Статьи журнала - International Journal of Mathematical Sciences and Computing

Все статьи: 63

A Fast Heuristic Algorithm for Solving High-Density Subset-Sum Problems

A Fast Heuristic Algorithm for Solving High-Density Subset-Sum Problems

Akash Nag

Статья научная

The subset sum problem is to decide whether for a given set of integers A and an integer S, a possible subset of A exists such that the sum of its elements is equal to S. The problem of determining whether such a subset exists is NP-complete; which is the basis for cryptosystems of knapsack type. In this paper a fast heuristic algorithm is proposed for solving subset sum problems in pseudo-polynomial time. Extensive computational evidence suggests that the algorithm almost always finds a solution to the problem when one exists. The runtime performance of the algorithm is also analyzed.

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A Fuzzy Approach for Text Mining

A Fuzzy Approach for Text Mining

Deepa B. Patil, Yashwant V. Dongre

Статья научная

Document clustering is an integral and important part of text mining. There are two types of clustering, namely, hard clustering and soft clustering. In case of hard clustering, data item belongs to only one cluster whereas in soft clustering, data point may fall into more than one cluster. Thus, soft clustering leads to fuzzy clustering wherein each data point is associated with a membership function that expresses the degree to which individual data points belong to the cluster. Accuracy is desired in information retrieval, which can be achieved by fuzzy clustering. In the work presented here, a fuzzy approach for text classification is used to classify the documents into appropriate clusters using Fuzzy C Means (FCM) clustering algorithm. Enron email dataset is used for experimental purpose. Using FCM clustering algorithm, emails are classified into different clusters. The results obtained are compared with the output produced by k means clustering algorithm. The comparative study showed that the fuzzy clusters are more appropriate than hard clusters.

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A Hybrid Approach based on Classification and Clustering for Intrusion Detection System

A Hybrid Approach based on Classification and Clustering for Intrusion Detection System

Jasmeen K. Chahal, Amanjot Kaur

Статья научная

Computer security plays an important role in everybody's life. Therefore, to protect the computer and sensitive information from the untrusted parties have great significance. Intrusion detection system helps us to detect these malicious activities and sends the reports to the administration. But there is a problem of high false positive rate and low false negative rate. To eliminate these problems, hybrid system is proposed which is divided into two main parts. First, cluster the data using K-Mean algorithm and second, is to classify the train data using Adaptive-SVM algorithm. The experiments is carried out to evaluate the performance of proposed system is on NSL-KDD dataset. The results of proposed system clearly give better accuracy and low false positive rule and high false negative rate.

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A Multi-view Comparison of Various Metaheuristic and Soft Computing Algorithms

A Multi-view Comparison of Various Metaheuristic and Soft Computing Algorithms

Abdulrahman Ahmed Bobakr Baqais

Статья научная

AI algorithms have been applied in a wide spectrum of articles across different domains with great success in finding solutions. There is an increasing trend of applying these techniques on newer problems. However, the numerous numbers of algorithms that are classified as AI algorithm hinder the ability of any researcher to select which algorithm is suitable for his problem. The invention of new algorithms increases the difficulty for researchers to be updated about AI algorithms. This paper is intended to provide a multi-facet comparison between various AI algorithms in order to aid researchers in understanding the differences between some of the popular algorithms and select the suitable candidate for their problems.

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A Natural Language Query Builder Interface for Structured Databases Using Dependency Parsing

A Natural Language Query Builder Interface for Structured Databases Using Dependency Parsing

Rohini Kokare, Kirti Wanjale

Статья научная

A natural language query builder interface retrieves the required data in structured form from database when query is entered in natural language. The user need not necessarily have sufficient technical knowledge of structured query language statements so nontechnical users can also use this proposed model. In natural language parsing, getting highly accurate syntactic analysis is a crucial step. Parsing of natural languages is the process of mapping an input string or a natural language sentence to its syntactic representation. Constituency parsing approach takes more time for parsing. So, natural language query builder interface is developed in which the parsing of natural language sentence is done by using dependency parsing approach. Dependency parsing technique is widespread in natural language domain because of its state-of-art accuracy and efficiency and also it performs best. In this paper, the buffering scheme is also proposed for natural language statements which will not load the whole sentence if it was done previously. Also there was a need of generalized access to all tables from database which is handled in this system.

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A New Method of Generating Optimal Addition Chain Based on Graph

A New Method of Generating Optimal Addition Chain Based on Graph

K. Mani, M. Viswambari

Статья научная

In many number theoretic cryptographic algorithms, encryption and decryption is of the form xn mod p, where n and p are integers. Exponentiation normally takes more time than any arithmetic operations. It may be performed by repeated multiplication which will reduce the computational time. To reduce the time further fewer multiplications are performed in computing the same exponentiation operation using addition chain. The problem of determining correct sequence of multiplications requires in performing modular exponentiation can be elegantly formulated using the concept of addition chains. There are several methods available in literature in generating the optimal addition chain. But novel graph based methods have been proposed in this paper to generate the optimal addition chain where the vertices of the graph represent the numbers used in the addition chain and edges represent the move from one number to another number in the addition chain. Method 1 termed as GBAPAC which generates all possible optimum addition chains for the given integer n by considering the edge weight of all possible numbers generated from every number in addition chain. Method 2 termed as GBMAC which generates the minimum number of optimum addition chains by considering mutually exclusive edges starting from every number. Further, the optimal addition chain generated for an integer using the proposed methods are verified with the conjectures which already existed in the literature with respect to addition chains.

