Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework. MobileNet, Xception, and InceptionV3 were the pretrained models used for classification. The facial images were taken from a publicly available dataset on Kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children. Given the accuracy of the classification results for the validation data, MobileNet reached 95% accuracy, Xception achieved 94%, and InceptionV3 attained 0.89%.

Recently, wireless networks and traffic requirements have been rapidly aggregated in diverse applications in 5G environments. For this reason, researchers have investigated the influences of this growth based on a user's requirements inside these networks. However, the stream of traffic has been considered a crucial role for the user's needs over 5G network. In this paper, gigantic data traffic is considered for enabling dynamic spectrum sharing over 5G networks. Thus, various accessing plans are covered to manage the overall network traffic. Additionally, it proposes a traffic predicting model for a technique of managing traffic when multiple requests are received to decrease delays. It has considered different significances related to a large size of traffic practices. Additionally, this work will guide us to enhance traffic solutions within massive requests over outsized networks. Systematically, it has focused on the traffic flow, starting from the accessing steps until passing on requests to suitable spectrum carriers.

Iteration is ubiquitous during software development and particularly notable in complex system development. It has both positive and negative effects; the positives of iteration include improving quality and understandability, reducing complexity and maintenance, leading to innovation, and being costeffective in the long run; Negatives of iteration include; time, cost, and effort overrun. Its management is a challenging task and becomes more complex due to the non-uniformity of the terminology used at various places. Although Software Development Life Cycles (SDLC) are highly iterative, not much work related to them has been reported in the literature. Insights into iteration are explained in this paper by defining different perspectives (Exploration, Refinement, Rework, and Negotiation) on iteration through literature review, modeling each perspective, and simulating the effect of each iterative perspective on project completion time. An attempt has been made to create awareness about efficient use of iteration during software development by informing which perspective of iteration has what kind of impact on project completion time to avoid delays.

No abstract

No abstract