Search results for: binary vector quantization (BVQ)

Authors: Soumya Sajjan

Abstract:

Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

Procedia PDF Downloads 388

Authors: Maryam S. Ghoraishi, John E. Hawk, Thomas Thundat

Abstract:

Understanding liquid properties in small scale has become important in recent decades as immerging new microelectromechanical systems (MEMS) devices have been widely used for micro pumps, drug delivery, and many other laboratory-on-microchips analysis. Often in microfluidic devices, fluids are transported electrokinetically. Therefore, extensive knowledge of fluid flow, heat transport, electrokinetics and electrochemistry are key to successful lab on a chip design. Among different microfluidic devices, recently developed hollow channel cantilever offers an ideal platform to study different fluid properties simultaneously without drastic decrease in quality factor which normally occurs when traditional cantilevers operate in the liquid phase. Using hollow channel cantilever, we monitor changes in density and viscosity of liquid while simultaneously investigating dielectric properties of alcohol water binary mixtures. Considerable research has been conducted on alcohol-water mixtures since such a mixture is a typical prototype for biomolecules, Micelle formation, and structural stability of proteins (to name a few). Here we show that hollow channel cantilever can be employed to investigate dielectric properties of ethanol/water mixtures in different concentrations. We study dynamic amplitude shifts of hollow channel cantilever oscillation at different concentrations of ethanol/water for different voltages. Our results show how interactions between solute and solvent, and possibly cluster formation, could change dielectric properties and dipole reorientation of the mixture, as well as the resulting force on the hollow cantilever. For comparison, we also examine higher conductivity ionic mixtures of sodium sulfate solution under the same conditions as low conductivity ethanol/water mixtures. We will show the results from systematic investigation of solvent effects on dielectric properties of the binary mixture. We will also address the question of resolution limits in dielectric study of analyte molecules imposed by solvent concentrations.

Keywords: dielectric constant, cantilever sensors, ethanol water mixtures, low frequency

Procedia PDF Downloads 179

Authors: Arindam Chaudhuri

Abstract:

Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

Procedia PDF Downloads 472

Authors: Sobar M. Johari, Ida Putri Anjarsari

Abstract:

In the transmission of monetary instrument towards real sector of the economy, Bank Indonesia as monetary authority has developed Islamic Bank Indonesia Certificate (abbreviated as SBIS) as an instrument in Islamic open market operation. One of the monetary transmission channels could take place through financing channel from which the fund is used as the source of banking financing. This study aims to analyse the impact of Islamic monetary instrument towards output or economic growth. Data used in this research is taken from Bank Indonesia and Central Board of Statistics for the period of January 2008 until December 2015. The study employs Granger Causality Test, Vector Error Correction Model (VECM), Impulse Response Function (IRF) technique and Forecast Error Variance Decomposition (FEVD) as its analytical methods. The results show that, first, the transmission mechanism of banking financing channel are not linked to output. Second, estimation results of VECM show that SBIS, PUAS, and FIN have significant impact in the long term towards output. When there is monetary shock, output or economic growth could be recovered and stabilized in the short term. FEVD results show that Islamic banking financing contributes 1.33 percent to increase economic growth.

Keywords: Islamic monetary instrument, Islamic banking financing channel, economic growth, Vector Error Correction Model (VECM)

Procedia PDF Downloads 253

Authors: Usman A. Usman

Abstract:

Undeniably, unlike the Classical side, the Keynesian perspective of the aggregate demand side indeed has a significant position in the policy, growth, and welfare of Nigeria due to government involvement and ineffective demand of the population living with poor per capita income. This study seeks to investigate the effect of Government Final Consumption Expenditure, Financial Deepening on Households, and NPISHs Final consumption expenditure using data on Nigeria from 1981 to 2019. This study employed the ADF stationarity test, Johansen Cointegration test, and Vector Error Correction Model. The results of the study revealed that the coefficient of Government final consumption expenditure has a positive effect on household consumption expenditure in the long run. There is a long-run and short-run relationship between gross fixed capital formation and household consumption expenditure. The coefficients cpsgdp (financial deepening and gross fixed capital formation posit a negative impact on household final consumption expenditure. The coefficients money supply lm2gdp, which is another proxy for financial deepening, and the coefficient FDI have a positive effect on household final consumption expenditure in the long run. Therefore, this study recommends that Gross fixed capital formation stimulates household consumption expenditure; a legal framework to support investment is a panacea to increasing hoodmold income and consumption and reducing poverty in Nigeria. Therefore, this should be a key central component of policy.

