The rapid expansion of the cloud service industry has raised the critical challenge of ensuring efficient job allocation and trust within a backdrop of heightened privacy concerns. Existing models often struggle to achieve an optimal balance between these factors, particularly in dynamic cloud environments. This research introduces a comprehensive approach that optimizes trust‐based job allocation in cloud services while addressing privacy issues. Our proposed hybrid model integrates k‐anonymity techniques for privacy preservation, coupled with a firefly‐Levenberg (Fireberg) optimization to bolster trust generation. It also employs the time‐aware modified best fit decreasing (T‐MBFD) allocation policy to make resource allocation time‐sensitive. This strategic allocation approach enhances cloud computing system performance and scalability. Simulations using a dataset of 95,000 records demonstrate that our model achieves an impressive 96% accuracy, surpassing existing literature by 5%–14%. The results highlight the model’s ability to provide robust privacy protection while ensuring efficient resource allocation. The proposed hybrid model promises cloud service users high‐quality, secure, and efficient job allocations, thereby improving customer satisfaction and trust. This research makes significant contributions to fortifying the reliability and appeal of cloud services in an evolving digital landscape.

Software defect prediction is a critical task in software engineering, enabling organizations to proactively identify and address potential issues in software systems, thereby improving quality and reducing costs. In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. The models were trained and tested on preprocessed and feature‐selected data, followed by optimization through hyperparameter tuning. Performance evaluation metrics were employed to analyze the results comprehensively, including classification reports, confusion matrices, receiver operating characteristic–area under the curve (ROC‐AUC) curves, precision–recall curves, and cumulative gain charts. The results revealed that XGBoost consistently outperformed other models, achieving the highest accuracy, precision, recall, and AUC scores across all metrics. This indicates its robustness and suitability for predicting software defects in real‐world applications.

Deep learning has transformed image classification tasks across many domains, including medical diagnostics. Medicine wrappers, boxes, and strips often contain valuable but complex information that can be difficult to read and comprehend manually. This complexity drives users to seek additional knowledge online. However, traditional search engines often present a large volume of results, requiring users to manually filter through multiple links to find relevant information, which can be time-consuming. To address this issue, we propose a lightweight pipeline that leverages MobileNetV2 for image classification and Optical Character Recognition (OCR) for extracting text content from medicine packaging. The extracted text is processed using the RAKE algorithm to identify significant keywords, which are then matched with relevant URLs through a Google Search API. To ensure relevance, retrieved links are ranked using ROUGE scores. Performance metrics demonstrate the model's efficiency, with ROUGE-1 achieving 90% Recall, 95% F1-Score, and 90% Accuracy, and ROUGE-L achieving 83% Recall, 91% F1-Score, and 83% Accuracy. The pipeline was trained and validated on a curated dataset of 3,000 real-world medicine packaging images, publicly available on GitHub. These results highlight the novelty and practicality of our solution for automating medical information retrieval from packaging, using an interpretable and scalable deep learning-driven approach.