Статьи журнала - International Journal of Mathematical Sciences and Computing
Все статьи: 240
Application of Mathematical Modeling: A Mathematical Model for Dengue Disease in Bangladesh
Статья научная
A virus spread by mosquitoes called dengue fever affects millions of people each year and is a serious threat to world health. More than 140 nations are affected by the illness of dengue fever. Therefore, in this paper, a Susceptible-Infectious-Recovered (SIR) mathematical model for the host (human) and vector (dengue mosquitoes) has been presented to describe the transmission of dengue in Bangladesh. In the model the vector are related with two compartments that are susceptible and infective and host are related with three compartments that are susceptible, infective, and recovered. By these five compartments, five connected nonlinear ordinary differential equations (ODEs) are produced. As a result of non dimensionalization, a system of three nonlinear ODEs has been generated. The reproductive number and equilibrium points have been estimated for different cases. In order to compute the infection rate, data for infected human populations have been gathered from multiple health institutes in Bangladesh. MATLAB has been utilized to construct numerical simulations of different compartments in order to examine the impact of critical parameters on the disease’s propagation and to bolster the analytical findings. The simulated outcomes for susceptible, infected, and eliminated in graphical formats have been displayed. The paper’s main goal is to emphasize the uniqueness of computational analysis of the SIR mathematical model for the dengue fever.
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Статья научная
The article focuses on the application of mathematical methods and models in product - service manufacturing processes in scientific innovative technoparks. The necessity of applying economic-mathematical models and methods in the activity of innovative structures is substantiated. A system of indicators and criteria for assessing the performance and effective management of technoparks has been developed. Based on this system, an information model of the technopark was proposed. A mathematical model of the general management of technoparks is proposed. An econometric model for the innovative product-service manufacturing has been developed. The technique of comparative evaluation of innovative technology parks based on a system of composite indicators and a composite index is proposed. As a result of the experimental implementation of these methods and models, the activity of technoparks.
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Application of the Flow Curvature Method in Lorenz-Haken Model
Статья научная
We consider a recently developed new approach so-called the flow curvature method based on the differential geometry to analyze the Lorenz-Haken model. According to this method, the trajectory curve or flow of any dynamical system of dimension considers as a curve in Euclidean space of dimension . Then the flow curvature or the curvature of the trajectory curve may be computed analytically. The set of points where the flow curvature is null or empty defines the flow curvature manifold. This manifold connected with the dynamical system of any dimension directly describes the analytical equation of the slow invariant manifold incorporated with the same dynamical system. In this article, we apply the flow curvature method for the first time on the three-dimensional Lorenz-Haken model to compute the analytical equation of the slow invariant manifold where we use the Darboux theorem to prove the invariance property of the slow manifold. After that, we determine the osculating plane of the dynamical system and find the relation between flow curvature manifold and osculating plane. Finally, we find the nature of the fixed point stability using flow curvature manifold.
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Asymptotic solutions of a semi-submerged sphere in a liquid under oscillations
Статья научная
One of the most widely used techniques to look into transient behaviour of vibrating systems is the Krylov-Bogoliubov-Mitropolskii (KBM) method, which was developed for obtaining the periodic solutions of second order nonlinear differential systems of small nonlinearities. Later on, this method was studied and modified by numerous scholars to obtain solutions of higher order nonlinear systems. This article modified the method to study the solutions of semi-submerged sphere in a liquid which is floating owing to the gravitational force and the upward pressure of the liquid. This paper suggests that the results obtained for different sets of initial conditions by the modified KBM method correspond well with those obtained by the numerical method.
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Augmented Apriori by Simulating Map-Reduce
Статья научная
Association rule mining is a data mining technique which is used to identify decision-making patterns by analyzing datasets. Many association rule mining techniques exist to find various relationships among itemsets. The techniques proposed in the literature are processed using non-distributed platform in which the entire dataset is sustained till all transactions are processed and the transactions are scanned sequentially. They require more space and are time consuming techniques when large amounts of data are considered. An efficient technique is needed to find association rules from big data set to minimize the space as well as time. Thus, this paper aims to enhance the efficiency of association rule mining of big transaction database both in terms of memory and speed by processing the big transaction database as distributed file system in Map-Reduce framework. The proposed method organizes the transactions into clusters and the clusters are distributed among many parallel processors in a distributed platform. This distribution makes the clusters to be processed simultaneously to find itemsets which enhances the performance both in memory and speed. Then, frequent itemsets are discovered using minimum support threshold. Associations are generated from frequent itemsets and finally interesting rules are found using minimum confidence threshold. The efficiency of the proposed method is enhanced in a noticeably higher level both in terms of memory and speed.
