MATLAB R2013a Research Articles

Probing Malware Propagation Model with Variable Infection Rates Under Integer, Fractional, and Fractal–Fractional Orders

Malware software has become a pervasive threat in computer and mobile technology attacks. Attackers use this software to obtain information about users of the digital world to obtain benefits by hijacking their data. Antivirus software has been developed to prevent the propagation of malware, but this problem is not yet under control. To develop this software, we have to check the propagation of malware. In this paper, we explore an advanced malware propagation model with a time-delay factor and a variable infection rate. To better understand this model, we use fractal–fractional theory. We use an exponential decay kernel for this. For theoretical purposes (existence, uniqueness, and stability), we use the results from fixed-point theory, and, for numerical purposes, a Lagrange two-point interpolation polynomial is used to develop an algorithm. Matlab R2016a is used for simulation, and the physical significance is assessed. We examine the impact of different fractal and fractional orders for various parameters. Moreover, we compare four different mathematical models (classical, fractional, fractal, and fractal–fractional). Also, constant and variable fractional and fractal orders are compared using graphs. We investigate the idea that significant perturbation in infected nodes might be due to minor changes. This work may help with developing antivirus strategies in real life.

Application Of Fuzzy Logic In Determining Full Cream Milk Drink RecommendationsBased On Milk Price And Nutrition

Fuzzy logic is part of the branch of mathematics that analyzes random number processes using computer science-based mathematical statistical theory. This research aims to use the fuzzy method to determine the recommended full-cream milk drink based on the price and nutrition of the packaged milk itself using fuzzy logic, observation, and literature study. The fuzzy method used is the Mamdani method with the assistance of the Fuzzy Inference System (FIS) in MATLAB R2015a application. Based on the research results, it is found that sample milks 4, 5, and 6 are suitable for recommendation to consumers because, through fuzzy logic analysis, they obtained membership degree values of 0.92, 0.92, and 0.91 respectively, which fall into the category of highly recommended.

Numerical approximation of space-fractional diffusion equation using Laguerre spectral collocation method

The space-fractional diffusion equation is extensively used to model various issues in engineering and mathematics. This paper addresses the numerical approximation of the space-fractional-order diffusion equation, using a fractional operator in the Caputo sense. The proposed equation is computed numerically through the Laguerre spectral collocation method combined with the finite difference scheme. The results were visualized using MATLAB R2016a, and the accuracy of the numerical scheme was validated by comparing the approximate results with those derived from exact values and the Legendre spectral collocation technique. The originality of this study lies in the application of the Laguerre spectral collocation method for fractional-order diffusion equations in the Caputo sense. Two numerical problems were included to demonstrate the validity and applicability of the proposed technique. The obtained results confirm that this method aligns well with the exact values and the Legendre spectral collocation method, as shown in tabular and graphical simulations.

Applying fractional calculus to malware spread: A fractal-based approach to threat analysis.

Malware is a common word in modern era. Everyone using computer is aware of it. Some users have to face the problem known as Cyber crimes. Nobody can survive without use of modern technologies based on computer networking. To avoid threat of malware, different companies provide antivirus strategies on a high cost. To prevent the data and keep privacy, companies using computers have to buy these antivirus programs (software). Software varies due to types of malware and is developed on structure of malware with a deep insight on behavior of nodes. We selected a mathematical malware propagation model having variable infection rate. We were interested in examining the impact of memory effects in this dynamical system in the sense of fractal fractional (FF) derivatives. In this paper, theoretical analysis is performed by concepts of fixed point theory. Existence, uniqueness and stability conditions are investigated for FF model. Numerical algorithm based on Lagrange two points interpolation polynomial is formed and simulation is done using Matlab R2016a on the deterministic model. We see the impact of different FF orders using power law kernel. Sensitivity analysis of different parameters such as initial infection rate, variable adjustment to sensitivity of infected nodes, immune rate of antivirus strategies and loss rate of immunity of removed nodes is investigated under FF model and is compared with classical. On investigation, we find that FF model describes the effects of memory on nodes in detail. Antivirus software can be developed considering the effect of FF orders and parameters to reduce persistence and eradication of infection. Small changes cause significant perturbation in infected nodes and malware can be driven into passive mode by understanding its propagation by FF derivatives and may take necessary actions to prevent the disaster caused by cyber crimes.

