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Dropout rates for students in correspondence and open courses are on increase. There is a need of analysis of factors causing increase in dropout rate. The discovery of hidden knowledge from the educational data system by the effective process of data mining technology to analyze factors affecting student drop out can lead to a better academic planning and management to reduce students drop out from...
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...
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...
Data stored in educational database is increasing day by day. Data mining algorithms can be used to find hidden patterns from the student's database. These patterns can be used to find academic performance of students. The main aim of this study was to determine factors that influence the student's performance. This paper proposes Generalized Sequential Pattern mining algorithm for finding frequent...
A waybill is a document that accompanies the freight during transportation. The document contains essential information such as, origin and destination of the freight, involved actors, and the type of freight being transported. We believe, the information from a waybill, when presented in an electronic format, can be utilized for building knowledge about the freight movement. The knowledge may be...
Image-based kinship recognition is an important problem in the reconstruction and analysis of social networks. Prior studies on image-based kinship recognition have focused solely on pair wise kinship verification, i.e. on the question of whether or not two people are kin. Such approaches fail to exploit the fact that many real-world photographs contain several family members, for instance, the probability...
Multi-label classification (MLC) is the task of automatically assigning an object to multiple categories. There are many important and modern applications of MLC such as text categorization (associating documents to various subjects) and functional genomics (determining the multiple biological functions of genes and proteins). MLC problems typically involve datasets that are both very large in size...
Medical databases contain massive volume of clinical data which could provide valuable information regarding diagnosis, prognosis and treatment plan when mining algorithms are used in appropriate manner. The irrelevant, redundant and incomplete data in medical databases makes the extraction of useful pattern a difficult process. Feature selection, a robust data preprocessing method selects attributes...
A signaling pathway, which is represented as a chain of interacting proteins for a biological process, can be predicted from protein-protein interaction (PPI) networks. However, pathway prediction is computationally challenging because of (1) inefficiency in searching all possible paths from the large-scale PPI networks and (2) unreliability of current PPI data generated by automated high-throughput...
The Slope One Predictor is suitable for predicting the online rating-base collaborative filtering which is used for analyzing data related to persons' likes or interests in the menu which are variously diverse and the menus are plenty. The system is considered a Personalized Recommender System by using collaborative filtering of satisfaction that the researchers consider the menu fit for the data...
The main difficulty faced by a learning algorithm is to find the appropriate knowledge inside of the huge search space of possible solutions. Typically, the researchers try to solve this problem developing more efficient search algorithms, defining “ad-hoc” heuristic for the specific problem or reducing the expressiveness of the knowledge representation. This work explores an alternative way that...
Location based services (LBS) is one of the fastest growing areas in recent years. Location update of mobile clients is fundamental in all types of LBS. But algorithms proposed in this field generally didn't concern the restricted context of road network and caused some unnecessary update. This paper proposed a revised vector-based update algorithm which taking characteristics of road network into...
A fusion scheme of phone duration models (PDMs) is presented in this work. Specifically, a support vector regression (SVR)-fusion model is fed with the predictions of a group of independent PDMs operating in parallel. The American-English KED TIMIT and the Greek WCL-1 databases are used for evaluating the PDMs and the fusion scheme. The fusion scheme contributes to the accuracy improvement over the...
The intended applications of automatic face recognition systems include venues that vary widely in demographic diversity. Formal evaluations of algorithms do not commonly consider the effects of population diversity on performance. We document the effects of racial and gender demographics on the accuracy of algorithms that match identity in pairs of face images. In particular, we focus on the effects...
Since RNA molecules regulate genes and control alternative splicing by allostery, that is, by switching between two distinct secondary structures, it is important to develop algorithms to predict RNA conformational switches. It has recently emerged that RNA secondary structure can be more accurately predicted by computing the maximum expected accurate (MEA) structure, rather than the minimum free...
Protein complexes are important entities to organize various biological systems. However, they are still limited in availability. Thus, it is a challenging problem to predict protein complexes computationally from existing genome-wide data sets, like protein-protein interaction (PPI) networks. In this paper, we propose an efficient algorithm for predicting protein complexes by random walking on a...
Collaborative filtering (CF) has been a comprehensive approach in recommendation system. But data are always sparse; any given user has seen or buys only a small fraction of all items. This becomes the bottleneck of CF. Cluster-based smoothing technique for nature language processing is successful to estimate probability of the unseen term by using the topic (cluster) of the term belongs to, which...
This work presents a system for knowledge discovery from protein databases, based on an Artificial Immune System. The discovered rules have the advantage of representing comprehensible knowledge to biologist users. This task leads to a very challenging problem since a protein can be assigned multiple classes (functions or Gene Ontology (GO) terms) across several levels of the GO's term hierarchy....
Existing trajectory prediction algorithms mainly employ kinematical models to approximate real world routes and always ignore spatial and temporal distance. In order to overcome the drawbacks of existing trajectory prediction approaches, this paper proposes a novel trajectory prediction algorithm. It works as: (1) mining the interesting regions from trajectory data sets; (2) extracting the trajectory...
The deep web integration system employs a set of semantic mappings between the mediated schema and the schemas of web data sources. In this dynamic environment, sources often undergo changes that invalidate the mappings. Such continuous monitoring is extremely labor intensive, and poses a key bottleneck to the widespread deployment of web data integration systems in practice. The paper describes DBMFR...
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