Search results for: classification algorithms.

Authors: Aissa Saoudi, Hassane Essafi

Abstract:

In this work we will present a new approach for shot transition auto-detection. Our approach is based on the analysis of Spatio-Temporal Video Slice (STVS) edges extracted from videos. The proposed approach is capable to efficiently detect both abrupt shot transitions 'cuts' and gradual ones such as fade-in, fade-out and dissolve. Compared to other techniques, our method is distinguished by its high level of precision and speed. Those performances are obtained due to minimizing the problem of the boundary shot detection to a simple 2D image partitioning problem.

Keywords: Boundary shot detection, Shot transition detection, Video analysis, Video indexing.

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Authors: Hamed Masoumi, Seyed Mohsen Safavi, Zahra Khani

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In this paper, an automated system is presented for identification and separation of plastic resins based on near infrared (NIR) reflectance spectroscopy. For identification and separation among resins, a "Two-Filter" identification method is proposed that is capable to distinguish among polyethylene terephthalate (PET), high density polyethylene (HDPE), polyvinyl chloride (PVC), polypropylene (PP) and polystyrene (PS). Through surveying effects of parameters such as surface contamination, sample thickness, label and cap existence, it was obvious that the "Two-Filter" method has a high efficiency in identification of resins. It is shown that accurate identification and separation of five major resins can be obtained through calculating the relative reflectance at two wavelengths in the NIR region.

Keywords: Identification, Near Infrared, Plastic, Separation, Spectroscopy

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Authors: Seo Young Kim, Jae Won Lee, Jong Sung Bae

Abstract:

Microarray experiments are information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. For biologists, a key aim when analyzing microarray data is to group genes based on the temporal patterns of their expression levels. In this paper, we used an iterative clustering method to find temporal patterns of gene expression. We evaluated the performance of this method by applying it to real sporulation data and simulated data. The patterns obtained using the iterative clustering were found to be superior to those obtained using existing clustering algorithms.

Keywords: Clustering, microarray experiment, temporal pattern of gene expression data.

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Authors: Samee Ullah Khan, C. Ardil

Abstract:

The problem of mapping tasks onto a computational grid with the aim to minimize the power consumption and the makespan subject to the constraints of deadlines and architectural requirements is considered in this paper. To solve this problem, we propose a solution from cooperative game theory based on the concept of Nash Bargaining Solution. The proposed game theoretical technique is compared against several traditional techniques. The experimental results show that when the deadline constraints are tight, the proposed technique achieves superior performance and reports competitive performance relative to the optimal solution.

Keywords: Energy efficient algorithms, resource allocation, resource management, cooperative game theory.

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Authors: Wang Shou-Dao, Xiao Chuang-Bai, Lin Yu

Abstract:

In high bitrate information hiding techniques, 1 bit is embedded within each 4 x 4 Discrete Cosine Transform (DCT) coefficient block by means of vector quantization, then the hidden bit can be effectively extracted in terminal end. In this paper high bitrate information hiding algorithms are summarized, and the scheme of video in video is implemented. Experimental result shows that the host video which is embedded numerous auxiliary information have little visually quality decline. Peak Signal to Noise Ratio (PSNR)Y of host video only degrades 0.22dB in average, while the hidden information has a high percentage of survives and keeps a high robustness in H.264/AVC compression, the average Bit Error Rate(BER) of hiding information is 0.015%.

Keywords: Information Hiding, Embed, Quantification, Extract

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Authors: Yali Zheng, Peipei Luo, Shinan Chen, Jiasheng Hao, Hong Cheng

Abstract:

Most of traditional visual indoor navigation algorithms and methods only consider the localization in ordinary daytime, while we focus on the indoor re-localization in low light in the paper. As RGB images are degraded in low light, less discriminative infrared and depth image pairs are taken, as the input, by RGB-D cameras, the most similar candidates, as the output, are searched from databases which is built in the bag-of-word framework. Epipolar constraints can be used to relocalize the query infrared and depth image sequence. We evaluate our method in two datasets captured by Kinect2. The results demonstrate very promising re-localization results for indoor navigation system in low light environments.

