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In order to improve the location accuracy in the complicated greenhouse environment, a positioning system is designed in this paper. According to RSSI data in the greenhouse, the parameters of the path-loss model are modified. Besides, least square estimation is used to filter RSSI data to eliminate random disturbance. Based on RSSI, the blind node is positioned by triangle centroid location method...
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...
Texture identification is an important preliminary step in many computer vision applications. There exists supervised and unsupervised approaches to solve this problem. One of the widely used unsupervised technique is KMeans which identifies the region of image based on clustering. The disadvantage of the KMeans technique is that it is an off-line approach that needs all the data prior to processing...
Graphs are a powerful way to model interactions and relationships in data from a wide variety of application domains. In this setting, entities represented by vertices at the 'center' of the graph are often more important than those associated with vertices on the 'fringes'. For example, central nodes tend to be more critical in the spread of information or disease and play an important role in clustering/community...
Classification is a supervised learning technique typically uses two-thirds of the given annotated data set for training and the remaining for test. In this paper, we developed a frame work which uses less than one-third of the data set for training and tests the remaining two-thirds of the data and still gives results comparable to other classifiers. To achieve good classification accuracy with small...
Mechatronics as an interdisciplinary field, which combines knowledge from multiple fields uses a comprehensive approach in the design of any technical object. It combines the requirements and their integration into all subsystems of the technical object. This approach, which is described by the VDI 2006 norm, can generally be used in the suggestion of regulation for the pressure pipe networks.
Most existing topic models focus either on extracting static topic-sentiment conjunctions or topic-wise evolution over time leaving out topic-sentiment dynamics and missing the opportunity to provide a more in-depth analysis of textual data. In this paper, we propose an LDA-based topic model for analyzing topic-sentiment evolution over time by modeling time jointly with topics and sentiments. We derive...
In this paper, we address the problem of recommending Point-of-Interests (POIs) to users in a location-based social network. To the best of our knowledge, we are the first to propose the ST (Social Topic) model capturing both the social and topic aspects of user check-ins. We conduct experiments on real life data sets from Foursquare and Yelp. We evaluate the effectiveness of ST by evaluating the...
Ranking objects is an essential problem in recommendation systems. Since comparing two objects is the simplest type of queries in order to measure the relevance of objects, the problem of aggregating pair wise comparisons to obtain a global ranking has been widely studied. In order to learn a ranking model, a training set of queries as well as their correct labels are supplied and a machine learning...
Research community has recently put more attention to the Extreme Learning Machines (ELMs) algorithm in Neural Network (NN) area. The ELMs are much faster than the traditional gradient-descent-based learning algorithms due to its analytical determination of output weights with the random choice of input weights and hidden layer bias. However, since the input weights and bias are randomly assigned...
Building accurate classifiers is difficult when using data that is skewed or imbalanced which is typical of real world data sets. Two popular approaches that have been applied for improving classification accuracy and statistical comparisons of imbalanced data sets are: synthetic minority over-sampling technique (SMOTE) and propensity score matching (PSM). A novel sampling approach is introduced referred...
Most of the studies working on point cloud data focused on complete and clean data (even though some of them took missing values into account), while in practice we often have to deal with incomplete and unclean data, just as there might be missing values and noise in data. We study noise handling, and we put our focus on processing a noisy point cloud of a visual object or a 3D model. We propose...
Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental...
The potential of Markov chain and cellular automata model with help of agents that play a vital role in a cities urbanisation through fuzziness in the data and hierarchal weights (for principal agents) have been used to understand and predict the urban growth for the Pune city, India. The model utilizes temporal land use changes with probable growth agents such as roads drainage networks, railway...
Recent surveys show that there is enormous increase of organizations intending to adopt cloud, but one of their major obstructions is the trustworthiness evaluation of cloud service candidates. Performing evaluations of cloud service candidates is expensive and time consuming, especially with the breadth of services available today. In this situation, this paper proposes a novel trustworthiness measurement...
Stream mining has gained popularity in recent years due to the availability of numerous data streams from sources such as social media and sensor networks. Data mining on such continuous streams possess a variety of challenges including concept drift and unbounded stream length. Traditional data mining approaches to these problems have difficulty incorporating relational domain knowledge and feature...
Linear data transformations constitute essential operations in various machine learning algorithms, ranging from linear regression up to adaptive metric transformation. Often, linear scalings are not only used to improve the model accuracy, rather feature coefficients as provided by the mapping are interpreted as an indicator for the relevance of the feature for the task at hand. This principle, however,...
This paper presents a hybrid position estimation technique that is based on the integration of Fingerprinting (FP), Time-of-flight (ToF), and Trilateration approach. When empirical channel models are used with Ultra Wideband (UWB) systems, there are plenty of challenges that must be taken into consideration due to their dependence on frequency. Thus, when the position of a Blind Node (BLN) is estimated...
We compared the accuracy measure between a single-stage classifier model and a multiple-stage classifier model in postural classifications using Kinect. Postural training sets were collected from Kinect's skeletal data streams, based on some of the common human postures during television watching. Three types of training sets were used, including Kinect's raw skeletal training set, skeletons with...
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...
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