Search results for: Clinical deterioration prediction

Authors: Pradeep Singh, Shrish Verma

Abstract:

Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. Earlier we predicted the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven datasets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: Software Metrics, Fault prediction, Cross project, Within project.

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: Building energy prediction, data mining, demand response, electricity market.

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Authors: K. Petcharaporn, S. Kumchoo

Abstract:

The acidity (citric acid) is the one of chemical content that can be refer to the internal quality and it’s a maturity index of tomato, The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR) spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomato.

Keywords: Tomato, quality, prediction, transmittance, titratable acidity, citric acid.

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Authors: Seyed Saeed Changiz Rezaei, Babak Hossein Khalaj

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As the demand for higher capacity in a cellular environment increases, the cell size decreases. This fact makes the role of suitable handoff algorithms to reduce both number of handoffs and handoff delay more important. In this paper we show that applying the grey prediction technique for handoff leads to considerable decrease in handoff delay with using a small number of handoffs, compared with traditional hystersis based handoff algorithms.

Keywords: Cellular network, Grey prediction, Handoff.

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Authors: Abdelsalam Omran Gebril, Ali Gh Saeid

Abstract:

Grazing and pastoral overloading through human factors result in significant land desertification. Failure to take into account the phenomenon of desertification as a serious problem can lead to an environmental disaster because of the damages caused by land encroachment. Therefore, soil on residential and urban areas is affected because of the deterioration of vegetation. Overgrazing or grazing in open and irregular lands is practiced in these areas almost throughout the year, especially during the growth cycle of edible plants, thereby leading to their disappearance. In addition, the large number of livestock in these areas exceeds the capacity of these pastures because of pastoral land overloading, which results in deterioration and desertification in the region. In addition, rare plants, the extinction of some edible plants in the region, and the emergence of plants unsuitable for grazing, must be taken into consideration, as along with the emergence of dust and sand storms during the dry seasons (summer to autumn) due to the degradation of vegetation. These results show that strategic plans and regulations that protect the environment from desertification must be developed. Therefore, increased pastoral load is a key human factor in the deterioration of vegetation cover, leading to land desertification in this region.

Keywords: Overloading, pastoral, grazing, desertification, Libya

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Authors: Raissa A. Muzychkina, Irina M. Korulkina, Dmitriy Yu. Korulkin

Abstract:

In article the data of pre-clinical researches of Ramon preparation is described. Antitumor activity of Ramon has been studied on 19 strains of transplantated tumors of different hystogenesis.

Keywords: Cancer, antitumor activity, pre-clinical testing, anthraquinones, phytopreparation, Ramon.

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Authors: Ryad Zemouri, Paul Ciprian Patic

Abstract:

An adaptive software reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available software failure time data, Fuzzy Min-Max algorithm is used to globally optimize the number of the k Gaussian nodes. The corresponding optimized neural network architecture is iteratively and dynamically reconfigured in real-time as new actual failure time data arrives. The performance of our proposed approach has been tested using sixteen real-time software failure data. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to next-steppredictability compared to existing neural network model for failure time prediction.

Keywords: Neural network, Prediction error, Recurrent RadialBasis Function Network, Reliability prediction.

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Authors: Brian A. Smith, Ronald W. McClendon, Gerrit Hoogenboom

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The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. An improved model for temperature prediction in Georgia was developed by including information on seasonality and modifying parameters of an existing artificial neural network model. Alternative models were compared by instantiating and training multiple networks for each model. The inclusion of up to 24 hours of prior weather information and inputs reflecting the day of year were among improvements that reduced average four-hour prediction error by 0.18°C compared to the prior model. Results strongly suggest model developers should instantiate and train multiple networks with different initial weights to establish appropriate model parameters.

Keywords: Time-series forecasting, weather modeling.

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Authors: Miloje S. Radenkovic, Tamal Bose

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This paper presents the convergence analysis of a prediction based blind equalizer for IIR channels. Predictor parameters are estimated by using the recursive least squares algorithm. It is shown that the prediction error converges almost surely (a.s.) toward a scalar multiple of the unknown input symbol sequence. It is also proved that the convergence rate of the parameter estimation error is of the same order as that in the iterated logarithm law.