Keywords: Indoor navigation, low light, RGB-D camera, vision based.

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Authors: S. G. Ponnambalam, Low Seng Kiat

Abstract:

In this paper, a particle swarm optimization (PSO) algorithm is proposed to solve machine loading problem in flexible manufacturing system (FMS), with bicriterion objectives of minimizing system unbalance and maximizing system throughput in the occurrence of technological constraints such as available machining time and tool slots. A mathematical model is used to select machines, assign operations and the required tools. The performance of the PSO is tested by using 10 sample dataset and the results are compared with the heuristics reported in the literature. The results support that the proposed PSO is comparable with the algorithms reported in the literature.

Keywords: Machine loading problem, FMS, Particle Swarm Optimization.

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Authors: G. Poonkuzhali, K.Thiagarajan, K.Sarukesi

Abstract:

With the enormous growth on the web, users get easily lost in the rich hyper structure. Thus developing user friendly and automated tools for providing relevant information without any redundant links to the users to cater to their needs is the primary task for the website owners. Most of the existing web mining algorithms have concentrated on finding frequent patterns while neglecting the less frequent one that are likely to contain the outlying data such as noise, irrelevant and redundant data. This paper proposes new algorithm for mining the web content by detecting the redundant links from the web documents using set theoretical(classical mathematics) such as subset, union, intersection etc,. Then the redundant links is removed from the original web content to get the required information by the user..

Keywords: Web documents, Web content mining, redundantlink, outliers, set theory.

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Authors: I. Davies, O. L. C. Haas

Abstract:

In this paper, the problem of stability and stabilization for neutral delay-differential systems with infinite delay is investigated. Using Lyapunov method, new delay-independent sufficient condition for the stability of neutral systems with infinite delay is obtained in terms of linear matrix inequality (LMI). Memory-less state feedback controllers are then designed for the stabilization of the system using the feasible solution of the resulting LMI, which are easily solved using any optimization algorithms. Numerical examples are given to illustrate the results of the proposed methods.

Keywords: Infinite delays, Lyapunov method, linear matrix inequality, neutral systems, stability.

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Authors: Sergey A. Burikov, Tatiana A. Dolenko, Kirill A. Gushchin, Sergey A. Dolenko

Abstract:

The paper presents the results of clusterization by Kohonen self-organizing maps (SOM) applied for analysis of array of Raman spectra of multi-component solutions of inorganic salts, for determination of types of salts present in the solution. It is demonstrated that use of SOM is a promising method for solution of clusterization and classification problems in spectroscopy of multicomponent objects, as attributing a pattern to some cluster may be used for recognition of component composition of the object.

Keywords: Kohonen self-organizing maps, clusterization, multicomponent solutions, Raman spectroscopy.

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Authors: Maryam Jasim Abdullah, Bassim. H. Graimed, Jalal. S. Hameed

Abstract:

Demand over web services is in growing with increases number of Web users. Web service is applied by Web application. Web application size is affected by its user-s requirements and interests. Differential in requirements and interests lead to growing of Web application size. The efficient way to save store spaces for more data and information is achieved by implementing algorithms to compress the contents of Web application documents. This paper introduces an algorithm to reduce Web application size based on reduction of the contents of HTML files. It removes unimportant contents regardless of the HTML file size. The removing is not ignored any character that is predicted in the HTML building process.

Keywords: HTML code, HTML tag, WEB applications, Document compression, DOM tree.

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Authors: N. Lassouaoui, L. Hamami, N. Nouali

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The tracking allows to detect the tumor affections of cervical cancer, it is particularly complex and consuming time, because it consists in seeking some abnormal cells among a cluster of normal cells. In this paper, we present our proposed computer system for helping the doctors in tracking the cervical cancer. Knowing that the diagnosis of the malignancy is based in the set of atypical morphological details of all cells, herein, we present an unsupervised genetic algorithm for the separation of cell components since the diagnosis is doing by analysis of the core and the cytoplasm. We give also the various algorithms used for computing the morphological characteristics of cells (Ratio core/cytoplasm, cellular deformity, ...) necessary for the recognition of illness.