Keywords: government final consumption expenditure, household consumption expenditure, vector error correction model, cointegration

Procedia PDF Downloads 29

Authors: Edokpa Idemudia Waziri, Salisu S. Umar

Abstract:

The Hotellng’s T^2 is a well-known statistic for detecting a shift in the mean vector of a multivariate normal distribution. Control charts based on T have been widely used in statistical process control for monitoring a multivariate process. Although it is a powerful tool, the T statistic is deficient when the shift to be detected in the mean vector of a multivariate process is small and consistent. The Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is one of the control statistics used to overcome the drawback of the Hotellng’s T statistic. In this paper, the probability distribution of the Average Run Length (ARL) of the MEWMA control chart when the quality characteristics exhibit substantial cross correlation and when the process is in-control and out-of-control was derived using the Markov Chain algorithm. The derivation of the probability functions and the moments of the run length distribution were also obtained and they were consistent with some existing results for the in-control and out-of-control situation. By simulation process, the procedure identified a class of ARL for the MEWMA control when the process is in-control and out-of-control. From our study, it was observed that the MEWMA scheme is quite adequate for detecting a small shift and a good way to improve the quality of goods and services in a multivariate situation. It was also observed that as the in-control average run length ARL0¬ or the number of variables (p) increases, the optimum value of the ARL0pt increases asymptotically and as the magnitude of the shift σ increases, the optimal ARLopt decreases. Finally, we use the example from the literature to illustrate our method and demonstrate its efficiency.

Keywords: average run length, markov chain, multivariate exponentially weighted moving average, optimal smoothing parameter

Procedia PDF Downloads 396

Authors: Muziwandile Luthuli

Abstract:

Although numerous factors predicting adherence to antiretroviral therapy (ART) among people living with HIV/AIDS (PLWHA) have been broadly studied on both regional and global level, up-to-date adherence of patients to ART remains an overarching, dynamic and multifaceted problem that needs to be investigated over time and across various contexts. There is a rarity of empirical data in the literature on interactive mechanisms by which psychosocial factors influence adherence to ART among PLWHA within the South African context. Therefore, this study was designed to investigate the relationship between depression, HIV stigma, and adherence to ART among adult patients living with HIV at a tertiary hospital in Durban, South Africa, and the mediating roles of self-efficacy and social support. The health locus of control theory and the social support theory were the underlying theoretical frameworks for this study. Using a cross-sectional research design, a total of 201 male and female adult patients aged between 18-75 years receiving ART at a tertiary hospital in Durban, KwaZulu-Natal were sampled, using time location sampling (TLS). A self-administered questionnaire was employed to collect the data in this study. Data were analysed through SPSS version 27. Several statistical analyses were conducted in this study, namely univariate statistical analysis, correlational analysis, Pearson’s chi-square analysis, cross-tabulation analysis, binary logistic regression analysis, and mediational analysis. Univariate analysis indicated that the sample mean age was 39.28 years (SD=12.115), while most participants were females 71.0% (n=142), never married 74.2% (n=147), and most were also secondary school educated 48.3% (n=97), as well as unemployed 65.7% (n=132). The prevalence rate of participants who had high adherence to ART was 53.7% (n=108), and 46.3% (n=93) of participants had low adherence to ART. Chi-square analysis revealed that employment status was the only statistically significant socio-demographic influence of adherence to ART in this study (χ2 (3) = 8.745; p < .033). Chi-square analysis showed that there was a statistically significant difference found between depression and adherence to ART (χ2 (4) = 16.140; p < .003), while between HIV stigma and adherence to ART, no statistically significant difference was found (χ2 (1) = .323; p >.570). Binary logistic regression indicated that depression was statistically associated with adherence to ART (OR= .853; 95% CI, .789–.922, P < 001), while the association between self-efficacy and adherence to ART was statistically significant (OR= 1.04; 95% CI, 1.001– 1.078, P < .045) after controlling for the effect of depression. However, the findings showed that the effect of depression on adherence to ART was not significantly mediated by self-efficacy (Sobel test for indirect effect, Z= 1.01, P > 0.31). Binary logistic regression showed that the effect of HIV stigma on adherence to ART was not statistically significant (OR= .980; 95% CI, .937– 1.025, P > .374), but the effect of social support on adherence to ART was statistically significant, only after the effect of HIV stigma was controlled for (OR= 1.017; 95% CI, 1.000– 1.035, P < .046). This study promotes behavioral and social change effected through evidence-based interventions by emphasizing the need for additional research that investigates the interactive mechanisms by which psychosocial factors influence adherence to ART. Depression is a significant predictor of adherence to ART. Thus, to alleviate the psychosocial impact of depression on adherence to ART, effective interventions must be devised, along with special consideration of self-efficacy and social support. Therefore, this study is helpful in informing and effecting change in health policy and healthcare services through its findings

