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

Communication is an essential part of day-to-day life. However, people who have disabling hearing loss, which in general is known as deafness, or have a severe loss of hearing use sign language for their communication. Those people learn this language through their respective schools, whereas hearing people rarely get to know this communication tool, leaving a wide communication gap. To bridge this gap, an automated communication system that can convert text to sign language without depending on human interpreters. This literature review overviews sign language and discusses the different technologies used for text-to-sign conversion systems. The paper evaluates computer-based text processing methods, machine translation techniques (rule-based, corpus-based, and neural architectures), and sign synthesis approaches (avatar animation, video concatenation, neural generation). The paper compares each method's strengths and limitations and presents quantitative performance data from recent systems. The review also provides a comprehensive summary of widely used datasets and introduces a visual taxonomy of translation approaches. Furthermore, the paper discusses the current limitations, including scalability, data limitations, the need for more expressive synthesis, and future scope for improvement. The expected outcome of this review is to find and gather information on various sign language systems worldwide, identify research gaps, and propose further enhancements in text-to-sign language conversion.

Diabetes is the body’s inability to efficiently break down sugar or secrete enough insulin required to process glucose, which supports normal bodily functions. Diabetes, as a prevalent chronic disorder, has contributed to numerous underlying health challenges among its carriers and is classified by the WHO as the world’s deadliest disease and silent killer. Its non-communicable nature makes early diagnosis difficult, allowing progression through various stages: type I, type II, pre-diabetes, and gestational. This challenge is further compounded by the imbalanced nature of diabetes datasets, which leads to high misclassification, poor generalization, and reduced accuracy. This study predicts diabetes using a bi-directional long short-term memory (BiLSTM) model applied to two datasets: (a) PIMA Indian Diabetes and (b) Iraqi Society Dataset, to evaluate the impact of six known balancing techniques and assess their effectiveness. Results show that for PID, the SMOTE-Tomek fused BiLSTM outperforms other balancing schemes with F1, Accuracy, Precision, Recall, and Specificity scores of 0.9182, 0.9198, 0.9128, 0.9248, and 0.9208, respectively. For ISD, it also achieves the best performance with values of 0.9367, 0.9369, 0.9386, 0.9388, and 0.9313, respectively. Other balancing approaches yielded F1 scores ranging from [0.6751 to 0.9347], accuracy [0.684 to 0.9358], Precision [0.6851 to 0.9296], Recall [0.6639 to 0.9356], and specificity [0.6658 to 0.9298]. These results imply that BiLSTM is resilient to the vanishing gradient problem and can effectively classify diabetes cases with enhanced performance.

This paper presents recent research in the field of human face recognition and detection. Technology development has marked a clear transformation in the implementation of recognition tasks. It is a transition from classical methods to methods with AI and deep learning applications. We have reviewed the latest works in famous journals to evaluate this field. This paper provides a comprehensive taxonomy across detection, identification, and tracking. It offers a comparative analysis of state-of-the-art AI approaches and highlights their strengths, limitations, and practical considerations. We have evaluated the level of compliance, completeness, and methodology of related studies. According to the results of our evaluation, significant improvements and developments have occurred in this field. However, some challenges still need to be focused on, such as improving the efficiency of training models in different environmental conditions. The negative effects of lighting conditions, occlusion of faces, or distortions due to different shooting angles are overcome.

Indoor positioning plays an important role in a wide range of applications, such as autonomous mobile robots (AMRs), healthcare smart homes, and industrial automation. Popular positioning methods include received signal strength indicator (RSSI)-based positioning, proximity-based positioning, angle-of-arrival (AoA)-based positioning, and fingerprinting. Each method has specific trade-offs regarding accuracy, cost, and deployment complexity. Recent research has focused on solutions to improve accuracy. These include noise filtering techniques, such as Kalman and particle filters, as well as the use of machine learning to optimize the positioning model. In particular, graph-based positioning methods have demonstrated the ability to improve accuracy by optimizing the spatial relationship between landmarks. In addition, regression-based error estimation helps predict and correct positioning errors, improving system reliability. This paper presents a focused review of state-of-the-art techniques to enhance indoor positioning systems' accuracy. They consist of filtering, hybrid methods, and Artificial Intelligence (AI)-based optimization, offering a practical reference for enhancing system performance in real-world applications. By reinforcing recent developments, this review serves as a foundation for researchers and engineers seeking effective positioning solutions for AMR and smart environments. Looking ahead, hybrid positioning systems integrate multiple technologies. AI and machine learning enhance them. Hybrid positioning systems are expected to offer greater accuracy, flexibility, and scalability. These advancements will be essential in optimizing operations and enhancing user experiences in future automation applications and the Internet of Things(IoT).

Secure image transmission is increasingly vital in the digital era, especially against emerging quantum threats. This study proposes a hybrid image encryption scheme that integrates Quantum Key Distribution (QKD) using the BB84 protocol with a combination of 7-dimensional (7D) and 2-dimensional (2D) hyperchaotic systems to achieve robust security. The BB84 protocol facilitates quantum-assisted key exchange, ensuring resistance to eavesdropping, while the hyperchaotic systems provide high entropy and complex randomness, utilized in a layered permutation-substitution encryption framework. The initial seeds for chaotic sequences are derived using a SHA-512 hash of both the input image and quantum-generated key, ensuring uniqueness and sensitivity. Experimental validation was conducted using several benchmark images. The information entropy values of the ciphered images reached up to 7.9993, indicating excellent randomness. Differential analysis showed high resistance to small perturbations, with NPCR exceeding 99.61% and UACI averaging around 33.47%, which meet standard security thresholds. Histogram and chi-square tests confirmed the uniform pixel distribution, with chi-square values below 280, satisfying the randomness criterion for 8-bit images. Furthermore, correlation coefficients of adjacent pixels dropped to near zero, evidencing effective decorrelation. The encryption scheme also demonstrated robustness to data loss, as shown by the successful decryption of partially corrupted cipher images. Robustness testing under partial data loss (200×200-pixel blocks) also demonstrated visual recoverability and algorithm resilience. Overall, the proposed BB84-assisted dual-hyperchaotic encryption scheme offers a secure and computationally effective solution for protecting sensitive image data, making it suitable for post-quantum secure communications.

