Set Of Threshold Values Research Articles

On nonparametric estimation of the latent distribution for ordinal data

Ordinal data collected in surveys often consist of numerical scores that have a natural ordering. Observed values of ordinal variables can be thought of as a manifestation of an underlying continuous latent variable that is related to the observed ordinal variable through a set of threshold values or “cut-points”. The “cut-points” partition the latent variable into intervals corresponding to the observed levels of the ordinal variable. This latent distribution is of interest to researchers for purposes of descriptive statistics and statistical modeling. However, restrictive parametric assumptions about the latent distribution are often not adequate. A nonparametric model based on mixtures of scaled Beta distributions is presented and estimation is carried out using a version of Anderson–Darling statistic-based criteria which is shown to be computationally more efficient than likelihood based criteria. A Monte Carlo simulation shows that the proposed model and estimation method performs well and is robust against any underlying continuous distribution. Several empirical examples based on ordinal data from the household section of the Agricultural Resource Management Survey (ARMS) illustrate the versatility and adaptability of the method in practice.

Research on energy stock market associated network structure based on financial indicators

A financial market is a complex system consisting of many interacting units. In general, due to the various types of information exchange within the industry, there is a relationship between the stocks that can reveal their clear structural characteristics. Complex network methods are powerful tools for studying the internal structure and function of the stock market, which allows us to better understand the stock market. Applying complex network methodology, a stock associated network model based on financial indicators is created. Accordingly, we set threshold value and use modularity to detect the community network, and we analyze the network structure and community cluster characteristics of different threshold situations. The study finds that the threshold value of 0.7 is the abrupt change point of the network. At the same time, as the threshold value increases, the independence of the community strengthens. This study provides a method of researching stock market based on the financial indicators, exploring the structural similarity of financial indicators of stocks. Also, it provides guidance for investment and corporate financial management.

Tangential velocity during impact with friction in three-dimensional rigid multibody systems

This paper introduces an analysis of tangential velocity of the contact point during a single-point impact with friction in three-dimensional rigid multibody systems. Considering the normal component of impulse at the contact point as the independent variable, a system of nonlinear differential equations describing the progress of the tangential velocity is derived. Quartic algebraic equations for the invariant and the flow change directions are obtained. The system of differential equations could have a discontinuity problem if the tangential velocity instantaneously vanishes. Effectively, this problem is mathematically solved and the new sliding direction is specified. During impact, the sliding direction could continuously change or the sliding could stop and the non-sliding continues or the sliding could restart along a new direction. The stick-slip effect could also have its influence. Two friction models are used, and an appropriate numerical procedure is suggested that can handle all the possible sliding scenarios. To verify the proposed method, the numerical solution is compared to the analytical solution for a special case of a homogeneous ellipsoid that collides with a horizontal fixed rough surface. A case study of a four-degrees-of-freedom spatial robot that collides with its environment is considered. The bifurcation associated with the variation of the kinetic coefficient of friction is investigated, and a set of threshold values of it are determined. Some phase portraits are drawn, and the invariant directions and flow change directions are drawn for all portraits. Each portrait represents all the flow trajectories of the tangential velocity for different initial conditions with a specific kinetic coefficient of friction. This qualitative analysis not only helps in understanding the behavior of multibody systems in such situations but also it gives insight to the physical phenomenon of impact itself.

Development of Personalized Urination Recognition Technology Using Smart Bands

PurposeThis study collected and analyzed activity data sensed through smart bands worn by patients in order to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination activity in patients, this study aimed to explore the feasibility of urination monitoring systems.MethodsThis study aimed to develop an algorithm that recognizes urination based on a patient’s posture and changes in posture. Motion data was obtained from a smart band on the arm. An algorithm that recognizes the 3 stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and from tilt angle data. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the absolute value of the signals was calculated and then compared with the set threshold value to determine the occurrence of vibration signals. In feature extraction, the most essential information describing each pattern was identified after analyzing the characteristics of the data. The results of the feature extraction process were sorted using a classifier to detect urination.ResultsAn experiment was carried out to assess the performance of the recognition technology proposed in this study. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 90.4%, proving the robustness of the proposed algorithm.ConclusionsThe proposed urination recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after comparative analyses with standardized feature patterns.

