Indoor positioning technology plays an important role in improving efficiency and automating industrial processes such as warehouse management, production lines, and mobile robot navigation. However, existing RFID-based and odometry-only localization methods still suffer from limited accuracy, drift, and dependence on predefined infrastructure. To address these challenges, this work proposes a lightweight sensor fusion framework that combines wheel encoder data and phase-based RFID signals using an Extended Kalman Filter (EKF) for accurate indoor localization of mobile robots. The proposed method does not require prior knowledge of the tag map and enables convergence even when the robot starts outside the reader's range. Simulation results demonstrate that the fused method achieves an average positioning error of 5.4 cm and a final error of less than 8 cm. An ablation study comparing odometry-only, RFID-only, and fusion scenarios confirms the superiority of the integrated approach in terms of accuracy and robustness. The system is suitable for real-time implementation in cost-effective embedded platforms and has potential for deployment in smart warehouses and logistics environments.

This paper presents a vehicle detection, tracking, and counting system for urban traffic videos based on integrating YOLOv9s and DeepSORT. The proposed method aims to address challenges such as occlusions, high vehicle density, and identity consistency in surveillance video analysis. A private dataset consisting of annotated traffic videos recorded in Thai Nguyen, Vietnam, was used for evaluation. The YOLOv9s model was selected for its balance between speed and accuracy, while DeepSORT provides robust multi-object tracking using appearance features and Kalman filtering. Experimental results demonstrate that the system achieves a mean Average Precision (mAP at 0.5) of 91.4%, an mAP at 0.5-0.95 of 82.7%, and operates at an average speed of 12.9 frames per second (FPS). Vehicle counting was validated against manually annotated ground truth with an average error rate of less than 4%. These results indicate that the proposed approach is both accurate and efficient for real-time traffic monitoring applications.

Breast cancer remains one of the leading causes of death among women worldwide, primarily due to late detection and diagnosis. Early and accurate prediction is essential to improve survival rates. Machine learning (ML) techniques have proven effective in supporting early diagnosis. This study aims to evaluate and compare the performance of three different approaches: traditional ML, ensemble ML, and deep learning (DL) for early prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The methodology includes data collection, preprocessing, and the design of predictive models. Traditional ML algorithms used include Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Decision Tree (DT). Ensemble ML techniques comprise Random Forest (RF), XGBoost, and AdaBoost, while DL models include Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The models were evaluated using precision, recall, F1-score, and accuracy. The results indicate that XGBoost achieved the highest accuracy (0.99), with strong recall (0.98) and F1-score (0.986), outperforming all other ensemble and traditional ML methods. CNN achieved 0.99 in all evaluation metrics, slightly outperforming RNN, which attained 0.98 accuracy and 0.985 F1-score. These findings confirm that ensemble ML techniques outperform traditional models, while CNN leads among DL models. Furthermore, the proposed models demonstrated superior prediction performance compared to existing studies, particularly in minimizing false negatives, which is critical for healthcare applications.

Object detection for sensing images is one of the promising research directions in computer vision. Applications for object detection from remote sensing images play an important role in analyzing aerial or satellite imagery. Benefits include applications in monitoring buildings and infrastructure, transportation, supporting search and rescue or responding to natural disasters, and environmental research. However, detecting objects in remote sensing images is difficult due to the diversity of shapes and sizes, viewing angles of objects, and complex background environments. In this paper, the authors present a Deep Learning (DL)-based object detection process from remotely sensed images, the main goal of which is to improve the ability to detect small objects in high-resolution aerial images. Implement and evaluate the super-slicing inference technique in the YOLOv11 model to improve the ability to detect very small and extremely small objects. Many simulation results are tested experimentally in the problem of detecting and tracking vehicles in Vietnam (Thai Nguyen). The results show that the system can accurately detect small objects such as pedestrians, motorbikes, and cars at a distance, with confidence ranging from 0.31 to 0.90. Some detection situations are successful even when the object is located at the edge of the slice. Finally, the authors discuss potential future research directions and unaddressed formulations.

