Search results for: font distribution algorithm

Authors: Yan-Ren Chen, Jenn-Kaie Lain

Abstract:

This paper proposes a novel indoor positioning algorithm based on visible light communications, implemented by light-emitting diode fixtures. In the proposed positioning algorithm, distances between light-emitting diode fixtures and mobile terminal are derived from the assumption of ideal Lambertian optic radiation model, and Trilateration positioning method is proceeded immediately to get the coordinates of mobile terminal. The proposed positioning algorithm directly obtains distance information from the optical signal modeling, and therefore, statistical distribution of received signal strength at different positions in interior space has no need to be pre-established. Numerically, simulation results have shown that the proposed indoor positioning algorithm can provide accurate location coordinates estimation.

Keywords: indoor positioning, received signal strength, trilateration, visible light communications

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Authors: Emad E. Abdallah, Alaa E. Abdallah, Bajes Y. Alskarnah

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We introduce a robust three-dimensional watermarking algorithm for copyright protection and indexing. The basic idea behind our technique is to measure the interquartile range or the spread of the 3D model vertices. The algorithm starts by converting all the vertices to spherical coordinate followed by partitioning them into small groups. The proposed algorithm is slightly altering the interquartile range distribution of the small groups based on predefined watermark. The experimental results on several 3D meshes prove perceptual invisibility and the robustness of the proposed technique against the most common attacks including compression, noise, smoothing, scaling, rotation as well as combinations of these attacks.

Keywords: watermarking, three-dimensional models, perceptual invisibility, interquartile range, 3D attacks

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

Procedia PDF Downloads 115

Authors: Thomas Yeboah

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Cloud Computing is newly paradigm in computing that promises a delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet). As Cloud Computing is a newly style of computing on the internet. It has many merits along with some crucial issues that need to be resolved in order to improve reliability of cloud environment. These issues are related with the load balancing, fault tolerance and different security issues in cloud environment.In this paper the main concern is to develop an effective load balancing algorithm that gives satisfactory performance to both, cloud users and providers. This proposed algorithm (hybrid Bee Ant Colony algorithm) is a combination of two dynamic algorithms: Ant Colony Optimization and Bees Life algorithm. Ant Colony algorithm is used in this hybrid Bee Ant Colony algorithm to solve load balancing issues whiles the Bees Life algorithm is used for optimization of job scheduling in cloud environment. The results of the proposed algorithm shows that the hybrid Bee Ant Colony algorithm outperforms the performances of both Ant Colony algorithm and Bees Life algorithm when evaluated the proposed algorithm performances in terms of Waiting time and Response time on a simulator called CloudSim.

Keywords: ant colony optimization algorithm, bees life algorithm, scheduling algorithm, performance, cloud computing, load balancing

Procedia PDF Downloads 596

Authors: Sardar Ameer Akram Khan, Shahzad Amin Sheikh

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Blind equalization is an important technique amongst equalization family. Multimodulus algorithms based on blind equalization removes the undesirable effects of ISI and cater ups the phase issues, saving the cost of rotator at the receiver end. In this paper a new algorithm combination of recursive least square and Multimodulus algorithm named as RLSMMA is proposed by providing few assumption, fast convergence and minimum Mean Square Error (MSE) is achieved. The excellence of this technique is shown in the simulations presenting MSE plots and the resulting filter results.

Keywords: blind equalizations, constant modulus algorithm, multi-modulus algorithm, recursive least square algorithm, quadrature amplitude modulation (QAM)

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Authors: J. Becker, R. Arnold

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The design of distribution logistics has a decisive impact on a company's logistics costs and performance. Hence, such solutions make an essential contribution to corporate success. This article describes a decision support system for analyzing the potential of distribution logistics in terms of logistics costs and performance. In contrast to previous procedures of business process re-engineering (BPR), this method maps distribution logistics holistically under variable distribution structures. Combined with qualitative measures the decision support system will contribute to a more efficient design of distribution logistics.

Keywords: decision support system, distribution logistics, potential analyses, supply chain management

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Authors: Safa Adi

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This paper will consider the problem of sequential mining patterns embedded in a database by handling the time constraints as defined in the GSP algorithm (level wise algorithms). We will compare two previous approaches GTC and PSP, that resumes the general principles of GSP. Furthermore this paper will discuss PG-hybrid algorithm, that using PSP and GTC. The results show that PSP and GTC are more efficient than GSP. On the other hand, the GTC algorithm performs better than PSP. The PG-hybrid algorithm use PSP algorithm for the two first passes on the database, and GTC approach for the following scans. Experiments show that the hybrid approach is very efficient for short, frequent sequences.

