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This paper presents detailed anomaly detection evaluation on operational time-series data of Internet of Things (IoT) based household devices in general and Heating, Ventilation and Air Conditioning (HVAC) systems in specific. Due to the number of issues observed during evaluation of widely used distance-based, statistical-based, and cluster-based anomaly detection techniques, we also present a pattern-based...
The paper considers the problem of feature selection in learning using privileged information (LUPI), where some of the features (referred to as privileged ones) are only available for training, while being absent for test data. In the latest implementation of LUPI, these privileged features are approximated using regressions constructed on standard data features, but this approach could lead to polluting...
One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under...
We consider the Similarity Sketching problem: Given a universe [u] = {0,..., u-1} we want a random function S mapping subsets A of [u] into vectors S(A) of size t, such that similarity is preserved. More precisely: Given subsets A,B of [u], define X_i = [S(A)[i] = S(B)[i]] and X = sum_{i in [t]} X_i. We want to have E[X] = t*J(A,B), where J(A,B) = |A intersect B|/|A union B| and furthermore to have...
This paper aims to classify a peripheral pulmonary lesion whether it is malignant or benign by proposing the new method to select a window of interest (WOI) using window slicing and the new feature called the "weight-sum of upper and lower gray level co-occurrence matrix (GLCM)" of an endobronchial ultrasound (EBUS) image. The proposed feature can be used to determine the heterogeneity of...
The complexity of the mechanism brings great difficulty to the calculation of the mechanism motion reliability, and the computer simulation algorithm based on Monte Carlo method needs a great number of simulation. The further-development of LMS Virtual. Lab was carried out, and the least square support vector machine algorithm was used to construct the response surface proxy model. The PSO-GA algorithm...
A hybrid sampling technique is proposed by combining Complementary Fuzzy Support Vector Machine (CMTFSVM) and Synthetic Minority Oversampling Technique (SMOTE) for handling the imbalanced classification problem. The proposed technique uses an optimised membership function to enhance the classification performance and it is compared with three different classifiers. The experiments consisted of four...
This paper considers a problem of reconstructing a typeface of Chinese characters. First, Chinese characters are generated by using the so-called dynamic font method as a basic tool. Then, we develop a scheme for reconstructing characters to cursive characters with natural "Renmen" which can depict continuity of the adjacent strokes. For realizing the natural Renmen, we propose a method...
The classification and identification of the bird species from the visual image is complex compared by using audio song. The knowledge of the features species type is very important as to ensure it is classified to the correct species. Color-based feature extraction is one of the procedure in extracting the color properties from the bird which to represent the species of the bird. However, it is a...
Evaluating motion quality has many applications in health promotion and exercise coaching. This study aimed to develop an approach for automatic and cost-efficient evaluation on motion quality using the Nintendo Wii Balance Board (WBB) and machine learning techniques. We conducted a pilot study with twelve participants to collect data of chest rotation and hip joint rotation. We used support vector...
There are many modulation formats (such as FSK, PSK, ASK) in the process of underwater acoustic communication, and the task of the signal modulation identification is to distinguish the format that used from signals which received from acoustic communication. This technique for communication management, such as underwater acoustic communication spectrum monitoring, interference listening and signal...
The paper describes the study on the problem of applying classification techniques in medical datasets with a class imbalance. The aim of the research is to identify factors that negatively affect classification results and propose actions that may be taken to improve the performance. To alleviate the impact of uneven and complex class distribution, methods of balancing the datasets are proposed and...
The paper presents the comparative analysis of the computer systems for face recognition. Autoencoder, the typical representative of deep learning is compared with the classical PCA transformation. Both, autoencoder and PCA serve as the tools for feature generation and selection. However, the important difference is the nonlinearity and multilayer structure applied in autoencoder. Final task of recognition...
Abnormality or inconsistency detection within a data is an attempt to make a distinction between usual and exceptional data instances. In this paper, we have proposed a novel methodAbnormality Prediction in High Dimensional Dataset among Semi Supervised Learning approaches (AP-HDD-SSL) to match the efficiencies of different semi supervised machine learning approaches using high dimensional KDD CUP...
Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in...
The electroencephalography (EEG) data records vast amounts of human cerebral activity yet is still reviewed primarily by human readers. Most of the times, the data is contaminated with non-cerebral originated signals, called artifacts, which could be very difficult to visually detect and, undiscovered, could damage the neural information analysis. The purpose of our work is to detect the artifacts...
Nature has surrounded us with lot of plants having medicinal values. But most of the time, we don't realize the importance and benefits of the plant and we just ignore it. In other cases, though we know names of the plants with medicinal values, it becomes difficult to identify the plant even if it is naturally grown in our backyard. And hence, a system is developed which would provide a solution...
Cancer classification is routinely done using gene expression data. With microarray technology, monitoring thousands of genes is an easy task. The reliable and precise classification of different tumour types is very important in cancer classification and drug discovery which is useful in providing better treatment. Microarray gene expression data analysis is extensively used for human cancer diagnosis...
In this paper, a method for reducing coding artifacts introduced by lossy image compression is proposed. The method is similar to sample adaptive offset (SAO) which is adopted in the H.265/HEVC video coding standard as one of in-loop filtering tools. In the SAO, samples of the reconstructed image are classified into several categories based on some simple algorithms, and an optimum offset value is...
Context: Software Bug Severity Classification can help to improve the software bug triaging process. However, severity levels present a high-level of data imbalance that needs to be taken into account. Aim: We investigate cost-sensitive strategies in multi-class bug severity classification to counteract data imbalance. Method: We transform datasets from three severity classification papers to a common...
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