This paper presents visually impaired assistive system (VIAS) which focuses on independent mobility of visually impaired or blind people who suffer in an unknown environment without any manual assistance. This system employs Radio Frequency Identification (RFID) to achieve an objective of identifying certain paths for the user navigation as well as provide certain features such as object recognition, log records of all users' tag access, emergency button and user track information. This proposed system on the user side include a mobile RFID reader module with an integrated microcontroller, zigbee transceiver for transmitting the tag's information and TTS for playing information to the user and on the server side zigbee transceiver for wireless communication. In path identification technique, RFID passive tag network is employed on the path and for object recognition required tools and other objects in the house or building will be embedded with passive RFID tags. A text data unique to each object and path location, resides on the server. The reader reads the tags and transmits the data wirelessly to the server PC which in turn scans for the received Tag ID in the database and respond to the user with its related text data which is played at the user side by converting it from text to speech with the help of TTS module. The feasibility and reliability of the developed system is tested by deploying the proposed system at Blind school of girls at Pune, India. As an overall this system will help visually impaired person to gain the feelings of visualization. General TermsBlind navigation system, visually impaired user navigation and object recognition system.

Cloud computing drive out the need of IT based companies to invest in high computing infrastructure and services used by them. In cloud, the data is dwell into set of networked resources that enable data to be accessed via virtual machines. These data centers are located in various parts of the world beyond the control and reach of the user, so there are multiple challenges and security issues that need to be addressed and understood. This review paper aims to analyze and elaborate deduplication issues in cloud computing which is the base for our future roadmap.

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Nowadays everything is digitized. We share information on network, even confidential information also get transferred in the form of files. So it require protection of data against corruption and fault tolerance. This needs secure and efficient way to protect private data from illegle usage. For that purpose we have proposed Parallel File protecting System using CPP (CPU Parallel protecting), CPUP (CPU Parallel Unprotecting), GPP(GPU Parallel Protecting), GPUP(GPU Parallel Unprotecting), HPP(Hybrid Parallel Protecting), HPUP (Hybrid Parallel Unprotecting) which secures file with the help of SHA3, AES and BLAKE2b algorithm. In proposed system we optimized SHA3-256 and parallel AES algorithm for security purpose with GPU Parallelism and CPU Parallelism for high performance. Parallelism is achieved with the help of NVIDIA's GPU. Along with SHA3 Blake2b is used for fast secure hashing. We have achieved better speed and security with the help of Blake2b. Thus Parallel File Protecting System is secure system and it can be used in computer equipped with NVIDIA's GPU.

Objectives:The main objective of this study is to review and compare the various methods used for Modi script recognition. Methods: The author has chosen various methods from 2010 to 2022 that are used to process MODI Script. The distinct methods employed for feature extraction and classification are compared for various datasets. A discussion on the significance of the selection of correct feature extraction and classification techniques and the comments on the methods suited to specific applications is provided. Findings: Currently, there are very few MODI translators. In contrast, millions of historical documents written in MODI remain unexplored. Novelty: The Convolutional Neural Networks (CNNs) has been used successfully for recognizing MODI Script characters. In the present study the author finds that, compared to all techniques, CNN provides maximum accuracy of 99.78%. Hence, CNN is the best character recognition technique for the MODI script.