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.

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.

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.

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.

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.

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.

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.

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

The asymptotic stability analysis in stochastic logistic model with Poisson growth coefficient

The asymptotic stability of a discrete logistic model with random growth coefficient is studied in this paper. Firstly, the discrete logistic model with random growth coefficient is built and reduced into its deterministic equivalent system by orthogonal polynomial approximation. Then, the linear stability theory and the Jury criterion of nonlinear deterministic discrete systems are applied to the equivalent one. At last, by mathematical analysis, we find that the parameter interval for asymptotic stability of nontrivial equilibrium in stochastic logistic system gets smaller as the random intensity or statistical parameters of random variable is increased and the random parameter’s influence on asymptotic stability in stochastic logistic system becomes prominent.

On a new fractional-order Logistic model with feedback control

In this paper,we formulate and analyze a new fractional-order Logistic model with feedback control,which is different from a recognized mathematical model proposed in our very recent work.Asymptotic stability of the proposed model and its numerical solutions are studied rigorously.By using the Lyapunov direct method for fractional dynamical systems and a suitable Lyapunov function,we show that a unique positive equilibrium point of the new model is asymptotically stable.As an important consequence of this,we obtain a new mathematical model in which the feedback control variables only change the position of the unique positive equilibrium point of the original model but retain its asymptotic stability.Furthermore,we construct unconditionally positive nonstandard finite difference(NSFD)schemes for the proposed model using the Mickens’methodology.It is worth noting that the constructed NSFD schemes not only preserve the positivity but also provide reliable numerical solutions that correctly reflect the dynamics of the new fractional-order model.Finally,we report some numerical examples to support and illustrate the theoretical results.The results indicate that there is a good agreement between the theoretical results and numerical ones.

The Stability of Logistic Model with Random Impulse

This paper is devoted to studying the stability of Logistic model with random impulse by using the theory of Markov skeleton processes and a convenient condition for Logistic model with random impulse to be stable is given.

Transmission Based Conditional Logistic Model for Testing Main and Interaction Effects

Transmission disequilibrium test (TDT) is a popular family based genetic association method. Under multiplicative assumption, a conditional logistic regression for matched pair, affected offspring with allele transmitted from parents and pseudo-offspring (control) with allele non-transmitted from parents, was built to detect the main effects of genes and gene-covariate interactions. When there exist genotype uncertainties, expectation-maximization (EM) algorithm was adopted to estimate the coefficients. The transmission model was applied to detect the association between M235T polymorphism in AGT gene and essential hypertension (ESH). Most of parents are not available in the 126 families from HongKong Chinese population. The results showed M235T is associated with hypertension and there is interaction between M235T and the case’s sex. The allele T is higher risk for male than female.

Analysis of Logistic Model with Constant Harvesting in a View of Non-Integer Derivative

The conformable fractional derivative method has been utilized in order to examine the logistic model with constant harvesting.Such method introduces a generalization to the classical analysis of Logistic model,and hence the features of the Logistic model,such as subcritical and supercritical harvesting,have been investigated in a view of fractional calculus.The positive auxiliary parameter,σ,with dimension of time is implemented to maintain the dimensionality of the system.The significant information of such parameter to the population has been discussed.The population expressions,obtained by conformable description,are compared with the expressions of the classical derivative.This comparison shows that the non-integer expressions are in a parallel line with that of the classical one.

Establishment of Fungal Decomposition Model Based on OLS and Logistic Model

By using the OLS model,an equation for the rate of decomposing wood by a variety of fungi was established.We analyzed the effects of various fungi in the experimental data under different temperature and humidity.Based on the growth performance of different fungi at different temperatures and humidity,we use the method of systematic cluster to divide the fungi into 5 categories,and introduce competition levels as the viability of different species of fungi.We have established a logistic model that introduces competition levels to obtain a fungal habitat model.The fungal habitat model includes predictions about the relative advantages and disadvantages for each species and combinations of species likely to persist,and do so for different environments including arid,semi-arid,temperate,arboreal,and tropical rain forests.

Model fitting of the seasonal population dynamics of the soybean aphid, Aphis glycines Matsumura, in the field

The soybean aphid, Aphis glycines Matsumura(Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integrated pest management(IPM). Based on systematically investigating soybean aphid populations in the field from 2018 to 2020, this study adopted the inverse logistic model for the first time, and combined it with the classical logistic model to describe the changes in seasonal population abundance from colonization to extinction in the field. Then, the increasing and decreasing phases of the population fluctuation were divided by calculating the inflection points of the models, which exhibited distinct seasonal trends of the soybean aphid populations in each year. In addition, multifactor logistic models were then established for the first time, in which the abundance of soybean aphids in the field changed with time and relevant environmental conditions. This model enabled the prediction of instantaneous aphid abundance at a given time based on relevant meteorological data. Taken as a whole, the successful approaches implemented in this study could be used to build a theoretical framework for practical IPM strategies for controlling soybean aphids.

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.

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.

Classification of breast lesions based on a dual S-shaped logistic model in dynamic contrast enhanced magnetic resonance imaging

This study proposes a novel dual S-shaped logistic model for automatically quantifying the characteristic kinetic curves of breast lesions and for distinguishing malignant from benign breast tumors on dynamic contrast enhanced (DCE) magnetic resonance (MR) images.D(,) is the diagnostic parameter derived from the logistic model.Significant differences were found in D(,) between the malignant benign groups.Fisher's Linear Discriminant analysis correctly classified more than 90% of the benign and malignant kinetic breast data using the derived diagnostic parameter (D(,)).Receiver operating characteristic curve analysis of the derived diagnostic parameter (D(,)) indicated high sensitivity and specificity to differentiate malignancy from benignancy.The dual S-shaped logistic model was effectively used to fit the kinetic curves of breast lesions in DCE-MR.Separation between benign and malignant breast lesions was achieved with sufficient accuracy by using the derived diagnostic parameter D(,) as the lesion's feature.The proposed method therefore has the potential for computer-aided diagnosis in breast tumors.

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.

Influencing factors analysis of helmet wearing for electric bicycle riders based on ordinal multinomial logistic model

The helmet of riders of electric bicycles plays an important role in reducing injuries and deaths in traffic accidents.This paper conducts a questionnaire survey,data analysis and modelling to investigate the influencing factors of electric bicycle helmet wearing.First,living area,gender,age,marital status and education level are selected as independent variables for data analysis.The factor analysis divides the sentiments of electric bicyclists for wearing helmets into four aspects:safety perception,practical sensation,satisfaction perception and emergency perception,and ordinal multiple logistic models are built to analyse the influencing factors.The result shows that people aged 18−25,26−35,36−45 and 46−55 are 1.3,1.8,2.0 and 2.3 times more likely,respectively,to have at least a grade higher safety perception than those aged 56 and over;men are 0.77 times more likely than women to feel at least one grade higher about the practical perception and 1.48 times more than women about the satisfaction perception;people with primary school,junior high school,senior high school,junior college and bachelor’s degree education are 1.64,2.44,1.50,1.70 and 1.55 times more likely,respectively,than people with a master’s degree to feel at least one grade higher about the satisfaction perception.