This study presents the development of a predictive nutritional assessment system using an Artificial Neural Network (ANN) tailored for adult vegetarians. The system integrates biometric, dietary, and lifestyle data derived from the Anthropometric, Biochemical, Clinical, and Dietary (ABCD) nutritional assessment framework to classify individuals into undernourished, normal, and overweight categories. A feedforward ANN architecture was implemented with 20 input nodes representing ABCD indicators, one hidden layer of 10 neurons, and an output layer of 3 nodes. The model was trained using backpropagation over 1000 epochs with a learning rate of 0.5 and momentum of 0.9, employing data normalization and one-hot encoding for categorical variables. The dataset was split into 80% for training and 20% for testing. The proposed system achieved a classification accuracy of 92.4%, with a 65.3% reduction in cross-entropy loss and an average precision of 89.7% across classes, while processing each input in under 0.8 seconds. Nutritional status predictions were validated using BMI thresholds and cross-referenced with clinical and dietary indicators. Compared to traditional assessment methods, the ANN significantly reduces processing time, improves scalability, and enables dynamic feedback through evolving health indicators. Features such as structured meal plans, real-time daily nutrition summaries, and tailored exercise recommendations enhanced user engagement and adherence to dietary guidelines. The results demonstrate the ANN’s potential as a reliable and scalable tool for personalized nutritional evaluation, enabling early intervention and adaptive planning to address the specific dietary needs and nutrient deficiencies commonly found in vegetarian populations, ultimately supporting predictive healthcare and dietary optimization.

This study proposes a lightweight hybrid model that integrates probabilistic feature augmentation, Gated Recurrent Units (GRU), and Multi-Head Attention to enhance chronic disease prediction on imbalanced clinical tabular data. The research addresses the challenge of low recall and poor minority-class detection in conventional models, aiming to improve predictive robustness and interpretability. The proposed model leverages logistic regression to generate probability-based feature augmentations, which are combined with sequential dependencies learned by GRU and refined through attention mechanisms. Evaluated on three benchmark medical datasets, Breast Cancer, Heart Disease, and Hepatitis C, the model achieves a maximum F1-score of 0.951, a recall of 0.944, and an AUC of 0.976, outperforming traditional machine learning baselines and single-path deep learning models. The attention module enhances interpretability by highlighting relevant features, supporting clinical insights. These findings confirm that probabilistic augmentation and attention-guided deep architectures can significantly improve prediction performance on imbalanced medical data. The results support the study’s objective to design an accurate, interpretable, and clinically relevant prediction model.

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.

Agricultural yields have significantly decreased over the years due to soil degradation and weather variability. Inefficient utilization of these farming resources has worsened the situation. To address this challenge, this study developed an AI-driven crop recommendation framework based on structured soil and climate data. The dataset comprises key features such as temperature, humidity, and rainfall, as well as soil pH, nitrogen, phosphorus, and potassium, collected from the Wannune axis of Tarka Local Government Area in Benue State, Nigeria. To handle the data imbalance in the dataset, the Synthetic Minority Over-Sampling Technique (SMOTE) was applied. Recursive Feature Elimination (RFE) was employed to optimize feature selection. Five tree-based models, including Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, and Extreme Gradient Boost (XGBoost), were evaluated. The XGBoost model yielded the best performance, with an accuracy of 99.65%. The use of indigenous data bridges the gap between reliability and the relevance of localization in precision agriculture. Future work includes integrating dynamic environmental variables and conducting field validation to improve scalability and adoption

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.

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.

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.