Keywords: ART adherence, depression, HIV/AIDS, PLWHA

Procedia PDF Downloads 163

Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

Abstract:

Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

Procedia PDF Downloads 58

Authors: Usman A. Usman

Abstract:

Undeniably, unlike the Classical side, the Keynesian perspective of the aggregate demand side indeed has a significant position in the policy, growth, and welfare of Nigeria due to government involvement and ineffective demand of the population living with poor per capita income. This study seeks to investigate the effect of Government Final Consumption Expenditure, Financial Deepening on Households, and NPISHs Final consumption expenditure using data on Nigeria from 1981 to 2019. This study employed the ADF stationarity test, Johansen Cointegration test, and Vector Error Correction Model. The results of the study revealed that the coefficient of Government final consumption expenditure has a positive effect on household consumption expenditure in the long run. There is a long-run and short-run relationship between gross fixed capital formation and household consumption expenditure. The coefficients cpsgdp financial deepening and gross fixed capital formation posit a negative impact on household final consumption expenditure. The coefficients money supply lm2gdp, which is another proxy for financial deepening, and the coefficient FDI have a positive effect on household final consumption expenditure in the long run. Therefore, this study recommends that Gross fixed capital formation stimulates household consumption expenditure; a legal framework to support investment is a panacea to increasing hoodmold income and consumption and reducing poverty in Nigeria. Therefore, this should be a key central component of policy.

Keywords: household, government expenditures, vector error correction model, johansen test

Procedia PDF Downloads 36

Authors: Cherifi Mehdi, Lahdir Mourad, Ameur Soltane

Abstract:

Image compression is used to reduce the number of bits required to represent an image. The Meteosat Second Generation satellite (MSG) allows the acquisition of 12 image files every 15 minutes. Which results a large databases sizes. The transform selected in the images compression should contribute to reduce the data representing the images. The Radon transform retrieves the Radon points that represent the sum of the pixels in a given angle for each direction. Linear predictive coding (LPC) with filtering provides a good decorrelation of Radon points using a Predictor constitute by the Symmetric Nearest Neighbor filter (SNN) coefficients, which result losses during decompression. Finally, Run Length Coding (RLC) gives us a high and fixed compression ratio regardless of the input image. In this paper, a novel image compression method based on the Radon transform and linear predictive coding (LPC) for MSG images is proposed. MSG image compression based on the Radon transform and the LPC provides a good compromise between compression and quality of reconstruction. A comparison of our method with other whose two based on DCT and one on DWT bi-orthogonal filtering is evaluated to show the power of the Radon transform in its resistibility against the quantization noise and to evaluate the performance of our method. Evaluation criteria like PSNR and the compression ratio allows showing the efficiency of our method of compression.