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BRAINSEG – Brain Structures Segmentation Pipeline Using Open Source Tools
Статья научная
Structure segmentation is often the first step in the diagnosis and treatment of various diseases. Because of the variations in the various brain structures and overlapping structures, segmenting brain structures is a very crucial step. Though a lot of research had been done in this area, still it is a challenging field. Using prior knowledge about the spatial relationships among structures, called as atlases, the structures with dissimilarities can be segmented efficiently. Multiple atlases prove a better one when compared to single atlas, especially when there are dissimilarities in the structures. In this paper, we proposed a pipeline for segmenting brain structures using open source tools. We test our pipeline for segmenting brain structures in MRI using the publicly available data provided by MIDAS.
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Bayesian Parameter Inference of Explosive Yields Using Markov Chain Monte Carlo Techniques
Статья научная
A Bayesian parameter inference problem is conducted to estimate the explosive yield of the first atomic explosion at Trinity in New Mexico. The first of its kind, the study advances understanding of fireball dynamics and provides an improved method for the determination of explosive yield. Using fireball radius-time data taken from archival film footage of the explosion and a physical model for the expansion characteristics of the resulting fireball, a yield estimate is made. Bayesian results from the Markov chain indicate that the estimated parameters are consistent with previous calculation except for the critical parameter that modifies the independent time variable. This unique result finds that this parameter deviates in a statistically significant way from previous predictions. Use of the Bayesian parameter estimates computed is found to greatly improve the ability of the fireball model to predict the observed data. In addition, parameter correlations are computed from the Markov chain and discussed. As a result, the method used increases basic understanding of fireball dynamics and provides an improved method for the determination of explosive yields.
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Bayesian approach to generalized normal distribution under non-informative and informative priors
Статья научная
The generalized Normal distribution is obtained from normal distribution by adding a shape parameter to it. This paper is based on the estimation of the shape and scale parameter of generalized Normal distribution by using the maximum likelihood estimation and Bayesian estimation method via Lindley approximation method under Jeffreys prior and informative priors. The objective of this paper is to see which is the suitable prior for the shape and scale parameter of generalized Normal distribution. Simulation study with varying sample sizes, based on MSE, is conducted in R-software for data analysis.
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Статья научная
This study describes the Bayesian approach as an alternative approach for estimating time axes parameters and the periodogram (power spectrum) associated with sinusoidal model when the white noise (sigma) is known or unknown. The conventional method of estimating the time axes parameters and the periodogram has been via the Schuster method that relies solely on Maximum Likelihood Estimation (MLE). The Bayesian alternative approach proposed in this work, on the other hand, adopted the Maximum A Posteriori (MAP) via the Markov Chain Monte Carlo (MCMC) in order to checkmate the problem of re-parameterization and over- parameterization associated with MLE in the conventional practice. The rates of heartbeat variability at exactly an hour and two hours after birth of one thousand eight hundred (1800) newly born babies in a state hospital were recorded and subjected to both the Bayesian approach and Schuster approach for inferences. The periodogram estimates, exactly an hour and two hours of after birth, were estimated to be 0.7395 and 0.7549, respectively - and it was deduced that rates of heartbeat (frequency) variability moderated and stabilized the pulse among the babies after two hours of birth. In addition, MAP mean estimates of the parameters approximately equals to the true mean of estimates when round up to curb the problem of re-parameterization and over- parameterization that do affect Schuster method via MLE.
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Статья научная
The present study is concerned with the estimation of Inverse Exponential distribution using various Bayesian approximation techniques like normal approximation, Tierney and Kadane (T-K) Approximation. Different informative and non-informative priors are used to obtain the Baye’s estimate of Inverse Exponential distribution under different approximation techniques. A simulation study has also been conducted for comparison of Baye’s estimates obtained under different approximation using different priors.
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Bayesian normal and T-K approximations for shape parameter of Type-I Dagum distribution
Статья научная
Dagum distribution is a statistical distribution used closely for fitting income and wealth distributions. This distribution has wide application in fields like reliability theory survival analysis, actuarial sciences, and meteorological data. In this article, we obtained Bayes estimators for the shape parameter of Dagum distribution using approximation techniques like normal and T-K approximations. Moreover different informative priors have been considered and a simulation study and three real data sets have been considered to study the efficiency of obtained results.