Indonesia’s waste management system struggles to keep pace with rapid population growth and urbanization, resulting in inefficient waste collection, environmental degradation, and low recycling rates. The country predominantly relies on open dumping and landfilling, which contribute significantly to pollution and greenhouse gas emissions. This study explores the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in waste management, focusing on smart waste collection, automated sorting, real-time monitoring, and predictive analytics. AI-driven waste classification enhances recycling efficiency, while IoT-enabled smart bins optimize collection routes, reducing operational costs and landfill dependency. Despite these advantages, challenges such as high implementation costs, digital infrastructure limitations, and data privacy concerns hinder widespread adoption. This study highlights that policy support, investment in digital infrastructure, and stakeholder collaboration are crucial for successful implementation. By leveraging AI and IoT, Indonesia can significantly improve waste management efficiency, minimize environmental impact, and advance circular economy initiatives. The findings suggest that, with the right policies and investments, AI-driven waste management can drive sustainability, reduce waste mismanagement, and promote resource optimization, making it a vital strategy for future urban development in Indonesia.
 

The rapid evolution of artificial intelligence (AI) has significantly transformed the field of steganography, extending its scope beyond conventional image-based techniques to novel domains such as linguistic and 3D mesh data hiding. This review presents a concise, accessible, and critical examination of recent AI-powered steganography methods, focusing on three distinct modalities: image, linguistic, and 3D mesh. Unlike most surveys focusing solely on one modality, this work highlights some modalities, identifies their unique challenges, and discusses how AI has reshaped embedding mechanisms, evaluation strategies, and security concerns. In image-based steganography, deep models such as GANs and Transformers have improved imperceptibility and extraction accuracy, but face limitations in computational efficiency and extraction consistency. Linguistic steganography, previously hindered by semantic fragility, has been revitalized by large language models (LLMs), enabling context-aware and reversible embedding, though still constrained by metric standardization and synchronization issues. Meanwhile, 3D mesh steganography remains dominated by non-AI methods, offering fertile ground for innovation through geometric deep learning. This review also provides a comparative summary of design principles, performance metrics, and modality-specific trade-offs. The analysis reveals a shift in evaluation paradigms, from numeric fidelity (e.g., PSNR, SSIM) to semantic and perceptual metrics (e.g., LPIPS, BERTScore, Hausdorff Distance). Looking ahead, future directions include cross-modal integration, domain adaptation, lightweight AI models, and the development of unified benchmarks. By presenting recent advances and critical perspectives across underexplored domains, this survey aims to inspire early-stage researchers and practitioners to explore new frontiers of steganography in the AI era.

Expanding extensive and intricate social networks has required sophisticated community detection techniques. This study presents an innovative hybrid methodology that utilizes node space similarity and local knowledge to enhance community identification. Node space similarity is defined by integrating eigenvector centrality (EC), which quantifies a node’s influence inside the network, with proximity metrics, such as closeness, to evaluate the connectivity between nodes. This enables us to identify cohorts of individuals with analogous influence and connectivity. We use local knowledge by concentrating on these pivotal nodes' direct connections and attributes, allowing the technique to broaden community discovery (CD) outward effectively. Our five-phase methodology, grounded in an iterative seed expansion algorithm, commences with identifying highly central nodes and progressively develops communities by integrating nodes exhibiting high similarity and local connectivity. The method incorporates graph statistical inference and embedding features to improve accuracy and capture extensive network patterns. This integrated approach facilitates the precise and effective identification of communities within extensive social networks, exceeding the constraints of conventional techniques. This research attained a modularity of 95.05% on the DBLP dataset and 94.50% on the Amazon dataset. This study achieved a Normalized Mutual Information (NMI) of 91.80% on the DBLP dataset, 92.50% on the Amazon dataset, and 90.43% on the football dataset, demonstrating superior performance relative to previous methodologies. The findings indicate that the hybrid method outperforms other recognized methods in large-scale graphs, showcasing notable robustness and efficiency.

Feature selection in most black-box machine learning algorithms, such as BERT, is based on the cor-relations between features and the target variable rather than causal relationships in the dataset. This makes their predictive power and decisions questionable because of their potential bias. This paper presents novel BERT models that learn from causal variables in a clinical discharge dataset. The causal-directed acyclic Graphs (DAG) identify input variables for patients’ survival rate prediction and decisions. The core idea behind our model lies in the ability of the BERT-based model to learn from the causal DAG semi-synthetic dataset, enabling it to model the underlying causal structure accurately in-stead of the generic spurious correlations devoid of causation. The results from Causal DAG Conditional Independence Test (CIT) validation metrics showed that the conceptual assumptions of the causal DAG were supported, the Pearson correlation coefficient ranges between -1 and 1, the p-value was (>0.05), and the confidence interval of 95% and 25% were satisfied. We further mapped the semi-synthetic dataset that evolved from the Causal DAG to three BERT models. Two metrics, pre-diction accuracy, and AUC score, were used to compare the performance of the BERT models. The accuracy of the BERT models showed that the regular BERT has a performance of 96%, while Clinical-BERT performance was 90%, and Clinical-BERT-Discharge-summary was 92%. On the other hand, the AUC score for BERT was 79%, ClinicalBERT was 77%, while ClinicalBERT-discharge summary was 84%. Our experiments on the synthetic dataset for the patient’s survival rate from the causal DAG datasets demonstrate high predictive performance and explainable input variables for human under-standing to justify prediction.