Keywords: image compression, radon transform, linear predictive coding (LPC), run lengthcoding (RLC), meteosat second generation (MSG)

Procedia PDF Downloads 392

Authors: Javed Mallick

Abstract:

In metropolitan areas, rapid changes in land use and land cover (LULC) have ecological and environmental consequences. Saudi Arabia's cities have experienced tremendous urban growth since the 1990s, resulting in urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, and so on. From 1990 to 2020, this study examines the variance and heterogeneity in land surface temperature (LST) caused by LULC changes in Abha-Khamis Mushyet, Saudi Arabia. LULC was mapped using the support vector machine (SVM). The mono-window algorithm was used to calculate the land surface temperature (LST). To identify LST clusters, the local indicator of spatial associations (LISA) model was applied to spatiotemporal LST maps. In addition, the parallel coordinate (PCP) method was used to investigate the relationship between LST clusters and urban biophysical variables as a proxy for LULC. According to LULC maps, urban areas increased by more than 330% between 1990 and 2018. Between 1990 and 2018, built-up areas had an 83.6% transitional probability. Furthermore, between 1990 and 2020, vegetation and agricultural land were converted into built-up areas at a rate of 17.9% and 21.8%, respectively. Uneven LULC changes in built-up areas result in more LST hotspots. LST hotspots were associated with high NDBI but not NDWI or NDVI. This study could assist policymakers in developing mitigation strategies for urban heat islands

Keywords: land use land cover mapping, land surface temperature, support vector machine, LISA model, parallel coordinate plot

Procedia PDF Downloads 54

Authors: Katyana A. Vert-Pre, James T. Thorson, Thomas Trancart, Eric Feunteun

Abstract:

In marine ecosystems, spatial and temporal species structure is an important component of ecosystems’ response to anthropological and environmental factors. Although spatial distribution patterns and fish temporal series of abundance have been studied in the past, little research has been allocated to the joint dynamic spatio-temporal functional patterns in marine ecosystems and their use in multispecies management and conservation. Each species represents a function to the ecosystem, and the distribution of these species might not be random. A heterogeneous functional distribution will lead to a more resilient ecosystem to external factors. Applying a Vector-Autoregressive Spatio-Temporal (VAST) model for count data, we estimate the spatio-temporal distribution, shift in time, and abundance of 140 species of the Eastern English Chanel, Bay of Biscay and Mediterranean Sea. From the model outputs, we determined spatio-temporal clusters, calculating p-values for hierarchical clustering via multiscale bootstrap resampling. Then, we designed a functional map given the defined cluster. We found that the species distribution within the ecosystem was not random. Indeed, species evolved in space and time in clusters. Moreover, these clusters remained similar over time deriving from the fact that species of a same cluster often shifted in sync, keeping the overall structure of the ecosystem similar overtime. Knowing the co-existing species within these clusters could help with predicting data-poor species distribution and abundance. Further analysis is being performed to assess the ecological functions represented in each cluster.

Keywords: cluster distribution shift, European marine ecosystems, functional distribution, spatio-temporal model

Procedia PDF Downloads 173

Authors: Risikat Oladoyin S. Dauda, Onyebuchi Iwegbu

Abstract:

This study investigates the response of human development outcomes to selected macroeconomic indicators in Nigeria. Human development outcomes is measured by human development index while the selected macroeconomic variables are inflation rate, real interest rate, government capital expenditure, real exchange rate, current account balance, and savings. Structural Vector Autoregression (SVAR) technique is employed in examining the response of human development index to the macroeconomic shocks. The result from the forecast error variance decomposition and Impulse-Response analysis reveals that fiscal policy (government capital expenditure) shock is the greatest determinant of human development outcomes. This result reiterates the role which the government plays in improving the welfare of the citizenry. The fiscal policy tool is pivotal in human development which comes in the form of investment in education, health, housing, and infrastructure. Further conclusion drawn from this study is that human development outcome positively and significantly responds to shocks from real interest rate, a monetary policy transmission variable and is felt greatly in the short run period. The policy implication of this study is that if capital budget implementation falls below expectations, human development will be engendered. Hence, efforts should be made to ensure that full implementation and appraisal of government capital expenditure is taken sacrosanct as any shock from such plan, engenders human development outcome.

