International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1238
Статья научная
Forecasting CPU availability in volunteer computing systems using a single prediction algorithm is insufficient due to the diversity of the world-wide distributed resources. In this paper, we draw-up the main guidelines to develop an appropriate CPU availability prediction system for such computing infrastructures. To reduce solution time and to enhance precision, we use simple prediction techniques, precisely vector autoregressive models and a tendency-based technique. We propose a predictor construction process which automatically checks assumptions of vector autoregressive models in time series. Three different past analyses are performed. For a given volunteer resource, the proposed prediction system selects the appropriate predictor using the multi-state based prediction technique. Then, it uses the selected predictor to forecast CPU availability indicators. We evaluated our prediction system using real traces of more than 226000 hosts of Seti@home. We found that the proposed prediction system improves the prediction accuracy by around 24%.
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Статья научная
Inspection task is traditionally carried out by human. However, Automated Visual Inspection (AVI) has gradually become more popular than human inspection due to the advantageous in the aspect of high precision and short processing time. Therefore, this paper proposed a system which identifies the object's position for industrial robot based on colors and shapes where, red, green, blue and circle, square, triangle are recognizable. The proposed system is capable to identify the object's position in three modes, either based on color, shape or both color and shape of the desired objects. During the image processing, RGB color space is utilized by the proposed system while winner take all approach is used to classify the color of the object through the evaluation of the pixel's intensity value of the R, G and B channel. Meanwhile, the shapes and position of the objects are determined based on the compactness and the centroid of the region respectively. Camera settings, such as brightness, contrast and exposure is another important factor which can affect the performance of the proposed system. Lastly, a Graphical User Interface was developed. The experimental result shows that the developed system is highly efficient when implemented in the selected database.
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Automated bug assignment in software maintenance using graph databases
Статья научная
Processes involved in maintaining a system play a crucial role in enhancing customer satisfaction and longevity of the system. Maintenance engineers are the most critical resources in Software Maintenance. They play a significant role in fixing bugs and ensuring the normal functioning of systems. Software maintenance is a tedious task for novice engineers who are new to the system domain. The lack of up-to-date documentation makes system comprehension more challenging for inexperienced engineers. Assignment of high priority bugs to novice engineers may lead to inappropriate fixes and delay in the revival of an impacted system. Such issues may degrade customer satisfaction and also poor fixes can have a severe impact on the functioning of the system at a later stage. Our research is focussed on identification of engineers with the right level of experience to fix a given bug. We have used the concept of page ranking and graph databases to compute the importance of bugs and assignees in a graph. A newly reported bug will be scored and matched with bugs that have a similar score in the graph database. Assignees who have fixed a bug that closely maps the score of the reported bug will be assigned the task of fixing the bug. We have implemented this methodology using bug reports from QT framework on neo4j graph database. Our results are promising and will definitely pave way for a new bug assignment strategy in software maintenance.
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Automatic Ethical Filtering using Semantic Vectors Creating Normative Tag Cloud from Big Data
Статья научная
Ethical filtering has been a painful and controversial issue seen by different angles worldwide. Stalwarts for freedom find newer methods to circumvent banned URLs while generative power of the Internet outpaces velocity of censorship. Hence, keeping online content safe from anti-religious and sexually provocative content is a growing issue in conservative countries in Asia and The Middle East. Solutions for online ethical filters are linearly upper bound given computation and big data growth scales. In this scenario, Semantic Vectors are applied as automatic ethical filters to calculate accuracy and efficiency metrics. The results show a normative tag cloud generated with superior performance to industry solutions.
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Automatic Ration Material Distributions Based on GSM and RFID Technology
Статья научная
Now a day ration card is very important for every home and used for various field such as family members details, to get gas connection, it act as address proof for various purposes etc. All the people having a ration card to buy the various materials (sugar, rice, oil, kerosene, etc) from the ration shops. But in this system having two draw backs, first one is weight of the material may be inaccurate due to human mistakes and secondly, if not buy the materials at the end of the month, they will sale to others without any intimation to the government and customers. In this paper, proposed an Automatic Ration Materials Distribution Based on GSM (Global System for Mobile) and RFID (Radio Frequency Identification) technology instead of ration cards. To get the materials in ration shops need to show the RFID tag into the RFID reader, then controller check the customer codes and details of amounts in the card. After verification, these systems show the amount details. Then customer need to enter they required materials by using keyboard, after receiving materials controller send the information to government office and customer through GSM technology. In this system provides the materials automatically without help of humans.
