Tạp chí Khoa học Lạc Hồng - Chuyên sang kỹ thuật
Tạp chí của Trường Đại học Lạc Hồng
12. Automated detection of concrete spalling in post-earthquake structures using deep learning
Post-earthquake structural assessment is critical in determining the extent of damage and guiding emergency response efforts. Spalling, characterized by the detachment of concrete layers, serves as a key indicator of seismic damage and can significantly impact structural integrity. This study develops an automated classification model utilizing deep learning to distinguish between spalling and non-spalling cases in concrete structures. The proposed method employs transfer learning with ResNet50 and EfficientNet-B3 to optimize accuracy and inference efficiency. The dataset, collected from real-world post-earthquake reconnaissance, consists of high-resolution images categorized into spalling and non-spalling classes. Key preprocessing techniques, including pixel normalization, data augmentation, and class balancing, were applied to improve model robustness and mitigate class imbalance issues. Performance evaluation showed that ResNet50 outperforms EfficientNet-B3 in overall accuracy (77% vs. 71%), while EfficientNet-B3 achieved higher recall (90% vs. 85%), making it more sensitive to detecting spalling cases. The study highlights the challenges posed by dataset variability and proposes future enhancements such as advanced augmentation, multi-modal data integration, and self-supervised learning. The findings contribute to the advancement of AI-driven structural health monitoring, offering an efficient tool for rapid post-disaster damage assessment.
11. Application of physics-informed neural networks in simulating heat transfer and mass diffusion
This paper presents a novel approach to simulating classical physical phenomena-specifically heat transfer and mass diffusion-using Physics-Informed Neural Networks (PINNs), a class of deep neural networks that incorporate physical constraints. Unlike conventional machine learning models, PINNs allow the integration of empirical data with partial differential equations (PDEs) governing the underlying physical systems. This results in models capable of making accurate predictions even in the presence of incomplete or noisy data. The study constructs and trains PINN models for two canonical problems: heat conduction in a one-dimensional (1D) rod and concentration diffusion in a closed medium. Simulation results demonstrate that the PINNs achieve significantly lower prediction errors compared to standard neural networks without physical constraints, while also exhibiting strong generalization capabilities and numerical stability. This method offers a promising new direction for simulating physical processes, particularly in scenarios where real-world data are limited-making it well-suited for applications in education, engineering, and scientific research.
A bibliometric study for mapping the Metaverse of Global research patterns and Country level differences
Metaverse research has gained global attention as a multidisciplinary field that brings together technology, media, and human interaction. This study presents a bibliometric analysis of Metaverse-related publications from 2012 to 2021, using data from the Scopus database and visualization tools such as VOSviewer to identify research trends, keyword associations, and patterns of international collaboration. The findings show that the field is growing rapidly, with the United States, China, and Germany leading in publication output. Virtual reality is identified as the most frequently studied topic. This study offers a concise overview of current research and provides direction for future academic and technological progress in the Metaverse.
Artificial Intelligence: A catalyst for sustainable education and enhanced learning quality
Artificial Intelligence (AI) is increasingly playing a central role in reshaping industries, economies, and social life, directly impacting the foundations of sustainable development as well as the education system. The application of AI across various sectors has led to significant advancements in work efficiency and decision-making processes. However, alongside these benefits, there are concerns regarding the ethical, social, and environmental consequences that this technology may bring. This paper analyzes how AI can be leveraged as a tool to support the achievement of the United Nations' Sustainable Development Goals (SDGs), with a focus on the education sector. Through three case studies, the paper examines the dual role of AI – both as a driver of innovation and a source of new challenges – and offers insights into policy-making, trade direction, and workforce training. The conclusion suggests that, with proper guidance, AI can become a key factor in advancing global sustainable development and significantly contribute to the reform of education, preparing learners to adapt to a continuously changing world.
Imbalanced data classification using random forest with Ward clustering
This study introduces a Modified Balanced Random Forest algorithm to improve classification performance on imbalanced datasets. The proposed method enhances the Balanced Random Forest by applying a clustering based under sampling strategy during each bootstrap iteration. Four clustering methods were evaluated including K Means, Spectral Clustering, Agglomerative Clustering, and Ward Hierarchical Clustering. Among these, the Ward Hierarchical Clustering technique achieved the best performance. Experimental results show that the proposed method outperforms standard Random Forest and Balanced Random Forest, reaching a true positive rate of 93.42 percent, a true negative rate of 93.60 percent, and an area under the curve accuracy of 93.51 percent, while also reducing processing time. These results confirm the effectiveness of the proposed approach for imbalanced data classification.
