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This paper presents a hybrid fault-diagnosis framework for milling cutting tools designed to address three persistent challenges in industrial monitoring: noisy vibration signals, limited fault labels, and variability across operating conditions. The framework begins by removing baseline drift from raw signals to improve the signal-to-noise ratio. Logarithmic continuous wavelet scalograms are then constructed to provide precise time-frequency localization and reveal fault-related harmonics. To enhance feature clarity, a Canny edge operator is applied, suppressing minor artifacts and reducing intra-class variation so that key diagnostic structures are emphasized. Feature representation is obtained through a dual-branch encoder, where one pathway captures localized patterns while the other preserves long-range dependencies, resulting in compact and discriminative fault descriptors. These descriptors are integrated by an ensemble decision mechanism that assigns validation-guided weights to individual learners, ensuring reliable fault identification, improved robustness under noise, and stable performance across diverse operating conditions. Experimental validation on real-world cutting tool data demonstrates an accuracy of 99.78%, strong resilience to environmental noise, and consistent diagnostic performance under variable conditions. The framework remains lightweight, scalable, and readily deployable, providing a practical solution for high-precision tool fault diagnosis in data-constrained industrial environments.

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.

Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks’ features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study’s innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future.

Breast cancer remains a significant global health challenge among women, with histopathological image analysis playing a critical role in early detection. However, existing diagnostic models often struggle to extract complex patterns from high-resolution tissue images, limiting their diagnostic accuracy and generalization. This study aims to develop a high-performance deep learning framework for accurate classification of breast cancer in histopathology images, addressing limitations in feature extraction and spatial dependency modeling. A hybrid deep learning model is proposed, integrating EfficientNetV2 for multi-scale feature extraction with a Gated Recurrent Unit (GRU) enhanced by an attention mechanism to model sequential dependencies. The model is trained and evaluated using the BreakHisand Camelyon17 dataset at 200× magnification. Evaluation metrics include precision, recall, F1-score, specificity, Intersection over Union (IoU), and accuracy. The proposed model achieved superior performance compared to existing architectures such as AlexNet, DenseNet, MobileNetV3, and EfficientNet. It attained a precision of 98.15%, recall of 95.68%, F1-score of 96.82%, specificity of 96%, IoU of 93.99%, and accuracy of 95.72% on the test set. The integration of EfficientNetV2 with GRU and attention mechanisms enables effective learning of spatial and contextual features, enhancing the accuracy and interpretability of breast cancer classification from histopathology images. This framework shows strong potential for aiding pathologists in real-time diagnostic workflows.

The exposure of Unmanned Aerial Vehicles (UAVs) to Global Positioning System (GPS) spoofing attacks constitutes a major cybersecurity challenge. In this work, we conduct a comparative performance analysis of LSTM, GRU, and sequential LSTM–GRU hybrid deep learning models for the detection of GPS spoofing attacks. The ‘UAV Attack’ dataset was preprocessed, and the 11 most significant features were selected using correlation and mutual information algorithms. The models were evaluated using a robust 5-fold cross-validation framework. A combination of 99.31% accuracy, 96.98% recall, and a 97.47% F1-score was achieved by the LSTM–GRU hybrid model, distinguishing it as the leading performer in the experimental study. The LSTM model achieved the highest precision, with a value of 98.49%. ROC curves and AUC values confirmed that the classification performance of all models was close to perfect for the simulated dataset. The findings indicate that deep-learning-based models incorporating the hybrid LSTM–GRU architectures provide an effective and reliable approach designed to identify GPS-spoofing threats affecting UAVs.

This paper presents a fault diagnosis technique for milling machines based on acoustic emission (AE) signals and a hybrid deep learning model optimized with a genetic algorithm. Mechanical failures in milling machines, particularly in critical components like cutting tools, gears, and bearings, account for a significant portion of operational breakdowns, leading to unplanned downtime and financial losses. To address this issue, the proposed method first acquires AE signals from the milling machine. AE signals, capturing the dynamic responses of machine components, are transformed into continuous wavelet transform (CWT) scalograms for further analysis. Gaussian filtering is applied to enhance the clarity of these scalograms, effectively reducing noise while maintaining essential features. A convolutional neural network (CNN) based on the VGG16 architecture is utilized for spatial feature extraction, followed by a bidirectional long short-term memory (BiLSTM) network to capture the temporal dependencies of the scalograms. The genetic algorithm (GA) is used to optimize feature selection and ensure the selection of the most relevant features to further improve the model’s performance. The optimized features are finally fed into a fully connected (FC) layer of the proposed hybrid model for fault classification. The proposed method achieves an accuracy of 99.6%, significantly outperforming traditional approaches. This method offers a highly accurate and efficient solution for fault detection in milling machines, allowing for more reliable predictive maintenance and operational efficiency in industrial settings.

