Статьи журнала - International Journal of Mathematical Sciences and Computing
Все статьи: 240
A 4-D HyperChaotic DNA Encryption/Decryption Algorithm for Securing Students Data System
Статья научная
Data security has become a significant issue nowadays with the increase of information capacity and its transmission rate. The most common and widely used techniques in the data security fields is cryptography. Cryptography is the process of concealing and transmitting data in an appropriate format, so that only authorized people can access and process it. The main goal of the cryptographic process is protecting data from being hijacked and altered. This paper proposes an algorithm for encrypting data through the use of Deoxyribo Nucleic Acid (DNA) sequence and four-dimensional hyper chaotic system. Whereby, the hyper chaotic system is applied to generate a binary sequence which is later passed to a permutation function for the key generation of the first level encryption. The proposed encryption algorithm includes several intermediate steps, which are binary-coded form and the generation of arbitrary keys. Experimental results were analyzed by calculating encryption time, key generation time, histogram and correlation coefficient entropy. Furthermore, the proposed text encryption algorithm is implemented on two different students’ datasets to improve the security of educational systems. Finally, experimental and comparative studies have shown that, the proposed encryption algorithm reported a uniform encrypted text distribution and correlation coefficient values nearer to ‘0’, which are close to the theoretical optimal value.
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A Comparative Analysis among Online and On-Campus Students Using Decision Tree
Статья научная
COVID-19 hit the world unexpectedly, forcing humans to isolate themselves. It has placed the lives of people in jeopardy with its fury. The global pandemic had a detrimental effect on the worlds' education spheres. It has imposed a global lockdown, with a negative impact on the students' lives. Continuing regular classes on-campus was out of the question. At that moment, online learning came to us as a savior. The quality of online education was yet to be tested on a large scale compared to regular schooling. Educational data mining is a modern arena that holds promise for those who work in education. Data mining strategies are developed to uncover latent information and identify valuable trends that can increase students' performance and, in turn, contribute to the improvement of the educational system in the long run. This research mainly aims to identify a comparative analysis of the students' academic performance between online and on-campus environments and distinguish the significant characteristics that influence their academic endeavors. The impact of the factors on the students' performance is visualized with the help of the Decision Tree Classification Model. This paper will assist in giving a good overview that influences the distinguished factors on students' academic performance. Moreover, educators will also be benefited from this paper while making any important decision regarding the educational activity.
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Статья научная
The majority of failure cases may occur because of the wrong selection of inappropriate material in the manufacture of wind turbine blades. Composites are used to increase reliability and reduce wind turbine manufacturing costs. Therefore, this research focuses on comparing the use of Epoxy S-Glass UD and Epoxy Carbon UD as manufacturing materials for wind turbine blades using 3D finite element analysis; to find out which of these materials have the best performance for its use as a manufacturing material for wind turbine blades. The distribution of Von Mises stresses in wind turbine blade models was investigated using Epoxy S-Glass UD and Epoxy Carbon UD under the wind loads that affect the blade of the turbine. The results showed that the value of the maximum stresses in the epoxy glass model was 3.495 × 107 Pa, while this value was in the epoxy carbon model 4.0494 × 107 Pa. As for the value of the minimum stresses, it was in the epoxy glass model 7431.8 Pa, while the value in another material model 17323 Pa. Therefore, it is not recommended to use Epoxy Carbon UD as a manufacturing material for wind turbine blades, but it is recommended to use Epoxy S-Glass UD, which reduces induced stresses and thus to prolong its lifespan.
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A Comparison of Simpson’s Rule Generalization through Lagrange and Hermite Interpolating Polynomials
Статья научная
Simpson's Rule is a widely used numerical integration technique, but it cannot be applied to unequally spaced data. This paper presents a new generalization of Simpson's Rule using both Lagrange and Hermite interpolating polynomials to address this limitation. I provide a geometric interpretation of the method, showing its relationship to the area calculation of a trapezoid and a triangle, where the accuracy is significantly influenced by the chosen interpolating polynomial for midpoint determination. A comprehensive comparative analysis across various functions reveals that the Hermite-based approach consistently exhibits higher accuracy and stability than the Lagrange method, particularly with an increasing number of subintervals. This improved performance stems from the Hermite polynomial's ability to better approximate the function's behavior between data points. The findings highlight the effectiveness of the proposed Hermite-based generalization of Simpson's Rule in improving the accuracy of numerical integration for unequally spaced data, which is commonly encountered in practical applications.