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Automatic brain tissues segmentation based on self initializing K-Means clustering technique
Статья научная
This paper proposed a self-initialization process to K-Means method for automatic segmentation of human brain Magnetic Resonance Image (MRI) scans. K-Means clustering method is an iterative approach and the initialization process is usually done either manually or randomly. In this work, a method has been proposed to make use of the histogram of the gray scale MRI brain images to automatically initialize the K-means clustering algorithm. This is done by taking the number of main peaks as well as their values as number of clusters and their initial centroids respectively. This makes the algorithm faster by reducing the number of iterations in segmenting the MRI image. The proposed method is named as Histogram Based Self Initializing K-Means (HBSIKM) method. Experiments were done with the MRI brain volumes available from Internet Brain Segmentation Repository (IBSR). Similarity validation was done by Dice coefficient with the available gold standards from the IBSR website. The performance of the proposed method is compared with the traditional K-Means method. For the IBSR volumes, the proposed method yields 3 to 4 times faster results and higher dice value than traditional K-Means method.
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Available Link Bandwidth Based Network Selection in Multi-access Networks
Статья научная
In a heterogeneous wireless environment, one of the important aspects of seamless communication for ubiquitous computing is the dynamic selection of the best access network. The problem of access network selection has been addressed through various decision methods based on available network information. Available link bandwidth is one of the important information parameters, which can be used as criterion for network selection. In this paper, we consider available bandwidth as a dynamic parameter to select the network in heterogeneous environment. First, we propose a bootstrap approximation based technique to estimate available bandwidth and then utilize it for the selection of the best suitable network in the heterogeneous environment consisting of 2G and 3G standards based wireless networks. The proposed algorithm is implemented in temporal and spatial domains to check its robustness. Estimation time with varying size of files is used as the performance metric. Through numerical results, it is shown that the proposed algorithm gives improved performance as compared to the existing algorithm.
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BER Analysis of OFDM Digital Communication Systems with Improved ICI Cancellation Technique
Статья научная
In this paper, performance of OFDM digital communication systems have been analyzed with improved ICI cancellation technique. The bit error rate has been regarded as a fundamental information theoretic measure of a communication system. A novel parallel ICI cancellation technique has been proposed for mitigating frequency offset of OFDM digital communication systems. The simulated results of the proposed technique is compared with ICI self cancellation scheme. The simulated results show better performance over ICI self cancellation scheme.
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Balanced Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem
Статья научная
0/1 Multiple Knapsack Problem, a generalization of more popular 0/1 Knapsack Problem, is NP-hard and considered harder than simple Knapsack Problem. 0/1 Multiple Knapsack Problem has many applications in disciplines related to computer science and operations research. Quantum Inspired Evolutionary Algorithms (QIEAs), a subclass of Evolutionary algorithms, are considered effective to solve difficult problems particularly NP-hard combinatorial optimization problems. A hybrid QIEA is presented for multiple knapsack problem which incorporates several features for better balance between exploration and exploitation. The proposed QIEA, dubbed QIEA-MKP, provides significantly improved performance over simple QIEA from both the perspectives viz., the quality of solutions and computational effort required to reach the best solution. QIEA-MKP is also able to provide the solutions that are better than those obtained using a well known heuristic alone.
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Balinese historian chatbot using full-text search and artificial intelligence markup language method
Статья научная
In the era of technology, various information could be obtained quickly and easily. The history of Bali is one of the information that could be obtained. Balinese have known their history through Babad and stories which are told through generations. Babad is traditional-historical writing which tells important event that has happened. As technology evolves, Balinese’s interest in studying their own history has been decreased. It is caused by people interest in studying history books and chronicles tend to decrease over time. Therefore, an innovation of technology, which able to convert historical data from printed media to digital media, is needed. The technology that could be used is Chatbot technology; a computer program that could carry out conversations. Chatbot technology is used to make people learning history easily by using Instant Messenger LINE as a platform to communicate. This Chatbot uses two methods, namely the Artificial Intelligence Markup Language method and the Full-Text Search method. The Artificial Intelligence Markup Language method is used as the process of making characteristic of questions and answers. The Full-Text Search method is the process of matching answers based on user input. This chatbot only uses Indonesian to communicate. The results of this study are a Chatbot that could be accessed by using Instant Messenger LINE and could communicate like historian expert.
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Bandwidth Extension of Speech Signals: A Comprehensive Review
Статья научная
Telephone systems commonly transmit narrowband (NB) speech with an audio bandwidth limited to the traditional telephone band of 300-3400 Hz. To improve the quality and intelligibility of speech degraded by narrow bandwidth, researchers have tried to standardize the telephonic networks by introducing wideband (50-7000 Hz) speech codecs. Wideband (WB) speech transmission requires the transmission network and terminal devices at both ends to be upgraded to the wideband that turns out to be time-consuming. In this situation, novel Bandwidth extension (BWE) techniques have been developed to overcome the limitations of NB speech. This paper discusses the basic principles, realization, and applications of BWE. Challenges and limitations of BWE are also addressed.