Artificial intelligence can change many industries as a global phenomenon. Over the years, this transformation has supported Electronic Markets in reengineering the processes and activities that take place in traditional markets, focusing on improving transaction effectiveness and efficiency. While our dependence on intelligent machines continues to grow, the demand for more transparent and interpretable models equally grows. Thus, explanations for machine decisions and predictions are needed to justify their reliability, which requires greater interpretability and often elaborates the need to understand the algorithms' underlying mechanism. This paper, therefore, proposed models based on Decision Tree (DT), Long Short-Term Memory (LSTM), and an ensemble of the two aforementioned models for improving CLV accuracy, interpretability, and explainability of AI-based models in the multi-category electronic market. An open-source e-commerce Behavior Data from a multi-category store, previously used by similar studies on XAI and CLV, was used in this experiment, ensuring the robustness of the product prediction and explanations and fair comparison. From the results, the models from this study demonstrated remarkable performance in terms of minimal error rates of MAE, MSE, and RMSE, with LSTM outperforming the other models. Regarding explainability and interpretation, the begin_time is ranked as the most relevant feature in CLV prediction.

This research presents a hybrid intrusion detection approach that integrates Generative Adversarial Networks (GANs) for synthetic data generation with Random Forest (RF) as the primary classifier. The study aims to improve detection performance in cybersecurity applications by enhancing dataset diversity and addressing challenges in traditional models, particularly in detecting minority attack classes often underrepresented in real-world datasets. The proposed method employs GANs to generate synthetic attack samples that mimic real-world intrusions, which are then combined with real data from the UNSW-NB15 dataset to create a more balanced training set. By leveraging synthetic data augmentation, our approach mitigates issues related to class imbalance and enhances the generalization capability of the classifier. Extensive experiments demonstrate that RF trained on the combined dataset of real and synthetic data achieves superior detection performance compared to models trained exclusively on real data. Specifically, RF trained solely on the original dataset achieves an accuracy of 97.58%, whereas integrating GAN-generated synthetic data improves accuracy to 98.27%. The proposed methodology is further evaluated through comparative analysis against alternative classifiers, including Support Vector Machine (SVM), XGBoost, Gated Recurrent Unit (GRU), and related studies in the field. Our findings indicate that GAN-augmented training significantly enhances detection rates, particularly for rare attack types, while maintaining computational efficiency. Furthermore, RF outperforms other classifiers, including deep learning models, demonstrating its effectiveness as a lightweight yet robust classification method. Integrating GANs with RF offers a scalable and adaptable framework for intrusion detection, ensuring improved resilience against evolving cyber threats.

Phishing is a fraudulent activity wherein an attacker impersonates a trusted individual or organization to acquire sensitive information from an online user. Phishing websites have become a major cyber-security issue in the contemporary digital landscape. As online activities expand in e-commerce, banking, and social media, the hazards presented by these fraudulent websites have intensified. Deep learning-based Natural Language Processing (NLP) approaches offer an effective solution for detecting phishing URLs. However, deploying large models like BERT or RoBERTa for real-time detection poses computational challenges. This study proposes BERTPHIURL, a Teacher-Student Learning framework that leverages RoBERTa as the Teacher model and DistilRoBERTa as the Student model to improve phishing detection efficiency while reducing computational overhead. By applying knowledge distillation, the Student model learns from the Teacher, preserving high detection accuracy with significantly lower resource consumption. The proposed approach effectively captures contextual relevance and local features in malicious URL detection tasks. The experiments were conducted on a dataset exceeding 50,000 URLs to evaluate performance. Results indicate that BERTPHIURL achieves a 94.22% accuracy, outperforming existing phishing detection methods while maintaining efficiency suitable for real-time applications.

The research focuses on developing an Optimized Multiclass Intrusion Classifier (OMIC), an advanced framework for large-scale network intrusion detection in IoT environments. Traditional intrusion detection systems face significant challenges with increasing network complexity, attack sophistication, and the exponential growth of IoT devices, particularly in handling class imbalance, computational efficiency, and real-time processing of massive data volumes. OMIC introduces a novel ensemble approach combining LightGBM and XGBoost classifiers with a memory-optimized processing pipeline to address these limitations. The framework implements sophisticated data handling techniques, including dynamic chunk-based processing, adaptive sampling methods, and cost-sensitive learning to manage class imbalance. Experimental evaluation using the comprehensive CICIoT2023 dataset, comprising over 1 million records and 33 distinct attack types, demonstrates OMIC's exceptional performance with an overall accuracy of 99.26%. The framework achieves perfect precision, recall, and F1-scores for most DDoS and DoS attack categories, significantly outperforming traditional machine learning and deep learning approaches. While excelling in most attack categories, OMIC shows limitations in detecting certain web-based attacks and reconnaissance activities, suggesting areas for future enhancement. The framework's superior performance in handling large-scale data while maintaining high detection accuracy positions it as a significant advancement in IoT network security, offering practical solutions for real-world deployments.

