Search Results (135)

The rapid proliferation of Narrowband Internet of Things (NB-IoT) devices necessitates robust, privacy-preserving intrusion detection systems. While Federated Learning (FL) mitigates data privacy risks through localized training, it introduces vulnerabilities to model poisoning and computational bottlenecks on edge devices. To address these challenges, we propose a secure, hardware-optimized Blockchain-Federated Learning (BC-FL) framework. Deploying a lightweight Hybrid CNN-RNN model on Edge Gateways, we relieve end-sensors of heavy computational tasks. To overcome the ‘cold-start’ problem, we introduce a Domain-Adaptive Transfer Learning strategy, dynamically adapting a pre-trained binary classifier to a multi-class task (Normal, Mirai, Bashlite). Furthermore, a lightweight blockchain ledger provides an immutable audit trail and a reputation-based isolation mechanism to penalize malicious nodes. Evaluated on the N-BaIoT dataset, the proposed 3-class CNN-RNN model achieves 95.62% overall accuracy, with precision/recall/F1-scores of 0.99/0.91/0.95 for Mirai and 0.93/0.99/0.96 for Bashlite attacks. The framework reduces communication bandwidth by 96% compared to centralized learning. During simulated Byzantine attacks, the reputation mechanism successfully banned malicious nodes, maintaining a robust 95.62% global accuracy. This framework offers a highly scalable, secure, and computationally feasible solution for real-time anomaly detection in resource-constrained IoT edge environments. Full article

This paper proposes a hybrid machine learning algorithm that enhances stock price prediction accuracy by selecting the optimal model based on the predicted probabilities of short-term upward and downward trends. First, a long short term memory (LSTM) network and a random forest (RF) model are employed to forecast the next-day closing price. Then, based on each model’s statistical performance in predicting upward (HR+) and downward (HR−) trends over the preceding 60 trading days, the optimal model is selected, and the ultimate forecast is determined accordingly. Experimental results based on nine stocks from the Shanghai and Shenzhen Stock Exchanges, covering the period from January 1, 2018 to December 31, 2023, demonstrate that the proposed method outperforms RF, CNN, LSTM, GRU, CNN-LSTM, LSTM-RNN, LSTM-GRU, and AE + LSTM models. Specifically, it achieves superior performance in direction accuracy metrics (HR, HR+, and HR−), with overall HR improving by approximately 2–5% and MAPE decreasing by about 1–2%. Furthermore, the results indicate that the LSTM model performs better in upward trend prediction, while the RF model is more effective in downward trend prediction. In addition, the tanh activation function is found to outperform ReLU in deep learning models for stock prediction. These findings suggest that the proposed algorithm has practical value for the research on stock investment-related algorithms. Full article

The routine culling of day-old male chicks represents a major ethical concern in the poultry industry. This practice has been banned in Germany, and a similar ban is being considered by the European Union. Each year, hundreds of millions of day-old male chicks are culled in the EU, with several billion culled worldwide. Various methods have been developed to determine the sex of chicks before hatching; however, most are invasive and identify sex relatively late, potentially after the onset of pain perception in embryos. Existing approaches include polymerase chain reaction analysis, spectroscopy, analysis of volatile organic compounds, morphological analysis, and machine vision. Previous studies have shown that machine vision can achieve accuracies of up to 89.25% by analyzing blood vessel patterns during early incubation. Despite this potential, research remains limited, particularly regarding different chicken breeds and the temporal development of embryos. In this study, we investigate the impact of both breed variation and temporal information on early-stage sex identification. Image data were collected on incubation days 4, 5, and 6 from a total of 208 chicken eggs. A convolutional neural network (CNN) and a hybrid convolutional neural network–recurrent neural network (CNN–RNN) model were evaluated to analyze spatial and temporal features. The results show that the CNN model achieved an accuracy of up to 71.43%, while the hybrid CNN–RNN model reached 67.85%. These findings indicate that incorporating temporal information did not improve performance compared to the baseline CNN. However, due to the limited size and quality of the dataset, no definitive conclusions can be drawn. Full article