An Algorithm for Automatic Low-contrast Detection System and Its Evaluation for Images with Various Phantom Rotations

The purpose of this study is to develop an algorithm for automatic low-contrast object detection on the ACR 464 CT phantom and to investigate its evaluation for images with various phantom rotations. A software for automatic low-contrast detection was implemented with MATLAB R2013a. An algorithm was based on a template matching method. The reference points for the template matching method was centers of phantom and largest low-contrast object. The centers of the phantom and the largest low-contrast object were calculated using centroid formulae from the segmented phantom and objects with specific threshold values. Region of interests (ROIs) were located at each low-contrast object and background. The mean CT number and noise were calculated from pixel values within each ROI. The contrast-to-noise ratio (CNR) was then calculated based on the contrast between low-contrast object and background. The CNR cut-off is one. Object that has a CNR value more than the cut-off is considered resolved object and object that has a CNR value less than the cut-off is considered unresolved object. The algorithm was evaluated on images of the ACR CT phantom with various rotations of 0°, 22.5°, 45°, and 60°. Statistic evaluation was performed by ANOVA one-way to compare results of mean CT number, noise, and CNR for various rotations. The p-value more than 0.05 indicated that there is no significant difference. The proposed method was successful in placing ROIs at all low-contrast objects and at background for various phantom rotations. The CNR increases with the increase of size of the low-contrast object. The p-values for contrast, noise and CNR were 0.99, indicating that there are no statistically significant differences for various rotations. The minimum resolved object detectability in various rotations was 4 mm. An automatic technique for detecting low-contrast objects is accurate for various rotations.

Kinematics Analysis and Oil Film Lubrication Characteristics in the Piston–Cylinder Interface of a Bent-Axis-Type Piston Motor

Hydraulic piston motors are characterized by their excellent starting performance, high transmission torque, good sealing performance, compact design, and lightweight. These attributes make them highly applicable in fields such as construction machinery and marine applications. With the advancement of hydraulic transmission technology, higher performance requirements have been set for hydraulic motors. While extensive research has been conducted on hydraulic pumps, studies focusing on the performance of hydraulic motors remain relatively limited. Therefore, this paper is novel in that, based on the motion and force conditions of the piston, it differs from previous research on swashplate-type machinery by considering the complex structure of the bent-axis motor; it employs a micro finite element method to analyze the oil film characteristics at the piston–cylinder interface in a bent-axis piston motor, the structural changes in the piston assembly in the bent-axis motor are comprehensively considered, and a fluid–structure coupling model for the piston–cylinder interface is established. The leakage and viscous friction power loss equations for the piston–cylinder interface are derived. Simulation analyses are conducted using MATLAB R2016a to reveal the variation patterns of leakage and viscous friction power loss under different operating conditions and structural parameters, providing valuable insights for the operation analysis, energy loss evaluation, structural design optimization, and engineering applications of bent-axis piston motors.

Research on Active–Passive Training Control Strategies for Upper Limb Rehabilitation Robot

Due to accidents, upper limb movement disorders have become increasingly common. Training can help restore muscle strength and rebuild neurological function. However, the existing single mode has limitations in adapting to the training needs of different rehabilitation stages. Therefore, this paper conducts research on active–passive training control strategies for an upper limb rehabilitation robot. It establishes an upper limb kinematic model based on the Lagrange method and builds a man–machine integration dynamics model for upper limb rehabilitation in MATLAB (R2016a)/Simulink. A design active controller, passive controller, and switching controller based on PI and feedforward compensation strategies are proposed to improve training control accuracy. The output moment of the system during active training is planned to ensure the safety and stability of the training process. By utilizing neural networks to train sample data during rehabilitation training, the fuzzy rules and membership functions in fuzzy intention recognition algorithm are optimized to improve the accuracy of intention recognition during training. By adopting the independently developed experimental platform for the upper limb rehabilitation robot, active–passive training, intention recognition, and training mode switching are achieved. The results show that the active and passive training processes are smooth, the training intention recognition is accurate, and the switching between active and passive training modes is steady. This verifies the feasibility and effectiveness of the established mathematical model in upper limb rehabilitation training.

