Статьи журнала - International Journal of Mathematical Sciences and Computing
Все статьи: 198
Статья научная
Precise extrapolative mining and analysis of relevant dataset during or after any disease outbreak can assist the government, stake holders and relevant agencies in the health sector to make important decisions with respect to the disease outbreak control and management. While prior works has concentrated on non-stationary long term data, this work focuses on a short term non-stationary and relatively noisy data. Particularly, a distinctive nonparametric machine learning method based kernel-controlled probabilistic Gaussian process regression model has been proposed and employed to model and analyze Covid-19 pandemic data acquired over a period of approximately six weeks. To accomplish the aim, the MATLAB 2018a computational and machine learning environment was engaged to develop and perform the Gaussian process extrapolative analysis. The results displayed high scalability and optimal performance over the commonly used machine learning methods such as the Neural networks, Neural-Fuzzy networks, Random forest, Regression tree, Support Vector machines, K-nearest neighbor and Discriminant linear regression models. These results offer a solid foundation for conducting research on reliable prognostic estimations and analysis of contagious disease emergence intensity and spread.
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Machine learning applied to cervical cancer data
Статья научная
Cervical Cancer is one of the main reason of deaths in countries having a low capita income. It becomes quite complicated while examining a patient on basis of the result obtained from various doctor’s preferred test for any automated system to determine if the patient is positive with the cancer. There were 898 new cases of cervical cancer diagnosed in Australia in 2014. The risk of a woman being diagnosed by age 85 is 1 in 167. We will try to use machine learning algorithms and determine if the patient has cancer based on numerous factors available in the dataset. Predicting the presence of cervical cancer can help the diagnosis process to start at an earlier stage.
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Статья научная
In countries with arid and semi-arid climate such as Iran with water constraints, the use of groundwater resources is very important. There are various mathematical based methods and software packages for modelling groundwater resources. This paper uses groundwater flow problems to illustrate possible approaches for providing the environment of active teaching. Mathematical models supported by software applications facilitate the gaining of an insight into the physical behaviors by investigating a host of scenarios and events but they are poor in training critical thinking for encapsulating the hardcore mathematical equations describing the problems. Whilst software engineering has transformed the intellectual capitals accumulated between the 20th century and the middle of the 21th century into working tools, it has the drawback of encapsulating core mathematics away from common experience of the students and practitioners. This diminishes critical thinking in a world of increasing risks and ought to be taken a serious side effect of software engineering. This paper suggests a solution by building up a library of solvers using spreadsheets, with the effect that the encapsulated knowledge of building modelling solvers can permanently be brought to life in education with the active learning culture. Implementation was carried out in the same way for steady state flow as well as explicit 2D and 3D finite difference approximation for transient flow. This study raises concern about the encapsulated body of knowledge contributed to the emergence and the establishment of modelling software applications since 1980. This body of knowledge comprise a deeper understanding of equations of often partial differential equations describing physical problems, as well as their numerical transformation into systems of equations and their subsequent properly- and improperly posed systems of equation in terms of their assumptions and quality conditions. The outcome is the emergence of a cookbook mentality among the new breed of mathematical modelers without any critical thinking. The results revealed that spreadsheet can be used with the aid of the Solver function. This idea capitalized on the capabilities of the net-generation and opens up the possibility for the emergence of bottom-up open source modelling platforms.
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Статья научная
The research is concerned with the development of a mathematical model for predicting the rate of human happiness and to outline factors that influence human happiness. The model was optimized and observation about the model’s extreme value was made. The outcome of the optimization result showed that happiness has neither minimum nor maximum level that should be required in human. It means someone’s happiness could be close to 0% or even be up to 100%. Thereafter, the model was analysed and the collated real-life data were correlated with those of the model data (H model) using suitable statistical tools. The findings from the correlation result showed that the questionnaire result attained a 70% degree of correlation with the estimated model result (H model), and thus recommending the model as a standard measure for predicting the rate of human happiness.
