Статьи журнала - International Journal of Modern Education and Computer Science

Все статьи: 968

A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

Sudip Mandal

Статья научная

Particle Swarm Optimization (PSO) is one of most widely used metaheuristics which is based on collective movement of swarm like birds or fishes. The inertia weight (w) of PSO is normally used for maintaining balance between exploration and exploitation capability. Many strategies for updating the inertia weight during iteration were already proposed by several researchers. In this paper, a Modified Particle Swarm Optimization (MPSO) algorithm based on self-adaptive acceleration constants along with Linear Decreasing Inertia Weight (LDIW) technique is proposed. Here, in spite of using fixed values of acceleration constants, the values are updated themselves during iteration depending on local and global best fitness value respectively. Six different benchmark functions and three others inertia weight strategies were used for validation and comparison with this proposed model. It was observed that proposed MPSO algorithm performed better than others three strategies for most of functions in term of accuracy and convergence although its execution time was larger than others techniques.

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A Module Coupling Slice Based Test Case Prioritization Technique

A Module Coupling Slice Based Test Case Prioritization Technique

Harish Kumar, Naresh Chauhan

Статья научная

Regression testing is a process that executes subset of tests that have already been conducted to ensure that changes have not propagated unintended side effects. Test case prioritization aims at reordering the regression test suit based on certain criteria, so that the test cases with higher priority can be executed first rather than those with lower priority. In this paper, a new approach for test case prioritization has been proposed which is based on a module-coupling effect that considers the module-coupling value for the purpose of prioritizing the modules in the software so that critical modules can be identified which in turn will find the prioritized set of test cases. In this way there will be high percentage of detecting critical errors that have been propagated to other modules due to any change in a module. The proposed approach has been evaluated with a case study of software consisting of ten modules.

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A Naïve Based approach of Model Pruned trees on Learner’s Response

A Naïve Based approach of Model Pruned trees on Learner’s Response

S.Anupama Kumar, Vijayalakshmi M.N.

Статья научная

Appraisal and feedback have a strong positive influence on teachers and their work. Teachers report that it increases their job satisfaction and, to some degree, their job security, and it significantly increases their development as teachers. Student’s appraisal towards a teacher plays a vital role in building a very good teaching-learning environment in an educational institution. The evaluation report of the student helps the stakeholders to retain qualified teachers for the course. It will also help the teacher to understand the need of the student and the course. Therefore it becomes necessary to evaluate the teacher using appropriate tool to improve the quality of the education. Teacher evaluation can be measured based on the technical knowledge, communication skills, clarity, attitude towards the student etc. Regression trees can be considered as a tool to analyze the teacher appraisal scores. Two regression trees namely the REP tree and M5P algorithms are applied on the data set to bring out new knowledge from it. The algorithms have identified Parameter A as an important factor in teacher’s appraisal. Pruning has been taken as parameter to find the accuracy of the algorithm. The performance of the algorithm is measured using the mean absolute error and the time taken by the algorithms to derive the regression tree. The REP tree algorithm performs better than the M5P algorithm in terms of accuracy as well as the performance.

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A Network-Based Peer Evaluation Strategy

A Network-Based Peer Evaluation Strategy

Mohamed A. Alket

Статья научная

The purpose of this study was to design a network-based peer evaluation strategy -which included two types of peer evaluation (known and unknown) to investigate the effect of these two types on developing problem-solving and critical thinking skills for students in Java object oriented programming language course (OOP). The participants of this study were twenty-four (n=24) students at College of Science and Arts, Qassim university. The results revealed that the two types of peer evaluation (in both known and unknown) had a positive effect on developing problem-solving and critical thinking skills for students. After, the comparison between the post-application of the two experimental groups in the problem solving and critical thinking skills, although there is a slightly higher between mean ranks in the sake of the unknown group, the results showed that there were no significant differences between the two groups. Finally, the researcher recommended to using a network-based peer evaluation strategy with other specializations and a large sample.

