The integration of modern authentication methods such as multifactor authentication (MFA), biometric verification, and password-less authentication in legacy systems presents significant challenges due to outdated infrastructure, compatibility issues, and security vulnerabilities. This study analyzes the difficulties associated with implementing these authentication techniques in legacy environments and proposes viable solutions. A combination of software-based adaptation layers, API gateways, and Zero Trust frameworks can help bridge the gap between traditional and modern security paradigms. The research also includes algorithmic approaches for retrofitting authentication mechanisms, security models for risk assessment, and implementation case studies. The results indicate that a hybrid approach combining incremental upgrades with identity federation mechanisms enhances authentication security while minimizing operational disruptions. The study concludes that legacy systems require a strategic balance between modernization efforts and system stability, ensuring long-term security and efficiency in authentication processes.

This paper presents a real-time assistive system designed to support visually impaired individuals through object detection, voice-guided navigation, and speech-based information retrieval. The system integrates the YOLOv5s deep learning model for object recognition and a speech interface for intuitive auditory feedback. The proposed model was implemented on a system powered by an Intel(R) Core (TM) i5-8250U CPU @ 1.60$\mathbf{G H z}$(up to 1.80 GHz) with 8 GB RAM and integrated Intel(R) UHD Graphics$\mathbf{6 2 0} \boldsymbol{(} \mathbf{1 2 8 ~ M B} \boldsymbol{)}$, utilizing the built-in system camera for real-time video input. Quantitative evaluation demonstrated an object detection accuracy of$89.6 \% \text{mAP}$, a navigation success rate of 96%, and a speech recognition accuracy of 93%. The system achieved an average latency of 0.72 s for object detection, 1.1s for navigation, and 0.68 s for voice retrieval, with a frame rate of 22-25 FPS on GPU hardware. Performance testing was conducted under varying lighting conditions, partial occlusions, and different noise levels$(40-70 ~\text{dB})$to evaluate environmental robustness. The results confirm that the system maintains stable operation in both indoor and outdoor environments, demonstrating practical applicability and real-time responsiveness for assistive navigation in dynamic and crowded settings.

Brain tumors, a significant health concern due to their potential to disrupt critical brain functions, require accurate and timely detection for effective management. Traditional diagnostic methods, reliant on manual examination of MRI scans by radiologists, often face challenges related to time constraints and susceptibility to human error. This study investigates the application of deep learning techniques to automate the detection and classification of brain tumors from MRI scans. We evaluated various neural network architectures, including CNN, VGG16, VGG19, and ResNet-50, to determine their effectiveness in identifying and classifying brain tumors from MRI images. Our results demonstrate that the CNN model outperforms VGG16, VGG19, and ResNet-50 in terms of both accuracy and generalization, making it the most effective choice for automated tumor detection. To address practical clinical needs, we developed a user-friendly web application that integrates the CNN model, enabling real-time tumor detection and classification. This application allows healthcare professionals to upload MRI images and receive immediate tumor detection and classification results, facilitating quicker and more precise diagnostic processes. The integration of deep learning models into this web-based platform marks notable progress in the automated detection of brain tumors. By enabling realtime analysis, the application supports clinicians in making informed decisions and planning treatment strategies with enhanced accuracy.

The development of drowsiness detection applications has become increasingly important within the automotive technology sector, success rates are far from optimal. Despite the potential life-saving impact of such applications, the possible role of dynamic risk management in the successful completion of drowsiness detection projects has not been directly explored in existing studies on the topic. Considering the theoretical, as well as practical significance of the issue, the research under consideration aimed to analyze the benefits, challenges, and best practices of dynamic risk management in projects like “Drive Guard” a drowsiness detection Android application, with emphasis on the development of a framework for its implementation in such applications.

The rapid spread of fake content across digital platforms, including text, and images, poses significant challenges to the integrity of information. While recent advancements in multimodal fake detection have shown promise, existing models often focus on a single modality or lack scalability and transferability to unseen events. This study addresses these limitations by developing a novel fake detection model based on the CLIP and BLIP architectures, designed to be scalable across text and image modalities. The proposed architecture makes use of a BLIP model to extract image-text similarity and a CLIP model to extract image and text embeddings. The hybrid fusion technique was used to fuse the features. The proposed model was evaluated against existing bimodal approaches, demonstrating superior performance with an accuracy of 90.63%, precision of 92.04%, recall of 90.73%, and an F1-score of 91.38%. These results highlight the model’s effectiveness in accurately detecting fake content across diverse modalities while maintaining a balanced performance between precision and recall. This research contributes to the advancement of multimodal fake detection by providing a scalable and comprehensive approach, paving the way for future developments in combating misinformation across various digital mediums.