This paper presents a novel ingredient-based recipe recommendation system that suggests dishes using only available ingredients, eliminating reliance on user preference data. As online culinary platforms host millions of recipes, users struggle to find dishes that match their pantry constraints, dietary needs, and culinary preferences. Unlike traditional methods like TF-IDF, which lack semantic depth, or existing methods such as graph neural networks (GNNs), which incur high computational costs and struggle with cold-start scenarios, our self-supervised contrastive learning approach maps ingredients and recipes into a 64-dimensional semantic space, capturing complex culinary relationships for precise, scalable recommendations. The system enhances representation quality without user history by leveraging dual-modal Word2Vec embeddings that incorporate ingredients and cooking instructions and optimizing them using triplet loss. Using a dataset of 122,265 standardized recipes, our model achieves a 79.11% ingredient match ratio, 70% recommendation diversity (vs. 50% for baselines), and sub-100ms query times. It outperforms TF-IDF and KNN by 14% in relevance. The implementation addresses ingredient normalization, rare ingredient handling, and diverse cuisine representation, promoting sustainable cooking by maximizing ingredient use and reducing food waste. This work advances the methodology of recommendation systems by addressing the cold-start problem and ensuring privacy through user-data-free modeling. It supports practical culinary applications, contributing to sustainable cooking practices and culinary informatics.

Hyperspectral unmixing is a fundamental task in remote sensing that aims to decompose mixed spectral pixels into their constituent materials and estimate their respective abundances. Traditional methods often face limitations due to nonlinear mixing effects and the lack of pure pixels. This paper proposes a deep learning-based comparative framework that integrates modified versions of the Minimum Simplex Convolutional Network (MiSiCNet) and Unsupervised Deep Image Prior (UnDIP), referred to as Blind MiSiCNet and Supervised UnDIP, respectively, to achieve robust unmixing and abundance estimation in real-world scenarios. The proposed Blind MiSiCNet removes downsampling and upsampling layers and leverages spatial and geometric priors through convolutional layers with a minimum simplex volume constraint, producing reliable initial abundance maps even in the absence of pure pixels. The Supervised UnDIP variant further refines these estimates using the implicit regularization of convolutional neural networks, incorporating known endmembers to generate noise-free and spatially coherent abundance maps. Experimental results on the real-world Jasper Ridge dataset demonstrate that the proposed supervised and blind unmixing methods, evaluated comparatively, significantly outperform existing approaches regarding Spectral Angle Distance (SAD) and Root Mean Square Error (RMSE). The results also highlight improved noise robustness and better preservation of spatial structures.

This study presents RetenNet, a comprehensive framework for managing customer churn in telecommunications, integrating predictive modelling, prescriptive optimization, and explainable artificial intelligence (XAI) incorporated with Large Language Models (LLMs). The process commences with the IBM Telco dataset, divided in an 80:20 ratio into training and testing sets. Categorical variables are converted by one-hot and label encoding, whilst class imbalance is mitigated using SMOTEENN. Min-max scaling and mutual information-based feature selection guarantee data appropriateness for machine learning models. Five classification algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) are assessed. The SVM model utilizing an RBF kernel exhibits optimal performance. In conjunction with nested cross-validation, Bayesian optimization guarantees excellent hyperparameter optimization and generalization. Performance is evaluated using the F1-score to highlight the implications of false negatives and false positives in churn situations. The methodology additionally incorporates fuzzy rule-based clustering, facilitating flexibility in customer segment identification for intervention priority. Prescriptive optimization uses linear integer programming to distribute retention budget according to model results and business constraints. SHAP waterfall plot employed to guarantee transparency and facilitate actionable insights. Furthermore, Gemini 1.5 Flash, a multimodal LLM, generates analysis and produces contextual recommendations derived from the SHAP waterfall plot. RetenNet offers a comprehensive and interpretable approach to the churn management pipeline, including classical machine learning, prescriptive optimization, and LLM-driven explainable artificial intelligence to enhance decision-making in customer retention efforts.