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Статья научная
At the end of 2019 the novel coronavirus disease (COVID-19) was declared as a major health hazard by the world health organization (WHO) and the only available way of stopping this threat was via non-pharmaceutical approach. Most authors have studied COVID-19 transmission dynamics using mathematical modeling by involving the basic (major) compartments. In this study we have formulated a mathematical model for the transmission dynamics of COVID-19 which incorporates almost all possible scenarios at present. We have also analyzed the impact of prevention and control strategies. The model has satisfied all the basic properties that infectious disease model should fulfill; Boundedness, positivity of its solutions, stability analysis, epidemic equilibrium point, basic reproduction number and local stability of the disease free equilibrium. We introduced a self-protection parameter, m to analyze the impact of physical distancing, staying at home, using masks, washing hands and so on. The impact of isolation and quarantine has been analyzed and their effects on the number of Exposed, infected and dead people were clearly discussed. In addition to these, the effects of symptomatic and asymptomatic individuals on the value of basic reproduction number have been examined. The numerical simulations of this study indicate that the government should increase isolation, quarantine and self-protection rates. Additionally to minimize the contact rate between susceptible and asymptotic individuals, self-protection at all cost and everywhere has to be done, so that both symptomatic and importantly asymptomatic individuals stop transmitting the virus.
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Means of the Semantic Search Personification on base of Ontological Approach
Статья научная
The main trends of information retrieval deal with its personification and semantization are analyzed. Sources of knowledge about main subjects and objects of the search process are considered. Ontological model of interaction between the Web information resources and information consumers is proposed as a base of the search personification. Methods of development, improvement and usage of this model are defined. User characteristics are supplemented with sociopsychophysiological properties and ontologically personalized readability criteria. Software realization of semantic search on base of this ontological approach is described.
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Mining maximal subspace clusters to deal with inter-subspace density divergence
Статья научная
In general, subspace clustering algorithms identify enormously large number of subspace clusters which may possibly involve redundant clusters. This paper presents Dynamic Epsilon based Maximal Subspace Clustering Algorithm (DEMSC) that handles both redundancy and inter-subspace density divergence, a phenomenon in density based subspace clustering. The proposed algorithm aims to mine maximal and non-redundant subspace clusters. A maximal subspace cluster is defined by a group of similar data objects that share maximal number of attributes. The DEMSC algorithm consists of four steps. In the first step, data points are assigned with random unique positive integers called labels. In the second step, dense units are identified based on the density notion using proposed dynamically computed epsilon-radius specific to each subspace separately and user specified input parameter minimum points, τ. In the third step, sum of the labels of each data object forming the dense unit is calculated to compute its signature and is hashed into the hash table. Finally, if a dense unit of a particular subspace collides with that of the other subspace in the hash table, then both the dense units exists with high probability in the subspace formed by combining the colliding subspaces. With this approach efficient maximal subspace clusters which are non-redundant are identified and outperforms the existing algorithms in terms of cluster quality and number of the resulted subspace clusters when experimented on different benchmark datasets.
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Статья научная
The socio-economic evolution of populations has in recent decades a rapid and multiple changes, including dietary habits that have been characterized by the consumption of fresh products out of season and widely available throughout the year. Culture under shelters of fruit, vegetable and flower species developed from the classical to the greenhouse agro - industrial, currently known for its modernity and high level of automation (heating, misting, of conditioning, control, regulation and control, supervisor of computer etc ...). new techniques have emerged, including the use of control devices and regulating climate variables in a greenhouse (temperature, humidity, CO2 concentration etc ...) to the exploitation of artificial intelligence such as neural networks and / or fuzzy logic. Currently the climate computer offers many benefits and solves problems related to the regulation, monitoring and controls. Greenhouse growers remain vigilant and attentive, facing this technological development. they ensure competitiveness and optimize their investments / production cost which continues to grow. The application of artificial intelligence in the industry known for considerable growth, which is not the case in the field of agricultural greenhouses, where enforcement remains timid. it is from this fact, we undertake research work in this area and conduct a simulation based on meteorological data through MATLAB Simulink to finally analyze the thermal behavior - greenhouse microclimate energy.
