International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1243
Study of Context Modelling Criteria in Information Retrieval
Статья научная
Whereas the majority of works and research about context-awareness in ubiquitous computing provide context models that make use of context features in a particular application, one of the main challenges these last years has been to come out with prospective standardization of context models. As for Information Retrieval, the lack of consensual Context Models represents the biggest issue. In this paper, we investigate the importance of good context modelling to overcome some of the issues surrounding a search task. Thus, after identifying those issues and listing and categorizing the modelling requirements, the objective of our research is to find correlations between the appreciations of context quality criteria taking into account the user dimension. Likewise, the results of a previous survey about search habits have been used such that many socio-demographic categories were considered and the Kendall's W evaluation performed together with the Friedman test provided very interesting results that encourage the feasibility of building large scale context models.
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Study of Covering Based Multi Granular Rough Sets and Their Topological Properties
Статья научная
The notions of basic rough sets introduced by Pawlak as a model of uncertainty, which depends upon a single equivalence relation has been extended in many directions. Over the years, several extensions to this rough set model have been proposed to improve its modeling capabilities. From the granular computing point of view these models are single granulations only. This single granulation model has been extended to multi-granulation set up by taking more than one equivalence relations simultaneously. This led to the notions of optimistic and pessimistic multi-granulation. One direction of extension of the basic rough set model is dependent upon covers of universes instead of partitions and has better modeling power as in many real life scenario objects cannot be grouped into partitions but into covers, which are general notions of partitions. So, multigranulations basing on covers called covering based multi-granulation rough sets (CBMGRS) were introduced. In the literature four types of CBMGRSs have been introduced. The first two types of CBMGRS are based on minimal descriptor and the other two are based on maximal descriptor. In this paper all these four types of CBMGRS are studied from their topological characterizations point of view. It is well known that there are four kinds of basic rough sets from the topological characterisation point of view. We introduce similar characterisation for CBMGRSs and obtained the kinds of the complement, union, and intersection of such sets. These results along with the accuracy measures of CBMGRSs are supposed to be applicable in real life situations. We provide proofs and counter examples as per the necessity of the situations to establish our claims.
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Study of Open-source Software Adoption Strategy in E-government: Madinah Development Authority
Статья научная
Open Source Software (OSS) has gained significant traction in the government sector due to its potential to reduce costs, enhance security, and offer diverse benefits. This study focuses on the adoption of OSS within the Madinah Development Authority (MDA), a Saudi Arabian governmental agency. It aims to explore the OSS adoption process, identify challenges, and propose solutions to maximize its benefits. Employing a hybrid approach, data were collected through preliminary interviews with managers and a structured questionnaire survey among MDA employees. A SWOT analysis was conducted to evaluate the organization's IT environment and staff capabilities. The study’s key contribution is the development of a phased strategy tailored for MDA to successfully adopt OSS, addressing identified challenges and optimizing the benefits of open-source solutions for government operations.
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Study of Parametric Performance Evaluation of Machine Learning and Statistical Classifiers
Статья научная
Most of the researchers/ scientists are facing data explosion problem presently. Large amount of data is available in the world i.e. data from science, industry, business, survey and many other areas. The main task is how to prune the data and extract valuable information from these data which can be used for decision making. The answer of this question is data mining. Data Mining is popular topic among researchers. There is lot of work that cannot be explored in the field of data mining till now. A large number of data mining tools/software’s are available which are used for mining the valuable information from the datasets and draw new conclusion based on the mined information. These tools used different type of classifiers to classify the data. Many researchers have used different type of tools with different classifiers to obtained desired results. In this paper three classifiers i.e. Bayes, Neural Network and Tree are used with two datasets to obtain desired results. The performance of these classifiers is analyzed with the help of Mean Absolute Error, Root Mean-Squared Error, Time Taken, Correctly Classified Instance, Incorrectly Classified instance and Kappa Statistic parameter.
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Статья научная
This paper explores the possibility of applying techniques for segmenting the regions of medical image. For this we need to investigate the use of different techniques which helps for detection and classification of image regions. We also discuss some segmentation methods classified by researchers. Region classification is an essential process in the visualization of brain tissues of MRI. Brain image is basically classified into three regions; WM, GM and CSF. The forth region can be called as the tumor region, if the image is not normal. In the paper; Segmentation and characterization of Brain MR image regions using SOM and neuro fuzzy techniques, we integrate Self Organizing Map(SOM) and Neuro Fuzzy scheme to automatically extract WM, GM, CSF and tumor region of brain MRI image tested on three normal and three abnormal brain MRI images. Now in this paper this scheme is further tested on axial view images to classify the regions of brain MRI and compare the results from the Keith‘s database. Using some statistical tests like accuracy, precision, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, false negative rate, likelihood ratio positive, likelihood ratio negative and prevalence of disease we calculate the effectiveness of the scheme.
