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Phase and Amplitude Coupling Feature Extraction and recognition of Ictal EEG using VMD 4

Phase and Amplitude Coupling Feature Extraction and Recognit...

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IEEE 4th international conference on Knowledge-Based Engineering and Innovation (KBEI)

作者： Ahmadi, Amirmasoud Behroozi, Mahsa Shalchyan, Vahid Daliri, Mohammad Reza Iran Univ Sci & Technol Sch Elect Engn Biomed Engn Dept Tehran Iran

ISBN: (纸本)9781538626405

Automatic classification of epileptic signals plays a critical role in long term monitoring and diagnosis. this study provides a novel feature extraction and pattern recognition technique for classifying epilepsy using phase-phase, phase amplitude and amplitude-amplitude coupling based on Variational Mode Decomposition(VMD). the EEG signal was decomposed to band limited intrinsic mode functions (BLIMFs) in the first place. Second, a coupling of BLIMFs was extracted as a feature and after determining an optimal feature two classifiers, strictly speaking, Random Forest and Extreme learning machine have classified the inputs. In order to evaluate and compare the performance of the new presented method Bonn University dataset of epilepsy have been used. Results indicate a more desirable performance than any other applied method suggested previously.

关键词： Epileptic Variational Mode Decomposition (VMD) Phase-Phase Coupling (PPC) Amplitude-Amplitude Coupling (AAC) Phase-Amplitude Coupling (PAC) Extreme learning machine (ELM) Random Forest Classifier

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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7th international conference on Computer and Communication Technology, ICCCT 2017

ISBN: (纸本)9781450353243

the proceedings contain 25 papers. the topics discussed include: global software development: a design framework to measure the risk of the global practitioners;a discourse linking tool for English language texts comprising lexicon building and ontology creation;three-dimensional SVPWM using Α-Β-Γ frame for solving 4-leg active shunt filter;travel recommendation system using geotagged photos;HDML : habit detection with machine learning;calculus of concurrent probabilistic reversible processes;flip error elimination and core map selection in patch and stitch algorithms for localization in wireless sensor network;comparative study for proposed algorithm for all-optical network with negative acknowledgement (AO-NACK);a load balancing cross clustering approach in wireless sensor network;component-based self-healing algorithm with dynamic range allocation for fault-tolerance in WSN;optimal sub-path selection for maximum data gathering using mobile sink in WSN;an improved tour length minimization for single hop data gathering in wireless sensor network;copy-move tampering detection based on local binary pattern histogram Fourier feature;a handheld gun detection using faster R-CNN deep learning;an automated approach for volume fraction measurement of titanium alloy using digital image processing;recognition of bird species from their sounds using data reduction techniques;designing an efficient methodology based on entropy-TOPSIS for evaluating efficiency of cloud services;and generation and proliferation of random directed acyclic graphs for workflow scheduling problem.

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Android malicious code detection and recognition based on depth learning

Android malicious code detection and recognition based on de...

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2017 4th international conference on machinery,Materials and Computer(MACMC 2017)

作者： YangJing Hubei University of Police

ISBN: (纸本)9781510871038

At present, most malicious code detection methods are based on the shallow machine learning model. these shallow machine learning methods are simple in the modeling process, and restrict the complex functions and classification problems. In order to improve the accuracy of Andriod malicious code detection and recognition, an algorithm of malicious code detection and recognition of the deep learning has been put forward in this paper, this algorithm based on neural network training and learning model. through the learning and training of malicious code sample data, the static, dynamic characteristics and malicious application characteristics of malicious code data are analyzed, including privilege feature, API feature and Op Codes characteristic data. the comprehensive performance of the algorithm was tested, the test results indicate that using depth learning detection algorithm, Andriod malicious code identification accuracy is higher, and false detection rate and undetected rate are low, which is a highly efficient and reliable malicious code detection and recognition algorithm.

关键词： malicious code detection algorithm depth learning neural network Andriod terminal

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Dimensionality reduction via representation and affinity learning

Dimensionality reduction via representation and affinity lea...