The Microbiome of the Prostate Tumor Microenvironment

BackgroundThe advent of molecular-based methods of identification and characterization of complex microbial populations has led to a new era of microbial discovery. A detailed and comprehensive analysis of the microbial ecosystem of the pathologic and healthy prostate tissues has not been yet reported. ObjectivesTo characterize the microbiome possibly associated to the pathologic prostate microenvironment. Design, setting, and participantsThe microbiome profile of tumor, peri-tumor, and nontumor tissues was assessed on 16 radical prostatectomy-specimens. Outcome measurements and statistical analysisMicrobiome analysis was assessed by massive ultradeep pyrosequencing. Bacteria load was expressed as a percentage of the total number of bacteria. The statistical significance of differences among specimen-groups was tested with Friedman's test (Dunn posthoc test) and Wilcoxon rank-sum test. Results and limitationsThree phyla, six classes, nine orders, 14 families, and 11 genera were above the set threshold value of 1%, respectively. Significant differences in specific microbial populations among tumor/peri-tumor and nontumor prostate specimens were observed at certain taxonomic levels. Among genera, Propionibacterium spp. were the most abundant. Staphylococcus spp. were more represented in the tumor/peri-tumor tissues (p<0.05). The restricted number of specimens represents a potential limitation. ConclusionsThe prostate contains a plethora of bacteria, which set themselves within the gland with a distribution dependent on the nature of the tissue, thus suggesting a possible pathophysiological correlation between the composition of the local microbial niche and the presence of the tumor itself. Future studies will help to clarify the role of these specific bacteria and their potential to be exploited as new biomarkers. Patient summaryThe pathological prostate is populated by specific microbial populations, whose distribution varies according to the nature of the tissue. This finding opens interesting perspectives for the identification of novel therapeutic approaches and biomarkers.

GEMAS: Establishing geochemical background and threshold for 53 chemical elements in European agricultural soil

The GEMAS (geochemical mapping of agricultural soil) project collected 2108 Ap horizon soil samples from regularly ploughed fields in 33 European countries, covering 5.6 million km2. The <2 mm fraction of these samples was analysed for 53 elements by ICP-MS and ICP-AES, following a HNO3/HCl/H2O (modified aqua regia) digestion. Results are used here to establish the geochemical background variation and threshold values, derived statistically from the data set, in order to identify unusually high element concentrations for these elements in the Ap samples. Potentially toxic elements (PTEs), namely Ag, B, As, Ba, Bi, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sb, Se, Sn, U, V and Zn, and emerging ‘high-tech’ critical elements (HTCEs), i.e., lanthanides (e.g., Ce, La), Be, Ga, Ge, In, Li and Tl, are of particular interest. For the latter, neither geochemical background nor threshold at the European scale has been established before. Large differences in the spatial distribution of many elements are observed between northern and southern Europe. It was thus necessary to establish three different sets of geochemical threshold values, one for the whole of Europe, a second for northern and a third for southern Europe. These values were then compared to existing soil guideline values for (eco)toxicological effects of these elements, as defined by various European authorities. The regional sample distribution with concentrations above the threshold values is studied, based on the GEMAS data set, following different methods of determination. Occasionally local contamination sources (e.g., cities, metal smelters, power plants, agriculture) can be identified. No indications could be detected at the continental scale for a significant impact of diffuse contamination on the regional distribution of element concentrations in the European agricultural soil samples. At this European scale, the variation in the natural background concentration of all investigated elements in the agricultural soil samples is much larger than any anthropogenic impact.

Single-Nucleotide Polymorphisms in the Whole-Genome Sequence Data of Shiga Toxin-Producing Escherichia coli O157:H7/H- Strains by Cultivation.

Nine Shiga toxin-producing Escherichia coli O157:H7/H- (O157) strains were serially cultured three times on LB agar plates. After each sub-culture, five colonies were picked for DNA isolation and whole genome sequence (WGS) analysis. After exclusion of possible recombination-related SNPs, 11, 9, and 34 single-nucleotide polymorphisms (SNPs) were detected in genes in the backbone, O-island, and mobile elements gene categories. This suggested that those SNPs due to cultivation could influence the threshold value set for molecular epidemiological studies of O157. Significant differences were observed by the Kruskal-Wallis test (P < 0.01) when the number of the SNPs in a strain was compared to that in other strains. This indicated that a specific number of strains could be used for setting the threshold value in molecular epidemiological studies. Due to cultivation, the SNPs were also detected in genes in a few core genome or core gene sets, suggesting that those SNPs could affect studies of phylogeny as well as molecular epidemiology. To improve the accuracy of phylogenetic and molecular epidemiological studies, genes in which the SNPs have arisen due to cultivation should be excluded from WGS data.

