PeerJ Computer Science最新文献

Background: Medication non-adherence remains a significant global healthcare challenge, resulting in inadequate disease management, increased hospitalisations, and higher healthcare costs. Mobile health (mHealth) applications have emerged as promising digital health tools for enhancing medication adherence through real-time monitoring, personalised reminders, artificial intelligence (AI)-driven interventions, and improved patient engagement.

Objectives: This scoping review examines the effectiveness, key features, and challenges of mHealth applications in promoting medication adherence across diverse patient populations and healthcare settings. It also seeks to identify research gaps and inform future development and implementation strategies for digital therapeutics.

Eligibility criteria: Studies published between 2020 and 2024 were included if they investigated the use of mHealth applications to improve medication adherence and reported outcomes related to adherence rates, patient health indicators, or user engagement. Only studies with empirical data, including randomised controlled trials, observational studies, or mixed-methods research, were considered.

Sources of evidence: A comprehensive search was conducted across Scopus, Web of Science, PubMed/MEDLINE, Google Scholar, and CINAHL databases. In total, 319 studies met the inclusion criteria following a systematic screening process based on Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines.

Charting methods: Data were extracted on study design, app functionalities, patient demographics, adherence outcomes, and barriers to adoption. The charted data were thematically synthesised to identify trends, success factors, and limitations.

Results: Among the included studies, 85% reported improved medication adherence associated with features such as personalised medication reminders, real-time health tracking, and AI-powered adherence prediction. Clinical outcomes were also frequently observed, including improved blood pressure, glucose control, and patient-reported quality of life. Key barriers to adoption included limited digital literacy, concerns about data privacy, socioeconomic disparities, and a lack of integration with electronic health records (EHRs).

Conclusions: mHealth applications show significant potential to improve medication adherence and health outcomes, particularly in the management of chronic diseases. However, inclusive design, robust data privacy frameworks, and evidence-based implementation strategies are essential for scalability and sustained impact. Future research should focus on long-term effectiveness, cost-efficiency, and integration of mHealth tools within broader healthcare systems.

In social network analysis, bridges play a critical role in maintaining connectivity and facilitating the dissemination of information between communities. Despite increasing interest in bridge structures, a systematic classification of their roles across various network types remains unexplored. This study introduces a categorization of bridges into structural and functional types. Structural bridges maintain connectivity by preventing network fragmentation, whereas functional bridges facilitate the flow of information between communities. We conducted a comprehensive literature review and classified existing studies within this framework. The findings clarify the distinct roles of bridges and provide valuable insight for devising effective strategies for network design and analysis.

Preschool education plays a vital role in the harmonious development of an individual. Understanding basic shapes, colors, and letters at an early age lays a strong foundation for academic excellence and emotional growth. At an early childhood stage, the skills of spatial reasoning and problem-solving can be developed by recognizing and comprehending the depicted objects. By exploring deep learning technology, this article presents a cognitive enhancement framework for recognizing nested objects. With cutting-edge models, such as You Only Look Once (YOLOv8) and Visual Geometry Group (VGG16), objects and intra-objects are detected. For semantic description, the neural network model, specifically long short-term memory (LSTM), is exploited, preceded by precise object recognition. The framework is implemented in Google Colab with the prominent packages of Ultralytics, PyTorch, and OpenCV. The models are trained and tested by a custom dataset: PreEduDS. The results of the systematic evaluation suggest that the framework has widespread applicability. A promising accuracy score of 94.4% is obtained for object recognition and 96.5% for predicting precise semantic textual description. The proposed system is well-suited for enhancing preschool education and training based on augmented reality (AR) applications.

