scholarly journals Perbandingan Kinerja Artificial Intelligence dalam Memprediksi Kuat Tekan Beton

2020 ◽  
Vol 7 (2) ◽  
pp. 1-17
Author(s):  
Nico Christiono ◽  
Doddy Prayogo
Keyword(s):  
Artificial Intelligence ◽  
Support Vector ◽  
Quality Prediction ◽  
Concrete Compressive Strength ◽  
Existing Problems ◽  
Concrete Mixtures ◽  
Vector Machines ◽  
Concrete Quality ◽  
Open Question ◽  
Error Indicators

Today, concrete quality prediction can be performed with the help of artificial intelligence (AI) to solve existing problems. However, determining the best AI method for predicting concrete compressive strength remains an open question. Therefore, this research evaluates the most accurate AI modeling for predicting various kinds of concrete mixtures. AI methods used in this study are artificial neural networks (ANN), support vector machines (SVM), classification and regression trees (CART), and linear regression (LR). Furthermore, these four AI methods are run with several parameters and tested with 4 different kinds of concrete dataset. Four error indicators and 1 normalization indicator are used to evaluate AI and determine the best AI method. From the Obtained results, indicate that ANN has the best performance when compared with 3 other AI methods. It can be seen that from  ANN produced smaller error values when compared to the other three AI methods.

Applying Support Vector Machines in Rebound Hammer Test

2013 ◽  
Vol 853 ◽  
pp. 600-604 ◽  
Author(s):  
Yu Ren Wang ◽  
Wen Ten Kuo ◽  
Shian Shien Lu ◽  
Yi Fan Shih ◽  
Shih Shian Wei
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Support Vector Machines ◽  
Prediction Model ◽  
Prediction Models ◽  
Percentage Error ◽  
Support Vector ◽  
Concrete Compressive Strength ◽  
Rebound Hammer ◽  
Vector Machines

There are several nondestructive testing techniques available to test the compressive strength of the concrete and the Rebound Hammer Test is among one of the fast and economical methods. Nevertheless, it is found that the prediction results from Rebound Hammer Test are not satisfying (over 20% mean absolute percentage error). In view of this, this research intends to develop a concrete compressive strength prediction model for the SilverSchmidt test hammer, using data collected from 838 lab tests. The Q-values yield from the concrete test hammer SilverSchmidt is set as the input variable and the concrete compressive strength is set as the output variable for the prediction model. For the non-linear relationships, artificial intelligence technique, Support Vector Machines (SVMs), are adopted to develop the prediction models. The results show that the mean absolute percentage errors for SVMs prediction model, 6.76%, improves a lot when comparing to SilverSchmidt predictions. It is recommended that the artificial intelligence prediction models can be applied in the SilverSchmidt tests to improve the prediction accuracy.

scholarly journals Mouse Behavior Analysis Based on Artificial Intelligence as a Second-Phase Authentication System

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 29
Author(s):  
Daniel Garabato ◽  
Jorge Rodríguez García ◽  
Francisco J. Novoa ◽  
Carlos Dafonte
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machines ◽  
Support Vector ◽  
Second Phase ◽  
Authentication Protocols ◽  
Mouse Behavior ◽  
Authentication Mechanism ◽  
Vector Machines ◽  
Authentication System ◽  
Current Stage

Nowadays, a wide variety of computer systems use authentication protocols based on several factors in order to enhance security. In this work, the viability of a second-phase authentication scheme based on users’ mouse behavior is analyzed by means of classical Artificial Intelligence techniques, such as the Support Vector Machines or Multi-Layer Perceptrons. Such methods were found to perform particularly well, demonstrating the feasibility of mouse behavior analytics as a second-phase authentication mechanism. In addition, in the current stage of the experiments, the classification techniques were found to be very stable for the extracted features.

Artificial Intelligence Based Automated Estimation of Sleep Stages Using Electrocardiograph Signals: A Perspective

2014 ◽  
Vol 573 ◽  
pp. 836-841 ◽  
Author(s):  
Baskaran Banu Rekha ◽  
Arumugam Kandaswamy ◽  
R.A. Keerthana
Keyword(s):  
Artificial Intelligence ◽  
Sleep Stages ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Sleep Study ◽  
Statistical Classifiers ◽  
Vector Machines ◽  
Stationary Signals ◽  
Automated Screening ◽  
Single Night

The primary goal of this study is to expound the Artificial Intelligence schemes utilized in developing an automated sleep staging sytem. Sleep stages, broadly classified as REM and Non-REM (Rapid Eye Movement) are recognized during sleep studies. Electrocardiogram signal is one among the multiple signals recorded during a sleep study. An effort to bring out the correlation between Electrocardiogram and sleep stages would facilitate in developing an automated screening system for identifying sleep disorders. This study assimilates such researches and their outcomes conducted during the last two decades. It is also emphasized that due to liberal availability of Electrocardiogram data in hospitals, using it to distinguish sleep stages would aid in developing better healthcare. The prime methods identified from the literature are the statistical classifiers and neural network based classifiers.The reports discussed are typical of single night polysomnographic recordings. The collective results are then compared with manually scored sleep stages. Out of the various methods, Support Vector Machines and Detrended Fluctuation analysis are the popular methods owing to their nature of analyzing non stationary signals.

scholarly journals Using Artificial Intelligence Techniques for Prediction and Estimation of Photovoltaic System Output Power