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A Note on Quasi-coincidence for Fuzzy Points of Fuzzy Topology on the Basis of Reference Function

A Note on Quasi-coincidence for Fuzzy Points of Fuzzy Topology on the Basis of Reference Function

Kangujam Priyokumar Singh, Bhimraj Basumatary

Статья научная

In this article our main aim is to revisit the definition of fuzzy point and fuzzy quasi-coincident of fuzzy topology which is accepted in the literature of fuzzy set theory. We analyse some results and also prove some proposition with extended definition of complementation of fuzzy sets on the basis of reference function and some new definitions have also been introduced whenever possible. In this work the main efforts have been made to show that the existing definition of complement of fuzzy point and definition of fuzzy quasi-coincident are not acceptable.

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A Novel Mathematical Model for Cross Dock Open-Close Vehicle Routing Problem with Splitting

A Novel Mathematical Model for Cross Dock Open-Close Vehicle Routing Problem with Splitting

Mahdi Alinaghian, Mina Rezaei Kalantari, Ali Bozorgi-Amiri, Nima Golghamat Raad

Статья научная

Cross docks play an important role in goods distribution. In most of the common models, the capacity of vehicles is not completely used as they assume that each node is met only by one vehicle. Also, due to high cost of purchasing vehicles with high capacity, rental vehicles are used in collecting section. In this paper, a novel mathematical model is presented in which, each node can be possibly visited by different vehicles (splitting). Besides, in the proposed model, existence of open routes in pickup section has been supposed. Then, one meta-heuristic method based on the simulation annealing algorithm with two different approaches has been developed. For testing the performance of the proposed algorithm, the obtained results compared with the exact answers in both small and large scales. The outcomes show that the algorithm works properly.

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A Novel Verifiable Secret Sharing with Detection and Identification of Cheaters' Group

A Novel Verifiable Secret Sharing with Detection and Identification of Cheaters' Group

Qassim Al Mahmoud

Статья научная

Shamir's (t, n)-SS scheme is very simple to generate and distribute the shares for a secret among n participants by using such polynomial. We assume the dealer a mutually trust parity when he distributes the shares to participants securely. In addition when the participants pooling their shares in the secret reconstruction phase a honest participants can always reconstruct the real secret by Pooling areal shares. The property of verifiability enables participants to verify that their shares are consistent. Tompa and Woll suggested an important cheating scenario in Shamir's secret reconstruction. They found a solution to remove a single cheater with small probability, unfortunately, their scheme is based on computational assumptions. In addition each participants will receive a huge number of shares. In this paper we will construct scheme to be information-theoretically secure verifiable secret sharing which does not contain a single cheater. On the other hand we will eliminate these problems in Tompa and Woll scheme. Our proposed scheme is not only to detect and identify a cheater, but to prevent him from recovering the secret when the honest participants cannot.

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A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

Enesi Femi Aminu, Emmanuel Onyebuchi Ogbonnia, Ibrahim Shehi Shehu

Статья научная

High costs of medical equipment and insufficient number of medical specialists have immensely contributed to the increment of death rate especially in rural areas of most developing countries. According to Roll Back Malaria there are 300 million acute cases of malaria per year worldwide, causing more than one million deaths. About 90% of these deaths happen in Africa, majorly in young children. Besides malaria when tested; a large number is coinfected with typhoid. Most often, symptoms of malaria and typhoid fevers do have common characteristics and clinicians do have difficulties in distinguishing them. For instance in Nigeria the existing diagnostic systems for malaria and typhoid in rural settlements are inefficient thereby making the result to be inaccurate and resulting to treatment of wrong ailments. Therefore in this paper, a predictive symptoms-based system for malaria and typhoid coinfection using Support Vector Machines (SVMs) is proposed for an improved classification results and the system is implemented using Microsoft Visual Basic 2013. Relatively high performance accuracy was achieved when tested on a reserved data set collected from a hospital. Hence the system will be of a great significant use in terms of affordable and quality health care services especially in rural settlement as an alternative and a reliable diagnostic system for the ailments.

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A Proposed Technique for Solving Scenario Based Multi-Period Stochastic Optimization Problems with Computer Application

A Proposed Technique for Solving Scenario Based Multi-Period Stochastic Optimization Problems with Computer Application

Sajal Chakroborty, M. Babul Hasan

Статья научная

In this paper, we have presented a new technique for solving scenario based multi-period stochastic programming problems and presented a case study for the business policy of a super shop market in Bangladesh. We have developed our technique based on decomposition based pricing method which is the latest and faster decomposition technique in use. To our knowledge, this is the first work in the field of stochastic programming for solving multi-period stochastic optimization problems by using decomposition based pricing method. We have also developed a model by collecting data of a year from a super shop market of Bangladesh and analyzed their profit by dividing the whole year into four periods for different scenarios of an uncertainty. We have developed a computer code by using mathematical programming language AMPL and analyzed the model by using our developed technique.