With the discovery of electricity and the widespread adoption of lighting technology, the extensive application of electricity has greatly increased productivity, making night-time factory production possible. At the same time, the rapid expansion of factories has led to a significant increase in particulate matter 2.5 (PM2.5) in the air. However, economic development heavily relies on lighting and factory production. To address this issue, researchers have focused on predicting urban gross domestic product (GDP) through night-time lights and PM2.5, but current studies often focus on the impact of a single factor on GDP, leaving room for improvement in model accuracy. In response to this problem, this article proposes the Relationship and Prediction Model between Night Light Data, PM2.5, and Urban GDP (R&P-NLPG model). Firstly, night light data, PM2.5 data, and GDP data are collected and preprocessed. Secondly, correlation analysis is conducted to analyze the correlation between data features. Then, data fusion methods are used to integrate features between night-time data and PM2.5 data, forming the third data features. Next, a neural network is constructed to establish a functional relationship between features and GDP. Finally, the trained neural network model is used to predict GDP. The experimental results demonstrate that the predictive capability of the R&P-NLPG model outperforms GDP prediction models constructed with single-feature input and existing multi-feature input.

Brain tumor diagnosis using magnetic resonance imaging (MRI) scans is critical for improving patient survival rates. However, automating the analysis of these scans faces significant challenges, including data privacy concerns and the scarcity of large, diverse datasets. A potential solution is federated learning (FL), which enables cooperative model training among multiple organizations without requiring the sharing of raw data; however, it faces various challenges. To address these, we propose Federated Adaptive Reputation-aware aggregation with CKKS (Cheon-Kim-Kim-Song) Homomorphic encryption (FedARCH), a novel FL framework designed for a cross-silo scenario, where client weights are aggregated based on reputation scores derived from performance evaluations. Our framework incorporates a weighted aggregation method using these reputation scores to enhance the robustness of the global model. To address sudden changes in client performance, a smoothing factor is introduced, while a decay factor ensures that recent updates have a greater influence on the global model. These factors work together for dynamic performance management. Additionally, we address potential privacy risks from model inversion attacks by implementing a simplified and computationally efficient CKKS homomorphic encryption, which allows secure operations on encrypted data. With FedARCH, encrypted model weights of each client are multiplied by a plaintext reputation score for weighted aggregation. Since we are multiplying ciphertexts by plaintexts, instead of ciphertexts, the need for relinearization is eliminated, efficiently reducing the computational overhead. FedARCH achieved an accuracy of 99.39%, highlighting its potential in distinguishing between brain tumor classes. Several experiments were conducted by adding noise to the clients' data and varying the number of noisy clients. An accuracy of 94% was maintained even with 50% of noisy clients at a high noise level, while the standard FL approach accuracy dropped to 33%. Our results and the security analysis demonstrate the effectiveness of FedARCH in improving model accuracy, its robustness to noisy data, and its ability to ensure data privacy, making it a viable approach for medical image analysis in federated settings.

Image processing and computer vision highly rely on data augmentation in machine learning models to increase the diversity and variability within training datasets for better performance. One of the most promising and widely used applications of data augmentation is in classifying waste object images. This research focuses on augmenting waste object images with generative adversarial networks (GANS). Here deep convolutional GAN (DCGAN), an extension of GAN is utilized, which uses convolutional and convolutional-transpose layers for better image generation. This approach helps generate realism and variability in images. Furthermore, object detection and classification techniques are used. By utilizing ensemble learning techniques with DenseNet121, ConvNext, and Resnet101, the network can accurately identify and classify waste objects in images, thereby contributing to improved waste management practices and environmental sustainability. With ensemble learning, a notable accuracy of 99.80% was achieved. Thus, by investigating the effectiveness of these models in conjunction with data augmentation techniques, this novel approach of GAN-based augmentation cooperated with ensemble models aims to provide valuable insights into optimizing waste object identification processes for real-world applications. Future work will focus on better data augmentation methods with other types of GANS architectures and introducing multimodal sources of data to further increase the performance of the classification and detection models.

