International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1227
Covering Based Pessimistic Multigranular Rough Equalities and their Properties
Статья научная
The basic rough set theory introduced by Pawlak as a model to capture imprecision in data has been extended in many directions and covering based rough set models are among them. Again from the granular computing point of view, the basic rough sets are unigranular by nature. Two types of extensions to the context of multigranular computing are done; called the optimistic and pessimistic multigranulation by Qian et al in 2006 and 2010 respectively. Combining these two concepts of covering and multigranulation, covering based multigranular models have been introduced by Liu et al in 2012. Extending the stringent concept of mathematical equality of sets rough equalities were introduced by Novotny and Pawlak in 1985. Three more types of such approximate equalities were introduced by Tripathy in 2011. In this paper we study the approximate equalities introduced by Novotny and Pawlak from the pessimistic multigranular computing point of view and establish several of their properties. These concepts and properties are shown to be useful in approximate reasoning.
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Статья научная
Over the last few years, the amount of video data has increased significantly. So, the necessity of video summarization has reached a new level. Video summarization is summarizing a large video with a fewer number of frames keeping the semantic content same. In this paper, we have proposed an approach which takes all the frames from a video and then shot boundaries are detected using the color moment and SURF (Speeded Up Robust Features). Then the redundancy of the similar frames is eliminated using the color histogram. Finally, a summary slide is generated with the remaining frames which are semantically similar to the total content of the original video. Our experimental result is calculated on the basis of a questionnaire-based user survey which shows on average 78% positive result whereas 3.5% negative result. This experimental result is quite satisfactory in comparison with the existing techniques.
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Credible Mechanism for More Reliable Search Engine Results
Статья научная
The number of websites on the Internet is growing randomly, thanks to HTML language. Consequently, a diversity of information is available on the Web, however, sometimes the content of it may be neither valuable nor trusted. This leads to a problem of a credibility of the existing information on these Websites. This paper investigates aspects affecting on the Websites credibility and then uses them along with dominant meaning of the query for improving information retrieval capabilities and to effectively manage contents. It presents a design and development of a credible mechanism that searches Web search engine and then ranks sites according to its reliability. Our experiments show that the credibility terms on the Websites can affect the ranking of the Web search engine and greatly improves retrieval effectiveness.
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Credit Card Fraud Detection System Using Machine Learning
Статья научная
The security of any system is a key factor toward its acceptability by the general public. We propose an intuitive approach to fraud detection in financial institutions using machine learning by designing a Hybrid Credit Card Fraud Detection (HCCFD) system which uses the technique of anomaly detection by applying genetic algorithm and multivariate normal distribution to identify fraudulent transactions on credit cards. An imbalance dataset of credit card transactions was used to the HCCFD and a target variable which indicates whether a transaction is deceitful or otherwise. Using F-score as performance metrics, the model was tested and it gave a prediction accuracy of 93.5%, as against artificial neural network, decision tree and support vector machine, which scored 84.2%, 80.0% and 68.5% respectively, when trained on the same data set. The results obtained showed a significant improvement as compared with the other widely used algorithms.
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Cross-platform Fake Review Detection: A Comparative Analysis of Supervised and Deep Learning Models
Статья научная
This project addresses the growing issue of fake reviews by developing models capable of detecting them across different platforms. By merging five distinct datasets, a comprehensive dataset was created, and various features were added to improve accuracy. The study compared traditional supervised models like Logistic Regression and SVM with deep learning models. Notably, simpler supervised models consistently outperformed deep learning approaches in identifying fake reviews. The findings highlight the importance of choosing the right model and feature engineering approach, with results showing that additional features don’t always improve model performance.
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Cuckoo Search Algorithm for Stellar Population Analysis of Galaxies
Статья научная
The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been applied to solve a wide range of real-world optimization problem. In this paper, an improved Cuckoo Search Algorithm (ICS) is presented for determining the age and relative contribution of different stellar populations in galaxies. The results indicate that the proposed method performs better than, or at least comparable to state-of-the-art method from literature when considering the quality of the solutions obtained. The proposed algorithm will be applied to integrated color of galaxy NGC 3384. Simulation results further demonstrate the proposed method is very effective. The study revealed that cuckoo search can successfully be applied to a wide range of stellar population and space optimization problems.
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Current State and Future Trends in Location Recommender Systems
Статья научная
Technological developments in mobile devices enabled the utilization of geographical data for social networks. Accordingly, location-based social networks have become very attractive. The popularity of location-based social networks has prompted researchers to study recommendation systems for location-based services. There are many studies that develop location recommendation systems using various variables and algorithms. However, articles detailing past and present studies, and making future suggestions, are limited. Therefore, this study aims to thoroughly review the research performed on location recommender systems. For this purpose, topic pairs; "location and recommender system" and "location and recommendation system" were searched in the Web of Knowledge database. Resulting articles were examined in detail with respect to data sources and variables, algorithms, and evaluation techniques used. Thus, the current state of location recommender systems research is summarized and future recommendations are provided for researchers and developers. It is expected that the issues presented in this paper will advance the discussion of next generation location recommendation systems.
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Customer Credit Risk Assessment using Artificial Neural Networks
Статья научная
Since the granting of banking facilities in recent years has faced problems such as customer credit risk and affects the profitability directly, customer credit risk assessment has become imperative for banks and it is used to distinguish good applicants from those who will probably default on repayments. In credit risk assessment, a score is assigned to each customer then by comparing it with the cut-off point score which distinguishes two classes of the applicants, customers are classified into two credit statuses either a good or bad applicant. Regarding good performance and their ability of classification, generalization and learning patterns, Multi-layer Perceptron Neural Network model trained using various Back-Propagation (BP) algorithms considered in designing an evaluation model in this study. The BP algorithms, Levenberg-Marquardt (LM), Gradient descent, Conjugate gradient, Resilient, BFGS Quasi-newton, and One-step secant were utilized. Each of these six networks runs and trains for different numbers of neurons within their hidden layer. Mean squared error (MSE) is used as a criterion to specify optimum number of neurons in the hidden layer. The results showed that LM algorithm converges faster to the network and achieves better performance than the other algorithms. At last, by comparing classification performance of neural network with a number of classification algorithms such as Logistic Regression and Decision Tree, the neural network model outperformed the others in customer credit risk assessment. In credit models, because the cost that Type II error rate imposes to the model is too high, therefore, Receiver Operating Characteristic curve is used to find appropriate cut-off point for a model that in addition to high Accuracy, has lower Type II error rate.
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