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How to embed the new observations (or samples) into the low-dimensional space is a crucial problem in non-linear manifold learning techniques. This issue can be converted into the problem of finding an accurate mapping that transfers the unseen data samples into an existing manifold. In this paper, a locality-constrained sparse representation algorithm is proposed to deal with the out-of-sample embedding...
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
Detecting anomaly behavior in large network traffic data has presented a great challenge in designing effective intrusion detection systems. We propose an adaptive model to learn majority patterns under a dynamic changing environment. We first propose unsupervised learning on data abstraction to extract essential features of samples. We then adopt incremental majority learning with iterative evolutions...
The research using computational intelligence methods to improve bad debt recovery is imperative due to the rapid increase in the cost of healthcare in the U.S. This study explores effectiveness of using cost-sensitive learning methods to classify the unknown cases in imbalanced bad debt datasets and compares the results with those of two other methods: undersampling and oversampling, often used in...
This work proposes a new method to identify non-technical losses in typical Electrical Power Distribution Systems (EPDS). Aiming to improve the efficiency of on-site inspection for fraudulent clients, the consumers are classified in suspects and non-suspects using the Optimum-Path Forest (OPF) classifier. The authors propose an improvement to this method using results of a Distribution State Estimator...
Classifying cash crops through satellite based remote sensing has proved to be effective for reliable ground based agricultural statistics. In this study, frequently used simple and fast classification algorithms i.e., Mahalanobis Distance and Maximum Likelihood Classification (MLC) are compared for classifying tobacco crops by the end of June in north-western Pakistan. High Geometric Resolution imagery...
In this paper, a voting based weighted online sequential extreme learning machine (VWOS-ELM) is proposed for class imbalance learning (CIL). VWOS-ELM is the first sequential classifier that can tackle the class imbalance problem in multi-class data streams. Utilizing WOS-ELM and the recently proposed voting based online sequential extreme learning machine (VOS-ELM) method, VWOS-ELM adapts better to...
This study is to classify satellite data based on traditional swarm intelligence technique. Attempts to classify remote sensed data with traditional statistical classification technique faced number of challenges as the traditional per-pixel classifier examine only the spectral variance ignoring the spatial distribution of the pixels, corresponding to the land cover classes and correlation between...
Debugging is an indispensable yet frustrating activity in software development and maintenance. Thus, numerous techniques have been proposed to aid this task. Despite the demonstrated effectiveness and future potential of these techniques, many of them have the unrealistic single-fault failure assumption. To alleviate this problem, we propose a technique that can be used to distinguish failing tests...
Institutions hassles to accommodate a large of student that couldn't passed in normal study period. Some of them pending the study period because couldn't passed TPB in two semesters. If many students didn't graduated on time, it would be a lot of difficulties involved by institutions. The impact of these problems such as human resources, supplying classroom and operational costs. This research tries...
Threats to computer networks are numerous and potentially devastating. Intrusion detection techniques provide protection to our data and track unauthorized access. Many algorithms and techniques have been proposed to improve the accuracy and minimize the false positive rate of the intrusion detection system (IDS). Statistical techniques, evolutionary techniques, and data mining techniques have also...
Arrhythmia is the major cause of cardiovascular events during space flight. Even though a number of physical tests are conducted to diagnose the disease, in most of the cases the issue remains undetected because of the hidden problems which cannot be pinpointed with regular physical tests. A computation system which can assist in decision making for astronaut selection for space flight is proposed...
Voting based Extreme learning machine was recently proposed to reduce the error due to variance in Extreme Learning Machine. This paper further refines the algorithm by using entropy based ensemble pruning. Results obtained shows significant improvement in performance along with reduction in computational and storage requirement.
Artificial intelligence and decision support systems offer a plethora of health monitoring capabilities in ambient assisted living environment. Continuous assessment of health indicators for elderly people living on their own is of utmost importance, so as to prolong their independence and quality of life. Slow varying, long-term deteriorating health trends are not easily identifiable in seniors....
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...
Exercise is a good alternative approach to be healthy. However, it can cause a body in negative outcome for people who over workout without proper manner. Therefore, the objective of this project is to develop an activity tracker called "Feelfit" that has a high accuracy to measure levels of activity (with 5 intensities of exercises). Besides, challenging and motivating the exercise via...
Automated understanding of human facial expression is an active and concerning research topic. It is expected that in near future full-fledged understanding of human facial expression will enable machines to behave more intelligently. In this paper we proposed a system for automatic facial expression recognition. A consistent combination of Self-Organizing Map (SOM), Learning Vector Quantization (LVQ)...
In the literature, there are some studies which investigate if there is a relationship between fingerprint and gender or not. In these studies, this relationship is examined based on some vectorial parts of fingerprints. The main problem in these studies is the lack of data, depending on ethnical background and country, and there is not an exact finding of true classification results. It is known...
An excersice prescription is a professionally designed excersice plan for improving one's health according to the results of his health-related physical fitness (HRPF) tests. Traditionally, an excersice prescription is formulated by manually checking the norm-referenced chart of HRPF; however, it is time consuming and a highly specialized and experienced expert on health-related physical fitness testing...
Vehicle detection is an important problem in computer vision. Several applications including robotics, surveillance and automotive safety are related to vehicle detection. In this paper, we build up a vehicle detection system by combing the active basis model and logistics regression. Active basis model provides a robust and reasonable representation for cars, while logistic regression gives us an...
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