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In an era dominated by digital communications and the increase in social media platforms, the exchange of sensitive information has become vulnerable to security violations and attacks of various kinds, which has led to the spread of leaked documents and the circulation of important decisions. All this led to the urgent need to find a robust way to maintain the privacy, confidentiality and integrity of transmitted data. In this paper a new method was designed in order to offer a multi-layered of data confidentiality sent via digital channels and avoid unauthorized access. The proposed method consists of two stage encoding text data and encrypting audio signals. In the first stage, the text data are mixed with random key bits and converted into Morce audio signals. The audio signals are encrypted using Advanced Encryption Standards (AES) in the second stage. The decryption process is performed in reverse to the previous steps. The results showed that the average value of the SNR difference between the original file and the encrypted file is 25.89, while the average value of the SNR difference between the original file and the decrypted file is 0.47. Which means lower Intelligibility of the encrypted file and higher quality of the decrypted file. Thus indicate the effectiveness of the proposed method that preserving time consumption into 0.46 seconds. [ABSTRACT FROM AUTHOR]

Children with autism spectrum disorders (ASD) have problems understanding and emotionally communicating via facial expressions, until with their parents. The likelihood of children being classified with autism spectrum condition has risen considerably in past few years. Children with ASD have difficulty with verbal abilities making it Extremely Difficult to guess what they need emotionally and fast. There are a variety of ways for recognizing autism symptoms in children. Health providers usually employ facial expression-based ways to identify autistic children's feelings patterns. Sign language takes the form of facial expression, which reflects a human's internal emotions and thoughts. As far as facial expressions are concerned, facial expression recognition (FER) has lately gotten a lot of attention. As the quickest means of exchanging information of any type, which can then be analyzed using the most up to date deep learning techniques. Facial expression identification is a method that assists for discover children's feelings. Children with ASD, on the other hand, have difficulty recognizing facial emotions. Globally there are seven types of facial emotions: angry, disgusted, pleased, sad, frightened, astonished, and indifferent. observed most researchers utilized CNNs and other deep learning approaches to help identify facial expressions and emotions in photos or videos of autistic children. This paper provides a survey of the research on various approaches for newly developed technologies that aim to recognize human emotions through facial expressions. [ABSTRACT FROM AUTHOR]

According to the Evaluation of Technology and the Internet, they have been incorporated into all parts of life because of the benefits that make life easier and faster, allowing us to do more difficult activities in a more productive manner. This paper focuses on the design and implementation of a website that locates the closest school for the purpose of verifying its location on Google Maps and displaying a review of the school's essential information, such as (name school, email for the school, phone number, address, and so on) about the school. The system is compatible with any web browser (Google Chrome, Microsoft Edge). Browsers can visit the website from any device, including desktops, tablets, iPads, and mobile phones. And it works with any operating system, including Android, IOS, and Windows. The concept behind the suggestion is to make the search process so simple and quick that it can be utilized by anyone, regardless of their age or level of familiarity with Google Maps. It also involves the administration of schools and their registration in databases, which allows for the retrieval of information about each school as well as the insertion of new information and the alteration of current data. [ABSTRACT FROM AUTHOR]

Automatic speech recognition is a research field for enabling computers to receive voice feedback from humans and to translate it with the greatest probability of correctness. Speech is the easiest way to communicate the message in basic context. Speech recognition is referring the allowing of machines to perceive and accept speech information from humans for the greatest probability of accuracy. Speech recognition will play an ever more important role in the future of human with machine interfaces. Arabic speech recognition (ASR) is a most complex job and has received little attention than other languages. In this paper we reviewing the most important deep learning methods used in speech recognition in Arabic language. Through the search the main finding in this review that the Arabic is one of the most spoken languages and least highlighted in terms of speech recognition. One of the emerging techniques is using neural networks with deep learning for speech recognition. Deep Neural Networks (DNNs) have reached to suitable performance, Recurrent Neural Networks (RNN) is one of the best models applied for sequential data and convolution neural network (CNN) has obtained a best accuracy rate in speech recognition. [ABSTRACT FROM AUTHOR]

Detecting driver inattentive is one of interested research area to reduce vehicle accidents. Driver safety can be improved by detecting driver drowsiness and distraction. The increase of in-vehicle information systems induces the biomechanical and cognitive of driver distraction which affects driving performance qualitatively. The impact of digital technology on driving performance is presented in this paper based on the relative influence of the state-of-the-art techniques. The paper presents the finding of Convolution Neural Networks (CNNs) literature review covering the driver distraction detection systems. The results observe that convolutional neural network (CNN) is a powerful deep learning method that fits the needs of researchers in detecting of driver distraction. It can extract the useful attributes from trained data and train the weights by feeding the data on each level then tune the CNN based on specific driver distraction measurements. Also, it can provide a high accuracy results in real-time environment. [ABSTRACT FROM AUTHOR]