Keywords: database, GTC algorithm, PSP algorithm, sequential patterns, time constraints

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Authors: Ali Nourollah, Mohsen Movahedinejad

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In this paper a new algorithm to generate random simple polygons from a given set of points in a two dimensional plane is designed. The proposed algorithm uses a genetic algorithm to generate polygons with few vertices. A new merge algorithm is presented which converts any two polygons into a simple polygon. This algorithm at first changes two polygons into a polygonal chain and then the polygonal chain is converted into a simple polygon. The process of converting a polygonal chain into a simple polygon is based on the removal of intersecting edges. The merge algorithm has the time complexity of O ((r+s) *l) where r and s are the size of merging polygons and l shows the number of intersecting edges removed from the polygonal chain. It will be shown that 1 < l < r+s. The experiments results show that the proposed algorithm has the ability to generate a great number of different simple polygons and has better performance in comparison to celebrated algorithms such as space partitioning and steady growth.

Keywords: Divide and conquer, genetic algorithm, merge polygons, Random simple polygon generation.

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Authors: Dewi Retno Sari Saputro, Purnami Widyaningsih, Hendrika Handayani

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Extreme data on an observation can occur due to unusual circumstances in the observation. The data can provide important information that can’t be provided by other data so that its existence needs to be further investigated. The method for obtaining extreme data is one of them using maxima block method. The distribution of extreme data sets taken with the maxima block method is called the distribution of extreme values. Distribution of extreme values is Gumbel distribution with two parameters. The parameter estimation of Gumbel distribution with maximum likelihood method (ML) is difficult to determine its exact value so that it is necessary to solve the approach. The purpose of this study was to determine the parameter estimation of Gumbel distribution with quasi-Newton BFGS method. The quasi-Newton BFGS method is a numerical method used for nonlinear function optimization without constraint so that the method can be used for parameter estimation from Gumbel distribution whose distribution function is in the form of exponential doubel function. The quasi-New BFGS method is a development of the Newton method. The Newton method uses the second derivative to calculate the parameter value changes on each iteration. Newton's method is then modified with the addition of a step length to provide a guarantee of convergence when the second derivative requires complex calculations. In the quasi-Newton BFGS method, Newton's method is modified by updating both derivatives on each iteration. The parameter estimation of the Gumbel distribution by a numerical approach using the quasi-Newton BFGS method is done by calculating the parameter values that make the distribution function maximum. In this method, we need gradient vector and hessian matrix. This research is a theory research and application by studying several journals and textbooks. The results of this study obtained the quasi-Newton BFGS algorithm and estimation of Gumbel distribution parameters. The estimation method is then applied to daily rainfall data in Purworejo District to estimate the distribution parameters. This indicates that the high rainfall that occurred in Purworejo District decreased its intensity and the range of rainfall that occurred decreased.

Keywords: parameter estimation, Gumbel distribution, maximum likelihood, broyden fletcher goldfarb shanno (BFGS)quasi newton

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Authors: Jiin-Yuh Jang, Yu-Feng Gan

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In order to improve the comprehensive mechanical properties of the steel, the cooling rate, and the temperature distribution must be controlled in the cooling process. A three-dimensional numerical model for the prediction of the heat transfer coefficient distribution of H-beam in the controlled cooling process was performed in order to obtain the uniform temperature distribution and minimize the maximum stress and the maximum deformation after the controlled cooling. An algorithm developed with a simplified conjugated-gradient method was used as an optimizer to optimize the heat transfer coefficient distribution. The numerical results showed that, for the case of air cooling 5 seconds followed by water cooling 6 seconds with uniform the heat transfer coefficient, the cooling rate is 15.5 (℃/s), the maximum temperature difference is 85℃, the maximum the stress is 125 MPa, and the maximum deformation is 1.280 mm. After optimize the heat transfer coefficient distribution in control cooling process with the same cooling time, the cooling rate is increased to 20.5 (℃/s), the maximum temperature difference is decreased to 52℃, the maximum stress is decreased to 82MPa and the maximum deformation is decreased to 1.167mm.

Keywords: controlled cooling, H-Beam, optimization, thermal stress

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Authors: Ying Li, Yan Li

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A new algorithm for single hidden layer feedforward neural networks (SLFN), Orthogonal Basis Extreme Learning (OBEL) algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layer(s) during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability.

