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Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. The speed-up process of detection and classification is crucial for effective cancer treatment. Medical image analysis methods and computer-aided diagnosis can enhance this process, providing training and assistance to less experienced clinicians. Deep Learning (DL) models play a great role in accurately detecting and classifying cancer in the huge dataset, especially when dealing with large medical images. This paper presents a novel hybrid model of DL models combined a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for binary breast cancer classification on two datasets available at the Kaggle repository. CNNs extract mammographic features, including spatial hierarchies and malignancy patterns, whereas LSTM networks characterize sequential dependencies and temporal interactions. Our method combines these structures to improve classification accuracy and resilience. We compared the proposed model with other DL models, such as CNN, LSTM, Gated Recurrent Units (GRUs), VGG-16, and RESNET-50. The CNN-LSTM model achieved superior performance with accuracies of 99.17% and 99.90% on the respective datasets. This paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that our model CNN-LSTM can enhance the performance of breast cancer classifiers compared with others with 99.90% accuracy on the second dataset.

Based on the assumption that characterizing the history of a disease will help in improving practice while offering a clue to research, this article aims at reviewing the history of nonalcoholic fatty liver disease (NAFLD) in adults and children. To this end, we address the history of NAFLD histopathology, which begins in 1980 with Ludwig’s seminal studies, although previous studies date back to the 19th century. Moreover, the principal milestones in the definition of genetic NAFLD are summarized. Next, a specific account is given of the evolution, over time, of our understanding of the association of NAFLD with metabolic syndrome, spanning from the outdated concept of “NAFLD as a manifestation of the Metabolic Syndrome”, to the more appropriate consideration that NAFLD has, with metabolic syndrome, a mutual and bi-directional relationship. In addition, we also report on the evolution from first intuitions to more recent studies, supporting NAFLD as an independent risk factor for cardiovascular disease. This association probably has deep roots, going back to ancient Middle Eastern cultures, wherein the liver had a significance similar to that which the heart holds in contemporary society. Conversely, the notions that NAFLD is a forerunner of hepatocellular carcinoma and extra-hepatic cancers is definitely more modern. Interestingly, guidelines issued by hepatological societies have lagged behind the identification of NAFLD by decades. A comparative analysis of these documents defines both shared attitudes (e.g., ultrasonography and lifestyle changes as the first approaches) and diverging key points (e.g., the threshold of alcohol consumption, screening methods, optimal non-invasive assessment of liver fibrosis and drug treatment options). Finally, the principal historical steps in the general, cellular and molecular pathogenesis of NAFLD are reviewed. We conclude that an in-depth understanding of the history of the disease permits us to better comprehend the disease itself, as well as to anticipate the lines of development of future NAFLD research.

The agricultural sector faces critical challenges, including significant crop losses due to undetected plant diseases, inefficient monitoring systems, and delays in disease management, all of which threaten food security worldwide. Traditional approaches to disease detection are often labor-intensive, time-consuming, and prone to errors, making early intervention difficult. This paper proposes an intelligent framework for automated crop health monitoring and early disease detection to overcome these limitations. The system leverages deep learning, cloud computing, embedded devices, and the Internet of Things (IoT) to provide real-time insights into plant health over large agricultural areas. The primary goal is to enhance early detection accuracy and recommend effective disease management strategies, including crop rotation and targeted treatment. Additionally, environmental parameters such as temperature, humidity, and water levels are continuously monitored to aid in informed decision-making. The proposed framework incorporates Convolutional Neural Network (CNN), MobileNet-1, MobileNet-2, Residual Network (ResNet-50), and ResNet-50 with InceptionV3 to ensure precise disease identification and improved agricultural productivity.

