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In this paper, a learning approach is proposed to classify the fog situations into no fog, fog and dense fog three types. Feature vectors designed according to the contrast and details of foggy images are extracted to form the training set. By using the Gaussian Mixture Model to model the probability density of three situations and learning the parameters of the model with the expectation maximization...
A new change detection method for heterogeneous remote sensing images (i.e. SAR & optics) has been proposed via pixel transformation. It is difficult to directly compare the pixels from heterogeneous images for detecting changes. We propose to transfer the pixels in different images to a common feature space for convenience of comparison. For each pixel in the 1st image, it will be transferred...
The paper focuses on using stacking and rotation-based technique to improve performance and generalization ability of the machine learning classification with data reduction. The aim of data reduction technique is decreasing the quantity of information required to learn a high quality classifiers, especially when the data are huge. The paper shows that merging both stacking and rotation-based ensemble...
In order to solve the problem of the lack of prior knowledge in intrusion detection, as an unsupervised learning algorithm, the clustering algorithm is applied to intrusion detection. Aiming at the shortcomings of intrusion detection algorithm based on traditional hierarchical clustering, such as high time complexity and high false positive rate, a new clustering algorithm for intrusion detection...
Public sentiment permeated through social media is usually regarded as an important measure for public opinion monitoring, policy making, and so forth. However, the deluge of user-generated content in web, especially in social platform, causes great challenge to public sentiment analysis tasks. Therefore, Web-derived Emotional Word Detection (WEWD) is proposed as a fundamental tool aims to alleviate...
An ultra-low power neural spike sorting technique for implantable, multi-channel neural implant is proposed. It involves spiking neural network (SNN) with binary weights as an energy and area efficient classifier, along with a suitable frontend for spike encoding of the recorded neuro-potential. The proposed scheme employs two step training to implement supervised learning for the classifier, in order...
This paper presents a novel two-stage regularized moving-horizon algorithm for PieceWise Affine (PWA) regression. At the first stage, the training samples are processed iteratively, and a Mixed-Integer Quadratic-Programming (MIQP) problem is solved to find the sequence of active modes and the model parameters which best match the training data, within a relatively short time window in the past. According...
Clustering techniques that group samples based on their attribute similarity have been widely used in many fields such as pattern recognition, feature extraction and malicious behavior characterization. Due to its importance, various clustering techniques have been developed with distributed frameworks such as K-means with Hadoop in Apache Mahout for scalable computation. While K-means requires the...
Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity...
In order to satisfy the higher precision of indoor location-based service (ILBS), scholars have explored a great deal of algorithms based on Wi-Fi, ultrasonic, RFID or infrared, but all of which need additional device settings for transmitting and receiving signals before implementing location recognition. This paper proposed an idea that how to conveniently find the optimal feature or composite features...
A new Cluster-based methodology for real-time Novelty Detection and Isolation (NDI) in sensor networks, is presented. The proposed algorithm enables uniform clustering across time-frames to indicate the presence of a “healthy” network. In the event of novelty, the associated sensor is seen to be clustered in a non-uniform manner with respect other sensors in the network, thereby facilitating fault...
Self-Organizing Maps (SOM) [ ] are a popular clustering and visualization algorithm. Several implementations of the SOM algorithm exist in different mathematical/statistical softwares, the main one being probably the SOM Toolbox [2]. In this presentation, we will introduce an R package, SOMbrero, which implements several variants of the stochastic SOM algorithm. The package includes several diagnosis...
To extract key topics from news articles, this paper researches into a new method to discover an efficient way to construct text vectors and improve the efficiency and accuracy of document clustering based on Word2Vec model. This paper proposes a novel algorithm, which combines Jaccard similarity coefficient and inverse dimension frequency to calculate the importance degree between each dimension...
Tag recommendation has gained significant popularity for annotating various web-based resources including web services. Compared with other approaches, tag recommendation based on supervised learning models usually lead to good accuracy. However, a high-quality training data set is needed, which demands manual tagging efforts from domain experts. While we could leverage the tags of existing web services...
This paper investigates the continued need for intrusion detection systems (IDS) in computer networks. It explores some of the ways that data mining techniques can be used to improve IDS, and looks at how others have implemented those techniques. It then highlights a method for developing an intrusion detection model using DBSCAN clustering and presents the results of the clustering algorithm as applied...
This paper presents Decanter AI, a new approach to machine learning that uses automated machine learning techniques to resolve the massive data problem in the rapidly industry of the Internet of Things (IoT). This solution is specialized in IoT data and applied to a real-world example of a smart building with over 100 connected sensors and its performance is compared to industry benchmarks.
The fine-grained object detection is an extremely challenging problem due to the subtle variances in the appearances. At present, faster R-CNN is one of the best detection systems. However, it not a wise decision to directly apply the faster R-CNN to the fine-grained object detection. By analyzing the characteristics of fine-grained objects, we found that the anchor mechanism in the faster R-CNN system...
In the event of a maritime disaster, casualties need to be found and rescued promptly. Image processing methods could help to perform automated detection from a UAV. The main current approaches make use of multispectral and thermal cameras, which can deal with lightning difficulties but are expensive and could suffer from high noise problems. This paper presents a method combining both color analysis...
Learning Management System, such as Moodle, has been utilized extensively as part of e-learning implementation for higher institutions. The flexibility of LMS to convey the learning materials in many ways and approaches enable the instructor to implement blended learning. The student's interaction and activities while learning are captured by Moodle in the log data file and are useful to identify...
Extracting intuitive and useful information from the high-dimensional, fuzzy and complex operational simulation training data is in urgent need. In this paper, the operational simulation training data refers to the quantitative, numeric data that is usually used as the simulation results. The traditional statistical analysis methods have some limitations in clustering, visualizing and evaluating the...
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