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DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring

INFORMATION SCIENCES 2020年第0期516卷 401-418页

作者： Plawiak, Pawel Abdar, Moloud Plawiak, Joanna Makarenkov, Vladimir Acharya, U. Rajendra Polish Acad Sci Inst Theoret & Appl Informat Baltycka 5 PL-44100 Gliwice Poland Cracow Univ Technol Fac Comp Sci & Telecommun Dept Informat & Commun Technol Warsaw 24 StF-3 PL-31155 Krakow Poland Univ Quebec Montreal Dept Comp Sci Montreal PQ H2X 3Y7 Canada AGH Univ Sci & Technol Dept Biocybernet & Biomed Engn Fac Elect Engn Automat Comp Sci & Biomed Engn PL-30059 Krakow Poland Ngee Ann Polytech Dept Elect & Comp Engn Clementi 599491 Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Clementi 599491 Singapore Kumamoto Univ IROAST Kumamoto Japan

Credit scoring (CS) is an effective and crucial approach used for risk management in banks and other financial institutions. It provides appropriate guidance on granting loans and reduces risks in the financial area. Hence, companies and banks are trying to use novel automated solutions to deal with CS challenge to protect their own finances and customers. Nowadays, different machine learning (ML) and data mining (DM) algorithms have been used to improve various aspects of CS prediction. In this paper, we introduce a novel methodology, named Deep Genetic Hierarchical Network of Learners (DGHNL). the proposed methodology comprises different types of learners, including Support Vector machines (SVM), k-Nearest Neighbors (kNN), Probabilistic Neural Networks (PNN), and fuzzy systems. the Statlog German (1000 instances) credit approval dataset available in the UCI machine learning repository is used to test the effectiveness of our model in the CS domain. Our DGHNL model encompasses five kinds of learners, two kinds of data normalization procedures, two extraction of features methods, three kinds of kernel functions, and three kinds of parameter optimizations. Furthermore, the model applies deep learning, ensemble learning, supervised training, layered learning, genetic selection of features (attributes), genetic optimization of learners parameters, and novel genetic layered training (selection of learners) approaches used along with the cross-validation (CV) train-ingtesting method (stratified 10-fold). the novelty of our approach relies on a proper flow and fusion of information (DGHNL structure and its optimization). We show that the proposed DGHNL model with a 29-layer structure is capable to achieve the prediction accuracy of 94.60% (54 errors per 1000 classifications) for the Statlog German credit approval data. It is the best prediction performance for this well-known credit scoring dataset, compared to the existing work in the field. (C) 2019 the Authors. Publish

关键词： Credit scoring machine learning Data mining Ensemble learning Deep learning Genetic algorithm Feature extraction and selection

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E-HEALth APPROACHES FOR DEVELOPING COUNTRIES 5

E-HEALTH APPROACHES FOR DEVELOPING COUNTRIES

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5th IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Kaur, Amandeep Gupta, Anuj Kumar IKGPTU KAPURTHALA Kapurthala Punjab India CGC Comp Sci & Engn Landran India

ISBN: (纸本)9781728139883

Online and telecommunication services reduce the gap between level of health care service quality between villages and cities. In this research work, a study of five e-health models has been done. these models are simultaneously in use these days in developing countries such as India. the models are discussed in terms of their evolution and their application in various kinds of eHealth services. the paper gives definitive information on the technical standards and stacks required to build such eHealth communication models and services. Our study has found that most of these models are running concurrently in most of the developing countries and there is no absolute winner when they are compared with each *** all depends upon the level of development in the respective country and commitment of their governments to provide affordable health care services to the masses. the paper also discusses the use of Cloud-based medical sensors, IoT and Fog computing approach as well the use of data mining and machine learning modelling to construct an e-Health System. In the second part of this paper, mathematical models and algorithms to diagnose the onset of health problems are given. the outcome of this study shows that there is a need for statistical as well as machine learning models to build eHealth solutions in developing countries. After this research a new model has been proposed which aims at providing better and affordable healthcare services and coincidentally benefits various other sectors.

关键词： E-health Cloud machine learning

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A Review on Automatic signal Classification Techniques for Software Defined Radios 5

A Review on Automatic Signal Classification Techniques for S...

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5th IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Manisha, Ghunsar Marriwala, Nikhil Kurukshetra Univ Univ Inst Engn & Technol Dept Elect & Commun Engn Kurukshetra Haryana India

ISBN: (纸本)9781728139883

In today's generation automatic modulation recognition for software defined radio (SDR) is a hot topic of research. their ability like re-configurability, flexibility, high capacity, improved bandwidth efficiency, rapid up-gradation of technology etc., has made it popular and widely acceptable. this paper represents a review about SDR infrastructure, modulation recognition problem and various existing modulation recognition techniques. this paper summarizes the work and contribution of various researchers in this field. this paper also holds discussion about various machine learning tools and their advantages and disadvantages. this paper helps the reader to have brief knowledge about development of SDR and automatic modulation recognition technique.

