Epileptic seizures, a leading cause of global morbidity and mortality, pose significant challenges in timely diagnosis and management. Epilepsy, a chronic neurological disorder characterized by recurrent and unpredictable seizures, affects over 70 million people worldwide, according to the World Health Organization (WHO). Despite significant advances in medical science, accurate and timely diagnosis of epileptic seizures remains a challenge, with misdiagnosis rates reported to be as high as 30%. The consequences of misdiagnosis or delayed diagnosis can be severe, leading to increased morbidity, mortality, and reduced quality of life for patients. Therefore, this paper presents a novel approach to enhancing epileptic seizure detection through the integration of Synthetic Minority Over-Sampling Technique (SMOTE) for data balancing and a Hybrid Feature Selection Technique—Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT). The proposed model aims to improve the accuracy and reliability of seizure detection systems by addressing data imbalance and extracting discriminative features from electroencephalograms (EEG) signals. Experimental results demonstrate substantial performance gains, with the Support Vector Machine (SVM) classifier achieving 97.30% accuracy, 99.62% Area Under the Curve (AUC), and 93.08% F1 score, which outperform the results of the existing studies from the literature. The results highlight the effectiveness of the proposed model in advancing seizure detection systems, highlighting the potential to improve diagnostic capabilities and patient outcomes.

The increasing use of social media networks on handheld devices, especially smartphones with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive volumes of digital media, including images and videos. Such media is full of visual information and holds immense value in today’s world. The volume of data involved calls for automated visual content analysis systems able to meet the demands of practice in terms of efficiency and effectiveness. Deep Learning (DL) has recently emerged as a prominent technique for visual content analysis. It is data-driven in nature and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined handcrafted feature extractors. Another appealing characteristic of DL solutions is the performance they can achieve, once the network is trained, under practical constraints. This paper identifies eight problem domains which require analysis of visual artefacts in multimedia. It surveys the recent, authoritative, and best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems. The paper also discusses the challenges that DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis.

Manually labelling datasets for training violence detection systems is time-consuming, expensive, and labor-intensive. Mind wandering, boredom, and short attention span can also cause labelling errors. Moreover, collecting and distributing sensitive images containing violence has ethical implications. Automation is the future for labelling sensitive image datasets. Deep labeller is a two-stage Deep Learning (DL) method that uses pre-trained DL object detection methods on MS-COCO for automatic labelling. The Deep Labeller method labels violent and nonviolent images in WVD and USI. In stage 1, WVD generates weak labels using synthetic images. In stage 2, the Deep labeller method is retrained on weak labels. USI dataset is used to test our method on real-world violence. Deep labeller generated weak and strong labels with an IoU of 0.80036 in stage 1 and 0.95 in stage 2 on the WVD. Automatically generated labels. To test our method’s generalisation power, violent and nonviolent image labels on USI dataset had a mean IoU of 0.7450.

The rising awareness of cybersecurity among governments and the public underscores the importance of effectively managing security incidents, especially zero-day attacks that exploit previously unknown software vulnerabilities. These zero-day attacks are particularly challenging because they exploit flaws that neither the public nor developers are aware of. In our study, we focused on dynamic application security testing (DAST) to investigate cross-site scripting (XSS) attacks. We closely examined 23 popular WordPress plugins, especially those requiring user or admin interactions, as these are frequent targets for XSS attacks. Our testing uncovered previously unknown zero-day vulnerabilities in three of these plugins. Through controlled environment testing, we accurately identified and thoroughly analyzed these XSS vulnerabilities, revealing their mechanisms, potential impacts, and the conditions under which they could be exploited. One of the most concerning findings was the potential for admin-side attacks, which could lead to multi-site insider threats. Specifically, we found vulnerabilities that allow for the insertion of malicious scripts, creating backdoors that unauthorized users can exploit. We demonstrated the severity of these vulnerabilities by employing a keylogger-based attack vector capable of silently capturing and extracting user data from the compromised plugins. Additionally, we tested a zero-click download strategy, allowing malware to be delivered without any user interaction, further highlighting the risks posed by these vulnerabilities. The National Institute of Standards and Technology (NIST) recognized these vulnerabilities and assigned them CVE numbers: CVE-2023-5119 for the Forminator plugin, CVE-2023-5228 for user registration and contact form issues, and CVE-2023-5955 for another critical plugin flaw. Our study emphasizes the critical importance of proactive security measures, such as rigorous input validation, regular security testing, and timely updates, to mitigate the risks posed by zero-day vulnerabilities. It also highlights the need for developers and administrators to stay vigilant and adopt strong security practices to defend against evolving threats.

In the current era, satisfying the appetite of data hungry models is becoming an increasingly challenging task. This challenge is particularly magnified in research areas characterised by sensitivity, where the quest for genuine data proves to be elusive. The study of violence serves as a poignant example, entailing ethical considerations and compounded by the scarcity of authentic, real-world data that is predominantly accessible only to law enforcement agencies. Existing datasets in this field often resort to using content from movies or open-source video platforms like YouTube, further emphasising the scarcity of authentic data. To address this, our dataset aims to pioneer a new approach by creating the first synthetic virtual dataset for violence detection, named the Weapon Violence Dataset (WVD). The dataset is generated by creating virtual violence scenarios inside the photo-realistic video game namely: Grand Theft Auto-V (GTA-V). This dataset includes carefully selected video clips of person-to-person fights captured from a frontal view, featuring various weapons—both hot and cold across different times of the day. Specifically, WVD contains three categories: Hot violence and Cold violence (representing the violence category) as well as No violence (constituting the control class). The dataset is designed and created in a way that will enable the research community to train deep models on such synthetic data with the ability to increase the data corpus if the needs arise. The dataset is publicly available on Kaggle and comprises normal RGB and optic flow videos.