The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Researchers in higher education are beginning to explore the potential of data mining in analyzing data for the purpose of giving quality service and needs of their graduates. Thus, educational data mining emerges as one tools to study academic data to identify patterns and help for decision making affecting the education. This paper predicts the employability of IT graduates using nine variables...
There exists a base classification system for classification of problem tickets in the Enterprise domain. Different deep learning algorithms (Gated Recursive Unit and Long Short Term Memory) were investigated for solving the classification problem. Experiments were conducted for different parameters and layers for these algorithms. Paper brings out the architectures tried, results obtained, our conclusions...
E-learning has become an essential factor in the modern educational system. In today's diverse student population, E-learning must recognize the differences in student personalities to make the learning process more personalized. The objective of this study is to create a data model to identify both the student personality type and the dominant preference based on the Myers-Briggs Type Indicator (MBTI)...
Associative Classification is a recent and rewarding approach which combines associative rule mining and classification. This technique has attracted many researchers as it derives accurate classifier with effective rules. Associative classifiers are useful for application where maximum predictive accuracy is desired. Increasing access to huge datasets and corresponding demands to analyze these data...
Rainfall prediction is an important part of weather prediction. Compared to conventional methods predicting rainfall rate, the approach applying historical records and data mining technology shows obviously advantage in computing cost. Many excellent works have been done attempting to build predicting model with data mining methods, however, most of them just test the predicting accuracy on data set...
Currently, various perspectives of neural networks are proposed for solving classification problems. Some of them are based on two types of mapping functions, namely, linear and nonlinear, for mapping an input space into a feature space. In addition, some neural networks are proposed based on probability theory. Since some models are appropriated for some kinds of data, depending on a distribution...
Pattern classification or clustering plays important role in a wide variety of applications in different areas like psychology and other social sciences, biology and medical sciences, pattern recognition and data mining. A lot of algorithms for supervised or unsupervised classification have been developed so far in order to achieve high classification accuracy with lower computational cost. However,...
This paper proposes a short-term energy price classification model using decision tree. The proposed model does not predict the exact value of future electricity price, but the class to which it belongs, established with respect to pre-specified threshold. This strategy is proposed since for some applications, the exact value of future prices is not required for the decision-making process. A feature...
Classification is a central problem in the fields of data mining and machine learning. Using a training set of labelled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more...
In big data universities, an understanding of how the individual learning style and preferences interacts with the instructional medium presented is needed. In this study we examined the VARK learning style inventory using the variable-centered, person-centered and social approaches. We worked on a big “data set” which encompasses two data sources the first was LMS while the second was social media...
Changes in the network topology such as large-scale power outages or Internet worm attacks are events that may induce routing information updates. Border Gateway Protocol (BGP) is by Autonomous Systems (ASes) to address these changes. Network reachability information, contained in BGP update messages, is stored in the Routing Information Base (RIB). Recent BGP anomaly detection systems employ machine...
The objective of the present work is to design a HADOOP based parallel Marathi content retrieval system using clustering technique to get the efficient and optimized result than existing systems. The system also focuses on providing the personalized documents in Marathi language to the end user based on their interests identified from the browsing history and using time session mechanism for re ranking...
Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases Now a day's large amount of data is. generated, that need to be analyse, and pattern have to be extracted from that to get some knowledge. Classification is a supervised machine learning task which builds a model from labelled...
Data mining approaches have been used in business purposes since its inception; however, at present it is used successfully in new and emerging areas like education systems. Government of Bangladesh emphasizes the need to improve the education system. In this research, we use data mining approaches to predict students' final outcome, i.e., final grade in a particular course by overcoming the problem...
Big Data though it is a hype up-springing many technical challenges that confront both academic research communities and commercial IT deployment, the root sources of Big Data are founded on data streams. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining or high-speed...
Diagnosis of the disease is one of the application areas where data mining techniques helps in the extraction of knowledge from medical database. Recently, researchers have been investigating the effect of cascading more than one technique showing enhanced results in the diagnosis of the disease. This paper proposes a hybrid model using K-means as a preprocessing algorithm. The proposed model is developed...
Chronic diseases are gradually becoming the principal factors of harm to people's health. Fortunately, the development of e-health provides a novel thought for chronic disease prevention and treatment. This paper focuses on the research of cardiovascular disease (CVDs) prevention and early warning techniques using e-health and data mining. In this paper, we will use weighted associative classification...
How to classify the data sets with vast information amount and large distribution fluctuation, which is always the research hotspot. This paper puts forward an improved SVM incremental learning algorithm by comparing the different incremental learning methods of SVM algorithm. In the algorithm, whether to violate the KTT conditions is regarded as an important basis for incremental data set. And the...
Post-pruning is a common method of decision tree pruning. However, various post-pruning tends to use a single measure as an evaluation standard of pruning effects. The single and exclusive index evaluation standard of decision tree is subjective and partial, and the decisions after pruning often have a bias. This paper proposes a decision tree post-pruning algorithm based on comprehensive considering...
This research considers simulated laser radar (LADAR) vibrometry for vehicle identification. Time sampled data is considered for developing multiple nonlinear autoregressive neural network (NARNet) classifier models. Emphasis is placed on robustness to sensor location and using small amounts of data. Decision level fusion is used to combine results from multiple classifiers. Results offer improved...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.