Keywords: Adaptive blind equalizer, Recursive leastsquares, Adaptive Filtering, Convergence analysis.

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Authors: Neeraj Mohan, Parvinder S. Sandhu, Hardeep Singh

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Software maintenance is extremely important activity in software development life cycle. It involves a lot of human efforts, cost and time. Software maintenance may be further subdivided into different activities such as fault prediction, fault detection, fault prevention, fault correction etc. This topic has gained substantial attention due to sophisticated and complex applications, commercial hardware, clustered architecture and artificial intelligence. In this paper we surveyed the work done in the field of software maintenance. Software fault prediction has been studied in context of fault prone modules, self healing systems, developer information, maintenance models etc. Still a lot of things like modeling and weightage of impact of different kind of faults in the various types of software systems need to be explored in the field of fault severity.

Keywords: Fault prediction, Software Maintenance, Automated Fault Prediction, and Failure Mode Analysis

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Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of a high performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice River catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: Flood prediction process, High performance computing, Online flood prediction system, Parallelization.

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper presents sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for CHD prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, coronary heart disease, ECG, electrocardiogram, ResNet, sliding window.

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. Our work applies modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Sae-Rom Pak, Seung Hwan Park, Jeong Ho Cho, Daewoong An, Cheong-Sool Park, Jun Seok Kim, Jun-Geol Baek

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It is important to predict yield in semiconductor test process in order to increase yield. In this study, yield prediction means finding out defective die, wafer or lot effectively. Semiconductor test process consists of some test steps and each test includes various test items. In other world, test data has a big and complicated characteristic. It also is disproportionably distributed as the number of data belonging to FAIL class is extremely low. For yield prediction, general data mining techniques have a limitation without any data preprocessing due to eigen properties of test data. Therefore, this study proposes an under-sampling method using support vector machine (SVM) to eliminate an imbalanced characteristic. For evaluating a performance, randomly under-sampling method is compared with the proposed method using actual semiconductor test data. As a result, sampling method using SVM is effective in generating robust model for yield prediction.

Keywords: Yield Prediction, Semiconductor Test Process, Support Vector Machine, Under Sampling

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Authors: Dawit Mengistu, Lars Lundberg, Paul Davidsson

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A major requirement for Grid application developers is ensuring performance and scalability of their applications. Predicting the performance of an application demands understanding its specific features. This paper discusses performance modeling and prediction of multi-agent based simulation (MABS) applications on the Grid. An experiment conducted using a synthetic MABS workload explains the key features to be included in the performance model. The results obtained from the experiment show that the prediction model developed for the synthetic workload can be used as a guideline to understand to estimate the performance characteristics of real world simulation applications.

Keywords: Grid computing, Performance modeling, Performance prediction, Multi-agent simulation.

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Authors: A. Elyousfi, A. Tamtaoui, E. Bouyakhf

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The H.264/AVC video coding standard contains a number of advanced features. Ones of the new features introduced in this standard is the multiple intramode prediction. Its function exploits directional spatial correlation with adjacent block for intra prediction. With this new features, intra coding of H.264/AVC offers a considerably higher improvement in coding efficiency compared to other compression standard, but computational complexity is increased significantly when brut force rate distortion optimization (RDO) algorithm is used. In this paper, we propose a new fast intra prediction mode decision method for the complexity reduction of H.264 video coding. for luma intra prediction, the proposed method consists of two step: in the first step, we make the RDO for four mode of intra 4x4 block, based the distribution of RDO cost of those modes and the idea that the fort correlation with adjacent mode, we select the best mode of intra 4x4 block. In the second step, we based the fact that the dominating direction of a smaller block is similar to that of bigger block, the candidate modes of 8x8 blocks and 16x16 macroblocks are determined. So, in case of chroma intra prediction, the variance of the chroma pixel values is much smaller than that of luma ones, since our proposed uses only the mode DC. Experimental results show that the new fast intra mode decision algorithm increases the speed of intra coding significantly with negligible loss of PSNR.

Keywords: Intra prediction, H264/AVC, video coding, encodercomplexity.