Maximum Energy Density for Evaluation of the Dynamic Accuracy of LVDT Sensors Applied in the Energy Industry

This paper presents a proposal in which the maximum energy density criterion is used to evaluate the dynamic accuracy of LVDT (Linear variable differential transformer) sensors for applications in the energy industry. The solutions proposed in the paper are based on a mathematical model of the LVDT sensor, represented by its frequency response. The mathematical foundations required for the synthesis of such a model and the formulae and algorithm necessary to determine the maximum energy density for the integral-square error criterion are presented. Numerical and simulation calculations are performed using MathCad 15 and MATLAB R2014a programs. The solutions presented in this paper can constitute a basis for the selection of LVDT sensors for applications in the energy industry, with a view to achieving accurate diagnostic measurements.

Mathematical Modeling of the Dynamics of Psychoactive Drug Abuse with Intervention of Control Agencies

Drug abuse continues to pose a significant threat to both developed and developing countries. This paper examines the dynamics of psychoactive drug abuse with intervention of control agencies. The study established that the drug abuse model remains positively invariant over time, as indicated by the positivity of the solutions to the model equations. The study identified both drug abuse endemic and drug abuse free equilibrium states. Basic reproduction number was obtained using the method of next generation matrix. The local stability of the drug abuse free equilibrium state was analyzed using Jacobian method and the analysis showed that the drug abuse free equilibrium state is locally asymptotically stable since all the Eigen values are negative. Similarly, the global stability analysis of the drug abuse free equilibrium state confirmed that the system is globally asymptotically stable. The sensitivity index was computed. The results indicate that parameters with positive sensitivity indices increase the prevalence, while those with negative sensitivity indices decrease it. This suggests that these parameters have the ability to either increase or decrease the persistence of drug abuse in the population. The numerical simulation of the model was performed using MATLAB R2015a software and results show that enlightenment and law enforcement agencies play a vital role in reducing the spread of drug abuse. This research recommends intensification of early enlightenment to curtail the spread of drug abuse. Also making more policies, enactment and enforcement of more laws that will check the use of hard drugs

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed technique can be applied to various field of life like security, surveillance systems.

Analysis of the nonlinear Fitzhugh–Nagumo equation and its derivative based on the Rabotnov fractional exponential function

The Fitzhugh–Nagumo equation is an essential governing equation that explains the process of impulse transmission from the nerves. This work suggests a novel generalized arbitrary-order Fitzhugh–Nagumo equation that is based on the recently developed nonsingular fractional operator. The modified Fitzhugh–Nagumo equation has been generalized using fractional Yang–Abdel–Cattani operator to provide a more accurate representation of the equation with memory effects and non-local behavior. Further, we discussed some interesting and new properties of the proposed fractional operator and the Rabotnov exponential fractional kernel with a modified Sumudu integral transform. In order to solve the proposed nonlinear differential equation, the homotopy perturbation method coupled with the modified Sumudu integral transform technique is implemented to examine the result of a newly proposed Yang–Abdel–Cattani fractional Fitzhugh–Nagumo equation, which converges to the exact solution. The nonlinear terms of this equation are handled in terms of He’s polynomials. The existence and uniqueness of the solution have been demonstrated using the Banach space fixed point theorem. The convergence and error analysis of the suggested technique are also discussed. The findings are expressed in terms of generalized Mittag-Leffler functions. The obtained results are plotted with the help of MATLAB R2016a mathematical software. The results in this work are more accurate, and it is proposed that the new Yang–Abdel–Cattani fractional derivative operator is an effective tool for finding the results of any other nonlinear problems arising in science and engineering. All three problems have been computed for different orders to confirm the significance of the fractionalizing concept in the proposed equation. The increase of the fractional order value ℘ to 1 confirms that our proposed time-fractional Fitzhugh–Nagumo equation in the Yang–Abdel–Cattani sense gets close to the analytic solution.

Mamdani's Fuzzy Logic-Based Tapioca Optimal Production Amount Prediction System

This research focuses on the imbalance between supply and demand in the tapioca industry, especially on the scale of Small and Medium Industries (SMI). Such challenges involve price fluctuations and lack of efficiency in determining the optimal production amount. Using fuzzy logic as an artificial intelligence method, this research aims to develop a system for predicting the optimal amount of tapioca production that can increase efficiency, reduce waste of raw materials and energy, and stabilize prices. The data used in this study was obtained through interviews with resource persons who are involved in the field of tapioca SMI. Furthermore, the data was processed using the Mamdani Fuzzy Inference System method. The results showed that in the case of production when demand is 400 kg and cassava availability is 2000 kg, the optimal production amount of tapioca is 630 kg. This value is also consistent when proven using the Matlab R2015a application. This shows that the model can be relied upon in determining the decision on the amount of tapioca production by considering demand factors and raw material availability.