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international conference on Systems and Informatics (ICSAI)

作者： Miao Qi Shuang Lu Xing Huang Lin Yang Chengrong Gong Jianzhong Wang Yuanyuan Gu Key Laboratory for Applied Statistics of MOE Northeast Normal University Chanzchun China School of Information Science and Technology Northeast Normal University Chanzchun China College of Humanities and information Changchun University of Technology Chanzchun China

Because graphs are crucial in modeling the data distribution or structure, graph-based dimensionality reduction methods have been widely employed in many machine learning and pattern recognition applications. However, in most existing graph-based dimensionality reduction techniques, the graph is usually specified empirically or learned from raw data, which may deteriorate their performances. In this paper, we propose a novel algorithm named Dimensionality Reduction via Representation and Affinity learning (DRRAL), which adaptively constructs graph and learns the projection matrix for dimensionality reduction. Our algorithm takes noise and different local structure of each sample into account. Moreover, an efficient optimization strategy based on the iterative updating Augmented Lagrange Multiplier (ALM) and eigenvectors is developed to solve the proposed DRRAL. Extensive experimental results on five databases are carried out to verify the feasibility and effectiveness of proposed approach.

关键词： Algorithm design and analysis Optimization databases Classification algorithms Manifolds

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Smart solution to improve water-energy nexus for water supply systems 18th

Smart solution to improve water-energy nexus for water suppl...

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18th international conference on Water Distribution System Analysis (WDSA)

作者： Helmbrecht, Jorge Pastor, Jordi Moya, Carolina Inclam Grp Water Idea Samaria 4 Madrid 28009 Spain Inclam Grp INCLAM Paseo Arco Ladrillo 882 Planta Of 15 Valladolid 47008 Spain

In the last years, there has been a great interest in the complex relations between energy and water, known as the Water-Energy Nexus [1]. Natural resources, such as energy and water, enable economy growth and support quality of life. the Water-Energy Nexus is considered as one of the most important multidisciplinary challenges [2] that the global growing water market [3] has to face in the forthcoming years. Currently, many water systems are not managed sustainably enough. Water Utilities face other challenges, such as infrastructure aging and poor cost-recovery, leading to a lack of finance for O&M (Operation and Maintenance). Energy is required in all stages of water production and distribution, from pumping and treatment to transportation. Energy costs are a top-of-mind concern for water utilities, regardless of geography, size and level of water network efficiency [4]. On the other hand, Water Utilities are having a hard time to either improve their services or expand their network to unserved neighborhoods in developing countries. the current trend of water transmission system to the creation of DMAs (District Metered Areas) offers great possibilities of non-structural solutions that use existing data and transform them into useful information to support decision making. the Smart Metering and the use of large amounts of data from a network enhance the use of software for decision support, but it is not the only way. Smart Solutions can also be applied to networks with less recorded data, which would enhance operators' knowledge to these data, turn them into useful information for decision-making either for the operation or the maintenance and network design. In this scope, a Smart Solution is presented. It is developed combining key factors of the energy consumption and the water supply into water networks management to obtain improvements from both water and energy fields. this non-structural solution increases resource efficiency and environmental performan

关键词： Nexus water energy smart water networks urban water distribution network management Energy Management in water Distribution Systems Water Utilities Artificial Intelligence machine learning pattern recognition Business Rules

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Deep Neural Networks for Emergency Detection 26th

Deep Neural Networks for Emergency Detection

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26th international conference on Artificial Neural Networks (ICANN)

作者： Cipolla, Emanuele Rizzo, Riccardo Vella, Filippo CNR Natl Res Council Italy ICAR Inst High Performance Comp & Networking Arcavacata Di Rende Italy

