GERMAN CREDIT RISKS CLASSIFICATION USING SUPPORT VECTOR MACHINES

Support Vector Machines (SVM) is one of the most popular classification algorithms. SVM penalty parameter and the kernel parameters have high impact over the classification performance and the complexity of the algorithm. So, this brings the problem of choosing the suitable values for SVM parameters. This problem can be solved using meta-heuristic optimization algorithms. Salp Swarm Algorithm (SSA) and Crow Search Algorithm (CSA) are new meta-heuristic algorithms. SSA is a swarm algorithm that is inspired from a mechanism salps forming in deep ocean called salp chain. CSA algorithm is inspired by the intelligent behavior of crows. In this paper, SVM parameter optimization is done using SSA and CSA. German Credit dataset from the UCI data repository is used for the experiments. All experiments results are gathered from a 10-fold cross validation block. Evaluation criteria determined as accuracy, sensitivity, specificity and AUC. SSA and CSA gave accuracy results of 0.72±4.62 and 0.71±3.53 respectively. Also, ROC curves and box plots of the algorithms are given. CSA algorithm draws better graphs

Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes.

A rich model-based motion vector (MV) steganalysis benefiting from both temporal and spatial correlations of MVs is proposed in this paper. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this paper. First, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring MVs for longer distances. Therefore, temporal MV dependency alongside the spatial dependency is utilized for rigorous MV steganalysis. Second, unlike the filters previously used, which were heuristically designed against a specific MV steganography, a diverse set of many filters, which can capture aberrations introduced by various MV steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in the previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent MV steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in MV steganalysis field, including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.Engineering and Physical Sciences Research Council through the CSIT 2 Project under Grant EP/N508664/1

Determining of Solar Power by Using Machine Learning Methods in a Specified Region

In this study, it is aimed to estimate the solar power according to the hourly meteorological data of the specified location measured between 2002 and 2006 by using different Machine Learning (ML) algorithms. Data Mining Processes (DMP) were used to select the most appropriate input variables from these measured data. Data groups created using DMP were evaluated according to three different ML algorithms such as Artificial Neural Network (ANN), Support Vector Regression (SVR) and K-Nearest Neighbors (KNN). It can be concluded that DMP-ML based prediction models are more successful than models developed using all available data. The most successful model developed among these models estimated the hourly solar power potential with an accuracy of 97%. Also, different error measurement statistics were used to evaluate ML algorithms. According to Symmetric Mean Absolute Percentage Error, 6.12%, 7.22% and 12.72% values were found in the most successful prediction models developed using ANN, KNN and SVR, respectively. In addition, from the meteorological data used in this study the most effective data on solar power as a result of DMP were shown to be Temperature and Hourly Sunshine Duration

DIMENSION AND COLOR CLASSIFICATION OF OLIVE FRUIT WITH IMAGE PROCESSING TECHNIQUES

DIMENSION AND COLOR CLASSIFICATION OF OLIVE FRUIT WITH IMAGE PROCESSING TECHNIQUESAbstractThe development of image processing technology appears in agriculture as well as in many other fields. Various classifications are carried out for fruits and vegetables. These are processes such as determining the harvest time according to their degree of maturity, deciding the way of collection and performing packaging operations according to their dimension. This study aims to classify the fruit according to its intended use in order to benefit more from the olive fruit that is important in industrial terms. In this study, olive fruit is classified as big, medium, and small according to its dimensions. Also classified as black and green according to their colors. This classification process was made in MATLAB environment and the KNN algorithm and decision trees was used. The results are obtained with Euclid and Manhattan methods used with the KNN algorithm and are given comparatively. According to the application results, 100% success was achieved in both methods in color classification. In dimension classification, 89.2% classification success was achieved in KNN algorithm and 86.7% in decision tree method.Keywords: Image processing, olive classification, KNN classification algorithm, decision tree

Cybersecurity Engineering: Bridging the Security Gaps in Avionics Architectures and DO-326A/ED-202A

Urban Air Mobility is envisioned as an on-demand, highly automated and autonomous air transportation modality. It requires the use of advanced sensing and data communication technologies to gather, process, and share flight-critical data. Where this sharing of mix-critical data brings opportunities, if compromised, presents serious cybersecurity threats and safety risks due to the cyber-physical nature of the airborne vehicles. Therefore the avionics system design approach of adhering to functional safety standards (DO-178C) alone is inadequate to protect the mission-critical avionics functions from cyber-attacks. To approach this challenge, the DO-326A/ED-202A standard provides a baseline to effectively manage cybersecurity risks and to ensure the airworthiness of airborne systems. In this regard, this paper pursues a holistic cybersecurity engineering and bridges the security gap by mapping the DO-326A/ED-202A system security risk assessment activities to the Threat Analysis and Risk Assessment process. It introduces Resilient Avionics Architecture as an experimental use case for Urban Air Mobility by apprehending the DO-326A/ED-202A standard guidelines. It also presents a comprehensive system security risk assessment of the use case and derives appropriate risk mitigation strategies. The presented work facilitates avionics system designers to identify, assess, protect, and manage the cybersecurity risks across the avionics system life cycle

Fuzzy expert system approach for determination of alfa-linolenic acid content of eggs obtained from hens by dietary flaxseed

This paper presents the development of a fuzzy expert system (FES) for determination of -linolenic acid content of eggs, obtained from hens fed dietary flaxseed. Based on experimental values FES models were designed using MATLAB 6.5 fuzzy logic toolbox in Windows XP running on Intel 1.9 Gh environment. It was used time and flaxseed ratio as input parameters and linolenic acid content as output. There was a good correlation (R20.9983) between experimental values and FES (P0.05,t-test)

Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), K-Nearest Neighbors (K-NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, R2, and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively