Search results for: Least Squares SupportVector Machines

Authors: Ilyas Khan. Liliane Pintelon, Harry Martin, Michael Shömig

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The increasing costs of healthcare on one hand, and the rise in aging population and associated chronic disease, on the other hand, are putting increasing burden on the current health care system in many Western countries. For instance, chronic kidney disease (CKD) is a common disease and in Europe, the cost of renal replacement therapy (RRT) is very significant to the total health care cost. However, the recent advancement in healthcare technology, provide the opportunity to treat patients at home in their own comfort. It is evident that home healthcare offers numerous advantages apparently, low costs and high patients’ quality of life. Despite these advantages, the intake of home hemodialysis (HHD) therapy is still low in particular in Germany. Many factors are accounted for the low number of HHD intake. However, this paper is focusing on patients’ safety-related factors of current HHD practices in Germany. The aim of this paper is to analyze the current HHD practices in Germany and to identify risks related factors if any exist. A case study has been conducted in a dialysis center which consists of four dialysis centers in the south of Germany. In total, these dialysis centers have 350 chronic dialysis patients, of which, four patients are on HHD. The centers have 126 staff which includes six nephrologists and 120 other staff i.e. nurses and administration. The results of the study revealed several risk-related factors. Most importantly, these centers do not offer allied health services at the pre-dialysis stage, the HHD training did not have an established curriculum; however, they have just recently developed the first version. Only a soft copy of the machine manual is offered to patients. Surprisingly, the management was not aware of any standard available for home assessment and installation. The home assessment is done by a third party (i.e. the machines and equipment provider) and they may not consider the hygienic quality of the patient’s home. The type of machine provided to patients at home is similar to the one in the center. The model may not be suitable at home because of its size and complexity. Even though portable hemodialysis machines, which are specially designed for home use, are available in the market such as the NxStage series. Besides the type of machine, no assistance is offered for space management at home in particular for placing the machine. Moreover, the centers do not offer remote assistance to patients and their carer at home. However, telephonic assistance is available. Furthermore, no alternative is offered if a carer is not available. In addition, the centers are lacking medical staff including nephrologists and renal nurses.

Keywords: Home hemodialysis, home hemodialysis practices, patients’ related risks in the current home hemodialysis practices, patient safety in home hemodialysis.

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Authors: A. Zare Bazghaleh, M. R. Naghashan, H. Mahmoudimanesh, M. R. Meshkatoddini

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Linear induction motors are used in various industries but they have some specific phenomena which are the causes for some problems. The most important phenomenon is called end effect. End effect decreases efficiency, power factor and output force and unbalances the phase currents. This phenomenon is more important in medium and high speeds machines. In this paper a factor, EEF , is obtained by an accurate equivalent circuit model, to determine the end effect intensity. In this way, all of effective design parameters on end effect is described. Accuracy of this equivalent circuit model is evaluated by two dimensional finite-element analysis using ANSYS. The results show the accuracy of the equivalent circuit model.

Keywords: Linear induction motor, end effect, equivalent circuitmodel, finite-element method.

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Authors: Harish Ramesh Babu, Marco Böcker, Mario Raddatz, Sebastian Henkel, Horst Biermann, Uwe Gampe

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Thermal power machines are subjected to cyclic loading conditions under elevated temperatures. At these extreme conditions, the durability of the components has a significant influence. The material mechanical behaviour has to be known in detail for a failsafe construction. For this study a nickel-based alloy is considered, the deformation and fatigue behaviour of the material is analysed under cyclic loading. A viscoplastic model is used for calculating the deformation behaviour as well as to simulate the rate-dependent and cyclic plasticity effects. Finally, the cyclic deformation results of the finite element simulations are compared with low cycle fatigue (LCF) experiments.

Keywords: Complex low cycle fatigue, elevated temperatures, IN718, viscoplastic.

