The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%.

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In the era of cloud, anything and everything as a service facilitates number of remote user connected from anywhere, anytime and any form of access to the storage services. Today, Cloud storage has become an essential aspect of cloud computing, stores information and support any kind of applications. Applications like Internet of Things, Big Data analytics, Data warehousing, Databases, Backups and archive applications all rely on some form or the other on cloud storage architecture. Users may have a different variants of smart devices such as tablet, PCs, notebook and smart phones. Cloud storage provides an interlink between these smart devices. Enterprises prefer to use cloud storage because they provide cost-friendly and flexible alternatives on locally implemented hardware. However, business process in the cloud needs secured transaction, confidential files are sometimes exposed to risk of leakage, as cloud- stored data resides outside of the local infrastructure, thus vulnerable to security risks. Cloud storage providers provides enough security at their end, but there is no system that provides client level security while using public clouds. The proposed system will provide client level security using hybrid encryption techniques. Using AES (Advance Encryption Standard) in CBC Mode (Cipher Block Chaining) and HMAC-SHA-1 (Hash-based Message Authentication Code) with light weight methods enhances the strong encryption at client level security. Fusion of these algorithms adds extra layers of security to the cloud storage data. The proposed method enhances the security measures for any client users, using storage as a service offered by several cloud service providers.