Background/Objectives: Traditional predictive equations derived from regression analyses exhibit varying degrees of accuracy in estimating resting energy expenditure (REE). AI models can increase the predictability of such equations, even for population-specific ones. This work aimed to improve the prediction of REE in a dataset of Saudi population-specific equations using suitable AI tools. Methods: The dataset from the previously published Saudi population-specific equation by Almajwal and Abulmeaty (AA) in 2019 was used to develop an artificial neural network (ANN)-based version to better predict REE in the adult population. Anthropometric and body composition parameters were used as proposed features. The proposed hybrid prediction model underwent an extensive two-stage, iterative training process. First, the Extreme Gradient Boosting (XGBoost) model is used to compute feature importance scores. Then, the most prominent features were identified and incorporated into the ANN model. These significant features were used to train the ANN model to capture nonlinear correlations among them and make accurate predictions. Subsequently, XGBoost and Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) are used for their ability to provide a multi-layer abstraction of complex input data. Results: A total of 423 participants (208 male, 215 female) were divided into three non-overlapping sets: training (295, 70%), validation (64, 15%), and testing (64, 15%). The ANN model, combined with XGBoost, helped us to develop two equations: AA_ANN1= 2.47 × BMI + 11.9 × AdjBW + 962.5 and AA_ANN2 = 4.29 × age + 9.4 × fat mass + 15.71 × FFMI + 1289.3, where BMI is Body Mass Index (kg/m²), AdjBW is Adjusted Body Weight (kg), and FFMI is Fat Free Mass Index (kg/m²). The AA_ANN1 presented a Root Mean Square Error (RMSE) of 215 and an accuracy of 66.2%, whereas AA_ANN2 presented a lower RMSE of 193 and a higher accuracy of 71.4%. The ANN model was trained on the top 10 features ranked by XGBoost, achieving an average accuracy of 90.2%. Conclusions: The two new predictive equations, developed using an ANN combined with XGBoost, significantly improved REE prediction accuracy to 90.2%, achieved only with the full ANN model. Future external validation in an independent cohort is essential before clinical application of these equations. Full article

Designing effective temporal modeling strategies for video-based sign language recognition (SLR) remains challenging, particularly in low-resource settings where the behavior of modern architectures is not fully understood. In this study, we present a controlled comparative evaluation of temporal models, including recurrent architectures (RNN, LSTM, GRU) and a Transformer encoder, within a unified spatio-temporal framework based on a shared MobileNetV2 feature extractor. All models are trained and evaluated under identical conditions on a curated subset of the WLASL dataset (37 classes), ensuring a fair and reproducible comparison. The results show that recurrent models consistently achieve higher performance than the Transformer-based approach in data-constrained scenarios, with the CNN–LSTM model reaching an accuracy of 90.02%. In contrast, the Transformer model exhibits lower generalization capability, which may be attributed to its higher data requirements. Additionally, increasing architectural complexity through hybrid temporal designs does not result in performance improvements. These findings suggest that simpler recurrent architectures remain effective for temporal modeling in limited data settings and highlight the importance of aligning model complexity with data availability for practical SLR applications. Full article

This study examines corporate carbon emissions of Korean firms from an ESG perspective and develops an AI-based screening framework to improve the identification of firms likely to exceed regulatory emission thresholds. As global climate policies and carbon pricing mechanisms expand, understanding the emission profiles of listed companies has become increasingly important for regulators, investors, and policymakers. Despite growing ESG disclosure, reliable firm-level screening tools for carbon emissions remain limited. Using a pooled annual panel of KOSPI-listed non-financial firms from 2019 to 2024, the study constructs a dataset of 552 firm-year observations. Firms are classified as high-emission when annual emissions exceed the Korean Emissions Trading Scheme (K-ETS) regulatory threshold of 125,000 tCO₂e. To evaluate predictive performance, the analysis compares multiple machine learning models (RF, SVM, XGBoost, LightGBM, and CatBoost) and deep learning models (CNN, RNN, GAN, LSTM, and Transformer). In addition, a hybrid ensemble combining CatBoost, GAN, and Transformer is proposed to enhance predictive reliability. The empirical results show that ESG-augmented models consistently outperform financial-only baselines across AUC and F1 metrics. Among individual models, the ESG-enhanced Transformer achieves the strongest discriminatory power, while the proposed hybrid ensemble delivers the best overall predictive performance. The findings contribute to the literature by demonstrating the incremental value of ESG information in predicting corporate carbon emissions and by presenting a practical AI-based framework for compliance-oriented screening under carbon regulation. From a policy and investment perspective, the model provides a useful decision support tool for anticipating potential inclusion in emissions trading schemes, assessing transition exposure, and supporting data-driven decarbonization strategies. Full article