Keywords: neural network, orthogonal basis extreme learning, function approximation

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Authors: Amir Lotfi, Seyed Hamid Hosseini

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During the recent past, due to the increase of electrical energy demand and governmental resources constraints in creating additional capacity in the generation, transmission, and distribution, privatization, and restructuring in electrical industry have been considered. So, in most of the countries, different parts of electrical industry like generation, transmission, and distribution have been separated in order to create competition. Considering these changes, environmental issues, energy growth, investment of private equity in energy generation units and difficulties of transmission lines expansion, distributed generation (DG) units have been used in power systems. Moreover, reduction in the need for transmission and distribution, the increase of reliability, improvement of power quality, and reduction of power loss have caused DG to be placed in power systems. On the other hand, considering low liquidity need, private investors tend to spend their money for DGs. In this project, the main goal is to offer an algorithm for planning and placing DGs in order to reduce the need for transmission and distribution network.

Keywords: planning, transmission, distributed generation, power security, power systems

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Authors: Wullapa Wongsinlatam

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Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

Keywords: artificial neural networks, back propagation algorithm, time series, local minima problem, metaheuristic optimization

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Authors: Jamileh Darsareh, Mohammad Nikafrooz

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Some teachers and e-content producers, based on their experiences, try to produce educational videos using screen recording software. There are many challenges that they may encounter while producing screen-recorded videos. These are in the domains of technical and pedagogical challenges like designing the roadmap, preparing the screen, setting the recording software and recording the screen, editing, etc. This study is a descriptive study and tries to present some procedures for producing acceptable and well-made videos. These procedures are presented in the form of an algorithm for producing screen-recorded video. This algorithm presents the main producing phases, including design, pre-production, production, post-production, and distribution. These phases consist of some steps which are supported by several technical and pedagogical considerations. Following these phases and steps according to the suggested order helps the producers to produce their intended and desired video by saving time and also facing fewer technical problems. It is expected that by using this algorithm, e-content producers and teachers gain better performance in producing educational videos.

Keywords: e-content producing algorithm, screen-recorded videos, screen recording software, technical and pedagogical considerations

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Authors: Wajih Abbassi, Zouhaier Ben Khelifa

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The present research points towards the empirical validation of three options valuation models, the ad-hoc Black-Scholes model as proposed by Berkowitz (2001), the constant elasticity of variance model of Cox and Ross (1976) and the Kou jump-diffusion model (2002). Our empirical analysis has been conducted on a sample of 26,974 options written on three indexes, the S&P 500, Nasdaq 100 and the Russell 2000 that were negotiated during the year 2007 just before the sub-prime crisis. We start by presenting the theoretical foundations of the models of interest. Then we use the technique of trust-region-reflective algorithm to estimate the structural parameters of these models from cross-section of option prices. The empirical analysis shows the superiority of the Kou jump-diffusion model. This superiority arises from the ability of this model to portray the behavior of market participants and to be closest to the true distribution that characterizes the evolution of these indices. Indeed the double-exponential distribution covers three interesting properties that are: the leptokurtic feature, the memory less property and the psychological aspect of market participants. Numerous empirical studies have shown that markets tend to have both overreaction and under reaction over good and bad news respectively. Despite of these advantages there are not many empirical studies based on this model partly because probability distribution and option valuation formula are rather complicated. This paper is the first to have used the technique of nonlinear curve-fitting through the trust-region-reflective algorithm and cross-section options to estimate the structural parameters of the Kou jump-diffusion model.

Keywords: jump-diffusion process, Kou model, Leptokurtic feature, trust-region-reflective algorithm, US index options

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Authors: El Asnaoui Khalid, Aksasse Brahim, Ouanan Mohammed

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In this paper, we are interested in the problem of finding similar images in a large database. For this purpose we propose a new algorithm based on a combination of the 2-D histogram intersection in the HSV space and statistical moments. The proposed histogram is based on a 3x3 window and not only on the intensity of the pixel. This approach can overcome the drawback of the conventional 1-D histogram which is ignoring the spatial distribution of pixels in the image, while the statistical moments are used to escape the effects of the discretisation of the color space which is intrinsic to the use of histograms. We compare the performance of our new algorithm to various methods of the state of the art and we show that it has several advantages. It is fast, consumes little memory and requires no learning. To validate our results, we apply this algorithm to search for similar images in different image databases.

Keywords: 2-D histogram, statistical moments, indexing, similarity distance, histograms intersection

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Authors: Hazem J. Smadi

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Performance of a supply chain is directly related to a distribution network that entails the location of storing materials or products and how products are delivered to the end customer through different stages in the supply chain. This study analyses the current distribution network used for delivering gas cylinders to end customer in Jordan. Evaluation of current distribution has been conducted across customer service components. A modification on the current distribution network in terms of central warehousing in each city in the country improves the response time and customer experience. 

