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Autism spectrum disorder (ASD) is a term used to describe a group of neurodevelopmental abnormalities. Differences in communication and social interaction are characteristics of these disorders. People with ASD may have limited and repetitive hobbies or behavioural patterns.ASD affects people of all races & ethnicities, cultures, and socioeconomic backgrounds. Early infancy is when ASD symptoms typically appear, usually between the ages of 12 and 24 months. Symptoms, on the other hand, could appear at any time. A severe delay in linguistic or social development could be one of the first indicators. Autism involves symptoms relating to physical movements and behaviours and speech and social difficulties. It's unclear why autism occurs. It might be caused by issues in the regions of your brain that interpret sensory information and process language. It may happen to persons of any race, nationality, or social status. The proposed study is reviewed to identify the autistic disorder and detect this ASD problem using machine learning, as well as to conclude the behavioral features of human nature during ASD. [ABSTRACT FROM AUTHOR]

Object detection is a significant task that forms the basis for many image processing and computer vision applications. Automobiles and vehicles are engrained in popular culture as one of the most versatile and convenient means of transportation. While it is a convenient and popular means of transportation, it is also quite dangerous. Every year, several people are killed in car accidents caused by another vehicle or object. A Method for Preventing Mishaps Many incidents can be prevented if drivers avoid exposure and evasion, which are caused by things like loss of control, irresponsible driving, exhausted or drunk drivers, and a lack of concentration on the road. The market currently does not provide a standard safety feature in any car, nor does it provide the use of sensory equipment to identify potentially dangerous objects at a defined distance. The goal of this study is to create intelligent driver monitoring software that identifies tiredness and helps to avoid traffic accidents. A driver and a publicly accessible online tracking program The technology measures him if he blinks his eyes. It is done by measuring the distance between the eyes or by staring at specified spots. Python is the programming language utilized in the development of the software system. It uses the OPENCV and Media Pipe libraries, a Media Pipe Face Mesh technique is used in this paper. The system can calculate the time of nap, and it triggers beep sound if the nap occurs in any time. the system take consider, if the driver haver 30 or more of that value in napping. It will be send alarm message IOT system with the current location of driver to driver family. [ABSTRACT FROM AUTHOR]

In modern days, various tools and technologies of cloud computing are widely used in different domains that are offered by cloud service providers on a usage-based payment method. As the number of resources availed may vary from tens to hundreds, it is very challenging to appraise the performance of resource management policies of the cloud computing arena. There are many tools in the form of kits available to simulate and model the cloud environment. Some of them are Green Cloud, Cloud Analyst, GridSim and CloudSim etc. In this research, we have used CloudSim as a simulator which is helpful to do a simulation in Data Center. With the help of scheduling policies, this toolkit may build several virtual machines on a single Data Center node, as well as assign user requests in the form of Cloudlets to virtual machines. In this paper, a study of scheduling of cloudlets using Time – Shared and Space – Shared policies using FCFS, and Round Robin resource allocation algorithms have been done and there is a comparison of the results obtained from both the processes based on the different variables like total execution time, the total number of resources etc. The compared result yields the optimized resource management at the end. [ABSTRACT FROM AUTHOR]

U-net is an image segmentation approach developed mostly for image segmentation tasks. The style includes a max pooling path to acquire context and a symmetrical up sampling track through concatenation that allows accurate localization. The success of U-net appears in its considerable use in mostly all considerable photo methods, from CT scans as well as likewise MRI to Xrays in addition to microscopy. In addition, while U-net is considerably a segmentation tool, there have actually been circumstances of utilizing U-net in various other applications. Considered that U-net's possibility is still elevating, in this paper, U-net model is trained on MADS dataset and afterwards the model's efficiency is reviewed with future suggestions and enhancements. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) has received a great deal of attention for its computer representation and evaluation of human language. AI, email spam location, data extraction, once-finished, clinical, and question addressing are only a couple of the applications. The article is broken into four areas, with the first talking about different degrees of NLP and parts of Natural LanguageGeneration (NLG), trailed by the arrangement of encounters and improvement of NLP, the high level, new things, and weights, and future expansion. We'll also look at the tools and methods utilized in Natural Language Processing, as well as how these procedures work when we apply them. The single correlation between operations and how each approach performs. Regular language handling has not yet attained flawlessness, although continued progress in this field can certainly approach the line of flawlessness. Today, numerous AIs recognize and respond to consumer voice directions using typical language handling calculations. [ABSTRACT FROM AUTHOR]

The estimation of software costs is the key step in the software growth process. It estimates the volume of time and effort required to develop a software set up. It is the very important duty, and a precise estimate gives a solid foundation for the growth process. The Constructive Cost Methodology (COCOMO), the most extensively used estimated cost model, is discussed. For perceptron learning technique the artificial neural networks utilized for creating model. The network is trained and tested using COCOMO dataset. The goal of the study is to improve COCOMO model's approximation validity by including artificial neural networks. [ABSTRACT FROM AUTHOR]