Keywords: human development outcome, macroeconomic outcomes, structural vector autoregression, SVAR

Procedia PDF Downloads 136

Authors: C. Q. Geng, Y. K. Hsiao

Abstract:

We study direct CP-violating asymmetries (CPAs) in the baryonic B decays of B- -> p\bar{p}M and Λb decays of Λb ®pM andΛb -> J/ΨpM with M=π-, K-,ρ-,K*- based on the generalized factorization method in the standard model (SM). In particular, we show that the CPAs in the vector modes of B-®p\bar{p}K* and Λb -> p K*- can be as large as 20%. We also discuss the simplest purely baryonic decays of Λb-> p\bar{p}n, p\bar{p}Λ, Λ\bar{p}Λ, and Λ\bar{Λ}Λ. We point out that some of CPAs are promising to be measured by the current as well as future B facilities.

Keywords: CP violation, B decays, baryonic decays, Λb decays

Procedia PDF Downloads 240

Authors: Vijaya K. Srivastava, Davide Spinello

Abstract:

This paper presents performance of two robust gradient-based heuristic optimization procedures based on 3ⁿ enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3ⁿ integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results.

Keywords: constrained integer problems, enumerative search algorithm, Heuristic algorithm, Tunneling algorithm

Procedia PDF Downloads 306

Authors: Mogale T. E., Ayisi K. K., Munjonji L., Kifle Y. G.

Abstract:

Grain sorghum and cowpea are important staple crops in many areas of South Africa, particularly the Limpopo Province. The two crops are produced under a wide range of unsustainable conventional methods, which reduces productivity in the long run. Climate-smart traditional methods such as intercropping can be adopted to ensure sustainable production of these important two crops in the province. A no-tillage field experiment was laid out in a randomised complete block design (RCBD) with four replications over two seasons in two distinct agro-ecological zones, Syferkuil and Ofcolacoin, the province to assess the productivity of sorghum-cowpea intercropped under two cowpea densities.LCi Ultra compact photosynthesis machine was used to collect photosynthetic rate data biweekly between 11h00 and 13h00 until physiological maturity. Biomass and grain yield of the component crops in binary and sole cultures were determined at harvest maturity from middle rows of 2.7 m2 area. The biomass was oven dried in the laboratory at 65oC till constant weight. To obtain grain yield, harvested sorghum heads and cowpea pods were threshed, cleaned, and weighed. Harvest index (HI) and land equivalent ratio (LER) of the two crops were calculated to assess intercrop productivity relative to sole cultures. Data was analysed using the statistical analysis software system (SAS) 9.4 version, followed by mean separation using the least significant difference method. The photosyntheticrate of sorghum-cowpea intercrop was influenced by cowpea density and sorghum cultivar. Photosynthetic rate under low density was higher compared to high density, but this was dependent on the growing conditions. Dry biomass accumulation, grain yield, and harvest index differed among the sorghum cultivars and cowpea in both binary and sole cultures at the two test locations during the 2018/19 and 2020/21 growing seasons. Cowpea grain and dry biomass yields werein excess of 60% under high density compared to low density in both binary and sole cultures. The results revealed that grain yield accumulation of sorghum cultivars was influenced by the density of the companion cowpea crop as well as the production season. For instant, at Syferkuil, Enforcer and Ns5511 accumulated high yield under low density, whereas, at Ofcolaco, the higher yield was recorded under high density. Generally, under low cowpea density, cultivar Enforcer produced relatively higher grain yield whereas, under higher density, Titan yield was superior. The partial and total LER varied with growing season and the treatments studied. The total LERs exceeded 1.0 at the two locations across seasons, ranging from 1.3 to 1.8. From the results, it can be concluded that resources were used more efficiently in sorghum-cowpea intercrop at both Syferkuil and Ofcolaco. Furthermore, intercropping system improved photosynthetic rate, grain yield, and dry matter accumulation of sorghum and cowpea depending on growing conditions and density of cowpea. Hence, the sorghum-cowpea intercropping system can be adopted as a climate-smart practice for sustainable production in the Limpopo province.