Keywords: distribution network, gas cylinder, Jordan, supply chain

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Authors: M. Al-Shamery, R. Young, P. Birch, C. Chatwin

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Computer Generated Holography (CGH) is employed to create digitally defined coherent wavefronts. A CGH can be created by using different techniques such as by using a detour-phase technique or by direct phase modulation to create a kinoform. The detour-phase technique was one of the first techniques that was used to generate holograms digitally. The disadvantage of this technique is that the reconstructed image often has poor quality due to the limited dynamic range it is possible to record using a medium with reasonable spatial resolution.. The kinoform (phase-only hologram) is an alternative technique. In this method, the phase of the original wavefront is recorded but the amplitude is constrained to be constant. The original object does not need to exist physically and so the kinoform can be used to reconstruct an almost arbitrary wavefront. However, the image reconstructed by this technique contains high levels of noise and is not identical to the reference image. To improve the reconstruction quality of the kinoform, iterative techniques such as the Gerchberg-Saxton algorithm (GS) are employed. In this paper the GS algorithm is described for the optimisation of a kinoform used for the reconstruction of a complex wavefront. Iterations of the GS algorithm are applied to determine the phase at a plane (with known amplitude distribution which is often taken as uniform), that satisfies given phase and amplitude constraints in a corresponding Fourier plane. The GS algorithm can be used in this way to enhance the reconstruction quality of the kinoform. Different images are employed as the reference object and their kinoform is synthesised using the GS algorithm. The quality of the reconstructed images is quantified to demonstrate the enhanced reconstruction quality achieved by using this method.

Keywords: computer generated holography, digital holography, Gerchberg-Saxton algorithm, kinoform

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Authors: Jean-Pierre Lomaliza, Kwang-Seok Moon, Hanhoon Park

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It is well-known that Ramer Douglas Peucker (RDP) algorithm greatly depends on the method of choosing starting points. Therefore, this paper focuses on finding such starting points that will optimize the results of RDP algorithm. Specifically, this paper proposes a curve approximation algorithm that finds flat points, called essential points, of an input curve, divides the curve into corner-like sub-curves using the essential points, and applies the RDP algorithm to the sub-curves. The number of essential points play a role on optimizing the approximation results by balancing the degree of shape information loss and the amount of data reduction. Through experiments with curves of various types and complexities of shape, we compared the performance of the proposed algorithm with three other methods, i.e., the RDP algorithm itself and its variants. As a result, the proposed algorithm outperformed the others in term of maintaining the original shapes of the input curve, which is important in various applications like pattern recognition.

Keywords: curve approximation, essential point, RDP algorithm

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Authors: Djendi Mohmaed

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In this paper, we propose a dual adaptive algorithm, which is based on the combination between the forward blind source separation (FBSS) structure and the affine projection algorithm (APA). This proposed algorithm combines the advantages of the source separation properties of the FBSS structure and the fast convergence characteristics of the APA algorithm. The proposed algorithm needs two noisy observations to provide an enhanced speech signal. This process is done in a blind manner without the need for ant priori information about the source signals. The proposed dual forward blind source separation affine projection algorithm is denoted (DFAPA) and used for the first time in an airplane cockpit context to enhance the communication from- and to- the airplane. Intensive experiments were carried out in this sense to evaluate the performance of the proposed DFAPA algorithm.

Keywords: adaptive algorithm, speech enhancement, system mismatch, SNR

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Authors: Bello Yusuf Idi

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The Gongola arm of the Upper Banue Trough, Northeastern Nigeria is predominantly covered by the outcrops of Limestone-bearing rocks in form of Sandstone with intercalation of carbonate clay, shale, basaltic, felsphatic and migmatide rocks at subpixel dimension. In this work, subpixel classification algorithm was used to classify the data acquired from landsat 7 Enhance Thematic Mapper (ETM+) satellite system with the aim of producing fractional distribution image for three most economically important solid minerals of the area: Limestone, Basalt and Migmatide. Linear Spectral Unmixing (LSU) algorithm was used to produce fractional distribution image of abundance of the three mineral resources within a 100Km2 portion of the area. The results show that the minerals occur at different proportion all over the area. The fractional map could therefore serve as a guide to the ongoing reconnaissance for the economic potentiality of the formation.

Keywords: linear spectral un-mixing, upper benue trough, gongola arm, geological engineering

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Authors: Cemil Turan, Mohammad Shukri Salman

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The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms.