Face spoofing detection is critical for biometric security systems to prevent unauthorized access. This study proposes a deep learning-based approach integrating FaceNet and DenseNet201 to enhance face spoofing detection performance. FaceNet generates identity-based embeddings, ensuring robust facial feature representation, while DenseNet201 extracts complementary texture-based features. These features are fused using the Concatenate function to form a more comprehensive representation for im-proved classification. The proposed method is evaluated on two widely used face spoofing datasets, NUAA Photograph Imposter and LCC-FASD, achieving 100% accuracy on NUAA and 99% on LCC-FASD. Ablation studies reveal that data augmentation does not always enhance performance, particularly on high-complexity datasets such as LCC-FASD, where augmentation increases the False Rejection Rate (FRR). Conversely, DenseNet201 benefits more from augmentation, while the proposed method performs best without augmentation. Comparative analysis with previous studies further confirms the superiority of the proposed approach in reducing error rates, particularly Half Total Error Rate (HTER), False Acceptance Rate (FAR), and FRR. These findings indicate that combining identity-based embeddings and texture-based feature extraction significantly improves spoofing detection and enhances model robustness across different attack scenarios. This study advances biometric security by introducing an efficient feature fusion strategy that strengthens deep learning-based spoof detection. Future research may explore further optimization strategies and evaluate the approach on more diverse datasets to enhance generalization.

This study aimed to predict and detect cyberattacks using hybrid machine-learning models. The CICIoT2023 dataset was utilized for attack prediction and detection, and model performance was evaluated separately by performing thirty-four class (33+1), eight class (7+1), and binary (1+1) classifications according to the types of attacks in the dataset. Voting and stacking hybrid machine learning models were employed in this study, with Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Random Forest (RF) algorithms selected as sub-models. Data preprocessing steps were applied to enhance model performance, and hyperparameter optimization was performed. As a result, this study achieved an accuracy of 98% in thirty-four class classifications, 88% in eight class classifications, and 99% in binary classifications with the Voting hybrid machine learning model. In contrast, the Stacking hybrid machine learning model reached an accuracy of 98% in both thirty-four class and eight class classifications and 99% in binary classifications. This study presents a significant innovation in the cybersecurity field by introducing an innovative approach to eliminating the disadvantages of single-model methods.

Project management is changing drastically due to the integration of artificial intelligence (AI) and quantum computing (QC), redefining traditional methods. This study explores Quantum AI (QAI) and AI-driven solutions to tackle enduring issues, including resource inefficiencies, schedule delays, and budget overruns. These technologies significantly enhance project outcomes by leveraging predictive analytics, dynamic scheduling, and high-dimensional optimization. A comparative analysis of prominent case studies, including the Crossrail Project, East Side Access, and the Montreal Olympics, highlights the superior performance of AI and QAI techniques compared to conventional methods. The study shows that QAI can cut delays by 60%, optimize resource allocation with 83% efficiency, and eliminate cost overruns by up to 40% using Monte Carlo simulations and Failure Mode Effects Analysis. These results demonstrate that quantum artificial intelligence is a ground-breaking tool for handling intricate, interconnected project settings. Additionally, this study emphasizes how QAI is scalable and applicable across industries, especially in fields that need real-time optimization and high-dimensional data processing. The proposed hybrid quantum-classical paradigm provides practical solutions and sets a benchmark for efficiency, scalability, and risk mitigation in project management.

Course recommendation aims to find suitable and attractive courses for students based on their needs, playing a significant role in the curricula-variable system. However, with the abundant available courses, students often face cognitive overload when selecting the most appropriate ones. This research proposes a course recommendation system called the Enhanced Hybrid Course Recommender to address this challenge. This system uses an ensemble learning approach to combine and leverage the power of multiple machine learning classifiers, including Random Forest, Naive Bayes, and Support Vector Machine. By utilizing TF-IDF vectorization for text data transformation and label encoding for target label compatibility, this experiment significantly enhances recommendation precision and relevance, easing students' decision-making process and improving the overall quality of course recommendations. A hybrid approach is applied to improve the recommendation quality by combining predictions from all three classifiers through weighted voting. This ensemble method improves overall robustness and accuracy. This approach not only mitigates the cognitive overload faced by students but also significantly improves the quality of recommendations. Our hybrid model represents a substantial advancement in personalized course recommendation technology by demonstrating superior performance across key evaluation metrics such as accuracy, precision, recall, F1-score, ARHR, and NDCG.

The identification and early treatment of skin diseases are crucial to mitigate serious health risks. The growing attention on researching skin disease analysis stems from the transformative impact of artificial intelligence (AI) in dermatology. In this systematic review, we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to comprehensively assess recent approaches for skin disease detection. Our study addressed four key research questions exploring the methods for skin disease detection, the evaluation techniques employed to measure the effectiveness of skin disease detection models, the datasets utilized, and the challenges encountered in applying machine learning and deep learning techniques for skin disease detection. We screened studies from 2019 to 2023 from reputable databases, including IEEE Explore, Science Direct, and Google Scholar. Our findings revealed that the CNN model outperformed other deep learning models. Additionally, our analysis identified the ISIC public dataset as the most frequently used dataset. The studies reviewed employed evaluation metrics such as accuracy, recall, precision, sensitivity, and F1 score to evaluate model performance. We identified several limitations in the studies we reviewed, including the use of limited datasets, challenges in distinguishing between diseases with similar features, and other related limitations. Overall, we provided a comprehensive overview of the current state-of-the-art techniques in skin disease detection and highlighted the future directions.

