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.

Currently, many deep learning models are being used to classify COVID‐19 and normal cases from chest X‐rays. However, the available data (X‐rays) for COVID‐19 is limited to train a robust deep‐learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high‐dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep‐convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID‐19). To validate whether the generated images are accurate, we used the k‐mean clustering technique with three clusters (Normal, Pneumonia, and COVID‐19). We only selected the X‐ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X‐rays, we used the Grad‐CAM technique to visualize the underlying pattern, which leads the network to its final decision.

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.

Breast cancer imaging is paramount to quickly detecting and accurately evaluating the disease. The scarcity of annotated mammogram data presents a significant obstacle when building deep learning models that can produce reliable outcomes. This paper proposes a novel approach that utilizes deep convolutional generative adversarial networks (DCGANs) to effectively tackle the issue of limited data availability. The main goal is to produce synthetic mammograms that accurately reproduce the intrinsic patterns observed in real data, enhancing the current dataset. The proposed synthesis method is supported by thorough experimentation, demonstrating its ability to reproduce diverse viewpoints of the breast accurately. A mean similarity assessment with a standard deviation was performed to evaluate the credibility of the synthesized images and establish the clinical significance of the data obtained. A thorough evaluation of the uniformity within each class was conducted, and any deviations from each class’s mean values were measured. Including outlier removal using a specified threshold is a crucial process element. This procedure improves the accuracy level of each image cluster and strengthens the synthetic dataset’s general dependability. The visualization of the class clustering results highlights the alignment between the produced images and the inherent distribution of the data. After removing outliers, distinct and consistent clusters of homogeneous data points were observed. The proposed similarity assessment demonstrates noteworthy effectiveness, eliminating redundant and dissimilar images from all classes. Specifically, there are 505 instances in the normal class, 495 instances in the benign class, and 490 instances in the malignant class out of 600 synthetic mammograms for each class. To check the further validity of the proposed model, human experts visually inspected and validated synthetic images. This highlights the effectiveness of our methodology in identifying substantial outliers.

Breast cancer is widespread throughout the world and can be cured if diagnosed early. Mammography is an irreplaceable and critical technique in modern medicine that serves as a foundation for the detection of breast cancer. In medical imaging, the reliability of synthetic mammogram images is produced by deep convolutional generative adversarial networks (DCGAN). Human validation to assess the quality of synthetic images to examine and calculate the perceptual variations between synthetic images and their real-world counterparts is a difficult task. Thus, this research focused on improving the quality and authenticity of synthetic mammogram images. For this, we explored and identified a new research gap because radiologists consistently expressed much higher confidence levels in real mammogram images in their assessment process. This research highlights the key difference between synthetic and real mammograms by defining mean scores. The defined mean identifies a large gap, with real mammographic images receiving an average score of 0.73 and a synthetic score of 0.31. A statistical analysis was performed, which produced a T-statistic of -6.35, a p-value less than 0.001, and a 95% confidence interval ranging from -0.50 to -0.28. These results have a wide range of implications. It emphasizes the urgent need for further improvements in the generative model, improving the legitimacy and caliber of synthetic mammogram images. Our research highlights how crucial it is to incorporate synthetic images into clinical practice with caution and thought. Ethical considerations must encompass the potential consequences of relying on synthetic data in medical decision-making, along with concerns related to diagnostic accuracy and patient safety.

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.