REAL-TIME OBJECT DETECTION IN AUTONOMOUS VEHICLES USING DEEP LEARNING

Object detection is a crucial component of autonomous driving technology. Accurate and real-time detection of every object on the road is required to ensure the safe operation of vehicles at high speeds. In recent years, there has been a lot of research into how to balance detection speed with accuracy. Real-time object detection is one of the important technologies applied to autonomous vehicles that allow vehicles to move safely through traffic. This paper focuses on the use of deep learning, the YOLOv8 algorithm in object detection of self-driving cars. The real-world data set of real driving scenarios involved includes streets, roads, and intersections/squares. The powerful interaction of the model with the deep learning algorithms defines the objects and allows for a fast decision-making process applied in autonomous systems. The metrics used to assess the models include detection rates, accuracy of the bounding die placement, and accuracy of the objects’ detection. The outcome is beneficial in refining the object detection methods and advancing the perception capability for self-driven vehicles as well as making driving automation safer

Secure and Flexible Key Protected Identity Framework for Mobile Devices

Mobile or IOT based applications are emerging rapidly across the globe and there is a massive digital transformation happening within each country. It is a need of an hour to improve and protect digital identity during online transactions through handheld devices. This paper proposes a Mobile ID solution based on Mobile-originated PKI without the need for the actual identity card or a card reader. The solution proposed focuses on security, privacy, and usability using open standards which will protect Personally Identifiable Information (PII) over handheld devices. The proposed mobile ID solution has better cost-efficacy and privacy than today’s scenario. It also explicates the Mobile ID solution with established secure identity among users, authorities, other organizations of public, and private sectors.</p

Blockchain Framework for Learner Performance Prediction using Life-Brain Storm-based Light GBM Coupled Neural Network

652-668E-learning is one of the dominant applications of digital techniques in the educational platform. Tutors can effectivelytailor their instruction to each student by using the automatic identification of the student's learning styles. Nowadays Deep learning techniques provide the preferable predictive model in the e-learning platform. Hence, this research article provides the prediction of the learner’s performance by using the Life-Brain Storm (Life-BS) based LightGBM coupled Neural Network (NN). A significant part of the research lies in the tuning of the hyper-parameters using the proposed Brain rule selection algorithm, which boosts the accuracy of the classifier. Furthermore, by lowering the dimensionality of the data, the feature extraction approach is developed in this study to reduce the computational complexity of the prediction framework. The suggested Life-BS-based LightGBM coupled NN model is shown to be effective by the experimental assessment, which yielded the lowest RMSE as well as the MSE for courses 1, 2, and 3, respectively. In addition, the evaluation metrics such as MAE and Kappa scores achieve better results for course-1, course-2, and course-3 respectively. Use of blockchain, including kappa score also in performance metrics along with Life-Brain Storm based LightGBM coupled Neural Network proposed learner performance prediction model are the keypoints of the presented work