This review explores the integration of Human-Computer Interaction (HCI) principles in AI to advance Human-Centered Artificial Intelligence (HCAI). It highlights how these fields intersect to create user-friendly AI systems that enhance human capabilities and align with human values. Given the recent interest of HCI in user-centered design and AI in technical innovation, this paper bridges this divide by infusing principles from HCI into AI systems. Relevant peer-reviewed articles, conference papers, and case studies have been selected from leading databases like IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar, encompassing publications from 2017 to 2024. The inclusion criteria for the review focus on interdisciplinary approaches, real-world applications, and challenges of HCAI, while studies that do not have a clear methodology or lack relevance to HCAI were excluded. This paper identifies some of the key gaps, highlights the successful applications of HCAI across healthcare, edu-cation, and entertainment, and discusses various challenges that have arisen, such as bias, transparency, and balancing automation with human control. Findings reveal that iterative design and hu-man-centered frameworks will lead to better usability and ethical fit for HCAI, but significant challenges remain. This study proposes an integrative framework for bringing HCI principles into AI design through interdisciplinary collaboration in developing systems that will enhance human capabilities while considering ethical aspects. Future directions include responsible AI, personalized healthcare, and effective human-AI collaboration.

Autonomous mobile robot navigation integrates localization, mapping, and path planning to enable effective operation in complex environments. This study compares a modified Q-learning algorithm with a Deep Q-Network (DQN) in a simulated gym environment, focusing on convergence speed, success rate, and computational efficiency. The modified Q-learning algorithm converged after 44 episodes, outperforming the DQN, which required 400 episodes. It achieved a success rate of 69.6% with a cumulative reward that surpassed the DQN in fewer episodes, while completing simulations in just 9 minutes compared to 400 minutes for the DQN. These results demonstrate the modified Q-learning’s efficiency in addressing the exploration-exploitation trade-off and navigating complex environments. This study highlights the potential of the modified Q-learning algorithm for real-world applications in robotics and autonomous navigation, providing a foundation for future research in intelligent path planning

This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achievinga perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterizedgate selection, and the integration of the quantum layer with classical neural network components.This work contributes to the growing body of research on hybrid quantum-classical models and theirapplicability to real-world datasets.

High blood pressure (or hypertension) is a causative disorder to a plethora of other ailments – as it succinctly masks other ailments, making them difficult to diagnose and manage with a targeted treatment plan effectively. While some patients living with elevated high blood pressure can effectively manage their condition via adjusted lifestyle and monitoring with follow-up treatments, Others in self-denial leads to unreported instances, mishandled cases, and in now rampant cases – result in death. Even with the usage of machine learning schemes in medicine, two (2) significant issues abound, namely: (a) utilization of dataset in the construction of the model, which often yields non-perfect scores, and (b) the exploration of complex deep learning models have yielded improved accuracy, which often requires large dataset. To curb these issues, our study explores the tree-based stacking ensemble with Decision tree, Adaptive Boosting, and Random Forest (base learners) while we explore the XGBoost as a meta-learner. With the Kaggle dataset as retrieved, our stacking ensemble yields a prediction accuracy of 1.00 and an F1-score of 1.00 that effectively correctly classified all instances of the test dataset.

Landslides pose significant threats to life, property, and infrastructure. This study explores applying unsupervised learning techniques to identify and understand landslide-prone areas. We analyzed topographic data by employing K-Means, Hierarchical Clustering, Spectral Clustering, Mean Shift Clustering, and DBSCAN to uncover hidden patterns in landslide occurrence. Evaluation metrics, including the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used to assess the performance of these algorithms. Hierarchical Clustering achieved the highest Silhouette Score of 0.635, indicating excellent cluster separation. However, Mean Shift Clustering outperformed the other methods with a superior Davies-Bouldin Index of 0.603 and the highest Calinski-Harabasz Index of 4121.75, demonstrating the best overall clustering performance. DBSCAN also performed well, with a Silhouette Score of 0.610 and 12 noise points identified. These findings contribute to a deeper understanding of landslide spatial distribution and can inform the development of effective early warning systems and mitigation strategies.

The efficacy of Cancer treatment often varies across different types of cancers. This study aims to investigate any pattern relationship in histopathological images of different cancer types to find any potential correlation between those patterns. Using deep image analysis techniques and artificial intelligence (AI), we extract, analyze, and compare the morphological parameters of cancer images to identify potential indicators of that treatment effective for one type that might be applicable for its correspondence. This research applied advanced image analysis, artificial intelligence (AI), machine learning, and more sophisticated statistical analysis to find the required pattern relationship for those parameters. The study answers the question regarding the correlation of different measurement parameters across different varieties of cancer cells. The model achieved an impressive ROC-AUC score of 0.967, an F1-score of 0.805, and Cohen's kappa coefficient of 0.767, indicating a high level of agreement and predictive performance. The overall accuracy of the model was 81%, with both macro and weighted averages also at 81%. These results provide strong evidence of meaningful pattern relationships across different cancer types, potentially enhancing treatments' applicability across various cancers.

