In the global fight against breast cancer, the importance of early diagnosis is unparalleled. Early identification not only improves treatment options but also significantly improves survival rates. Our research introduces an innovative ensemble method that synergistically combines the strengths of four state‐of‐the‐art convolutional neural networks (CNNs): EfficientNet, AlexNet, ResNet, and DenseNet. These networks were chosen for their architectural advances and proven efficacy in image classification tasks, particularly in medical imaging. Each network within our ensemble is uniquely optimized: EfficientNet is fine‐tuned with customized scaling to address dataset specifics; AlexNet employs a variable dropout mechanism to reduce overfitting; ResNet benefits from learnable weighted skip connections for better gradient flow; and DenseNet uses selective connectivity to balance computational efficiency and feature extraction. This ensembling strategy combines the predictive output of multiple CNNs, each trained with an individually optimized network, to enhance the ensemble’s overall diagnostic performance. This provides higher precision and stability than any model and shows outstanding performance in the early stage of breast cancer with a precision of up to 94.6%, sensitivity of 92.4%, specificity of 96.1%, and area under the curve (AUC) of 98.0%. This ensemble framework indicated a leap in the early diagnosis of breast cancer as it is a powerful tool that combines several state‐of‐the‐art techniques, hence providing better results.

The widespread adoption of cloud services has posed several challenges, primarily revolving around energy and resource efficiency. Integrating cloud and fog resources can help address these challenges by improving fog-cloud computing environments. Nevertheless, the search for optimal task allocation and energy management in such environments continues. Existing studies have introduced notable solutions; however, it is still a challenging issue to efficiently utilize these heterogeneous cloud resources and achieve energy-efficient task scheduling in fog-cloud of things environment. To tackle these challenges, we propose a novel ML-based EcoTaskSched model, which leverages deep learning for energy-efficient task scheduling in fog-cloud networks. The proposed hybrid model integrates Convolutional Neural Networks (CNNs) with Bidirectional Log-Short Term Memory (BiLSTM) to enhance energy-efficient schedulability and reduce energy usage while ensuring QoS provisioning. The CNN model efficiently extracts workload features from tasks and resources, while the BiLSTM captures complex sequential information, predicting optimal task placement sequences. A real fog-cloud environment is implemented using the COSCO framework for the simulation setup together with four physical nodes from the Azure B2s plan to test the proposed model. The DeFog benchmark is used to develop task workloads, and data collection was conducted for both normal and intense workload scenarios. Before preprocessing the data was normalized, treated with feature engineering and augmentation, and then split into training and test sets. To evaluate performance, the proposed EcoTaskSched model demonstrated superiority by significantly reducing energy consumption and improving job completion rates compared to baseline models. Additionally, the EcoTaskSched model maintained a high job completion rate of 85%, outperforming GGCN and BiGGCN. It also achieved a lower average response time, and SLA violation rates, as well as increased throughput, and reduced execution cost compared to other baseline models. In its optimal configuration, the EcoTaskSched model is successfully applied to fog-cloud computing environments, increasing task handling efficiency and reducing energy consumption while maintaining the required QoS parameters. Our future studies will focus on long-term testing of the EcoTaskSched model in real-world IoT environments. We will also assess its applicability by integrating other ML models, which could provide enhanced insights for optimizing scheduling algorithms across diverse fog-cloud settings.

The rapid proliferation of Internet of Things (IoT) devices has introduced significant security challenges, with Distributed Denial of Service (DDoS) attacks posing a critical threat to network integrity. Traditional detection methods often rely on computationally intensive models, rendering them unsuitable for resource-constrained IoT environments. To address this limitation, this study proposes a lightweight and scalable machine learning-based DDoS detection framework specifically designed for IoT networks. Utilizing the NSL-KDD dataset, the framework employs an Extra Trees Classifier (ETC) for feature selection, reducing dimensionality while retaining critical attributes. Reduced features were selected to enhance performance and reduce processing cost. Three supervised learning models, Random Forest, Logistic Regression, and Naïve Bayes, were implemented and evaluated based on their detection accuracy, precision, recall, and F1-score. Experimental results demonstrate that the Random Forest model achieves exceptional accuracy (99.88%), precision (99.93%), recall (99.81%), and F1-score (99.87%), outperforming both Logistic Regression (91.61% accuracy) and Naïve Bayes (87.62% accuracy). Furthermore, the proposed framework significantly reduces computational overhead compared to deep learning-based approaches, making it highly suitable for IoT deployments. This research advances IoT security by providing a scalable, efficient, and accurate solution for detecting DDoS attacks, thereby bridging the gap between high-performance requirements and resource limitations in real-world IoT applications.

