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System call analysis is a behavioral malware detection technique that is popular due to its promising detection results and ease of implementation. This study describes a system that uses system call analysis to detect malware that evade traditional defenses. The system monitors executing processes to identify compromised hosts in production environments. Experimental results compare the effectiveness...
In addition to the traditional video surveillance, various audio processing techniques can also be added to the existing CCTV cameras. They can be used as additional features to help in analyzing the scene better and autonomously detecting violence or any unwanted activity in the scene. For this purpose, a deep learning based scream sound detection approach is proposed in this paper. MFCC features...
In digital retinal images, positive means of texture feature detection around the macula region with specified radius is still an open issue. Diabetic macular edema is a complication caused due to Diabetic Retinopathy (DR) and is the true cause of blindness and visual loss. In this paper, we have presented a computerized method for texture feature extraction around the specified radius taking macula...
Routability is one of the primary objectives in placement. There have been many researches on forecasting routing problems and improving routability in placement but no perfect solution is found. Most traditional routability-driven placers aim to improve global routing result, but true routability lies in detailed routing. Predicting detailed routing routability in placement is extremely difficult...
Classification is an important technique in data mining. The K-Nearest neighbor (K-NN) algorithm is a memory based algorithm and is capable of producing satisfactory results when applied on certain data but the distance measures used in this algorithm is not capable of handling the data sets containing the uncertain attribute values. Data uncertainty is common in real word applications. In this paper...
Individuals with severe neurologic injuries often cannot participate in robotic rehabilitation because they do not retain sufficient residual motor control to initiate the robotic assistance. In these situations, brain- and body-computer interfaces have emerged as promising solutions to control robotic devices. In a previous experiment conducted with healthy subjects, we showed that detecting motor...
This paper aims at building a portable robotic hand for physically disabled people to perform basic hand movements. Surface Electromyography(EMG) signal is collected from muscles of human forearm to extract the subject's intentions of action, where six kinds of gestures are selected for discussion. An Artificial Neural Network(ANN) is trained and utilized to distinguish the desired movement according...
Randomized algorithms have good performances for regression and classification problems by using random hidden weights and pseudoinverse computing for the output weights. They have one single hidden layer structure. On the other hand, deep learning techniques have been successfully used for pattern recognition due to their deep structure and effective unsupervised learning. In this paper, the randomized...
Parts of speech tagging is an important research topic in Natural Language Processing research are. Since it is one among the first steps of any natural language processing (NLP) techniques such as machine translation, if any error happens for tagging the same will repeat in the whole NLP process. So far works had been done on POS tagging based on SVM, MBLP, HMM, Ngram. All of these methods were not...
Support Vector Machines (SVMs) were primarily designed for 2-class classification. But they have been extended for N-class classification also based on the requirement of multiclasses in the practical applications. Although N-class classification using SVM has considerable research attention, getting minimum number of classifiers at the time of training and testing is still a continuing research....
Laser induced breakdown spectroscopy (LIBS) is an atomic emission based spectroscopy that uses a laser pulse as the source of excitation. The laser is focused to form hot plasma, which atomizes and excites the sample. In the LIBS spectrum each “feature” is the amplitude or intensity detected at different wavelengths in the range of 200–1000 nm. Pattern recognition techniques were applied on samples...
In this paper, we propose a fast and economic strategy for the integration of new classes on the fly into evolving fuzzy classifiers (EFC) during data stream mining processes. Fastness addresses the assurance that a newly arising class in the stream can be integrated in a way such that the classifier is able to correctly return the new class after receiving only a few training samples of it. Economic...
In this paper we investigate the use of fuzzy rule-based classifiers for multi-label classification. This classification task deals with problems where more than one label could be assigned simultaneously to a given instance. We concentrate on problem transformation methods, which use different strategies to transform a multi-label problem into a different single-label classification problems. This...
Random forests have proved to be very effective classifiers, which can achieve very high accuracies. Although a number of papers have discussed the use of fuzzy sets for coping with uncertain data in decision tree learning, fuzzy random forests have not been particularly investigated in the fuzzy community. In this paper, we first propose a simple method for generating fuzzy decision trees by creating...
The sequential covering strategy has been and still is a very common way to develop rule learning algorithms. This strategy follows a greedy procedure to learn rules, where, after each step one rule is obtained. Recently, we proposed a new sequential covering strategy that allowed the review of previously learned knowledge during the learning process itself. This review of knowledge allowed the algorithm...
This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper...
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...
The advantages of multi-classification schemes based on decomposition strategies, and especially the One-vs-One framework, have been stressed even for those algorithms that can address multiple classes. However, there is an inherent hitch for the One-vs-One learning scheme related to the decision process: the non-competent classifier problem. This issue refers to the case where a binary classifier...
Enterprise in financial trouble is a comprehensive event and the enterprise financial situation can be reflected through the liquidity ratio, earnings per share and net assets per share and cash content per share. Artificial neural network method is used to establish the financial early warning model to find the potential financial crisis at an early age. The experiment results show that BP neural...
There is a need for rapid response during disasters. However, there is a paucity of training data which leads to classification models that do not generalize well. If the pre disaster data is used to augment the training data, the models perform poorly due to statistical distribution differences between pre and post disaster conditions. Also, it is challenging to analyze large areas for identifying...
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