The number of vehicles in traffic and the use of traffic surveillance systems are increasing day by day. This situation has revealed the necessity of control and analysis processes on traffic surveillance systems. Vehicle detection and vehicle tracking studies for the purpose of analyzing video sequences obtained from surveillance systems have recently become a popular field of study. Despite the increase in studies in this field, the aimed level has not been reached. Many reasons such as weather changes, day-night difference, vehicles blocking each other in traffic, background complexity make vehicle detection and tracking difficult. This study is presented to guide researchers who want to work in the field. In order to determine the common trends of the studies and to analyze the studies, a data set was created by searching the Web of Science database using the keywords "vehicle detection" and "vehicle tracking". In order to analyze the obtained data, the Voswiever (version 1.6.20) program and the R studio programs "bibliometrix" package and the biblioshiny application were used.

Software engineering is a systematic discipline that covers the creation of a software product at the end of software development processes. Many different models are used in software development processes. The most widely used model today is the Agile Approaches model. Methods that focus on increasing customer orientation and speed in software development projects are considered agile approaches. Agile approaches are used to overcome the limitations of traditional methods such as the waterfall approach and to adapt to constantly changing requirements. Agile approaches increase collaboration in a project team established to develop a software product and contribute to product success by increasing customer satisfaction. Agile approaches are highly preferred for distributed software development environments and remote teams. The reviewed literature shows that agile approaches increase flexibility and speed of delivery in software engineering projects, but they also bring many difficulties when they are not applied with an expert team in large-scale projects or when they are applied incorrectly. Agile approaches have transformed software engineering processes, prioritizing customer requirements and supporting high quality software development processes. However, factors such as the experience of teams, organizational culture and process management skills are critical for the successful implementation of agile methods. Within the scope of the study, bibliometric analysis was conducted on 1372 data obtained from the Web of Science database. The study provides a roadmap for future applications of agile methods in software engineering.

Background/Objectives: Artificial intelligence (AI) has attracted great interest due to its applicability in many fields. The adoption of visual illustration techniques produced by AI in the field of graphic design has further expanded the field of use of this technology. This study focuses on foot anatomy illustrations generated by Adobe Firefly and Microsoft Designer Image Creator applications, evaluating them based on detail, clarity, anatomical realism, accuracy, and aesthetic appeal. Methods: The illustrations were created using text-based scripts, and five anatomists compared them to traditional illustrations from the Sobotta Atlas of Human Anatomy. Results: Fleiss’ Kappa statistic was used to analyze consistency among expert evaluations. For the four figures generated by both AI applications, Fleiss’ Kappa agreement was high. Adobe Firefly performed slightly better in illustrating phalanx and ankle bones, but its anatomical accuracy was lower for tarsal and metatarsal bones. Microsoft Designer Image Creator excelled in illustrating metatarsal bones, while its tarsal and phalanx illustrations were less anatomically accurate than Adobe Firefly and the atlas drawings. Both programs showed average realism in ankle structures, while the tarsal bones had low realism. Conclusions: Artificial intelligence applications within the scope of the study showed fast performance. Aesthetic appeal is dominant at first glance in the resulting drawings. In general, both applications have struggled to reflect anatomical reality.

Background: Alzheimer’s disease is the most common type of dementia and a progressive neurodegenerative disease that begins with neuronal damage and leads to a reduction in brain tissue. Currently, there is no cure for this disease, and existing approaches focus on alleviating symptoms. Methods: This study proposes NeuroFusion-ViT, a highly accurate and computationally efficient hybrid deep learning model for early-stage detection of Alzheimer’s disease. The model combines an EVA-02-based Vision Transformer (ViT) with the ConvNeXt-Small CNN architecture, providing powerful representation learning that can process both global context and local details. The proposed Gated Cross-Attention Fusion (G-CAF) mechanism dynamically combines two different features, offering high discriminative power and model stability. Results: In experiments conducted on the OASIS MRI dataset, the model achieved 99.86% accuracy, 0.9989 Macro F1, and 0.999 ROC-AUC values, demonstrating clear superiority over single-modal and hybrid models described in the literature. Furthermore, 5-fold cross-validation results also support the model’s high generalizability. Ablation studies showed that each of the components—cross-attention, gate mechanism, Dual LayerNorm, and FFN-Dropout—made a meaningful contribution to performance. Conclusions: The results demonstrate that the NeuroFusion-ViT architecture offers a reliable, stable, and clinically applicable solution for Alzheimer’s stage classification.