Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Medical Image Segmentation through Bat-Active Contour Algorithm
Статья научная
In this research work, an improved active contour method called Bat-Active Contour Method (BA-ACM) using bat algorithm has been developed. The bat algorithm is incorporated in order to escape local minima entrapped into by the classical active contour method, stabilize contour (snake) movement and accurately, reach boundary concavity. Then, the developed Bat-Active Contour Method was applied to a dataset of medical images of the human heart, bone of knee and vertebra which were obtained from Auckland MRI Research Group (Cardiac Atlas Website), University of Auckland. Set of similarity metrics, including Jaccard index and Dice similarity measures were adopted to evaluate the performance of the developed algorithm. Jaccard index values of 0.9310, 0.9234 and 0.8947 and Dice similarity values of 0.8341, 0.8616 and 0.9138 were obtained from the human heart, vertebra and bone of knee images respectively. The results obtained show high similarity measures between BA-ACM algorithm and expert segmented images. Moreso, traditional ACM produced Jaccard index values 0.5873, 0.5601, 0.6009 and Dice similarity values of 0.5974, 0.6079, 0.6102 in the human heart, vertebra and bone of knee images respectively. The results obtained for traditional ACM show low similarity measures between it and expertly segmented images. It is evident from the results obtained that the developed algorithm performed better compared to the traditional ACM.
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Статья научная
An efficient classification algorithm used recently in many big data applications is the Random forest classifier algorithm. Large complex data include patient record, medicine details, and staff data etc., comprises the medical big data. Such massive data is not easy to be classified and handled in an efficient manner. Because of less accuracy and there is a chance of data deletion and also data missing using traditional methods such as Linear Classifier K-Nearest Neighbor, Random Clustering K-Nearest Neighbor. Hence we adapt the Random Forest Classification using K-means clustering algorithm to overcome the complexity and accuracy issue. In this paper, at first the medical big data is partitioned into various clusters by utilizing k- means algorithm based upon some dimension. Then each cluster is classified by utilizing random forest classifier algorithm then it generating decision tree and it is classified based upon the specified criteria. When compared to the existing systems, the experimental results indicate that the proposed algorithm increases the data accuracy.
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Memory Enhancer Games: A General-purpose Game-based Intelligent Tutoring System
Статья научная
Students of today are exposed to technologies that are either educationally effective or distractive. Most of them are having a hard time learning in a traditional classroom setup, are easily distracted, and have difficulty remembering lessons just learned and prerequisite skills needed in learning new lessons. Game-Based Intelligent Tutoring System (GB-ITS) is a technology that provides an individualized learning experience based on student’s learning needs. GB-ITS mimics a teacher doing one-on-one teaching, also known as tutoring, which is more cost-efficient than human tutors. This study developed a general-purpose Memory Enhancer Games system, in a form of a GB-ITS. This study was conducted at Calasiao Comprehensive National High School, identified the game type that best enhances memory and the game features for this proposed system through a questionnaire by (9) ICT teacher respondents. The developed system in this study has undergone validity testing by (8) ICT teachers and professors from Schools Division I of Pangasinan, and of a University in Dagupan City, and acceptability testing by (100) senior high school students of Calasiao Comprehensive National High School, 1st semester of school year 2022-2023, using Likert scale to determine its appropriateness as an intelligent learning tool. The results of the game design questionnaire confirmed the studies of which elements were ideal for a GB-ITS, and both the validity and acceptability survey questionnaires with overall weighted means of 4.57 and 4.08, show that the system is a valid and acceptable intelligent learning tool. The developed MEG can also be of use for testing game features for educational effectiveness and can also contribute to any future study which will conduct to test whether a general-purpose GBL or GB-ITS model would compare; if won’t equal the effectiveness of GBLs designed for delivering specific contents or subjects.