Wheat plays a vital role in Pakistan’s economy and food security, making accurate yield forecasting essential for planning and resource management. Traditional approaches—such as manual field surveys and remote sensing—have been widely used, but their effectiveness in capturing yield variation across different growth stages remains uncertain. This study explores a multi-phase approach to wheat yield prediction by dividing the crop cycle into four key stages and analyzing data from 2017 to 2022. Using the Google Earth Engine platform, the analysis integrates satellite imagery, seasonal weather variables, and soil information. A range of machine learning and deep learning models were tested to assess their predictive performance. The results show that combining diverse data sources with advanced AI techniques significantly improves prediction accuracy, with model performance reaching R² values between 0.4 and 0.88. Among the tested models, those capable of capturing spatial and temporal patterns reduced prediction errors most effectively. These findings demonstrate the value of integrating environmental data and AI methods for enhancing crop yield forecasting across complex agricultural regions. This study presents a comparative analysis of AI-based approaches against conventional methods, demonstrating their superiority in three key areas: (1) improved computational efficiency through optimized learning architectures, (2) enhanced spatial generalizability by capturing complex, nonlinear patterns, and (3) greater prediction stability under varying conditions. Our findings underscore the transformative potential of AI models, offering a more reliable and scalable alternative for predictive tasks in our framework. This research offers a robust framework for forecasting wheat yields, aiding decision-makers in improving food security and agricultural planning.

Accurate forecasting of crop yields is essential for ensuring food security and promoting sustainable agricultural practices. Winter wheat, a key staple crop in Pakistan, faces challenges in yield prediction because of the complex interactions among climatic, soil, and environmental factors. This study introduces DeepAgroNet, a novel three-branch deep learning framework that integrates satellite imagery, meteorological data, and soil characteristics to estimate winter wheat yields at the district level in southern Pakistan. The framework employs three leading deep learning models—convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN)—trained on detrended yield data from 2017 to 2022. The Google Earth Engine platform was used to process and integrate remote sensing, climate, and soil data. CNN emerged as the most effective model, achieving an R² value of 0.77 and a forecast accuracy of 98% one month before harvest. The RNN and ANN models also demonstrated moderate predictive capabilities, with R² values of 0.72 and 0.66, respectively. The results showed that all models achieved less than 10% yield error rates, highlighting their ability to effectively integrate spatial, temporal, and static data. This study emphasizes the importance of deep learning in addressing the limitations of traditional manual methods for yield prediction. By benchmarking the results against Crop Report Services data, this study confirms the reliability and scalability of the proposed framework. The findings demonstrate the potential of DeepAgroNet to improve precision agriculture practices, contributing to food security and sustainable agricultural development in Pakistan. Furthermore, this adaptable framework can serve as a model for similar applications in other agricultural regions around the world.

Breast cancer (BC) remains a major global health problem designed for early diagnosis and requires innovative solutions. Mammography is the most common method of detecting breast abnormalities, but it is difficult to interpret the mammogram due to the complexities of the breast tissue and tumor characteristics. The EfficientViewNet model is designed to overcome false predictions of BC. The model consists of two pathways designed to analyze breast mass characteristics from craniocaudal (CC) and mediolateral oblique (MLO) views. These pathways comprehensively analyze the characteristics of breast tumors from each view. The proposed study possesses several significant strengths, with a high F 1 score and recall of 0.99. It shows the robust discriminatory ability of the proposed model compared to other state‐of‐the‐art models. The study also explored the effects of different learning rates on the model’s training dynamics. It showed that the widely used stepwise reduction strategy of the learning rate played a key role in the convergence and performance of the model. It enabled fast early progress and careful fine‐tuning of the learning rate as the model nears optimum. The model opens the door to achieving a high level of patient outcomes through a very rigorous methodology.