Software-defined networking (SDN) represents a paradigm shift in network management, but its centralized control plane introduces new and severe security vulnerabilities. Conventional intrusion detection systems, including signature- and rule-based methods, lack adaptability and interpretability in the face of evolving threats. This paper proposes a multi-hybrid deep learning fusion ensemble (MHDLFE) to enhance intrusion detection in SDN environments. The framework integrates Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models via feature fusion and a meta-classifier, thereby improving both detection performance and robustness. To address the critical need for transparency in security systems, the proposed approach incorporates Explainable AI techniques, specifically Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), providing interpretable insights into model decisions. The proposed model achieves strong performance on the NSL-KDD and CIC-IDS2017 datasets, attaining near-perfect binary classification scores of 97.91% and 93.30%, and multiclass accuracies of 98.61% and 97.91%, respectively. These results demonstrate that the proposed framework delivers an effective and trustworthy SDN intrusion detection system by combining deep learning, ensemble fusion, and explainable AI to support accurate, transparent, and reliable cybersecurity decision-making. Full article

This paper addresses the critical communication barrier experienced by deaf and hearing-impaired individuals in the Arab world through the development of an affordable, video-based Arabic Sign Language (ArSL) recognition system. Designed for broad accessibility, the system eliminates specialized hardware by leveraging standard mobile or laptop cameras. Our methodology employs Mediapipe for real-time extraction of hand, face, and pose landmarks from video streams. These anatomical features are then processed by a hybrid deep learning model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), specifically Bidirectional Long Short-Term Memory (BiLSTM) layers. The CNN component captures spatial features, such as intricate hand shapes and body movements, within individual frames. Concurrently, BiLSTMs model long-term temporal dependencies and motion trajectories across consecutive frames. This integrated CNN-BiLSTM architecture is critical for generating a comprehensive spatiotemporal representation, enabling accurate differentiation of complex signs where meaning relies on both static gestures and dynamic transitions, thus preventing misclassification that CNN-only or RNN-only models would incur. Rigorously evaluated on the author-created JUST-SL dataset and the publicly available KArSL dataset, the system achieved 96% overall accuracy for JUST-SL and an impressive 99% for KArSL. These results demonstrate the system’s superior accuracy compared to previous research, particularly for recognizing full Arabic words, thereby significantly enhancing communication accessibility for the deaf and hearing-impaired community. Full article

Non-Intrusive Load Monitoring (NILM) systems are increasingly applied in residential and commercial environments to disaggregate energy consumption without requiring additional hardware sensors. The integration of Machine Learning (ML) techniques has enhanced the accuracy and efficiency of load identification and classification in smart meter-based systems. This study presents a systematic review and meta-analysis aimed at identifying, classifying, and quantitatively evaluating ML models applied to NILM. Searches were conducted in the IEEE Xplore and Scopus databases, restricted to peer-reviewed publications from 2017 to 2024. Thirty studies met the eligibility criteria and were included in the quantitative synthesis using a random-effects meta-analysis model (DerSimonian–Laird estimator). The primary effect measure was the F1-score. Statistical analyses were performed using R (version 4.5.0) and Python (version 3.10.0), including heterogeneity assessment and subgroup analyses according to model type. Hybrid models, such as SVDT-KNN-MLP, LE-CRNN, and RBFNN-MOGA, achieved the highest pooled F1-scores, although supported by a limited number of studies. Traditional approaches, including CNN, KNN, and Random Forest, demonstrated consistently strong performance and broader validation, whereas Boosted Trees and RNN-based models showed lower or more variable results. Substantial heterogeneity was observed across studies, highlighting the need for dataset standardization, reproducible evaluation frameworks, and further validation of emerging hybrid architectures in diverse operational scenarios. This study contributes by providing a quantitative synthesis of machine learning models applied to NILM using a structured PRISMA-based methodology and subgroup analysis by model architecture. Unlike previous narrative reviews, this work integrates scientometric analysis with meta-analytic performance aggregation, offering a consolidated and comparative evidence base for future NILM research. Full article