Is it still worthwhile to perform quarterly cd4+ t lymphocyte cell counts on hiv-1 infected stable patients?

BackgroundIn the last 20 years routine T CD4+ lymphocyte (CD4+) cell count has proved to be a key factor to determine the stage of HIV infection and start or discontinue of prophylaxis for opportunistic infections. However, several studies recently showed that in stable patients on cART a quarterly CD4+ cell count monitoring results in limited (or null) clinical relevance. The research is intended to investigate whether performing quarterly CD4+ cell counts in stable HIV-1 patients is still recommendable and to provide a forecast of the cost saving that could be achieved by reducing CD4+ monitoring in such a category of patients.MethodsThe study is based on data referring to all HIV-infected patients > 18 years of age being treated at two large infectious diseases units located in the metropolitan area of Genoa, Italy. The probability of CD4+ cell counts dropping below a threshold value set at 350 cells/mm3 is assessed using confidence intervals and Kaplan-Meier survival estimates, whereas multivariate Cox analysis and logistic regression are implemented in order to identify factors associated with CD4+ cell count falls below 350 cells/mm3.ResultsStatistical analysis reveals that among stable patients the probability of maintaining CD4+ >350 cell/mm3 is more than 98%. Econometric models indicate that HCV co-infection and HIV-RNA values >50 copies/mL in previous examinations are associated with CD4+ falls below 350 cells/mm3. Moreover, results suggest that the cost saving that could be obtained by reducing CD4+ examinations ranges from 33 to 67%.ConclusionsEmpirical findings shows that patients defined as stable at enrollment are highly unlikely to experience a CD4+ value <350 cell/mm3 in the space/arc of a year. The research supports a recommendation for annual CD4+ monitoring in stable HIV-1 patients.

Mitigating cadmium accumulation in greenhouse lettuce production using biochar.

Greenhouse experiments were conducted to investigate the influence of rice straw biochar (RSB) on soil cadmium (Cd) availability and accumulation in lettuce. The RSB was applied either in bands or broadcast in the test site of four greenhouses with soil Cd concentrations ranging from 1.70-3.14μgg-1. Biochar doses applied in bands were half of those broadcast. The Cd levels in the shoots of lettuce were observed to be reduced by up to 57% with increasing RSB application rate (0, 6, 12, 18tha-1). Following RSB application, shoot Cd concentrations of lettuce were reduced to below the Chinese threshold value set for food, and hazard quotients for Cd associated with vegetable consumption were reduced from 0.70-1.11 to 0.42-0.65. A decrease in soil bulk density (11%) and increases in water holding capacity (16%), available phosphorus (30%), available potassium (197%), and lettuce yield (15%) were observed after RSB application. Multiple linear regression analysis suggested that the soil extractable Cd level (but not biomass dilution) and soil bulk density, as influenced by RSB addition, were the dominant contributors to the shoot Cd levels in lettuce and lettuce yield, respectively. These results highlight the potential for RSB to mitigate the phytoaccumulation of Cd and thereby to reduce human exposure from vegetable consumption. Application of biochar in band, rather than broadcasting over the entire area, represents an opportunity to halve the biochar cost while retaining a good remediation effect.

Substation Equipment Abnormal Voice Analysis Method Based on Image Processing

An audio signal analysis method using digital image processing technology is proposed, which is used to detect the abnormal signal of substation equipment. First of all, the sound signal acquisition with pickups that robot carrying. Secondly, the waveform of the sound signal is transformed into the image, and the image processing technique is used to process the image, and then the horizontal projection of the waveform image are analyzed. Finally, edge detection and Hough line detection are performed on the horizontal projection, the ratio of area which the line segment is calculated. The ratio of the area compare with the set threshold value, which greater than the threshold value is abnormal signal. The experimental results show that the algorithm can achieve the abnormal analysis of the sound signal, and the algorithm is simple and accurate.