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A Decision-Making Technique for Software Architecture Design
Статья научная
The process of making decisions on software architecture is the greatest significance for the achievement of a software system's success. Software architecture establishes the framework of the system, specifies its characteristics, and has significant and major effects across the whole life cycle of the system. The complicated characteristics of the software development context and the significance of the problem have caused the research community to build various methodologies focused on supporting software architects to improve their decision-making abilities. With these efforts, the implementation of such systematic methodologies looks to be somewhat constrained in practical application. Moreover, the decision-makers must overcome unexpected difficulties due to the varying software development processes that propose distinct approaches for architecture design. The understanding of these design approaches helps to develop the architectural design framework. In the area of software architecture, a significant change has occurred wherein the focus has shifted from primarily identifying the result of the architecting process, which was primarily expressed through the representation of components and connectors, to the documentation of architectural design decisions and the underlying reasoning behind them. This shift finally concludes in the creation of an architectural design framework. So, a correct decision- making approach is needed to design the software architecture. The present study analyzes the design decisions and proposes a new design decision model for the software architecture. This study introduces a new approach to the decision-making model, wherein software architecture design is viewed based on specific decisions.
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A Fast Heuristic Algorithm for Solving High-Density Subset-Sum Problems
Статья научная
The subset sum problem is to decide whether for a given set of integers A and an integer S, a possible subset of A exists such that the sum of its elements is equal to S. The problem of determining whether such a subset exists is NP-complete; which is the basis for cryptosystems of knapsack type. In this paper a fast heuristic algorithm is proposed for solving subset sum problems in pseudo-polynomial time. Extensive computational evidence suggests that the algorithm almost always finds a solution to the problem when one exists. The runtime performance of the algorithm is also analyzed.
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Статья научная
Today unauthorized access to sensitive information and cybercrimes is rising because of increasing access to the Internet. Improvement in software and hardware technologies have made it possible to detect some attacks and anomalies effectively. In recent years, many researchers have considered flow-based approaches through machine learning algorithms and techniques to reveal anomalies. But, they have some serious defects. By way of illustration, they require a tremendous amount of data across a network to train and model network’s behaviors. This problem has been caused these methods to suffer from desirable performance in the learning phase. In this paper, a technique to disclose intrusions by Support Vector Regression (SVR) is suggested and assessed over a standard dataset. The main intension of this technique is pruning the remarkable portion of the dataset through mathematics concepts. Firstly, the input dataset is modeled as a Directed Graph (DG), then some well-known features are extracted in which these ones represent the nature of the dataset. Afterward, they are utilized to feed our model in the learning phase. The results indicate the satisfactory performance of the proposed technique in the learning phase and accuracy over the other ones.
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A Fuzzy Approach for Text Mining
Статья научная
Document clustering is an integral and important part of text mining. There are two types of clustering, namely, hard clustering and soft clustering. In case of hard clustering, data item belongs to only one cluster whereas in soft clustering, data point may fall into more than one cluster. Thus, soft clustering leads to fuzzy clustering wherein each data point is associated with a membership function that expresses the degree to which individual data points belong to the cluster. Accuracy is desired in information retrieval, which can be achieved by fuzzy clustering. In the work presented here, a fuzzy approach for text classification is used to classify the documents into appropriate clusters using Fuzzy C Means (FCM) clustering algorithm. Enron email dataset is used for experimental purpose. Using FCM clustering algorithm, emails are classified into different clusters. The results obtained are compared with the output produced by k means clustering algorithm. The comparative study showed that the fuzzy clusters are more appropriate than hard clusters.
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A Gaussian Process Regression Model to Predict Path Loss for an Urban Environment
Статья научная
This research paper presents a Gaussian process regression (GPR) model for predicting path loss signal in an urban environment. The Gaussian process regression model was developed using a dataset of path loss signal measurements acquired in two urban environments in Nigeria. Three different kernel functions were selected and compared for their performance in the Gaussian process regression model, including the squared exponential kernel, the Matern kernel, and the rotational quadratic kernel. The GPR model was validated and evaluated using various performance metrics and compared with different regression models. The results show that the Gaussian process regression model with the Matern kernel outperforms the linear regression and the support vector regression, but the decision tree and the random forest regression did better than the GPR in both cities. In the city of Port Harcourt, the GPR has a RMSE value of 3.0776 dB, the DTR has 2.0005 dB, the SVR has 3.6047 dB, the RFR has 1.0459 dB, and the LR 3.5947dB. The proposed GPR model provides more accurate and efficient approach to predict path loss compared to traditional methods. The extensive data collection and analysis conducted has resulted in a well-developed and accurate model.