Keywords: cowpea, climate-smart, grain sorghum, intercropping

Procedia PDF Downloads 189

Authors: Jose Francisco Ferreira Ribeiro, Alexandre Bevilacqua Leoneti, Andre Lucirton Costa

Abstract:

This paper presents a mathematical model in binary variables 0/1 to make the assignment of surgical procedures to the operating rooms in a hospital. The proposed mathematical model is based on the generalized assignment problem, which maximizes the sum of preferences for the use of the operating rooms by doctors, respecting the time available in each room. The corresponding program was written in Visual Basic of Microsoft Excel, and tested to schedule surgeries at St. Lydia Hospital in Ribeirao Preto, Brazil.

Keywords: generalized assignment problem, logistics, optimization, scheduling

Procedia PDF Downloads 266

Authors: Tooraj Karimi, Mohammadreza Sadeghi Moghadam

Abstract:

Grey set theory has the advantage of using fewer data to analyze many factors, and it is therefore more appropriate for system study rather than traditional statistical regression which require massive data, normal distribution in the data and few variant factors. So, in this paper grey clustering and entropy of coefficient vector of grey evaluations are used to analyze energy consumption in buildings of the Oil Ministry in Tehran. In fact, this article intends to analyze the results of energy audit reports and defines most favorable characteristics of system, which is energy consumption of buildings, and most favorable factors affecting these characteristics in order to modify and improve them. According to the results of the model, ‘the real Building Load Coefficient’ has been selected as the most important system characteristic and ‘uncontrolled area of the building’ has been diagnosed as the most favorable factor which has the greatest effect on energy consumption of building. Grey clustering in this study has been used for two purposes: First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, grey clustering with variable weights has been used to classify all buildings in three categories named ‘no standard deviation’, ‘low standard deviation’ and ‘non- standard’. Entropy of coefficient vector of Grey evaluations is calculated to investigate greyness of results. It shows that among the 38 buildings surveyed in terms of energy consumption, 3 cases are in standard group, 24 cases are in ‘low standard deviation’ group and 11 buildings are completely non-standard. In addition, clustering greyness of 13 buildings is less than 0.5 and average uncertainly of clustering results is 66%.

Keywords: energy audit, grey set theory, grey incidence matrixes, grey clustering, Iran oil ministry

Procedia PDF Downloads 353

Authors: M. Govindarajan

Abstract:

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

Procedia PDF Downloads 230

Authors: Ahmed A. Abdel-Khalek, Reham A. Mohamed

Abstract:

The kinetics of oxidation of binary complex [CrIII(DMG)2(H2O)4 ]+ to Cr(VI) by periodate has been investigated spectrophotometrically where, [DMG= Dimethylglyoxime] at 370nm under pseudo first order reaction conditions in aqueous medium over 20- 40ºC range, PH 2-3, and I=0.07 mol dm-3. The reaction is first order with respect to both [IO4-] and Cr(III), and the reaction increased with PH increased. Thermodymanic activation parameters have been calculated. It is suggested that electron transfer proceeds through an inner sphere mechanism via coordination of IO4- to Cr (III). The reaction obeys the following rate law Rate= {k1 K5+ k2 K6 K2 } [Cr III (DMG)2(H2O)4 ]+ [H5IO6].

Keywords: chromium, dimethylglyoxime, kinetics, oxidation, periodate

Procedia PDF Downloads 394

Authors: Mohammad Reza Shojaei, Mahdeih Mirzaeinia

Abstract:

We have studied the cluster structure of even-even stable isotopes of Ge and Sr. The Schrodinger equation has been solved using the generalized parametric Nikiforov-Uvarov method with a phenomenological potential. This potential is the sum of the attractive Yukawa-like potential, a Manning-Rosen-type potential, and the repulsive Yukawa potential for interaction between the cluster and the core. We have shown that the available experimental data of the first rotational band energies can be well described by assuming a binary system of the α cluster and the core and using an analytical solution. Our results were consistent with experimental values. Hence, this model can be applied to study the other even-even isotopes