Keywords: Cervical cell, morphological analysis, recognition, segmentation.

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Authors: Klathea Sevilla, Marjorie Remolador, Bryan Baltazar, Imee Saladaga, Loureal Camille Inocencio, Ma. Rosario Concepcion Ang

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The underutilization of biomass resources in the Philippines, combined with its growing population and the rise in fossil fuel prices confirms demand for alternative energy sources. The goal of this paper is to provide a comparison of MODIS-based and Landsat-based agricultural land cover maps when used in the estimation of rice hull’s available energy potential. Biomass resource assessment was done using mathematical models and remote sensing techniques employed in a GIS platform.

Keywords: Biomass, geographic information system, GIS, renewable energy.

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Authors: Nikolai Krainiukov, Boris Melnikov

Abstract:

We study the properties of maximal prefix codes. The codes have many applications in computer science, theory of formal languages, data processing and data classification. Our approaches to study use finite state automata (so-called flower automata) for the representation of prefix codes. An important task is the decomposition of prefix codes into prime prefix codes (factors). We discuss properties of such prefix code decompositions. A linear time algorithm is designed to find the prime decomposition. We used the GAP computer algebra system, which allows us to perform algebraic operations for free semigroups, monoids and automata.

Keywords: Maximal prefix code, regular languages, flower automata, prefix code decomposing.

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Authors: W. Jaipahkdee, C. Srinilta

Abstract:

The overall service performance of I/O intensive system depends mainly on workload on its storage system. In heterogeneous storage environment where storage elements from different vendors with different capacity and performance are put together, workload should be distributed according to storage capability. This paper addresses data placement issue in short video sharing website. Workload contributed by a video is estimated by the number of views and life time span of existing videos in same category. Experiment was conducted on 42,000 video titles in six weeks. Result showed that the proposed algorithm distributed workload and maintained balance better than round robin and random algorithms.

Keywords: data placement, heterogeneous storage system, YouTube, short videos

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Authors: Masahiro Kuzunishi, Tetsuya Furukawa, Ke Lu

Abstract:

Classifying data hierarchically is an efficient approach to analyze data. Data is usually classified into multiple categories, or annotated with a set of labels. To analyze multi-labeled data, such data must be specified by giving a set of labels as a semantic range. There are some certain purposes to analyze data. This paper shows which multi-labeled data should be the target to be analyzed for those purposes, and discusses the role of a label against a set of labels by investigating the change when a label is added to the set of labels. These discussions give the methods for the advanced analysis of multi-labeled data, which are based on the role of a label against a semantic range.

Keywords: Classification Hierarchies, Data Analysis, Multilabeled Data, Orders of Sets of Labels

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Authors: Young-Seok Choi

Abstract:

We present a new subband adaptive filter (R-SAF) which is robust against impulsive noise in system identification. To address the vulnerability of adaptive filters based on the L2-norm optimization criterion against impulsive noise, the R-SAF comes from the L1-norm optimization criterion with a constraint on the energy of the weight update. Minimizing L1-norm of the a posteriori error in each subband with a constraint on minimum disturbance gives rise to the robustness against the impulsive noise and the capable convergence performance. Experimental results clearly demonstrate that the proposed R-SAF outperforms the classical adaptive filtering algorithms when impulsive noise as well as background noise exist.

Keywords: Subband adaptive filter, L1-norm, system identification, robustness, impulsive interference.

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Authors: Alawode Kehinde O., Jubril Abimbola M. Komolafe Olusola A.

