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Predicting the location of a user in indoor settings in a practical and energy-efficient manner is (still) a very non-trivial task. The latest challenge in indoor localization is not to design specialized sensors but to design and implement practical data fusion methods using the already available technologies. Current state-of-the-art indoor localization techniques utilize Wi-Fi and a variety of...
“Collaborative filtering” (CF) methods provide a good solution for recommendation systems. Neighborhood formation is considered as the main phase in memory approaches. Unfortunately, this phase encounters many problems such as sparsity and scalability, especially for huge datasets which consists of a large number of users and items. This paper presents a new hybrid approach for collaborative filtering...
In this paper, a model for traffic jam prediction using data about traffic, weather and noise is presented. It is based on data coming from a Smart City in Spain called Santander. The project in this city is called ”Smart Santander” and provides a platform for large-scale experiment based on realtime data. This paper demonstrates the possibility of predicting traffic jams and is a basis to integrate...
Collaborative filtering method was widely used in the recommendation system. This method was able to provide recommendations to the user through the similarity values between users. However, the central issues in this method were new user issue and sparsity. This paper discusses about how to use matrix factorization and nearest-neighbour in film recommendation systems. Both of methods will be used...
For extorting the helpful comprehension concealed in the biggest compilation of a database the data mining technology is used. There are some negative approaches occurred about the data mining technology, among which the potential privacy incursion and potential discrimination. The latter consists of irrationally considering individuals on the source of their fitting to an exact group. Data mining...
Recent emerging growth of data created so many challenges in data mining. Data mining is the process of extracting valid, previously known & comprehensive datasets for the future decision making. As the improved technology by World Wide Web the streaming data come into picture with its challenges. The data which change with time & update its value is known as streaming data. As the...
We proposed an approach to predict the availability of volunteer sensor networks (VSN) node. It is based on the Stronger Intelligent selection (SIS). First, the availability of VSN node is analyzed and predicted based on its location. The stronger model is defined and Studied on the Optimization Rules and Solution Tactics of availability. A simple and efficient stronger searching mechanism is presented...
Recommender systems are designed in such a way that they sort through massive amounts of data so as to help users in finding their preferred items. Currently much research on recommender systems focus on improving the prediction or classification accuracy of the respective algorithms while behavioral aspects are often overlooked. In this paper we focus on a particular behavioral property called monotonicity...
Physical interactions between the proteins in a living organism helps in identification of most protein-protein interaction data. The annotated proteins are previously known by their functions. Their knowledge is definite. The un-annotated proteins are annotated based on estimation of such similar functions. Generally a cluster containing annotated nodes with their adjacent unlabeled nodes is assumed...
For improving the forecasting accuracy of bank cash flow, a combined model based on back propagation (BP) neural network and grey prediction method is put forward based on the merits and demerits of both BP neural network and grey model prediction method. The proposed method has the advantage of two methods and makes up the deficiencies of single model as well. It can efficiently reduce the influence...
Traditional classification algorithms often perform well when training and testing data are drawn from the identical distribution. However, in real applications, this condition may be not satisfied. Domain adaptation is an effective approach to deal with this problem. In this paper, we propose an efficient two-stage algorithm for domain adaptation. In the label transfer stage, we utilize training...
Elasticity is a key feature in cloud computing, and perhaps what distinguishes it from other computing paradigms. Despite the advantages of elasticity, realizing its full potential is hard due to multiple challenges stemming from the need to estimate workload demand. A desirable solution would require predicting system workload and allocating resources a priori, i.e., A predictive approach. Instead,...
the present study utilizes social computing techniques to enhance the content-based recommender systems. Coined as Enhanced Content-based Algorithm using Social Networking (ECSN), this recommender algorithm is applied in academic social networks to suggest the most relevant items to members of these online societies. In addition to considering user's own preferences, ECSN takes advantage of the interest...
Dealing with multiple labels is a supervised learning problem of increasing importance. Multi-label classifiers face the challenge of exploiting correlations between labels. While in existing work these correlations are often modelled globally, in this paper we use the divide-and-conquer approach of decision trees which enables taking local decisions about how best to model label dependency. The resulting...
Number of defects remaining in a system provides an insight into the quality of the system. Defect detection systems predict defects by using software metrics and data mining techniques. Clustering analysis is adopted to build the software defect prediction models. Cluster ensembles have emerged as a prominent method for improving robustness, stability and accuracy of clustering solutions. The clustering...
One-bit transform (1BT), followed by binary motion estimation, is an effective alternative for accelerating traditional 8-bit motion estimation (ME) in video coding. The underlining assumption in the design of 1BT methods is that natural videos contain noise. For screen content videos, however, the special characteristics (e.g. screen content is typically noise-free) can be exploited to further improve...
Crime detection and prevention is a very crucial wok which is in the hands of police, law enforcement agencies and local government. Experts in crime analyzing use crime scene evidences to capture unique ways a criminal has acted during a crime, which is also called as Modus Operandi(MO). Using MO as the main focus, the efforts taken in this research is to shortlist and predict criminals and criminal...
The proposed methodology involves to compares classification techniques for predicting the cognitive skill of students which can be evaluate by conducting the online test. The paper focuses the comparative performance of C4.5 algorithm, Naïve Bayes classifier algorithm which one is well suited accuracy for predicting the skill of expertise by experimenting in Rapid miner.
Classification of data points in a data stream is a fundamentally different set of challenges than data mining on static data. While streaming data is often placed into the context of "Big Data" (or more specifically "Fast Data") wherein one-pass algorithms are used, true data streams offer additional hurdles due to their dynamic, evolving, and non-stationary nature. During the...
Missing data imputation is an important task in cases where it is crucial to use all available data and no discard records with missing values. However, most of the existing algorithms are focused on missing at random (MAR) or missing completely at random (MCAR). In this paper, an information decomposition imputation (IDIM) algorithm using fuzzy membership function is proposed for addressing the missing...
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