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Study of the Characteristics and Computation Analysis Results of Electromechanical Systems Models
Статья научная
Today, simulation of electrical drives with asynchronous motors based on systems of differential equations is regarded as one of the principal means of their research study. The difficulty of the simulation is determined by the need for accuracy of the results obtained and the complexity of the mathematical model’s differential equations. In this article, we present a study of the particularities of the simulation of electrical drives systems with asynchronous motors. We have studied models composed of three-phase and orthogonal coordinates systems and we have shown that qualitative and quantitative differences exist in the process of changing the angular speed of the rotor and electromagnetic torque. The result obtained is above all influenced by the non-linear character of the load opposing a fan-type or “dry friction”-type resistant torque. For dual-earthed electromagnetic actuation with the moments of the resistant torques indicated, integration of differential equation systems was carried out with various digital methods used in professional mathematical software for simulation.
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Study on QoS Gains in Migration from IPv4 to IPv6 Internet
Статья научная
IPv6 has features, like a) "no header checksum calculation" and b) "no IP packet fragmentation at intermediate routers", which makes it better than IPv4 from router/routing point of view. Existing Internet technology supports both IPv6 and IPv4 protocols for transport of packets and hence dual addressed machines are widely present. Maximizing QoS in IPv6 networks, as compared to IPv4 networks, for sites having dual addresses is an active area of research. Results of our study on QoS gains in networks connected to IPv6 Internet as compared to IPv4 Internet for a network of about 2500 nodes are presented here. The technique used to estimate QoS gains in the migration from IPv4 to IPv6 is also presented. The test-bed data of one month with 25000 most visited websites was analyzed. The results show that an alternate IPv6 channel exists for a large number of major global websites and substantial QoS gains in terms of reduced access times – averaging up to 35% for some websites - can be expected by intelligent per site IP address selection for dual stack machines.
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Study on the Effectiveness of Spam Detection Technologies
Статья научная
Nowadays, spam has become serious issue for computer security, because it becomes a main source for disseminating threats, including viruses, worms and phishing attacks. Currently, a large volume of received emails are spam. Different approaches to combating these unwanted messages, including challenge response model, whitelisting, blacklisting, email signatures and different machine learning methods, are in place to deal with this issue. These solutions are available for end users but due to dynamic nature of Web, there is no 100% secure systems around the world which can handle this problem. In most of the cases spam detectors use machine learning techniques to filter web traffic. This work focuses on systematically analyzing the strength and weakness of current technologies for spam detection and taxonomy of known approaches is introduced.
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Study on the Impact Breakup Model of the Space Target Based on the Thin Plate
Статья научная
In the paper, an engineering model for the im-pact breakup of the space target is studied based on the thin plate. The average fragment size model for the impact breakup of the thin plate is established depending on the strain rate, according as Poisson statistic fragments are discrete and distribution model is figured out. On the foundation of the constitution analysis for the target and projectile, the target equivalent model based on the thin plate is established, and projectile equivalent model is also given. The length and velocity degraded model are set up against the cylindrical projectile. The simulation case is analyzed and the result indicates that the paper model is effective, flexible and has important engineering reference value.
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Study the Performance of SLM for Different Number of Subcarriers
Статья научная
Orthogonal Frequency Division Multiplexing (OFDM) is an attractive modulation technique for transmitting large amounts of digital data over radio waves. One major disadvantage of OFDM is that the time domain OFDM signal which is a sum of several sinusoids leads to high peak to average power ratio (PAPR) which leads to power inefficiency in RF section of the transmitter and increased complexity in the analog to digital and digital to analog Converter. Selected mapping (SLM) is a well-known method for reducing the PAPR in OFDM. In this paper, we have studied the performance of SLM for Different Number of Subcarriers. Simulation result shows that the PAPR is reduced significantly when the number of phase sequences is increased and PAPR is increased when the number of subcarriers is increased. It also shows that data speed increases when subcarriers increase where N-point IFFT/FFT circuit depends on N-subcarriers.
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Subset matching based selection and ranking (SMSR) of web services
Статья научная
Web service is a software application, which is accessible using platform independent and language neutral web protocols. However, selecting the most relevant services became one of the vital challenges. Quality of services plays very important role in web service selection, as it determines the quality and usability of a service, including its non-functional properties such as scalability, accessibility, integrity, efficiency, etc. When agent application send request with a set of quality attributes, it becomes challenging to find out the best service for satisfying maximum quality requirements. Among the existing approaches, the single value decomposition technique is popular one; however, it suffers for computational complexity. To overcome this limitation, this paper proposed a subset matching based web service selection and ranking by considering the quality of service attributes. This proposed method creates a quality-web matrix to store available web services and associated quality of service attributes. Then, matrix subsets are created using web service repository and requested quality attributes. Finally, web services are efficiently selected and ranked based on calculated weights of corresponding web services to reduce composition time. Experimental results showed that proposed method performs more efficient and scalable than existing several techniques such as single value decomposition.