关键词： Software radio

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Arrhythmia Detection - A machine learning based Comparative Analysis with MIT-BIH ECG Data 5

Arrhythmia Detection - A Machine Learning based Comparative ...

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IEEE 5th international conference for Convergence in Technology (I2CT)

作者： Singh, Vishavpreet Tewary, Suman Sardana, Viren Sardana, H. K. Acad Sci & Innovat Res AcSIR CSIO Chandigarh India CSIR Cent Sci Instruments Org Chandigarh India

ISBN: (纸本)9781538680759

Cardiovascular arrhythmias are most common cardiac problem in the world. this work while focusing on development of automated detection and classification of arrhythmia using MIT-BIH database compared five machine learning algorithms with three different features. Pre-processing followed by beat detection is applied on one channel to get individual beats having PQRST window. three features (viz. simple amplitude feature of 300 samples, area feature with non-overlapped sliding window, and area feature with overlapped sliding window) were used for five classifiers used in this study. It was observed that Artificial Neural Network with amplitude features gave best result with 99.59% accuracy which is comparable to state-of-the art methods.

关键词： ANN Arrhythmia Decision Tree ECG MIT-BIH machine learning signal processing Random Forest SVM

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Dataset of Raw and Pre-processed Speech signals, Mel Frequency Cepstral Coefficients of Speech and Heart Rate Measurements 5

Dataset of Raw and Pre-processed Speech Signals, Mel Frequen...

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5th IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Usman, Mohammed Ahmad, Zeeshan Wajid, Mohammad King Khalid Univ Dept Elect Engn Abha Saudi Arabia Aligarh Muslim Univ Dept Elect Engn ZHCET Aligrah 202002 India

ISBN: (纸本)9781728139883

Heart rate is an important vital sign used in the diagnosis of many medical conditions. Conventionally, heart rate is measured using a medical device such as pulse oximeter. Physiological parameters such as heart rate bear a correlation to speech characteristics of an individual. Hence, there is a possibility to measure heart rate from speech signals using machine learning and deep learning, which would also allow non-invasive, non-contact based and remote monitoring of patients. However, to design such a scheme and verify its accuracy, it is necessary to collect speech recordings along with heart rates measured using a medical device, simultaneously during the recording. this article provides a dataset as well as the procedure used to create the dataset which could be used to facilitate research in developing techniques to estimate heart rate accurately by observing speech signal.

关键词： Heart rate measurement speech as a biomedical signal heart rate from speech speech biomedical dataset speech-heart rate dataset

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Investigating Deep CNNs Models Applied in Kinship Verification through Facial Images 5

Investigating Deep CNNs Models Applied in Kinship Verificati...

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5th international conference on Frontiers of signal processing (ICFSP)

作者： Chergui, Abdelhakim Ouchtati, Salim Mavromatis, Sebastien Bekhouche, Salah Eddine Sequeira, Jean Skikda Univ Elect Res Lab Skikda LRES Skikda Algeria Aix Marseille Univ CNRS UMR 7020 LIS Lab Marseille France Univ Oulu Ctr Machine Vision & Signal Anal Oulu Finland

ISBN: (纸本)9781728152585

the kinship verification through facial images is ana ctive research topic due to its potential applications. In this paper, we propose an approach which takes two images as input then give kinship result (kinship / No-kinship) as an output. our approach based on the deep learning model (ResNet) for the feature extraction step, alongside with our proposed pair feature representation function and RankFeatures (Ttest) for feature selection to reduce the number of features finally we use the SVM classifier for the decision of kinship verification. the approach contains three steps which are: (1) face preprocessing, (2) deep features extraction and pair features representation (3) Classification. Experiments are conducted on five public databases. the experimental results show that our approach is comparable with existed approaches.

关键词： Kinship veri cation Deep learning CNNs ResNet Rank Features Ttest SVM

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Interferometric radar for activity recognition and benchmarking in different radar geometries 5

Interferometric radar for activity recognition and benchmark...

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5th IET international Radar conference, IET IRC 2020

作者： Zhou, Boyu Le Kernec, Julien Yang, Shufan Fioranelli, Francesco Romain, Olivier Zhao, Zhiqin Department of Computer Science The University of Hong Kong Hong Kong Hong Kong School of Information and Communication University of Electronic Science and Technology of China Chengdu China James Watt School of Engineering University of Glasgow Glasgow United Kingdom ETIS – Signal and Information Processing lab University Cergy-Pontoise Cergy France MS3-Microwave Sensing Signals and Systems TU Delft Delft Netherlands

ISBN: (纸本)9781839535406

Radar micro-Doppler signatures have been proposed for human activity classification for surveillance and ambient assisted living in healthcare-related applications. A known issue is the performance reduction when the target is moving tangentially to the line-of-sight of the radar. Multiple techniques have been proposed to address this, such as multistatic radar and to some extent, interferometric radar. A simulator is presented to generate synthetic data representative of 8 different radar systems (including configurations as monostatic, multistatic, and interferometric) to quantify classification performances as a function of aspect angles and deployment geometries. this simulator allows an unbiased performance evaluation of the different radar systems. 6 human activities are considered with signatures originating from motion-captured data of 14 different subjects. the results show that interferometric radar data with fusion outperforms the other methods with over 97.6% accuracy consistently across all aspect angles, as well as the potential for simplified indoor deployment. © 2020 IET conference Proceedings. All rights reserved.

