Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1165
Статья научная
The focus of this work is on how the congestion experienced on the GSM network can be minimized. The voice calls is broken into sub-classes of services and a level of priority is established among the classes so that the most urgent and important service will have access to the channel by preempting the lower priority services during congestion. The voice communications over the GSM network using the different classes of subscribers were analyzed with Markov chain’s model. The steady state probabilities for voice services were derived. The blocking and dropping probabilities models for the different services were developed using the Multi-dimensional Erlang B. To give a new call a fair sharing of the channel, Time-Threshold scheme is employed. This scheme classifies handoff call as either prioritised call or new call according to its associated elapsed real time value. The models were implemented based on the blocking and dropping probabilities models to show how the congestion can be minimised for different subscribers based on their priority levels. The work shows that the models used gave significant reduction in congestion when compared to the traditional Erlang-B model used in GSM.
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Статья научная
This paper presents solution of multi-objective optimal dispatch (MOOD) problem of solar-wind-thermal system by improved stochastic fractal search (ISFSA) algorithm. Stochastic fractal search (SFSA) is inspired by the phenomenon of natural growth called fractal. It utilizes the concept of creating fractals for conducting a search through the problem domain with the help of two main operations diffusion and updating. To improve the exploration and exploitation capability of SFSA, scale factor is used in place of random operator. The SFSA and proposed ISFSA is implemented and tested on six different multi objective complex test systems of power system. TOPSIS is used here as a decision making tool to find the best compromise solution between the two conflicting objectives. The outcomes of simulation results are also compared with recent reported methods to confirm the superiority and validation of proposed approach.
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Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization
Статья научная
Cloud computing is the latest emerging trend in distributed computing, where shared resources are provided to end-users in an on demand fashion that brings many advantages, including data ubiquity, flexibility of access, high availability of resources, and flexibility. In this type of systems many challenges are existed that the task scheduling problem is one of them. The task scheduling problem in Cloud computing is an NP-hard problem. Therefore, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks. In this paper, we propose a new algorithm based on PSO to schedule the tasks in the Cloud. The results demonstrated that the proposed algorithm has a better operation in terms of task execution time, waiting time and missed tasks in comparison of First Come First Served (FCFS), Shortest Process Next (SPN) and Highest Response Ratio Next (HRRN).
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Multi-agent System for Management of Data from Electrical Smart Meters
Статья научная
The smart meter can process sensor data in a residential grid. These sensors transmit different parameters or measurement data (index, power, temperature, fluctuation of voltage and electricity, etc.) to the smart meter. All of these measurement data can come in different ways at the smart meter. The sensors transmit each measurement data to the smart meter. In addition, the collection of this data to a central system is a significant concern to ensure data integrity and protect the privacy of residents. The complexity of these data management also lies in their volume, frequency, and scheduling. This work presents a scheduling and a collection mechanism in private power consumption data between both sensors and smart meters on one hand and between smart meters and the central data collection system on other hand. We have found several approaches to intelligent meter data management in scientific researches. We propose another approach in response to this concern for the scheduling and collection of measurement data to a central system from residential areas of sensors’ network connected to smart meters. This work is also an example of a link between data collection and data scheduling in intelligent information management, transmission, and protection. We also propose a modeling of the measurement objects of smart grid and highlight the changes made to these objects throughout the process of data processing. It should be noted that this smart grid system consists of three main active systems namely sensors, smart meters and central system. In addition to these three systems, there are other systems that communicate with the smart meters and the central system. We have identified three implementation models for the smart metering system. We also present an intelligent architecture based on multi-agent systems for the smart grid. Most current electricity management systems are not adapted to the new challenges imposed by social and economic development in Africa. The objectives of this study are to initiate the design of a smart grid system for the management of electricity data.
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Multi-platform code generation supported by domain-specific modeling
Статья научная
Code generation is widely used to make software development more efficient and less prone to human errors. A significant use case of code generation is processing of Domain-Specific Languages (DSLs) and Domain-Specific Models (DSMs). Sometimes, it is desired to generate semantically equivalent or similar functionality to different languages to better support multiple platforms and achieve better reuse in the tooling. For example, it is convenient if a single tool supports code generating from a DSM to either Java or C#. There has been relevant research on using modeling and model transformations for code generation to multiple platforms. The Model-Driven Architecture (MDA) inherently supports multi-platform code generation based on models. Nevertheless, the MDA standard is a high-level general framework that includes standards, notions and principles but does not specify more concrete methods or workflows about their efficient adoption. Our research focuses on the efficient and practically usable application of MDA principles to generate multi-platform code. This paper reports on our results on multi-platform code generation and the difficulties that we are about to addressed in future research. The approach and the challenges presented in the paper are useful for tool developers, such as developers of DSLs, who generates code for several platforms.