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Authors: Husam El-Asfour, Fateh Adhnouss, Kenneth McIsaac, AbdulMutalib Wahaishi, Raafat Aburukba, Idris El-Feghia

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Although intelligence is commonly referred to as the observable behavior in various fields and domains, it must also be shown how it develops by exhibiting multiple forms and without observing the inherent behavior. There have been several official and informal definitions of intelligence in various areas; however, no scientific agreement on a definition has been agreed upon. There must be a single definition, structure, and precise modeling for articulating how intelligence is perceived by people and machines in order to comprehend intelligence. Another key challenge is defining the different environment types based on the integral elements (agents) and their possible interactions. On the basis of conceptualization, this paper proposes a formal model for defining and developing intelligence. Forms of intelligence are derived from an ontological view, and thus intelligence is defined, described, and modeled based on the various types of environments.

Keywords: Intelligence, forms, transformation, conceptualization, ontological view.

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Authors: M. Sedighizadeh, A. Rezazadeh

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Based on the component approach, three kinds of dynamic load models, including a single –motor model, a two-motor model and composite load model have been developed for the stability studies of Khuzestan power system. The study results are presented in this paper. Voltage instability is a dynamic phenomenon and therefore requires dynamic representation of the power system components. Industrial loads contain a large fraction of induction machines. Several models of different complexity are available for the description investigations. This study evaluates the dynamic performances of several dynamic load models in combination with the dynamics of a load changing transformer. Case study is steel industrial substation in Khuzestan power systems.

Keywords: Dynamic load, modeling, Voltage Stability.

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Authors: Samee Ullah Khan, C. Ardil

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We model the process of a data center as a multi- objective problem of mapping independent tasks onto a set of data center machines that simultaneously minimizes the energy consump¬tion and response time (makespan) subject to the constraints of deadlines and architectural requirements. A simple technique based on multi-objective goal programming is proposed that guarantees Pareto optimal solution with excellence in convergence process. The proposed technique also is compared with other traditional approach. The simulation results show that the proposed technique achieves superior performance compared to the min-min heuristics, and com¬petitive performance relative to the optimal solution implemented in UNDO for small-scale problems.

Keywords: Energy-efficient computing, distributed systems, multi-objective optimization.

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Authors: Qiong Wang

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One of the most important problems to solve is eye location for a driver fatigue monitoring system. This paper presents an efficient method to achieve fast and accurate eye location in grey level images obtained in the real-word driving conditions. The structure of eye region is used as a robust cue to find possible eye pairs. Candidates of eye pair at different scales are selected by finding regions which roughly match with the binary eye pair template. To obtain real one, all the eye pair candidates are then verified by using support vector machines. Finally, eyes are precisely located by using binary vertical projection and eye classifier in eye pair images. The proposed method is robust to deal with illumination changes, moderate rotations, glasses wearing and different eye states. Experimental results demonstrate its effectiveness.

Keywords: eye location, structure feature, driver fatiguemonitoring

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Authors: Soe Sandar Aung, Han Phyo Wai, Nyein Nyein Soe

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The induction hardening machines are utilized in the industries which modify machine parts and tools needed to achieve high ware resistance. This paper describes the model of induction heating process design of inverter circuit and the results of induction surface hardening of heating coil. In the design of heating coil, the shape and the turn numbers of the coil are very important design factors because they decide the overall operating performance of induction heater including resonant frequency, Q factor, efficiency and power factor. The performance will be tested by experiments in some cases high frequency induction hardening machine.

Keywords: Induction Heating, Resonant Circuit, InverterCircuit, Coil Design, Induction Hardening Machine.

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Authors: Sara Felloni, Giuliano Strini

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We explore entanglement in composite quantum systems and how its peculiar properties are exploited in quantum information and communication protocols by means of Diagrams of States, a novel method to graphically represent and analyze how quantum information is elaborated during computations performed by quantum circuits. We present quantum diagrams of states for Bell states generation, measurements and projections, for dense coding and quantum teleportation, for probabilistic quantum machines designed to perform approximate quantum cloning and universal NOT and, finally, for quantum privacy amplification based on entanglement purification. Diagrams of states prove to be a useful approach to analyze quantum computations, by offering an intuitive graphic representation of the processing of quantum information. They also help in conceiving novel quantum computations, from describing the desired information processing to deriving the final implementation by quantum gate arrays.

Keywords: Diagrams of states, entanglement, quantum circuits, quantum information.

