Enhancing PDF Malware Detection through Logistic Model Trees

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Analysis of gender＇s role on voluntary tendency of potential/active volunteers via logistic regression modeling： The case of Canakkale Onsekiz Mart University

From economy to political administrations, education to health, environment to human rights, many problems we met have gained a global importance in recent days. Existing state systems, political parties and nation states are not adequate for solving these problems in question effectively on their own. Not only governments and local authorities but also voluntary organizations based on completely voluntary activities have significant roles in solving these problems. Effective performance of voluntary organizations depends on increasing volunteer population. Individuals＇ attitudes or their perception of understanding volunteerism play an important role in their contributions to voluntary organizations. The aim of this study is to determine individuals＇ ways of perceiving volunteerism concept and their tendency towards it. Furthermore, differences between men and women＇s perception and attitudes towards volunteerism concept have been examined. For this purpose, a survey has been conducted over university students of bachelor＇s degree. Tendencies and attitudes towards volunteerism compared to gender differences have been tested via logistic regression method. Research results reveal that women take part in voluntary activities more than men and women perceive volunteerism as ＂a political position＂ while men perceive volunteerism as ＂a learning atmosphere and learning process＂.

Landslide-Dammed Mapping and Logistic Regression Modeling Using GIS and R Statistical Software in the Northeast Afghanistan

A complex terrain and topography resulted in an enormous landslide-dammed area northeast of Afghanistan. Moreover, debris, rock avalanches, and landslides occurrences are the primary source of lakes created within the area. Recently, instances have increased because of the high displacement and mass movement by glacial and seismic activities. In this study, using GIS and R statistical software, we performed a logistic regression modeling in order to map and predict the probability of landslides-dammed occurrences. Totally, 361 lakes were mapped using Google Earth historical imagery. This total was divided into 253 （70%） lakes for modeling and 801 （30%） lakes for the model validation. They were randomly selected by creating a fishnet for the study area using Arc toolbox in GIS. Four independent variables that are mostly contributed to landslide-dammed occurrences consisting of slope angles, relief classes, distances to major water sources and earthquake epicenters, were extracted from DEM （digital elevation model） data using 85-meter resolution. The result is a grid map that classified the area into Low （16,834.98 km2）, Medium （2,217.302 kin：） and High （2,013.55 km2） vulnerability to landslide-dammed occurrences. Overall, the model result has been validated by using a ROC （receiver operator characteristic） curve available in SPSS software. The model validation showed a 95.1 percent prediction accuracy that is considered satisfactory.

Impulsive Logistic Model for Gray Leaf Spots Caused by Cercospora zeae-maydi

[ Objectlve] Impulsive Logistic Model was used to simulate epidemic process of Gray Leaf Spots caused by C. zeae-maydi. [ Method] The pathogen was inoculated in different maize varieties, and the incidence were observed and recorded. Impulsive Logistic Model was used to simulate the development process of the disease, which was compared with actual incidence. [ Result] Artificial inoculation tests showed that impulsive Logistic Model could reflect time dynamic of C. zeae-maydi. Through derivation, exponential growth phase was from maize seedling emergence to eady July in each year, logistic phase was from early July to late August, terminal phase was from eady September to the end of maize growth stage. [ Conclusion] The derivation result from model was consistent with the development biological laws of C. zeae-maydi.

Genetic variation of height growth rhythm between clones of Larix kaempferi × L. gmelini based on logistic models

Fifty-three larch interspecific hybrid clones（Larix kaempferi × L.gmelini） and their parent clones were used for growth curve analysis of height variations.The growth curves of the 55 clones were ＇S＇-shaped and 36 exhibited similar curves as the male parent.The coefficients of the logistic models were higher than 0.943,indicating that our results were effective in the simulation of the growth curves.ANOVA analysis showed significant differences in height of different clones （P／0.01）.Average date of maximum height growth was Day 173,and average duration of rapid growth lasted for 50 days.Annual average increase in height was 9.7cm d^（-1） and daily average increase was 0.2 cm.The ratio of GR to the total annual increase in height ranged from 51.2 to 68.8%,with the average being 59.8%.There was a positive correlation between k values and plant heights which benefited from the evaluation of early plant height.There was also a positive correlation between GR（growth stage）,GD（plant height） and annual increase in height.These results are informative to the evaluation of the elite clone selection and provide a theoretical basis for breeding and management.

