Human metapneumovirus (hMPV) is a significant cause of respiratory illness, particularly in children, elderly individuals, and immunocompromised patients. Despite its clinical relevance, hMPV poses diagnostic challenges due to its symptom similarity with other respiratory illnesses, such as influenza and respiratory syncytial virus (RSV), and the lack of specialized detection systems. Traditional diagnostic methods are often inadequate for providing rapid and accurate results, particularly in low-resource settings. This study proposes a novel deep learning framework, referred to as hMPV-Net, which leverages Convolutional Neural Networks (CNNs) to facilitate the precise detection and classification of hMPV infections. The CNN model is designed to perform binary classification by differentiating between hMPV-positive and hMPV-negative cases. To address the lack of real-world patient data, simulated image datasets were used for model training and evaluation, allowing the model to generalize to various clinical scenarios. A key challenge in developing this model is the imbalance within the dataset, where hMPV-positive cases are often underrepresented. To mitigate this, the framework incorporates advanced techniques such as data augmentation, weighted loss functions, and dropout regularization, which help to balance the dataset, improve model robustness, and enhance classification accuracy. These techniques are crucial in addressing issues such as overfitting and generalization, which are common when working with limited datasets in medical imaging tasks. The dataset used for model training and testing consists of 10,000 samples, with an equal distribution of hMPV-positive and hMPV-negative cases. Experimental results demonstrate that the hMPV-Net model achieves a high test accuracy of 91.8%, along with impressive test precision, recall, and F1-score values around 92%. These metrics indicate that the model performs exceptionally well in classifying both hMPV-positive and hMPV-negative cases. Furthermore, the model exhibits superior computational efficiency, requiring only 3.2 GFLOPs, which is significantly lower than other state-of-the-art models such as ResNet-50 and VGG-16. This reduction in computational cost makes the model suitable for deployment in resource-constrained healthcare environments, where computing power and infrastructure may be limited.

The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by conventional signal recognition techniques has significantly increased due to the growing number of antennas and higher-order modulations. To overcome these challenges, deep learning approaches are adopted as they offer versatility, nonlinear modelling capabilities, and parallel computation efficiency for large-scale MIMO detection. Therefore, a deep network for channel estimation and massive MIMO detection is developed to reduce computational complexity issues. Initially, a channel estimation scheme is developed to enhance the channel capacity of the MIMO system. It correlates the transmitted and received signals using a confusion matrix. The proposed Modified Squid Game Optimizer (MSGO) is employed for channel state estimation. Based on the obtained channel state information, MIMO detection is performed within the communication system. Here, Multiuser Interference Cancellation (MIC)-based iterative sequential detection is initially conducted. Then, massive MIMO detection is performed using the Dilated Adaptive Recurrent Neural Network with Attention Mechanism (DARNN-AM) through learnable parameters. Moreover, to further optimize the detection performance by fine-tuning the attributes of DARNN-AM, the MSGO is utilized. The proposed network performs multi-segment mapping across multiple constellation points with different modulation schemes. The effectiveness of the proposed deep learning-based MIMO detection system is evaluated by comparing it with existing techniques and algorithms to validate its superior performance.