The escalating complexity of cyber threats in smart microgrids necessitates advanced detection frameworks to counter sophisticated attacks. Existing methods often underutilize optimization techniques like Harris hawks optimization (HHO) and struggle with class imbalance in cybersecurity datasets. This study proposes a novel framework integrating HHO with extreme gradient boosting (XGBoost) and a hybrid convolutional neural network with support vector machine (Cnn-SVM) to enhance cyber threat detection. Using the distributed denial of service (DDoS) botnet attack and KDD CUP99 datasets, the proposed models leverage HHO for hyperparameter optimization, achieving accuracies of 99.97% and 99.99%, respectively, alongside improved area under curve (AUC) metrics. These results highlight the framework's ability to capture complex nonlinearities and address class imbalance through RandomOverSampler. The findings demonstrate the potential of HHO-optimized models to advance automated threat detection, offering robust and scalable solutions for securing critical infrastructures.

Background: Reliable measurement of left ventricular mass (LVM) in echocardiography is essential for early detection of left ventricular dysfunction, coronary artery disease, and arrhythmia risk, yet growing patient volumes have created critical shortage of experts in echocardiography. Recent deep learning approaches reduce inter-operator variability but require large, fully labeled datasets for each standard view-an impractical demand in many clinical settings.

Methods: To overcome these limitations, we propose a heatmap-based point-estimation segmentation model trained via model-agnostic meta-learning (MAML) for few-shot LVM quantification across multiple echocardiographic views. Our framework adapts rapidly to new views by learning a shared representation and view-specific head performing K inner-loop updates, and then meta-updating in the outer loop. We used the EchoNet-LVH dataset for the PLAX view, the TMED-2 dataset for the PSAX view and the CAMUS dataset for both the apical 2-chamber and apical 4-chamber views under 1-, 5-, and 10-shot scenarios.

Results: As a result, the proposed MAML methods demonstrated comparable performance using mean distance error, mean angle error, successful distance error and spatial angular similarity in a few-shot setting compared to models trained with larger labeled datasets for each view of the echocardiogram.

Abnormal cardiac activity can lead to severe health complications, emphasizing the importance of timely diagnosis. It is essential to save lives if diseases are diagnosed in a reasonable timeframe. The intelligent telehealth system has the potential to transform the healthcare industry by continuously monitoring cardiac diseases remotely and non-invasively. A cloud-based telehealth system utilizing an Internet of Things (IoT)-enabled electrocardiogram (ECG) monitor gathers and analyzes ECG signals to predict cardiac complications and notify physicians in crises, facilitating prompt and precise diagnosis of cardiovascular disorders. Abnormal cardiac activity can lead to severe health complications, making early detection crucial for effective treatment. This study provides an efficient method based on deep learning convolutional neural network (CNN) and long short-term memory (LSTM) approaches to categorize and detect cardiovascular problems utilizing ECG data to increase classifications (referring to distinguishing between different ECG signal categories) and precision. Additionally, a threshold-based classifier is developed for the telehealth system's security and privacy to enable user identification (for selecting the correct user from a group) using ECG data. A data preprocessing and augmentation technique was applied to improve the data quality and quantity. The proposed LSTM model attained 99.5% accuracy in the classification of cardiac diseases and 98.6% accuracy in user authentication utilizing ECG signals. These results exhibit enhanced performance compared to conventional machine learning and convolutional neural network models.

As a key natural language processing (NLP) task, question generation (QG) is crucial for boosting educational quality and fostering personalized learning. This article offers an in-depth review of the research advancements and future directions in QG in education (QGEd). We start by tracing the evolution of QG and QGEd. Next, we explore the current state of QGEd research through three dimensions: its three core objectives, commonly used datasets, and question quality evaluation methods. This article also underscores its unique contributions to QGEd, including a systematic analysis of the research landscape and an identification of pivotal challenges and opportunities. Lastly, we highlight future research directions, emphasizing the need for deeper exploration in QGEd regarding multimodal data processing, controllability of fine-grained cognitive and difficulty levels, specialized educational dataset construction, automatic evaluation technology development, and system architecture design. Overall, this review aims to provide a comprehensive overview of the field, offering valuable insights for researchers and practitioners in educational technology.