Search results for: numerical algorithms

Authors: Rishabh Srivastav, Divyam Sharma

Abstract:

We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic.

Keywords: categorical data, fuzzy logic, genetic algorithm, K modes clustering, rough sets

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Authors: Bastien Batardière, Joon Kwon

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For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

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Authors: Luke Ukpebor, C. E. Abhulimen

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Stiff initial value problems in ordinary differential equations are problems for which a typical solution is rapidly decaying exponentially, and their numerical investigations are very tedious. Conventional numerical integration solvers cannot cope effectively with stiff problems as they lack adequate stability characteristics. In this article, we developed a new family of four-step second derivative exponentially fitted method of order six for the numerical integration of stiff initial value problem of general first order differential equations. In deriving our method, we employed the idea of breaking down the general multi-derivative multistep method into predator and corrector schemes which possess free parameters that allow for automatic fitting into exponential functions. The stability analysis of the method was discussed and the method was implemented with numerical examples. The result shows that the method is A-stable and competes favorably with existing methods in terms of efficiency and accuracy.

Keywords: A-stable, exponentially fitted, four step, predator-corrector, second derivative, stiff initial value problems

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: K. Boualem, T. Yahiaoui, A. Azzi

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Numerical investigations are performed to analyze the flow behavior over NACA0015 and to evaluate the efficiency of synthetic jet as active control device. The second objective of this work is to investigate the influence of momentum coefficient of synthetic jet on the flow behaviour. The unsteady Reynolds-averaged Navier-Stokes equations of the turbulent flow are solved using, k-ω SST provided by ANSYS CFX-CFD code. The model presented in this paper is a comprehensive representation of the information found in the literature. Comparison of obtained numerical flow parameters with the experimental ones shows that the adopted computational procedure reflects nearly the real flow nature. Also, numerical results state that use of synthetic jets devices has positive effects on the flow separation, and thus, aerodynamic performance improvement of NACA0015 airfoil. It can also be observed that the use of synthetic jet increases the lift coefficient about 13.3% and reduces the drag coefficient about 52.7%.

Keywords: active control, synthetic jet, NACA airfoil, CFD

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Issa Qabaja, Fadi Thabtah

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Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

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Authors: Ahlem Fatnassi, Hamza Gharsellaoui, Sadok Bouamama

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This paper deals with the study of interest in the fields of Steganography and Steganalysis. Steganography involves hiding information in a cover media to obtain the stego media in such a way that the cover media is perceived not to have any embedded message for its unintended recipients. Steganalysis is the mechanism of detecting the presence of hidden information in the stego media and it can lead to the prevention of disastrous security incidents. In this paper, we provide a critical review of the steganalysis algorithms available to analyze the characteristics of an image stego media against the corresponding cover media and understand the process of embedding the information and its detection. We anticipate that this paper can also give a clear picture of the current trends in steganography so that we can develop and improvise appropriate steganalysis algorithms.

Keywords: optimization, heuristics and metaheuristics algorithms, embedded systems, low-power consumption, steganalysis heuristic approach

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Authors: Abbas Ebrahimi, Mohammad Zandsalimy

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The main purpose of this study is to investigate the feasibility of using FPGA (Field Programmable Gate Arrays) chips as alternatives for the conventional CPUs to accelerate the numerical solution of the Laplace equation. FPGA is an integrated circuit that contains an array of logic blocks, and its architecture can be reprogrammed and reconfigured after manufacturing. Complex circuits for various applications can be designed and implemented using FPGA hardware. The reconfigurable hardware used in this paper is an SoC (System on a Chip) FPGA type that integrates both microprocessor and FPGA architectures into a single device. In the present study the Laplace equation is implemented and solved numerically on both reconfigurable hardware and CPU. The precision of results and speedups of the calculations are compared together. The computational process on FPGA, is up to 20 times faster than a conventional CPU, with the same data precision. An analytical solution is used to validate the results.

Keywords: accelerating numerical solutions, CFD, FPGA, hardware definition language, numerical solutions, reconfigurable hardware

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Authors: Faruk Ortes, Baris Sayin, Tarik Serhat Bozkurt, Cemil Akcay

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The technique using near-surface mounted (NSM) carbon fiber-reinforced polymer (CFRP) composites has proved to be an reliable strengthening technique. However, the effects of different parameters for the use of NSM CFRP are not fully developed yet. This study focuses on the development of a numerical modeling that can predict the behavior of reinforced concrete (RC) beams strengthened with NSM FRP rods exposed to bending loading and the efficiency of various parameters such as CFRP rod size and filling material type are evaluated by using prepared models. For this purpose, three different models are developed and implemented in the ANSYS® software using Finite Element Analysis (FEA). The numerical results indicate that CFRP rod size and filling material type are significant factors in the behavior of the analyzed RC beams.