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Birth Rate Study of Henan Province Based on Ridge Regression Model
Статья научная
In order to explore the underlying reasons for the decline in birth rate, this article selects 12 explanatory variables and uses ridge regression method to study the birth rate in Henan Province from 2015 to 2021. Research has shown that four factors, namely the average salary of urban unit employees, the urbanization degree, the ratio of female employees with a university degree or above, and the population mortality rate, can not explain the birth rate. However, the proportion of gross domestic product of the second and third industries, as well as the proportion of female population over 15 years of age who are illiterate, has a positive impact on the car success rate. The gross domestic product per capita, the number of beds per 10000 people in medical institutions, the per capita disposable income of urban residents, the per capita disposable income of rural residents, the adolescent dependency ratio, and the elderly dependency ratio have a negative impact on the birth rate. Through the research in this article, the main factors affecting the birth rate in Henan Province have been identified, and policy recommendations for improving the birth rate have been proposed. The positive impact represents increasing investment in these factors, which can effectively improve the birth rate in Henan Province and solve the serious problems we are currently facing. The negative factor is the opposite.
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Blockchain: A Comparative Study of Consensus Algorithms PoW, PoS, PoA, PoV
Статья научная
Since the inception of Blockchain, the computer database has been evolving into innovative technologies. Recent technologies emerge, the use of Blockchain is also flourishing. All the technologies from Blockchain use a mutual algorithm to operate. The consensus algorithm is the process that assures mutual agreements and stores information in the decentralized database of the network. Blockchain’s biggest drawback is the exposure to scalability. However, using the correct consensus for the relevant work can ensure efficiency in data storage, transaction finality, and data integrity. In this paper, a comparison study has been made among the following consensus algorithms: Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Vote (PoV). This study aims to provide readers with elementary knowledge about blockchain, more specifically its consensus protocols. It covers their origins, how they operate, and their strengths and weaknesses. We have made a significant study of these consensus protocols and uncovered some of their advantages and disadvantages in relation to characteristics details such as security, energy efficiency, scalability, and IoT (Internet of Things) compatibility. This information will assist future researchers to understand the characteristics of our selected consensus algorithms.
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Building Background to the Elgamal Algorithm
Статья научная
In this paper, we develop a new encryption scheme based on the ELGAMAL encryption algorithm and the degree of difficulty of the discrete logarithm problem (DLP). In public key cryptography, a secret key is often used for a long period of time, thus expelling the secret key. Moreover, devices used to calculate cryptography can also be physically attacked, leading to the secret key being exposed. This paper proposes a new encryption scheme to reduce the risk of revealing a secret key.
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Category specific prediction modules for visual relation recognition
Статья научная
Object classification in an image does not provide a complete understanding of the information contained in it. Visual relation information such as “person playing with dog” provides substantially more understanding than just “person, dog”. The visual inter-relations of the objects can provide substantial insight for truly understanding the complete picture. Due to the complex nature of such combinations, conventional computer vision techniques have not been able to show significant promise. Monolithic approaches are lacking in precision and accuracy due to the vastness of possible relation combinations. Solving this problem is crucial to development of advanced computer vision applications that impact every sector of the modern world. We propose a model using recent advances in novel applications of Convolution Neural Networks (Deep Learning) combined with a divide and conquer approach to relation detection. The possible relations are broken down to categories such as spatial (left, right), vehicle-related (riding, driving), etc. Then the task is divided to segmenting the objects, estimating possible relationship category and performing recognition on modules specially built for that relation category. The training process can be done for each module on significantly smaller datasets with less computation required. Additionally this approach provides recall rates that are comparable to state of the art research, while still being precise and accurate for the specific relation categories.
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Clustering of Multi Scripts Isolated Characters Using k-Means Algorithm
Статья научная
The aim of this paper is script identification problem of handwritten text which facilitates the clustering of data according to their type of script. In this paper, collection of different types of handwritten text document i.e. Devanagari, Gurumukhi and Roman is taken as input and then cluster of all these documents according to script type whether i.e. Devanagari, Gurumukhi, or Roman was prepared. Clustering of handwritten multi-script document scheme proposed in this paper is divided into two phases. First phase used to extract the features of given text images. In the second phase, features extracted in the previous phase were used for clustering with k-Means algorithm. In feature extraction phase, we have extracted four types of features, namely, circular curvature feature, horizontal stroke density feature, pixel density feature value and zoning based feature. In this study, we have considered 4,850 samples of isolated characters of Devanagari, Gurumukhi and Roman script.
