International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1173
Urinary System Diseases Diagnosis Using Machine Learning Techniques
Статья научная
The urinary system is the organ system responsible for the production, storage and elimination of urine. This system includes kidneys, bladder, ureters and urethra. It represents the major system which filters the blood and any imbalance of this organ can increases the rate of being infected with diseases. The aim of this paper is to evaluate the performance of different variants of Support Vector Machines and k-Nearest Neighbor with different distances and try to achieve a satisfactory rate of diagnosis (infected or non-infected urinary system). We consider both diseases that affect the urinary system: inflammation of urinary bladder and nephritis of renal pelvis origin. Our experimentation will be conducted on the database “Acute Inflammations Data Set” obtained from UCI Machine Learning Repository. We use the following measures to evaluate the results: classification accuracy rate, classification time, sensitivity, specificity, positive and negative predictive values.
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Статья научная
For more relevant informational retrieval and matching of user request with metadata about biomedical informational recourses it is necessary to formulize the user knowledge about this subject domain. We propose to use the ontologies and associated with them thesauri of the appropriate subject domains for representation of biomedicine knowledge. The algorithms of formation and normalization of the multilinguistic thesauruses, and also methods of their comparison are given in this work.
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Using Artificial Immune Recognition Systems in Order to Detect Early Breast Cancer
Статья научная
In this work, a decision support system for early breast cancer detection is presented. In hard to diagnose cases, different examinations (i.e. mammography, ultrasonography and magnetic resonance imaging) provide contradictory findings and patient is guided to biopsy for definite results. The proposed method employs a Correlation Feature Selection procedure and an Artificial Immune Recognition System (AIRS) and is evaluated using real data collected from 53 subjects with contradictory diagnoses. Comparative results with commonly used artificial intelligence classifiers verify the suitability of the AIRS classifier. The application of such an approach can reduce the number of unnecessary biopsies.
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Using Description Logics to specify a Document Synthesis System
Статья научная
This paper deals with an automatic document’s synthesis system. Our approach is based on the prior formal description of the semantics of the main elements (document, reader and his request) in the synthesis system. In this approach, semantic capture is based on ontology definition that is specified formally using Description Logics (DL). The DL inference techniques associated to production rules are then used to compute a document synthesis. Moreover, DL inference techniques are used to reason about each component.
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Using Fuzzy Models and Time Series Analysis to Predict Water Quality
Статья научная
Water quality prediction is very important for both water resource scheduling and management. Simple linear regression analysis and artificial neural network models cannot accurately forecast water quality because of complicated linear and nonlinear relationships in the water quality dataset. An adaptive neuro-fuzzy inference system (ANFIS) that can integrate linear and nonlinear relationships has been proposed to address the problem. However, the ANFIS model can only work in scenarios where input and target parameters have strong correlations. In this paper, a fuzzy model integrated with a time series data analysis method is proposed to address the water quality prediction problem when the correlation between the input and target parameters is weak. The water quality datasets collected from the Las Vegas Wash between the years 2005 and 2010, and the Boulder Basin, Nevada-Arizona from the years 2011 to 2016 are used to test the proposed model. The prediction accuracy of the proposed model is measured by three different statistical indices: mean average percentage error, root mean square error, and coefficient of determination. The experimental results have proven that the ANFIS model combined with a time series analysis method achieves the best prediction accuracy for predicting electrical conductivity and total dissolved solids in the Las Vegas Wash, with the testing value of coefficient of determination reaching 0.999 and 0.997, respectively. The fuzzy time series analysis has the best performance for dissolved oxygen and electrical conductivity prediction in the Boulder Basin, and dissolved oxygen prediction in the Las Vegas Wash, with testing value of coefficients of determination equal to 0.990, 90975, and 0.960, respectively.
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Using Heuristic-based Search for Zinc Models
Статья научная
The Zinc modelling language provides a rich set of constraints, data structures and expressions to support high-level modelling. Zinc is the only modelling language that supports all solving techniques: constraint programming, mathematical methods, and local search. By providing search patterns, it allows users to implement their search methods in a declarative way. There are currently three search patterns implemented in Zinc: backtracking search, branch and bound search, and local search. In this paper we explain how Zinc efficiently implements user-defined local search algorithms.
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Статья научная
The current Learning Management Systems used in e-learning lack intelligent mechanisms which can be used by an instructor to group learners during an online group task based on the learners' collaboration competence level. In this paper, we discuss a novel approach for grouping students in an online learning group task based on individual learners' collaboration competence level. We demonstrate how it can be applied in a Learning Management System such as Moodle using forum data. To create the collaboration competence levels, two machine learning algorithms for clustering namely Skmeans and Expectation Maximization (EM) were applied to cluster data and generate clusters based on learner's collaboration competence. We develop an intelligent grouping algorithm which utilizes these machine learning generated clusters to form heterogeneous groups. These groups are automatically made available to the instructor who can proceed to assign them to group tasks. This approach has the advantage of dynamically changing the group membership based on learners' collaboration competence level.