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Metadata based Classification Techniques for Knowledge Discovery from Facebook Multimedia Database
Статья научная
Classification is a parlance of Data Mining to genre data of different kinds in particular classes. As we observe, social media is an immense manifesto that allows billions of people share their thoughts, updates and multimedia information as status, photo, video, link, audio and graphics. Because of this flexibility cloud has enormous data. Most of the times, this data is much complicated to retrieve and to understand. And the data may contain lot of noise and at most the data will be incomplete. To make this complication easier, the data existed on the cloud has to be classified with labels which is viable through data mining Classification techniques. In the present work, we have considered Facebook dataset which holds meta data of cosmetic company’s Facebook page. 19 different Meta Data are used as main attributes. Out of those, Meta Data ‘Type’ is concentrated for Classification. Meta data ‘Type’ is classified into four different classes such as link, status, photo and video. We have used two favored Classifiers of Data Mining that are, Bayes Classifier and Decision Tree Classifier. Data Mining Classifiers contain several classification algorithms. Few algorithms from Bayes and Decision Tree have been chosen for the experiment and explained in detail in the present work. Percentage split method is used to split the dataset as training and testing data which helps in calculating the Accuracy level of Classification and to form confusion matrix. The Accuracy results, kappa statistics, root mean squared error, relative absolute error, root relative squared error and confusion matrix of all the algorithms are compared, studied and analyzed in depth to produce the best Classifier which can label the company’s Facebook data into appropriate classes thus Knowledge Discovery is the ultimate goal of this experiment.
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Method for determination of cyber threats based on machine learning for real-time information system
Статья научная
This work is about the definition of cyber threats in the information system. The cyber threats lead to significant loss of network resources and cause the system disability as a whole. Detecting countermeasures in certain threats can reduce the impact on the system by changing the topology of the network in advance. Consequently, the interruption of a cyberattack forces the intruders to seek for alternative ways to damage the system. The most important task in the information system work is the state of network equipment monitoring. Also it’s the support of the network infrastructure in working order. The purpose of the work is to develop a method for detecting cyber threats for the information system. The system can independently detect cyber threats and develop countermeasures against them. The main feature of the counteractions is to protect network nodes from compromising. To ensure the functional stability, the most important issues are providing safety metrics. This technique allows to increase the functional stability of the system, which works in real time.
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Method for optimization of information security systems behavior under conditions of influences
Статья научная
The paper analyzes modern methods of modeling impacts on information systems, which made it possible to determine the most effective approaches and use them to optimize the parameters of security systems. And also as a method to optimize data security, taking in the security settings account (number of security measures, the type of security subsystems, safety resources and total cost information) allows to determine the optimal behavior in the “impact-security”. Also developed special software that allowed to verify the proposed method.
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Method for unit self-diagnosis at system level
Статья научная
This paper suggests unconventional approach to system level self-diagnosis. Traditionally, system level self-diagnosis focuses on determining the state of the units which are tested by other system units. In contrast, the suggested approach utilizes the results of tests performed by a system unit to determine its own state. Such diagnosis is in many respects close to self-testing, since a unit evaluates its own state, which is inherent in self-testing. However, as distinct from self-testing, in the suggested approach a unit evaluates it on the basis of tests that it does not performs on itself, but on other system units. The paper considers different diagnosis models with various testing assignments and diferent faulty assumptions including permanent and intermittent faults, and hybrid- fault situations. The diagnosis algorithm for identifying the unit’s state has been developed, and correctness of the algorithm has been verified by computer simulation experiments.
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Method of medical images similarity estimation based on feature analysis
Статья научная
The paper presents the method of medical images similarity estimation based on feature extraction and analysis. The proposed method has been developed for and tested on rat brain histological images, however, it can be applied for other types of medical images, since the general approach is based on consideration of the shape of core components present in a given template image. The proposed method can be used in image analysis tools in a wide range of image-based medical investigations, in particular, in the brain researches. The theoretical background of the proposed method is presented in the paper. The expert evaluation approach used for assessment of the proposed method effectiveness is explained and illustrated by examples. The method of medical images similarity estimation based on feature analysis consists of several stages: colour model conversion, image normalization, anti-noise filtering, contours search, conversion, and feature analysis. The results of the proposed method algorithmic realization are demonstrated and discussed.
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Metrics for Evaluating Pervasive Middleware
Статья научная
Pervasive computing aims at developing smart environments which enable user to interact with other devices. Pervasive computing includes a middleware to support interoperability, heterogeneity and self-management among different platforms. It provides efficient communications and context awareness among devices. Middleware for pervasive computing provides much more attention to coordinate the devices in the smart environment. The evaluation of the pervasive middleware is a challenging endeavor. The scope of evaluating smart environment is mainly increasing due to various devices involved in that environment. In this paper evaluation metrics are proposed based on the contexts available in the environment, how the devices are used, security and autonomy of smart applications. These metrics are used for evaluating different kind of smart applications.
