Статьи журнала - International Journal of Mathematical Sciences and Computing

Все статьи: 246

The better pseudo-random number generator derived from the library function rand() in C/C++

The better pseudo-random number generator derived from the library function rand() in C/C++

Pushpam Kumar Sinha, Sonali Sinha

Статья научная

We choose a better pseudo-random number generator from a list of eight pseudo-random number generators derived from the library function rand() in C/C++, including rand(); i.e. a random number generator which is more random than all the others in the list. rand() is a repeatable pseudo-random number generator. It is called pseudo because it uses a specific formulae to generate random numbers, i.e. to speak the numbers generated are not truly random in strict literal sense. There are available several tests of randomness, some are easy to pass and others are difficult to pass. However we do not subject the eight set of pseudo random numbers we generate in this work to any known tests of randomness available in literature. We use statistical technique to compare these eight set of random numbers. The statistical technique used is correlation coefficient.

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Transmission Dynamics of Malware in Networks Using Caputo Fractional Order Derivative

Transmission Dynamics of Malware in Networks Using Caputo Fractional Order Derivative

Jyoti Kumari Gupta, Bimal Kumar Mishra

Статья научная

Fractional calculus plays a crucial role in the representation of various natural and physical phenomena by incorporating the inherent non-locality and long-term memory effect of fractional operators. These models offer a more precise and systematic depiction of the underlying phenomena. The focus of this research paper is on the utilization of fractional calculus in the context of the epidemic model. Specifically, the model considers a fractional order ρ, where 0<ρ≤1, and employs the Caputo fractional order derivative to describe the transmission of malware in both wireless and wired networks. The basic reproduction number, along with the fractional order ρ, is identified as the threshold parameter in this model. The stability of the system is analysed at different stages of the reproduction number, considering both local and global asymptotic stability. Additionally, sensitivity analysis is conducted on the model parameters to determine the direction of change in the reproduction number. This analysis aids in understanding whether the reproduction number will increase or decrease under different scenarios. To obtain numerical results, the Fractional Forward Euler Method is utilized for simulation purposes. This method enables the computation of the model's dynamics and offers insights into the behaviour of the system. While the Caputo fractional order derivative offers a promising framework for modelling epidemic dynamics, they often entail significant computational overhead, limiting the scalability and practical utility of fractional calculus-based epidemic models, especially in real-time simulation and forecasting scenarios.

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Trend Analysis and Forecasting of Water Level in Mtera Dam Using Exponential Smoothing

Trend Analysis and Forecasting of Water Level in Mtera Dam Using Exponential Smoothing

Filimon Abel Mgandu, Mashaka Mkandawile, Mohamed Rashid

Статья научная

This study presents trend analysis and forecasting of water level in Mtera dam. Data for water level were obtained from Rufiji Basin Development Authority (RUBADA). The study analyzed trend of water level using time series regression while forecasting of water level in Mtera dam was done using Exponential smoothing. Results revealed that both maximum and minimum water level trends were decreasing. Forecasted values show that daily water level will be below 690 (m.a.s.l) which is the minimum level required for electricity generation on 2023. It was recommended that proper strategies should be taken by responsible authorities to reduce effects that may arise. Strategies my include constructing small dams on upper side of Mtera dam to harvest rain water during rainy season as reserves to be used on dry season. In long run Tanzania Electric Supply Company (TANESCO) should invest into alternative sources of energy.

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Use of the Ontological Model for Personification of the Semantic Search

Use of the Ontological Model for Personification of the Semantic Search

J. Rogushina

Статья научная

Semantic search is an important component of modern intelligent applications oriented on work in open information environment. The intelligence level of application depends of it's capabilities in knowledge processing and defines it's facilities. Now applications widely use ontologies for knowledge representation. Therefore criteria of intelligence level estimation (that can analyze ontologies) of applications and retrieval systems as their particular case are proposed. In this paper, an ontological model of the intelligent interaction of the main objects and subjects of the semantic search (the Web information resources, information objects and information consumers etc.) is developed. Software realization of semantic search on base of this ontological model and integration of this search instrument with applied systems are describes.

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Using Deep Learning Towards Biomedical Knowledge Discovery

Using Deep Learning Towards Biomedical Knowledge Discovery

Nadeem N. Rather, Chintan O. Patel, Sharib A. Khan

Статья научная

A vast amount of knowledge exists within biomedical literature, publications, clinical notes and online content. Identifying hidden, interesting or previously unknown biomedical knowledge from free text resources using an automated approach remains an important challenge. Towards this problem, we investigate the use of deep learning methods that have shown significant promise in identifying hidden patterns from large corpus of text in an unsupervised manner. For example, it can deduce that 'husband' - 'man' + 'woman' = 'wife'. We use the text corpus from MRDEF file in the Unified Medical Language System (UMLS) dataset as training set to discover potential relationships. To evaluate our approach, we cross-verify new relationships against the UMLS MRREL dataset and conduct a manual evaluation from a sample of the non-overlapping set. The algorithm found 32% of new relationships not originally represented in the UMLS. The deep learning methods provide a promising approach in discovering potential new biomedical knowledge from free text.

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Vertex Connected Domination Polynomial of some Coalescence of Complete and Wheel Graphs

Vertex Connected Domination Polynomial of some Coalescence of Complete and Wheel Graphs

Nechirvan Badal Ibrahim, Hariwan Fadhil M.Salih

Статья научная

In this paper, we introduce new results of vertex connected dominating set and vertex connected domination polynomial of vertex identification, edge introduced and t-tuple of complete graph, also we determine new results of vertex connected dominating set and vertex connected domination polynomial of vertex identification, edge introduced and t-tuple of wheel graph.

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