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A New Approach for Dynamic Virtual Machine Consolidation in Cloud Data Centers

A New Approach for Dynamic Virtual Machine Consolidation in Cloud Data Centers

Esmail Asyabi, Mohsen Sharifi

Статья научная

Cloud computing environments have introduced a new model of computing by shifting the location of computing infrastructure to the Internet network to reduce the cost associated with the management of hardware and software resources. The Cloud model uses virtualization technology to effectively consolidate virtual machines (VMs) into physical machines (PMs) to improve the utilization of PMs. Studies however have shown that the average utilization of PMs in many Cloud data centers is still lower than expected. The Cloud model is expected to improve the existing level of utilization by employing new approaches of consolidation mechanisms. In this paper we propose a new approach for dynamic consolidation of VMs in order to maximize the utilization of PMs. This is achieved by a dynamic programing algorithm that selects the best VMs for migration from an overloaded PM, considering the migration overhead of a VM. Evaluation results demonstrate that our algorithms achieve good performance.

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A New Classification Algorithm for Data Stream

A New Classification Algorithm for Data Stream

Li Su, Hong-yan Liu, Zhen-Hui Song

Статья научная

Associative classification (AC) which is based on association rules has shown great promise over many other classification techniques on static dataset. Meanwhile, a new challenge have been proposed in that the increasing prominence of data streams arising in a wide range of advanced application. This paper describes and evaluates a new associative classification algorithm for data streams AC-DS, which is based on the estimation mechanism of the Lossy Counting (LC) and landmark window model. And AC-DS was applied to mining several datasets obtained from the UCI Machine Learning Repository and the result show that the algorithm is effective and efficient.

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A New Diagnosis Loseless Compression Method for Digital Mammography Based on Multiple Arbitrary Shape ROIs Coding Framework

A New Diagnosis Loseless Compression Method for Digital Mammography Based on Multiple Arbitrary Shape ROIs Coding Framework

Ping Xu, Yan Zuo, Wei-Dong Xu, Hua-Jie Chen

Статья

With the rapidly growing use of digital images in medical archival and communication, image compression technology, especially diagnosis lossless compression technology, plays a more and more important role for medical applications. In this thesis, a novel diagnosis loseless compression algorithm is presented for digital mammography. The mammogram is divided into breast region, pectoral muscle and background using the CAD technology. Then mutiple arbitrary shape ROIs coding framework is used to compress the mammogram in which the breast region and pectoral muscle are compressed losslessly and lossily respectively, and the background can be discarded or compressed lossily as user’s will. Experimental results show that the proposed method offer potential advantage in medical applications of digital mammography compression.

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A New High-Performance Bridge Structure for 4-to-2 Compressor using CMOS and CNFET Technology

A New High-Performance Bridge Structure for 4-to-2 Compressor using CMOS and CNFET Technology

Mehdi Darvishi, Mehdi Bagherizadeh

Статья научная

In this paper, a new high-speed and energy efficient 4-to-2 compressor cell was presented using carbon nanotube field effect transistors (CNFETs). CNFET is very suitable for high-frequency and low-voltage applications. In addition, in this paper several conventional and state-of-the-art 4-to-2 compressor cells are surveyed and compared. In order to evaluate the proposed designs, computer simulations are carried out using 32nm-CMOS and 32nm-CNFET technologies. Simulations are conducted using various low voltage power supplies, different temperatures, frequencies and load capacitors. Results of simulation demonstrate predominance of the proposed design in terms of power consumption, delay, and power-delay product (PDP) compared to other 4-to-2 compressor cells and they confirm that the proposed design is the fastest 4-to-2 compressor in various working circumstances.

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A New Hybrid Classification Method for Condensing of Large Datasets: A Case Study in the Field of Intrusion Detection

A New Hybrid Classification Method for Condensing of Large Datasets: A Case Study in the Field of Intrusion Detection

SAEED Khazaee, ALI Bozorgmehr

Статья научная

In large data sets data pre-processing always has been the most essential data processing stages. Sampling and using small volumes of data has been an integrated part of data pre-processing to decrease training errors and increase speed of learning. In this study, instead of sampling from all data and using small parts of them, a method has been proposed to not only benefit from sampling but all data be used during training process. In this way, outliers would be detected and even used in completely different way. Using artificial neural networks, new features for instances will be built and the problem of intrusion detection will be mapped as a 10- feature problem. In fact, such a classification is for feature creation and as features in new problem only have discrete values, in final classification decision tree will be used. The results of proposed method on KDDCUP'99 datasets and Cambridge datasets show that this has improved classification in many classes dramatically.

