Search results for: soil–machine

Authors: Rafik Djemili, Mouldi Bedda, Hocine Bourouba

Abstract:

This paper proposes a novel approach that combines statistical models and support vector machines. A hybrid scheme which appropriately incorporates the advantages of both the generative and discriminant model paradigms is described and evaluated. Support vector machines (SVMs) are trained to divide the whole speakers' space into small subsets of speakers within a hierarchical tree structure. During testing a speech token is assigned to its corresponding group and evaluation using gaussian mixture models (GMMs) is then processed. Experimental results show that the proposed method can significantly improve the performance of text independent speaker identification task. We report improvements of up to 50% reduction in identification error rate compared to the baseline statistical model.

Keywords: Speaker identification, Gaussian mixture model (GMM), support vector machine (SVM), hybrid GMM/SVM.

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Authors: R. Amooaghaie

Abstract:

Principally, plants grown in soilless culture may be attacked by the same pests and diseases as cultivated traditionally in soil. The most destructive phytopathogens are fungi, such as Phythium, Phytophthora and Fusarium, followed by viruses, bacteria and nematodes. We investigated effect of carbon nanotube filters on disease management of soilless culture. Tomato seedlings transplant in plastic pots filled with a soilless media of vermiculite. The crop irrigated and fertilized using a hydroponic nutrient solution. We used carbon nanotube filters for nutrient solution disinfection. Our results show that carbon nanotube filtration significantly reduces pathogens on tomato plants. Fungal elimination (Fusarium oxysporum and Pythium spp.) was usually successful at about 96 to 99.9% all over the cultural season. It is seem that in tomato soilless culture, nanofiltration constitutes a reliable method that allows control of the development of diseases caused by pathogenic fungi

Keywords: Fusarium oxysporum, Nanofilteration, Pythium spp., Soilless culture, Tomato

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Authors: Gayadhar Panda, P. K. Rautraya

Abstract:

Damping of inter-area electromechanical oscillations is one of the major challenges to the electric power system operators. This paper presents Gravitational Search Algorithm (GSA) for tuning Static Synchronous Series Compensator (SSSC) based damping controller to improve power system oscillation stability. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The effectiveness of the scheme in damping power system oscillations during system faults at different loading conditions is demonstrated through time-domain simulation.

Keywords: FACTS, Damping controller design, Gravitational search algorithm (GSA), Power system oscillations, Single-machine infinite Bus power system, SSSC.

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

Abstract:

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: P. Gnanasekar, A. Nagappan, S. Sharavanan, O. Saravanan, D. Vinodkumar, T. Elayabharathi, G. Karthik

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In this paper we present the deep study about the Bio- Medical Images and tag it with some basic extracting features (e.g. color, pixel value etc). The classification is done by using a nearest neighbor classifier with various distance measures as well as the automatic combination of classifier results. This process selects a subset of relevant features from a group of features of the image. It also helps to acquire better understanding about the image by describing which the important features are. The accuracy can be improved by increasing the number of features selected. Various types of classifications were evolved for the medical images like Support Vector Machine (SVM) which is used for classifying the Bacterial types. Ant Colony Optimization method is used for optimal results. It has high approximation capability and much faster convergence, Texture feature extraction method based on Gabor wavelets etc..

Keywords: ACO Ant Colony Optimization, Correlogram, CCM Co-Occurrence Matrix, RTS Rough-Set theory

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Authors: Fathi N. Mayoof

Abstract:

Rolling element bearings are widely used in industry, especially where high load capacity is required. The diagnosis of their conditions is essential matter for downtime reduction and saving cost of maintenance. Therefore, an intensive analysis of frequency spectrum of their faults must be carried out in order to determine the main reason of the fault. This paper focus on a beating phenomena observed in the waveform (time domain) of a cylindrical rolling element bearing. The beating frequencies were not related to any sources nearby the machine nor any other malfunctions (unbalance, misalignment ...etc). More investigation on the spike energy and the frequency spectrum indicated a problem with races of the bearing. Multi-harmonics of the fundamental defects frequencies were observed. Two of them were close to each other in magnitude those were the source of the beating phenomena.