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Authors: Virginia Bassily, Sherif Goubran

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In heritage conservation and revival, much of the focus is placed on the techniques and methods to preserve, restore, and revive heritage structures and locations. However, more attention needs to be drawn to how deterioration happens and its effect on the area’s character and socio-economic status. To this end, this research aims to examine the decline and its effect in the El Korba area in Heliopolis, Cairo, Egypt. El Korba was designed with a unique architectural character to stimulate social and economic life. However, the area has been on a path of physical deterioration that is corroding the social life on its streets. This research uses diachronic analysis in Ibrahim El-Lakkani Boulevard of El Korba based on a previously developed framework that connects buildings’ architectural features to the degree of social interaction in the street to document the changes that the building deterioration could have caused. Architectural features of the street level during both the original state (1906) and the current state (2021) are broken down and categorized in those six parameters to understand their decline or improvement over time. We find that the parameters that have decreased over the years and caused the deterioration are complexity and architectural character, permeability, territoriality and personalization, and physical comfort.  Based on these findings, revival projects can focus on physical parameters that create synergistic benefits by preserving and renewing heritage locations and revitalizing their socio-economic potential.

Keywords: Architectural character, heritage building conservation, enclosure, ground-floor use, El Korba, visual and physical permeability, personalization, physical comfort, social life, territoriality.

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Authors: Engin Eyceyurt, Josko Zec

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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: Drive test, LTE, machine learning, uplink throughput prediction.

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Authors: Jacqueline M. Dias, Amina A Khowaja

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Clinical education is one of the most important components of a nursing curriculum as it develops the students’ cognitive, psychomotor and affective skills. Clinical teaching ensures the integration of knowledge into practice. As the numbers of students increase in the field of nursing coupled with the faculty shortage, clinical preceptors are the best choice to ensure student learning in the clinical settings. The clinical preceptor role has been introduced in the undergraduate nursing programme. In Pakistan, this role emerged due to a faculty shortage. Initially, two clinical preceptors were hired. This study will explore clinical preceptors views and experiences of precepting Bachelor of Science in Nursing (BScN) students in an undergraduate program. A case study design was used. As case studies explore a single unit of study such as a person or very small number of subjects; the two clinical preceptors were fundamental to the study and served as a single case. Qualitative data were obtained through an iterative process using in depth interviews and written accounts from reflective journals that were kept by the clinical preceptors. The findings revealed that the clinical preceptors were dedicated to their roles and responsibilities. Another, key finding was that clinical preceptors’ prior knowledge and clinical experience were valuable assets to perform their role effectively. The clinical preceptors found their new role innovative and challenging; it was stressful at the same time. Findings also revealed that in the clinical agencies there were unclear expectations and role ambiguity. Furthermore, clinical preceptors had difficulty integrating theory into practice in the clinical area and they had difficulty in giving feedback to the students. Although this study is localized to one university, generalizations can be drawn from the results. The key findings indicate that the role of a clinical preceptor is demanding and stressful. Clinical preceptors need preparation prior to precepting students on clinicals. Also, institutional support is fundamental for their acceptance. This paper focuses on the views and experiences of clinical preceptors undertaking a newly established role and resonates with the literature. The following recommendations are drawn to strengthen the role of the clinical preceptors: A structured program for clinical preceptors is needed along with mentorship. Clinical preceptors should be provided with formal training in teaching and learning with emphasis on clinical teaching and giving feedback to students. Additionally, for improving integration of theory into practice, clinical modules should be provided ahead of the clinical. In spite of all the challenges, ten more clinical preceptors have been hired as the faculty shortage continues to persist.

Keywords: Baccalaureate nursing education, clinical education, clinical preceptors, nursing curriculum.

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: Classification, classifier fusion, CNN, Deep Learning, prediction, SNR.

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Authors: Muhammad Nizam, Azah Mohamed, Majid Al-Dabbagh, Aini Hussain

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This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.

Keywords: Dynamic voltage collapse, prediction, artificial neural network, support vector machines

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Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: Decision tree, genetic algorithm, machine learning, software defect prediction.