Keywords: numerical model, FEA, RC beam, NSM technique, CFRP rod, filling material

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Authors: Muhammad Arslan Usman, Muhammad Rehan Usman, Soo Young Shin

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Live video streaming is one of the most widely used service among end users, yet it is a big challenge for the network operators in terms of quality. The only way to provide excellent Quality of Experience (QoE) to the end users is continuous monitoring of live video streaming. For this purpose, there are several objective algorithms available that monitor the quality of the video in a live stream. Subjective tests play a very important role in fine tuning the results of objective algorithms. As human perception is considered to be the most reliable source for assessing the quality of a video stream, subjective tests are conducted in order to develop more reliable objective algorithms. Temporal impairments in a live video stream can have a negative impact on the end users. In this paper we have conducted subjective evaluation tests on a set of video sequences containing temporal impairment known as frame freezing. Frame Freezing is considered as a transmission error as well as a hardware error which can result in loss of video frames on the reception side of a transmission system. In our subjective tests, we have performed tests on videos that contain a single freezing event and also for videos that contain multiple freezing events. We have recorded our subjective test results for all the videos in order to give a comparison on the available No Reference (NR) objective algorithms. Finally, we have shown the performance of no reference algorithms used for objective evaluation of videos and suggested the algorithm that works better. The outcome of this study shows the importance of QoE and its effect on human perception. The results for the subjective evaluation can serve the purpose for validating objective algorithms.

Keywords: objective evaluation, subjective evaluation, quality of experience (QoE), video quality assessment (VQA)

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Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Mehdi Ameur, Ahmed Essakdi, Tamou Nasser

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The purpose of this paper is the analysis and modeling of the photovoltaic system with MPPT techniques. This system is developed by combining the models of established solar module and DC-DC converter with the algorithms of perturb and observe (P&O), incremental conductance (INC) and fuzzy logic controller(FLC). The system is simulated under different climate conditions and MPPT algorithms to determine the influence of these conditions on characteristic power-voltage of PV system. According to the comparisons of the simulation results, the photovoltaic system can extract the maximum power with precision and rapidity using the MPPT algorithms discussed in this paper.

Keywords: photovoltaic array, maximum power point tracking, MPPT, perturb and observe, P&O, incremental conductance, INC, hill climbing, HC, fuzzy logic controller, FLC

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Authors: Chulsang Yoo, Gildo Kim

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Dual polarization radar provides comprehensive information about rainfall by measuring multiple parameters. In Korea, for the rainfall estimation, JPOLE and CSU-HIDRO algorithms are generally used. This study evaluated the local applicability of JPOLE and CSU-HIDRO algorithms in Korea by using the observed rainfall data collected on August, 2014 by the Biseulsan dual polarization radar data and KMA AWS. A total of 11,372 pairs of radar-ground rain rate data were classified according to thresholds of synthetic algorithms into suitable and unsuitable data. Then, evaluation criteria were derived by comparing radar rain rate and ground rain rate, respectively, for entire, suitable, unsuitable data. The results are as follows: (1) The radar rain rate equation including KDP, was found better in the rainfall estimation than the other equations for both JPOLE and CSU-HIDRO algorithms. The thresholds were found to be adequately applied for both algorithms including specific differential phase. (2) The radar rain rate equation including horizontal reflectivity and differential reflectivity were found poor compared to the others. The result was not improved even when only the suitable data were applied. Acknowledgments: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (NRF-2013R1A1A2011012).

Keywords: CSU-HIDRO algorithm, dual polarization radar, JPOLE algorithm, radar rainfall estimation algorithm

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Authors: Indri Mahadiraka Rumamby, R. R. Dwinanti Rika Marthanty, Jessica Sjah

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Smoothed particle hydrodynamics (SPH) was originally created to simulate nonaxisymmetric phenomena in astrophysics. However, this method still has several shortcomings, namely the high computational cost required to model values with high resolution and problems with boundary conditions. The difficulty of modeling boundary conditions occurs because the SPH method is influenced by particle deficiency due to the integral of the kernel function being truncated by boundary conditions. This research aims to answer if SPH modeling with a focus on boundary layer interactions and continuous flow can produce quantifiably accurate values with low computational cost. This research will combine algorithms and coding in the main program of meandering river, continuous flow algorithm, and solid-fluid algorithm with the aim of obtaining quantitatively accurate results on solid-fluid interactions with the continuous flow on a meandering channel using the SPH method. This study uses the Fortran programming language for modeling the SPH (Smoothed Particle Hydrodynamics) numerical method; the model is conducted in the form of a U-shaped meandering open channel in 3D, where the channel walls are soil particles and uses a continuous flow with a limited number of particles.