Keywords: Bearing, beating, spike energy, vibration.

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Authors: Jana Novakova, Lenka Petrkovska, Josef Brychta, Robert Cep, Lenka Ocenasova

Abstract:

The contribution is dealing with the influence of high speed parameters on the quality of machined surface. In general the principle of high speed cutting lies in achieving faster machine times with concurrent increase in accuracy and quality of the machined areas in largely irregular, mathematically hard to define shapes. High speed machining is a highly effective method of machining with the following goals: increasing of machining productivity, increasing of quality of the machined surface, improving of machining economy, improving of ecological aspects of machining. This article is based on an experiment performed by the Department of Machining and Assembly of the Faculty of Mechanical Engineering of VŠBTechnical University of Ostrava.

Keywords: High speed cutting, measurement, surface integrity, surface roughness, residual stress/

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Authors: Mahesh Chudasama, Harit Raval

Abstract:

Conical sections and shells of metal plates manufactured by 3-roller conical bending process are widely used in the industries. The process is completed by first bending the metal plates statically and then dynamic roller bending sequentially. It is required to have an analytical model to get maximum bending force, for optimum design of the machine, for static bending stage. Analytical models assuming various stress conditions are considered and these analytical models are compared considering various parameters and reported in this paper. It is concluded from the study that for higher bottom roller inclination, the shear stress affects greatly to the static bending force whereas for lower bottom roller inclination it can be neglected.

Keywords: Roller-bending, static-bending, stress-conditions, analytical-modeling.

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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: Hrudaya Ku. Tripathy, B. K. Tripathy, Pradip K. Das

Abstract:

Knowledge Discovery in Databases (KDD) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. In this paper we presented the current status of research on applying rough set theory to KDD, which will be helpful for handle the characteristics of real-world databases. The main aim is to show how rough set and rough set analysis can be effectively used to extract knowledge from large databases.

Keywords: Data mining, Data tables, Knowledge discovery in database (KDD), Rough sets.

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Authors: J. K. Alhassan, B. Attah, S. Misra

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Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. Medical dataset is a vital ingredient used in predicting patient’s health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. WEKA software was used for the implementation of the algorithms. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. From the results obtained, DTA performed better than ANN. The Root Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: Artificial neural network, classification, decision tree, diabetes mellitus.

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Authors: M. Aslam, Farrukh, A. R. Gardezi, Nasir Hayat

Abstract:

Automated material handling is given prime importance in the semi automated and automated facilities since it provides solution to the gigantic problems related to inventory and also support the latest philosophies like just in time production JIT and lean production. Automated storage and retrieval system is an antidote (if designed properly) to the facility sufferings like getting the right material , materials getting perished, long cycle times or many other similar kind of problems. A working model of automated storage and retrieval system (AS/RS) is designed and developed under the design parameters specified by Material Handling Industry of America (MHIA). Later on analysis was carried out to calculate the throughput and size of the machine. The possible implementation of this technology in local scenario is also discussed in this paper.

Keywords: Automated storage and retrieval system (AS/RS), Material handling, Computer integrated manufacturing (CIM), Lightdependent resistor (LDR)

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Authors: Rahele Hasani, Iraj Mehregan, Kambiz Larijani, Taher Nejadsattari, Romain Scalone

Abstract:

Essential oils of Heracleum persicum (Apiaceae) have been widely used from many years ago, but the difference of its properties among different populations have not been identified up to now. Hydrodistilation Clevenger type was used to obtaining the fruit essential oils of four populations of H. persicum from different localities in Iran, then they were characterized by GC-FID and GC-MS analyses. Some ecological factors were also measured. The oils of four populations were compared to determine the similarities and differences and the relationships between these factors and ecological factors. Based on the result, 18-32 different components were identified in four populations, while the percentage of the main components was higher in population with lower number of components. According to the statistical analyses of chemical components and ecological factors, it can be concluded that some ecological factors such as altitude, less humidity, high difference between day and night temperature and salty soil would lead to lower number of components in essential oil, whereas they consist the higher percentage.

