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A Low Cost Implementation of Multi-label Classification Algorithm Using Mathematica on Raspberry Pi

Procedia Computer Science 2015年 46卷 306-313页

作者： Neethu John R. Surya R. Ashwini S. Sachin Kumar K.P. Soman Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Campus Tamilnadu-641112 India

Implementation of data mining algorithms with low cost is one of the challenging tasks in the present world of massively increasing data. The key idea of this paper is to utilize the functionalities of Mathematica which is freely accessible on Raspberry Pi for the purpose of implementing Multi-label classification algorithm with low cost. With the facilities available in Mathematica software for Raspberry Pi, the line of code required for implementing data mining algorithms can be reduced sufficiently. Use of Random Kitchen Sink algorithm improves the accuracy of Multi-label classification and brings improvement in terms of memory usage for large dataset.

关键词： Multi-label classification Mathematica Raspberry Pi Random Kitchen Sink Data Mining

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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Fast Fourier Transform and Nonlinear Circuits Based Approach for Smart Meter Data Security

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Procedia Technology 2015年 21卷 287-294页

作者： M. Jocelyn Babu V. Sowmya K.P. Soman Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Campus Tamilnadu-641112 India

Smart meters take measurements at minute intervals of time. The energy disaggregation of smart meter data provides accurate information regarding the electricity consumption but also reveals detailed information pertaining to the customer. Load signatures-that is unique for every appliance can be used to declare the behavioral pattern of the customers such as their sleep-wake cycles, usage period of various appliances, time during which the house is empty and by far, which particular channel is being viewed on the television. The smart meter data is transmitted to the utility company at regular intervals of time over the internet. Data that has such potential to access private information can be tampered with, by an undesirable third party during transmission. This is a vital privacy threat to the customer and has hyped the researches pertaining to smart meter data security in the recent times. To assure security, we introduce an algorithm that aims at encryption of data prior to transmission. The algorithm employs the FFT algorithm and various nonlinear systems to generate chaotic signals. The obtained chaotic signal is amalgamated with a transformed version of the smart meter data and securely transmitted over a suitable network. The proposed algorithm is tested on a real household's power signal data.

关键词： Fast Fourier Transform Chua circuit Rossler attractor Chen-Lee attractor Hash code

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ADMM Based Algorithm for Spike and Smooth Signal Separation using Over-Complete Dictionary

ADMM Based Algorithm for Spike and Smooth Signal Separation ...

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International Conference on Communications and Signal Processing

作者： K. Harikumar S. Athira Yedu C. Nair V. Sowmya K. P. Soman Centre for Excellence in Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham

ISBN: (纸本)9781479980826

In signal processing, many a time people deal with smooth stationary signals mixed with sharp spikes and most of the time their analysis demands separation of the smooth and spike elements. In this paper, we propose a methodology for this kind of separation based on the well-known notion of using an over complete dictionary to define an underdetermined system of linear equations and picking out its sparsest solution. The Alternating Direction Method of Multipliers (ADMM) framework is proposed for formulating and solving this optimization problem. The study of the performance of the algorithm with respect to certain signal parameters is also included. Performance of the algorithm was tested for different sparsity values at different signal energies and the results are reported. Cases involving group sparsity is also studied.

关键词： ADMM Optimization Over-complete dictionary Smooth Sparsity Spike

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A synchronised tree adjoining Grammar for English to Tamil Machine Translation

A synchronised tree adjoining Grammar for English to Tamil M...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Vijay Krishna Menon Rajendran S Soman K P Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Coimbatore India

ISBN: (纸本)9781479987931

Tree adjoining Grammar (TAG) is a rich formalism for capturing syntax and some limited semantics of Natural languages. The XTAG project has contributed a very comprehensive TAG for English Language. Although TAGs have been proposed nearly 40 years ago by Joshi et al, 1975, their usage and application in the Indian Languages have been very rare, predominantly due to their complexity and lack of resources. In this paper we discuss a new TAG system and methodology of development for Tamil Language that can be extended for other Indian languages. The trees are developed synchronously with a minimalistic grammar obtained by careful pruning of XTAG English Grammar. We also apply Chomskian minimalism on these TAG trees, so as to make them simple and easily parsable. Furthermore we have also developed a parser that can parse simple sentences using the above mentioned grammar, and generating a TAG derivation that can be used for dependency resolution. Due to the synchronous nature of these TAG pairs they can be readily adapted for Formalism based Machine Translation (MT) from English to Tamil and vice versa.

关键词： Grammar Syntactics Semantics Informatics computational linguistics Natural languages Complexity theory

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Heart murmur detection and classification using wavelet transform and Hilbert phase envelope

Heart murmur detection and classification using wavelet tran...