Atomic Absorption Spectroscopic Analysis of Heavy Metals in Cancerous Breast Tissues Among Women in Jos, Nigeria

Breast cancer is prevalent in northern Nigerian women most especially Jos, Plateau State owing to anthropogenic activities such as solid earth mineral mining. In this study, Atomic Absorption Spectrometry was used to determine the levels of eight heavy metals (Cd, As, Cr, Cu, Fe, Pb, Ni and Zn) in cancerous and non-cancerous breast tissues of Jos Nigerian Women. The concentration of heavy metals ranged from 1.08 to 29.34 mg/kg, 0.29 to 10.76 mg/kg, 0.35 to 51.93 mg/kg, 5.15 to 62.93 mg/kg, 11.64 to 51.10 mg/kg, 0.42 to 83.16 mg/kg, 2.08 to 43.07 mg/kg and 1.67 to 71.53 mg/kg for Cd, As, Cr, Cu, Fe, Pb, Ni and Zn respectively. Using MATLAB R2016a, significant differences (t<sub>v</sub> = 0.0041 – 0.0317) existed between the levels of all the heavy metals in cancerous and non-cancerous breast tissues except Fe. At 0.01 level of significance, positive significant correlation existed between Pb and Fe, Pb and Cu, Pb and Fe, Ni and Fe, Cr and Pb, as well as Ni and Cr (r = 0.583 – 0.998) in cancerous breast tissues. Using ANOVA, significant differences also occurred in the levels of these heavy metals in cancerous breast tissues (p = 1.910510×10<sup>-26</sup>). The relatively high levels of the cancer-induced heavy metals (Cd, As, Cr and Pb) compared with control indicated contamination or exposure to heavy metals which could be the major cause of cancer in these female subjects.

ANN Based Location of Fault for 500KV Transmission Line in Sudan from Merowe to Atbara

Today, transmission lines have become one of the important core components in systems of electrical power that are used to transport energy. Since transmission lines are prone in nature, the probability of failures in transmission lines is generally higher than that of other major components. This paper presents the fault of the transmission line from Merowe to Atbara in northern Sudan using artificial neural networks. A transmission line model was created in MATLAB R2014a using SIMULINK and SIMSCAPE with the SIMPOWERSYSTEM toolbox. The current and voltage values obtained from the transmission line model have been used as an entry for artificial neural networks. The results obtained from the proposed artificial neural networks were acceptable and the networks were found to be practically practicable for implementation. The significance of picking the most proper artificial neural network, to get the best performance from the neural networks.

Numerical analysis of multi-dimensional Navier–Stokes equation based on Yang–Abdel–Cattani fractional operator

The Navier–Stokes equation is a key governing equation for the motion of viscous fluid flow. The main target of our work is to obtain the solution to multi-dimensional Navier–Stokes equations in the Yang–Abdel–Cattani fractional sense. The results of the model are in terms of the three-parameter Prabhakar function. Three examples are discussed that depend on the recommended method for a novel Yang–Abdel–Cattani arbitrary order operator. The obtained results are plotted with the help of MATLAB R2016a mathematical software.

Enhanced Local Binary Pattern Algorithm for Facial Recognition Using Chinese Remainder Theorem

Current biometrics research focuses on achieving a high authentication success rate for identity management and discussing the threat of various security attacks. Local Binary Pattern (LBP), one of the methods for feature extraction, and Chicken Swarm Optimization (CSO), one of the strategies for feature selection, were used for user identification and authentication. LBP requires high computational time to extract features from the facial images. The Chinese Remainder Theorem (CRT) was used to reduce its computational time by formulating an Enhanced Local Binary Pattern (ELBP). Michael Olugbenga Banji Abolarinwa (MOBA) database was created specifically for this study. 600 frontal facial images of 200 people were collected, each with three images. 360 images were used for training while 240 images were used for testing. MATLAB (R2016a) was used to run the simulation. The time it took to classify the facial images when LBP and CSO were combined and when ELBP and CSO were combined were enumerated. The LBP-CSO achieved a false-positive rate (FPR) of 11.67%, a sensitivity (SEN) of 92.78%, a specificity (SPEC) of 88.33%, a precision (PREC) of 95.98%, and an accuracy of 91.67% in 119.10 seconds at 0.80 thresholds for face recognition. ELBP-CSO obtained an FPR of 5.00%, SEN of 95.00%, SPEC of 95.00%, PREC of 98.28%, and accuracy of 95.00% in 79.16 seconds. The results showed that LBP-CSO took an average of 119.10 seconds and ELBP-CSO took an average of 79.16 seconds. In conclusion, the performance of CSO-ELBP justifies the usage of LBP enhancement with CRT.

