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32nd IEEE International Conference on Computer Design (ICCD)

作者： Kumar, Raghavan Burleson, Wayne Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA

ISBN: (纸本)9781479964925

Physically Unclonable Functions have emerged as a possible candidate to replace traditional cryptography. However, majority of the strong PUFs are vulnerable to modeling attacks. In this work, we take a closer look at the possible attacks on one of the strong PUF architectures known as Current-based PUFs, which exploit irregularities in transistor currents to generate unique signatures. We demonstrate that the fault-injection attacks when coupled with a machine learning (ML) algorithm can considerably push the limits of prediction accuracies. Based on simulations, we observed that the stand-alone ML algorithms suffer from error prone CRPs especially for higher length PUFs. In such scenarios, hybrid attacks exploiting the unreliable responses pushed the prediction accuracies up to 99% for higher length Current-based PUF circuits.

关键词： cryptography learning (artificial intelligence) current-based PUF error prone CRP fault-injection attacks hybrid attacks machine learning algorithm modeling attacks physically unclonable functions strong PUF architectures transistor currents Accuracy Circuit faults Integrated circuit modeling Logic gates Prediction algorithms Security Transistors

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Comparison of ML algorithms for Identification of Automated Number Plate Recognition 3

Comparison of ML Algorithms for Identification of Automated ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Bhardwaj, Dinesh Kaur, Harjinder Chandigarh Univ Mohali Dept Comp Sci & Engn Chandigarh India Golden Coll Engn & Tech Dept Elect & Commun Engn Gurdaspur India

ISBN: (纸本)9781479968961

Automated Number Plate Recognition (ANPR) has become essential part of traffic control system because of unconstrained increased of vehicles on roads which make it difficult to control. On or after analysis of diverse author's exertion, ANPR has six imperative parts like Image Acquisition, Pre-processing, edge detection, segmentation, feature extraction and recognition. Characters recognition is turn out to be difficult everyday jobs due to variations of number plate. The intentioned system is mainly focus on classification and recognition of characters using Kstar machine learning algorithm. The classification characters present better performance using Kstar. A comparative study is done between these classifiers based upon their considerations like average accuracy, precision, recall and F-measure. Performance is deliberated using precision, recall and F-measure. The results give you an idea about that K STAR algorithm completes better with Recognition Rate and it achieved an accuracy of 99%.

关键词： ANPR Kstar Pre-processing Segmentation Edge detection Feature extraction and recognition machine learning algorithm

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Detection and Utilization of Emotional State for Disabled Users

Detection and Utilization of Emotional State for Disabled Us...

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14th International Conference on Computers Helping People with Special Needs (ICCHP)

作者： Mohamad, Yehya Hettich, Dirk T. Bolinger, Elaina Birbaumer, Niels Rosenstiel, Wolfgang Bogdan, Martin Matuz, Tamara Fraunhofer Inst Appl Informat Technol FIT St Augustin Germany Eberhard Karls Univ Tubingen Dept Comp Engn Fac Sci Tubingen Germany Eberhard Karls Univ Tubingen Inst Med Psychol & Behav Neurobiol Tubingen Germany

ISBN: (纸本)9783319085968;9783319085951

In this paper, we present an experimental approach to design systems sensitive to emotion. We describe a system for the detection of emotional states based on physiological signals and an application use case utilizing the detected emotional state. The application is an emotion management system to be used for the support in the improvement of life conditions of users suffering from cerebral palsy (CP). The system presented here combines effectively biofeedback sensors and a set of software algorithms to detect the current emotional state of the user and to react to them appropriately.

关键词： Affective Computing machine learning algorithm Disabled Person Context Emotion Emotion Management User Interface E-learning Web-Based

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Human and machine annotation in the Orchive, a large scale bioacoustic archive

Human and machine annotation in the Orchive, a large scale b...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Ness, Steven Tzanetakis, George Univ Victoria Dept Comp Sci Victoria BC V8W 2Y2 Canada

ISBN: (纸本)9781479970889

Advances in computer technology have enabled the collection, digitization, and automated processing of huge archives of bioacoustic sound. Many of the tools previously used in bioacoustics research work well with small to medium-sized audio collections, but are challenged when processing large collections ranging from tens of terabytes to petabyte size. The Orchive is a system that assists researchers to listen to, view, annotate and run advanced audio feature extraction and machine learning algorithms on large bioacoustic archives. Annotation is one of the biggest challenges in our work. In this paper, we describe our efforts to utilize experts as well as citizen scientists to participate in the process of annotating recordings. The Orchive contains over 23,000 hours of orca vocalizations collected over the course of 30 years, and represents one of the largest continuous collections of bioacoustic recordings in the world. Manual annotation is practically impossible and therefore we investigate the effectiveness of a semi-automatic approach for extracting information from these recordings, and show various experimental results. Finally we have been able to apply our automatic analysis over the a large portion of the archive and describe the computational resources required. To the best of our knowledge this is the largest archive of bioacoustic data that has even been automatically analyzed.

关键词： audio signal processing bioacoustics data analysis Orchive advanced audio feature extraction algorithm annotating recordings automatic analysis bioacoustic data bioacoustic recordings bioacoustic sound human annotation large scale bioacoustic archive machine annotation machine learning algorithm manual annotation medium-sized audio collections orca vocalization Accuracy Kernel Logistics machine learning algorithms Support vector machines Whales machine learning algorithms bioacoustics automatic analysis audio signal processing archives Whale Logistics Support Vector Network ANNOTATIONS Computer technology Kernel Recordings Data analysis

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Characterization of Essential Proteins based on Network Topology in Proteins Interaction Networks

Characterization of Essential Proteins based on Network Topo...