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Authors: Ilya Kavalchuk, Mehdi Seyedmahmoudian, Ben Horan, Aman Than Oo, Alex Stojcevski

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The power electronic components within Electric Vehicles (EV) need to operate in several important modes. Some modes directly influence safety, while others influence vehicle performance. Given the variety of functions and operational modes required of the power electronics, it needs to meet efficiency requirements to minimize power losses. Another challenge in the control and construction of such systems is the ability to support bidirectional power flow. This paper considers the construction, operation, and feasibility of available converters for electric vehicles with feasible configurations of electrical buses and loads. This paper describes logic and control signals for the converters for different operations conditions based on the efficiency and energy usage bases.

Keywords: Electric Vehicles, Electrical Machines Control, Power Electronics, Powerflow Regulations.

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Authors: Arash Hassanpour Isfahani, Siavash Sadeghi

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Permanent magnet synchronous machines are known as a good candidate for hybrid electric vehicles due to their unique merits. However they have two major drawbacks i.e. high cost and small speed range. In this paper an optimal design of a permanent magnet machine is presented. A reduction of permanent magnet material for a constant torque and an extension in speed and torque ranges are chosen as the optimization aims. For this purpose the analytical model of the permanent magnet synchronous machine is derived and the appropriate design algorithm is devised. The genetic algorithm is then employed to optimize some machine specifications. Finally the finite element method is used to validate the designed machine.

Keywords: Design, Finite Element, Hybrid electric vehicle, Optimization, Permanent magnet synchronous machine.

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Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: Classification, data mining, evaluation measures, groundwater.

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: Clusters of Microcalcifications, Ductal Carcinoma in Situ, Least-Square Support Vector Machine, Particle Swarm Optimization.

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Authors: Suresh S. Salankar, Balasaheb M. Patre

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Research into the problem of classification of sonar signals has been taken up as a challenging task for the neural networks. This paper investigates the design of an optimal classifier using a Multi layer Perceptron Neural Network (MLP NN) and Support Vector Machines (SVM). Results obtained using sonar data sets suggest that SVM classifier perform well in comparison with well-known MLP NN classifier. An average classification accuracy of 91.974% is achieved with SVM classifier and 90.3609% with MLP NN classifier, on the test instances. The area under the Receiver Operating Characteristics (ROC) curve for the proposed SVM classifier on test data set is found as 0.981183, which is very close to unity and this clearly confirms the excellent quality of the proposed classifier. The SVM classifier employed in this paper is implemented using kernel Adatron algorithm is seen to be robust and relatively insensitive to the parameter initialization in comparison to MLP NN.

Keywords: Classification, MLP NN, backpropagation algorithm, SVM, Receiver Operating Characteristics.

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Authors: Youssef Chahir, Messaoud Mostefai, Salah Khodja

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This paper presents the development of a 2D visual marker, derived from a recent patented work in the field of numbering systems. The proposed fiducial uses a group of projective invariant straight-line patterns, easily detectable and remotely recognizable. Based on an efficient data coding scheme, the developed marker enables producing a large panel of unique real time identifiers with highly distinguishable patterns. The proposed marker Incorporates simultaneously decimal and binary information, making it readable by both humans and machines. This important feature opens up new opportunities for the development of efficient visual human-machine communication and monitoring protocols. Extensive experiment tests validate the robustness of the marker against acquisition and geometric distortions.

Keywords: visual marker, projective invariants, distance map, level set

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Authors: N. M. Nawi, M. R. Ransing, R. S. Ransing

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The conjugate gradient optimization algorithm usually used for nonlinear least squares is presented and is combined with the modified back propagation algorithm yielding a new fast training multilayer perceptron (MLP) algorithm (CGFR/AG). The approaches presented in the paper consist of three steps: (1) Modification on standard back propagation algorithm by introducing gain variation term of the activation function, (2) Calculating the gradient descent on error with respect to the weights and gains values and (3) the determination of the new search direction by exploiting the information calculated by gradient descent in step (2) as well as the previous search direction. The proposed method improved the training efficiency of back propagation algorithm by adaptively modifying the initial search direction. Performance of the proposed method is demonstrated by comparing to the conjugate gradient algorithm from neural network toolbox for the chosen benchmark. The results show that the number of iterations required by the proposed method to converge is less than 20% of what is required by the standard conjugate gradient and neural network toolbox algorithm.