Bioremediation of phenolic compounds in coal‐contaminated environments: Bio‐kinetic studies using a mixed bacterial culture

AbstractTwo distinct bacterial strains were combined in a mixed culture to investigate the degradation of phenol. The mixed cultures consisting of species like Rhodococcus and Bacillus, were isolated from the waste effluent collected from Dankuni coal complex (DCC) in West Bengal. The experimental phenol biodegradation were performed batch wise at an optimized temperature and pH conditions of 37°C and 7.0 for a wide range of phenol concentrations initially, with saline solutions which is not supplemented with nutrients initially. Experimental results concluded a highest possible specific growth rate at 150 mg/L for phenol biodegradation of 2400 mg/L within a span of 72 h. Several significant physicochemical parameters were investigated in order to determine the ideal conditions for phenol breakdown. It was also observed that a spectroscopic study of the intermediate at 260 nm of the phenol biodegradation commenced through the formation of a cis, cis intermediate, indicating ortho‐cleavage pathway. A simulated substrate utilization and growth profiles was obtained by using substrate inhibition model equation using MATLAB R2017a were used to compare for the validation of the obtained experimental growth and substrate characterization data.

Room Steering Temperature Determination with Variable Concerned of Driver Conditions Who are Sleep Deprived and Road Condition

Among the main reasons for the high death rate worldwide are traffic accidents. The most common cause of road accidents is driver fatigue. Not getting enough sleep is one thing that makes people tired. A study on how sleep-deprived sleep-deprived drivers felt about themselves was conducted, and the results showed that they were tired. A potential preventive measure against the driver's extreme weariness is the room steering temperature. The goal of this research is to find the room temperature range that produces the most minor drowsiness is this research's goal. The research used six treatments in a 40-minute driving simulator: hot, cold, pleasant room temperature steering, and monotonous and non-monotonous road conditions. With the Electroencephalogram, one may measure one's state of sleepiness by monitoring brain wave activity. Six treatments were administered to each of the study's four young adult male participants. Teta, alpha, and beta wave powers are derived by processing data from brain wave activity using Matlab R2009A. An ANOVA test is used to discover which factors affect the degree of drowsiness by using the ratio of sleepiness level, which is determined using the equation (θ + α) /β. ANOVA test results indicated that while road conditions and room steering temperature impacted sleepiness, their interaction had no effect. Just the hot-cold level is found to be significantly different by the Tukey and Newman Keuls tests. For sleep-deprived drivers, the temperature differences between hot and cold conditions result in varying drowsiness. The research's practical conclusion is that, for sleep-deprived drivers, a hot temperature range (>26°C–29°C) can result in the lowest level of tiredness.

Analysis of the Dynamic Evolution Game of Government, Enterprise and the Public to Control Industrial Pollution

This paper proposes a two-party evolutionary game model of government and enterprise to solve the dilemma of industrial pollution control and explore the mode of government and enterprise collaborative governance. The local equilibrium points of the game model in four cases are calculated and analyzed, and the results show that government power alone cannot help enterprises achieve an ideal level of pollution reduction, and it is necessary to introduce public power for supervision. Based on the above, a tripartite evolutionary game model comprising the government, the public, and the enterprise is proposed. When the costs and benefits of the tripartite game players meet certain conditions, the system will evolve to a state of equilibrium (0,1,1). Following the current situation of economic development in China, the parameters of the two-party and tripartite evolutionary game are assigned, and the operating path and system’s evolution trajectory of the two-party and tripartite industrial pollution control are simulated by Matlab R2016a software. It is indicated that whether the government participates in supervision or not, an enterprise will actively control pollution under strong public supervision, which can provide feasible suggestions for the selection of industrial pollution control policies.