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Multi-stage Transfer Learning for Fake News Detection Using AWD-LSTM Network
Статья научная
In the recent decades, the automatic veracity verification of rumors is essential, since online social media platforms allow users to post news item or express opinion towards a circulating piece of information without much restriction. The intention of fake news is to make the readers believe in inaccurate information, where the detection of fake news by using content is a difficult task. So, the auxiliary information: user profile, social engagement of the users, and other user’s comments are useful in the detection of fake news. In this manuscript, a novel multi-stage transfer learning approach is introduced for an effective fake news detection, where it utilizes user’s comments as auxiliary information to detect whether the given tweet is true or false. The stances of the response tweets contain opinions on news/rumors are often used for verifying the veracity of the circulating information. In order to devastate the effects of the specific rumors at the earliest, the multi-stage transfer learning approach automatically predict veracity of rumors jointly with the stances of their response tweets. The proposed multi-stage transfer learning is an inductive transfer learning variation that is used to forecast the stance of responses, then to identify fake news. The proposed model’s effectiveness is evaluated on the two-benchmark datasets: semEval-2017 task 8 and PHEME. The proposed model outperformed the existing approaches by obtaining a classification accuracy of 64.30% and 65.30%, an F-measure of 65.95% and 63.90% on semEval-2017 task 8, and PHEME on event-wise datasets.
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Multiple Ranks Weighting Score for Microscopic Image Retrieval System
Статья научная
Content based medical images have become a major necessity with the growing retrieving Advancements. CBIR access to medical images for supporting clinical decision making has been proposed that would be ease to manage large number of image in the database system. [4] In real time case only few systems has been developed and used in clinical environment. Content-Based Image Retrieval refers to image retrieval system that is based on visual properties of image objects other than textual annotation. Query image features compare with the database image features which is not exactly matching so image feature can be compare with the two tier approach in the database image in order to improve the accuracy of the retrieval system. Every day, large volume of different types of medical images such as MRI, CT images ultrasound, x-ray, radiology, etc are produced in different medical centre’s .microscopic image classification and discrimination (sub-type) [12] is the most difficult problem in medical image retrieval system. In this paper, the survey provides the suitable algorithm for retrieval and classification of medical image to improve the overall accuracy of the MIMS.
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Multiresolution Fuzzy C-Means Clustering Using Markov Random Field for Image Segmentation
Статья научная
In this paper, an unsupervised multiresolution image segmentation algorithm is put forward, which combines interscale and intrascale Markov random field and fuzzy c-means clustering with spatial constraints. In the initial label determination of wavelet coefficient phase, the statistical distribution property of wavelet coefficients is characterized by Gaussian mixture model, the properties of intrascale clustering and interscale persistence of wavelet coefficients are captured by Markov prior probability model. According to maximum a posterior rule, the initial label of wavelet coefficient from coarse to fine scale is determined. In the image segmentation phase, in order to overcome the shortcomings of conventional fuzzy c-means clustering, such as being sensitive to noise and lacking of spatial constraints, we construct the novel fuzzy c-means objective function based on the property of intrascale clustering and interscale persistence of wavelet coefficients, taking advantage of Lagrange multipliers, the improved objective function with spatial constraints is optimized, the final label of wavelet coefficient is determined by iteratively updating the membership degree and cluster centers. The experimental results on real magnetic resonance image and peppers image with noise show that the proposed algorithm obtains much better segmentation results, such as accurately differentiating different regions and being immune to noise.
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Myanmar-English Bidirectional Machine Translation System with Numerical Particles Identification
Статья научная
This paper the development of Myanmar-English bidirectional machine translation system is implemented applying Rule based machine translation approach. Stanford and ML2KR parsers are used for preprocessing step. From this step, parsers generate corresponding parse tree structures. Used parsers generate corresponding CFG rules which are collected and created as synchronous context free grammar SCFG rules. Myanmar language can be written free order style, but it must be verb final structure. Therefore, CFG rules are required for reordering the structure of the two languages. After that tree to tree transformation is carried on the source tree structure which corresponds with used parser (Stanford parser or ML2KR's parser). When source parse tree is transformed as target parse tree, it is changed according to the SCFG rules. And then system carries out the morphological synthesis. In this stage, we need to solve only for English to Myanmar machine translation because Myanmar language is morphologically rich language. Therefore, particles for Myanmar language can be solved in this system by proposed algorithm. After finishing morphological synthesis, this system generates meaningful and appropriate smoothing sentences.