Research and development of a method to select the segmentation algorithm for the problem of detecting irregularities in time series data
Research and Enhancement of Segmentation Methods for Anomaly Detection in Time Series Data. This paper presents a study and improvement of segmentation techniques applied to the problem of anomaly detection in time series data. The work outlines the process of collecting and constructing essential datasets, including both periodic and non-periodic time series, along with a detailed description of the SWAT 2019 dataset. Subsequently, the paper delves into the segmentation process, focusing on the extraction of anomalous segments and the evaluation of experimental results. Finally, the study discusses the selection of the maximum allowable error (max_error), a critical parameter for optimizing the segmentation process and improving the performance of anomaly detection.
6. Applying machine learning and computer vision for Planogram compliance evaluation in retail environments
This paper presents a novel approach for automated planogram compliance assessment in retail environments, with a focus on the Vietnamese market. Addressing the limitations of manual inspection methods—which are time-consuming, error-prone, and difficult to scale—the proposed system integrates recent advances in computer vision and machine learning. Specifically, the method leverages state-of-the-art object detection models, YOLOv11 and YOLOv12, trained on annotated shelf images collected from real retail settings. Detected products are spatially organized using the DBSCAN clustering algorithm, while the Hungarian algorithm is employed to match detected layouts with predefined planograms and compute compliance scores. Experimental results demonstrate high detection accuracy and reliable compliance evaluation, even under complex retail conditions. The combination of advanced YOLO models with spatial reasoning techniques proves effective in handling challenges unique to the Vietnamese retail landscape, such as inconsistent shelf organization and varied packaging. This work contributes a scalable, accurate, and practical solution for enhancing retail execution and operational efficiency.
Applying clustering methods to classify customers based on shopping behaviour
Customer segmentation is crucial for optimizing marketing strategies. This study applies and compares the effectiveness of three common clustering algorithms: K-Means, Hierarchical Clustering, and Gaussian Mixture Models (GMM) to classify customers based on shopping behavior and demographics (age, gender, total spending). Utilizing three retail datasets (two from Kaggle, one from Sling Academy), the research performs data preprocessing, applies the clustering algorithms, and evaluates their performance using Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The results indicate that GMM performs most effectively for segmenting based on total spending and gender, creating distinct clusters. Hierarchical Clustering proves suitable for detailed age-based analysis on specific datasets, while K-Means offers a balanced solution, particularly effective when cluster structures are clear or rapid results are needed. The study recommends selecting appropriate algorithms based on specific business objectives and data characteristics, enabling businesses to develop more effective personalized marketing strategies.
Application of MediaPipe and SVM in sign language recognition to support the hearing impaired
Sign language serves as an essential means of communication for the hearing-impaired community. However, access to and adoption of sign language in Vietnam remain limited due to a lack of resources and supporting tools. To address this issue, this study proposes a Vietnamese Sign Language recognition system that combines MediaPipe technology with the Support Vector Machine (SVM) classification algorithm. The training dataset is constructed from hand gesture images, with MediaPipe responsible for detecting and extracting hand landmark features. These features are then classified using the SVM model. Experimental results demonstrate that the system achieves an accuracy rate between 85% and 90%, confirming its potential to support communication for hearing-impaired individuals through sign language recognition technology.
Improving accuracy in facial emotion recognition through PCA and Artificial Neural Networks
This research addresses the challenge of accurately identifying human emotions through facial expressions using Principal Component Analysis (PCA) combined with Artificial Neural Networks (ANN). The method involves preprocessing facial images, extracting critical features using PCA, and classifying emotional states with ANN. We utilized two standard facial expression datasets JAFFE and FEI for evaluation, focusing on basic emotions: happiness, sadness, surprise, and neutrality. Experimental results demonstrated that the proposed PCA-ANN approach achieved average accuracy rates of 96.3% on JAFFE and 93.8% on FEI datasets, outperforming several traditional methods in terms of computational efficiency and classification accuracy. Despite limitations concerning dataset size and emotion diversity, this research contributes to developing robust systems for real-world applications such as interactive technologies, assistive communication, and security systems. Future directions include expanding emotion recognition capabilities and integrating multimodal data for improved accuracy.