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Bank Customer Credit Scoring by Using Fuzzy Expert System
Статья научная
Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.
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Basis path based test suite minimization using genetic algorithm
Статья научная
UML State Diagram is used to represent the behavior of the System Under Test (SUT) when an event occurs. The state of the system is determined by the event that occurs randomly. The system state changes when the transition relationship between the States is satisfied. Test cases are generated from State Chart Diagram to test the behavior of the system. When multiple decision nodes are present in the same path, path explosion occurs. A method is proposed to generate Basis Path (BP) test cases with node coverage using Genetic Algorithm (GA) to overcome this problem. Experiments are conducted upon various Android applications and the efficiency of the algorithm is evaluated through the code coverage and the mutation analysis. Using this approach, BP test cases, Robotium test scripts are generated for 10 Android applications and observed an average of 70% reduction in the test case number concerning all path test cases. The resulted average code coverage is 74%, and Defect Removal Efficiency (DRE) is 95%. The experimental results show that the proposed method is effective when compared to other methods.
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Bat-Genetic Encryption Technique
Статья научная
Nowadays, the security of confidential data is the vital issue in the digital world. Information security becomes even more essential in storing and transmitting data while online. For protecting digital data and achieving security and confidentiality over an insecure internet, the iterative Bat-Genetic Encryption Technique (B-GET) is proposed. The main stages of B-GET are pre-processing, encryption process, bat algorithm steps, and genetic processes. B-GET also comprises an arithmetic and logical operators that increase encryption quality. Empirical results show that the reconstructed data is a copy of the original. It also demonstrates that B-GET technique has a large space key and several defensive stages that resist many attacks and it has strong security based on multiple steps, multiple variables, and the main stages of the B-GET technique. Encrypted data is nearly random and does not contain any indication to secret data.
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Batik Classification with Artificial Neural Network Based on Texture-Shape Feature of Main Ornament
Статья научная
Batik is a textile with motifs of Indonesian culture which has been recognized by UNESCO as world cultural heritage. Batik has many motifs which are classified in various classes of batik. This study aims to combine the features of texture and the feature of shapes' ornament in batik to classify images using artificial neural networks. The value of texture features of images in batik is extracted using a gray level co-occurrence matrices (GLCM) which include Angular Second Moment (ASM) / energy), contrast, correlation, and inverse different moment (IDM). The value of shape features is extracted using a binary morphological operation which includes compactness, eccentricity, rectangularity and solidity. At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, their shape, and the combination of texture and shape features. From the three features used in the classification of batik image with artificial neural networks, it was obtained that shape feature has the lowest accuracy rate of 80.95% and the combination of texture and shape features produces a greater value of accuracy by 90.48%. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the artificial neural network with the combination of texture and shape features in batik image.
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Статья научная
In this article, novel mixture of conditional volatility models of Generalized Autoregressive Conditional Heteroscedasticity (GARCH); Exponential GARCH (EGARCH); Glosten, Jagannathan, and Runkle GARCH (GJR-GARCH); and dependent variable-GARCH (TGARCH) were thoroughly expounded in a Bayesian paradigm. Expectation-Maximization (EM) algorithm was employed as the parameter estimation technique to work-out posterior distributions of the involved hyper-parameters after setting-up their corresponding prior distributions. Mode was considered as the stable location parameter instead of the mean, because it could robustly adapt to symmetric, skewedness, heteroscedasticity and multimodality effects simulteanously needed to redefine switching conditional variance processes conceived as mixture components based on shifting number of modes in the marginal density of Skewed Generalized Error Distribution (SGED) set as the prior random noise. In application to ten (10) most used cryptocurrency coins and tokens via their daily open, high, low, close and volume converted and transacted in USD from the same date of inception. Binance Coin (BNB) via its daily lower units transacted in USD (that is, low-BNB), yielded the most reduced Deviance Information Criteria (DIC) of 3651.1935. The low-BNB process yielded a two-regime process of TGARCH, that is, Mixture dependent variable-GARCH (TGARCH (2: 2, 2)) with stable probabilities of 33% and 66% respectively. The first regime was attributed with low unconditional volatility of 16.96664, while the second regime was traded with high unconditional volatility of 585.6190. In summary, Binance Coin (BNB) was a mixture of tranquil market conditions and stormy market conditions. Implicatively, this implies that the first regime of the low-BNB was characterized with strong fluctuating reaction to past negative daily returns of low-BNB converted to USD, while the second regime was attributed with weak fluctuating reaction. Additionally, the first regime was attributed with low repetitive volatility process, while the second regime was characterized with high persistence fluctuating process. For financial and economic decision-making, crypocurrency users and financial bodies should look-out for financial and economic sabotage agents, like war, exchange rate instability, political crises, inflation, browsing network fluctuation etc. that arose, declined or fluctuated doing the ten (10) years to study of the coins and tokens to ascertain which of this/these agent(s) contributed to the volatility process. Mixture models from a Bayesian perspective were of interest because; some of the classical (traditional) models cannot accommodate and absolve regime-switching traits, and as well contain prior information known about cryptocurrency coins and tokens. In light of model performance, DIC values were compared on the basis of most performed to less perform via lower to higher values of DICs.