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A Hybrid Approach based on Classification and Clustering for Intrusion Detection System
Статья научная
Computer security plays an important role in everybody's life. Therefore, to protect the computer and sensitive information from the untrusted parties have great significance. Intrusion detection system helps us to detect these malicious activities and sends the reports to the administration. But there is a problem of high false positive rate and low false negative rate. To eliminate these problems, hybrid system is proposed which is divided into two main parts. First, cluster the data using K-Mean algorithm and second, is to classify the train data using Adaptive-SVM algorithm. The experiments is carried out to evaluate the performance of proposed system is on NSL-KDD dataset. The results of proposed system clearly give better accuracy and low false positive rule and high false negative rate.
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A Hybrid Spectral Conjugate Gradient Method with Global Convergence
Статья научная
The spectral conjugate gradient (SCG) method is one of the most commonly used methods to solve large- scale nonlinear unconstrained optimization problems. It is also the research and application hot spot of optimization theorists and optimization practitioners. In this paper, a new hybrid spectral conjugate gradient method is proposed based on the classical nonlinear spectral conjugate gradient method. A new parameter is given. Under the usual assumptions, the descending direction independent of any line search is generated, and it has good convergence performance under the strong Wolfe line search condition . On a set of test problems, the numerical results show that the algorithm is effective.
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A LSB Based Image Steganography Using Random Pixel and Bit Selection for High Payload
Статья научная
Security in digital communication is becoming more important as the number of systems is connected to the internet day by day. It is necessary to protect secret message during transmission over insecure channels of the internet. Thus, data security becomes an important research issue. Steganography is a technique that embeds secret information into a carrier such as images, audio files, text files, and video files so that it cannot be observed. In this paper, based on spatial domain, a new image steganography method is proposed to ensure the privacy of the digital data during transmission over the internet. In this method, least significant bit substitution is proposed where the information embedded in the random bit position of a random pixel location of the cover image using Pseudo Random Number Generator (PRNG). The proposed method used a 3-3-2 approach to hide a byte in a pixel of a 24 bit color image. The method uses Pseudo Random Number Generator (PRNG) in two different stages of embedding process. The first one is used to select random pixels and the second PRNG is used select random bit position into the R, G and B values of a pixel to embed one byte of information. Due to this randomization, the security of the system is expected to increase and the method achieves a very high maximum hiding capacity which signifies the importance of the proposed method.
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Статья научная
Given that divorce has become a recurring challenge with increasing intensity in our world today accompanying broken families, economy and social contagion, as well as the existence of difference in happiness between the couple and their children. Thus, a mathematical model for predicting the rate of divorce tendencies in the Nigerian society is hereby developed. Factors influencing the rate of divorce were outlined and mathematical relationships between these factors were established. Afterwards, the developed model was validated and the real life data collected were contrasted with the model data predictions using suitable statistical tools. The findings from the comparison showed that the real life data and the model data predictions have a higher degree of correlation; consequently, recommending the model as a benchmark measure for predicting rate of divorce/marital instability in Nigeria. In the same vein, recommendations were made at the end of the model analysis which when adhered to would yield.
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Статья научная
Preparing a class timetable or routine is a difficult task because it requires an iterative trial and error method to handle all the constraints. Moreover, it has to be beneficial both for the students and teachers. Therefore, the problem becomes a multi-objective optimization problem with a good number of constraints. There are two types of constraints: hard and soft constraint. As the problem is an NP-hard problem, population based multi-objective optimization algorithms (multi-objective evolutionary algorithm) is a good choice for solving the problem. There are well established hard constraints handling techniques for multi-objective evolutionary algorithms, however, the technique is not enough to solve the problem efficiently. In the paper, a smart initialization technique is proposed to generate fewer constraints violated solutions in the initial phase of the algorithm so that it can find feasible solutions quickly. An experimental analysis supports the assumption. Moreover, there are no well-known techniques available for handling soft constraints. A new soft constraints handing technique is proposed. Experimental results show a significant improvement can be achieved. Finally, proposed combined approach integrates smart initialization and soft constraints handling techniques. Better results are reported when comparing with a standard algorithm.