2020 ◽  
Vol 3 (1) ◽  
pp. 15-21
Author(s):  
Deogratias Nurwaha
Keyword(s):  
Artificial Intelligence ◽  
Output Power ◽  
Gene Expression Programming ◽  
Photovoltaic System ◽  
Support Vector ◽  
Independent Variables ◽  
Vector Machines ◽  
System Output ◽  
Two Parameters ◽  
Statistical Metrics

Two artificial intelligence methods, namely, support vector machines (SVM) and gene expression programming (GEP), were explored for prediction and estimation of the Photovoltaic (PV)output power. Measured values of temperature T (°C) and irradiance E (kWh/㎡) were used as inputs (independent variables) and PV output power P (Kw) was used as output (dependent variable). The statistical metrics were used to assess the predictive performances of the methods. The results of the two models were estimated and compared. The results showed that the two techniques performances are better and similar. Using GEP technique, the relationships between the two parameters and output power were established. Importance of each parameter as contributor to PV output power was also investigated. The results indicated that the SVM and GEP would become the powerful tools that could help estimate the PV output power capacity reserve.

Realization methods of computer-aided diagnosis system of medical images

2019 ◽  
Vol 13 ◽  
Author(s):  
Muhammad Aqeel Ashraf ◽  
Shahreen Kasim
Keyword(s):  
Data Model ◽  
Object Segmentation ◽  
Medical Images ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Cad System ◽  
Existing Problems ◽  
Vector Machines ◽  
Computer Aided ◽  
Aided Diagnosis

: In this paper, medical images are used to realize the computer-aided diagnosis (CAD) system which develops targeted solutions to existing problems. Relying on the Mi COM platform, this system has collected and collated cases of all kinds, based on which a unified data model is constructed according to the gold standard derived by deducting each instance. Afterwards, the object segmentation algorithm is employed to segment the diseased tissues. Edge modification and feature extraction are performed for the tissue block segmented. The features extracted are classified by applying support vector machines or the Naive Bayesian classification algorithm. From the simulation results, the CAD system developed in this paper allows realization of diagnosis and treatment and sharing of data resources.

Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks

2012 ◽  
pp. 414-427 ◽  
Author(s):  
Marco Vannucci ◽  
Valentina Colla ◽  
Silvia Cateni ◽  
Mirko Sgarbi
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machines ◽  
Decision Trees ◽  
Real World ◽  
Support Vector ◽  
Artificial Intelligence Techniques ◽  
Vector Machines ◽  
Classification Tasks

In this chapter a survey on the problem of classification tasks in unbalanced datasets is presented. The effect of the imbalance of the distribution of target classes in databases is analyzed with respect to the performance of standard classifiers such as decision trees and support vector machines, and the main approaches to improve the generally not satisfactory results obtained by such methods are described. Finally, two typical applications coming from real world frameworks are introduced, and the uses of the techniques employed for the related classification tasks are shown in practice.

Artificial Intelligence in Electricity Market Operations and Management

2011 ◽  
pp. 1821-1840
Author(s):  
Zhao Yang Dong ◽  
Tapan Kumar Saha ◽  
Kit Po Wong
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Neural Networks ◽  
Time Series ◽  
Electricity Market ◽  
Support Vector ◽  
Market Demand ◽  
Deregulated Electricity Market ◽  
Vector Machines ◽  
Spike Analysis

This chapter introduces advanced techniques such as artificial neural networks, wavelet decomposition, support vector machines, and data-mining techniques in electricity market demand and price forecasts. It argues that various techniques can offer different advantages in providing satisfactory demand and price signal forecast results for a deregulated electricity market, depending on the specific needs in forecasting. Furthermore, the authors hope that an understanding of these techniques and their application will help the reader to form a comprehensive view of electricity market data analysis needs, not only for the traditional time-series based forecast, but also the new correlation-based, price spike analysis.

Diabetes Diagnosis System Based on Support Vector Machines Trained by Vortex Optimization Algorithm

2018 ◽  
pp. 203-218 ◽  
Author(s):  
Sadi Fuat Cankaya ◽  
Ibrahim Arda Cankaya ◽  
Tuncay Yigit ◽  
Arif Koyun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Support Vector Machines ◽  
Optimization Algorithm ◽  
Intelligent Systems ◽  
Support Vector ◽  
Diabetes Diagnosis ◽  
Diagnosis System ◽  
Vector Machines ◽  
Expert Support

Artificial intelligence is widely enrolled in different types of real-world problems. In this context, developing diagnosis-based systems is one of the most popular research interests. Considering medical service purposes, using such systems has enabled doctors and other individuals taking roles in medical services to take instant, efficient expert support from computers. One cannot deny that intelligent systems are able to make diagnosis over any type of disease. That just depends on decision-making infrastructure of the formed intelligent diagnosis system. In the context of the explanations, this chapter introduces a diagnosis system formed by support vector machines (SVM) trained by vortex optimization algorithm (VOA). As a continuation of previously done works, the research considered here aims to diagnose diabetes. The chapter briefly gives information about details of the system and findings reached after using the developed system.

scholarly journals Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Igor Peško ◽  
Vladimir Mučenski ◽  
Miloš Šešlija ◽  
Nebojša Radović ◽  
Aleksandra Vujkov ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Construction Projects ◽  
Percentage Error ◽  
Support Vector ◽  
Building Process ◽  
Vector Machines ◽  
Specific Part

Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE) of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.

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