FEATURES OF APPROACHES TO ASSESSING ECONOMIC SECURITY OF REGIONS

Approaches to assessing the economic security of the region used in a particular interweaving are analyzed, in which the basis is often a functional (or complex) approach, which involves the allocation of functional areas (components or aspects) of economic security, each of which reflects a particular aspect of the region's economic system. The basic rules of formation of the set of primary indicators for assessing the economic security of the region are considered: limited number of primary indicators of the model, quantitative (or quasi- quantitative) nature of indicators, information capacity of the indicator, determining the value of the indicator by direct measurement or direct algorithms. An indicator approach is highlighted, through which the economic security of the region is assessed by comparing (absolute or relative) the actual values of indicators with their indicators and the appropriate treatment of the identified deviations. The indicator approach looks very attractive when describing the sequence of techniques, procedures, calculation operations and algorithms used. The main problematic issues in the use of indicator approach are formulated: plurality of sets of indicators, unambiguous limit or threshold values of indicators, lack of rules for selection of indicators, limited application of a systematic approach in forming a set of indicators, setting values of indicators. The use of interval indicators of economic security of the region is revealed, which provide an opportunity to identify some dangerous zone, within which its state is gradually changing; The economic security of the region may be in this danger zone for some time, the duration of which depends on the influence of a number of factors. It is noted that in determining the number of indices for assessing the economic security of the region, two approaches are possible: a small number of indices, each of which evaluates a significant number of aspects of economic security of the region; a significant number of indices, each of which assesses a small number of aspects of the region's economic security.

Experimental study on f-ω regulation model under abnormal methane emission

In view of the difficulty of automatic adjustment, the recovery lag and the major accident potential of the mine ventilation system, an experimental model of the pipe net was established according to the typical one mine and one working face ventilation system of Daliuta coal mine. Using the best uniform approximation method of Chebyshev interpolation to fit the fan performance curve, we experimentally determined fan characteristics with different frequencies and establish the data base for the curves. Based on ventilation network monitoring theory, we designed a monitoring system for ventilation network parameter monitoring and fan operating frequency automatic control. Using the absolute methane emission quantity to predict the air quantity requirement of branch and fan frequency, we established a f-ω regulation model based on fan frequency and absolute methane emission quantity. After analysing methane emission and distribution characteristics, using CO2 to simulate the methane emission characteristics from a working face, we verified the correctness and rationality of the f-ω regulation model. The fan operation frequency is adjusted by the method of air adjustment change with methane emission quantity and the curve searching method after determining air quantity requirements. The results show that the air quantity in a branch strictly changes according to the f-ω regulation model, in the air-increasing dilution by fan frequency regulation, the CO2 concentration is limited to the set threshold value. The paper verifies the practicability of a frequency regulation system and the feasibility of the frequency adjustment scheme and provides guidance for the construction of automatic frequency conversion control system in coal mine ventilation networks.

Improving droplet sizing methodology for spray dynamics investigation

Spray modelling is one of the most useful techniques to characterize engine performance, efficiency and emissions. The size of droplets is one of the key variables that govern the efficiency of combustion of the liquid fuel. This study aims to develop an interactive tool using MATLAB codes that identifies the droplets and their sizes from the image taken with the long distance microscope in the spray chamber setup. In this developed method, firstly the background of the image was removed and then image processing techniques, dilation and erosion, were applied to the image file to refine the image files. Subsequently, circle detection method based on the Hough Transform algorithm with the function of imfindcircles was implemented. This function of the program allows the user to identify size droplets from the image files. A statistical study was conducted with the results automated from the MATLAB program using a different set of threshold values of black and white contrast. The results showed an optimal range for the threshold (black and white) values between 40 and 70. This optimal threshold range was established based on consideration of the correct and incorrect identification of the droplets. The results indicated that the program has the ability to identify the droplet providing size and numbers. The MATLAB program was developed using MATLAB compiler and can be used at different workstations.

Optimum threshold value to segment potato image in hand-held potato grading machine

The rising demands of quality food products these days lead to requirement of high speed machine vision systems which can detect the defects of food products. An accuracy and cost effectiveness are two key features which a good machine vision system should include. Potato is one of the most popular food product consumed by humans and its production is rising day by day. But post-harvest losses are reported very high due to poor packing and grading facilities. So qualitative grading of potatoes in Machine vision system was performed by extracting the best thresholds. This algorithm grade the potato into defected or healthy using entropy feature defined earlier in the literature. This technique can be efficiently used in hand held potato grading machines due to its low complexity as no background removal is required in this technique before grading the potato. The threshold value set in this algorithm is analyzed using various threshold values and optimal value which can grade potato efficiently is used. The potatoes used for testing this algorithm were of chipsona-1 variety and were purchased from local Indian market. This algorithm was found to be efficient for detecting common defects found on potato skin like cracks and rots. Keywords– Potato, Entropy, Texture feature, Segmentation, Machine Vision, Cracks, Rots.