ISBN: (纸本)9783319686004;9783319685991

the increasing deployment of sensor networks has resulted in an high availability of geophysical data that can be used for classification and predictions of environmental features and conditions. In particular, detecting emergency situations would be desirable to reduce damages to people and things. In this work we propose the use of a deep neural network architecture to detect pluvial-flood emergencies, building upon and extending our previous works [1, 2] in which we gathered a large set of rain measures coming from a sensor and surveillance network deployed in the last decade in the Italian region of Tuscany and built a database of verified emergency events using a manually annotated set of resources found on the World Wide Web. We used a stacked LSTM [3] network to classify 4-day-long sequences (the measures for a given day and the three days prior) of pluvial measurements gathered from the whole set of stations belonging to Servizio Idrogeologico Regionale in Tuscany. After empirical tests, we chose two 2 LSTM layers with 256 outputs each for the hidden part of the network. Using multiple layers we exploit the abstraction power for pattern recognition in time sequences that has been previously recognized for LSTMs: lower layers are able to detect the most significant variations, while the higher ones use these patterns to spot emergency events. As they are very infrequent, a balanced subset of quiet days has to be considered to build a binary classifier to avoid overfitting. To increase the number of relevant true examples we performed a linear interpolation of existing sequences, generating 10 new examples for each original one. After training the network using 560 examples, we tested its performance using 1276 sequences. We had 131 true positives, 1074 true negatives, 64 false positives and 7 false negatives. this leads to a precision of 0.67 and a recall of 0.95, so the F₁-score is 0.79. Accuracy is also high (0.94). We are planning to train this

关键词： LSTM Neural network Rain Flood Emergency Deep learning

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Real time direction-sensitive fall detection system using accelerometer and learning classifier

Real time direction-sensitive fall detection system using ac...

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international conference on Advances in Electrical Engineering (ICAEE)

作者： Shaikh Farhad Hossain Md. Zahurul Islam Md. Liakot Ali Institute of Information and Communication Technology Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

Continuous fall recognition framework screens the day by day action of particularly elderly individuals to enroll somebody's assistance as quick as conceivable if there should be an occurrence of crisis. this paper presents a real-time fall detection using a single 3D commercial accelerometer (3DCA) and support vector machine learning algorithm (SVMLA). In past, two machine learning (ML) based calculations SVMLA and k-Nearest Neighbors (K-NN) were executed for mandate fall discovery in reproduction. Among the two strategies, SVMLA give better exhibitions which prompts 96.45% of exactness utilizing PCA mean and standard deviation highlights, surpassing the exhibitions detailed in the writing. the performances of the developed system in real time are also evaluated and they are found same accuracy, precision and recall. When applied to experimental data from 13 male subjects, the real time system discriminates between falls and activities of daily living (ADL) with same level like simulation. the system utilizes privacy preserving sensor. the system is reliable, user friendly and cost effective with less technical error rate and high classification accuracy.

关键词： Real-time systems three-dimensional displays Servers Feature extraction Senior citizens Standards

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Determination of expected output signals of the neural network model intended for image recognition

Determination of expected output signals of the neural netwo...

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international Scientific-Practical conference Problems of Infocommunications Science and Technology

作者： Oleksandr Oksiiuk Liudmyla Tereikovska Ihor Tereikovskiy Taras Shevchenko National University of Kiev Kiev Ukraine Kyiv National University of Construction and Architecture Kiev Ukraine National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kiev Ukraine

ISBN: (纸本)9781538609842

the article is devoted to the problem of further development of the methodology of using neural network models based on a multilayer perceptron for image recognition. It is shown that a significant shortcoming of modern methodologies is the insufficient efficiency of learning neural network models, is associated with the insufficient quality of processing statistical data that are used in the formation of the parameters of the training example. One of the important reasons for this is the lack of correlation between the expected output of the training examples with the closeness of the standards of the classes that should be recognized. It is suggested to improve the quality of the training examples by using the method of neural network coding the magnitude of the expected output signal of the training examples, which will allow taking into account the proximity of the standards of recognized classes in such a signal. A method for determining the encoding of the expected output signal is developed, which provides for estimating the closeness of standards using a probabilistic neural network. the expediency of using a probabilistic neural network is determined on the basis of the low resource intensity of its training. In addition, in the training examples of such a network, the expected output signal can be represented not by a number, but by the name of the expected class. the mathematical apparatus of the method is formed. As a result of numerous experiments it was shown that the application of the developed method makes it possible to reduce the number of training iterations in 1.3-1.5 times to achieve an allowable error of training within 1%. It is shown that the prospects for further research are to adapt the method to neural network systems used in industries in which the computational complexity of learning is of critical importance.

