Статьи журнала - International Journal of Mathematical Sciences and Computing
Все статьи: 240
Accuracy Analysis for the Solution of Initial Value Problem of ODEs Using Modified Euler Method
Статья научная
There exist numerous numerical methods for solving the initial value problems of ordinary differential equations. The accuracy level and computational time are not the same for all of these methods. In this article, the Modified Euler method has been discussed for solving and finding the accurate solution of Ordinary Differential Equations using different step sizes. Approximate Results obtained by different step sizes are shown using the result analysis table. Some problems are solved by the proposed method then approximated results are shown graphically compare to the exact solution for a better understanding of the accuracy level of this method. Errors are estimated for each step and are represented graphically using Matlab Programming Language and MS Excel, which reveals that so much small step size gives better accuracy with less computational error. It is observed that this method is suitable for obtaining the accurate solution of ODEs when the taken step sizes are too much small.
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Action Recognition Based on the Modified Twostream CNN
Статья научная
Human action recognition is an important research direction in computer vision areas. Its main content is to simulate human brain to analyze and recognize human action in video. It usually includes individual actions, interactions between people and the external environment. Space-time dual-channel neural network can represent the features of video from both spatial and temporal perspectives. Compared with other neural network models, it has more advantages in human action recognition. In this paper, a action recognition method based on improved space-time two-channel convolutional neural network is proposed. First, the video is divided into several equal length non-overlapping segments, and a frame image representing the static feature of the video and a stacked optical flow image representing the motion feature are sampled at random part from each segment. Then these two kinds of images are input into the spatial domain and the temporal domain convolutional neural network respectively for feature extraction, and then the segmented features of each video are fused in the two channels respectively to obtain the category prediction features of the spatial domain and the temporal domain. Finally, the video action recognition results are obtained by integrating the predictive features of the two channels. Through experiments, various data enhancement methods and transfer learning schemes are discussed to solve the over-fitting problem caused by insufficient training samples, and the effects of different segmental number, pre-training network, segmental feature fusion scheme and dual-channel integration strategy on action recognition performance are analyzed. The experiment results show that the proposed model can better learn the human action features in a complex video and better recognize the action.
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Adaptive Social Acceleration Constant Based Particle Swarm Optimization
Статья научная
In this paper, an attempt has been made to develop an Adaptive Social Acceleration Constant based PSO (ASACPSO). ASACPSO converge faster in comparison to basic PSO. The best value has been selected based on the minimum number of kounts required to minimize the function. Adaptive Social Acceleration Constant based PSO (ASACPSO) has been developed using the best value of adaptive social acceleration constant. The Adaptive Social Acceleration Constant has been searched using three formulations which led to the development of three algorithms-ALDPSO, AELDPSO-I and AELDPSO-II. All three were implemented on Rosenbrock function to get the best value of adaptive social acceleration constant. Similarly it has been implemented on seven mathematical benchmark functions and its performance has been compared to Basic Particle Swarm Optimization (BPSO). ASACPSO was observed to converge faster and give better accuracy. Results show that Kounts required for convergence of mathematical function is lesser for ASACPSO in comparison to basic PSO.ASACPSO reduces the computational time to optimize the function.
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An Application of the Two-Factor Mixed Model Design in Educational Research
Статья научная
As with any ANOVA, a repeated measure ANOVA tests the equality of means. However, a repeated measure ANOVA is used when all members of a random sample are measured under a number of different conditions. As the sample is exposed to each condition in turn, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: the data violate the ANOVA assumption of independence. Some ANOVA designs combine repeated measures factors and independent group factors. These types of designs are called mixed-model ANOVA and they have a split plot structure since they involve a mixture of one between-groups factor and one within-subjects factor. The work present an application of the mixed model factorial ANOVA, using scores obtained by 120 secondary school students in mathematics. The between group factor is the different categories of students (science, commercial humanities) with three levels while the within group factor is the three years spent in senior secondary School.
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An Approach of Securing Data using Combined Cryptography and Steganography
Статья научная
The recent advance in information technology field forcing us to ensure the privacy of the digital data. It is very important to develop the method that may satisfy the needs. Many methods/techniques applied to reach that goal. One of efficient way to reach that secrecy can be achieved by combining Cryptography and Steganography. In this paper, a new RGB shuffling method proposed. The concept of encryption using RGB Shuffling is shuffling all of RGB element to distort the image. RGB Shuffling method will shuffle the RGB each pixel of image depends on the input password from user. The basic step of RGB shuffling is adding RGB element with ASCII password, invers and shuffle it.