Accurate prediction of distribution network operating states is essential for implementing proactive fault warning systems. However, with the high penetration of distributed energy resources, measurement data exhibit strong nonlinearity and multi-scale temporal characteristics, posing significant challenges to existing prediction methods. Current mainstream approaches face a critical dilemma: traditional recurrent neural network (RNN) models (e.g., LSTM) suffer from vanishing gradients and memory bottlenecks in long-sequence forecasting, making it difficult to capture long-term evolutionary trends. In contrast, while standard Transformer models excel at global modeling, their smoothing effect renders them insensitive to subtle transient abrupt changes such as voltage sags, and they incur high computational complexity. To address the dual challenges of “difficulty in capturing transient abrupt changes” and “inability to simultaneously handle long-term trends,” this paper proposes a fault precursor trend prediction model that integrates Extended Long Short-Term Memory (XLSTM) with Informer, termed XLSTM-Informer. To tackle the challenge of extracting transient features, an XLSTM-based local encoder is constructed. By replacing the conventional Sigmoid activation with an improved exponential gating mechanism, the model achieves significantly enhanced sensitivity to instantaneous fluctuations in voltage and current. Additionally, a matrix memory structure is introduced to effectively mitigate information forgetting issues during long-sequence training. To overcome the challenge of modeling long-term dependencies, Informer is employed as the global decoder. Leveraging its ProbSparse sparse self-attention mechanism, the model substantially reduces computational complexity while accurately capturing long-range temporal dependencies. Experimental results on a real-world distribution network dataset demonstrate that the proposed model achieves substantially lower Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) compared to standalone CNN, LSTM, and other baseline models, as well as conventional LSTM–Informer hybrid approaches. Particularly under extreme operating conditions—such as sustained high summer loads and winter heating peak loads—the model successfully overcomes the trade-off limitations of traditional methods, enabling simultaneous and accurate prediction of both local precursors and global trends. This provides a reliable technical foundation for proactive warning systems in distribution networks. Full article

Accurate prediction of tuna distribution is essential for sustainable fisheries management. This study develops a two-stage hybrid model combining Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Random Forest (RF) to predict tuna distribution around drifting fish aggregating devices (DFAD) in the Western and Central Pacific Ocean (WCPO). Echo-sounder buoy data from DFAD were aggregated into 2° × 2° grid cells and matched with oceanographic variables from the Copernicus Marine Service. Random Forest-based variable importance analysis identified primary productivity (27%), chlorophyll-a (22%), and dissolved oxygen (18%) as the three dominant environmental drivers. The CNN-RNN component extracts spatiotemporal features from multi-layer ocean data, while the RF classifier performs binary classification of tuna aggregation zones (high-yield vs. low-yield). All five models (Decision Tree, RF, CNN, Transformer, and CNN-RNN-RF) were evaluated on 557 samples using 5-fold stratified cross-validation, with each fold further split 80:20 for training and validation. The proposed CNN-RNN-RF model achieved the highest performance with an AUC of 0.830, accuracy of 82.6%, and F1-scores of 86.3% (high-yield) and 76.2% (low-yield), outperforming the best baseline model (RF: AUC 0.761, accuracy 75.4%). Predicted high-yield zones showed strong consistency with fishing log records, demonstrating the potential of integrating echo-sounder data with hybrid deep learning for data-driven tuna fisheries management. Full article

Background: Correct preoperative evaluation of glioma grade and molecular profile is a prerequisite for tailored treatment strategies. Specifically, the 1p/19q codeletion status represents a major prognostic and therapeutic marker in low-grade gliomas (LGGs). Nevertheless, its assessment is presently performed through invasive histopathological and genetic studies, thus underlining the need for non-invasive alternative approaches. Methods: We introduce a non-invasive radiomics framework that combines quantitative MRI features with sophisticated ML and DL approaches for glioma grading and 1p/19q codeletion status prediction. High-dimensional radiomic features characterizing tumor geometry, intensity, and texture were derived from preoperative MRI-based tumor delineations. Features were normalized and optimized using correlation-based feature selection. Several traditional ML classifiers were compared and contrasted with DL models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a CNN-Long Short-Term Memory (LSTM) hybrid model tailored to exploit both spatial feature hierarchies and feature correlations. Model validation was conducted using five-fold cross-validation and an independent test dataset, with accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics. Results: Among all the models tested, the hybrid CNN-LSTM model performed the best, with an accuracy of 88.1% and an AUC of 0.93, outperforming conventional ML approaches and single-model DL architectures. Explainability analysis showed that the radiomic features of tumor heterogeneity and morphology had the most prominent impact on model performance. Conclusions: These findings indicate that the combination of radiomic features with hybrid DL models is capable of making non-invasive predictions of glioma grade and 1p/19q codeletion status. The new computational model has the potential to be used as a supplementary approach in precision neuro-oncology. Full article

