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Journal ArticleDOI

Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication

24 Nov 2021-International Journal of Speech Technology (Springer Science and Business Media LLC)-
About: This article is published in International Journal of Speech Technology.The article was published on 2021-11-24 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Dyslexia & Augmentative.
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Book ChapterDOI
TL;DR: In this article , a framework of ethical recommendations for using artificial intelligence in tele-habilitation for children with neurodevelopmental disorders is presented and the European Union requirements for trustworthy artificial intelligence for children are explored.
Abstract: Neurodevelopmental disorders are a cluster of mental disorders with neurobiological origins that occur during the development of children and lead to cognitive deficits with possible behavioral and emotional consequences. Intensive and individualized interventions are required to take action on these deficits timely. Recently, telerehabilitation techniques for neurodevelopmental disorders have been implemented by automating the rules to set up the intervention protocol. The use of artificial intelligence algorithms primarily applies to this automation. Although these methods have several advantages, such as automatizing personalization and self-adaptation, ethical implications emerged. In detail, it remains unclear how ethical principles can be applied to these new interventions. The present paper outlines a framework of ethical recommendations for using artificial intelligence in telerehabilitation for children with neurodevelopmental disorders. For this aim, a review of the use of artificial intelligence in adults as users is presented and the European Union requirements for trustworthy artificial intelligence for children are explored. The paper proposes some practical applications of ethical principles for artificial intelligence systems in the telerehabilitation of neurodevelopmental disorders and research strategies in line with the European Union guidance. This review of the ethical implication of artificial intelligence is intended to be an opportunity to improve artificial intelligence telerehabilitation of children with neurodevelopmental disorders.
References
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Journal ArticleDOI
TL;DR: The role of artificial intelligence (AI), machine learning (ML), and deep reinforcement learning (DRL) in the evolution of smart cities is explored and various research challenges and future research directions where the aforementioned techniques can play an outstanding role to realize the concept of a smart city are presented.

305 citations

Journal ArticleDOI
TL;DR: A hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets and experimental results confirm the fact that the proposed model performs better than the existing machine learning models.
Abstract: The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

226 citations

Journal ArticleDOI
TL;DR: An automatic online assessment method for the reliability of CPS is proposed, which builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time.
Abstract: The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

190 citations

Journal ArticleDOI
TL;DR: In AIDWRP, Markov Decision Process (MDP) discusses the dynamic water resource management issue with annual use and released locational constraints that develop sensitivity-driven methods to optimize several efficient environmental planning and management policies.

158 citations

Journal ArticleDOI
TL;DR: A face expression recognition method based on a convolutional neural network (CNN) and an image edge detection and the dimensionality reduction of the extracted implicit features is processed by the maximum pooling method.
Abstract: To avoid the complex process of explicit feature extraction in traditional facial expression recognition, a face expression recognition method based on a convolutional neural network (CNN) and an image edge detection is proposed. Firstly, the facial expression image is normalized, and the edge of each layer of the image is extracted in the convolution process. The extracted edge information is superimposed on each feature image to preserve the edge structure information of the texture image. Then, the dimensionality reduction of the extracted implicit features is processed by the maximum pooling method. Finally, the expression of the test sample image is classified and recognized by using a Softmax classifier. To verify the robustness of this method for facial expression recognition under a complex background, a simulation experiment is designed by scientifically mixing the Fer-2013 facial expression database with the LFW data set. The experimental results show that the proposed algorithm can achieve an average recognition rate of 88.56% with fewer iterations, and the training speed on the training set is about 1.5 times faster than that on the contrast algorithm.

106 citations