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An Automated System for Detecting Property Insurance Fraud Using Machine Learning
Статья научная
Detecting property insurance fraud is critical for reducing financial losses and ensuring fair claim processing. Traditional methods of detecting insurance fraud had several drawbacks, including no feature selection process, no hyper parameter tuning, lower accuracy, and class imbalance problems. To address the aforementioned shortcomings, this paper examines advanced ML (machine learning) techniques for accurately detecting property insurance fraud. To determine the best model for predicting fraudulent activities, this paper tested several machine learning models, including Gradient Boosting, classical ML classifiers, and Stacking Ensemble methods. To address class imbalance and improve model performance, the selected model incorporates proper feature selection, hyper parameter tuning, and SMOTE techniques (synthetic minority over-sampling). The Stacking Ensemble method outperformed the other ML models, achieving an accuracy of 96% and a recall of 94%. The experimental results show that the proposed stacking ensemble-based prediction scheme improves accuracy by 3.4% and recall by 2.7% over previous works. This article also includes a web application for assisting with property insurance fraud, which includes ML-based fraud prediction, question submission, answer checking, and blog post access. According to the findings, more than 54% of users expressed satisfaction with the web application's usefulness for detecting property fraud.
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An Efficient Impulse Noise Removal Image Denoising Technique for MRI Brain Images
Статья научная
Image enhancement is an important challenge in medical field. There are various techniques for image enhancement during last two decades. The objective of this paper is to remove impulse noise for MRI brain image. This paper proposed an efficient filter for removing impulse noise. The shape of the filter is changed to diamond. Experiments are conducted for various noise levels. The proposed method is compared with the existing Denoising techniques. The experimental results proved that the proposed filter performed well than the other methods.
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An Empirical Predictive Model for Formation Rate of the Day 5 Blastocyst
Статья научная
Day 5 (D5) blastocyst transfers present higher clinical pregnancy and live birth rates than day 6 in both fresh and frozen transfers [1]. To investigate the D5 blastocyst formation rate, in this study, we first collected clinical data from a hospital in Jiaozuo and partitioned the data into training set and validation set. We conducted univariate logistic regression analyses, which were possible predictors of the D5 blastocyst formation rate, on 12 patient covariates. According to the univariate analysis, we determined 10 covariates were suitable for multivariate analysis. Finally, we identified five covariates to construct a logistic regression model to predict the D5 blastocyst formation rate. We also used the receiver operating characteristic curve, the Hosmer–Lemeshow test, and the calibration curve to verify the accuracy of this model. The results showed that logistic regression model of D5 blastocyst formation rate directly reflected the relationship between transplantation results and covariates. According to the model, doctors can provide guidance to patients before treatment and improve the rate of blastocyst formation by changing patients' physical fitness. The model has certain clinical application value.
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An Improved Security Schematic based on Coordinate Transformation
Статья научная
An earlier research project that dealt with converting ASCII codes into 2D Cartesian coordinates and then applying translation and rotation transformations to construct an encryption system, is improved by this study. Here, we present a variation of the Cantor Pairing Function to convert ASCII values into distinctive 2D Coordinates. Then, we apply some novel methods to jumble the ciphertext generated as a result of the transformations. We suggest numerous improvements to the earlier research via simple tweaks in the existing code and by introducing a novel key generation protocol that generates an infinite integral key space with no decryption failures. The only way to break this protocol with no prior information would be brute force attack. With the help of elementary combinatorics and probability topics, we prove that this encryption protocol is seemingly infeasible to overcome by an unwelcome adversary.
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An Individualized Face Pairing Model for Age-Invariant Face Recognition
Статья научная
Among other factors affecting face recognition and verification, the aging of individuals is a particularly challenging one. Unlike other factors such as pose, expression, and illumination, aging is uncontrollable, personalized, and takes place throughout human life. Thus, while the effects of factors such as head pose, illumination, and facial expression on face recognition can be minimized by using images from controlled environments, the effect of aging cannot be so controlled. This work exploits the personalized nature of aging to reduce the effect of aging on face recognition so that an individual can be correctly recognized across his/her different age-separated face images. To achieve this, an individualized face pairing method was developed in this work to pair faces against entire sets of faces grouped by individuals then, similarity score vectors are obtained for both matching and non-matching image-individual pairs, and the vectors are then used for age-invariant face recognition. This model has the advantage of being able to capture all possible face matchings (intra-class and inter-class) within a face dataset without having to compute all possible image-to-image pairs. This reduces the computational demand of the model without compromising the impact of the ageing factor on the identity of the human face. The developed model was evaluated on the publicly available FG-NET dataset, two subsets of the CACD dataset, and a locally obtained FAGE dataset using leave-one-person (LOPO) cross-validation. The model achieved recognition accuracies of 97.01%, 99.89%, 99.92%, and 99.53% respectively. The developed model can be used to improve face recognition models by making them robust to age-variations in individuals in the dataset.