Keywords: Back-propagation, activation function, conjugategradient, search direction, gain variation.

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Authors: Raksha Sharma, Vishnu Kant Soni, Manoj Kumar Mishra, Prachet Bhuyan

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Grid computing is a form of distributed computing that involves coordinating and sharing computational power, data storage and network resources across dynamic and geographically dispersed organizations. Scheduling onto the Grid is NP-complete, so there is no best scheduling algorithm for all grid computing systems. An alternative is to select an appropriate scheduling algorithm to use in a given grid environment because of the characteristics of the tasks, machines and network connectivity. Job and resource scheduling is one of the key research area in grid computing. The goal of scheduling is to achieve highest possible system throughput and to match the application need with the available computing resources. Motivation of the survey is to encourage the amateur researcher in the field of grid computing, so that they can understand easily the concept of scheduling and can contribute in developing more efficient scheduling algorithm. This will benefit interested researchers to carry out further work in this thrust area of research.

Keywords: Grid Computing, Job Scheduling, ResourceScheduling.

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Authors: Amir Rashid, M. Saleem Khan, Freeha Zafar

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This work presents a new approach of securing a wireless network. The configuration is focused on securing & Protecting wireless network traffic for a small network such as a home or dorm room. The security Mechanism provided both authentication, allowing only known authorized users access to the wireless network, and encryption, preventing anyone from reading the wireless traffic. The mentioned solution utilizes the open source free S/WAN software which implements the Internet Protocol Security –IPSEC. In addition to wireless components, wireless NIC in PC and wireless access point needs a machine running Linux to act as security gateway. While the current configuration assumes that the wireless PC clients are running Linux, Windows XP/VISTA/7 based machines equipped with VPN software which will allow to interface with this configuration.

Keywords: Wireless network security, security network, authentication, encryption and internet protocol security.

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Authors: Nisa Özge Önal, Kamil Karaçuha, Göksu Hazar Erdinç, Banu Bahar Karaçuha, Ertuğrul Karaçuha

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The study aims to use a mathematical approach with the fractional calculus which is developed to have the ability to continuously analyze the factors related to the children’s height development. Until now, tracking the development of the child is getting more important and meaningful. Knowing and determining the factors related to the physical development of the child any desired time would provide better, reliable and accurate results for childcare. In this frame, 7 groups for height percentile curve (3th, 10th, 25th, 50th, 75th, 90th, and 97th) of Turkey are used. By using discrete height data of 0-18 years old children and the least squares method, a continuous curve is developed valid for any time interval. By doing so, in any desired instant, it is possible to find the percentage and location of the child in Percentage Chart. Here, with the help of the fractional calculus theory, a mathematical model is developed. The outcomes of the proposed approach are quite promising compared to the linear and the polynomial method. The approach also yields to predict the expected values of children in the sense of height.

Keywords: Children growth percentile, children physical development, fractional calculus, linear and polynomial model.

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Authors: Akash Rathee, Harish Parthasarathy

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In this paper, a frequency-variation based method has been proposed for transistor parameter estimation in a commonemitter transistor amplifier circuit. We design an algorithm to estimate the transistor parameters, based on noisy measurements of the output voltage when the input voltage is a sine wave of variable frequency and constant amplitude. The common emitter amplifier circuit has been modelled using the transistor Ebers-Moll equations and the perturbation technique has been used for separating the linear and nonlinear parts of the Ebers-Moll equations. This model of the amplifier has been used to determine the amplitude of the output sinusoid as a function of the frequency and the parameter vector. Then, applying the proposed method to the frequency components, the transistor parameters have been estimated. As compared to the conventional time-domain least squares method, the proposed method requires much less data storage and it results in more accurate parameter estimation, as it exploits the information in the time and frequency domain, simultaneously. The proposed method can be utilized for parameter estimation of an analog device in its operating range of frequencies, as it uses data collected from different frequencies output signals for parameter estimation.

Keywords: Perturbation Technique, Parameter estimation, frequency-variation based method.