This paper critically examines artificial intelligence in the healthcare sector and aims to identify concrete points of challenges and business value propositions first in radiology and then across healthcare more broadly. It discusses current applications in radiology and future uses of AI in healthcare, focusing on three main areas: (i) emergency incidents handling, (ii) intensive care unit treatment and (iii) augmented telemedicine, to which emergency radiology is a critical success factor. Despite some risks and compliance issues that need to be taken care of, this paper clearly shows that AI has the potential (a) to reengineer the business processes of the healthcare sector, using AI-assisted radiology as a driver and (b) to improve the effectiveness of the healthcare system as well as (c) to increase the quality provision of healthcare services. Despite its slow adoption, AI-assisted healthcare can indeed offer business/operational solutions that benefit all healthcare stakeholders.

The rapid growth in vehicle ownership has led to increased traffic congestion, making the need for autonomous driving solutions more urgent. Autonomous Vehicles (AVs) offer a promising solution to improve road safety and reduce traffic accidents by adapting to various driving conditions without human intervention. This research focuses on implementing Deep Q-Network (DQN) to enhance AV performance in different driving modes: safe, normal, and aggressive. DQN was selected for its ability to handle complex, dynamic environments through experience replay, asynchronous training, and epsilon-greedy exploration. We designed a simulation environment using the Highway-env platform and evaluated the DQN model under varying traffic densities. The performance of the AV was assessed based on two key metrics: success rate and total reward. Our findings show that the DQN model achieved a success rate of 90.75%, 94.625%, and 95.875% in safe, normal, and aggressive modes, respectively. Although the success rate increased with traffic intensity, the total reward remained lower in aggressive driving scenarios, indicating room for optimization in decision-making processes under highly dynamic conditions. This study demonstrates that DQN can adapt effectively to different driving needs, but further optimization is needed to enhance performance in more challenging environments. Future work will focus on improving the DQN algorithm to maximize both success rate and reward in high-traffic scenarios and testing the model in more diverse and complex environments.

Incorporating deep learning models has marked a significant advancement in integrating trends and technology within the fashion industry. These models are extensively applied in the realm of image recognition, product recommendation, and trend prediction, employing deep learning techniques such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Autoencoders. This paper aims to cover various aspects of the textile industry’s supply chain processes, highlighting these deep learning techniques' present influence and potential future directions. It includes a comprehensive analysis of some of the most recent and well-recognized studies in the industry that focus on different parts of a product’s lifecycle in the industry, such as Design and Trend Forecasting, Production and Quality Control, Marketing and Sales, and Distribution and Retail. While deep learning has significantly improved the efficiency of processes across the supply chain, our review highlights some of the existing challenges, such as dependency on large datasets, manual annotation needs, and limitations in creative design generation, encouraging future research to focus on more sophisticated models incorporating multimodal data and personalized factors like body types and aesthetic preferences. Additionally, areas like sewing pattern generation, body-aware designs, and ethical sourcing are critical areas of the fashion industry that require further exploration.

This paper offers a brief analysis highlighting the effectiveness of several well-known Artificial Intelligence (AI) technologies applied in education, particularly in teaching and learning. It provides an overview of how modern classrooms can benefit from more effective teaching strategies that encourage students to engage in hands-on learning. Advanced technologies are changing how knowledge is found and shared, as well as how teaching is delivered. Memorization has been emphasized in educational models as a crucial learning skill until relatively recently. The technologies alter how knowledge is accessed and taught in schools today. Based on that, most knowledge is readily available, quickly accessible, and available online. The skills of reading, sharing, listening, and acting are now prerequisites for schooling. Most recent developments in advanced technologies in education are provided. Some analyses related to opening issues and challenges are shown for future work.

This is one of the greatest challenges in computer network security and cannot be dealt with without a set of most recent detection techniques. This paper lays down a new hybrid technique that combines Clustering-Based Local Outlier Factor (CBLOF) and Extreme Gradient Boosting (XGBoost) to enhance accuracy while detecting Distributed Denial of Service (DDoS) from network traffic. The proposed hybrid model utilizes a CBLOF for outlier detection as feature engineering. Over the detected anomalies, classification is to be done using XGBoost classification to attain the objective. The proposed hybrid model was tested extensively on CICIDS 2017 and CICIDS 2018 datasets Compared with traditional ones, the proposed model outperformed the traditional ones with an accuracy rate of 99.99%, precision of 100%, and F1 score reflecting perfection. These results confirm this model's efficiency in terms of known and novel attack patterns and introduce a further reliable framework for the timely detection of DDoS attacks. Even if it is computation-heavy, optimization could be made towards real-time large-scale data.

Face recognition occluded by occlusions, such as glasses or shadows, remains a challenge in many security and surveillance applications. This study aims to analyze the performance of various machine learning and deep learning techniques in face recognition scenarios with occlusions. We evaluate KNN (standard and FisherFace), CNN, DenseNet, Inception, and FaceNet methods combined with a pre-trained DeepFace model using three public datasets: YALE, Essex Grimace, and Georgia Tech. The results show that KNN maintains the highest accuracy, reaching 100% on two datasets (Essex Grimace and YALE), even in the presence of occlusions. Meanwhile, CNN shows strong performance, with accuracy remaining 100% on YALE, both with and without occlusions, although its performance drops slightly on Essex Grimace (94% with occlusion). DenseNet and Inception show a more significant drop in accuracy when faced with occlusion, with DenseNet dropping from 81% to 72% on Essex Grimace and Inception dropping from 100% to 92% on the same dataset. FaceNet + DeepFace excels on more large dataset (Georgia Tech) with 98% accuracy, but its performance drops dramatically to 53% and 70% on Essex Grimace and YALE with occlusion. These findings indicate that while deep learning methods show high accuracy under ideal conditions, machine learning methods such as KNN are more flexible and robust to occlusion in face recognition.