Keywords: cluser model, NU method, ge and Sr, potential central

Procedia PDF Downloads 51

Authors: Zhuomin Zou, Thijs Van Landeghem, Elke Gruyaert

Abstract:

Using supplementary cementitious materials, such as blast-furnace slag and limestone, to replace cement clinker is a promising method to reduce the carbon emissions from cement production. To efficiently use slag and limestone, it is necessary to carefully select the particle size distribution (PSD) of the binders. This study investigated the effects of the PSD of binders on the performance of slag-limestone ternary cement. The Portland cement (PC) was prepared by grinding 95% clinker + 5% gypsum. Based on the PSD parameters of the binders, three types of ternary cements with a similar overall PSD were designed, i.e., NO.1 fine slag, medium PC, and coarse limestone; NO.2 fine limestone, medium PC, and coarse slag; NO.3. fine PC, medium slag, and coarse limestone. The binder contents in the ternary cements were (a) 50 % PC, 40 % slag, and 10 % limestone (called high cement group) or (b) 35 % PC, 55 % slag, and 10 % limestone (called low cement group). The pure PC and binary cement with 50% slag and 50% PC prepared with the same binders as the ternary cement were considered as reference cements. All these cements were used to investigate the mortar performance in terms of workability, strength at 2, 7, 28, and 90 days, carbonation resistance, and non-steady state chloride migration resistance at 28 and 56 days. Results show that blending medium PC with fine slag could exhibit comparable performance to blending fine PC with medium/coarse slag in binary cement. For the three ternary cements in the high cement group, ternary cement with fine limestone (NO.2) shows the lowest strength, carbonation, and chloride migration performance. Ternary cements with fine slag (NO.1) and with fine PC (NO.3) show the highest flexural strength at early and late ages, respectively. In addition, compared with ternary cement with fine PC (NO.3), ternary cement with fine slag (NO.1) has a similar carbonation resistance and a better chloride migration resistance. For the low cement group, three ternary cements have a similar flexural and compressive strength before 7 days. After 28 days, ternary cement with fine limestone (NO.2) shows the highest flexural strength while fine PC (NO.3) has the highest compressive strength. In addition, ternary cement with fine slag (NO.1) shows a better chloride migration resistance but a lower carbonation resistance compared with the other two ternary cements. Moreover, the durability performance of ternary cement with fine PC (NO.3) is better than that of fine limestone (NO.2).

Keywords: limestone, particle size distribution, slag, ternary cement

Procedia PDF Downloads 107

Authors: Moquan Gong

Abstract:

In recent years, with the rapid development of deep learning, pre-training technology for natural language processing has made great progress. The early field of natural language processing has long used word vector methods such as Word2Vec to encode text. These word vector methods can also be regarded as static pre-training techniques. However, this context-free text representation brings very limited improvement to subsequent natural language processing tasks and cannot solve the problem of word polysemy. ELMo proposes a context-sensitive text representation method that can effectively handle polysemy problems. Since then, pre-training language models such as GPT and BERT have been proposed one after another. Among them, the BERT model has significantly improved its performance on many typical downstream tasks, greatly promoting the technological development in the field of natural language processing, and has since entered the field of natural language processing. The era of dynamic pre-training technology. Since then, a large number of pre-trained language models based on BERT and XLNet have continued to emerge, and pre-training technology has become an indispensable mainstream technology in the field of natural language processing. This article first gives an overview of pre-training technology and its development history, and introduces in detail the classic pre-training technology in the field of natural language processing, including early static pre-training technology and classic dynamic pre-training technology; and then briefly sorts out a series of enlightening technologies. Pre-training technology, including improved models based on BERT and XLNet; on this basis, analyze the problems faced by current pre-training technology research; finally, look forward to the future development trend of pre-training technology.