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Substitute and Communication Pattern for an Internet Banking System
Статья научная
The design patterns are the reusable component used in the development of the software, which delivers enhanced quality software to the end users. The design patterns are available for user interface, mobile applications, text classification and so on. There are no design patterns for internet banking applications. This motivated to mine the design patterns for internet banking application from the document of Business Process Management (BPM) by using the qualitative research technique. The nonfunctional quality attribute of software architecture is enhanced by using the design patterns. In this paper the mined two patterns are presented namely substitute pattern and communication pattern for internet banking application.
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Suggestive Approaches to Create a Recommender System for GitHub
Статья научная
Recommender system suggests users with options that may be of use to them or may be of their interest or liking. These days recommender systems are used widely on most systems and especially on those which are connected to World Wide Web, it may be a mobile app, a desktop application, or a website. Most advertisements on these systems are focused on targeting a specific group. Recommender systems provide a solution to such a scenario where the recommendations need to be targeted based on a user profile. Almost all commercial, collaborative or even social networking websites rely on recommender systems. In this paper, we specifically focus on GitHub, a source code hosting site and one of the most popular platforms for online collaborative coding and sharing. GitHub offers an opportunity for researchers to perform analysis by providing REST-based APIs for downloading its data. GitHub hosts a vast amount of user repositories so it is quite difficult for a GitHub user to decide to which repository she should contribute on GitHub. So, our paper aims to review different approaches that can be used for creating a recommender system for GitHub, to provide personalized suggestions to GitHub users to which repositories they should contribute. In this paper, we have discussed collaborative filtering, content-based filtering, and hybrid filtering, knowledge-based and utility-based approaches of a recommender system.
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Статья научная
Internal combustion (IC) engines are optimized to meet exhaust emission requirements with the best fuel economy. Closed loop combustion control is a key technology that is used to optimize the engine combustion process to achieve this goal. In order to conduct research in the area of closed loop combustion control, a control oriented cycle-to-cycle engine model, containing engine combustion information for each individual engine cycle as a function of engine crank angle, is a necessity. In this research, the IC engine is modeled according to fuel ratio, which is represented by the mass of air. In this research, a multi-input-multi-output baseline computed fuel control scheme is used to simultaneously control the mass flow rate of both port fuel injection (PFI) and direct injection (DI) systems to regulate the fuel ratio of PFI to DI to desired levels. The control target is to maintain the fuel ratio at stoichiometry and the fuel ratio to a desired value between zero and one. The performance of the baseline computed fuel controller is compared with that of a baseline proportional, integral, and derivative (PID) controller.
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Supervision Architecture Design for Programmer Logical Controller Including Crash Mode
Статья научная
This paper is a contribution for development of a high level of security for the Programmer Logic Controller (PLC). Many industrial adopt the redundant PLC architecture (or Standby PLC) designed to replace the failed (out of order) PLC without stopping associated automated equipments. We propose a formal method to choose a Standby PLC based on probability study, by comparing normal functioning to misbehavior one leading to residue generation process. Any generated difference reveals a presence of anomaly. The proposed method begins by listing all PLC components failures leading to their stopping according to failures criticalities. Two models; functional and dysfunctional are obtained by using formal specifications. Probability’s calculus of dysfunction of each Standby PLC is obtained by the sum of the probabilities of dysfunction of its critical components. These probabilities are allocated each transition which leads to the dysfunction in the dysfunctional model. The dysfunctional model is obtained by using the FMECA method (Failure Modes, Effects and Criticality Analysis). We shall see that this global vision of functioning of the whole PLC leads to a higher level of security where the chosen Standby PLC works continuously.
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Survey of MAC Layer Protocols for vehicular Ad Hoc Network
Статья научная
Vehicular Ad Hoc Network is currently challenge for the wireless networking and its researchers. In this paper we have proposed the survey of the different MAC Layer protocols of the wireless medium those can be implemented in the Vehicular Ad Hoc Networking. The survey is based upon the study of the different protocol on their MAC level and its performance factors is evaluated. The performance factor is extracted from the studied materials and current working conditions of the protocols. These all factors are mobility, accuracy, privacy and confidentiality, safety critical message priority, delay control and suitability on vehicle and roadside. According to analyzed performance factor the IEEE 802.11p Wave is most suitable protocol for the Vehicular Ad Hoc Network but it does not cover up all conditional requirements.