Keywords: adaptive filtering, sparse system identification, TD-LMS algorithm, VSSLMS algorithm

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Authors: Omar Ramzi Jasim, Jalal Sultan Ashour

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Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problems in operation and it can potentially lead to poor customer satisfaction. In this paper, a hybrid evolutionary algorithm (ICA-GA) is presented, which integrates the merits of both imperialist competitive algorithm (ICA) and genetic algorithm (GA) for solving multi-objective MPS problems. In the presented algorithm, the colonies in each empire has be represented a small population and communicate with each other using genetic operators. By testing on 5 production scenarios, the numerical results of ICA-GA algorithm show the efficiency and capabilities of the hybrid algorithm in finding the optimum solutions. The ICA-GA solutions yield the lower inventory level and keep customer satisfaction high and the required overtime is also lower, compared with results of GA and SA in all production scenarios.

Keywords: master production scheduling, genetic algorithm, imperialist competitive algorithm, hybrid algorithm

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Authors: Gamze Ozel, Selen Cakmakyapan

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We introduce a new model called the Marshall-Olkin Rayleigh distribution which extends the Rayleigh distribution using Marshall-Olkin transformation and has increasing and decreasing shapes for the hazard rate function. Various structural properties of the new distribution are derived including explicit expressions for the moments, generating and quantile function, some entropy measures, and order statistics are presented. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is determined. The potentiality of the new model is illustrated by means of real life data set.

Keywords: Marshall-Olkin distribution, Rayleigh distribution, estimation, maximum likelihood

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Authors: Jeryes Danial, Yosi Ben Asher

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Algorithms for controlling a swarm of robots is an active research field, out of which cattle herding is one of the most complex problems to solve. In this paper, we derive an independent herding algorithm that is specifically designed for a swarm of quadcopters. The algorithm works by devising flight trajectories that cause the cows to run-away in the desired direction and hence herd cows that are distributed in a given field towards a common gathering point. Unlike previously proposed swarm herding algorithms, this algorithm does not use a flocking model but rather stars each cow separately. The effectiveness of this algorithm is verified experimentally using a simulator. We use a special set of experiments attempting to demonstrate that the herding times of this algorithm correspond to field diameter small constant regardless of the number of cows in the field. This is an optimal result indicating that the algorithm groups the cows into intermediate groups and herd them as one forming ever closing bigger groups.

Keywords: swarm, independent, distributed, algorithm

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

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Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Sikander Singh Cheema, Jasmeen Kaur

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In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields.

Keywords: data mining, KDD, genetic algorithm, descriptive mining, predictive mining

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Authors: Sidi Mohamed Mesli, Mohamed Habchi, Arslane Boudghene Stambouli, Rafik Benallal

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The BaMnMF7 (M=Fe,V, transition metal fluoride glass, assuming isomorphous replacement) have been structurally studied through the simultaneous simulation of their neutron diffraction patterns by reverse Monte Carlo (RMC) and by the Hybrid Reverse Monte Carlo (HRMC) analysis. This last is applied to remedy the problem of the artificial satellite peaks that appear in the partial pair distribution functions (PDFs) by the RMC simulation. The HRMC simulation is an extension of the RMC algorithm, which introduces an energy penalty term (potential) in acceptance criteria. The idea of this work is to apply the Buckingham potential at the title glass by ignoring the van der Waals terms, in order to make a fit of the partial pair distribution functions and give the most possible realistic features. When displaying the partial PDFs, we suggest that the Buckingham potential is useful to describe average correlations especially in similar interactions.

Keywords: fluoride glasses, RMC simulation, hybrid RMC simulation, Buckingham potential, partial pair distribution functions

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Authors: Phutthiwat Waiyawuththanapoom

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This research is objected to study the pattern and channel of distribution of Ang Thong’s court doll OTOP product and try to develop the quality of distribution of the court doll product. The population of this research is 50 court doll manufacturers of Ang Thong’s court doll. The data and information was collected by using the questionnaire and use percentage, mean and standard deviation as an analysis tools. The distribution channel of Ang Thong’s court doll can be separated into 3 channels which are direct distribution from the manufacturer, via the middleman and via the co-operated manufacturing group. In the direct distribution from the manufacturer channel, it was found that the manufacturer is given the highest rate of importance to how they keep the inventory. In the distribution via the middleman channel, it was found that the manufacturer is given the highest rate of importance to the distribution efficiency. But in the distribution via the co-operated manufacturing group, it was found that the manufacturer is given the highest rate of importance to the public relationship.

Keywords: distribution, court doll, Ang Thong province, business and social sciences

Procedia PDF Downloads 280