Energy is the most appropriate method to fill the hole of social, financial, and ecological factors for improving human growth across the world. Renewable energy prediction is of paramount importance for reliable operation and efficient management of power systems in a continuously changing climate. Nevertheless, there are still deficits in the accuracy of existing forecaster model, poor generalization, and ineffective optimization results of parameters. To cope with those difficulties, we present in this study a new global renewable energy forecasting model using hybrid deep learning and two‐tier optimization technics. Our proposed model integrates wisdom of features, attention‐guided spatiotemporal learning, and automatic determination of hyperparameters to ensure robust and accurate prediction. Through extensive experiments done on a benchmark global renewable energy dataset, the experimental results show that our proposed approach outperforms other state‐of‐the‐art methods in terms of prediction accuracy, stability, and generalization ability. For the past few years, developments in technology, computing power, and artificial intelligence (AI) have delivered an effective method for tackling numerous urban computing issues such as short‐term renewable energy prediction. This study presents a novel global renewable energy forecasting using a hybrid deep learning and two‐Tier optimization models (GREF‐HDLTOM). The proposed GREF‐HDLTOM model’s main intention is to enhance the prediction model of renewable energy using advanced DL and optimization algorithms. At first, the data preprocessing stage contains input scaling using a standard scaler and output scaling using MaxAbsScaler for converting the categorical data into a numerical format. For the feature selection process, the proposed GREF‐HDLTOM model designs an African vulture optimization algorithm (AVOA). This study introduces a new global renewable energy forecasting model using hybrid deep learning and two‐tier optimization method. Its components consist of AVOA for feature selection and attention‐convolutional gated recurrent neural networks (A‐CGRNNs) for spatiotemporal predictions, while improved northern goshawk optimization (INGO) is employed to search optimal hyperparameters. Experiments show that the new method significantly outperforms existing methods. Furthermore, the hybrid of A‐CGRNN has been deployed for the prediction process. At last, the INGO algorithm adjusts the hyperparameter values of the A‐CGRNN model optimally and outcomes in greater prediction performance. The experimental evaluation of the GREF‐HDLTOM system occurs utilizing a benchmark database. The simulation outcomes indicated the enhanced performance of the GREF‐HDLTOM system compared to existing approaches.

Numerous learning-based techniques for effective human behavior identification have emerged in recent years. These techniques focus only on fundamental human activities, excluding transitional activities due to their infrequent occurrence and short period. Nevertheless, postural transitions play a critical role in implementing a system for recognizing human activity and cannot be ignored. This study aims to present a hybrid deep residual model for transitional activity recognition utilizing signal data from wearable sensors. The developed model enhances the ResNet model with hybrid Squeeze-and-Excitation (SE) residual blocks combining a Bidirectional Gated Recurrent Unit (BiGRU) to extract deep spatio-temporal features hierarchically, and to distinguish transitional activities efficiently. To evaluate recognition performance, the experiments are conducted on two public benchmark datasets (HAPT and MobiAct v2.0). The proposed hybrid approach achieved classification accuracies of 98.03% and 98.92% for the HAPT and MobiAct v2.0 datasets, respectively. Moreover, the outcomes show that the proposed method is superior to the state-of-the-art methods in terms of overall accuracy. To analyze the improvement, we have investigated the effects of combining SE modules and BiGRUs into the deep residual network. The findings indicates that the SE module is efficient in improving transitional activity recognition.

Abnormal electrolyte levels can lead to failures in lead-acid batteries. The capacitive method, as a non-invasive liquid level inspection technique, can be applied to the nondestructive detection of electrolyte level abnormalities in lead-acid batteries. However, due to the high viscosity of sulfuric acid in lead-acid batteries, residual liquid films are easily adhered to the tube walls during rapid liquid level drops, resulting in significant dynamic measurement errors in capacitive methods. To eliminate dynamic measurement errors caused by residual liquid film adhesion, this study proposes a hybrid deep learning model—Poly-LSTM. This model combines polynomial feature generation with a Long Short-Term Memory (LSTM) network. First, polynomial features are generated to explicitly capture the complex nonlinear and coupling effects in the sensor inputs. Subsequently, the LSTM network processes these features to model their temporal dependencies. Finally, the time information encoded by the LSTM is used to generate accurate liquid level predictions. Experimental results show that this method outperforms other comparative models in terms of liquid level estimation accuracy. At a rapid drop rate of 0.12 mm/s, the average absolute error (MAE) is 0.5319 mm, the root mean square error (RMSE) is 0.7180 mm, and the mean absolute percentage error (MAPE) is 0.1320%.

In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.