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National Conference on Communications (NCC)

作者： V. Nivitha Varghees K. I. Ramachandran Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham University Tamilnadu India

Detection and classification of heart murmurs play an important role in accurate diagnosis of different types of heart dysfunctions. In this paper, we present a noise-robust method for detection and classification of heart murmurs using stationary wavelet transform (SWT) and Hilbert phase envelope. The proposed method consists of five major stages: SWT based PCG signal decomposition for identifying heart sound (HS) including S1, S2, S3 and S4, and heart murmur(HM) subbands, Hilbert phase envelope based boundary determination, temporal feature extraction, murmur detection and classification rule. The boundaries of local acoustic HS segments are determined using the positive slope of instantaneous phase waveform of the smooth absolute envelope. The temporal features such as amplitude, duration, zerocrossing rate, interval, onset and offset time-instants of the detected HS and HM segments are used at the classification stage. The performance of the proposed method is tested and validated using a wide variety of normal and pathological signals containing different patterns of heart sounds and murmurs. The method achieves a probability of correctly detecting HM segments P ms =100%, a probability of correctly detecting HS segments P hs =97.33% and probability of falsely detecting segments P fs =1.33% for SNR value of 15 dB, and murmur classification accuracy ranging from 82.76% to 100%.

关键词： Heart Phonocardiography Hafnium Feature extraction Signal to noise ratio Signal resolution Acoustics

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Hyperspectral image classification using Random Kitchen Sink and Regularized Least Squares

Hyperspectral image classification using Random Kitchen Sink...

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International Conference on Communications and Signal Processing

作者： Nikhila Haridas Sowmya V Soman K.P Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu

ISBN: (纸本)9781479980826

Kernel machines has gained considerable attention in the field of remote sensing for solving machine learning tasks, particularly in classification. Despite the fact that, kernel based methods produce comparatively better performance than traditional learning approaches, they are computationally expensive and requires large memory storage. In recent years, the concept of random features was introduced in kernel machines to solve this problem. This paper presents a new framework for hyperspectral image classification using Random Kitchen Sink (RKS) and Regularized Least Squares (RLS) classifier. The study shows that randomized features are economically powerful tool for hyperspectral image classification which produces significant improvement in classification accuracy. The proposed approach is tested on two standard hyperspectral datasets namely, Salinas-A and Indian Pines subset scene acquired by Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor. A statistical comparison of the accuracies obtained on standard hyperspectral data with and without using Random Kitchen Sink algorithm for Regularized Least Squares classifier is analysed to show the effectiveness of the proposed method. The experimental results shows that the proposed method leads to improvement in Overall Accuracy from 85.12% to 98.58% and Kappa Coefficient from 0.8154 to 0.9822 for Salinas-A data scene. The analysis of Indian Pines subset scene shows that the proposed work results in significant improvement in Overall Accuracy from 62.76% to 93.79% and Kappa Coefficient from 0.5061 to 0.9160. The result analysis proves that random features of hyperspectral data as input to a standard linear classifier without the aid of any preprocessing produces better classification accuracy.

关键词： Standards Kernel Xenon Hyperspectral imaging Accuracy Splines (mathematics)

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Recursive Variational Mode Decomposition Algorithm for Real Time Power Signal Decomposition

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Procedia Technology 2015年 21卷 540-546页

作者： Soman K.P. Prabaharan Poornachandran Athira S. Harikumar K. Center for excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Amrita Centre for Cyber Security systems and Networks Amrita Vishwa Vidyapeetham

Conventional methods of signal decomposition are observed to fail in power system applications and computationally intensive algorithms like EMD, VMD, EWT are found to give better performance. The heavy computations associated with them restricts their use in real time applications and stream processing. This paper presents a recursive block processing technique for real time signal decomposition. The use of recursive FFT and the clever initializations of the center frequencies in the existing VMD algorithm helps in reducing the computational complexity and hence speeds up the process. This low complexity algorithm was tested on synthetically generated power signals and the results were observed to be consistent with the existing VMD algorithm.

关键词： Recursive FFT VMD computational complexity power signal decomposition.

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An Optimization Algorithm for Voltage Flicker Analysis

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Procedia Technology 2015年 21卷 589-595页

作者： Athira S. Harikumar K. Soman K.P. Prabaharan Poornachandran Center for excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Amrita Centre for Cyber Security systems and Networks Amrita Vishwa Vidyapeetham

Maintenance of power quality standards is a critical issue in the electrical distribution networks today. Proper quality can be maintained only by continuously monitoring and analyzing the signals of interest so as to identify the sources of distortion. This paper presents an algorithm for analysis of voltage flickers in power signals, a common phenomenon in most of the networks involving heavy loads. The low frequency component responsible for modulating the power signal is extracted using an optimization algorithm. Further the algorithm separately identifies the signals affecting the fundamental component and the harmonics present in the power signal.

关键词： Flicker low freqency envelope optimization.

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Implementation of CDMA in GNU Radio

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Procedia Computer Science 2015年 46卷 981-988页

作者： P. Kavitha K. Meera Mohan R. Surya R. Gandhiraj K.P. Soman Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Coimbatore India

This paper proposes the real time implementation of CDMA, a multiple access technique, which brings forth the complete bandwidth usage by spreading the data of same transmitted power, over the whole bandwidth thereby ensuring safe communication and preventing the occurrence of jamming. GNU Radio is a software defined radio which puts experiments into practice using software rather than the normal hardware implementation. Blocks for data spreading, code despreading with and without code tracking are created using Zero Correlation Zone code (ZCZ, a combination of ternary codes that is 1, 0 and -1 which is specified in the program). Thus the real time implementation of CDMA checks the feasibility of an asynchronous multiuser communication among users with good results.

关键词： CDMA Spread Spectrum Zero Correlation Zone(ZCZ) Auto Correlation Cross correlation Software Defined Radio(SDR) GNU Radio Companion(GRC)

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