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A New Method for Content based Image Retrieval using Primitive Features

A New Method for Content based Image Retrieval using Primitive Features

S.Maruthuperumal, G. Rosline Nesa Kumari

Статья научная

The diminishing expenditure of consumer electronic devices such as digital cameras and digital camcorders along with ease of transportation facilitated by the internet, has lead to a phenomenal rise in the quantity of multimedia data. The need to find a desired image from a collection is shared by many professional groups, including journalists, design engineers and art historians. While the requirements of image users, it can be characterize image queries into three levels. The proposed method based on primitive features such as color and shapes. These features are extracted and used as the basis for a similarity check between images. The shape and color features are extracted through Gradient Edge Detection and color histogram the combination of these features is robust. The experiment results show that the proposed image retrieval is more efficient and effective in retrieving the user interested images.

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A New Method for Graph Queries Processing without Index Reconstruction on Dynamic Graph Databases

A New Method for Graph Queries Processing without Index Reconstruction on Dynamic Graph Databases

Hamed Dinari

Статья научная

Graphs play notable role in daily life. For instance, they are used in variety fields such as social networks, malware detection, and biological networks. Graph data processing performed to extract useful information is known as graph mining. A critical field of graph mining is graph containment problem, in which all graphs containing the query are returned by a graph query q. Scanning the whole database (graph query as a subgraph) for a query is a time consuming process. To improve query performance, an inverted index is constructed on the graph database and then the query is performed based on the query. The problem in this process is that when a graph is added to or removed from a database, the inverted index must be reconstructed. The present study proposes a method in which index updating is not needed upon a change in the database. This feature enables simultaneous inverted index updating and querying. The assessment results showed optimum and satisfactory performance of the proposed method.

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A New Method of Equivalent Material Model Deformation Observation

A New Method of Equivalent Material Model Deformation Observation

Lailiang CAI, Kan WU, Qisheng YU,Jinpeng FENG

Статья

Equivalent Material Model is a staple method that can be used to imitate the strata and the ground surface movement which caused by human’s underground activities, such as coal resources extraction. The precision of indoor imitations are mainly decided by ways of models’ deformation observation. This paper proposes a new method of measuring models after analyzing the status of deformation observation of equivalent material models. In this paper, the industrial measuring system is used to measure deformations of the model. The system includes two main parts: photographic surveying of industry instruments and structure lighting scanning devices. The first part is used to get the coordinates of the target points which are set on the model; the second one is used to scan the model surface for catching the cracks on the model surface. Due to the high accuracy of photographic surveying of industry method, it can meet the need of monitoring imperceptible movements of target points; also the structure lighting scanning has a high precision on scanning the model surface, which can get the very thick points cloud of the model surface. The system is originally used for reverse engineering, and it scarcely used for Equivalent material model until our research used, so there is no mature method on data management. This paper researches the special data procession method for the model, and results show that methods in the paper are suit for the industrial measuring system.

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A Novel 4×4 Universal Reversible Gate as a Cost Efficient Full Adder/Subtractor in Terms of Reversible and Quantum Metrics

A Novel 4×4 Universal Reversible Gate as a Cost Efficient Full Adder/Subtractor in Terms of Reversible and Quantum Metrics

Shekoofeh Moghimi, Mohammad R. Reshadinezhad

Статья научная

This paper proposes a new 4×4 reversible logic gate which is named as MOG. Reversible gates are logical basic units, having equal number of input and output lines, which can reduce power dissipation in digital systems design through their reversibility feature; because there is a one-to-one corresponding between their input and outputs vectors. The most significant aspect of the MOG gate is that it is a universal gate and has the ability of calculating any logical function on its own. We have also proposed quantum representation of the MOG gate with optimal quantum cost equal to 11. Then, it has been proved that MOG gate can be used to produce a cost efficient reversible full adder/subtractor cell in terms of reversible and quantum metrics. The proposed reversible full adder/subtractor design using MOG gate is a completely optimized circuit in terms of the number of reversible gates, the number of constant inputs, and the number of garbage outputs because it can work with the minimum possible amounts of these reversible metrics. Additionally, it is more efficient than the existing counterparts in terms of quantum cost. The full adder/subtractor cell is an important circuit in VLSI and digital signal processing applications. A lot of works have been done toward designing reversible full adder/subtractors in the literature; but there is no an optimized design with quantum implementation. To prove the applicability of the proposed design in large processing scales, we have constructed 8-bits reversible ripple carry full adder/subtractor circuit using MOG gates. Results have shown the superiority of our proposed design compared with other 8-bits similar designs