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Статья научная
This study presents the modelling of impacts of climate change on water resources. Mtera dam in Tanzania was taken as a case study. Data for climate variables on four stations were obtained from Tanzania Meteorological Agency (TMA) while data for water level were obtained from Rufiji Basin Development Authority (RUBADA). The study aimed at doing regression analysis on all stations to analyze the impacts of change in climate variables on water level. Results show that rainfall was significant predictor of water level at Iringa and Dodoma while temperature and sunshine were significant at Mbeya station. Change in climate variables accounted for 37% of the fluctuations of water level in the dam. It was recommended that TANESCO should construct small dams on upper side of Mtera dam to harvest rain water during rainy season. In long run TANESCO should invest into alternative sources of energy.
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Modification on AES-GCM to increment ciphertext randomness
Статья научная
Today, there are many cryptographic algorithms that are designed to maintain the data confidentiality, from these algorithms is AES. In AES-GCM, the key in addition to the IV are used to encrypt the plaintext to obtain the ciphertext instead of just the key in the traditional AES. The Use of the IV with the key in order to gain different ciphertext for the same plaintext that was encrypted more than ones, with the same key. In this paper, the mechanism of change the IV each time in AES-GCM was modified to get more randomness in the ciphertext, thus increase the difficulty of breaking the encrypted text through analysis to obtain the original text. NIST statistical function were used to measure the randomness ratio in the encrypted text before and after modification, where there was a clear rise in the randomness ratio in the encoded text which obtained by using the modified algorithm against ciphertext by using the normal AES_GCM.
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Modified DES using Different Keystreams Based On Primitive Pythagorean Triples
Статья научная
Symmetric-key encryption is a traditional form of cryptography, in which a single key is used to encrypt and decrypt a message. In symmetric–key algorithm before any encrypted message is being transmitted, the sender and receiver must know the key value in advance. There are several drawbacks in symmetric-key algorithms. In some algorithms, the size of the key should be same as the size of the original plaintext and maintaining and remembering such a key is very difficult. Further, in symmetric-key algorithms, several round has to be performed to produce the ciphertext and perhaps the same key is used in each round which results in subkey generated from the current round is fully depending on the previous round. To avoid these, a novel approach in generating the key from the keystream for any symmetric-key algorithms using the Primitive Pythagorean Triples(PPT) has been proposed in this paper. The main advantage of this method is that the key value generated from the keystream is chosen by both the sender and the receiver. Further, the size of the key sequence is not limited but its size is arbitrary in length. Since, the keystream generated is random, no need to remember such keys by both the sender and the receiver.
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Статья научная
When we are given a data set where in based upon the values and or characteristics of attributes each data point is assigned a class, it is known as classification. In machine learning a very simple and powerful tool to do this is the k-Nearest Neighbor (kNN) algorithm. It is based on the concept that the data points of a particular class are neighbors of each other. For a given test data or an unknown data, to find the class to which it is the neighbor one measures in kNN the Euclidean distances of the test data or the unknown data from all the data points of all the classes in the training data. Then out of the k nearest distances, where k is any number greater than or equal to 1, the class to which the test data or unknown data is the nearest most number of times is the class assigned to the test data or unknown data. In this paper, I propose a variation of kNN, which I call the ANN method (Alternative Nearest Neighbor) to distinguish it from kNN. The striking feature of ANN that makes it different from kNN is its definition of neighbor. In ANN the class from whose data points the maximum Euclidean distance of the unknown data is less than or equal to the maximum Euclidean distance between all the training data points of the class, is the class to which the unknown data is neighbor. It follows, henceforth, naturally that ANN gives a unique solution to each unknown data. Where as , in kNN the solution may vary depending on the value of the number of nearest neighbors k. So, in kNN, as k is varied the performance may vary too. But this is not the case in ANN, its performance for a particular training data is unique. For the training data [1] considered in this paper, the ANN gives 100% accurate result.