关键词： Neural networks conferences Image recognition Handwriting recognition machine learning Acoustics Speech

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Influence of Legal Form and Non-Anonymous Ownership Structure on Corporate Financial Performance 4

Influence of Legal Form and Non-Anonymous Ownership Structur...

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4th international conference on Management, Leadership and Governance (ICMG)

作者： Pudil, Pavel Pirozek, Petr Somol, Petr Komarkova, Lenka Univ Econ Fac Management Jindrichuv Hradec Prague Czech Republic Masaryk Univ Fac Econ & Adm Brno Czech Republic

ISBN: (纸本)9781910810859

the paper represents a continuation of our previous results, which were closely linked to the topic of automated search for factors of corporate competitiveness and financial performance. As opposed to the results presented at ICMLG 2014 and ECMLG 2014, the current research deals with other characteristics of organizations not investigated before and related to Corporate Governance, like their legal form, the international diversity of top management (domestic only or also foreign) and particularly the non-anonymity of the ownership structure. the data were gathered in the period 2011 to 2013 from 222 companies with various legal forms. the main purpose of our research was to investigate which of these characteristics have an impact on corporate financial performance that has been assessed by the Return on Assets (ROA) index. the paper attempts to answer the research question of whether the ownership structure, particularly the fact whether the organization or enterprise owners are or are not anonymous, has a major influence on corporate financial performance. the methodology used in analysing and processing the data had to respect the fact that the characteristics (variables) of the companies which were individually investigated are not mutually independent, thus multidimensional methods have to be used. therefore, we used here our non-linear kernel regression model which had already been successfully verified, having been developed in the field of statistical pattern recognition. Prediction error of the proposed model is then used as a feature selection criterion in the process of identifying factors that affect Corporate Governance and the financial performance the most. the actual feature selection algorithm applied is the recent Dependency-Aware Feature Selection algorithm due to its good performance on low-sample high-dimensional data and its suitability for use with the regression model. the results presented in the paper demonstrate that the type of ownershi

关键词： anonymous ownership legal form of companies enterprises performance machine learning approach pattern recognition feature selection

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Fall recognition using Wearable Technologies and machine learning Algorithms 4

Fall Recognition using Wearable Technologies and Machine Lea...

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4th IEEE international conference on Big data (Big data)

作者： Harris, Austin True, Hanna Hu, Zhen Cho, Jin Fell, Nancy Sartipi, Mina Univ Tennessee Chattanooga Dept Comp Sci & Engn Chattanooga TN 37403 USA Univ Tennessee Chattanooga Dept Phys Therapy Chattanooga TN 37403 USA

ISBN: (纸本)9781467390057

Falls are common and dangerous for the elderly or individuals with decreased independence or functional limitations. Fall recognition is extremely important for fallers, healthcare providers, and society. Immediate fall recognition triggers emergency services and potentially decreases individuals time with injury without care. Acute post-fall intervention works to mitigate life threatening fall consequences, decrease fall risk through rehabilitation, and improve quality of life. Extended from our research on real-time fall risk estimation with the functional reach test and Timed Up and Go test built in mStroke, a real-time and automatic mobile health system for post-stroke recovery and rehabilitation, our investigation here is expanded to include fall recognition by taking advantage of wearable technologies and machine learning algorithms. Up to three wearable sensors are employed to acquire raw motion data related to activities of daily living or falls. Feature selection and classification on the basis of machine learning algorithms are explored for fall recognition. the fall recognition performances are presented to justify their accuracy and reliability. Meanwhile, the effects of sensor placement/ location and the feature number on the recognition performance are also discussed in this paper.

关键词： fall recognition wearable feature selection machine learning

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