Feasibility and performance study of LLMS on mobile devices for supporting C++ programming learning
Learning C++ programming is a complex process that requires mastering both syntax and algorithmic thinking. This study aims to evaluate the feasibility of deploying large language models (LLMs) on mobile devices to support users in learning C++ more effectively. The research involved testing models such as DeepSeek-Coder, Llama, and Gemma, and applying optimization techniques like 4-bit and 8-bit quantization to reduce hardware resource consumption. Experiments measured model accuracy on C++ tasks, memory usage (VRAM, RAM), and inference speed under different optimization levels. Results showed that DeepSeek-Coder-1.3B achieved the highest accuracy among mobile-friendly models, solving around 40% of C++ problems with 3.2GB of VRAM—suitable for smartphones. Meanwhile, DeepSeek-V2-Lite-Instruct (4-bit) reached 64% accuracy but consumed 6GB VRAM, making it more appropriate for laptops. After quantization, the model ran stably on devices such as the Samsung A52S (8GB RAM), requiring approximately 1.9GB of system RAM (excluding OS usage), which ensures acceptable performance on mid-range mobile devices. The findings confirm that deploying LLMs on mobile platforms is feasible and holds significant potential in supporting programming education. In the future, the research team will continue to optimize performance and improve the user interface to enhance the overall learning experience.
Automated orange quality classification using convolutional neural networks: A deep learning approach for smart agriculture
Quality control is the core activity of an agribusiness and food processing industry just to make sure that the customers have access to quality oranges in a reduced wastage system. This study molds a deep learning idea to classify oranges as either good or bad. These images capture critical features such as consistency of color, surface texture, and apparent defects. Brightness adjustments, enhanced contrasts, and even the addition of some noise are among the possible scenes to improve model generalization error performance. The proposed system would give an automated and scalable real-time orange grading system that would gradually reduce the influence of time-based human inspection practices and improve quality. The finding that even a simple CNN without any pre-train models can be used to achieve high accuracy in this classification task indeed, the results provide for deep learning to be effective in fruit sorting, with scope for much else based on larger data sets, as well as real-world deployment potential.
Predicting employee attrition using machine learning approaches
Employee attrition poses a critical challenge to organizations, both in terms of financial costs and operational continuity, with the average replacement cost per hire estimated at USD 4,129 and a reported attrition rate of 57.3% in 2021. This study applies machine learning techniques to predict employee attrition and identify its primary organizational drivers. Four supervised learning models were evaluated, Support Vector Machine (SVM), Support Vector Machine (LR), Decision Tree Classifier (DTC), and Extra Trees Classifier (ETC), in which the optimized ETC achieving the highest prediction accuracy of 93%, surpassing existing state-of-the-art methods. An Employee Exploratory Data Analysis (EEDA) revealed that monthly income, hourly rate, job level, and age are key factors influencing attrition. These findings highlight the effectiveness of AI-driven approaches in workforce analytics and provide actionable insights for organizational leaders aiming to improve retention through data-informed strategies.
Application of AI technology in safety monitoring and warning at Secondary schools
The research "application of ai technology in safety monitoring and warning at secondary schools" aims to develop a system for monitoring and ensuring railing safety in secondary schools by integrating Artificial Intelligence (AI) with IoT sensors. The system is designed to detect potential risks of students falling from railings and provide timely alerts to minimize accidents. The research methodology involves utilizing an AI camera for image recognition, a motion sensor to detect movement, and a distance sensor to measure the gap between individuals and the railing. Additionally, the system integrates a warning speaker and sends alert messages to mobile phones in hazardous situations. Data is collected and processed using an image recognition algorithm with an accuracy of over 80%, combined with sensor signals to determine safe or unsafe conditions. The motion sensor operates with an accuracy of 92%, while the distance sensor has an error margin of less than 5%. Experimental results indicate that the system achieves an image recognition accuracy of 85% in good lighting conditions and 78% in low-light environments. The findings demonstrate that the system effectively detects risks and issues timely warnings, paving the way for further research and applications in public spaces to enhance user safety.