In recent times, the control of engineering processes has gained attention, making it critical to protect devices from operating beyond their designed capabilities. Therefore, this study investigates the modelling and optimization of gain factors for a MIMO-based PID controller employing a machine learning classifier approach. Traditional approaches like Ziegler-Nichols and Cohen-Coon may not handle dynamic coupling between channels well, struggle with nonlinearity, and always require manual tuning, which is tediously challenging and time-consuming. Machine-learning (ML) algorithms are introduced in this study to redesign and remodel the general model (GM) in MIMO fashion. We train and test the proposed model in Anaconda3 using Python programming on synthetic and iteratively generated datasets. Four different ML classifiers, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB), are employed to generate confusion matrices with accuracies of 30.67%, 72%, 25.33%, and 76%, respectively, using the PM system. Furthermore, the dataset corresponding to 76% of the NB is predicted to give an optimal output, as the output of the GM serves as the initial solution of the controller, and the test numerical results are fed back into the MATLAB scripts to produce an optimized version of the GM. Comparatively speaking, the GM, with an average settling time of 238.33 s, and the PM, with an average of 93.33 s, demonstrate faster steady-state response times, smaller gain, phase margins, and better overlap of the ideal gain factors on their target open-loop gain curves, depicting excellent stability. In addition, it can be inferred that the PM is more efficient than the existing model (GM) as it yields better-desired results. As a future direction, it is suggested that more machine learning classifiers, such as XGBOOST, neural networks, and K-nearest neighbour, be incorporated into the training and testing of larger datasets for better, improved, and optimized outputs.

Distributed Denial of Service (DDoS) attacks remain a critical threat to network infrastructure, necessitating timely and precise detection techniques to mitigate their impact. This study presents a hybrid deep learning framework that integrates the Three Conditions for Feature Aggregation (3ConFA) framework with a one-dimensional Convolutional Neural Network (1D-CNN) for effective DDoS detection. Initially, salient features were selected using the 3ConFA approach, which combines multi-filter feature ranking—based on Chi-square, Information Gain, and Decision Tree Recursive Feature Elimination (DT-RFE) to extract robust and relevant features from raw network traffic data. The training samples were balanced using the Adaptive Synthetic Sampling Approach (ADASYN) to address the high-class imbalance typically present in DDoS datasets. The refined features were then passed through a 1D-CNN model designed to learn temporal and spatial attack behavior patterns. Feature fusion was applied by concatenating the aggregated statistical features and the deep features learned by the CNN, then re-selected the most informative features using Recursive Feature Elimination with Cross-Validation (RFECV). The final classification was performed using a Softmax output layer, and the model was evaluated using 5-fold cross-validation and a separate test set. Experimental results demonstrated an average training accuracy of 99.42%, an F1-score of 99.35%, and an AUC-ROC of 99.87%. On the test set, the model achieved a detection accuracy of 99.56%, a precision of 99.61%, and an F1-score of 99.50%, with an AUC-ROC of 0.9982. The proposed hybrid approach outperforms traditional models such as Random Forest, Decision Tree, XGBoost, and standalone CNN, validating the synergistic impact of integrating 3ConFA with deep temporal convolutional modeling for accurate and interpretable DDoS attack detection.

Nowadays, the construction, monitoring, quality control, and maintenance of roads always require high-precision, easy-to-use measuring devices in the field. This study develops an embedded computer program using the YOLOv8 model integrated into a smart road length measuring device to detect road surface cracks. First, the study analyzes and clarifies the outstanding points of the YOLOv8 model, including anchor-free: Eliminates the use of traditional anchor boxes, helping to simplify the training process, increase accuracy and reduce computational costs; C2f (Cross-Stage Partial with feature fusion); Replacing CSSPLayer in YOLOv5, this module improves feature extraction and maintains good computational performance; SPFF (Spatial Pyramid Pooling - Fast): Helps expand the receptive field without increasing computational costs, supporting object recognition at multiple scales; PAN++: Enhances the transfer of features from lower layers to the output, helping to detect small objects such as cracks well. Then, this work evaluated the performance of the proposed model by applying YOLOv8 on the surface crack dataset taken from the open-source Roboflow Universe. The results showed that the developed YOLOv8 model achieved good results with the recall indexes of 57.3%, mAP50 59.3%, precision of 64.4% mAP50-95 48.9% for YOLOv8n model, and can improve the accuracy further, reaching 79.8% using the YOLOv8m model, while still meeting the real-time processing speed of the device. These experimental results using the YOLOv8 model for detecting cracks on DongAnh road, Hanoi, Vietnam, confirm the possibility of applying the integrated road crack detection model on smart road length measuring devices in practice, achieved a precision of 43.3%, supporting the survey, inspection, supervision, construction and management of traffic infrastructure more scientifically and effectively