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Using Rough Set Theory for Reasoning on Vague Ontologies
Статья научная
Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. A prototype of Tableau's extended algorithm is developed and tested on examples where encouraging results are given by this method to demonstrate that unlike other methods, it is possible to answer queries even in the presence of incomplete information.
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Using the Euler-Maruyama Method for Finding a Solution to Stochastic Financial Problems
Статья научная
The purpose of this paper is to survey stochastic differential equations and Euler-Maruyama method for approximating the solution to these equations in financial problems. It is not possible to get explicit solution and analytically answer for many of stochastic differential equations, but in the case of linear stochastic differential equations it may be possible to get an explicit answer. We can approximate the solution with standard numerical methods, such as Euler-Maruyama method, Milstein method and Runge-Kutta method. We will use Euler-Maruyama method for simulation of stochastic differential equations for financial problems, such as asset pricing model, square-root asset pricing model, payoff for a European call option and estimating value of European call option and Asian option to buy the asset at the future time. We will discuss how to find the approximated solutions to stochastic differential equations for financial problems with examples.
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Статья научная
In computational study and automatic recognition of opinions in free texts, certain words in sentences are used to decide its sentiments. While analysing each customer’s opinion per time in churn management will be effective for personalised recommendations. Oftentimes, the opinion is not sufficient for contextualised content mining. While personalised recommendations are time consuming, it also does not provide complete picture of an overall sentiment in the business community of customers. To help businesses identify widespread issues affecting a large segment of their customers towards engendering patterns and trends of different customer churn behaviour, here, we developed a clustered contextualised conversation as opinions set for integration with Roberta Model. The developed churn behavioural opinion clusters disambiguated short messages while charactering contents collectively based on context beyond keyword-based sentiment matching for effective mining. Based on the predicted opinion threshold, customer churn category for group-based personalised decision support was generated, with matching concepts. The baseline RoBERTa model on the contextually clustered opinions, trained with a batch size of 16, a learning rate of 2e-5, over 8 epochs, using a maximum sequence length of 128 and standard hyperparameters, achieved an accuracy of 92%, Precision of 88%, Recall of 86% and F1 score of 84% over a test set of 30%.
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VLSI Circuit Configuration Using Satisfiability Logic in Hopfield Network
Статья научная
Very large scale integration (VLSI) circuit comprises of integrated circuit (IC) with transistors in a single chip, widely used in many sophisticated electronic devices. In our paper, we proposed VLSI circuit design by implementing satisfiability problem in Hopfield neural network as circuit verification technique. We restrict our logic construction to 2-Satisfiability (2-SAT) and 3-Satisfiability (3-SAT) clauses in order to suit with the transistor configuration in VLSI circuit. In addition, we developed VLSI circuit based on Hopfield neural network in order to detect any possible error earlier than the manual circuit design. 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 evaluated based on global VLSI configuration, circuit accuracy and the runtime. It has been observed that the VLSI circuits (HNN-2SAT and HNN-3SAT circuit) developed by proposed design are better than the conventional circuit due to the early error detection in our circuit.
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Vague Logic Approach to Disk Scheduling
Статья научная
Vague sets theory separates the evidences in favour and against of an element in a set which provides better mechanism to handle impreciseness and uncertainty. This research paper aims to handle the incompleteness and impreciseness of data associated with the disk access requests. Here, we propose a new disk scheduling algorithm, Vague Disk Scheduling (VDS) Algorithm, based on vague logic. The proposed framework includes Vague-Fuzzification Technique, Priority Expression, and VDS Algorithm. The Vague-Fuzzification Technique is applied to the input data of each disk access request and generates a priority for each request in the queue. Based on the priority allotted the requests are serviced. Finally work is evaluated on different datasets and finally compared with Fuzzy Disk Scheduling (FDS) Algorithm. The results prove that VDS algorithm performs better than FDS Algorithm.
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Varna-based optimization: a new method for solving global optimization
Статья научная
A new and simple optimization algorithm known as Varna-based Optimization (VBO) is introduced in this paper for solving optimization problems. It is inspired by the human-society structure and human behavior. Varna (a Sanskrit word, which means Class) is decided by people’s Karma (a Sanskrit word, which means Action), not by their birth. The performance of the proposed method is examined by experimenting it on six unconstrained, and five constrained benchmark functions having different characteristics. Its results are compared with other well-known optimization methods (PSO, TLBO, and Jaya) for multi-dimensional numeric problems. Our experimental results show that the VBO outperforms other optimization algorithms and have proved the better effectiveness of the proposed algorithm.
