Early detection of obstructive sleep apnea (OSA) is extremely necessary to control its’ rising prevalence worldwide. Conventional diagnostic method like polysomnography (PSG) is uncomfortable, intrusive, and costly, thus, limiting its’ easy accessibility among people. To address this, we propose a novel deep learning method for detecting OSA using photoplethysmography (PPG) signal, a non-invasive method that is commonly available in wearable devices. We introduce a novel methodology using Multivariate Long Short-Term Memory-Fully Convolutional Network (MLSTM-FCN) model that effectively captures both temporal dependencies and local features in PPG signals for OSA detection. A new windowing technique was introduced to ensure apneic events are centered within each window to enhance the model’s ability to detect delayed physiological responses to apnea. The model was trained and evaluated on the Multi-Ethnic Study of Atherosclerosis (MESA) dataset, achieving an improvement of 11.3% in accuracy over the state-of-the-art method. The method obtained an accuracy of 93.44%, precision of 0.94, recall of 0.91, and an F1-score of 0.93. These results demonstrate the potential of our method in accurately identifying OSA events. This method offers a unobtrusive, comfortable, and cost-effective alternative to traditional diagnostic tools, making it suitable for long-term, home-based monitoring.

Obstructive sleep apnea (OSA) has become a serious health concern with increasing morbidity worldwide. Even though polysomnography is widely used by physicians for diagnosing OSA, the process is costly, time-consuming, and uncomfortable for patients. This increases the demand for developing unobtrusive, cost-effective, and reliable solutions for detecting OSA and reducing patient discomfort. Several machine learning-based and some deep learning based methods using extracted ECG features for OSA detection from ECG are found to be less reliable due to the manual feature extraction process, very few studies(included in the comparison table of below section) have used only deep learning methods for OSA detection from ECG signals. In this study, we proposed a novel deep learning method that leverages convolutional neural networks (CNN) and long short-term memory (LSTM) networks to learn spatial and temporal features for detecting OSA from ECG data. Our model was trained and evaluated on the publicly available MESA and Apnea-ECG datasets to assess its robustness. In a prudent data windowing step, we center the apnea data within each data window, enabling the model to better learn apnea patterns and resulting in achieving an accuracy of 95.4%, 92.2%, Specificity of 95.1%, 92.8%, Sensitivity of 94.8%, 91.6% and F1 score of 94.1%, 92.1% respectively on MESA and Apnea-ECG dataset. Our model yields an increase of 0.57% and 9.31% in specificity, 1.87% and 50.47% in sensitivity, 4.89% in F1-score, and 3.47% and 17.77% in accuracy against the best result we found using the Apnea-ECG and MESA dataset respectively. The results show our proposed model outperforms state-of-the-art methods on the MESA dataset and achieves equally good results on the Apnea-ECG dataset. Implementation of our model on Jetson orin AGX board gains comparable results with the above-stated accuracy showing the possible practical use of our methodology. These findings highlight the model’s stability, robustness, and high accuracy in detecting OSA.

The interpretability of machine learning model is crucial in healthcare as it fosters reliability and supports clinicians in making informed decisions based on predictions. However, recent approaches for vital parameter estimation often act as black boxes, lacking clarity in their predictive reasoning. Therefore, accurate estimation of vital parameters is crucial not only for timely diagnosis and effective patient monitoring but also for ensuring that clinicians can trust and understand the model’s predictions. In this study, we introduce XVPE-Net, an explainable framework aimed at improving the state-of-the-art VPE-Net model for estimating vital parameters: heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our model enhances interpretability by visually highlighting key parts of the PPG signal that significantly influence VPE-Net’s predictions of vital parameters. We test our model using 1,000 PPG segments from 38 subjects in the MIMIC-III dataset, which is available in the Physionet repository. The results show strong predictive capabilities, along with better transparency and reliability. This study helps us understand how the model makes decisions and supports the use of explainable AI in health monitoring, contributing to the development of more reliable models for medical applications.

Social media is a popular medium for the dissemination of real-time news all over the world. Easy and quick information proliferation is one of the reasons for its popularity. An extensive number of users with different age groups, gender, and societal beliefs are engaged in social media websites. Despite these favorable aspects, a significant disadvantage comes in the form of fake news, as people usually read and share information without caring about its genuineness. Therefore, it is imperative to research methods for the authentication of news. To address this issue, this article proposes a two-phase benchmark model named WELFake based on word embedding (WE) over linguistic features for fake news detection using machine learning classification. The first phase preprocesses the data set and validates the veracity of news content by using linguistic features. The second phase merges the linguistic feature sets with WE and applies voting classification. To validate its approach, this article also carefully designs a novel WELFake data set with approximately 72 000 articles, which incorporates different data sets to generate an unbiased classification output. Experimental results show that the WELFake model categorizes the news in real and fake with a 96.73% which improves the overall accuracy by 1.31% compared to bidirectional encoder representations from transformer (BERT) and 4.25% compared to convolutional neural network (CNN) models. Our frequency-based and focused analyzing writing patterns model outperforms predictive-based related works implemented using the Word2vec WE method by up to 1.73%.

Nowadays, massive amounts of data are acquired, transferred, and analyzed nearly in real-time by utilizing a large number of computing and storage elements interconnected through high-speed communication networks. However, one issue that still requires research effort is to enable efficient monitoring of applications and infrastructures of such complex systems. In this paper, we introduce an Integer Linear Programming (ILP) model called M3AT for optimized assignment of monitoring agents and aggregators on large-scale computing systems. We identified a set of requirements from three representative data-intensive applications and exploited them to define the model's input parameters. We evaluated the scalability of M3AT using the Constraint Integer Programing (SCIP) solver with default configuration based on synthetic data sets. Preliminary results show that the model provides optimal assignments for subsystems composed of up to 200 monitoring agents with complex I/O policies, while keeping the number of aggregators constant and demonstrates variable sensitivity with respect to the scale of monitoring data aggregators and limitation policies imposed.

Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.