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Microarray gene retrieval system based on LFDA and SVM
Статья научная
The DNA microarray technology enables the biologists to observe the expressions of multiple thousands of genes in parallel fashion. However, processing and gaining knowledge from the voluminous microarray gene data is serious issue. It is necessary for the biologists to retrieve the required data in a reasonable time. In order to address this issue, this work presents a gene retrieval system, which is based on feature dimensionality minimization and classification of the microarray gene data. The feature dimensionality minimization is achieved by Local Fisher Discriminant Analysis (LFDA), which inherits the merits of both Fisher Discriminant Analysis (FDA) and Locality Preserving Projection (LPP). Support Vector Machine (SVM) is employed as the classifier to classify between the genes. The LFDA is chosen for reducing the dimensionality of the features, owing to its better performance on multimodal data. The SVM is trained with the feature dimensionality reduced microarray gene data, which improves the efficiency and overthrows the computational complexity. The performance of the proposed approach is compared with the LPP and FDA. Additionally, the performance of SVM is compared with the k-Nearest Neighbour (k-NN) classifier. The combination of LFDA and SVM serves better in terms of accuracy, sensitivity and specificity.
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Microcantilever: an efficient tool for biosensing applications
Статья научная
Most of the biosensing applications involving analysis and detection of a particular specimen demands fast, easy to use, less expensive, highly reliable and sensitive method for the recognition of biomolecules. The reason behind this increasing demand is that most of the available laboratory equipment require large space, are highly expensive and have other preconditions. Most of the viscometers available for measuring the rheological properties of blood require cleaning after each use which can be challenging due to the capillary geometry. The substitute to this is microcantilever that has emerged as an ideal candidate for biosensing applications. Microcantilever is capable of being used in air, vacuum or liquid medium. This paper consists of seven sections in which working principle of a cantilever, different modes of vibration, their comparative analysis, analytical equations of hydrodynamic equations exerted by the fluid on the cantilever and their impact on the resonant frequency and quality factor, applications of microcantilever in liquid medium specifically in biomedical field are discussed.
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Microgrid Restoration after Major Faults in Main Grid with Automatic and Constant Time Switching
Статья научная
When a microgrid and distributed generation resources are disconnected from the grid for protection reasons, the restoration of microgrid (restoring distributed generation resources to feed the loads in microgrid) causes to increase the reliability of microgrid. When a fault occurs in the main grid, the reliability of islanded microgrid will be increased. In this paper a novel method for restoration of the microgrid is proposed when the fault occurred in the main grid. Therefore, we can take advantage of selling power energy during the fault. In addition, because of increasing in reliability, the price of energy will be increased. This paper selected a microgrid with two type of distributed generation resources, power electronic based distributed generation and small gas turbine with synchronous generator. Another purpose of this paper is to reduce restoration time. The proposed algorithm for automatic switching time is provided. This paper selected a microgrid system in medium voltage. The limitation voltage and frequency is according to IEEE 1547 standards, and simulation will be done by EMTP-RV with automatic and constant time switching separately.
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Статья научная
In this paper, we would like to propose a new microring resonator structure based on 6x6 Generalized Mach Zehnder interferometer (GMZI) using silicon waveguides. It is showed that this new kind of the devices works as three separated microring resonators. This characteristic of the device leads to a variety of tasks important to optical communications, including switching, filtering, add-drop multiplexing, sensing and modulation. In our study, silicon waveguides are used for designing the proposed devices. The transfer matrix method (TMM) and the three dimensional beam propagation methods (3D-BPM) are used to optimally design the device.
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Mining Data to Find Adept Teachers in Dealing with Students
Статья научная
Higher education faculty staffs lack behind any prior training program of teaching. Mostly staffs teach students in his/her ways. They are unaware of the qualities of a teacher which they must possess as how to tackle the problems arising in teaching, what key points must be remembered while teaching etc. This may cause a teacher to be unsuccessful in classroom. So the problem is the amount of knowledge a staff has of a teaching process. Educationist finds few qualities of a good teacher. But their method is qualitative. In this paper a quantitative approach i.e. data mining is used to measure the quality of a teacher and suggest them what qualities they have.