Improving facial expression recognition through PCA and LBP with SVM classifier
This paper proposes a facial expression recognition method using a combination of Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms for feature extraction from facial images. Experiments were conducted on two datasets: the Japanese Female Facial Expression (JAFFE) database and the Cohn-Kanade Extended (CK+) database. Support Vector Machine (SVM) was used as the primary classifier, compared to Euclidean distance (L2), to evaluate the performance of the classification methods. The experimental results show that the combination of PCA and SVM achieves a recognition rate of 87% on the JAFFE database and 81% on the CK+ database, with the difference attributed to the complexity and diversity of the CK+ dataset. The study indicates that the PCA and LBP method combined with SVM, outperforms methods using Euclidean distance, demonstrating that SVM is an effective classifier for facial expression recognition in complex experimental setting.
Overview of machine learning applications in manufacturing: From specialization to transfer learning
The shift of manufacturing systems toward the paradigms of Industry 4.0 and 5.0 has significantly boosted the integration of Machine Learning (ML) technologies in this field. With the rapid growth of research leveraging ML to enhance manufacturing functions, this review aims to provide a thorough and up-to-date overview of these applications. A total of 114 journal articles were collected, analyzed, and categorized based on supervision approaches, ML algorithms, and application domains. The study highlights the benefits of ML in manufacturing, alongside identifying potential avenues for future research. Notably, the paper highlights that the prevalent focus on highly specialized applications in manufacturing could be mitigated by encouraging the implementation of transfer learning within the industry.
Applying AI technology to detect and rescue children stuck in cars
Artificial Intelligence (AI) is a technology that enables computers to perform tasks that require human intelligence such as learning, reasoning, and problem solving. AI can be applied to solve serious problems such as children suffocating due to being left in cars. By using facial and voice recognition technology, AI can detect the presence of children in cars and sound a warning to the occupants and guide them to get out of the car. To achieve this, AI must be taught to recognize the presence of children through analyzing a large amount of image and sound data. Set the recognition sensitivity from 50% to 100% to activate the warning system. When the camera recognizes less than 50%, the system will not work. From 50 to 100%, the system will work. A 95% image recognition result will activate the warning speaker and turn on 2 relay switches. The noise recognition result of 80% will activate the warning speaker and the light on the board will change to red. When detecting someone, the PIR sensor will change the light color on the board to red. The recognition result will be continuously checked and adjusted to ensure accuracy. When the system detects a child in the car, it will immediately warn on the circuit board light and speaker. At the same time, it will send a message to the phone immediately. Thanks to the warnings’ high accuracy of up to 95%, this system can reduce the risk of accidents and thus contribute to the safety of children.
Integrating pre-trained LLMS with RAG for efficient content retrieval
Large Language Models (LLMs) are highly effective at replicating human tasks and boosting productivity but face challenges in accurate data extraction due to prioritizing fluency over factual precision. Researchers are addressing these limitations by combining LLMs with Retrieval-Augmented Generation (RAG) models. This approach utilizes chunking, searching, and ranking algorithms to streamline data retrieval from unstructured text, improving LLMs’ precision and processing. The findings provide key insights into optimizing chunking strategies and set the stage for the advancement and broader application of RAG-enhanced systems.
Exploring tourist behavior using natural language processing: Evidence from tripadvisor reviews
This study applies natural language processing (NLP) and Non-negative Matrix Factorization (NMF) to analyze TripAdvisor reviews of short-day tours in Southern Vietnam. Key topics representing traveler experiences and motivations are identified, framed within the push and pull theory. Push factors, like the desire for knowledge and convenience, and pull factors, such as unique activities and cultural immersion, influence tourist behaviors. The findings provide actionable recommendations for tour operators to create experiences that meet both intellectual and experiential needs, enhancing customer satisfaction and deepening connections with the destination.
Modeling user success in online social networks using advanced GNN architectures
Online social networks (OSNs) provide extensive data reflecting users’ personalities, interests, and social connections. The study explores how graph convolutional neural networks (GCNNs) can be used to analyze data from the VKontakte social network to predict users' professional success. Using features like user profiles and social connections, it evaluates various GCNN architectures, including GCNConv, SAGEConv, and GINConv. The Graph Isomorphism Network (GIN) layer achieved the highest accuracy (0.88). This research highlights the effectiveness of advanced neural networks in understanding professional success metrics in online social networks. Hong Bang International University, Ho Chi Minh City, Vietnam.