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A Novel Active Data Filtration for the Cloud based Architecture against Packet Flooding Attacks

A Novel Active Data Filtration for the Cloud based Architecture against Packet Flooding Attacks

Shikha Vashisht, Mandeep kaur

Статья научная

The usage of remote servers network on the Internet to process data, store and manage, instead of using a local server or any computer" is called cloud computing. Cloud computing is that which totally based on resource sharing rather than any other device to handle applications. Today cloud computing is facing numerous challenges and one of those is Attack on the cloud environment. There are many types of hazardous attack on cloud, as the attack is always in wait for some important data or resource. The most common and most affective attack is Packet Flooding attack and there are many faces of packet flooding. EDoS Attack one of the most commonly and strong packet flood attack on the cloud to make the resources almost inaccessible to the user by flooding the unnecessary packet to the network or site more that its capacity. This paper deals with the analysis of EDoS and a mechanism is proposed to mitigate the EDoS by using filtration mechanism. The filtration is done on the basis of secure key Exchange which differentiate legitimate user from attacker. The simulation is done by cloud sim as well as Net-Beans and the performance is analyzed over time and data. Using filter the packet loss and time delay occurs in EDoS attack is much reduced.

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A Novel Approach for Data Cleaning by Selecting the Optimal Data to Fill the Missing Values for Maintaining Reliable Data Warehouse

A Novel Approach for Data Cleaning by Selecting the Optimal Data to Fill the Missing Values for Maintaining Reliable Data Warehouse

Raju Dara, Ch. Satyanarayana, A. Govardhan

Статья научная

At present trillion of bytes of information is being created by projects particularly in web. To accomplish the best choice for business benefits, access to that information in a very much arranged and intuitive way is dependably a fantasy of business administrators and chiefs. Information warehouse is the main feasible arrangement that can bring the fantasy into reality. The upgrade of future attempts to settle on choices relies on upon the accessibility of right data that depends on nature of information basic. The quality information must be created by cleaning information preceding stacking into information distribution center following the information gathered from diverse sources will be grimy. Once the information have been pre-prepared and purified then it produces exact results on applying the information mining question. There are numerous cases where the data is sparse in nature. To get accurate results with sparse data is hard. In this paper the main goal is to fill the missing values in acquired data which is sparse in nature. Precisely caution must be taken to choose minimum number of text pieces to fill the holes for which we have used Jaccard Dissimilarity function for clustering the data which is frequent in nature.

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A Novel Approach for Diagnosis of Glaucoma through Optic Nerve Head (ONH) Analysis using Fractal Dimension Technique

A Novel Approach for Diagnosis of Glaucoma through Optic Nerve Head (ONH) Analysis using Fractal Dimension Technique

Dharmanna L, Chandrappa S, T. C. Manjunath, Pavithra G

Статья научная

According to the survey of World Health Organization (WHO), the number of people getting affected by glaucoma eye disease in worldwide will be 79.11 million by the year 2020. Glaucoma is a dangerous eye disease, which can lead to permanent vision loss if not provided proper treatment at the right time. Currently ophthalmologists detect the glaucoma disease based on estimation of cup to disk ratio, but this method suffers from accurate segmentation of regions like optic disk and optic cup. However, this introduces errors in the diagnosis. Therefore in this paper, Hausdrop Fractal Dimension (HFD) technique is adopted for identification of the glaucoma eye disease. Here, Optic disk perimeter parameter is used in HFD technique for classification of healthy or glaucomatous retinas. Average fractal dimension is calculated for a set of healthy optic disks and the fractal dimension is found to be 0.998, whereas for glaucomatous optic disks obtained average fractal dimension value 1.342.

