With the outbreak of COVID-19 that has prompted an increased focus on self-care, more and more people hope to obtain disease knowledge from the Internet. In response to this demand, medical question answering and question generation tasks have become an important part of natural language processing (NLP). However, there are limited samples of medical questions and answers, and the question generation systems cannot fully meet the needs of non-professionals for medical questions. In this research, we propose a BERT medical pretraining model, using GPT-2 for question augmentation and T5-Small for topic extraction, calculating the cosine similarity of the extracted topic and using XGBoost for prediction. With augmentation using GPT-2, the prediction accuracy of our model outperforms the state-of-the-art (SOTA) model performance. Our experiment results demonstrate the outstanding performance of our model in medical question answering and question generation tasks, and its great potential to solve other biomedical question answering challenges.

Medical image classification plays an essential role in disease diagnosis and clinical treatment. More and more research efforts have been dedicated to the design of effective methods for medical image classification. As an effective framework, the capsule network (CapsNet) can realize translation equivariance. Lots of current research applies capsule networks in medical image analysis. In this paper, we propose an attentive octave convolutional capsule network (AOC-Caps) for medical image classification. In AOC-Caps, an AOC module is used to replace the traditional convolution operation. The purpose of the AOC module is to process and fuse the high- and low-frequency information in the input image simultaneously, and weigh the important parts automatically. Following the AOC module, a matrix capsule is used and the expectation maximization (EM) algorithm is applied to update the routing weights. The proposed AOC-Caps and comparative methods are tested on seven datasets, including PathMNIST, DermaMNIST, OCTMNIST, PneumoniaMNIST, OrganMNIST_Axial, OrganMNIST_Coronal, and OrganMNIST_Sagittal, which are from MedMNIST. In the experiments, baselines include the traditional CNN models, automated machine learning (AutoML) methods, and related capsule network methods. The experimental results demonstrate that the proposed AOC-Caps achieves better performance on most of the seven medical image datasets.

COVID-19 has led to a severe impact on the society and healthcare system, with early diagnosis and effective treatment becoming critical. The Chest X-ray (CXR) is the most time-saving and cost-effective tool for diagnosing COVID-19. However, manual diagnosis through human eyes is time-consuming and tends to introduce errors. With the challenge of a large number of infections and a shortage of medical resources, a fast and accurate diagnosis technique is required. Manual detection is time-consuming, depends on individual experience, and tends to easily introduce errors. Deep learning methods can be used to develop automated detection and computer-aided diagnosis. However, they require a large amount of data, which is not practical due to the limited annotated CXR images. In this research, SDViT, an approach based on transformers, is proposed for COVID-19 diagnosis through image classification. We propose three innovations, namely, self-supervised learning, detail correction path (DCP), and domain transfer, then add them to the ViT Transformer architecture. Based on experimental results, our proposed method achieves an accuracy of 95.2381%, which is better performance compared to well-established methods on the X-ray Image dataset, along with the highest precision (0.952310), recall (0.963964), and F1-score (0.958102). Extensive experiments show that our model achieves the best performance on the synthetic-covid-cxr dataset as well. The experimental results demonstrate the advantages of our design for the classification task of COVID-19 X-ray images.

Social media provides a public and convenient platform for people to communicate. However, it is also open to hateful behavior and toxic comments. Social networks, like Facebook, Twitter, and many others, have been working on developing...

Cybersecurity has become a critical concern in the digital world, demanding robust and intelligent systems capable of defending against increasingly complex and evolving threats. This paper comprehensively explores the intersection between machine learning (ML) and cybersecurity, highlighting how data-driven approaches are reshaping threat detection, prevention, and response mechanisms. We provide an in-depth overview of core ML paradigms and analyze their relevance and effectiveness in various cybersecurity applications. A systematic literature review was conducted using major academic databases including IEEE Xplore, ACM Digital Library, SpringerLink, and ScienceDirect, covering peer-reviewed journal articles and conference papers published primarily between 2018 and 2025. Studies were selected based on relevance to ML-driven cybersecurity applications, methodological rigor, and demonstrated practical impact, while works lacking empirical validation or cybersecurity relevance were excluded. The review examines core ML paradigms: supervised, unsupervised, semi-supervised, and reinforcement learning, and analyzes their effectiveness across key cybersecurity domains such as malware detection, phishing detection, financial crime detection, anomaly detection, mobile security, and intrusion detection systems. We review prominent ML algorithms and detail their application in detecting malicious behavior across network traffic, system logs, and user activities. In addition, we survey open-source tools and platforms commonly used in cybersecurity-focused ML research and practice. The practical component of the paper showcases real-world use cases, including spam filtering, fraud detection, and endpoint security, illustrating how organizations can integrate ML techniques into operational security infrastructures. The paper also investigates the challenges and limitations of applying ML in cybersecurity, including adversarial ML, data imbalance, concept drift, and model interpretability. Finally, emerging research directions such as explainable AI, edge AI, federated learning, and quantum ML are discussed as promising avenues for advancing robust, transparent, and future-ready cybersecurity solutions. By bridging theoretical foundations with real-world practices, this paper aims to equip researchers, practitioners, and decision-makers with a holistic understanding of the capabilities, tools, and future potential of ML in advancing cybersecurity resilience.