Keywords: Chemotaxonomy, Persian hogweed, ecological factors, Apiaceae.

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Authors: M. A. Abd-Allah, Mahmoud N. Ali, A. Said

Abstract:

Lightning protection systems (LPS) for wind power generation is becoming an important public issue. A serious damage of blades, accidents where low-voltage and control circuit breakdowns are frequently occur in many wind farms. A grounding system is one of the most important components required for appropriate LPSs in wind turbines WTs. Proper design of a wind turbine grounding system is demanding and several factors for the proper and effective implementation must taken into account. In this paper proposed procedure of proper design of grounding systems for a wind turbine was introduced. This procedure depends on measuring of ground current of simulated wind farm under lightning taking into consideration the soil ionization. The procedure also includes the Ground Potential Rise (GPR) and the voltage distributions at ground surface level and Touch potential. In particular, the contribution of mitigating techniques, such as rings, rods and the proposed design were investigated.

Keywords: WTs, LPS, GPR, Grounding System, Mitigating techniques.

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Authors: Jheng-Long Wu, Pei-Chann Chang, Yi-Fang Pan

Abstract:

This research focus on developing a new segmentation method for improving forecasting model which is call trend based segmentation method (TBSM). Generally, the piece-wise linear representation (PLR) can finds some of pair of trading points is well for time series data, but in the complicated stock environment it is not well for stock forecasting because of the stock has more trends of trading. If we consider the trends of trading in stock price for the trading signal which it will improve the precision of forecasting model. Therefore, a TBSM with SVR model used to detect the trading points for various stocks of Taiwanese and America under different trend tendencies. The experimental results show our trading system is more profitable and can be implemented in real time of stock market

Keywords: Trend based segmentation method, support vector machine, turning detection, stock forecasting.

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Authors: A. K. Sharma, N. P. Patidar, G. Agnihotri, D. K. Palwalia

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This paper presents the performance state analysis of Self-Excited Induction Generator (SEIG) using Artificial Bee Colony (ABC) optimization technique. The total admittance of the induction machine is minimized to calculate the frequency and magnetizing reactance corresponding to any rotor speed, load impedance and excitation capacitance. The performance of SEIG is calculated using the optimized parameter found. The results obtained by ABC algorithm are compared with results from numerical method. The results obtained coincide with the numerical method results. This technique proves to be efficient in solving nonlinear constrained optimization problems and analyzing the performance of SEIG.

Keywords: Artificial bee colony, Steady state analysis, Selfexcited induction generator, Nonlinear constrained optimization.

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Authors: K. Bouhadef, S. Chikh, A. Boumedien, A. Benabdesselam

Abstract:

A solar refrigeration system based on the adsorptiondesorption phenomena is designed and analyzed. An annular tubular generator filled with silica gel adsorbent and with a perforated inner cylinder is integrated within a flat solar collector. The working fluid in the refrigeration cycle is water. The thermodynamic analysis and because of the temperature level that could be attained with a flat solar collector it is required that the system operates under vacuum conditions. In order to enhance the performance of the system and to get uniform temperature in the silica gel and higher desorbed mass, an apparatus for rotation of the generator is incorporated in the system. Testing is carried out and measurements are taken on the designed installation. The effect of rotation is checked on the temperature distribution and on the performance of this machine and compared to the flat solar collector with fixed generator.

Keywords: Refrigeration cycle, solar energy, rotating collector, adsorption, silica gel.

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Authors: D. Sumathi, P. Poongodi

Abstract:

Cloud Computing refers to applications delivered as services over the internet, and the datacenters that provide those services with hardware and systems software. These were earlier referred to as Software as a Service (SaaS). Scheduling is justified by job components (called tasks), lack of information. In fact, in a large fraction of jobs from machine learning, bio-computing, and image processing domains, it is possible to estimate the maximum time required for a task in the job. This study focuses on Trust based scheduling to improve cloud security by modifying Heterogeneous Earliest Finish Time (HEFT) algorithm. It also proposes TR-HEFT (Trust Reputation HEFT) which is then compared to Dynamic Load Scheduling.