BI-RADS Density Classification From Areometric and Volumetric Automatic Breast Density Measurements

The aim of our study was to classify breast density using areometric and volumetric automatic measurements to best match Breast Imaging-Reporting and Data System (BI-RADS) density scores, and determine which technique best agrees with BI-RADS. Second, this study aimed to provide a set of threshold values for areometric and volumetric density to estimate BI-RADS categories. We randomly selected 537 full-field digital mammography examinations from a population-based screening program. Five radiologists assessed breast density using BI-RADS with all views available. A commercial program calculated areometric and volumetric breast density automatically. We compared automatically calculated density to all BI-RADS density thresholds using area under the receiver operating characteristic curve, and used Youden's index to estimate thresholds in automatic densities, with matching sensitivity and specificity. The 95% confidence intervals were estimated by bootstrapping. Areometric density correlated well with volumetric density (r(2) = 0.76, excluding outliers, n = 2). For the BI-RADS threshold between II and III, areometric and volumetric assessment showed about equal area under the curve (0.94 vs. 0.93). For the threshold between I and II, areometric assessment was better than volumetric assessment (0.91 vs. 0.86). For the threshold between III and IV, volumetric assessment was better than areometric assessment (0.97 vs. 0.92). Volumetric assessment is equal to or better than areometric assessment for the most clinically relevant thresholds (ie, between scattered fibroglandular and heterogeneously dense, and between heterogeneously dense and extremely dense breasts). Thresholds found in this study can be applied in daily practice to automatic measurements of density to estimate BI-RADS classification.

ECG Signal Compression Using the High Frequency Components of Wavelet Transform

Electrocardiography (ECG) is the method of recording electrical activity of the heart by using electrodes. In ambulatory and continuous monitoring of ECG, the data that need to be handled is huge. Hence we require an efficient compression technique. The data also must retain the clinically important features after compression. For most of the signals, the low frequency component is considered as most important part of the signal. In wavelet analysis, the approximation coefficients are the low frequency components of the signal. The detail coefficients are the high frequency components of the signal. Most of the time the detail coefficients (high frequency components) are not considered. In this paper, we propose to use detail coefficients of Wavelet transform for ECG signal compression. The Compression Ratio (CR) of both the approximation and detail coefficients are compared. Threshold based technique is adopted. The Threshold value helps to remove the coefficients below the set threshold value of coefficients. Experiment is carried out using different types of Wavelet transforms. MIT BIH ECG data base is used for experimentation. MATLAB tool is used for simulation purpose. The novelty of the method is that the CR achieved by detail coefficients is better. CR of about 88% is achieved using Sym3 Wavelet. The performance measure of the reconstructed signal is carried out by PRD.

Towards adaptive open learning environments: Evaluating the precision of identifying learning styles by tracking learners’ behaviours

Open learning represents a new form of online learning where courses are provided freely online for large numbers of learners. MOOCs are examples of this form of learning. The authors see an opportunity for personalising open learning environments by adapting to learners' learning styles and providing adaptive support to meet individual learner needs and preferences. Identifying learning styles of learners in open learning environments is crucial to providing adaptive support. Learning styles refer to the manner in which learners receive and perceive information. In the literature, a number of learning style models have been proposed. The Felder and Silverman Learning Styles Model (FSLSM) has been selected as the most appropriate model for open learning. In previous studies two approaches have been used to automatically identify learning styles based on the FSLSM. These approaches are known as the data-driven method and the literature-based method. In the literature, the literature-based method has been shown to be more accurate in identifying learning styles. This method relies on tracking learners' interactions with the provided learning objects based on a set of pre-determined patterns that help in inferring learning styles. The patterns are monitored based on pre-identified threshold values. This paper aims to apply the literature-based method to open learning environments and introduce the optimal patterns and threshold values for identifying learning styles based on the FSLSM. To achieve this aim, a study was conducted whereby a prototype that simulates the open learning environment was developed and piloted on an undergraduate IT course so that learner behaviour could be tracked and data could be collected. Next, different sets of threshold values from the literature were considered along with some updated threshold values considering the context of open learning environments, and the precision of identifying learning styles was calculated. Eighty-three students participated in the study and used the developed prototype. Precision results from different threshold values presented in the literature along with customised threshold values for this study are reported and analysed in this paper. It is shown that threshold values derived from literature and customised to suit open learning environments provide a high level of accuracy in identifying learning styles. The paper presents the first study of its kind in evaluating threshold values and precision in identifying learning styles based on the FSLSM in open learning environments. The results are promising and indicate that the proposed methodology is efficient in detecting learning styles in open learning environments and useful for developing an adaptive framework.

