International Journal of Mathematical Sciences and Computing @ijmsc
Статьи журнала - International Journal of Mathematical Sciences and Computing
Все статьи: 246
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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Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting
Статья научная
In today's world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. In the literature, Many different time series methods have been frequency compared together in order to choose the most efficient once. In this paper, the performances of four different interval ARIMA-base time series methods are evaluated in financial markets forecasting. These methods are including Auto-Regressive Integrated Moving Average (ARIMA), Fuzzy Auto-Regressive Integrated Moving Average (FARIMA), Fuzzy Artificial Neural Network (FANN) and Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH). Empirical results of exchange rate forecasting indicate that the fuzzy artificial neural network model is more satisfactory than other models.
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Comparison of Machine Learning Algorithms in Domain Specific Information Extraction
Статья научная
Information Extraction is an essential task in Natural Language Processing. It is the process of extracting useful information from unstructured text. Information extraction helps in most of the NLP applications like sentiment analysis, named entity recognition, medical data extraction, features extraction from research articles, feature extraction from agriculture, etc. Most of the applications in information extraction are performed by machine learning models. Many research work shave been carried out on machine learning based information extraction from various domain texts in English such as Bio medical, Share market, Weather, Business, Social media, Agriculture, Engineering, and Tourism. However domain specific information extraction for a particular regional language is still a challenge. There are different types of classification algorithms. However, for a selected domain to select the appropriate classification algorithm is very difficult. In this paper three famous classification algorithms are selected to do information extraction by classifying the Gynecological domain data in Tamil Language. The main objective or this research work is to analyze the machine learning methods which is suitable for Tamil domain specific text documents. There are 1635 documents being involved in classification task to extract the features by these selected three algorithms. By evaluating the classification task of each model it has been found that the Naive Bayes classification model provides highest accuracy value (84%) for the gynecological domain data. The F1-Score, Error rate and Execution time also evaluated for the selected machine learning models. The evaluation of performance has proved that the Naïve Bayes classification model gives optimal results. It has been concluded that the Naïve Bayes classification model is the best model to classify the gynaecological domain text in Tamil language
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Comparison of fog computing & cloud computing
Статья научная
Fog computing is extending cloud computing by transferring computation on the edge of networks such as mobile collaborative devices or fixed nodes with built-in data storage, computing, and communication devices. Fog gives focal points of enhanced proficiency, better security, organize data transfer capacity sparing and versatility. With a specific end goal to give imperative subtle elements of Fog registering, we propose attributes of this region and separate from cloud computing research. Cloud computing is developing innovation which gives figuring assets to a specific assignment on pay per utilize. Cloud computing gives benefit three unique models and the cloud gives shoddy; midway oversaw assets for dependable registering for performing required errands. This paper gives correlation and attributes both Fog and cloud computing differs by outline, arrangement, administrations and devices for associations and clients. This comparison shows that Fog provides more flexible infrastructure and better service of data processing by consuming low network bandwidth instead of shifting whole data to the cloud.
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Comparison on Trapezoidal and Simpson’s Rule for Unequal Data Space
Статья научная
Numerical integration compromises a broad family of algorithm for calculating the numerical value of a definite integral. Since some of the integration cannot be solved analytically, numerical integration is the most popular way to obtain the solution. Many different methods are applied and used in an attempt to solve numerical integration for unequal data space. Trapezoidal and Simpson’s rule are widely used to solve numerical integration problems. Our paper mainly concentrates on identifying the method which provides more accurate result. In order to accomplish the exactness we use some numerical examples and find their solutions. Then we compare them with the analytical result and calculate their corresponding error. The minimum error represents the best method. The numerical solutions are in good agreement with the exact result and get a higher accuracy in the solutions.
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Статья научная
Many different methods are applied and used in an attempt to solve higher order nonlinear boundary value problems (BVPs). Galerkin weighted residual method (GWRM) are widely used to solve BVPs. The main aim of this paper is to find the approximate solutions of fifth, seventh and ninth order nonlinear boundary value problems using GWRM. A trial function namely, Bezier Polynomials is assumed which is made to satisfy the given essential boundary conditions. Investigate the effectiveness of the current method; some numerical examples were considered. The results are depicted both graphically and numerically. The numerical solutions are in good agreement with the exact result and get a higher accuracy in the solutions. The present method is quit efficient and yields better results when compared with the existing methods. All problems are performed using the software MATLAB R2017a.
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Construction of Fractals based on Catalan Solids
Статья научная
The deterministic fractals play an important role in computer graphics and mathematical sciences. The understanding of construction of such fractals, especially an ability of fractals construction from various types of polytopes is of crucial importance in several problems related both to the pure mathematical issues as well as some issues of theoretical physics. In the present paper the possibility of construction of fractals based on the Catalan solids is presented and discussed. The method and algorithm of construction of polyhedral strictly deterministic fractals is presented. It is shown that the fractals can be constructed only from a limited number of the Catalan solids due to the specific geometric properties of these solids. The contraction ratios and fractal dimensions are presented for existing fractals with adjacent contractions constructed based on the Catalan solids.
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