Keywords: smoothed particle hydrodynamics, computational fluid dynamics, numerical simulation, fluid mechanics

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Authors: M. W. Sun, Y. Zhang, L. M. Zhang, Z. H. Wang, Z. Q. Chen

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In the realm of flight control, the Proportional- Derivative (PD) control is still widely used for the attitude control in practice, particularly for the pitch control, and the attitude dynamics using PD controller should be investigated deeply. According to the empirical knowledge about the unstable flight dynamics, the control parameter combination conditions to generate sole or finite number of closed-loop oscillations, which is a quite smooth response and is more preferred by practitioners, are presented in analytical or numerical manners. To analyze the effects of the combination conditions of the control parameters, the roots of several polynomials are sought to obtain feasible solutions. These conditions can also be plotted in a 2-D plane which makes the conditions be more explicit by using multiple interval operations. Finally, numerical examples are used to validate the proposed methods and some comparisons are also performed.

Keywords: attitude control, dynamic performance, numerical solving method, interval, unstable flight dynamics

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Authors: Helen Zhang

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Accurate segmentation of the airways from CT scans is crucial for early diagnosis of lung cancer. However, the existing airway segmentation algorithms often rely on thin-slice CT scans, which can be inconvenient and costly. This paper presents a set of machine learning-based 3D super-resolution algorithms along heterogeneous dimensions to improve the resolution of thicker CT scans to reduce the reliance on thin-slice scans. To evaluate the efficacy of the super-resolution algorithms, quantitative assessments using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural SIMilarity index) were performed. The impact of super-resolution on airway segmentation accuracy is also studied. The proposed approach has the potential to make airway segmentation more accessible and affordable, thereby facilitating early diagnosis and treatment of lung cancer.

Keywords: 3D super-resolution, airway segmentation, thin-slice CT scans, machine learning

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Authors: Raza Abdulla Saeed

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In this study, the three-dimensional cavitating turbulent flow in a complete Francis turbine is simulated using mixture model for cavity/liquid two-phase flows. Numerical analysis is carried out using ANSYS CFX software release 12, and standard k-ε turbulence model is adopted for this analysis. The computational fluid domain consist of spiral casing, stay vanes, guide vanes, runner and draft tube. The computational domain is discretized with a three-dimensional mesh system of unstructured tetrahedron mesh. The finite volume method (FVM) is used to solve the governing equations of the mixture model. Results of cavitation on the runner’s blades under three different boundary conditions are presented and discussed. From the numerical results it has been found that the numerical method was successfully applied to simulate the cavitating two-phase turbulent flow through a Francis turbine, and also cavitation is clearly predicted in the form of water vapor formation inside the turbine. By comparison the numerical prediction results with a real runner; it’s shown that the region of higher volume fraction obtained by simulation is consistent with the region of runner cavitation damage.

Keywords: computational fluid dynamics, hydraulic francis turbine, numerical simulation, two-phase mixture cavitation model

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Authors: Hsien Hao Teng

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This study uses numerical simulation to analyze the aerodynamic characteristics of the 53-degree Lambda wing with a sweep angle and mainly discusses the numerical simulation results and physical characteristics of the wall y+. Use the commercial software Fluent to execute Mach number 0.15; when the angle of attack attitude is between 0 degrees and 27 degrees, the physical characteristics of the overall aerodynamic force are analyzed, especially when the fluid separation and vortex structure changes are discussed under the condition of high angle of attack, it will affect The instability of pitching moment. In the numerical calculation, the use of wall y+ and turbulence model will affect the prediction of vortex generation and the difference in structure. The analysis results are compared with experimental data to discuss the trend of the aerodynamic characteristics of the Lambda wing.

Keywords: lambda wing, wall function, turbulence model, computational fluid dynamics

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Authors: Payel Das, Gnaneshwar Nelakanti

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In this paper we analyse the iterated discrete Legendre Galerkin method for Fredholm-Hammerstein integral equations with smooth kernel. Using sufficiently accurate numerical quadrature rule, we obtain superconvergence rates for the iterated discrete Legendre Galerkin solutions in both infinity and $L^2$-norm. Numerical examples are given to illustrate the theoretical results.