Brain MRI segmentation for tumor detection via entropy maximization using Grammatical Swarm

This paper presents a new method for the segmentation of Magnetic Resonance Imaging (MRI) of brain tumor. First, discrete wavelet transform (DWT)-based soft-thresholding technique is used for removing noise in the MRI. Second, intensity inhomogeneity (IIH) independent of noise is removed from the MRI image. Third, again DWT is used to sharpen the de-noised and IIH corrected image. In this method, the image is decomposed into first level using wavelet decomposition and approximate values are assigned to zero and reconstruct the image results in detailed image. The detailed image is added with the pre-processed image to produce sharpened image. Entropy maximization using Grammatical Swarm (GS) algorithm is used to obtain a set of threshold values and a threshold value is selected with the expert knowledge to separate the lesion part from the other non-diseased cells in the image.

Inter-comparison of the infrared channels of the meteorological imager onboard COMS and hyperspectral IASI data

The successful launch and commissioning of the first geostationary meteorological satellite of Korea has the potential to enhance earth observation capability over the Asia Pacific region. Although the specifications of the payload, the meteorological imager (MI), have been verified during both ground and in-orbit tests, there is the possibility of variation and/or degradation of data quality due to many different reasons, such as the accumulation of contaminants, the aging of instrument components, and unexpected external disturbance. Thus, for better utilization of MI data, it is imperative to continuously monitor and maintain the data quality. As a part of such activity, this study presents an inter-calibration, based on the Global Space-based Inter-Calibration System (GSICS), between the MI data and the high quality hyperspectral data from the Infrared Atmospheric Sounding Interferometer (IASI) of the Metop-A satellite. Both sets of data, acquired for three years from April 2011 to March 2014, are processed to prepare the matchup dataset, which is spatially collocated, temporally concurrent, angularly coincident, and spectrally comparable. The results show that the MI data are stable within the specifications and show no significant degradation during the study period. However, the water vapor channel shows a rather large bias value of −0.77 K, with a root-mean-square difference (RMSD) of around 1.1 K, which is thought to be due to the shift in the spectral response function. The shortwave channel shows a maximum RMSD of around 1.39 K, mainly due to the coarse digitization at the lower temperature. The inter-comparison results are re-checked through a sensitivity analysis with different sets of threshold values used for the matchup dataset. Based on this, we confirm that the overall quality of the MI data meets the user requirements and maintains the expected performance, although the water vapor channel requires further investigation.

Improving segmentation velocity using an evolutionary method

Image segmentation plays an important role in image processing and computer vision. It is often used to classify an image into separate regions, which ideally correspond to different real-world objects. Several segmentation methods have been proposed in the literature, being thresholding techniques the most popular. In such techniques, it is selected a set of proper threshold values that optimize a determined functional criterion, so that each pixel is assigned to a determined class according to its corresponding threshold points. One interesting functional criterion is the Tsallis entropy, which gives excellent results in bi-level thresholding. However, when it is applied to multilevel thresholding, its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. Therefore, in the process of finding the appropriate threshold values, it is desired to limit the number of evaluations of the objective function (Tsallis entropy). Under such circumstances, most of the optimization algorithms do not seem to be suited to face such problems as they usually require many evaluations before delivering an acceptable result. On the other hand, the Electromagnetism-Like algorithm is an evolutionary optimization approach which emulates the attraction–repulsion mechanism among charges for evolving the individuals of a population. This technique exhibits interesting search capabilities whereas maintains a low number of function evaluations. In this paper, a new algorithm for multilevel segmentation based on the Electromagnetism-Like algorithm is proposed. In the approach, the optimization algorithm based on the electromagnetism theory is used to find the optimal threshold values by maximizing the Tsallis entropy. Experimental results over several images demonstrate that the proposed approach is able to improve the convergence velocity, compared with similar methods such as Cuckoo search, and Particle Swarm Optimization.