Abstract:

This paper solves the environmental/ economic dispatch power system problem using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and its hybrid with a Convergence Accelerator Operator (CAO), called the NSGA-II/CAO. These multiobjective evolutionary algorithms were applied to the standard IEEE 30-bus six-generator test system. Several optimization runs were carried out on different cases of problem complexity. Different quality measure which compare the performance of the two solution techniques were considered. The results demonstrated that the inclusion of the CAO in the original NSGA-II improves its convergence while preserving the diversity properties of the solution set.

Keywords: optimal power flow, multiobjective power dispatch, evolutionary algorithm

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Authors: Sangsuree Vasupongayya

Abstract:

To provide a better understanding of fair share policies supported by current production schedulers and their impact on scheduling performance, A relative fair share policy supported in four well-known production job schedulers is evaluated in this study. The experimental results show that fair share indeed reduces heavy-demand users from dominating the system resources. However, the detailed per-user performance analysis show that some types of users may suffer unfairness under fair share, possibly due to priority mechanisms used by the current production schedulers. These users typically are not heavy-demands users but they have mixture of jobs that do not spread out.

Keywords: Fair share, Parallel job scheduler, Backfill, Measures

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Authors: Shaimaa M. Mohamed, Haitham S. Hamza, Imane A. Saroit

Abstract:

Many wireless sensor network applications require K-coverage of the monitored area. In this paper, we propose a scalable harmony search based algorithm in terms of execution time, K-Coverage Enhancement Algorithm (KCEA), it attempts to enhance initial coverage, and achieve the required K-coverage degree for a specific application efficiently. Simulation results show that the proposed algorithm achieves coverage improvement of 5.34% compared to K-Coverage Rate Deployment (K-CRD), which achieves 1.31% when deploying one additional sensor. Moreover, the proposed algorithm is more time efficient.

Keywords: Wireless Sensor Networks (WSN), Harmony Search Algorithms, K-Coverage, Mobile WSN.

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Authors: M. Almeshal, M. O. Tokhi, K. M. Goher

Abstract:

A novel design of two-wheeled robotic vehicle with moving payload is presented in this paper. A mathematical model describing the vehicle dynamics is derived and simulated in Matlab Simulink environment. Two control strategies were developed to stabilise the vehicle in the upright position. A robust Proportional- Integral-Derivative (PID) control strategy has been implemented and initially tested to measure the system performance, while the second control strategy is to use a hybrid fuzzy logic controller (FLC). The results are given on a comparative basis for the system performance in terms of disturbance rejection, control algorithms robustness as well as the control effort in terms of input torque.

Keywords: double inverted pendulum, modelling, robust control, simulation,

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Authors: Nebi Gedik

Abstract:

Regardless of age or gender, cardiovascular illnesses are a serious health concern because of things like poor eating habits, stress, a sedentary lifestyle, hard work schedules, alcohol use, and weight. It tends to happen suddenly and has a high rate of recurrence. Machine learning models can be implemented to assist healthcare systems in the accurate detection and diagnosis of cardiovascular disease (CVD) in patients. Improved heart failure prediction is one of the primary goals of researchers using the heart disease dataset. The purpose of this study is to identify the feature or features that offer the best classification prediction for CVD detection. The support vector machine classifier is used to compare each feature's performance. It has been determined which feature produces the best results.

Keywords: Cardiovascular disease, feature extraction, supervised learning, support vector machine.

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Authors: Maciej Norberciak

Abstract:

Timetabling problems are often hard and timeconsuming to solve. Most of the methods of solving them concern only one problem instance or class. This paper describes a universal method for solving large, highly constrained timetabling problems from different domains. The solution is based on evolutionary algorithm-s framework and operates on two levels – first-level evolutionary algorithm tries to find a solution basing on given set of operating parameters, second-level algorithm is used to establish those parameters. Tabu search is employed to speed up the solution finding process on first level. The method has been used to solve three different timetabling problems with promising results.

Keywords: Evolutionary algorithms, tabu search, timetabling.