Optimizing hidden layers in LSTM networks to generate detailed summaries of hotel reviews
Online hotel booking platforms often lack the ability to provide detailed summaries of user reviews across key service areas, such as food, accommodation, service quality, and location. This study introduces a breakthrough solution using Long Short-Term Memory (LSTM) networks with optimized hidden layer configurations. With an F1- score of 75.28%—an increase of 10.18% compared to the standard LSTM model—the model has proven effective in generating review summaries for specific aspects. This advancement offers users a smarter and more efficient hotel booking experience while addressing the limitations of current review systems.
Unlocking the potential of blockchain in education: An innovative approach
Modern technological advancements such as 5G networks, smart devices, and the Internet of Things (IoT) have established a new digital landscape where dataflow security is a mandatory factor. In this context, blockchain technology stands out as a promising solution with the ability to provide a secure and invulnerable platform for systems. Blockchain is a distributed ledger, not only serving financial transactions but also securely and sustainably storing any type of data. Although blockchain originated from cryptocurrency (Bitcoin), its applicability has expanded into various fields such as data storage, product certification, healthcare, science, and education. In the field of education, blockchain is used to issue and store certificates, support degree management, assess learning outcomes, maintain academic records, and oversee training processes. This article analyzes the features and benefits of blockchain, presenting its current applications in education. At the same time, it discusses the benefits and challenges of implementing this technology in the field of education.
Malware detection in PE files using deep learning with self-supervised learning techniques
In recent years, there has been a surge in new malware created by hackers globally, posing challenges for traditional detection methods. This paper explores using advanced artificial intelligence, specifically Deep Learning with Self-supervised learning, to identify malware in executable files. Our study focuses on comparing the effectiveness of popular deep learning techniques like CNN models and fine-tuned CNN models, against Autoencoder models. The key contribution of this paper lies in comparing the results of these different approaches to malware detection.
Camera-based security system featured with laser detection and warning
Recent advancements in camera-based technologies have heightened security demands across various sectors. Cameras now play a pivotal role in traffic management, disease screening, industrial automation, and facial recognition. Nonetheless, many existing solutions are prohibitively expensive and complex, limiting the accessibility for broader applications. This paper introduces an innovative, low-cost, and compact security system utilizing the ESP32-CAM kit, which integrates a microcontroller with a built-in camera and Wi-Fi . Our system is engineered to detect unauthorized individuals in residential settings and parking facilities, employing a laser sensor to trigger alerts via a buzzer and capture photographic evidence for subsequent analysis. Anomalies identified by the system are seamlessly transmitted to cloud storage over Wi-Fi, ensuring real-time monitoring and response. Designed for easy installation and affordability, our system holds significant promise for enhancing security in homes and public areas. Through comprehensive experiments and evaluations, we validate the system's feasibility and effectiveness, demonstrating its potential as a transformative solution in the security landscape.
Phân tích ứng xử kết cấu móng bè dạng bản không sườn ứng lực trước trên nền đàn hồi
Lực ứng suất trước được xác định theo tải trọng cần cân bằng. Thông thường, các giá trị ứng suất hiệu quả nằm trong khoảng 0,75 – 1,5 MPa, giá trị cân bằng tải trọng khoảng 50% - 100% tải trọng thường xuyên để có thể kiểm soát độ võng, cải thiện khả năng chống xuyên, tránh vồng quá mức hay rút ngắn bản. Phân tích sự ảnh hưởng của ứng lực trước đến sự phân bố phản lực đất nền khi thay đổi ứng suất đất nền trong các trường hợp cân bằng tải trọng tương ứng với từng hệ số nền.
Mô phỏng bộ điều khiển mờ thích nghi và Kalman mở rộng trong điều khiển tốc độ động cơ PMSM không sử dụng cảm biến
Bài báo này trình bày thuật toán mờ thích nghi dùng để điều khiển tốc độ cho động cơ đồng bộ nam châm vĩnh cửu. Tốc độ của rotor được ước lượng dựa trên bộ lọc kalman mở rộng giảm bậc. Toàn bộ thuật toán điều khiển của động cơ được lập trình bằng ngôn ngữ mô tả phần cứng VHDL. Hệ thống mô phỏng được kết hợp giữa Matlab/Simulink và ModelSim. Kết quả mô phỏng thể hiện, tốc độ động cơ tốc độ động cơ đáp ứng tốt với tốc độ đặt và không ảnh hưởng khi thông số của hệ thống thay đổi.