关键词： machine learning

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Hardware Implementation of Automated Seizure Detection System using EEG signals and Edge Computing 6

Hardware Implementation of Automated Seizure Detection Syste...

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6th international conference on Trends in Electronics and Informatics (ICOEI)

作者： Torse, Dattaprasad A. Khanai, Rajashri Pai, Krishna Iyer, Sridhar KLE Dr MSSCET Belagavi India

ISBN: (纸本)9781665483285;9781665483292

Epilepsy is an unrelenting, constantly recurring neurological disorder characterized by abnormal electrical activity in the brain. Epilepsy acts as a condition with inconsistent symptoms involving spasmodic abnormal electrical activities in the brain. Clinical data pertinent to such abnormalities is nonlinear, non-stationary, and multi-dimensional. the processing and analysis of such data generates a combination of computing research challenges. Electroencephalogram (EEG) is one of the most clinically and scientifically utilized signals recorded from human brain. It is a powerful source for providing valuable insight of the brain dynamics. Accurate and careful analyses of EEG signals play a prominent role in diagnosis of brain disorders such as epilepsy. EEG signal has exceptional temporal resolution, usability, and non-invasive recording set-up with low costs which makes it popular in the field of biomedical research. Currently, trained neurologists take diagnostic decision by observing EEG recordings visually to decide probable treatment plan. the visual inspection of non-stationary and high-dimensional EEG recordings is a time-consuming task and results in errors. An automated seizure detector is an essential system not only for the better understanding of seizure generation but also a boon for medical professionals who are subjected to fatigue in visualizing a continuous and enormous long-term recording. the multichannel automated seizure detection is a bulky system as it involves recording from at least 16 channels. the use of such system on subjects who are remotely located is a challenge. In this paper, a unique embedded system based on envelop generation technique while sifting of EEG signals by Ensemble Empirical Mode Decomposition (EEMD) has been proposed to classify seizure signals. the modifications in the algorithm have resulted in an equivalent performance on multivariate signals when compared to simulation results. the use of 5-channel EMOTIV (R) head

关键词： Electrocochleogram seizure classification machine learning edge computing

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Cloud Infrastructure for Storing and processing EEG and ERP Experimental Data 5

Cloud Infrastructure for Storing and Processing EEG and ERP ...

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5th international conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE)

作者： Jezek, Petr Vareka, Lukas Univ West Bohemia Fac Appl Sci Dept Comp Sci & Engn New Technol Informat Soc Plzen Czech Republic

ISBN: (纸本)9789897583681

Current infrastructures for experimental data, results and computational tools make a shift from locally maintained solutions to remote cloud-based infrastructures. It brings a higher availability, sustainability and performance. However, specifics of different research areas require development of customized solutions for individual research domains. For example, electroencephalography and event-related potentials (EEG/ERP) use specific devices, data formats and machine learning workflows. As a solution, a cloud-based system for the EEG/ERP domain containing a distributed data storage, a signal processing method library and a client GUI is presented. the signal processing method library is used for training of classifiers and classifying the data in the cloud-based system controlled by the GUI. the presented system was tested using a machine learning workflow based on the data stored in the system. In the workflow, various classifiers were trained and their parameters stored into the system. Finally, testing data were classified using previously trained classifiers.

关键词： EEG ERP Cloud HDFS Hadoop Spark Experiment Infrastructure

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Automatic Recognition of Epileptiform EEG Abnormalities Using machine learning Approaches

Automatic Recognition of Epileptiform EEG Abnormalities Usin...

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international conference on Advanced Technologies for signal and Image processing (ATSIP)

作者： Itaf Ben Slimen Hassene Seddik CEREP Research Lab ENSIT University of Tunis Tunisia

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

Epilepsy is one of the various neurological disorders with 1% of the world population. It is characterized by the anomalous of a large number of neurons. In this paper, a proposed automated system for seizure detection and diagnosis using EEG signals records. Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes, spikes and the amplitude. the epileptiform is used as an indicator to anticipate the EEG signal class using machine learning methods. Based on EEG characterizes the proposed approach achieves a perfect classification rates with 99.8% using the Bonn database.

关键词： Electroencephalography Support vector machines Radio frequency Feature extraction Epilepsy Neurons Databases

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