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Статья научная
Problem of queue management has been a great barrier to the financial institutions. Another way of measuring efficiency in banking industries is how fast the service of saving and withdraw is been rendered. Imagine customers that spend the whole day in the banking hall for one service or the other, due to poor service delivery and long stay on the queue will not hesitate to change his bank. Data was collected by direct observation in two banks, one old generation bank and one new generation bank, queue model and other statistical tools were used to analyze the data. Result of the analysis shows that Guaranty Trust Bank is more efficient than First Bank in that the later has a prolonged service time attributed to the preference of it by a pool of customers for many reasons.
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Comparative Analysis of Steganography Technique for Information Security
Статья научная
Organisations need information security to reduce the risk of unauthorized information disclosure, use, modification and destruction. To avoid this risk and ensure security diverse solutions are available such as Cryptography, Steganography and Watermarking. Encryption changes the form of information but latter two hide records or watermark in some medium. This paper is an effort to explore one of the solutions i.e. Steganography. It is a mechanism of hiding secret information in text, image, audio or video carriers. Broadly, these are classified in various categories such as Spatial domain, Transform domain and Distortion Technique. This work intends to give an overview of above mentioned techniques in detail by comparing algorithms based on performance metrics such as Bhattacharyya Coefficient, Correlation Coefficient, Intersection Coefficient, Jaccard Index, MAE, MSE, PSNR and UIQI. After analysing the MATLAB simulation and comparison based on different performance metrics, LSB Substitution and Pseudorandom technique are best suited for generating highly matched stego image with respect to their cover image.
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Comparative Analysis of Threat Detection Techniques in Drone Networks
Статья научная
With the rapid proliferation of drones and drone networks across various application domains, ensuring their security against cyber threats has become imperative. This paper presents a comprehensive analysis and comparative analysis of the state-of-the-art techniques for detecting cyber threats in drone networks. The background provides a primer on drones, networks, drone network architectures, communication mechanisms, and enabling technologies like wireless protocols, satellite navigation, onboard computers, sensors, and flight control systems. The landscape of emerging technologies including blockchain, software-defined networking, machine learning, fog computing, ad-hoc networks, and swarm intelligence is reviewed in the context of transforming drone network capabilities while also introducing potential vulnerabilities. The paper delves into common cyber threats faced by drone networks such as hacking, DoS attacks, data breaches, and GPS spoofing. A detailed literature review of proposed threat detection techniques is provided, categorized into machine learning, multi-agent systems, blockchain, intrusion detection systems, software solutions, and miscellaneous methods. A key gap identified is handling increasingly sophisticated attacks, complex environments, and resource limitations in aerial platforms. The analysis highlights accuracy, overhead and real-time trade-offs between techniques, while factors like model optimization can influence efficacy. A comparative analysis highlights the advantages and limitations of each approach considering metrics like accuracy, scalability, flexibility, and overhead. Key observations include the trade-offs between computational complexity and real-time performance, the challenges in handling evolving attack techniques, and the dependencies between detection accuracy and factors like model selection and training data quality. The analysis provides a comprehensive reference for cyber threat detection in drone networks, benefiting researchers and practitioners aiming to advance this crucial area of drone security through robust detection systems tailored for resource-constrained aerial environments.
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Статья научная
It’s is no longer news that the Local Government (Third-tier of government in Nigeria) has not been able to optimally discharge its responsibilities according to its mandate. This had raised serious policy and research concerns that resulted to several reformative approaches in a bid to restructuring the system for efficient service delivery in the past decade. One major unpopular reason for inefficient service delivery was poor administration in the Local Government as a result of unbalanced distribution of personnel by cadre and gender in each local government in the state. This had not only hampered local government administration but also impeded adequate provision of expected services to local populace in line with its mandate as enshrined in the fourth schedule of the 1999 Constitution of the Federal Republic of Nigeria. Application of Statistical analysis using Chi-square Test of Independence showed that distribution of employees by cadre depends on local government in post in the year under study. Percentage distributions employed revealed that some local governments that are located around the capital city had more workers than those located outside the state capital city except some local governments with more viable socio-economic benefits. Sex ratio showed that there was gender imbalance as female personnel are more than their male counterpart in the local government service is.
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