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Authors: Rajender Singh Chillar, Barjesh Kochar

Abstract:

All Text processing systems allow their users to search a pattern of string from a given text. String matching is fundamental to database and text processing applications. Every text editor must contain a mechanism to search the current document for arbitrary strings. Spelling checkers scan an input text for words in the dictionary and reject any strings that do not match. We store our information in data bases so that later on we can retrieve the same and this retrieval can be done by using various string matching algorithms. This paper is describing a new string matching algorithm for various applications. A new algorithm has been designed with the help of Rabin Karp Matcher, to improve string matching process.

Keywords: Algorithm, Complexity, Matching-patterns, Pattern, Rabin-Karp, String, text-processing.

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Authors: Hadjira Bentoumi, Pascal Gautron, Kadi Bouatouch

Abstract:

We present a method for fast volume rendering using graphics hardware (GPU). To our knowledge, it is the first implementation on the GPU. Based on the Shear-Warp algorithm, our GPU-based method provides real-time frame rates and outperforms the CPU-based implementation. When the number of slices is not sufficient, we add in-between slices computed by interpolation. This improves then the quality of the rendered images. We have also implemented the ray marching algorithm on the GPU. The results generated by the three algorithms (CPU-based and GPU-based Shear- Warp, GPU-based Ray Marching) for two test models has proved that the ray marching algorithm outperforms the shear-warp methods in terms of speed up and image quality.

Keywords: Volume rendering, graphics processors

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Authors: A. Greco, N. Mammone, F.C. Morabito, M.Versaci

Abstract:

This paper is mainly concerned with the application of a novel technique of data interpretation to the characterization and classification of measurements of plasma columns in Tokamak reactors for nuclear fusion applications. The proposed method exploits several concepts derived from soft computing theory. In particular, Artifical Neural Networks have been exploited to classify magnetic variables useful to determine shape and position of the plasma with a reduced computational complexity. The proposed technique is used to analyze simulated databases of plasma equilibria based on ITER geometry configuration. As well as demonstrating the successful recovery of scalar equilibrium parameters, we show that the technique can yield practical advantages compares with earlier methods.

Keywords: Tokamak, sensors, artificial neural network.

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Authors: Myeongjin Park, Ohmin Kwon, Juhyun Park, Sangmoon Lee

Abstract:

This paper proposes improved delay-dependent stability conditions of the linear time-delay systems of neutral type. The proposed methods employ a suitable Lyapunov-Krasovskii’s functional and a new form of the augmented system. New delay-dependent stability criteria for the systems are established in terms of Linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Numerical examples showed that the proposed method is effective and can provide less conservative results.

Keywords: Neutral systems, Time-delay, Stability, Lyapunovmethod, LMI.

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Authors: Benalia Nadia, Bensiali Nadia, Mekki Mounira

Abstract:

In this paper, we propose an easily computable proximity index for predicting voltage collapse of a load bus using only measured values of the bus voltage and power; Using these measurements a polynomial of fourth order is obtained by using LES estimation algorithms. The sum of the absolute values of the polynomial coefficient gives an idea of the critical bus. We demonstrate the applicability of our proposed method on 6 bus test system. The results obtained verify its applicability, as well as its accuracy and the simplicity. From this indicator, it is allowed to predict the voltage instability or the proximity of a collapse. Results obtained by the PV curve are compared with corresponding values by QV curves and are observed to be in close agreement.

Keywords: least square method, Voltage Collapse, Voltage Stability, PV curve

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Authors: Dipankar Dhabak, Soumya Pandit

Abstract:

This paper presents an adaptive technique for generation of data required for construction of artificial neural network-based performance model of nano-scale CMOS inverter circuit. The training data are generated from the samples through SPICE simulation. The proposed algorithm has been compared to standard progressive sampling algorithms like arithmetic sampling and geometric sampling. The advantages of the present approach over the others have been demonstrated. The ANN predicted results have been compared with actual SPICE results. A very good accuracy has been obtained.

Keywords: CMOS Inverter, Nano-scale, Adaptive Sampling, ArtificialNeural Network

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Authors: Hy Dang, Bo Mei

Abstract:

The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: Classification, machine learning, time representation, stock prediction.

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