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Статья научная
Wireless sensor networks are often deployed for data-gathering or monitoring in a geographical region. This paper explains an important issue to maintain the fidelity of the sensed data while minimizing energy usage in the network. Nature inspired computation like evolutionary computation, swarm intelligence etc., which offers practical advantages to the researcher facing difficult optimization problems. The genetic algorithms are used for efficient connectivity and coverage. Single Objective Genetic Algorithms (SOGA) method is used to yield good results in terms of Coverage, but the objective’s graph had shown Pareto optimal designs with differing Endurance. However it is attractive to offer Pareto optimal designs to a user willing to settle for a poorer Coverage in order to gain in Endurance, so that the sensor network lasts longer. This explains concept of Multiple Objective Genetic Algorithm (MOGA) and its implementation and results which are compared to those of the SOGA. Endurance and Robustness to deployment inaccuracy tend to work in the same direction. A MOGA was conducted with the Coverage and Robustness as objectives. The main objective of this paper is to propose new Strength Perito Evolutionary Algorithm (SPEA) method along with clustering, this will reduce the distances between the sensor nodes that increase the efficiency of the nodes and also increase the connectivity. This will increase lifetime of sensors and connectivity.
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Статья научная
Recent worldwide medical research is focusing on new intelligence approaches for diagnosis of various infections. The sporadic occurrence of malaria diseases in human has pushed the need to develop computational approaches for its diagnoses. Most existing conventional malaria models for classification problems examine the dynamics of asymptomatic and morphological characteristics of malaria parasite in the thick blood smear, but this study examine the symptomatic characteristics of malaria parasite combined with effects of climatic factor which is a great determinant of malaria severity. The need to predict the occurrence of malaria disease and its outbreak will be helpful to take appropriate actions by individuals, World Health Organizations and Government Agencies and its devastating impact will be reduced. This paper proposed Feed-Forward Back-Propagation (FF_BP) Neural Network model to determine the rate of malaria transmission. Monthly averages of climatic factors; rainfall, temperature and relative humidity with monthly malaria incidences were used as input variables. An optimum threshold value of 0.7100 with classification accuracy 87.56%, sensitivity 96.67% and specificity 76.67% and mean square error of 0.100 were achieved. While, the model malaria threat detection rate was 87.56%, positive predictive value was 89.23%, negative predictive value was 92.00% and the standard deviation is 2.533. The statistical analysis of Feed-Forward Back-Propagation Neural Network model was conducted and its results were compared with other existing models to check its robustness and viability.
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Synchronization New 3D Chaotic System Using Brain Emotional Learning Based Intelligent Controller
Статья научная
One of the most important phenomena of some systems is chaos which is caused by nonlinear dynamics. In this paper, the new 3 dimensional chaotic system is firstly investigated and then utilizing an intelligent controller which based on brain emotional learning (BELBIC), this new chaotic system is synchronized. The BELBIC consists of reward signal which accept positive values. Improper selection of the parameters causes an improper behavior which may cause serious problems such as instability of system. It is needed to optimize these parameters. Genetic Algorithm (GA), Cuckoo Optimization Algorithm (COA), Particle Swarm Optimization Algorithm (PSO) and Imperialist Competitive Algorithm (ICA) are used to compute the optimal parameters for the reward signal of BELBIC. These algorithms can select appropriate and optimal values for the parameters. These minimize the Cost Function, so the optimal values for the parameters will be founded. Selected cost function is defined to minimizing the least square errors. Cost function enforce the system errors to decay to zero rapidly. Numerical simulation results are presented to show the effectiveness of the proposed method.
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Syntactic and Sentence Feature Based Hybrid Approach for Text Summarization
Статья научная
Recently, there has been a significant research in automatic text summarization using feature-based techniques in which most of them utilized any one of the soft computing techniques. But, making use of syntactic structure of the sentences for text summarization has not widely applied due to its difficulty of handling it in summarization process. On the other hand, feature-based technique available in the literature showed efficient results in most of the techniques. So, combining syntactic structure into the feature-based techniques is surely smooth the summarization process in a way that the efficiency can be achieved. With the intention of combining two different techniques, we have presented an approach of text summarization that combines feature and syntactic structure of the sentences. Here, two neural networks are trained based on the feature score and the syntactic structure of sentences. Finally, the two neural networks are combined with weighted average to find the sentence score of the sentences. The experimentation is carried out using DUC 2002 dataset for various compression ratios. The results showed that the proposed approach achieved F-measure of 80% for the compression ratio 50 % that proved the better results compared with the existing techniques.
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