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Authors: Leena Korpinen, Rauno Pääkkönen, Fabriziomaria Gobba, Vesa Virtanen

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The number of persons with implanted cardiac pacemakers (PM) has increased in Western countries. The aim of this paper is to investigate the possible situations where persons with a PM may be exposed to extremely low frequency (ELF) electric (EF) and magnetic fields (MF) that may disturb their PM. Based on our earlier studies, it is possible to find such high public exposure to EFs only in some places near 400 kV power lines, where an EF may disturb a PM in unipolar mode. Such EFs cannot be found near 110 kV power lines. Disturbing MFs can be found near welding machines. However, we do not have measurement data from welding. Based on literature and earlier studies at Tampere University of Technology, it is difficult to find public EF or MF exposure that is high enough to interfere with PMs.

Keywords: Cardiac Pacemaker, Electric Field, Magnetic Field.

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Authors: Tatjana Eitrich, Bruno Lang

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This work deals with aspects of support vector learning for large-scale data mining tasks. Based on a decomposition algorithm that can be run in serial and parallel mode we introduce a data transformation that allows for the usage of an expensive generalized kernel without additional costs. In order to speed up the decomposition algorithm we analyze the problem of working set selection for large data sets and analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our modifications and settings lead to improvement of support vector learning performance and thus allow using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machines, Shared Memory Parallel Computing, Large Data

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Authors: Gilbert Makanda, Roelf Sypkens

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A mathematical model for knowledge acquisition in teaching and learning is proposed. In this study we adopt the mathematical model that is normally used for disease modelling into teaching and learning. We derive mathematical conditions which facilitate knowledge acquisition. This study compares the effects of dropping out of the course at early stages with later stages of learning. The study also investigates effect of individual interaction and learning from other sources to facilitate learning. The study fits actual data to a general mathematical model using Matlab ODE45 and lsqnonlin to obtain a unique mathematical model that can be used to predict knowledge acquisition. The data used in this study was obtained from the tutorial test results for mathematics 2 students from the Central University of Technology, Free State, South Africa in the department of Mathematical and Physical Sciences. The study confirms already known results that increasing dropout rates and forgetting taught concepts reduce the population of knowledgeable students. Increasing teaching contacts and access to other learning materials facilitate knowledge acquisition. The effect of increasing dropout rates is more enhanced in the later stages of learning than earlier stages. The study opens up a new direction in further investigations in teaching and learning using differential equations.

Keywords: Differential equations, knowledge acquisition, least squares nonlinear, dynamical systems.

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Authors: Ahsan Bin Tufail, Ali Abidi, Adil Masood Siddiqui, Muhammad Shahzad Younis

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An early and accurate detection of Alzheimer's disease (AD) is an important stage in the treatment of individuals suffering from AD. We present an approach based on the use of structural magnetic resonance imaging (sMRI) phase images to distinguish between normal controls (NC), mild cognitive impairment (MCI) and AD patients with clinical dementia rating (CDR) of 1. Independent component analysis (ICA) technique is used for extracting useful features which form the inputs to the support vector machines (SVM), K nearest neighbour (kNN) and multilayer artificial neural network (ANN) classifiers to discriminate between the three classes. The obtained results are encouraging in terms of classification accuracy and effectively ascertain the usefulness of phase images for the classification of different stages of Alzheimer-s disease.

Keywords: Biomedical image processing, classification algorithms, feature extraction, statistical learning.

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Authors: Murat Toren, Mehmet Çelebi

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Reducing the weight of transformers while providing good performance, cost reduction and increased efficiency is important. Weight is one of the most significant factors in all electrical machines, and as such, many transformer design parameters are related to weight calculations. This study presents a comparison of the weight of oil type transformers and dry type transformer weight. Oil type transformers are mainly used in industry; however, dry type transformers are becoming more widespread in recent years. MATLAB is typically used for designing transformers and design parameters (rated voltages, core loss, etc.) along with design in ANSYS Maxwell. Similar to other studies, this study presented that the dry type transformer option is limited. Moreover, the commonly-used 50 kVA distribution transformers in the industry are oil type and dry type transformers are designed and considered in terms of weight. Currently, the preference for low-cost oil-type transformers would change if costs for dry-type transformer were more competitive. The aim of this study was to compare the weight of transformers, which is a substantial cost factor, and to provide an evaluation about increasing the use of dry type transformers.

Keywords: Weight, oil-type transformers, dry-type transformers.