Synergistic effects of planting density and nitrogen fertilization on chlorophyll degradation and leaf senescence after silking in maize

Regulating planting density and nitrogen(N)fertilization could delay chlorophyll(Chl)degradation and leaf senescence in maize cultivars.This study measured changes in ear leaf green area(GLA_(ear)),Chl content,the activities of Chl a-degrading enzymes after silking,and the post-silking dry matter accumulation and grain yield under multiple planting densities and N fertilization rates.The dynamic change of GLA_(ear)after silking fitted to the logistic model,and the GLA_(ear) duration and the GLAearat 42 d after silking were affected mainly by the duration of the initial senescence period(T_(1))which was a key factor of the leaf senescence.The average chlorophyllase(CLH)activity was 8.3 times higher than pheophytinase activity and contributed most to the Chl content,indicating that CLH is a key enzyme for degrading Chl a in maize.Increasing density increased the CLH activity and decreased the Chl content,T1,GLAear,and GLA_(ear) duration.Under high density,appropriate N application reduced CLH activity,increased Chl content,prolonged T1,alleviated high-density-induced leaf senescence,and increased post-silking dry matter accumulation and grain yield.

Sustainable land management in Mali

This study uses logistic and Poisson regression models to examine the factors influencing the adoption of sustain-able land management(SLM)practices in Mali using two rounds of the nationally representative survey Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages.The SLMs considered include the applica-tion of organic fertilizers,the application of inorganic fertilizers,the use of improved seeds,and the practice of intercropping.On average the application of organic fertilizers(39.2%),and inorganic fertilizers(28.7%)are the most frequent SLM practices among Malian farmers,and between 2014 and 2017,we observe a decline in the practice of intercropping.The regression results show that farmers’adoption of different SLMs is significantly associated with biophysical factors(average temperature,climate type,plot size,plot shape,and location),de-mographic factors(age,gender,education,household size),and socioeconomic factors(number of cultivated plots,livelihood diversification,type of crop grown,market access,credit access,economic shocks,and social capital).Our findings suggest that policymakers and agricultural development agencies in Mali need to adopt a multidimensional policy framework to unlock the untapped potential of SLM practices in promoting sustainable agriculture and food security.

Prediction of cyanotic and acyanotic congenital heart disease using machine learning models

BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.AIM To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors.METHODS The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan,Pakistan from December 2017 to October 2019.A sample of 3900 mothers whose children were diagnosed with identify the potential outliers.Different machine learning models were compared,and the best-fitted model was selected using the area under the curve,sensitivity,and specificity of the models.RESULTS Out of 3900 patients included,about 69.5%had acyanotic and 30.5%had cyanotic congenital heart disease.Males had more cases of acyanotic(53.6%)and cyanotic(54.5%)congenital heart disease as compared to females.The odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during pregnancy.The artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012,sensitivity of 65.76%,and specificity of 97.23%.CONCLUSION Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease.Males are more at risk and their mothers need more care,good food,and physical activity during pregnancy.The best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural network.The results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan.

Extracting Vegetation Phenology Metrics in Changbai Mountains Using an Improved Logistic Model

Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this model with SPOT/NDVI data,three key vegetation phenology metrics,the start of growing season (SOS),the end of growing season (EOS) and the length of growing season (LOS),were extracted and mapped in the Changbai Mountains,and the relationship between the key phenology metrics and elevation were established.Results show that average SOS of forest,cropland and grassland in the Changbai Mountains are on the 119th,145th,and 133rd day of year,respectively.The EOS of forest and grassland are similar,with the average on the 280th and 278th,respectively.In comparison,average EOS of the cropland is relatively earlier.The LOS of forest is mainly from the 160th to 180th,that of the grassland extends from the 140th to the 160th,and that of cropland stretches from the 110th to the 130th.As the latitude increases for the same land cover in the study area,the SOS significantly delays and the EOS becomes earlier.The SOS delays approximately three days as the elevation increases 100 m in the areas with elevation higher than 900 m above sea level (a.s.l.).The EOS is slightly earlier as the elevation increases especially in the areas with elevation below 1200 m a.s.l.The LOS shortens approximately four days as the elevation increases 100 m in the areas with elevation higher than 900 m a.s.l.The relationships between vegetation phenology metrics and elevation may be greatly influenced by the land covers.Validation by comparing with the field data and previous research results indicates that the improved logistic model is reliable and effective for extracting vegetation phenology metrics.

Predictors of work injury in underground mines—an application of a logistic regression model

Mine accidents and injuries are complex and generally characterized by several factors starting from personal to technical, and technical to social characteristics.In this study, an attempt has been made to identify the various factors responsible for work related injuries in mines and to estimate the risk of work injury to mine workers.The prediction of work injury in mines was done by a step-by-step multivariate logistic regression modeling with an application to case study mines in India.In total, 18 variables were considered in this study.Most of the variables are not directly quantifiable.Instruments were developed to quantify them through a questionnaire type survey.Underground mine workers were randomly selected for the survey.Responses from 300 participants were used for the analysis.Four variables, age, negative affectivity, job dissatisfaction, and physical hazards, bear significant discriminating power for risk of injury to the workers, comparing between cases and controls in a multivariate situation while controlling all the personal and socio-technical variables.The analysis reveals that negatively affected workers are 2.54 times more prone to injuries than the less negatively affected workers and this factor is a more important risk factor for the case-study mines.Long term planning through identification of the negative individuals, proper counseling regarding the adverse effects of negative behaviors and special training is urgently required.Care should be taken for the aged and experienced workers in terms of their job responsibility and training requirements.Management should provide a friendly atmosphere during work to increase the confidence of the injury prone miners.

