Metaheuristic Algorithms for Convolution Neural Network

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).Comment: Article ID 1537325, 13 pages. Received 29 January 2016; Revised 15 April 2016; Accepted 10 May 2016. Academic Editor: Martin Hagan. in Hindawi Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016

Face Spoofing Detection using Inception-v3 on RGB Modal and Depth Modal

Face spoofing can provide inaccurate face verification results in the face recognition system. Deep learning has been widely used to solve face spoofing problems. In face spoofing detection, it is unnecessary to use the entire network layer to represent the difference between real and spoof features. This study detects face spoofing by cutting the Inception-v3 network and utilizing RGB modal, depth, and fusion approaches. The results showed that face spoofing detection has a good performance on the RGB and fusion models. Both models have better performance than the depth model because RGB modal can represent the difference between real and spoof features, and RGB modal dominate the fusion model. The RGB model has accuracy, precision, recall, F1-score, and AUC values obtained respectively 98.78%, 99.22%, 99.31.2%, 99.27%, and 0.9997 while the fusion model is 98.5%, 99.31%, 98.88%. 99.09%, and 0.9995, respectively. Our proposed method with cutting the Inception-v3 network to mixed6 successfully outperforms the previous study with accuracy up to 100% using the MSU MFSD benchmark dataset

Forest and Land Fire Vulnerability Assessment and Mapping using Machine Learning Method in East Nusa Tenggara Province, Indonesia

Forest and land fires are severe disasters for forest ecosystems, diminishing their functionality. Accurate prediction of fire-prone areas aids in effective management and prevention. Machine learning methods have shown promise in this regard. By 2022, East Nusa Tenggara (NTT) had the highest incidence of such fires. This study aims to assess NTT's forest and land fire vulnerability using seven machine learning methods: Gaussian Naive Bayes, Support Vector Machine, Logistic Regression, Artificial Neural Network, Random Forest, Gradient Boosting Machine, and Extreme Gradient Boost. A geospatial dataset integrating NTT's 2022 fire data and fourteen fire-related factors were created using ArcGIS. Feature selection, employing the Information Gain Ratio, identified nine key features: Degree of Slope, Land Cover, NDVI, Annual Rainfall, Distance to Road, Distance to River, Distance to Buildings, Wind Speed, and Solar Radiation. The Random Forest model emerged as optimal, with AUC values of 0.864 and 0.742 for training and testing, respectively. The resulting vulnerability map highlighted factors contributing to NTT's forest fires, including gentle slopes, forest cover, unhealthy vegetation, low rainfall, human activities, remote water access, soil moisture, distant firefighting facilities, low wind speeds, and high solar radiation. Recommendations include land management, fire-resistant vegetation, policy enforcement, community education, and infrastructure enhancement

CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING

Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance

A Hybrid CNN-SVR for Airfoil Aerodynamic Coefficient Prediction

The prediction of aerodynamic coefficients on airfoils using machine learning is increasingly popular due to its efficiency in time and cost. Research typically focuses on a single image type without comparing various types and output quantities (single or multi-output). Although convolutional neural networks (CNN) are widely used, their final layer is often suboptimal as a linear operator, and feature extraction results contain many parameters that can still be trained. Support vector regression (SVR) with kernel functions effectively reduces common errors in feature vectors. We propose a hybrid method, AeroCNNSVR, combining CNN as a feature extractor and SVR as a regressor to predict aerodynamic coefficients on airfoils. This study focuses on the shape and position of airfoils according to the angle of attack (AoA) without considering flow conditions. Using 14533 aerodynamic coefficients from 563 airfoil types, we created a dataset of grayscale and RGB airfoil images. Results show the proposed method with grayscale images performs better because combining SVR strengthens the predictive model, while grayscale images accurately represent the airfoil's shape and position. AeroCNNSVR achieves lower RMSE values for Cl (0.101522), Cd (0.016450), and Cm (0.129661) compared to the CNN model’s Cl (0.112493), Cd (0.019060), and Cm (0.130041). Additionally, AeroCNNSVR's R² values for Cl (0.976071), Cd (0.928700), and Cm (0.860574) surpass those of the CNN model (Cl 0.970620, Cd 0.904282, Cm 0.816355). This research contributes by 1) proposing an alternative besides CFD for predicting and identifying trends in aerodynamic coefficients of airfoils in a much shorter time during the design stage; 2) offering wind tunnel practitioners for early detection of configuration errors; 3) providing an overview of the aerodynamic characteristics of the airfoil under test, including the angle at which stall conditions occur

Psikologi Agama

buku ini membahas tentang fungsi agama dalam kehidupan sehari har

Combining Deep Belief Networks and Bidirectional Long Short-Term Memory

This paper proposes a new combination of Deep Belief Networks (DBN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for Sleep Stage Classification. Tests were performed using sleep stages of 25 patients with sleep disorders. The recording comes from electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG) represented in signal form. All three of these signals processed and extracted to produce 28 features. The next stage, DBN Bi-LSTM is applied. The analysis of this combination compared with the DBN, DBN HMM (Hidden Markov Models), and Bi-LSTM. The results obtained that DBN Bi-LSTM is the best based on precision, recall, and F1 score

CIELab Color Moments: Alternative Descriptors for LANDSAT Images Classification System

This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors.  It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model.  The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively.  Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images.This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors.  It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model.  The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively.  Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images

Triangular fuzzy number for similarity measurement of Y chromosome DNA profile

This study measures the similarity of the short tandem repeat (STR) profile of human DNA. The similarity measurement had been done to the STR value of the allele loci in DNA profile between the query’s DNA to the reference’s DNA profile. The measurements were conducted on 27 DNA profile loci including the Y chromosome loci (YSTR). The YSTR loci were used as the main comparison of similarity measurements to determine the biological kinship relationship between the query DNA profile and the alleged male biological family. To measure the similarity of two STR values that have shifted due to several factors in the DNA source extraction process, a fuzzy similarity measure was used. The STR values of the DNA profile loci are described as triangular fuzzy numbers. Similarity value of the STR is the intersection of two isosecle that been compared. To conclude that the query has a biological relationship with the male reference, the similarity of the YSTR locus is equal or more than 0.75 and the similarity value of the other 24 DNA profile loci is greater or equal to 0.5. From the trial that have been done, 90% give the right results