Background: Falls are a leading cause of morbidity and mortality among older adults, creating a need for technologies that can automatically detect falls and summon timely assistance. The rapid evolution of sensor technologies and artificial intelligence has led to a proliferation of fall detection systems (FDS). This systematic review synthesizes the recent literature to provide a comprehensive overview of the current technological landscape. Objective: The objective of this review is to systematically analyze and synthesize the evidence from the academic literature on fall detection technologies. The review focuses on three primary areas: the sensor modalities used for data acquisition, the computational models employed for fall classification, and the emerging trend of shifting from reactive detection to proactive fall risk prediction. Methods: A systematic search of electronic databases was conducted for studies published between 2008 and 2025. Following the PRISMA guidelines, 130 studies met the inclusion criteria and were selected for analysis. Information regarding sensor technology, algorithm type, validation methods, and key performance outcomes was extracted and thematically synthesized. Results: The analysis identified three dominant categories of sensor technologies: wearable systems (primarily Inertial Measurement Units), ambient systems (including vision-based, radar, WiFi, and LiDAR), and hybrid systems that fuse multiple data sources. Computationally, the field has shown a progression from threshold-based algorithms to classical machine learning and is now dominated by deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Many studies report high performance, with accuracy, sensitivity, and specificity often exceeding 95%. An important trend is the expansion of research from post-fall detection to proactive fall risk assessment and pre-impact fall prediction, which aim to prevent falls before they cause injury. Conclusions: The technological capabilities for fall detection are well-developed, with deep learning models and a variety of sensor modalities demonstrating high accuracy in controlled settings. However, a critical gap remains; our analysis reveals that 98.5% of studies rely on simulated falls, with only two studies validating against real-world, unanticipated falls in the target demographic. Future research should prioritize real-world validation, address practical implementation challenges such as energy efficiency and user acceptance, and advance the development of integrated, multi-modal systems for effective fall risk management. Full article

Feature extraction (FE) is an important step in electroencephalogram (EEG)-based classification for brain–computer interface (BCI) systems and neurocognitive monitoring. However, the dynamic and low-signal-to-noise nature of EEG data makes achieving robust FE challenging. Recent deep learning (DL) advances have offered alternatives to traditional manual feature engineering by enabling end-to-end learning from raw signals. In this paper, we present a comparative review of 88 DL models published over the last decade, focusing on EEG FE. We examine convolutional neural networks (CNNs), Transformer-based mechanisms, recurrent architectures including recurrent neural networks (RNNs) and long short-term memory (LSTM), and hybrid models. Our analysis focuses on architectural adaptations, computational efficiency, and classification performance across EEG tasks. Our findings reveal that efficient EEG FE depends more on architectural design than model depth. Compact CNNs offer the best efficiency–performance trade-offs in data-limited settings, while Transformers and hybrid models improve long-range temporal representation at a higher computational cost. Thus, the field is shifting toward lightweight hybrid designs that balance local FE with global temporal modeling. This review aims to guide BCI developers and future neurotechnology research toward efficient, scalable, and interpretable EEG-based classification frameworks. Full article

Unplanned downtime in industrial machinery remains a major challenge, causing substantial economic losses and safety risks across sectors such as manufacturing, food processing, oil and gas, and transportation. This systematic review investigates the application of machine learning (ML) techniques for anomaly and fault detection within the broader context of predictive maintenance. Following a hybrid review methodology, relevant studies published between 2010 and 2025 were collected from major databases including IEEE Xplore, ScienceDirect, SpringerLink, Scopus, Web of Science, and arXiv. The review categorizes approaches into supervised, unsupervised, and hybrid paradigms, analyzing their pipelines from data collection and preprocessing to model deployment. Findings highlight the effectiveness of deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and hybrid frameworks in detecting faults from time series and multimodal sensor data. At the same time, key limitations persist, including data scarcity, class imbalance, limited generalizability across equipment types, and a lack of interpretability in deep models. This review concludes that while ML-based predictive maintenance systems are enabling a transition from reactive to proactive strategies, future progress requires improved hybrid architectures, Explainable AI, and scalable real-time deployment. Full article