بحوث سكوبس — علي حيدر صادق العزام

We are currently seeing a rapid spread of Autism Spectrum Disorder (ASD), so when studying autis0m behaviors, researchers note that this study requires substantial costs and time to characterize autism. Through the use of machine learning techniques, autism can be detected early. There are studies using machine learning techniques, but they have not provided any conclusion in determining the characteristics of autism due to the different ages of people. This study aims is to predict autism in any age group (children, adolescents, adults), using a classification system based on machine learning techniques (random forest (RF), decision tree (CART), Naive Bayes (NB), and Support Vector Machine (SVM). The results from algorithms (CART, SVM, NB, and RF) are evaluated using several metrics (Accuracy, Precision, Recall, F1 Score) based on the AQ dataset- 10. The model used showed advanced results in evaluating the accuracy of the types of datasets. The results provide superior performance for ASD classification. Random Forest and Support Vector Machine accuracy have been improved between (98% and 100%) with features selected by correlation technology and K fold split data. © 2023 IEEE.

The internet of things (IoT) is a rapidly developing area that consists of a globally linked network architecture based on the Internet. The internet of healthcare things (IoHT) is a subset of IoT that comprises of smart healthcare devices that are critical in monitoring, processing, storing, and transferring sensitive data. It is confronted with new issues in terms of data privacy protection. To safeguard healthcare information, this work proposes hybrid lightweight ciphers (PRESENT and TEA) that leverage elliptic curve cryptography (ECC) in the key generation phase. The proposed system evaluated using the main network evaluation parameters as throughput in Kbps, delay in ms, packet loss rate (%). The proposed approach provides secure data transmission of IoT devices based on the used lightweight security algorithms, in addition it provides conserving network performance, improving channel resource usage, network latency is increased due to the security level added by PRESENT and TEA with ECC, and decrease number of loss packets compared without security case study. © 2022, Institute of Advanced Engineering and Science. All rights reserved.