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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...
In this paper we propose a system for the problem of facade segmentation. Building facades are highly structured images and consequently most methods that have been proposed for this problem, aim to make use of this strong prior information. We are describing a system that is almost domain independent and consists of standard segmentation methods. A sequence of boosted decision trees is stacked using...
Since the Web of Things (WoT) term was first proposed, there has a big trend in IT vendors providing users with various services through their smart products. To enable users to discover and leverage these services, SDPs play an important role. However, so many variations of SDPs have been introduced it has caused a heterogeneity issue. Including standardization, many solutions have been proposed,...
The software defined networking (SDN) allows separating control and data plane, which provides better network management and higher utilization for data center network. Among these topical applications in SDN, such as traffic engineering, QoS and network management, there is significant interest on classifying the flows and predict future traffic. Classification plays an important role in SDN, especially...
As the need of internet is increasing day by day, the significance of security is also increasing. The enormous usage of internet has greatly affected the security of the system. Hackers do monitor the system minutely or keenly, therefore the security of the network is under observation. A conventional intrusion detection technology indicates more limitation like low detection rate, high false alarm...
Telemedicine applications provide healthcare services through communications technologies overcoming the geographical separation between patients and caregivers. These services can be provided via wireless devices, such as smart-phones with dedicated applications. An interesting application concerns the so-called situation awareness algorithms and, in particular, the Activity Recognition (AR) aimed...
The aim of this study is to compares some classification techniques used to predict the performance of student. It is helps to analyse the slow leaner in the semester exams that are likely study in poor which are used to improve their skill as early to achieve the goal in end semester. The task can be processed based on the several attributes to predict the performance of the student activity respectively...
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...
Anaphora resolution (AR) is the process of resolving references to an entity in the discourse. The paper presents an algorithm to identify the pronominals and its antecedents in the Malayalam text input. Anaphora resolution is achieved by employing a hybrid of statistical machine learning and rule based approaches. The system is implemented by exploiting the morphological richness of the language...
The paper proposes cognitive learning technique for predicting the destination in a video conference being held over an organizational network. The dataset comprised of 22801 connectivity records of video conferences held during the year 2010–2013. Naive Bayes, k-NN and decision tree were trained on the dataset and the performance of the learning algorithms were evaluated. The destination has been...
Now a day's most of the people suffer from brain related neurodegenerative disorders. These disorders lead to various diseases. Dementia is one such disease. Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Alzheimer's disease is the most common type of dementia. Alzheimer's disease is one of the types of the dementia which accounts to 60–80% of...
Attribute space extension for decision forests often contribute to improving the ensemble accuracy. In this paper we suggest the use of a recent method for attribute space extension where the newly generated attributes that have high classification capacity are chosen for extension. In literature, it is shown that the inclusion of these new attributes in the attribute space increases the prediction...
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...
Bugs can be reopened after they have been closed due to identification of the actual cause, previous incorrect fixing, or better reproducing, etc. Reopened bugs may increase the cost in maintenance, degrade the overall quality of the software product, reduce the trust of users, and bring unnecessary work to the already-busy developers. To minimize the occurrence of bug reopenings, the potential causes...
For the last few years, text mining has been gaining significant importance. Since Knowledge is now available to users through variety of sources i.e. electronic media, digital media, print media, and many more. Due to huge availability of text in numerous forms, a lot of unstructured data has been recorded by research experts and have found numerous ways in literature to convert this scattered text...
Falling can cause significant injury, where quick medical response and fall information are critical to providing aid. In this paper we present a wearable wireless fall detection system utilising a Shimmer accelerometer device, where important additional information is obtained, such as direction and strength of the occurred fall instance. Discrete Wavelet Transforms and multiresolution wavelet analysis...
In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed. In multi-label classification, each of the input data samples belongs to one or more than one class labels. The traditional binary and multi-class classification problems are the subset of the multi-label problem with the number of labels corresponding to each sample...
In recent days, researchers are actively analysing the human brain to understand the underlying mechanism of heterogeneous psychiatric conditions. Schizophrenia is a severe neurological disorder which has been characterized by varying symptoms namely hallucinations, delusions and cognitive problems. In this paper, we have investigated the resting state fMRI images of 15 normal controls and 12 Schizophrenia...
The Random Forests algorithm belongs to the class of ensemble learning methods, which are common used in classification problem. In this paper, we studied the problem of adopting the Random Forests algorithm to learn raw data from real usage scenario. An improvement, which is stable, strict, high efficient, data-driven, problem independent and has no impact on algorithm performance, is proposed to...
Given a spatial raster framework F, a set of explanatory feature maps, training and test samples with class labels on F, as well as a base classifier type, the problem of ensemble learning in raster classification aims to learn a collection of base classifiers to minimize classification errors. The problem has important societal applications such as land cover classification but is challenging due...
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