Each year, more than 1.9 million cases of colorectal cancer (CRC) are diagnosed worldwide. By 2040, the burden of colorectal cancer is estimated to reach 3.2 million new cases per year and 1.6 million deaths per year worldwide. As of 2024, it ranks as the third most prevalent form of cancer, contributing to over 10% of all new cancer cases annually, with a 5-year survival rate of only 65%. With effective early detection mechanisms in place, the survival rate could potentially increase to 90%. However, current detection mechanisms are manual and error-prone.
This study presents a deep learning-based approach to automating the detection of polyps, the tumor that causes colorectal cancer, in the human colon. Various state-of-the-art deep learning models  – including VGG, ResNet, DenseNet, and EfficientNet were trained and tested on a publicly available dataset. The findings of this study show that deep learning models can significantly automate the early diagnosis process of colorectal cancer with high accuracy, especially the DenseNet and EfficientNet models – attaining 99% and 99.4% respectively for both accuracy and F1 score metrics on the test dataset. This study validates the potential of deep learning to enhance the accuracy and reliability of colorectal cancer detection and prevention, ultimately contributing to better quality of diagnosis and patient outcomes.

This research aims to enhance mental well-being by addressing symptoms of anxiety and depression through a personalized, culturally specific multimodal emotion prediction system. It employs an emotionally aware Reinforcement Learning (RL) agent to suggest tailored Cognitive Behavioral Therapy (CBT) activities. The study focuses on developing precise, individualized emotion prediction models using facial expressions, vocal tones, and text, and integrates these models with the RL agent for emotionally aware CBT recommendations. The mHealth approach combines deep learning models with RL, achieving accuracies of 72% for facial expressions, 73% for vocal tones, and 86% for text, all fine-tuned for the Sri Lankan context. Validation through real-world use and user feedback consistently demonstrated that each model exceeds 70% accuracy, fulfilling the objective of precise emotion prediction. A weighted algorithm was introduced to refine the emotion prediction experience and personalize forecasts across the three modalities to enhance mental well-being. The RL-enabled agent suggests CBT activities approved by mental health professionals, tailored based on predicted emotions, and delivered through the same mHealth application. The effectiveness of these interventions was assessed using the DASS-21 questionnaire, revealing significant reductions in depression scores (from 21.08 to 13.54) and anxiety scores (from 19.85 to 10.46) in the study group compared to the control group. The study concludes that integrating multimodal emotion prediction models with RL-based CBT suggestions positively impacts mental well-being and contributes to personalized mental health interventions.

The digital revolution frontiers have rippled across society today – with various web content shared online for users as they seek to promote monetization and asset exchange, with clients constantly seeking improved alternatives at lowered costs to meet their value demands. From item upgrades to their replacement, businesses are poised with retention strategies to help curb the challenge of customer attrition. The birth of smartphones has proliferated feats such as mobility, ease of accessibility, and portability – which, in turn, have continued to ease their rise in adoption, exposing user device vulnerability as they are quite susceptible to phishing. With users classified as more susceptible than others due to online presence and personality traits, studies have sought to reveal lures/cues as exploited by adversaries to enhance phishing success and classify web content as genuine and malicious. Our study explores the tree-based Random Forest to effectively identify phishing cues via sentiment analysis on phishing website datasets as scrapped from user accounts on social network sites. The dataset is scrapped via Python Google Scrapper and divided into train/test subsets to effectively classify contents as genuine or malicious with data balancing and feature selection techniques. With Random Forest as the machine learning of choice, the result shows the ensemble yields a prediction accuracy of 97 percent with an F1-score of 98.19% that effectively correctly classified 2089 instances with 85 incorrectly classified instances for the test-dataset.

Rice is a staple food for most Nigerians, making accurate yield prediction is crucial for food security. This study addresses the limitations of traditional forecasting methods by employing Multiple Linear Regression (MLR) coupled with Recursive Feature Elimination (RFE) to predict rice yield in Adamawa and Cross River states, characterized by distinct agroclimatic conditions. Utilizing climatic data and historical yield records from 1990 to 2022, we trained and evaluated MLR and compared the MLR results with two other machine learning models (XGBoost, and K Nearest Neighbours). RFE-optimized feature selection identified All-sky Photosynthetically Active Radiation (PAR) as a key factor. MLR demonstrated a very stable prediction performance with R² values of 0.90 and 0.92 for Adamawa and Cross River, respectively, after RFE. This research contributes to developing advanced Agro-information systems, supporting informed agricultural decision-making, and enhancing Nigeria's food security.

Malaria continues to pose a significant global health threat, and the emergence of drug-resistant malaria exacerbates the challenge, underscoring the urgent need for new antimalarial drugs. While several machine learning algorithms have been applied to quantitative structure-activity relationship (QSAR) modeling for antimalarial compounds, there remains a need for more interpretable models that can provide insights into the underlying mechanisms of drug action, facilitating the rational design of new compounds. This study develops a QSAR model using Light Gradient Boosting Machine (LightGBM). The model is integrated with SHapley Additive exPlanations (SHAP) to enhance interpretability. The LightGBM model demonstrated superior performance in predicting antimalarial activity, with an ac-curacy of 86%, precision of 85%, sensitivity of 81%, specificity of 89%, and an F1-score of 83%. SHAP analysis identified key molecular descriptors such as maxdO and GATS2m as significant contributors to antimalarial activity. The integration of LightGBM with SHAP not only enhances the predictive ac-curacy of the QSAR model but also provides valuable insights into the importance of features, aiding in the rational design of new antimalarial drugs. This approach bridges the gap between model accuracy and interpretability, offering a robust framework for efficient and effective drug discovery against drug-resistant malaria strains.