Keywords: Software as a Service (SaaS), Trust, Heterogeneous Earliest Finish Time (HEFT) algorithm, Dynamic Load Scheduling.

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Authors: Yang Li, Xiaorong Guan, Cheng Xu

Abstract:

In order to design a suitable control scheme for human extremity exoskeleton, the interaction force control scheme with traditional PI controller was presented, and the simulation study of the electromechanical system of the human extremity exoskeleton was carried out by using a MATLAB/Simulink module. By analyzing the simulation calculation results, it was shown that the traditional PI controller is not very suitable for every movement speed of human body. So, at last the fuzzy self-adaptive PI controller was presented to solve this problem. Eventually, the superiority and feasibility of the fuzzy self-adaptive PI controller was proved by the simulation results and experimental results.

Keywords: Human extremity exoskeleton, interaction force control scheme, simulation study, fuzzy self-adaptive pi controller, man-machine coordinated walking, bear payload.

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Authors: S. Massoum, A. Bentaallah, A. Massoum, F. Benaimeche, P. Wira, A. Meroufel

Abstract:

This paper presents a speed fuzzy sliding mode controller for a vector controlled induction machine (IM) fed by a voltage source inverter (PWM). The sliding mode based fuzzy control method is developed to achieve fast response, a best disturbance rejection and to maintain a good decoupling. The problem with sliding mode control is that there is high frequency switching around the sliding mode surface. The FSMC is the combination of the robustness of Sliding Mode Control (SMC) and the smoothness of Fuzzy Logic (FL). To reduce the torque fluctuations (chattering), the sign function used in the conventional SMC is substituted with a fuzzy logic algorithm. The proposed algorithm was simulated by Matlab/Simulink software and simulation results show that the performance of the control scheme is robust and the chattering problem is solved.

Keywords: IM, FOC, FLC, SMC, and FSMC.

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Authors: Swetha Priya Darshini Thammadi, Sateesh Kumar Pisini, Sanjay Kumar Shukla

Abstract:

Source apportionment using Dispersion model depends primarily on the quality of Emission Inventory. In the present study, a CMB receptor model has been used to identify the sources of PM_2.5, while the AERMOD dispersion model has been used to account for missing sources of PM_2.5 in the Emission Inventory. A statistical approach has been developed to quantify the missing sources not considered in the Emission Inventory. The inventory of each grid was improved by adjusting emissions based on road lengths and deficit in measured and modelled concentrations. The results showed that in CMB analyses, fugitive sources - soil and road dust - contribute significantly to ambient PM_2.5 pollution. As a result, AERMOD significantly underestimated the ambient air concentration at most locations. The revised Emission Inventory showed a significant improvement in AERMOD performance which is evident through statistical tests.

Keywords: CMB, GIS, AERMOD, PM2.5, fugitive, emission inventory.

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Authors: Jin Sung Kim, Hoon Huh

Abstract:

The material properties of OFHC copper film was investigated with the High-Speed Material Micro Testing Machine (HSMMTM) at the high strain rates. The rate-dependent stress-strain curves from the experiment and the Johnson−Cook curve fitting showed large discrepancies as the plastic strain increases since the constitutive model implies no rate-dependent strain hardening effect. A new constitutive model was proposed in consideration of rate-dependent strain hardening effect. The strain rate hardening term in the new constitutive model consists of the strain rate sensitivity coefficients of the yield strength and strain hardening.

Keywords: Rate dependent material properties, Dynamic constitutive model, OFHC copper film, Strain rate.

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. However, accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER benefiting from deep learning, especially CNN and VGG16. First, the data are pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning.