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Статья научная
Water loss in water distribution systems (WDS) is a serious problem in Tanzania and the third world countries at large. A lot of water is lost on its way before reaching the consumers. This causes a shortage of water supply which leads to loss of revenues of the concerned water authorities. The control or reduction of water loss in the WDS is closely dependent on the commitment of the decision-makers and on the strategies and budget, they set for that purpose. This paper presents a combined model of Multi-Criteria Decision Making (MCDM) and Numerical optimization techniques which may help decision-makers to prioritize and select the best strategies to be used in the management of water loss in the WDS at Moshi Urban Water Supply and Sanitation Authority (MUWSA), Tanzania. The Multi-Criteria Decision Making family methods namely the Multi-Attribute Value Theory (MAVT), Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER), and Complex Proportional Assessment (COPRAS) were used to evaluate and prioritize the strategies, whereas the Integer Linear Programming (ILP) technique a numerical optimization technique was used to select the best strategies or alternatives to be employed in water loss management. The results show that the most preferable alternative is replacement of dilapidated pipes while the least preferable alternative is network zoning. The model selects thirteen out of sixteen alternatives, which cost 97% (TZS 235.71 million) of the total budgets set by the water authority to form a portfolio of the best alternatives for water loss management. Furthermore, the model showed robustness as the selected portfolio of alternatives remained the same even when the weights of the evaluation criteria changed.
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Multiobjective artificial bee colony based job scheduling for cloud computing environment
Статья научная
Cloud computing has become the hottest issue due to its wide range of services. Due to a large number of users, it becomes more significant to provide high availability of services to cloud users. The majority of existing scheduling techniques in the cloud environment is NP-Complete in nature. Many researchers have utilized meta-heuristic techniques to schedule the jobs in cloud data centers. The majority of existing techniques such as Genetic Algorithm, Ant colony optimization, Non-dominated Sorting Genetic Algorithm (NSGA-III), etc. suffer from poor convergence speed. Also, most of these techniques are either based upon scheduling or load balancing. Therefore, to overcome these issues, a new Variance Honey Bee Behavior with multi-objective optimization method (VHBBMO) is proposed in this paper. Extensive experiments have been conducted by considering the various set of jobs. The experimental results have shown that the proposed method provides more significant results than available methods.
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Non-singular Transformation Based Encryption Scheme
Статья научная
In this paper, we propose a novel variant of the Hill cipher based on vector spaces. In the classical Hill cipher, a non-singular matrix is used for encryption but it is well known that this cipher is vulnerable to the known-plaintext attack. In our proposed cryptosystem, we eradicate this problem by encrypting each plaintext block with a new invertible key matrix. This makes our scheme immune to all existing attacks in literature on this type of ciphers and so the resulting cipher can be used as other state-of-art block cipher. To generate the invertible matrices which serve as the dynamic keys, we make use of the vector spaces along with randomly generated basis and non-singular linear transformation. In addition to this, we also study the computational complexity of the proposed cryptosystem and compare this with the computational complexities of other schemes based on Hill cipher.