Phishing attacks continue to be a significant threat to internet users, necessitating the development of advanced detection systems. This study explores the efficacy of a Bidirectional Gated Recurrent Unit (BiGRU) model combined with feature selection techniques for detecting phishing websites. The dataset used for this research is sourced from the UCI Machine Learning Repository, specifically the Phishing Websites dataset. This approach involves cleaning and preprocessing the data, then normalizing features and employing feature selection to identify the most relevant attributes for classification. The BiGRU model, known for its ability to capture temporal dependencies in data, is then applied. To ensure robust evaluation, we utilized cross-validation, dividing the data into five folds. The experimental results are highly promising, demonstrating a Mean Accuracy, Mean Precision, Mean Recall, Mean F1 Score, and Mean AUC of 1.0. These results indicate the model's exceptional performance distinguishing between phishing and legitimate websites. This study highlights the potential of combining BiGRU models with feature selection and cross-validation to create highly accurate phishing detection systems, providing a reliable solution to enhance cybersecurity measures.

In the clinical treatment of skin diseases and cancer, cryotherapy and immunotherapy offer effective and minimally invasive alternatives. However, the complexity of patient response demands more sophisticated analytical strategies for accurate outcome prediction. This research focuses on analyzing the effect of preprocessing in various machine learning models on the prediction performance of cryotherapy and immunotherapy. The preprocessing techniques analyzed are advanced feature engineering and Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links as resampling techniques and their combination. Various classifiers, including support vector machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), XGBoost, and Bidirectional Gated Recurrent Unit (BiGRU), were tested. The findings of this study show that preprocessing methods can significantly improve model performance, especially in the XGBoost model. Random Forest also gets the same results as XGBoost, but it can also work better without significant preprocessing. The best results were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790, respectively, for accuracy, recall, specificity, precision, and f1 on the Immunotherapy dataset, while on the Cryotherapy dataset, respectively, they were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790. This study confirms the potential of customized preprocessing and machine learning models to provide deep insights into treatment dynamics, ultimately improving the quality of diagnosis.

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.

This research aims to improve the accuracy of liver disease classification using Quantum Feature Engineering (QFE) and the Synthetic Minority Over-sampling Tech-nique and Tomek Links (SMOTE-Tomek) data balancing technique. Four machine learning models were compared in this research, namely eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) on the Indian Liver Patient Dataset (ILPD) dataset. QFE is applied to capture correlations and complex patterns in the data, while SMOTE-Tomek is used to address data imbalances. The results showed that QFE significantly improved LR performance in terms of recall and specificity up to 99%, which is very important in medical diagnosis. The combination of QFE and SMOTE-Tomek gives the best results for the XGB method with an accuracy of 81%, recall of 90%, and f1-score of 83%. This study concludes that the use of QFE and data balancing techniques can improve liver disease classification performance in general.

This research aims to evaluate and compare the performance of several deep learning architectures, especially InceptionV3 and InceptionResNetV2, with other models, such as EfficientNetB3, ResNet50, and VGG19, in classifying rice leaf diseases. In addition, this research also evaluates the impact of using data augmentation on model performance. Three different datasets were used in this experiment, varying the number of images and class distribution. The results show that InceptionV3 and InceptionResNetV2 consistently perform excellently and accurately on most datasets. Data augmentation has varying effects, providing slight advantages on datasets with lower variation. The findings from this research are that the InceptionV3 model is the best model for classifying rice diseases based on leaf images. The InceptionV3 model produces accuracies of 99.53, 58.94, and 90.00 for datasets 1, 2, and 3, respectively. It is also necessary to be wise in carrying out data augmentation by considering the dataset's characteristics to ensure the resulting model can generalize well.

This research explores the effectiveness of machine learning (ML) and deep learning (DL) classification methods in Aspect-Based Sentiment Analysis (ABSA) on product reviews, incorporating Latent Dirichlet Allocation (LDA) for topic modeling. Using the Amazon reviews dataset, this research tests models such as Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Units(GRU). Important aspects such as the product's quality, practicality, and reliability are discussed. The results show that the RF and DL models provide competitive performance, with the RF achieving an accuracy of up to 94.50% and an F1 score of 95.45% for the reliability aspect. The study's conclusions emphasize the importance of selecting an appropriate model based on specifications and data requirements for ABSA, as well as recognizing the need to strike a balance between accuracy and computational efficiency.

Segmentation of pneumonia areas on chest X-rays is essential to improve the accuracy of recognition tasks and subsequent diagnosis. The capabilities of deep learning techniques, U-Net, SegNet, and DeepLabV3, are assessed to achieve these purposes. Using transfer learning, these models were adapted to pneumonia-specific datasets. The evaluation focuses on Intersection over Union (IoU) and accuracy metrics. Results show that DeepLabV3 outperforms U-Net and SegNet, achieving 84.4% accuracy and 81% IoU. U-Net achieves 80.3% accuracy and 68% IoU, while SegNet achieves 81.0% accuracy and 70% IoU. These findings highlight the potential of transfer learning models to automate the segmentation of pneumonia-affected regions, thereby facilitating timely and accurate medical intervention.