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Bespoke Shuffled Frog Leaping Algorithm and its Engineering Applications
Статья научная
Shuffled Frog Leap Algorithm (SFLA), a metaheuristic algorithms inspired by PSO and DE has proved its efficacy in solving discrete optimization problems. In this paper we have modified SFLA to solve constrained engineering design problems. The proposed modification integrates a simple mechanism to update the position of frog in its memeplex in order to accelerate the basic SFLA algorithm. The proposal is validated on four engineering design problems and the statistical results are compared with the state-of-art algorithms. The simulated statistical results indicate that our proposal is a promising alternative to solve these types of optimization problems in terms of convergence speed.
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Bezier Curves Satisfiability Model in Enhanced Hopfield Network
Статья научная
Bezier curve is one of the most pragmatic curves that has vast application in computer aided geometry design. Unlike other normal curves, any Bezier curve model must follow the properties of Bezier curve. In our paper, we proposed the reconstruction of Bezier models by implementing satisfiability problem in Hopfield neural network as Bezier properties verification technique. We represent our logic construction to 2-satisfiability (2SAT) clauses in order to represent the properties of the Bezier curve model. The developed Bezier model will be integrated with Hopfield neural network in order to detect the existence of any non-Bezier curve. Microsoft Visual C++ 2013 is used as a platform for training, testing and validating of our proposed design. Hence, the performance of our proposed technique is evaluated based on global Bezier model and computation time. It has been observed that most of the model produced by HNN-2SAT are Bezier curve models.
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Статья научная
Biometric authentication systems operating in real world environments using a single modality are found to be insecure and unreliable due to numerous limitations. Multimodal biometric systems have better accuracy and reliability due to the use of multiple biometric traits to authenticate a claimed identity or perform identification. In this paper a novel method for person identification using multimodal biometrics with hand geometry and palmprint biometric traits is proposed. The geometrical information embedded in the user hand and palmprint images are brought out through the graph representations. The topological characterization of the image moments, represented as the virtual nodes of the palmprint image graph is a novel feature of this work. The user hand and palmprint images are represented as weighted undirected graphs and spectral characteristics of the graphs are extracted as features vectors. The feature vectors of the hand geometry and palmprint are fused at feature level to obtain a graph spectral feature vector to represent the person. User identification is performed by using a multiclass support vector machine (SVM) classifier. The experimental results demonstrate, an appreciable performance giving identification rate of 99.19% for multimodal biometric after feature level fusion of hand geometry and palmprint modalities. The performance is investigated by conducting the experiments separately for handgeometry, palmprint and fused feature vectors for person identification. Experimental results show that the proposed multimodal system achieves better performance than the unimodal cues, and can be used in high security applications. Further comparison show that it is better than similar other multimodal techniques.
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Biorthogonal Wavelet Transform Using Bilateral Filter and Adaptive Histogram Equalization
Статья научная
Image fusion is a process of combining data from multiple sources to achieve refined or improved information for making decisions. It has many applications. When we use images with a similar acquisition time, the expected result is to obtain a fused image that retains the spatial resolution from the panchromatic image and color content from the multi-spectral image. In recent time different methods have been developed. These methods are both in spatial domain and in wavelet domain. Out of these two the wavelet domain based methods are more suitable as they are capable to handle the spatial distortion produced by the spatial domain. In this paper the proposed method is compared with principle component analysis, discrete cosine transform and also with biorthogonal wavelet transform in which bilateral filter and adaptive histogram is not present. This comparison is on the bases of different parameters. Biorthogonal wavelet transform is capable to preserve edge information and hence reducing the distortions in the fused image. It has two important properties wavelet symmetry and linear phase which are not present in spatial domain. The performance of the proposed method has been extensively tested on several pairs of multi-focus and multimodal images. Experimental results show that the proposed method improves fusion quality by reducing loss of significant information available in individual images.
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