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Authors: Khin S. Aye

Abstract:

The present work was conducted to find out the effect of biofertilizer formulated with four species of bacteria (two species of Azotobacter and two species of Lysobacter) and zinc sulphate. Field experiments with mustard plant were conducted to study the effectiveness of soil application of zinc sulphate and biofertilizer at 0, 10, 20, 30, 40, 50 days after sowing. Plant height and condition of plant was found to be increased significantly using a mixture of biofertilizer and zinc sulphate than other treatments after 40 days sowing. Three treatments were also used in this field experiment such as bacteria only, zinc sulphate only and mixture of biofertilizer and zinc sulphate. The treatment using a mixture of zinc sulphate and biofertilizer had the best yield (4688.008 kg/ha) within 50 days of sowing and performed better than other treatments. Field experiment using zinc sulphate only was second best yield (3380.75Kg/ha) and biofertilizer only treatment gave (2639.04kg/ha).

Keywords: biofertilizer, zinc sulphate, mustard plant, bacteria

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Authors: Uzu-Kuei Hsu, Chang-Hsien Tai, Kai-Wun Jin

Abstract:

When sinters are filled into the cooler from the sintering machine, and the non-uniform distribution of the sinters leads to uneven cooling. This causes the temperature difference of the sinters leaving the cooler to be so large that it results in the conveyors being deformed by the heat. The present work applies CFD method to investigate the thermo flowfield phenomena in a sinter cooler by the Porous Media Model. Using the obtained experimental data to simulate porosity (Ε), permeability (κ), inertial coefficient (F), specific heat (Cp) and effective thermal conductivity (keff) of the sinter packed beds. The physical model is a similar geometry whose Darcy numbers (Da) are similar to the sinter cooler. Using the Cooling Index (CI) and Uniformity Index (UI) to analyze the thermo flowfield in the sinter packed bed obtains the cooling performance of the sinter cooler.

Keywords: Porous media, sinter, cooling index, uniformity index, CFD.

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Authors: Mwafak Shakoor

Abstract:

The purpose of this study was to reduce patient waiting times, improve system throughput and improve resources utilization in radiology department. A discrete event simulation model was developed using Arena simulation software to investigate different alternatives to improve the overall system delivery based on adding resource scenarios due to the linkage between patient waiting times and resource availability. The study revealed that there is no addition investment need to procure additional scanner but hospital management deploy managerial tactics to enhance machine utilization and reduce the long waiting time in the department.

Keywords: Arena, Computed Tomography (CT), Discrete event simulation, Healthcare modeling, Radiology department, Waiting time.

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Authors: Hany Osman, M. F. Baki

Abstract:

We address the balancing problem of transfer lines in this paper to find the optimal line balancing that minimizes the nonproductive time. We focus on the tool change time and face orientation change time both of which influence the makespane. We consider machine capacity limitations and technological constraints associated with the manufacturing process of auto cylinder heads. The problem is represented by a mixed integer programming model that aims at distributing the design features to workstations and sequencing the machining processes at a minimum non-productive time. The proposed model is solved by an algorithm established using linearization schemes and Benders- decomposition approach. The experiments show the efficiency of the algorithm in reaching the exact solution of small and medium problem instances at reasonable time.

Keywords: Transfer line balancing, Benders' decomposition, Linearization.

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Authors: Vinh Quang La, Dirk Jansen

Abstract:

There are some existing Java benchmarks, application benchmarks as well as micro benchmarks or mixture both of them,such as: Java Grande, Spec98, CaffeMark, HBech, etc. But none of them deal with behaviors of multi tasks operating systems. As a result, the achieved outputs are not satisfied for performance evaluation engineers. Behaviors of multi tasks operating systems are based on a schedule management which is employed in these systems. Different processes can have different priority to share the same resources. The time is measured by estimating from applications started to it is finished does not reflect the real time value which the system need for running those programs. New approach to this problem should be done. Having said that, in this paper we present a new Java benchmark, named FHOJ benchmark, which directly deals with multi tasks behaviors of a system. Our study shows that in some cases, results from FHOJ benchmark are far more reliable in comparison with some existing Java benchmarks.

Keywords: Java Virtual Machine, Java benchmark, FHOJ framework.

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

Abstract:

Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: Causal relation identification, convolutional neural networks, natural Language Processing, Machine Learning.

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Authors: Nebi Gedik

Abstract:

One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: Wave atom transform, statistical features, multi-resolution representation, mammogram.

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