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A routing algorithm that leads to extended driving range and battery longevity of electric vehicles (EV) is proposed. In addition to locating the time and energy efficient routes, the proposed algorithm provides a desired speed profile to be tracked by the driver. Data mining techniques are employed for extracting the desired speed profile for the goal driver from a set of historical driving data...
Time series motifs are approximately repeating patterns in real-valued time series data. They are useful for exploratory data mining and are often used as inputs for various time series clustering, classification, segmentation, rule discovery, and visualization algorithms. Since the introduction of the first motif discovery algorithm for univariate time series in 2002, multiple efforts have been made...
Many real-world datasets suffer from the problem of missing values. Imputation which replaces missing values with plausible values is a major method for classification with data containing missing values. However, powerful imputation methods including multiple imputation are usually computationally intensive for estimating missing values in unseen incomplete instances. Rule-based classification algorithms...
By analyzing the disadvantages of the traditional KNN using lazy learning that directly classify the data based on the K neighboring classes using the majority voting method, a new Sigmoid weighted classification algorithm WKS (Weighted KNN Based On Sigmoid) was proposed. WKS provides a new method for learning and training, since each training data di ∊ D contributes to the correct classification...
Objective: Medical data mining is a research hotspot. But medical data often contains missing values, which brings difficulties to the medical data analysis. This work evaluates the performance of several imputation methods. Methods: In this paper, we first simulate the missing data set by completely deleting some data from the complete data set, and use the Euclidean distance KNN, the correlation...
Clustering is an important tool for analyzing gene expression data. Many clustering algorithms have been proposed for the analysis of gene expression data. In this article we have clustered real life gene expression data via K-Means which is one of clustering algorithms. Also, we have proposed a new method determining the initial cluster centers for K-means. We have compared results of our method...
Digital libraries can provide information services for users with diverse needs. Due to a large amount of data that exists in digital library systems, including text and multimedia resources, with different cohorts of users, and the challenges with existing digital library systems in terms of maintaining privacy and confidentiality, it is very difficult to provide personalised library services and...
Unemployment, poverty and similar problems that have come to the fore with the increase in population in our country have caused the municipalities to take charge in the field of social assistance and social services. For this purpose, it is very important that the municipalities that undertake social assistance and social service tasks are able to use the present data quickly during distribution...
ID3 is a classical algorithm of decision tree of classification with fast speed and easily understandable classification results. ID3 based on information gain tend to select test attribute with a variety of values, thus unable to deal with continuous attributes. In order to solve the above problems, this paper introduces the support in rough sets to discretize the continuous attributes dynamically,...
Association rule mining is a very essential data mining technique in different fields. The enormous development of the information needs increased computational power. To address this issue, it is important to study executions of mining algorithms. To find out the frequent itemsets is an essential and vital issue in numerous information mining applications. There are many algorithms present to extract...
This paper aims to build data mining model to predict the performance of candidate teachers who apply for employment in education of high schools of Gaza Strip. We apply three classification algorithms on our dataset which are Decision Tree, Naïve Bays and KNN. Our dataset contains 8000 teacher records collected from ministry of education in Gaza Strip. Although there are a lot of researchers...
It is a simple task for humans to visually identify objects. However, computer-based image recognition remains challenging. In this paper we describe an approach for image recognition with specific focus on automated recognition of plants and flowers. The approach taken utilizes deep learning capabilities and unlike other approaches that focus on static images for feature classification, we utilize...
The paper exposes the behavior of the Decision Trees (DT) algorithms on a big database with many cases and many attributes: Forest Covertype (FC) from UCI Knowledge Discovery in Databases Archive. In classification experiments considered have been taken into account 22 splitting criteria and two pruning methods whose performances were presented in terms of classification error rate on test data, data...
In recent years advent of social networking services has created large amounts of data. Microblogging website is a kind of social network in which users share short messages with others. One of the most popular microblogging services is Twitter. Every day millions of people post their opinions and sentiments in this microblog. Due to the large numbers of tweets, finding new approaches to discover...
The prediction of short term adverse events occurrence in phototherapy treatment is important for the dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique which can detect the potential short term adverse events occurrence in phototherapy treatments is required for clinicians. Based on data mining, this study tends...
One of the major causes of death in the world is Heart Failure. This disease affects directly the heart's pumping job. Because of this perturbation, nutriments and oxygen are not well circulated and distributed. The New York Heart Association has classified this disease into four different classes based on patient symptoms. In this paper, we are using a data mining technique, more precisely a sequential...
There is a need to exploit the available data collected on environment for the development of smart cities in order to improve the air quality which in turn can improve the quality of life for a city. Air pollution is becoming a serious concern to the society as the air pollutants are very hazardous in nature. Pollutants affect the health and causes respiratory and cardiac problems. If air pollutants...
The unprecedented interest in big data has paved way for augmented technologies. One of the major usefulness of big data is found in the field of healthcare analytics. The healthcare data come from varied sources. Specifically EHR data provide a comprehensive view of patient's health. People are paying more attention to their health and want the best possible healthcare especially with new technologies...
In this paper, we propose to determine whether the viewer's behavior changes or not before, during and after watching a TV program. Are there any behaviors specific to each particular phase of viewing? Here, we propose a flexible and nonintrusive method based on the use of three categories of everyday connected objects (i.e. Smartphone, smartwatch and remote control). Data were collected during participants'...
Event relation knowledge is important for deep language understanding and inference. Previous work has established automatic acquisition methods of event relations that focus on common sense knowledge acquisition from large-scale unlabeled corpus. However, in the case of domain-specific knowledge acquisition, such a method can not acquire much knowledge due to the limited amount of available knowledge...
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