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A Novel Approach for Optimization Auto-Scaling in Cloud Computing Environment

A Novel Approach for Optimization Auto-Scaling in Cloud Computing Environment

Khosro Mogouie, Mostafa Ghobaei Arani, Mahboubeh Shamsi

Статья научная

In recent years, applications of cloud services have been increasingly expanded. Cloud services, are distributed infrastructures which develop the communication and services. Auto scaling is one of the most important features of cloud services which dedicates and retakes the allocated dynamic resource in proportion to the volume of requests. Scaling tries to utilize maximum power of the available resources also to use idle resources, in order to maximize the efficiency or shut down unnecessary resources to reduce the cost of running requests. In this paper, we have suggested an approach based on learning automata auto- scaling, in order to manage and optimize factors like cost, rate of violations of user-level agreements (SLA Violation) as well as stability in the presence of traffic workload. Results of simulation show that proposed approach has been able to optimize cost and rate of SLA violation in order to manage their trade off. Also, it decreases number of operation needed for scaling to increase stability of system compared to the other approaches.

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A Novel Classification Method Using Hybridization of Fuzzy Clustering and Neural Networks for Intrusion Detection

A Novel Classification Method Using Hybridization of Fuzzy Clustering and Neural Networks for Intrusion Detection

Saeed Khazaee, Karim Faez

Статья научная

In this paper, a hybrid classifier using fuzzy clustering and several neural networks has been proposed. With using the fuzzy C-means algorithm, training samples will be clustered and the inappropriate data will be detected and moved to another dataset (Removed-Dataset) and used differently in the classification phase. Also, in the proposed method using the membership degree of samples to the clusters, the class of samples will be changed to the fuzzy class. Thus, for example in KDD cup99 dataset, any sample will have 5 membership degrees to classes DoS, Probe, Normal, U2R, and R2L. Afterwards, the neural networks will be trained by new labels then using a combination of regression and classification methods, the hybrid classifier will be created. Also to classify the outlier data, a fuzzy ARTMAP neural network is employed which is a part of the hybrid classifier. Evaluation of the proposed method is performed by KDDCup99 dataset for intrusion detection and Cambridge datasets for traffic classification problems. Our experimental results indicate that the proposed system has performed better than the previous works in the case of precision, recall and f-value also detection and false alarm rate. Also, ROC curve analysis shows that the proposed hybrid classifier has been better than the famous non-hybrid classifiers.

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A Novel Hybrid Flower Pollination Algorithm with Chaotic Harmony Search for Solving Sudoku Puzzles

A Novel Hybrid Flower Pollination Algorithm with Chaotic Harmony Search for Solving Sudoku Puzzles

Osama Abdel-Raouf, Ibrahim El-henawy, Mohamed Abdel-Baset

Статья научная

Flower Pollination algorithm (FPA) is a new nature-inspired algorithm, based on the characteristics of flowering plants.In this paper, a new hybrid optimization method called improved Flower Pollination Algorithm with Chaotic Harmony Search (FPCHS) is proposed. The method combines the standard Flower Pollination algorithm (FPA) with the chaotic Harmony Search (HS) algorithm to improve the searching accuracy. The FPCHS algorithm is used to solve Sudoku puzzles. Numerical results show that the FPCHS is accurate and efficient in comparison with standard Harmony Search, (HS) algorithm.

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A Novel Image Encryption Scheme Based on Multi-orbit Hybrid of Discrete Dynamical System

A Novel Image Encryption Scheme Based on Multi-orbit Hybrid of Discrete Dynamical System

Ruisong Ye, Huiqing Huang, Xiangbo Tan

Статья научная

A multi-orbit hybrid image encryption scheme based on discrete chaotic dynamical systems is proposed. One generalized Arnold map is adopted to generate three orbits for three initial conditions. Another chaotic dynamical system, tent map, is applied to generate one pseudo-random sequence to determine the hybrid orbit points from which one of the three orbits of generalized Arnold map. The hybrid orbit sequence is then utilized to shuffle the pixels' positions of plain-image so as to get one permuted image. To enhance the encryption security, two rounds of pixel gray values' diffusion is employed as well. The proposed encryption scheme is simple and easy to manipulate. The security and performance of the proposed image encryption have been analyzed, including histograms, correlation coefficients, information entropy, key sensitivity analysis, key space analysis, differential analysis, etc. All the experimental results suggest that the proposed image encryption scheme is robust and secure and can be used for secure image and video communication applications.

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