International Journal of Mathematical Sciences and Computing @ijmsc
Статьи журнала - International Journal of Mathematical Sciences and Computing
Все статьи: 285
Cryptographic Security using Various Encryption and Decryption Method
Статья научная
Fast development in universal computing and the growth in radio/wireless and mobile strategies have led to the extended use of application space for Radio Frequency (RFID), wireless sensors, Internet of things (IoT). There are numerous applications that are safe and privacy sensitive. The increase of the new equipments has permitted intellectual methods of linking physical strategies and the computing worlds through numerous network interfaces. Consequently, it is compulsory to take note of the essential risks subsequent from these communications. In Wireless systems, RFID and sensor linkages are extremely organized in soldierly, profitable and locomotive submissions. With the extensive use of the wireless and mobile devices, safety has therefore become a major concern. As a consequence, need for extremely protected encryption and decryption primitives in such devices is very important than before.
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Cyber Bullying Detection and Classification using Multinomial Naïve Bayes and Fuzzy Logic
Статья научная
The advent of different social networking sites has enabled people to easily connect all over the world and share their interests. However, Social Networking Sites are providing opportunities for cyber bullying activities that poses significant threat to physical and mental health of the victims. Social media platforms like Facebook, Twitter, Instagram etc. are vulnerable to cyber bullying and incidents like these are very common now-a-days. A large number of victims may be saved from the impacts of cyber bullying if it can be detected and the criminals are identified. In this work, a machine learning based approach is proposed to detect cyber bullying activities from social network data. Multinomial Naïve Bayes classifier is used to classify the type of bullying. With training, the algorithm classifies cyber bullying as- Shaming, Sexual harassment and Racism. Experimental results show that the accuracy of the classifier for considered data set is 88.76%. Fuzzy rule sets are designed as well to specify the strength of different types of bullying.
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Data Privacy System Using Steganography and Cryptography
Статья научная
Data privacy is being breached occasionally whether in storage or in transmission. This is due to the spate of attack occasioned by the movement of data and information on an insecure internet. This study aimed to design a system that would be used by both sender and receiver of a secret message. The system used the combination of Steganography (MSB) and Cryptography (RSA) approaches to ensure data privacy protection. The system generates two keys: public and private keys, for the sender and receiver to encrypt and decrypt the message respectively. The steganography method used does not affect the size of cover image. The software was designed using python programming language in PyCharmIDE. The designed system enhanced the security and privacy of data. The results of this study reveal the effectiveness of combination of steganography and cryptography over the use of either cryptography or steganography and other existing systems.
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Data- and Workflow Customer-Oriented Software Process Models
Статья научная
This paper presents a dataflow model to control the flow of data in each phase of a customer-oriented software process model. In addition, we suggest a workflow model to describe the transaction between the model phases, and a role model to govern the personnel participation and roles. Our goal is to develop models that involve the customer frequently and effectively during project development. Testing the models using CHAOS Report and shows that our models are capable of achieving this goal.
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Deep Classifier for Conjunctivitis – A Three-Fold Binary Approach
Статья научная
Alterations in environmental and demographic equations have resulted in phenomenal rise of human centric diseases, ocular being one of them. Technological advancements have witnessed early diagnosis of much of the previously un-ciphered diseases. This paper addresses two research questions (RQs) with the study being focused on conjunctivitis (the most prevalent eye ailment in adults as well as minors). The motive of both the RQs rests in implementing three state-of-the art deep learning framework for classification of the ocular disease and validation of the frameworks. Validation of the frameworks is seconded by improvised proposals for enhancements. RQ1 establishes and validates whether the three off the shelf Deep Learning frameworks VGG19, ResNet50, and Inception V3 properly classify the disease or not. RQ2 analyses the effectiveness of each classifier with further enhancement proposals. The algorithms were implemented on 210 images and generated an accuracy of 87.3%, 93.6%, and 95.2% for VGG19, ResNet50, and Inception V3 using Adam optimizer, with slightly variant results when applying Adadelta optimizer. These results were typical of the classification frameworks with enhancements. With pervasive penetration of Artificial Intelligence in healthcare, this paper presents the efficacy of Deep Learning Frameworks in conjunctivitis classification.
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Статья научная
This paper outlines the application of various classification methods and analytical techniques to identify a potential fraud. The aim of this document is to showcase the usefulness of such classification and analytical techniques for fraud detection. Considering the fact that there are hundreds of statistical methods and procedures to perform such analysis. In this paper, I would like to present a hybrid fraud detection method by using the Bayesian Classification technique to identify the risk group; followed by Benford's Law (The Law of First Digit) to detect a fraudulent transaction done by the identified risk group. Though this analysis focuses on the healthcare dataset, however, this technique can be replicated in any industry setup. Also, by adding the Voice of the Customer data to these classification and statistical methods, makes this analysis even more powerful and robust with improved accuracy.
