Natural Language Processing (NLP) is a group of theoretically inspired computer structures for analyzing and modeling clearly going on texts at one or extra degrees of linguistic evaluation to acquire human-like language processing for quite a few activities and applications. Hearing and visually impaired people are unable to see entirely or have very low vision, as well as being unable to hear completely or having a hard time hearing. It is difficult to get information since both hearing and vision, which are crucial organs for receiving information, are harmed. Hearing and visually impaired people are considered to have a substantial information deficit, as opposed to people who just have one handicap, such as blindness or deafness. Visually and hearing-impaired people who are unable to communicate with the outside world may experience emotional loneliness, which can lead to stress and, in extreme cases, serious mental illness. As a result, overcoming information handicap is a critical issue for visually and hearing-impaired people who want to live active, independent lives in society. The major objective of this study is to recognize Arabic speech in real time and convert it to Arabic text using Convolutional Neural Network-based algorithms before saving it to an SD card. The Arabic text is then translated into Arabic Braille characters, which are then used to control the Braille pattern via a Braille display with a solenoid drive. The Braille lettering triggered on the finger was deciphered by visually and hearing challenged participants who were proficient in Braille reading. The CNN, in combination with the ReLU model learning parameters, is fine-tuned for optimization, resulting in a model training accuracy of 90%. The tuned parameters model's testing results show that adding the ReLU activation function to the CNN model improves recognition accuracy by 84 % when speaking Arabic digits.

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Data visualization is an arrangement that presents data in manners that enable the utilization of human subjective and visual capacities. It is the method used to convert crude information into some visual structure. It uses designs and visuals to help with the psychological burden of comprehending big data. The burden put on general well-being because of the coronavirus is exacerbated by the ceaseless rise of new strains and unanswered inquiries concerning viral spread inside the host. Observations regarding the course of coronaviruses in people have been step by step expanded and extended to numerous regions worldwide. These observation programs have created an enormous amount of genomic information regarding coronaviruses, which encourages the investigation of the infection by computational strategies that are proficient and cost effective.The main focus of this chapter is the development of visualization techniques to comprehend the advancement of coronaviruses. The strategies depend on unaided dimensional decrease methods, which can be applied to every individual genome fragment or to the total genome succession of the infection. These strategies are a takeoff from the customary phylogenetic tree development worldview in light of the fact that an exceptionally enormous number of high-dimensional info arrangements can be prepared and results are seen legitimately in an a few-dimensional Euclidean space.

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