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An Overview on Quantum Computing as a Service (QCaaS): Probability or Possibility
Статья научная
Cloud computing is a worldwide classical system. Quantum computing is theoretical concept still in experimental review. Where cloud system is facing vulnerability in security, backup, processing and locality, there quantum computing shows a strong solution to overcome it. Most researchers are optimistic in quantum computing that it will improve cloud system. But to associate physics based subatomic computing system with software based cloud system is not an easy option. Our paper will show all the major advantages and disadvantages of quantum computing in the perspective to integrate it with cloud system. And review some recent progress with some foremost doubtful future aspects of quantum cloud computing. Also we will review the reality of quantum computation and internet system in applied viewpoint until present.
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An Unorthodox Trapdoor Function
Статья научная
At the bedrock of cryptosystems lie trapdoor functions, serving as the fundamental building blocks that determine the security and efficacy of encryption mechanisms. These functions operate as one-way transformations, demonstrating an inherent asymmetry: they are designed to be easily computable in one direction, while proving computationally challenging, if not infeasible, in the opposite direction. This paper contributes to the evolving landscape of cryptographic research by introducing a novel trapdoor function, offering a fresh perspective on the intricate balance between computational efficiency and security in cryptographic protocols. The primary objective of this paper is to present and scrutinize the proposed trapdoor function, delving into a comprehensive analysis that unveils both its strengths and weaknesses. By subjecting the function to rigorous examination, we aim to shed light on its robustness as well as potential vulnerabilities, contributing valuable insights to the broader cryptographic community. Understanding the intricacies of this new trapdoor function is essential for assessing its viability in practical applications, particularly in securing sensitive information in real-world scenarios. Moreover, this paper does not shy away from addressing the pragmatic challenges associated with deploying the proposed trapdoor function at scale. A thorough discussion unfolds, highlighting the potential hurdles and limitations when attempting to integrate this function into large-scale environments. Considering the practicality and scalability of cryptographic solutions is pivotal, and our analysis strives to provide a clear understanding of the circumstances under which the proposed trapdoor function may encounter obstacles in widespread implementation. In essence, this paper contributes to the ongoing discourse surrounding trapdoor functions by introducing a new entrant into the cryptographic arena. By meticulously exploring its attributes, strengths, and limitations, we aim to foster a deeper understanding of the intricate interplay between cryptographic theory and real-world applicability.
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An efficient genetic algorithm for numerical function optimization with two new crossover operators
Статья научная
Selection criteria, crossover and mutation are three main operators of genetic algorithm’s performance. A lot of work has been done on these operators, but the crossover operator has a vital role in the operation of genetic algorithms. In literature, multiple crossover operators already exist with varying impact on the final results. In this article, we propose two new crossover operators for the genetic algorithms. One of them is based on the natural concept of crossover i.e. the upcoming offspring takes one bit from a parent and next from other parent and continuously takes bits till last one. The other proposed scheme is the extension of two-point crossover with the concept of multiplication rule. These operators are applied for eight benchmark problems in parallel with some traditional crossover operators. Empirical studies show a remarkable performance of the proposed crossover operators.
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An encoding schematic based on coordinate transformations
Статья научная
This paper outlines an encoding schematic that is dependent on simple Cartesian coordinate transformations. Namely, the change of axes and the rotation of axes. A combination of these two is incorporated after turning singular ASCII values into 2D points. This system is based on multiple private keys that can also act as a potential candidate for threshold cryptography. Comprehensive initial testing has been performed on certain parameters by altering their values within a range. Further testing is required for more insights about the system. For now, the list of parameters that amounts to successful decryption is to be noted down for future use with this system.