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Vehicle Tracking and Locking System Based on GSM and GPS
Статья научная
Currently almost of the public having an own vehicle, theft is happening on parking and sometimes driving insecurity places. The safe of vehicles is extremely essential for public vehicles. Vehicle tracking and locking system installed in the vehicle, to track the place and locking engine motor. The place of the vehicle identified using Global Positioning system (GPS) and Global system mobile communication (GSM). These systems constantly watch a moving Vehicle and report the status on demand. When the theft identified, the responsible person send SMS to the microcontroller, then microcontroller issue the control signals to stop the engine motor. Authorized person need to send the password to controller to restart the vehicle and open the door. This is more secured, reliable and low cost.
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Veins Based Personal Identification Systems: A Review
Статья научная
Identification of people among each other has always been a tough and challenging task for the researchers. There are many techniques which are used for identifying a person but biometric technique is the standard one which allows us for online identification of individuals on the basis of their physiological and behavioral features. The veins based systems include finger veins, face veins, palm veins, head veins, heart veins, iris, palatal veins of the rogue etc. The multi-veins based systems use the veins of different physiological traits for identifying a person. This paper illustrates an overview of veins based personal identification systems. The performance of different single and multi-veins based identification systems are analyzed in this paper. The features like reliability, security, accuracy, robustness and long term stability along with the strengths and weaknesses of various veins based biometric approaches were taken into considerations while analyzing the results of existing research papers published so far. At last the future research directions in the field of veins based identification systems have also been outlined.
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Velocity Feedback Control of a Mechatronics System
Статья научная
Increasing demands in performance and quality make drive systems fundamental parts in the progressive automation of industrial process. The analysis and design of Mechatronics systems are often based on linear or linearized models which may not accurately represent the servo system characteristics when the system is subject to inputs of large amplitude. The impact of the nonlinearities of the dynamic system and its stability needs to be clarified. The objective of this paper is to present a nonlinear mathematical model which allows studying and analysis of the dynamic characteristic of an electro hydraulic position control servo. The angular displacement response of motor shaft due to large amplitude step input is obtained by applying velocity feedback control strategy. The simulation results are found to be in agreement with the experimental data that were generated under similar conditions.
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Video shots’ matching via various length of multidimensional time sequences
Статья научная
Temporal clustering (segmentation) for video streams has revolutionized the world of multimedia. Detected shots are principle units of consecutive sets of images for semantic structuring. Evaluation of time series similarity is based on Dynamic Time Warping and provides various solutions for Content Based Video Information Retrieval. Time series clustering in terms of the iterative Dynamic Time Warping and time series reduction are discussed in the paper.
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Статья научная
Techniques to detect the flame at an early stage are necessary in order to prevent the fire and minimize the damage. The flame detection technique based on the physical sensor has limited disadvantages in detecting the fire early. This paper presents the results of using local binary patterns for solving flames detecting problem and proposes modifications to improve the quality of detector work. Experimentally found that using support vector machines classifier with a kernel based on Gaussian radial basis functions shows the best results compared to other SVM cores or classifier k-nearest neighbors.
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Статья научная
An inclement dusty weather can significantly reduce the visual quality of captured images and this consequently leads to hamper the observation of meaningful image details. Capturing images in such weather often leads to undesirable artifacts such as poor contrast, deficient colors or color cast. Hence, various methods have been proposed to process such unwanted event and recover vivid results with acceptable colors. These methods vary from simple to complex due to the variation of the used processing concepts. In this article, an innovative technique that utilizes tuned fuzzy intensification operators is introduced to expeditiously process poor quality images captured in an inclement dusty weather. Intensive experiments were carried out to check the processing ability of the proposed technique, wherein the obtained results exhibited its competence in filtering various degraded images. Specifically, it performed well in providing acceptable colors and unveiling fine details for the processed images.
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Статья научная
Parkinson is a neurological disease and occurs due to lack of dopamine neurons. These dopamine neurons manage all body movements. Parkinson patients have difficulty in doing all daily routine activities, and also have disturbed vocal fold movements. Using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. In this paper, we have used 23 features dataset, all the features are analyzed and 15 features are selected from the total dataset. As in Parkinson tremor is present in the voice box muscles, so the variation in the period and amplitude of consecutive vocal cycles is present. The feature dataset selected consist of jitter, shimmer, harmonic to noise ratio, DFA, spread1 and PPE. Various classifiers are used and their comparison is done to find out which classifier is perfect in this environment. It is concluded that support vector classifiers as the best one with an accuracy of 96%. In the neural network classifiers with different transfer functions, there is tradeoff among the performance parameters.
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