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Статья научная
Twitter being one of the most sophisticated social networking platforms whose users base is growing exponentially, terabytes of data is being generated every day. Technology Giants invest billions of dollars in drawing insights from these tweets. The huge amount of data is still going underutilized. The main of this paper is to solve two tasks. Firstly, to build a sentiment analysis model using BERT (Bidirectional Encoder Representations from Transformers) which analyses the tweets and predicts the sentiments of the users. Secondly to build a personality prediction model using various machine learning classifiers under the umbrella of Myers-Briggs Personality Type Indicator. MBTI is one of the most widely used psychological instruments in the world. Using this we intend to predict the traits and qualities of people based on their posts and interactions in Twitter. The model succeeds to predict the personality traits and qualities on twitter users. We intend to use the analyzed results in various applications like market research, recruitment, psychological tests, consulting, etc, in future.
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New RLS Wiener Smoother for Colored Observation Noise in Linear Discrete-time Stochastic Systems
Статья научная
In the estimation problems, rather than the white observation noise, there are cases where the observation noise is modeled by the colored noise process. In the observation equation, the observed value y(k) is given as a sum of the signal z(k)=Hx(k) and the colored observation noise v_c(k). In this paper, the observation equation is converted to the new observation equation for the white observation noise. In accordance with the observation equation for the white observation noise, this paper proposes new RLS Wiener estimation algorithms for the fixed-point smoothing and filtering estimates in linear discrete-time wide-sense stationary stochastic systems. The RLS Wiener estimators require the following information: (a) the system matrix for the state vector x(k); (b) the observation matrix H; (c) the variance of the state vector x(k); (d) the system matrix for the colored observation noise v_c(k); (e) the variance of the colored observation noise.
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NoC Research and Practice: Design and Implementation of 2×4 2D-Torus Topology
Статья научная
Design and Implementation of network on chip interconnection architecture for eight compute-intensive processors are mainly presented in this paper. Firstly, it introduces the basic concept and architecture of the NoC, through analysis and comparison of three common NoC topologies, 2×4 2D Turos is chosen as the final topology, and the single routing node architecture is designed, including packet format, routing and arbitration. Secondly, routing nodes coding, routing algorithm and node degree routing direction are designed. Thirdly, the programming and simulation of 2×4 NoC interconnection architecture are designed, and it achieves uninterrupted operation. The result shows the correctness of the interconnection architecture design. Finally, it chooses XC4VSX55-12ff1148 of vertext 4 to synthesize, the maximum frequency can up to 268 MHz, which provides foundation of subsequent research and application.
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Статья научная
Social networks constitute a suitable forum for debate, and exchange on any sort of topic especially highly sensitive issues. They offer a fertile platform for debate on thorny societal issues such as politics, sex, culture and religion among others. Given the fact that they favor anonymity, openness and non-accountability for voiced opinion, a good number of Nigerians have found them suitable for, "hot", "aggressive" and very passionate discussions over subjects like sex, sexuality and religious convictions – issues which have remarkably remained somehow taboos in the Nigerian society. This paper investigates the conduct of online debates and opinion formation on sex in the prolific Nigerian motion picture industry (Nollywood). It is based on the content analysis of 516 comments by Nigerians, reacting or debating online (in social networks) on pornography in the Nigerian film industry. The paper seeks to explore and quantify the phenomenon of noise in online communication (conservation and debate) on sex by Nigerians. It equally examines how this noise affects communication flow in online debate on pornography in the Nigerian film industry. It argues that being somewhat considerable, noise in such a communication context, is mainly psychological in nature, due principally to the dominance of conservative beliefs on sex and pornography in the Nigerian society. This conservatism motivates most Nigerians to mainly have preconceived stereotypes, notions and biases on sex and pornography and to adopt judgmental and censuring reactions to most attempts to celebrate pornography. The effect of the psychological noise (as observed in online conversation on pornography) is mainly to orchestrate a change of topic from sex to other sensitive issues as politics and religion or engender insults and counter insults which further negatively affect communication.
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Non-polynomial Spline Difference Schemes for Solving Second-order Hyperbolic Equations
Статья научная
In this paper, a class of improved methods based on non-polynomial cubic splines in space and finite difference in time direction are constructed for the second-order hyperbolic equations with initial boundary value problems. Truncation error and stability analysis of the methods have been carried out. It is shown that by suitably choosing the parameters, many known methods can be derived from ours. We also obtain a new high accuracy scheme of , which is conditionally stable for .Finally, a numerical experiment is tested and results are compared with other published numerical solutions.