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Статья научная
Standard collocation (SCM) and perturbed collocation (PCM) are utilized as effective numerical techniques for solving fractional-order differential equations (FODEs) which focus on constructing orthogonal polynomials to serve as basis functions for approximating the solutions to these equations. The approach began by assuming an approximate solution, expressed in the constructed orthogonal polynomials. These assumed solutions were then substituted into the original FODEs. Following this, the problem was converted into a system of algebraic linear equations by collocating the equations at evenly spaced interior points. Numerical examples and the results indicated that the SCM and PCM are easy, efficient, and in good agreement compared with some existing methods and the results presented in the tables and graphs unequivocally demonstrate the efficacy of the proposed methods in solving fractional-order differential equations, yielding solutions of remarkable accuracy. However, the SCM and PCM exhibit comparable accuracy, making it difficult to identify a single superior approach, we conclude that both the proposed methods are effective and viable options for solving fractional order differential equations.
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Design Approaches for a Novel Reversible 4-bit Comparator
Статья научная
Reversible logic has shown considerable acceptance and growth in the research fields like quantum computing, Nano computing and optical computing promising lower power dissipation. This paper proposes an optimised design single-bit reversible comparator called SKAR gate with a purpose of reducing quantum cost. Besides, this novel SKAR gate is used as a single-bit reversible comparator to construct an optimised design for a four-bit reversible comparator. The paper discusses two designs, one with the use of SKAR gate and other one using a derivative gate constructed from SKAR gate. Since the reversible logic aims at reducing the value of its fundamental parameters viz. quantum cost, garbage outputs, ancillary inputs, delay and number of gates; Both the proposed designs for single-bit and four-bit reversible comparator are compared with other existing designs on the basis of elementary parameters of reversible logic.
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Design and Development of Multidimensional Chaotic Maps with Genetic Operator
Статья научная
Security of a digital image can be achieved in number of ways including image Encryption and Decryption. Encryption technique tries to convert a plain image into the cipher image which is hard to understand. The decryption technique tries to convert a cipher image into the plain image. The image encryption is done inorder to provide security from attacks. In this work we aim to develop an image encryption technique based on multidimensional chaotic map with genetic operator. One of the powerful features of genetic operator is crossover which is used to confuse the pixels of the image. A combination of multidimensional chaotic maps, namely, Logistic, Henon and Chebyshev will be used to generate pseudorandom sequence which will be XOR'ed to obtain an unpredictable sequence. This sequence will be then applied to the Crossover unit and upon XOR'ed to obtain an encrypted image. Later the same upredictable sequence is generated while decrypting the image. By combining the entire different dimensional chaotic map namely Logistic, Henon, Chebyshev map along with the gentic operator called crossover will enhance the extra security to the digital image.
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Design and Implementation of Intelligent Traffic Control Systems with Vehicular Ad Hoc Networks
Статья научная
Urban traffic congestion can be considered as a significant problem, and it contributes to long travel periods, fuel usage, and environmental influence. This paper introduces an Intelligent Traffic Control System (ITCS) that consists of Vehicular Ad Hoc Networks (VANETs) and Reinforcement Learning (RL) to optimise the control of traffic signals. The system facilitates real-time two-way communication between vehicles and roadside units, which means that an RL agent can control signal phases adaptively according to the traffic metrics like the average delay, the queue length, and traffic throughput. The Kaggle VANET Malicious Node Dataset was used to simulate malicious or unreliable nodes and test the robustness of the systems. The RL agent has been trained on the SUMO simulator trained on TraCI through various episodes and learns to take actions that increase traffic movement with a minimum amount of congestion. The results of training are progressive, as cumulative rewards grow, and average delays and queue length reduce with epochs. Performance evaluation of the ITCS under peak-hour, off-peak, incident, and malicious node scenarios demonstrated substantial gains over conventional fixed-time controllers, with average delays reduced by 48–55%, queue lengths by 49–57%, and throughput increased by 28–35%. These results indicate the success of the blend of reinforcement learning with VANET-supported traffic control, which is an adaptive, data-driven, and robust solution to an urban intersection. Not only the RL-based ITCS enhances traffic flow and congestion, but is also resistant to communication anomalies, which indicates its scalability to be deployed in the current smart city traffic management.
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Desirable Dog-Rabies Control Methods in an Urban setting in Africa -a Mathematical Model
Статья научная
Rabies is a fatal, zoonotic, viral disease that causes an acute inflammation of the brain in humans and other mammals. It is transmitted through contact with bodily fluids of infected mammals, usually via bites or scratches. In this paper, we formulate a deterministic model which measures the effects of different rabies control methods (mass-culling and vaccination of dogs) for urban areas near wildlife, using the Arusha region in Tanzania as an example. Values for various parameters were deduced from five years’ worth of survey data on Arusha’s dog population. Data included vaccination coverage, dog bites and rabies deaths recorded by a local non-governmental organization and the Ministry of Agriculture, Livestock Development and Fisheries of the United Republic of Tanzania. The basic reproduction number R_0 and effective reproduction number Re were computed and found to be 1.9 and 1.2 respectively. These imply that the disease is endemic in Arusha. The numerical simulation of the reproduction number shows that vaccination is the most appropriate control method for rabies transmission in urban areas near wildlife reservoirs. The disease free equilibrium ε_0 is also computed. If the effective reproduction number R_e is computed and found to be less than 1, it implies that it is globally asymptotically stable in the feasible region Φ. If R_e> 1 it is implied that there is one equilibrium point which is endemic and it is locally asymptotically stable.
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