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Authors: A. Nicknam, N. Eftekhari, A. Mazarei, M. Ganjvar

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This article presents an alternative collapse capacity intensity measure in the three elements form which is influenced by the spectral ordinates at periods longer than that of the first mode period at near and far source sites. A parameter, denoted by β, is defined by which the spectral ordinate effects, up to the effective period (2T1), on the intensity measure are taken into account. The methodology permits to meet the hazard-levelled target extreme event in the probabilistic and deterministic forms. A MATLAB code is developed involving OpenSees to calculate the collapse capacities of the 8 archetype RC structures having 2 to 20 stories for regression process. The incremental dynamic analysis (IDA) method is used to calculate the structure’s collapse values accounting for the element stiffness and strength deterioration. The general near field set presented by FEMA is used in a series of performing nonlinear analyses. 8 linear relationships are developed for the 8structutres leading to the correlation coefficient up to 0.93. A collapse capacity near field prediction equation is developed taking into account the results of regression processes obtained from the 8 structures. The proposed prediction equation is validated against a set of actual near field records leading to a good agreement. Implementation of the proposed equation to the four archetype RC structures demonstrated different collapse capacities at near field site compared to those of FEMA. The reasons of differences are believed to be due to accounting for the spectral shape effects.

Keywords: Collapse capacity, fragility analysis, spectral shape effects, IDA method.

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Authors: Yu Bin, Zhang Yan

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The prediction of transmembrane helical segments (TMHs) in membrane proteins is an important field in the bioinformatics research. In this paper, a new method based on discrete wavelet transform (DWT) has been developed to predict the number and location of TMHs in membrane proteins. PDB coded as 1KQG was chosen as an example to describe the prediction of the number and location of TMHs in membrane proteins by using this method. To access the effect of the method, 80 proteins with known 3D-structure from Mptopo database are chosen at random as the test objects (including 325 TMHs), 308 of which can be predicted accurately, the average predicted accuracy is 96.3%. In addition, the above 80 membrane proteins are divided into 13 groups according to their function and type. In particular, the results of the prediction of TMHs of the 13 groups are satisfying.

Keywords: discrete wavelet transform, hydrophobicity, membrane protein, transmembrane helical segments

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

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Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: Bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks.

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Authors: Emad Amar, Tawfik Khattab, Fatma Zada

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Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information about Earthquake Existed throughout history & the Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of the object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.

Keywords: BP neural network, Prediction, RBF neural network.

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Authors: Yiannis G. Smirlis

Abstract:

The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: Data envelopment analysis, interval DEA, efficiency classification, efficiency prediction.

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Authors: Radouane Iqdour, Abdelouhab Zeroual

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The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also performed, and the obtained results show that the neural networks are more efficient and gave the best results.

Keywords: Daily solar radiation, Prediction, MLP neural networks, linear model

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Authors: Nooshin Salari, Viliam Makis, Jane Doe

Abstract:

This paper presents a maintenance policy for a system consisting of two units. Unit 1 is gradually deteriorating and is subject to soft failure. Unit 2 has a general lifetime distribution and is subject to hard failure. Condition of unit 1 of the system is monitored periodically and it is considered as failed when its deterioration level reaches or exceeds a critical level N. At the failure time of unit 2 system is considered as failed, and unit 2 will be correctively replaced by the next inspection epoch. Unit 1 or 2 are preventively replaced when deterioration level of unit 1 or age of unit 2 exceeds the related preventive maintenance (PM) levels. At the time of corrective or preventive replacement of unit 2, there is an opportunity to replace unit 1 if its deterioration level reaches the opportunistic maintenance (OM) level. If unit 2 fails in an inspection interval, system stops operating although unit 1 has not failed. A mathematical model is derived to find the preventive and opportunistic replacement levels for unit 1 and preventive replacement age for unit 2, that minimize the long run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process (SMDP) framework. Numerical example is provided to illustrate the performance of the proposed model and the comparison of the proposed model with an optimal policy without opportunistic maintenance level for unit 1 is carried out.

Keywords: Condition-based maintenance, opportunistic maintenance, preventive maintenance, two-unit system.

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Authors: Raissa A. Muzychkina, Irina M. Korulkina, Dmitriy Yu. Korulkin

Abstract:

In article the data of chronic toxicity for pre-clinical researches of Ramon preparation is described. Ramon effects to hormone system and gastrointestinal tract; local irritative effect, allergic, pyrogenic properties and reaction to the immune system were studied.

Keywords: Cancer, toxicity, antitumor activity, pre-clinical testing, anthraquinones, phytopreparation, Ramon.

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