This study describes the creation of an intelligent surveillance system based on deep learning that aims to improve real-time security monitoring by automatically identifying suspicious activity. By using cutting-edge computer vision techniques, the suggested system overcomes the drawbacks of conventional surveillance that depends on human observation to spot irregularities in public spaces. The system successfully completes motion detection, trajectory analysis, and emotion recognition by using the YOLOv8 model for object detection and DeepFace for facial emotion analysis. Roboflow is used for dataset annotation, model training with optimized parameters, and visualization of object trajectories and detection confidence. The findings show that abnormal behaviors can be accurately identified, with noteworthy observations made about the emotional expressions and movement patterns of those deemed to be threats. Even though the system performs well in real time, issues like misclassification, model explainability, and a lack of diversity in the dataset still exist. Future research will concentrate on integrating multimodal data fusion, deeper models, and temporal sequence analysis to further enhance detection robustness and system intelligence.

Over the last few decades, there is a remarkable increase in air traffic worldwide; however, despite of rapid technological development and improved airport infrastructure, there is still an increasing number of aviation accidents and fatalities. In order to meet safety demands of Air Traffic Control (ATC), Machine Learning (ML) based aircraft conflict detection techniques are focused to predict events before they come to reality. This research presents a systematic literature review (SLR) about the applications of ML techniques in safety prediction of ATC system. Initially, the existing aircraft conflict prediction models are classified into three broad categories; logical, geometric and probabilistic. A formal process of SLR is then conducted which is initiated with preprocessing and effective searching. A series of filtering called title, abstract and objective-based are then applied to extract the most relevant articles. Detailed and critical reviews of selected articles are performed to identify challenges which are segregated into dataset, model and evaluation techniques, furthermore, the optimal solutions for each are proposed. Finally the matrices to map the identified challenged with their proposed solutions are constructed. This research serves as a foundation for future research and towards integration of ML based safety prediction techniques into modern ATC system.

At present, polypharmacy—which is the use of several medications to treat a single case at the same time—has become a fairly common medical practice, particularly in chronic illnesses or with older patients. But this relatively ‘faster’ form of treatment brings the problem of cumulative polypharmacy, which occurs when there is an increase in drug–drug interactions (DDIs) due to the large number of medicines taken. While the aftermath, such as the reduction in strength of medication taken or catastrophic and fatal responses to certain drugs, is clearly not worth the initial effort put into trying to ease the condition, attempting to resolve these issues requires excessive research. With these difficulties in mind, we describe our research that uses graph neural networks (GNNs) focused on DDI prediction by modeling drugs and their interactions in the form of graphs. The research is divided into two parts. In this research, the relevant literature is reviewed in order to understand how modern GNN-based algorithms can be applied for the detection of optimal drugs.

With the emergence of Advance Logistics, the logistics industry has witnessed significant advancements, leading to a greater reliance on intelligent technologies. These technologies play a crucial role in gathering and transmitting logistics data, but they also introduce security and privacy risks to logistics management systems. The sharing of customer-sensitive information among stakeholders for efficient operations becomes vulnerable to unauthorized access, thus compromising privacy. This, amongst others, is a critical problem that needs to be addressed. The unique features of a cryptographic hash function, including immutability, efficient verification, and anonymity, make it a transformative technology that has the potential to address the mentioned challenges in various sectors. This disruptive technology offers solutions by ensuring data integrity, enabling quick verification, and preserving privacy. The findings suggest that employing Cryptographic Hash Functions to share customer data among logistics partners offers robust protection against cyber-attacks. The proposed system guarantees asset security, and as demonstrated by the case study evaluation, exhibits strong resilience in safeguarding against various security and privacy threats. Although this study is significant, it does have limitations. One of these limitations is the considerable overlap between logistics research and supply chain management (SCM). While our focus was on logistics tasks, we did not extensively delve into the broader context of SCM

For organizations, protecting computer networks has always been a very tough and demanding task. In the current technological era digital resources can now be protected without the need for outdated traditional perimeter-based security techniques. Organizations can use the Zero Trust Network Access (ZTNA) approach to safeguard and filter their vital digital assets for the company’s benefit. This platform uses sophisticated logical authentication to test the system’s ability to authenticate users, and network monitoring is used to look for possible security flaws and system vulnerabilities. By evaluating users’ interactions with the system and their handling of assigned digital resources, multi-factor authentication filters out unwanted access attempts. Three fundamental access control styles are provided by network segmentation, giving administrators the option to manage access in a democratic, strict, or flexible manner (least privilege approach).