According to the World Health Organization estimation, globally the number of people with some visual impairment is estimated to be 285 million, of whom 39 million are blind. The inability to use features such as sending and reading of email, schedule management, pathfinding or outdoor navigation, and reading SMS is a disadvantage for blind people in many professional and educational situations. Speech or text analysis can help improve support for visually-impaired people. Users can speak a command to perform a task. The spoken command will be interpreted by the Speech Recognition Engine (SRE) and can be converted into text or perform suitable actions. In this paper, an application that allows schedule management, emailing, and SMS reading completely based on voice command is proposed, implemented, and validated. The System hopes to provide blind people to simply speak the desired functionality and be guided thereby the system’s audio instructions. The proposed and designed app is implemented to support three languages which are English, Hindi, and Kannada.
The consensus is a critical operation of any decision-making process.
It involves a set of eligible members; whose decision need to be honored by taking their acknowledgment before making any decision. The traditional consensus process follows centralized architecture, the members need to rely on and trust this architecture. The proposed system aims to develop a secure decentralized consensus application in the untrusted environment by making use of blockchain technology along with smart contract and interplanetary file system (IPFS).
Automatic software vulnerability detection has caught the eyes of researchers as because software vulnerabilities are exploited vehemently causing major cyber-attacks. Thus, designing a vulnerability detector is an inevitable approach to eliminate vulnerabilities. With the advances of Natural language processing in the application of interpreting source code as text, AI approaches based on Machine Learning, Deep Learning and Graph Neural Network have impactful research works. The key requirement for developing an AI based vulnerability detector model from a developer perspective is to identify which AI model to adopt, availability of labelled dataset, how to represent essential feature and tokenizing the extracted feature vectors, specification of vulnerability coverage with detection granularity. Most of the literature review work explores AI approaches based on either Machine Learning or Deep Learning model. The existing literature work either highlight only feature representation technique or identifying granularity level and dataset. A qualitative comparative analysis on ML, DL, GNN based model is presented in this work to get a complete picture on VDM thus addressing the challenges of a researcher to choose suitable architecture, feature representation and processing required for designing a VDM. This work focuses on putting together all the essential bits required for designing an automated software vulnerability detection model using any various AI approaches.
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