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3rd International Conference on Mathematical Sciences

作者： Abu Bakar, Sakhinah Taheri, Javid Zomaya, Albert Y. Univ Kebangsaan Malaysia Fak Sains & Teknol Pusat Pengajian Sains Matemat Bangi 43600 Selangor Malaysia Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp\ Sydney NSW 2006 Australia

ISBN: (纸本)9780735412361

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest;it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

关键词： Neural networks machine learning algorithm essential proteins protein interaction networks

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High Productivity Data Processing Analytics Methods with Applications

High Productivity Data Processing Analytics Methods with App...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Riedel, M. Memon, A. S. Memon, M. S. Forschungszentrum Julich Juelich Supercomp Ctr D-52425 Julich Germany

ISBN: (纸本)9789532330816

The term 'big data analytics' emerged in order to engage in the ever increasing amount of scientific and engineering data with general analytics techniques that support the often more domain-specific data analysis process. It is recognized that the big data challenge can only be adequately addressed when knowledge of various different fields such as data mining, machine learning algorithms, parallel processing, and data management practices are effectively combined. This paper thus describes some of the 'smart data analytics methods' that enable a high productivity data processing of large quantities of scientific data in order to enhance the data analysis efficiency. The paper thus aims to provide new insights how various fields can be successfully combined. Contributions of this paper include the concretization of the cross-industry standard process for data mining (CRISPDM) process model in scientific environments using concrete machine learning algorithms (e.g. support vector machines that enable data classification) or data mining mechanisms (e.g. outlier detection in measurements). Serial and parallel approaches to specific data analysis challenges are discussed in the context of concrete earth science application data sets. Solutions also include various data visualizations that enable a better insight in the corresponding data analytics and analysis process.

关键词： data analysis data mining learning (artificial intelligence) big data analytics data management data mining mechanism data visualization high productivity data processing analytics method machine learning algorithm parallel processing smart data analytics method Analytical models Big data Concrete Context Data mining Data models Systematics data mining big data machine learning algorithms Data analysis Data visualisation data models PARALLEL PROCESSING (COMPUTERS) Data management ANALYSIS MODELS Concrete Statistical analysis

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Improving the recognition performance of NIALM algorithms through technical labeling 12

Improving the recognition performance of NIALM algorithms th...

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12th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC)

作者： Mathis, Marcel Rumsch, Andreas Kistler, Rolf Andrushevich, Aliaksei Klapproth, Alexander Lucerne Univ Appl Sci & Arts CEESAR iHomeLab CH-6048 Horw Switzerland

ISBN: (纸本)9780769552491

A myriad of different electrical devices populate a typical household nowadays. Non-intrusive appliance load monitoring (NIALM) is an approach to find out how much energy each of them consumes in order to take measures to improve the overall energy efficiency. This article describes the ongoing research on improving electric loads recognition performed by NIALM algorithms within the context of smart homes and intelligent environments. The recognition performance can be significantly improved by decreasing the number of categories to be analyzed. The authors studied several labeling methods to categorize and group loads in order to increase the overall recognition rate. 31 different devices have been measured and labeled in different device states. Their input curves have been compared with 5 different machine learning algorithms. The best results could be reached by dividing all the loads into groups with small divergence in their normalized current curve. This approach has significantly increased the performance of NIALM recognition algorithms.

关键词： NIALM NILM load disaggregation technical labeling device categorisation device labeling load recognition machine learning algorithm intelligent environment Wavelet Fourier Transformation

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A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

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BMC BIOINFORMATICS 2013年第1期14卷 1-13页

作者： Goodswen, Stephen J. Kennedy, Paul J. Ellis, John T. Univ Technol Sydney Sch Med & Mol Biosci Ithree Inst Sydney NSW 2007 Australia Univ Technol Sydney Sch Software Fac Engn & Informat Technol Sydney NSW 2007 Australia Univ Technol Sydney Ctr Quantum Computat & Intelligent Syst Sydney NSW 2007 Australia

Background: An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. Results: The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. Conclusions: Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.

关键词： Training Dataset Vaccine Candidate machine learning algorithm Benchmark Dataset Laboratory Validation

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A prediction model for Clostridium difficile recurrence

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JOURNAL OF COMMUNITY HOSPITAL INTERNAL MEDICINE PERSPECTIVES 2015年第1期5卷 26033页

作者： LaBarbera, Francis D. Nikiforov, Ivan Parvathenani, Arvin Pramil, Varsha Gorrepati, Subhash PinnacleHlth Hosp Dept Internal Med 205 S Front St 3rd Floor Harrisburg PA 17110 USA

Background: Clostridium difficile infection (CDI) is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR);however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR) from January 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF) to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

关键词： Random Forest machine learning algorithm hospital infection

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Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor algorithm

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FORESTS 2014年第7期5卷 1635-1652页

作者： Suchenwirth, Leonhard Stuemer, Wolfgang Schmidt, Tobias Foerster, Michael Kleinschmit, Birgit Tech Univ Berlin Geoinformat Environm Planning Lab D-10623 Berlin Germany GeoVille Informat Syst GmbH A-6020 Innsbruck Austria Thunen Inst Forest Ecosyst D-16225 Eberswalde Germany

Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of organic carbon (C-org) in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial C-org distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total C-org stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE). Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest C-org stocks. While vegetation C-org has very high RMSEs, outcomes for soil and total C-org stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations.

关键词： organic matter machine learning algorithm neural network Danube Floodplain RapidEye additional geodata Austria

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