Keywords: natural language processing, pre-training, language model, word vectors

Procedia PDF Downloads 31

Authors: Kudzanayi Chiteka, Wellington Makondo

Abstract:

The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

Procedia PDF Downloads 254

Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

Abstract:

This paper presents an approach for the easy creation of an institutional risk profile for endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support risk factors set up with just the most important values that are important for a particular organisation. Subsequently, the risk profile employs fuzzy models and associated configurations for the file format metadata aggregator to support digital preservation experts with a semi-automatic estimation of endangerment level for file formats. Our goal is to make use of a domain expert knowledge base aggregated from a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation and analysis of risk factors for a requried dimension. The proposed methods improve the visibility of risk factor information and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and automatically aggregated file format metadata from linked open data sources. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: digital information management, file format, endangerment analysis, fuzzy models

Procedia PDF Downloads 387

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

Procedia PDF Downloads 14

Authors: Hamid Merzouk, D. T. Talantikite, H. Haddad, Amel Tounsi

Abstract:

ZnS is an important II-VI binary compound with large band-gap energy at room temperature. We present in this work preparation and characterization of ZnS and Cu doped ZnS thin films. The depositions are performed by a simple chemical bath deposition route. Structural properties are carried out by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Optical transmittance is investigated by the UV-visible spectroscopy at room temperature.

Keywords: chemical, bath, method, Cu, doped, ZnS, thin, films

Procedia PDF Downloads 532

Authors: N. V. Kuznetsov, O. A. Kuznetsova, G. A. Leonov, M. V. Yuldashev, R. V. Yuldashev

Abstract:

Nonlinear analysis of the phase locked loop (PLL)-based circuits is a challenging task. Thus, the simulation is widely used for their study. In this work, we consider a mathematical model of the optical Costas loop and demonstrate the limitations of simulation approach related to the existence of so-called hidden oscillations in the phase space of the model.

Keywords: optical Costas loop, mathematical model, simulation, hidden oscillation

Procedia PDF Downloads 414

Authors: Amina R. Issae, Godfrey C. Katusi, Beda J. Mwang’Onde, Ladslaus L. Mnyone, Allen L. Malisa

Abstract:

Background: The fluctuating composition of mosquito species over time, driven by ecological changes in specific regions, plays a pivotal role in the transmission of malaria. Grasping these dynamics is fundamental for establishing a baseline understanding and is crucial for identifying transmission patterns. This knowledge is essential in devising effective strategies for managing and controlling vector populations. Our study focused on examining the species composition and Plasmodium infection rates of malaria vectors, aiming to enhance the health and well-being of communities affected by malaria. Methods: Species composition was determined through a cross-sectional collection of mosquitoes, conducted once in the village, in four selected villages of Kilosa district, Tanzania. Mosquitoes were collected indoors and outdoors using CDC light traps. A sub-sample of all collected mosquitoes was subjected to PCR identification and assayed for Plasmodium porozoites. Results: A total of 6493 female Anophelines mosquitoes were collected, of which eight species were identified as Anopheles gambiaes.l., An. funestus group, An. coustani, An. pharoensis, An. squamosus, and An. rufipes. The abundance of the Anopheles gambiaes.s.and An. funestuss.s. varied with location and village. A total of 5 sporozoite-positive mosquitoes were found, of which 4 were An. funestuss.s. and 1 was An. gambiaes.s. Conclusions: Anopheles gambiaes.s.and An. funestuss.s. were identified as the most abundant malaria vectors, respectively. Sporozoite analysis indicated this for An. funestuss.s. contribute to most of the malaria transmission in the area. Further studies are required to assess the role of seasonal shifts in vector abundance, insecticide resistance and malaria transmission of the vectors.

Keywords: mosquito, composition, malaria, sporozoites

Procedia PDF Downloads 20

Authors: Marcin Michalak

Abstract:

Biclustering is the way of two-dimensional data analysis. For several years it became possible to express such issue in terms of Boolean reasoning, for processing continuous, discrete and binary data. The mathematical backgrounds of such approach — proved ability of induction of exact and inclusion–maximal biclusters fulfilling assumed criteria — are strong advantages of the method. Unfortunately, the core of the method has quite high computational complexity. In the paper the basics of Boolean reasoning approach for biclustering are presented. In such context the problems of computation parallelization are risen.

Keywords: Boolean reasoning, biclustering, parallelization, prime implicant

Procedia PDF Downloads 102