Data Analytics and Artificial Intelligence最新文献_第6页

Brain Controlled Car for Disabled Using Artificial Intelligence 利用人工智能为残疾人提供脑控汽车

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/14

N. Bindhu, N. Mageswari

{"title":"Brain Controlled Car for Disabled Using Artificial Intelligence","authors":"N. Bindhu, N. Mageswari","doi":"10.46632/daai/3/2/14","DOIUrl":"https://doi.org/10.46632/daai/3/2/14","url":null,"abstract":"This paper considers the development of a brain driven car, which would be of great help to the physically disabled people. Since these cars will rely only on what the individual is thinking they will hence not require any physical movement on the part of the individual. The car integrates signals from a variety of sensors like video, weather monitor, anti-collision etc. it also has an automatic navigation system in case of emergency. The car works on the asynchronous mechanism of artificial intelligence. It’s a great advance of technology which will make the disabled, abled. In the 40s and 50s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton and the Ratio Club in England. Most researchers hope that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them. A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129491812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of Stress Distribution in a Railway Embankment Reinforced By Geogrid Based Weak Soil Formation Using Hybrid Rnn-Eho 基于复合Rnn-Eho的土工格栅软弱土加固铁路路堤应力分布研究

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/39

{"title":"Investigation of Stress Distribution in a Railway Embankment Reinforced By Geogrid Based Weak Soil Formation Using Hybrid Rnn-Eho","authors":"","doi":"10.46632/daai/3/2/39","DOIUrl":"https://doi.org/10.46632/daai/3/2/39","url":null,"abstract":"As the primary method of track support, traditional sloping embankments are typically used by railroad lines. Geosynthetically Reinforced Soil (GRS) systems, as an alternative to traditional embankments, have gained appeal, notably for high-speed lines in India. This system's reduced base area compared to traditional embankments means that\u0000less ground stabilization, improvement, and land taking is necessary. The research's findings provide intriguing strategies that may be implemented into the way tracks are designed now to accommodate faster freight trains pulling greater loads.\u0000This research explains how to anticipate the bearing capacity of weak sand supported by a method of compacted granular fill over natural clay soil using a hybrid Recurrent Neural Network (RNN) and Elephant Herding Optimization (EHO) with Georgic reinforced soil foundation. The exact prediction target for the proposed model was developed by using displacement amplitude as an output index. A number of elements influencing the foundation bed's properties, Georgic reinforcement, and dynamic excitation have been taken into account as input variables. The RNN-anticipated EHO's accuracy was compared to that of three other popular approaches, including ANN, HHO, CFA, and MOA. Strict statistical criteria and a multi-criteria approach were principally used to assess the predictive power of the developed models. The model is also examined using fresh, independent data that wasn't part of the initial dataset. The hybrid RNNEHO model performed better in predicting the displacement amplitude of footing lying on Geogrid-reinforced beds than the other benchmark models. Last but not least, the sensitivity analysis was used to highlight how input parameters might affect the estimate of displacement amplitude.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134344985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

YouTube Clone by Using Ethereum Block Chain 使用以太坊区块链克隆YouTube

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/1/9

引用次数: 0

Thyroid Detection using Machine learning 使用机器学习进行甲状腺检测

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/29

Savita Adhav, Vipul Jadhao, Abhishek Markad, Suraj Jadhav

引用次数: 0

Covid-19 Patient Health Prediction Using Boosted Random Forest Algorithm 使用增强随机森林算法预测Covid-19患者健康状况

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/13

S. Saranya, S. Bobby

{"title":"Covid-19 Patient Health Prediction Using Boosted Random Forest Algorithm","authors":"S. Saranya, S. Bobby","doi":"10.46632/daai/3/2/13","DOIUrl":"https://doi.org/10.46632/daai/3/2/13","url":null,"abstract":"COVID-19, also known as 2019-nCoV, is no longer a pandemic but an endemic disease that has killed many people worldwide. COVID-19 has no precise treatment or remedy at this time, but it is unavoidable to live with the disease and its implications. By quickly and efficiently screening for covid, one may determine whether or not one has COVID-19 and thus limit the financial and administrative burdens on healthcare systems. This reality puts a huge demand on these countries' healthcare systems, especially in emerging nations, due to the poor healthcare systems around the world. Although the COVID-19 pandemic cannot be stopped by any licenced vaccine or antiviral medicine, there are other possible solutions that could lighten the burden of the virus on healthcare systems and the economy. The most promising approaches for usage outside of a clinical environment include non-clinical approaches like machine learning, data mining, deep learning, and other artificial intelligence technologies. Artificial intelligence (AI) approaches are increasingly being integrated into wireless infrastructure, real-time data collection, and end-user device processing. A positive and negative COVID-19 case dataset is used to validate artificial intelligence (AI) systems such decision trees, support vector machines, artificial neural networks, and naive Bayesian models. The correlation coefficients between various dependent and independent variables were examined to determine the strength of the relationship between the dependent features. The model was tested 20% of the time while being trained 80% of the time during the preparation phase. The Random Forest had the highest precision (94.99%), according to the evaluation of success.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124434651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strengthen Password Using Image Authentication 使用镜像认证加强密码

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/1/8

引用次数: 0

Energy Consumption Analysis in Post Quantum Cryptography Using Multivariate Signature Algorithms 基于多元签名算法的后量子密码的能量消耗分析

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/30

引用次数: 0

Assistance and Rehabilitation for Visually Impaired using Image Processing 视障人士影像处理之辅助与复健

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/1/10

引用次数: 0

Gendroid- A Conceptual Bio-Info Android App and Its Challenges Gendroid-一个概念性的生物信息Android应用程序及其挑战

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/22

R. M. Manjunath, Kumuda valli, Yathindra Sai Ganesh, Marzan G

引用次数: 0

Mobile Robotic Arm for Opening Doors Using Proximal Policy Optimization 基于近端策略优化的移动机械臂开门

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/20

M. Kokila, G. Amalredge

引用次数: 0