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Authors: Sanjay Kumar Parjapati, Ajai Jain

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This paper presents optimization of makespan for ‘n’ jobs and ‘m’ machines flexible job shop scheduling problem with sequence dependent setup time using genetic algorithm (GA) approach. A restart scheme has also been applied to prevent the premature convergence. Two case studies are taken into consideration. Results are obtained by considering crossover probability (pc = 0.85) and mutation probability (pm = 0.15). Five simulation runs for each case study are taken and minimum value among them is taken as optimal makespan. Results indicate that optimal makespan can be achieved with more than one sequence of jobs in a production order.

Keywords: Flexible Job Shop, Genetic Algorithm, Makespan, Sequence Dependent Setup Times.

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Authors: Metehan Makinacı

Abstract:

The objective of this paper, is to apply support vector machine (SVM) approach for the classification of cancerous and normal regions of prostate images. Three kinds of textural features are extracted and used for the analysis: parameters of the Gauss- Markov random field (GMRF), correlation function and relative entropy. Prostate images are acquired by the system consisting of a microscope, video camera and a digitizing board. Cross-validated classification over a database of 46 images is implemented to evaluate the performance. In SVM classification, sensitivity and specificity of 96.2% and 97.0% are achieved for the 32x32 pixel block sized data, respectively, with an overall accuracy of 96.6%. Classification performance is compared with artificial neural network and k-nearest neighbor classifiers. Experimental results demonstrate that the SVM approach gives the best performance.

Keywords: Computer-aided diagnosis, support vector machines, Gauss-Markov random fields, texture classification.

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: Fractional differential (FD), Computed Tomography (CT), fusion.

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Authors: Khalid Ahmed Elrabie Abdelrasoul

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The present study was carried out on Sahiwal cattle bulls maintained at the Artificial Breeding Complex, NDRI, Karnal, Hayana, India, to assess the effect of cooling using mist cooling and fanning on Sahiwal bulls in the dry hot summer season. Fourteen Sahiwal bulls were divided into two groups of seven each. Sexual behavior and semen quality traits considered were: Reaction time (RT), Dismounting time (DMT), Total time taken in mounts (TTTM), Flehmen response (FR), Erection Score (ES), Protrusion Score (PS), Intensity of thrust (ITS), Temperament Score (TS), Libido Score (LS), Semen volume, Physical appearance, Mass activity, Initial progressive motility, Non-eosinophilic spermatozoa count (NESC) and post thaw motility percent. Data were analyzed by least squares technique. Group-1 was the control, whereas group-2 (treatment group) bulls were exposed to mist cooling and fanning (thrice a day 15 min each) in the dry hot summer season. Group-2 showed significantly (p < 0.01) higher value in DMT (sec), ES, PS, ITS, LS, semen volume (ml), semen color density, mass activity, initial motility, progressive motility and live sperm.

Keywords: Mist cooling, Sahiwal bulls, semen quality, sexual behavior.

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Authors: Madhusudan Purohit, Stephen Philip, Bharathkumar Inturi

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In this paper we report the quantitative structure activity relationship of novel bis-triazole derivatives for predicting the activity profile. The full model encompassed a dataset of 46 Bis- triazoles. Tripos Sybyl X 2.0 program was used to conduct CoMSIA QSAR modeling. The Partial Least-Squares (PLS) analysis method was used to conduct statistical analysis and to derive a QSAR model based on the field values of CoMSIA descriptor. The compounds were divided into test and training set. The compounds were evaluated by various CoMSIA parameters to predict the best QSAR model. An optimum numbers of components were first determined separately by cross-validation regression for CoMSIA model, which were then applied in the final analysis. A series of parameters were used for the study and the best fit model was obtained using donor, partition coefficient and steric parameters. The CoMSIA models demonstrated good statistical results with regression coefficient (r²) and the cross-validated coefficient (q²) of 0.575 and 0.830 respectively. The standard error for the predicted model was 0.16322. In the CoMSIA model, the steric descriptors make a marginally larger contribution than the electrostatic descriptors. The finding that the steric descriptor is the largest contributor for the CoMSIA QSAR models is consistent with the observation that more than half of the binding site area is occupied by steric regions.

Keywords: 3D QSAR, CoMSIA, Triazoles.

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