems has not been getting much attention. Thus, this paper susgests to apply knowledge base to solve co- reference between object and aspect with sentim...

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied...

Aspect-based Opinion Summary (AOS), consisting of aspect discovery and sentiment classification steps, has recently been emerging as one of the most crucial data mining tasks in e-commerce systems. Along this direction, the LDA-based model is considered as a notably suitable approach, since this model offers both topic modeling and sentiment classi...