Wireless sensor networks (WSNs) for precision agriculture are constrained by limited node energy, weather-induced link variability, and latency requirements. This work introduces SWADS, a clustered WSN architecture in which an unmanned aerial vehicle (UAV) serves as a mobile sink and cooperates with two intelligence layers: (i) a long short-term memory (LSTM) forecaster for short-horizon weather prediction that triggers proactive UAV/fixed-sink handover during adverse conditions, and (ii) a reinforcement-learning (RL) policy for energy-aware cluster-head (CH) selection. In MATLAB simulations with 200 nodes over 8,000 rounds (first-order radio model, AWGN channel), the LSTM achieves ≈ 96% validation accuracy (> 97% training), enabling timely handovers that avoid predicted fades, while the RL policy selects near-optimal CHs with ≈ 95% accuracy, mitigating energy hotspots. SWADS sustains operation from first-node death (FND) at ~ 5,760 rounds to last-node death (LND) at ~ 7,032 rounds, demonstrating extended lifetime under clustered, mobility-aware routing. End-to-end delay remains low at ~ 1–1.2 ms on average, and packet loss is limited to ~ 6.04% despite channel noise, reflecting reduced contention via aggregation and shortened sink–CH distances. Throughput remains stable up to ~ 7,000 rounds with a peak of ~ 160 packets/round. Across baselines (static-sink LEACH-style, UAV mobile sink without weather awareness, and RL-based clustering without mobility), SWADS consistently delivers longer lifetime, lower delay, and more stable throughput. These results indicate that coupling weather-aware sink mobility with RL-driven clustering provides a robust and energy-efficient path to practical, long-lived agricultural WSN deployments.

Since 2019, humanity has been suffering from the negative impact of COVID-19, and the virus did not stop in its usual state but began to pivot to become more harmful until it reached its form now, which is the omicron variant. Therefore, in an attempt to reduce the risk of the virus, which has caused nearly 6 million deaths to this day, it is serious to focus on one of the most important causes of disease resistance, which is nutrition. It has been proven recently that death rates dangerously depend on what enters the human stomach from fat, protein, or even healthy vegetables. This study aims to investigate a relationship between what people eat and the Covid-19 death rate. The study applies five machine learning (ML) models as follows: gradient boosting regressor (GBR), random forest (RF), lasso regression, decision tree (DT), and Bayesian ridge (BR). The study utilizes an available Covid-19 nutrition dataset which consists of 4 attributes as follows: fat percentage, caloric consumption (kcal), food supply amount (kg), and protein levels of various dietary categories for the experiment. The experiment shows the GBR model without optimization obtained optimal results during comparison with other models. The GBR model achieved a mean squared error (MSE) of 0.1512, a mean absolute error (MAE) of 0.2262, mean absolute percentage error (MAPE) of 0.1351, and r 2 value of 0.963. The settings of the GBR model were refined using grid search (GS) hyperparameter optimization to find an optimal solution. This work employs evaluation strategies such as R 2 , MAE, MAPE and MSE to find the best-fitted model. The results displayed that the GS-GBR can enhance the performance of the original classifier compared with others from 96.3 to 99.4%. GS-optimized GBR predicts COVID-19 mortality rates better than other models, suggesting improvement in nutrition-related disease resistance predictions.

Climate change, which causes long-term temperature and weather changes, threatens natural ecosystems and cities. It has worldwide economic consequences. Climate change trends up to 2050 are predicted using the hybrid model that consists of Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM), a unique deep learning architecture. With a focus on Al-Qassim Region, Saudi Arabia, the model assesses temperature, air temperature dew point, visibility distance, and atmospheric sea-level pressure. We used Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) to reduce dataset imbalance. The CNN-GRU-LSTM model was compared to 5 classic regression models: DTR, RFR, ETR, BRR, and K-Nearest Neighbors. Five main measures were used to evaluate model performance: MSE, MAE, MedAE, RMSE, and R². After Min-Max normalization, the dataset was split into training (70%), validation (15%), and testing (15%) sets. The paper shows that the CNN-GRU-LSTM model beats standard regression methods in all four climatic scenarios, with R² values of 99.62%, 99.15%, 99.71%, and 99.60%. Deep learning predicts climate change well and can guide environmental policy and urban development decisions.