Tổ chức quản lý tuyến (xe buýt nhanh) BRT cho thành phố Biên Hòa hiện nay
Phát triển giao thông bền vững, hiệu quả nhất chính là phát triển hệ thống giao thông công cộng thay thế giao thông cá nhân và xây dựng mạng lưới vận tải hành khách công cộng bằng xe buýt là một xu thế tất yếu phù hợp với yêu cầu phát triển của xã hội và quá trình đô thị hoá. Bài báo này đưa ra vấn đề về tổ chức và quản lý một tuyến BRT cụ thể tại thành phố Biên Hòa. Nhằm hoàn thiện hơn hệ thống giao thông công cộng hiện nay và trong tương lai một cách hợp lý, hiệu quả đáp ứng khả năng phục vụ nhu cầu đi lại của nhân dân với chất lượng phục vụ tốt, giá cả hợp lý, đảm bảo an toàn, thuận lợi.
Kiến trúc cầu trên địa bàn thành phố Hồ Chí Minh
Kiến trúc cầu là một dạng công trình kiến trúc xây dựng mang tính đặc thù về mặt hình thái trong đô thị. Nhằm tạo điểm nhấn nghệ thuật quan trọng cho cảnh quan chung thông qua hình dáng kiến trúc và vị trí của những cây cầu trong quy hoạch đô thị. Lịch sử của một công trình kiến trúc cầu gắn liền với tiến trình phát triển lịch sử đô thị đó. Bàibáo này nghiên cứu về các công trình kiến trúc cầu ở Thành Phố Hồ Chí Minh đã, đang và sắp xây dựng trong tương lai gần.
Mô phỏng và thực nghiệm bộ điều khiển tốc độ động cơ PMSM bằng phương pháp vector và thuật toán SVPWM dựa trên công nghệ FPGA
Động cơ đồng bộ nam châm vĩnh cửu có hiệu suất cao vì thế được sử dụng rộng rãi trong công nghiệp. Bài báo này trình bày các bước thiết kế, mô phỏng bằng Simulink/Modelsim và thực nghiệm trên Kit DE2-70 bộ điều khiển tốc độ cho PMSM bằng phương pháp vector dựa trên công nghệ FPGA. Nội dung bài báo gồm 3 phần chính: Đầu tiên thuật toán SVPWM và phương pháp điều khiển vector được xây dựng và áp dụng; Tiếp theo, ngôn ngữ lập trình mô tả phần cứng được sử dụng để thực thi thuật toán điều khiển; Các bước mô phỏng chi tiết được xây dựng nhằm kiểm tra tính đúng đắn của từng phần thuật toán điều khiển. Toàn bộ thuật toán được đánh giá tính hiệu quả và độ chính xác lần nữa thông qua thực nghiệm trực tiếp trên kit FPGA. Cuối cùng kết quả mô phỏng và thực nghiệm được so sánh và thảo luận.
Đánh giá biện pháp gia cố ổn định và biến dạng của móng cọc mố cầu bằng phần mềm plaxis 3D foundation
Công trình xây dựng trên nền đất yếu thường đòi hỏi xử lý kỹthuật cao, đặc biệt là mố cầu. Dưới tác dụng của khối đất đắp cũng như lớp đất yếu dày nằm dưới gây tác động vào hệ thống móng cọc mố cầu làm cho công trình bị dịch chuyển và biến dạng. Vì vậy bài báo này sử dụng phần mềm Plaxis 3D Foundation mô phỏng và phân tích ảnh hưởng của khối đất đắp đường dẫn sau mố đến chuyển vị(có kết hợp quan trắc) của móng cọc mố cầu Kỳ Hà IV, quận 9, Tp. Hồ Chí Minh đồng thời gia cố cấu tạo hệ đài, móng cọc mố cầu bằng cọc khoan nhồi đảm bảo ổn định dưới tác dụng khối đất đắp vào mố làm cho mố được ổn định, an toàn.