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

PERSISTENCE AND EXTINCTION OF A STOCHASTIC LOGISTIC MODEL WITH DELAYS AND IMPULSIVE PERTURBATION

A stochastic logistic model with delays and impulsive perturbation is proposed and investigated. Sufficient conditions for extinction are established as well as nonpersistence in the mean, weak persistence and stochastic permanence. The threshold between weak persistence and extinction is obtained. Furthermore, the theoretical analysis results are also derivated with the help of numerical simulations.

Transitions in a Logistic Growth Model Induced by Noise Coupling and Noise Color

With unified colored noise approximation, the logistic growth model is used to analyze cancer cell population when colored noise is included. It is found that both the coupling between noise terms and the noise color can induce continuous first-order-like and re-entrance-like phase transitions in the system. The coupling and the noise color can also increase tumor cell growth for small number of cell mass and repress tumor cell growth for large number of cell mass. It is shown that the approximate analytic expressions are consistent with the numerical simulations.

Predictors of in-hospital mortality by logistic regression analysis among melioidosis patients in Northern Malaysia:A retrospective study

Objective:To identify the predictors of mortality among in-hospital melioidosis patients.Methods:A total of 453 patients in Hospital Sultanah Bahiyah,Kedah,and Hospital Tuanku Fauziah,Perlis with culture-confirmed melioidosis were retrospectively included in the study.Advanced multiple logistic regression was used to obtain the final model of predictors of mortality from melioidosis.The analysis was performed using STATA/SE 14.0.Results:A total of 50.11%(227/453)of the patients died at the hospital,and a majority(86.75%,393/453)of cases were bacteremic.The logistic regression estimated that the bacteremic type of melioidosis,low platelet count,abnormal white blood cell counts,and increased urea value were predictors of mortality.The results showed that bacteremic melioidosis increased the risk of death by 4.39 times(OR 4.39,95%CI 1.83-10.55,P=0.001)compared to non-bacteremic melioidosis.Based on laboratory test,the adjusted ORs from the final model showed that all three blood investigations were included as the associated factors of mortality for the disease[high white blood cell(>10×10^(9)/L):OR 2.43,95%CI1.41-4.17,P<0.001;low white blood cell(<4×10^(9)/L):OR 3.82,95%CI 1.09-13.34,P=0.036;low platelet(<100×10^(9)/L):OR 4.19,95%CI 1.89-9.30,P<0.001;high urea(>7800μmol/L):OR 5.53,95%CI 2.50-12.30,P<0.001;and low level of urea(<2500μmol/L):OR 3.52,95%CI 1.71-7.23,P=0.001].Conclusions:Routine blood investigations during a hospital admission can early identify predictors of mortality in melioidosis patients.

User growth and penetration modeling for converged heterogeneous network

To evaluate the trail potential of converged heterogeneous network （CHN） market, the logistic method for adoption modeling of CHN is used. User growth ＆ penetration have been taken as two variants to find saturation condition in market. Model is continuous in time but modifications are done for discrete recurrence equation, commonly known as logistic map. Dynamic and static phases are taken into consideration while penetration decay is not covered in this model.

The walking distance decay law of amenity selection based on binary logistic model

The current measuring methods of walkability,such as the walk score,consider that walking distance decay laws for all amenities are the same,which is not applicable to typical communities in China with plentiful resources.Therefore,the walking distance decay laws of multi-type and multi-scale facilities are studied.Firstly,based on the residents'amenity selection survey,the walking distance decay law of residents'choice of amenity was studied from three aspects,including the law of all amenities,the laws of different types of amenities and the laws of different scales of amenities.It was proved that the walking distance decay laws of different kinds of amenities showed a significant difference.Secondly,different amenities'acceptable walking distance and optimum walking distance were obtained according to previous studies and the decay curve.Amenities with higher attraction and/or a larger scale showed a longer acceptable walking distance and optimum walking distance.Finally,the binary logistic model was used to describe the relationships between walking distance,amenity type,amenity scale and the probability of one amenity being selected,the prediction accuracy of which reached 80.4%.The calculated probability obtained from the model can be used as the decay coefficient of amenities in the measurement of walkability,providing a reference for the site selection and evaluation of amenities.

Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.

Partial Oscillation of m-dimensional Logistic Ecologic Models

We present and discuss the partial oscillation with respect to equilibrium state ofm-dimensional Logistic delay ecologic models, and obtain some simple criteria.

POSITIVE PERIODIC SOLUTION FOR A NONAUTONOMOUS LOGISTIC MODEL WITH LINEAR FEEDBACK REGULATION

A nonautonomous delayed logistic model with linear feedback regulation is proposed in this paper. Sufficient conditions are derived for the existence, uniqueness and global asymptotic stability of positive periodic solution of the model