The use of Large Language Models (LLMs) for autonomous code generation is gaining attention in emerging technologies. As LLM capabilities expand, they offer new possibilities such as code refactoring, security enhancements, and legacy application upgrades. Many outdated web applications pose security and reliability challenges, yet companies continue using them due to the complexity and cost of upgrades. To address this, we propose an LLM-based multi-agent system that autonomously upgrades legacy web applications to the latest versions. The system distributes tasks across multiple phases, updating all relevant files. To evaluate its effectiveness, we employed Zero-Shot Learning (ZSL) and One-Shot Learning (OSL) prompts, applying identical instructions in both cases. The evaluation involved updating view files and measuring the number and types of errors in the output. For complex tasks, we counted the successfully met requirements. The experiments compared the proposed system with standalone LLM execution, repeated multiple times to account for stochastic behavior. Results indicate that our system maintains context across tasks and agents, improving solution quality over the base model in some cases. This study provides a foundation for future model implementations in legacy code updates. Additionally, findings highlight LLMs' ability to update small outdated files with high precision, even with basic prompts. The source code is publicly available on GitHub: https://github.com/alasalm1/Multi-agent-pipeline.

The presence of different malicious regions on a single breast reveal some necessary information for breast cancer early detection. In current computer-aided diagnosis models, different lesions contained in a single mammogram are not detected and segmented individually. Therefore, the multidetection and segmentation of the breast lesions can help the radiologists for an accurate diagnosis. This study aims to develop a model based on regional learning technique and RoI-based Convolutional neural network (CNN), which is known as Masked Regional Convolutional Neural Network embedded with Feature Pyramid Network. By using Mask RCNN-FPN, we can handle multi-detection, instance segmentation, and classification simultaneously. FPN extracts semantic features at different resolution scales and it can exhibit lesions at multiple scales. The training and testing of the model are performed on the DDSM and Inbreast respectively. In comparison, this model achieved mean average precision 0.84 for multi-detection and segmentation and 91% overall accuracy performance over SegNet and U-Net CNN encoder and decoder segmentation architecture.

Lung diseases are among the most widely recognized reasons for serious disease and passing around the world. The timely analysis is crucial to lessen the risk of any disease and if any disease is diagnosed, then precautions or medicines are immediately given. Therefore, for the diagnosis of lung sound auscultation, progressive computational tools are established and play a very vital role in the detection of disease-related anomalies. The aim of this study is the joint learning of the model that only extracts important breathing samples without generating redundant noise, and then uses this information to train lung sounds into four categories: normal, wheezes, crackles, and wheezes and crackles. This paper signified an unsupervised approach that depends on a Denoising Auto-Encoder (DAE). It utilizes the rebuild errors between the input and the output of the Auto-encoder as an activation audio signal to identify noisiness. A novel design of Recurrent Neural Network (RNN) called noise-masking anomalies recurrent neural network (NMA-RNN) for lung sound order is projected. ICBHI database used in this paper and some results of previous models were compared and achieved 95% accuracy.