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An Efficient Impulse Noise Removal Image Denoising Technique for MRI Brain Images

An Efficient Impulse Noise Removal Image Denoising Technique for MRI Brain Images

Murugan, Balasubramanian

Статья научная

Image enhancement is an important challenge in medical field. There are various techniques for image enhancement during last two decades. The objective of this paper is to remove impulse noise for MRI brain image. This paper proposed an efficient filter for removing impulse noise. The shape of the filter is changed to diamond. Experiments are conducted for various noise levels. The proposed method is compared with the existing Denoising techniques. The experimental results proved that the proposed filter performed well than the other methods.

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An Overview on Quantum Computing as a Service (QCaaS): Probability or Possibility

An Overview on Quantum Computing as a Service (QCaaS): Probability or Possibility

Mijanur Rahaman, Md. Masudul Islam

Статья научная

Cloud computing is a worldwide classical system. Quantum computing is theoretical concept still in experimental review. Where cloud system is facing vulnerability in security, backup, processing and locality, there quantum computing shows a strong solution to overcome it. Most researchers are optimistic in quantum computing that it will improve cloud system. But to associate physics based subatomic computing system with software based cloud system is not an easy option. Our paper will show all the major advantages and disadvantages of quantum computing in the perspective to integrate it with cloud system. And review some recent progress with some foremost doubtful future aspects of quantum cloud computing. Also we will review the reality of quantum computation and internet system in applied viewpoint until present.

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Analysis of Automated Matching of the Semantic Wiki Resources with Elements of Domain Ontologies

Analysis of Automated Matching of the Semantic Wiki Resources with Elements of Domain Ontologies

Rogushina J.

Статья научная

Intelligent information systems oriented on the Web open environment need in dynamic and interoperable ontological knowledge bases. We propose an approach for integration of ontological analysis with semantic Wiki resources: domain ontologies are used as a base of semantic markup of the Wiki pages, and this markup becomes the source for improving of these ontologies by new information

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Analysis of Signalling Time of Community Model

Analysis of Signalling Time of Community Model

Boudhayan Bhattacharya, Banani Saha

Статья научная

Data fusion is generally defined as the application of methods that combines data from multiple sources and collect information in order to get conclusions. This paper analyzes the signalling time of different data fusion filter models available in the literature with the new community model. The signalling time is calculated based on the data transmission time and processing delay. These parameters reduce the signalling burden on master fusion filter and improves throughput. A comparison of signalling time of the existing data fusion models along with the new community model has also been presented in this paper. The results show that our community model incurs improvement with respect to the existing models in terms of signalling time.

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Analysis of Some Software Reliability Growth Models with Learning Effects

Analysis of Some Software Reliability Growth Models with Learning Effects

Javaid Iqbal

Статья научная

A newly developed software system before its deployment is subjected to vigorous testing so as to minimize the probability of occurrence of failure very soon. Software solutions for safety critical and mission-critical application areas need a much focused level of testing. The testing process is basically carried out to build confidence in the software for its use in real world applications. Thus, reliability of systems is always a matter of concern for us. As we keep on performing the error detection and correction process on our software, the reliability of the system grows. In order to model this growth in the system reliability, many formulations in Software Reliability Growth Models (SRGMs) have been proposed including some based on Non-Homogeneous Poisson Process (NHPP). The role of human learning and experiential pattern gains are being studied and incorporated in such models. The realistic assumptions about human learning behavior and experiential gains of new skill-sets for better detection and correction of faults on software are being incorporated and studied in such models. In this paper, a detailed analysis of some select SRGMs with learning effects is presented based on use of seven data sets. The estimation of parameters and comparative analysis based on goodness of fit using seven data sets are presented. Moreover, model comparisons on the basis of total defects predicted by the select models are also tabulated.

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Analysis of Vascular Pattern Recognition Using Neural Network

Analysis of Vascular Pattern Recognition Using Neural Network

Navjot Kaur, Amardeep Singh

Статья научная

Biometric identification using vein patterns is a recent technique. The vein patterns are unique to each individual even in twins and they don't change over age except their size. As veins are beneath the skin it is difficult to forge. BOSPHOROUS hand vein database is used in this work. Hand vein images are uploaded first and key points using Scale Invariant Feature Transform (SIFT) are extracted. Then the neural network is used for training these images. Finally neural network is used for testing these images to check whether the image used for testing matches with the existing database or not. Results are computed like False Acceptation Rate (FAR), False Rejection Rate (FRR), accuracy and error per bit stream.

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Augmented Apriori by Simulating Map-Reduce

Augmented Apriori by Simulating Map-Reduce

R.Akila, K.Mani

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

BRAINSEG – Brain Structures Segmentation Pipeline Using Open Source Tools

R. Neela, R. Kalaimagal

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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Building Background to the Elgamal Algorithm

Building Background to the Elgamal Algorithm

Toan Nguyen Duc, Hong Bui The

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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