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A Multi-channel Character Relationship Classification Model Based on Attention Mechanism
Статья научная
Relation classification is an important semantic processing task in the field of natural language processing. The deep learning technology, which combines Convolutional Neural Network and Recurrent Neural Network with attention mechanism, has always been the mainstream and state-of-art method. The LSTM model based on recurrent neural network dynamically controls the weight by gating, which can better extract the context state information in time series and effectively solve the long-standing problem of recurrent neural network. The pre-trained model BERT has also achieved excellent results in many natural language processing tasks. This paper proposes a multi-channel character relationship classification model of BERT and LSTM based on attention mechanism. Through the attention mechanism, the semantic information of the two models is fused to get the final classification result. Using this model to process the text, we can extract and classify the relationship between the characters, and finally get the relationship between the characters included in this paper. Experimental results show that the proposed method performs better than the previous deep learning model on the SemEval-2010 task 8 dataset and the COAE-2016-Task3 dataset.
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A Multi-view Comparison of Various Metaheuristic and Soft Computing Algorithms
Статья научная
AI algorithms have been applied in a wide spectrum of articles across different domains with great success in finding solutions. There is an increasing trend of applying these techniques on newer problems. However, the numerous numbers of algorithms that are classified as AI algorithm hinder the ability of any researcher to select which algorithm is suitable for his problem. The invention of new algorithms increases the difficulty for researchers to be updated about AI algorithms. This paper is intended to provide a multi-facet comparison between various AI algorithms in order to aid researchers in understanding the differences between some of the popular algorithms and select the suitable candidate for their problems.
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A Natural Language Query Builder Interface for Structured Databases Using Dependency Parsing
Статья научная
A natural language query builder interface retrieves the required data in structured form from database when query is entered in natural language. The user need not necessarily have sufficient technical knowledge of structured query language statements so nontechnical users can also use this proposed model. In natural language parsing, getting highly accurate syntactic analysis is a crucial step. Parsing of natural languages is the process of mapping an input string or a natural language sentence to its syntactic representation. Constituency parsing approach takes more time for parsing. So, natural language query builder interface is developed in which the parsing of natural language sentence is done by using dependency parsing approach. Dependency parsing technique is widespread in natural language domain because of its state-of-art accuracy and efficiency and also it performs best. In this paper, the buffering scheme is also proposed for natural language statements which will not load the whole sentence if it was done previously. Also there was a need of generalized access to all tables from database which is handled in this system.
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A New Method of Generating Optimal Addition Chain Based on Graph
Статья научная
In many number theoretic cryptographic algorithms, encryption and decryption is of the form xn mod p, where n and p are integers. Exponentiation normally takes more time than any arithmetic operations. It may be performed by repeated multiplication which will reduce the computational time. To reduce the time further fewer multiplications are performed in computing the same exponentiation operation using addition chain. The problem of determining correct sequence of multiplications requires in performing modular exponentiation can be elegantly formulated using the concept of addition chains. There are several methods available in literature in generating the optimal addition chain. But novel graph based methods have been proposed in this paper to generate the optimal addition chain where the vertices of the graph represent the numbers used in the addition chain and edges represent the move from one number to another number in the addition chain. Method 1 termed as GBAPAC which generates all possible optimum addition chains for the given integer n by considering the edge weight of all possible numbers generated from every number in addition chain. Method 2 termed as GBMAC which generates the minimum number of optimum addition chains by considering mutually exclusive edges starting from every number. Further, the optimal addition chain generated for an integer using the proposed methods are verified with the conjectures which already existed in the literature with respect to addition chains.
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Статья научная
Picture fuzzy set is an extension of fuzzy sets and intuitionistic sets. It is demonstrated have a wide application in the fact and theoretical. In this paper, we propose some novel similarity measures between picture fuzzy sets. The novel similarity measure is constructed by combining negative functions of each degree membership of picture fuzzy set. This similarity is shown that is better other similarity measures of picture fuzzy sets in some cases. Next, we apply them in several pattern recognition problems. Finally, we apply them to find the fault diagnosis of the steam turbine.
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A Note on Quasi-coincidence for Fuzzy Points of Fuzzy Topology on the Basis of Reference Function
Статья научная
In this article our main aim is to revisit the definition of fuzzy point and fuzzy quasi-coincident of fuzzy topology which is accepted in the literature of fuzzy set theory. We analyse some results and also prove some proposition with extended definition of complementation of fuzzy sets on the basis of reference function and some new definitions have also been introduced whenever possible. In this work the main efforts have been made to show that the existing definition of complement of fuzzy point and definition of fuzzy quasi-coincident are not acceptable.
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