Keywords: hammerstein integral equations, spectral method, discrete galerkin, numerical quadrature, superconvergence

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Authors: Shubham Jaiswal

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In this study, the numerical solution of two-dimensional solute transport system in a homogeneous porous medium of finite-length is obtained. The considered transport system have the terms accounting for advection, dispersion and first-order decay with first-type boundary conditions. Initially, the aquifer is considered solute free and a constant input-concentration is considered at inlet boundary. The solution is describing the solute concentration in rectangular inflow-region of the homogeneous porous media. The numerical solution is derived using a powerful method viz., spectral collocation method. The numerical computation and graphical presentations exhibit that the method is effective and reliable during solution of the physical model with complicated boundary conditions even in the presence of reaction term.

Keywords: two-dimensional solute transport system, spectral collocation method, Chebyshev polynomials, Chebyshev differentiation matrix

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Authors: Terence Soule, Tami Al Ghamdi

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To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population.

Keywords: transfer learning, genetic algorithm, evolutionary computation, source and target

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Authors: Volkan Uygun, H. Birkan Yilmaz, Tuna Tugcu

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This study focuses on the development and use of algorithms that address the issue of resource management in response to emergency and disaster situations. The presented system, named Disaster Management Platform (DMP), takes the data from the data sources of service providers and distributes the incoming requests accordingly both to manage load balancing and minimize service time, which results in improved user satisfaction. Three different resource management algorithms, which give different levels of importance to load balancing and service time, are proposed for the study. The first one is the Minimum Distance algorithm, which assigns the request to the closest resource. The second one is the Minimum Load algorithm, which assigns the request to the resource with the minimum load. Finally, the last one is the Hybrid algorithm, which combines the previous two approaches. The performance of the proposed algorithms is evaluated with respect to waiting time, success ratio, and maximum load ratio. The metrics are monitored from simulations, to find the optimal scheme for different loads. Two different simulations are performed in the study, one is time-based and the other is lambda-based. The results indicate that, the Minimum Load algorithm is generally the best in all metrics whereas the Minimum Distance algorithm is the worst in all cases and in all metrics. The leading position in performance is switched between the Minimum Distance and the Hybrid algorithms, as lambda values change.

Keywords: emergency and disaster response, resource management algorithm, disaster situations, disaster management platform

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Authors: Vesile Evrim, Aliyu Awwal

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Today as in the other domains, there are an enormous number of political transcripts available in the Web which is waiting to be mined and used for various purposes such as statistics and recommendations. Therefore, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do the automatic classification are based on different features such as Linguistic. Considering the ideology differences between Liberals and Conservatives, in this paper, the effect of Personality Traits on political orientation classification is studied. This is done by considering the correlation between LIWC features and the BIG Five Personality Traits. Several experiments are conducted on Convote U.S. Congressional-Speech dataset with seven benchmark classification algorithms. The different methodologies are applied on selecting different feature sets that constituted by 8 to 64 varying number of features. While Neuroticism is obtained to be the most differentiating personality trait on classification of political polarity, when its top 10 representative features are combined with several classification algorithms, it outperformed the results presented in previous research.

Keywords: politics, personality traits, LIWC, machine learning

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Authors: Daliyah S. Aljutaili, Redna A. Almutlaq, Suha A. Alharbi, Dina M. Ibrahim

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All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.

Keywords: currency recognition, feature detection and description, SIFT algorithm, SURF algorithm, speeded up and robust features

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Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

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Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: approach instance-based, area under the ROC curve, patient-specific decision path, clinical predictions

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Authors: Aditya Verma

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E-voting using blockchain provides us with a distributed system where data is present on each node present in the network and is reliable and secure too due to its immutability property. This work compares various blockchain consensus algorithms used for e-voting applications in the past, based on performance and node scalability, and chooses the optimal one and improves on one such previous implementation by proposing solutions for the loopholes of the optimally working blockchain consensus algorithm, in our chosen application, e-voting.

Keywords: blockchain, parallel bft, consensus algorithms, performance

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Authors: Abdulrahman Alenezi

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This research paper investigates the surface heat transfer characteristics using computational fluid dynamics for orthogonal and inclined impinging jet. A jet Reynolds number (Rₑ) of 10,000, jet-to- plate spacing (H/D) of two and eight and two angles of impingement (α) of 45° and 90° (orthogonal) were employed in this study. An unconfined jet impinges steadily a constant temperature flat surface using air as working fluid. The numerical investigation is validated with an experimental study. This numerical study employs grid dependency investigation and four different types of turbulence models including the transition SSD to accurately predict the second local maximum in Nusselt number. A full analysis of the effect of both turbulence models and mesh size is reported. Numerical values showed excellent agreement with the experimental data for the case of orthogonal impingement. For the case of H/D =6 and α=45° a maximum percentage error of approximately 8.8% occurs of local Nusselt number at stagnation point. Experimental and numerical correlations are presented for four different cases

Keywords: turbulence model, inclined jet impingement, single jet impingement, heat transfer, stagnation point

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