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An enhanced rough set based feature grouping approach for supervised feature selection
Статья научная
Selection of useful information from a large data collection is an important and challenging problem. Feature selection refers to the problem of selecting relevant features from a given dataset which produces the most predictive outcome as the original features maintain before the selection. Rough set theory (RST) and its extension are the most successful mathematical tools for feature selection from a given dataset. This paper starts with an outline of the fundamental concepts behind the rough set and fuzzy rough set based feature grouping techniques which are related to supervise feature selection. Supervised Quickreduct (QR) and fuzzy-rough feature grouping Quickreduct (FQR) algorithms are highlighted here. Then an enhanced version of FQR method is proposed here which is based on rough set dependency criteria with feature significance measure that select a minimal subset of features. Also, the termination condition of the base method is modified. Experimental studies of the algorithms are carried out on five public domain benchmark datasets available in UCI machine learning repository. JRip and J48 classifier are used to measure the classification accuracy. The performance of the proposed method is found to be satisfactory in comparison with other methods.
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Analysis of Automated Matching of the Semantic Wiki Resources with Elements of Domain Ontologies
Статья научная
Intelligent information systems oriented on the Web open environment need in dynamic and interoperable ontological knowledge bases. We propose an approach for integration of ontological analysis with semantic Wiki resources: domain ontologies are used as a base of semantic markup of the Wiki pages, and this markup becomes the source for improving of these ontologies by new information
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Analysis of Signalling Time of Community Model
Статья научная
Data fusion is generally defined as the application of methods that combines data from multiple sources and collect information in order to get conclusions. This paper analyzes the signalling time of different data fusion filter models available in the literature with the new community model. The signalling time is calculated based on the data transmission time and processing delay. These parameters reduce the signalling burden on master fusion filter and improves throughput. A comparison of signalling time of the existing data fusion models along with the new community model has also been presented in this paper. The results show that our community model incurs improvement with respect to the existing models in terms of signalling time.
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Analysis of Some Software Reliability Growth Models with Learning Effects
Статья научная
A newly developed software system before its deployment is subjected to vigorous testing so as to minimize the probability of occurrence of failure very soon. Software solutions for safety critical and mission-critical application areas need a much focused level of testing. The testing process is basically carried out to build confidence in the software for its use in real world applications. Thus, reliability of systems is always a matter of concern for us. As we keep on performing the error detection and correction process on our software, the reliability of the system grows. In order to model this growth in the system reliability, many formulations in Software Reliability Growth Models (SRGMs) have been proposed including some based on Non-Homogeneous Poisson Process (NHPP). The role of human learning and experiential pattern gains are being studied and incorporated in such models. The realistic assumptions about human learning behavior and experiential gains of new skill-sets for better detection and correction of faults on software are being incorporated and studied in such models. In this paper, a detailed analysis of some select SRGMs with learning effects is presented based on use of seven data sets. The estimation of parameters and comparative analysis based on goodness of fit using seven data sets are presented. Moreover, model comparisons on the basis of total defects predicted by the select models are also tabulated.
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Analysis of Vascular Pattern Recognition Using Neural Network
Статья научная
Biometric identification using vein patterns is a recent technique. The vein patterns are unique to each individual even in twins and they don't change over age except their size. As veins are beneath the skin it is difficult to forge. BOSPHOROUS hand vein database is used in this work. Hand vein images are uploaded first and key points using Scale Invariant Feature Transform (SIFT) are extracted. Then the neural network is used for training these images. Finally neural network is used for testing these images to check whether the image used for testing matches with the existing database or not. Results are computed like False Acceptation Rate (FAR), False Rejection Rate (FRR), accuracy and error per bit stream.
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Application of Differential Geometry on a Chemical Dynamical Model via Flow Curvature Method
Статья научная
Slow invariant manifolds can contribute major rules in many slow-fast dynamical systems. This slow manifold can be obtained by eliminating the fast mode from the slow-fast system and allows us to reduce the dimension of the system where the asymptotic dynamics of the system occurs on that slow manifold and a low dimensional slow invariant manifold can reduce the computational cost. This article considers a trimolecular chemical dynamical Brusselator model of the mixture of two components that represents a chemical reaction-diffusion system. We convert this system of two-dimensional partial differential equations into four-dimensional ordinary differential equations by considering the new wave variable and obtain a new system of chemical Brusselator flow model. We observe that the onset of the chemical instability does not depend on the flow rate. We particularly study the slow manifold of the four-dimensional Brusselator flow model at zero flow speed. We apply the flow curvature method to the dynamical Brusselator flow model and acquire the analytical equation of the flow curvature manifold. Then we prove the invariance of this slow manifold equation with respect to the flow by using the Darboux invariance theorem. Finally, we find the osculating plane equation by using the flow curvature manifold.
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