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Mining Interesting Infrequent Itemsets from Very Large Data based on MapReduce Framework
Статья научная
Mining frequent and infrequent itemsets from a given dataset is the most important field of data mining. When we mine frequent and infrequent itemsets simultaneously, infrequent itemsets become very important because there are many valued negative association rules in them. Mining frequent Itemset is highly expensive, if the minimum threshold is low, whereas mining infrequent itemsets is highly expensive, if the minimum threshold is high. When the dataset size is very large, both memory usage and computational cost of mining infrequent items is very expensive. In addition, single processor’s memory and CPU resources are not enough to handle very large datasets. Parallel and distributed computing are effective approaches to handle large datasets. In this paper we proposed a method based on Hadoop-MapReduce model, which can handle massive datasets in mining infrequent itemsets. Experiments are performed on 8 node cluster with a synthetic dataset. The performance study shows that the proposed method is efficient in handling very large datasets.
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Mining Social Data to Extract Intellectual Knowledge
Статья научная
Social data mining is an interesting phe-nomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pattern recognition, social mapping, responsibility distribution and many other applications. This paper presents a systematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect different attributes such as about me, comments, wall post and age from facebook as raw data and use advanced data mining approaches to excavate intellectual knowledge. We also analyze our mined knowledge with comparison for possible usages like as human behavior prediction, pattern recognition, job responsibility distribution, decision making and product promoting.
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Mining Wikipedia to Rank Rock Guitarists
Статья научная
We present a method to find the most influential rock guitarist by applying Google PageRank algorithm to information extracted from Wikipedia articles. The influence of a guitarist was estimated by the number of guitarists citing him/her as an influence and the influence of the latter. We extracted this who-influenced-whom data from the Wikipedia biographies and converted them to a directed graph where a node represented a guitarist and an edge between two nodes indicated the influence of one guitarist over the other. Next we used Google PageRank algorithm to rank the guitarists. The results are most interesting and provide a quantitative foundation to the idea that most of the contemporary rock guitarists are influenced by early blues guitarists. Although no direct comparison exist, the list was still validated against a number of other best-of lists available online and found to be mostly compatible.
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Minutiae Fusion Based Framework for Thumbprint Identification of Identical Twins
Статья научная
Identical twins identification is a challenging task because they share the same DNA sequence. This research paper presents minutiae coordinates and orientation angles fusion based technique for thumbprint identification of identical twins. Six different thumbprint images of identical twins were taken at a fixed time interval using H3 T&A terminal. The minutiae coordinates and orientation angles of these thumbprints were fused to form a union set. The union set values were stored in the smartcard memory for further identification. The minutiae coordinates and orientation angles of a thumbprint of the person to be identified are computed and fused together for online identification. The fused minutiae are compared with the minutiae union set values stored in the smartcard memory for identity verification. The proposed method was tested on a self generated identical twin dataset and 50 identical twins of standard FVC04 and FVC06 datasets. We observed in experiments that the proposed method is accurately differentiating the identical twins of self generated and FVC datasets.
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Mismatch Calibration in LINC Power Amplifiers Using Modified Gradient Algorithm
Статья научная
One of the power amplifiers linearization technique is linear amplification with nonlinear components (LINC).The effects of phase and gain imbalances between two signal branches in LINC transmitters have been analyzed in this paper. Then a feedback path has been added to compensate this mismatches, using two complex gain in each path.This complex gains are controlled in a way to calibrate any gain and phase mismatches between two path using Modified Gradient Algorithm (MGA) adaptively. The main advantages of this algorithm over other algorithms are zero residual error and fast convergence time. In the proposedarchitecture power amplifiers in each path are modeled as a complex gain which its phase and amplitude depend on input signal level. Many simulations have been performed to validate the proposed self calibrating LINC transmitter. Simulation results have confirmed the analyticalpredictions. According to simulation results the proposed structure has around 40 dB/Hz improvement in the first adjacent channel of the output signal spectrum.
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Mobile Robot Navigation using Fuzzy Limit-Cycles in Cluttered Environment
Статья научная
This paper proposes a hybrid approach based on limit-cycles method and fuzzy logic controller for the problem of obstacle avoidance of mobile robots in unknown environment. The purpose of hybridization consists on the improvement of basic limit-cycle method in order to obtain safe and flexible navigation. The proposed algorithm has been successfully tested in different configurations on simulation.
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