Deep learning (DL) has emerged as a powerful tool for intelligent cyberattack detection, especially Distributed Denial-of-Service (DDoS) in Software-Defined Networking (SDN), where rapid and accurate traffic classification is essential for ensuring security. This paper presents a comprehensive evaluation of six deep learning models (Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and a proposed hybrid CNN-GRU model) for binary classification of network traffic into benign or attack classes. The experiments were conducted on an SDN traffic dataset initially exhibiting class imbalance. To address this, Synthetic Minority Over-sampling Technique (SMOTE) was applied, resulting in a balanced dataset of 24,500 samples (12,250 benign and 12,250 attacks). A robust preprocessing pipeline followed, including missing value verification (no missing values were found), feature normalization using StandardScaler to standardize numerical values, reshaping the data into 3D format to fit temporal models like CNN and GRU, and stratified train-test split (80% training, 20% testing) to maintain class distribution. The CNN-GRU model integrates a 1D convolutional layer for spatial pattern extraction and a GRU layer for temporal sequence learning, followed by dense layers with dropout regularization. The model was trained using the Adam optimizer with early stopping to prevent overfitting. Among all models, the CNN-GRU hybrid achieved perfect test performance, with 100% accuracy, 1.0000 precision, recall, and F1-score, and an ROC AUC of 1.0000. It also demonstrated exceptional generalization, achieving a mean cross-validation (CV) accuracy of 99.70% ± 0.09% and a mean AUC of 1.0000 ± 0.0000 across 5-fold stratified cross-validation. While individual models such as GRU, 1D-CNN, and LSTM also showed strong performance, the CNN-GRU hybrid consistently outperformed them in both accuracy and stability. These results validate the effectiveness of combining convolutional and recurrent architectures, augmented with data balancing via SMOTE, for highly accurate SDN-based intrusion detection.

While metabolic dysfunction-associated fatty liver disease (MAFLD) includes the homeostatic model assessment for insulin resistance (HOMA-IR) as one of the criteria to define metabolic dysregulation, the newly proposed metabolic dysfunction-associated steatotic liver disease (MASLD) has removed this criterion. We investigated whether the HOMA-IR can serve as an independent predictive marker for significant fibrosis in subjects with MAFLD. This is a cross-sectional multicenter study. We enrolled a total of 364 patients diagnosed with MAFLD. We conducted a multiple logistic regression analysis to assess the relationship between HOMA-IR and advanced stages of liver fibrosis (F ≥ 2), as assessed by the FIB-4 score and liver stiffness measurement (LSM). Each unit increase in insulin resistance, as measured by HOMA-IR, was associated with a 16% higher likelihood of displaying significant fibrosis, as determined by a non-invasive scoring test, regardless of diabetes or BMI status. HOMA-IR was independently associated with significant fibrosis in non-diabetic (OR: 1.14, 95% CI: 1.07–1.21, P < 0.001) and diabetic (OR: 1.03, 95% CI: 1.00–1.06, P = 0.03) patients. Moreover, significant fibrosis in lean was independently linked to HOMA-IR (OR: 1.06, 95% CI: 1.01–1.12, P = 0.03) and non-lean (OR: 1.04, 95% CI: 1.02–1.07, P < 0.001) patients. Insulin resistance measured by HOMA-IR should be assessed in patients with MAFLD as a key factor of disease progression and incorporated into the disease diagnostic criteria.

In deregulated power markets (DPMs), transmission-line congestion has become more severe and frequent than in traditional power systems. This congestion hinders electricity markets from operating in normal competitive equilibrium. The independent system operator (ISO) is responsible for implementing appropriate measures to mitigate congestion and ensure the proper functioning of the power market. This study utilized generation rescheduling (GR) to address congestion in spot or day-ahead power markets. Rapid alleviation of congestion is essential to prevent tripping of overloaded lines. Owing to their computational inefficiency, evolutionary algorithms (EAs) are ineffective for real-time congestion management, necessitating hybrid models to deliver rapid solutions. This paper proposes a hybrid deep neural network (DNN)-based congestion management (CM) approach for real-time congestion control and reduced computation time. The proposed CM system consists of three cascaded long short-term memory (LSTM) DNNs that operate sequentially. These LSTM modules predict congestion status, violated power, and adjusted active power for rescheduling generation. The LSTM-DNN was trained using data from a grey wolf optimization (GWO). This method provides a rapid solution with approximately 98% accuracy for managing congestion in the spot power market. The suggested approach was evaluated on an IEEE 30 bus system and demonstrated to be highly effective.