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Статья научная
This paper derives a new procedure for age classification of facial image based on the local region of facial image. The local region of facial image is extracted from a Significant Binary Pattern of Local Maximum Edge (SBPLME). The SBPLME is generated by calculating the absolute value of local difference between the average of local 3×3 sub window pixel values and its neighbors instead of the center pixel value. In the case of Local Maximum Edge Binary Pattern (LMEBP) calculating the absolute value of local difference between the center pixel value of local 3×3 sub window and its neighbors. The proposed SBPLME can generate 512 (0 to 511) different patterns. The present paper utilized Prominent LBP (PLBP) on the proposed SBPLME. The PLBP contains the significant patterns of Uniform LBP (ULBP) and Non Uniform LBP (NULBP). Thus the derived Significant PLBP of Local Maximum Edge (SPBPLME) becomes an efficient image classification and analysis, which will have a significant role in many areas. The novelty of the proposed SPBPLME method is, it has shown excellent age classification results by reducing the overall dimension, thus reducing the overall complexity.
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Novel Hybrid Model: Integrating Scrum and XP
Статья научная
Scrum does not provide any direction about how to engineer a software product. The project team has to adopt suitable agile process model for the engineering of software. XP process model is mainly focused on engineering practices rather than management practices. The design of XP process makes it suitable for simple and small size projects and not appropriate for medium and large projects. A fine integration of management and engineering practices is desperately required to build quality product to make it valuable for customers. In this research a novel framework hybrid model is proposed to achieve this integration. The proposed hybrid model is actually an express version of Scrum model. It possesses features of engineering practices that are necessary to develop quality software as per customer requirements and company objectives. A case study is conducted to validate the proposal of hybrid model. The results of the case study reveal that proposed model is an improved version of XP and Scrum model.
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Novel Optimized Designs for QCA Serial Adders
Статья научная
Quantum-dot Cellular Automata (QCA) is a new and efficient technology to implement logic Gates and digital circuits at the nanoscale range. In comparison with the conventional CMOS technology, QCA has many attractive features such as: low-power, extremely dense and high speed structures. Adders are the most important part of an arithmetic logic unit (ALU). In this paper, four optimized designs of QCA serial adders are presented. One of the proposed designs is optimized in terms of the number of cells, area and delay without any wire crossing methods. Also, two new designs of QCA serial adders and a QCA layout equivalent to the internal circuit of TM4006 IC are presented. QCADesigner software is used to simulate the proposed designs. Finally, the proposed QCA designs are compared with the previous QCA, CNTFET-based and CMOS technologies.
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Novel Spectrum Handoff in Cognitive Radio Networks Using Fuzzy Logic
Статья научная
Cognitive radio is a technology initiated by many research organizations and academic institutions to raise the spectrum utilization of underutilized channels in order to alleviate spectrum scarcity problem to a larger extent. Spectrum handoff is initiated due to appearance of primary user (PU) on the channels occupied by the secondary user (SU) at that time and location or interference to the PU exceeds the certain threshold. In this paper, we propose a novel spectrum handoff algorithm using fuzzy logic based approach that does two important functions: 1) adjusts transmission power of SU intelligently in order to avoid handoff by reducing harmful interference to PUs and 2) takes handoff decisions intelligently in the light of new parameter such as expected holding time (HT) of the channel as one of its antecedent. Simulated results show impact analysis of selection of the channel in the light of HT information and the comparison with random selection algorithm demonstrates that there is considerable reduction in handoff rate of SU.
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Numerical Implementation of Nonlinear Implicit Iterative Method for Solving Ill-posed Problems
Статья научная
Many nonlinear regularization methods may converge to local minima in numerical implementation for the complexity of nonlinear operator. Under some not very strict assumptions, we implement our proposed nonlinear implicit iterative method and have a global convergence results. Using the convexity property of the modified Tikhonov functional, it combines nonlinear implicit iterative method with a gradient method for solving ill-posed problems. Finally we present two numerical results for integral equation and parameter identification.
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Off-line sindhi handwritten character identification
Статья научная
Handwritten Identification is an ability of the computer to receive and translate the intelligible handwritten text into machine-editable text. It is classified into two types based on the way input is given namely: off-line and online. In Off-line handwritten recognition, the input is given in the form of the image while in online input is entered on a touch screen device. The research on off-line and online handwritten Sindhi character identification is on its very initial stage in comparison to other languages. Sindhi is one of the subcontinent's oldest languages with extensive literature and rich culture. Therefore, this paper aims to identify off-line Sindhi handwritten characters. In the proposed work, major steps involve in characters identification are training and testing of the system. Training is performed using a feed-forward neural network based on the efficient accelerative technique, the Back Propagation (BP) learning algorithm with momentum term and adaptive learning rate. The dataset of 304 Sindhi handwritten characters is collected from 16 different Sindhi writers, each with 19 characters. The novelty of proposed work is the comparison of the recognition rate for the single character, two characters and three characters at a time. Results showed that the recognition rate achieved for a single character is more than the recognition rate of multiple characters at a time.
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