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Novel Quantum Random Number Generator with the Improved Certification Method
Статья научная
Random numbers play an important role in many areas, for example, encryption, cryptography, static analysis, simulations. It is also a fundamental resource in science and engineering. There are algorithmically generated numbers that are similar to random distributions, but are not actually random, called pseudo random number generators. In many cases the tasks to be solved are based on the unpredictability of random numbers, which cannot be guaranteed in the case of pseudo random number generators, true randomness is required. In such situations, we use real random number generators whose source of randomness is unpredictable random events. Quantum Random Number Generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. Our goal is to generate fast random numbers at a lower cost. At the same time, a high level of randomness is essential. Through quantum mechanics, we can obtain true numbers using the unpredictable behavior of a photon, which is the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the operation of the device and the quality of the random bits generated. We present the improved novel quantum random number generator, which is based the on time of arrival QRNG. It uses the simple version of the detectors with few requirements. The novel QRNG produces more than one random bit per each photon detection. It is rather efficient and has a high level of randomness. Self-testing as well as device independent quantum random number generation methods are analyzed. The advantages and disadvantages of both methods are identified. The model of a novel semi self-testing certification method for quantum random number generators (QRNG) is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. Finally, the novel certification method is integrated into the model of the new quantum random number generator. The paper analyzes its security and efficiency.
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Numerical Double Integration for Unequal Data Spaces
Статья научная
Numerical integral is one of the mathematical branches that connect between analytical mathematics and computer. Numerical integration is a primary tool used by engineers and scientists to obtain an approximate result for definite integrals that cannot be solved analytically. Numerical double integration is widely used in calculating surface area, the intrinsic limitations of flat surfaces and finding the volume under the surface. A wide range of method is applied to solve numerical double integration for equal data space but the difficulty is arisen when the data values are not equal. In this paper we have tried to generate a mathematical formula of numerical double integration for unequal data spaces. Trapezoidal rule for unequal space is used to evaluate the formula. We also verified our proposed model by demonstrating some numerical examples and compared the numerical result with the analytical result.
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Статья научная
In this paper, we studied to obtain numerical solutions of partial differential equations with fractional variable coefficient by MAPLE 18 software algorithm on New Iterative Method. We examined and investigated behaviours of the fractional variable coefficients (Even and Odd) on first order partial differential equation; we obtain numerical solution and plot 2D/3D graphs representation of eight (8) cases for the study of the sequential trend of the fractional coefficients. The simplicity and the accuracy of the proposed numerical scheme are verified. More numerical examples will be used in the future for further testing the ability of the proposed scheme for solving some classical problems in engineering sciences.
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On the Relations between Lucas Sequence and Fibonacci-like Sequence by Matrix Methods
Статья научная
In the present paper first and foremost we introduce a generalization of a classical Fibonacci sequence which is called a Fibonacci-Like sequence and at hindmost we obtain some relationships between Lucas sequence and Fibonacci-Like sequence by using two cross two matrix representation to the Fibonacci-Like sequence. The most worth noticing cause of this article is our proof method, since all the identities are proved by using matrix methods.
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Статья научная
It has been of concern for the most appropriate control mechanism associated with the growing complexity of dual HIV-HBV infectivity. Moreso, the scientific ineptitude towards an articulated mathematical model for co-infection dynamics and accompanying methodological application of desired chemotherapies inform this present investigation. Therefore, the uniqueness of this present study is not only ascribed by the quantitative maximization of susceptible state components but opined to an insight into the epidemiological identifiability of dual HIV-HBV infection transmission routes and the methodological application of triple-dual control functions. Using ODEs, the model was formulated as a penultimate 7-Dimensional mathematical dynamic HIV-HBV model, which was then transformed to an optimal control problem, following the introduction of multi-therapies in the presence of dual adaptive immune system and time delay lags. Applying classical Pontryagin’s maximum principle, the system was analyzed, leading to the derivation of the model optimality system and uniqueness of the system. Specifically, following the dual role of the adaptive immune system, which culminated into triple-dual application of multi-therapies, the investigation was characterized by dual delayed HIV-HBV virions decays from infected double-lymphocytes in a biphasic manner, accompanied by more complex decay profiles of infectious dual HIV-HBV virions. The result further led to significant triphasic maximization of susceptible double-lymphocytes and dual adaptive immune system (cytotoxic T-lymphocytes and humeral immune response) achieved under minimal systemic cost. Therefore, the model is comparatively a monumental and intellectual accomplishment, worthy of emulation for related and future dual infectivity.
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