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Analysis of Meta-Learning Approaches for TCGA Pan-cancer datasets

Analysis of Meta-Learning Approaches for TCGA Pan-cancer Dat...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Jingyuan Chou Stefan Bekiranov Chongzhi Zang Mengdi Huai Aidong Zhang University of Virginia Charlottesville VA USA Center for Public Health Genomics University of Virginia Charlottesville VA USA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cancer has been characterized as a heterogeneous disease, and the classification of cancer subtypes has become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients and provide clinical decision support for clinicians. With the advance of machine learning in the last decade, many researchers employ machine learning to tackle the cancer classification problem. Importantly, traditional machine learning algorithms require a large amount of annotated data for model training. However, collection of large amounts of annotated data is time-consuming and expensive and may not be realistic in real-world activities. Facing data scarcity, meta-learning is proposed to tackle this problem. Meta-learning utilizes prior knowledge learned from related tasks and generalizes to new tasks of limited supervised experience, and it has been applied in many fields to tackle scarce annotated data problem, such as few-shot image classification, drug discovery, etc. As data scarcity is common in cancer research and diagnosis studies, and there are only few previous studies that classify cancers based on limited annotated data. We explore the meta-learning algorithm (MAML) to tackle the scenario where only limited annotated data are available. In this work, our objective is to comprehensively compare MAML among few-shot learning methods (matching network and prototypical network) and traditional machine learning methods (random forest and K-nearest neighbor). Experimental results on The Cancer Genome Atlas (TCGA) cancer patient data demonstrates the effectiveness and superiority of MAML over other methods, including its ability to outperform the other methods using 4.5-fold fewer features.

关键词： Task analysis Cancer data models Adaptation models Training Bioinformatics Genomics

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Research on P2P Credit Assessment Based on Random Forest ― from the Perspective of Lender’s Profit

Research on P2P Credit Assessment Based on Random Forest ― ...

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Big data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), international conference on

作者： Xiong Li Zhang Zengyi School of Management Shanghai University Shanghai china

ISBN: (数字)9781728164991

ISBN: (纸本)9781728165004

Without the guarantee of traditional intermediary, the benefit of lenders is often damaged by the default of borrowers. Now, many machine learning models have been widely used on credit assessment in P2P online lending. To further improve the validity of credit assessment and the profit of lenders, the random forest algorithm with the profit of the lender as the primary evaluation criterion is proposed. We choose fifteen features as the input vector of our model, then the profit of lenders is obtained by formula. The dataset from Lending Club was applied to validate the effectiveness of this model. The findings of research show that the interest rate, debt-to-income ratio and loan amount are the three most important factors of default. Furthermore, random forest model can achieve higher lender's profit compared with other traditional methods, so it could be applied to credit process control in reality.

关键词： Forestry Big data Artificial intelligence Internet of Things Business Information processing Presses

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Automatic Classification of Antepartum Cardiotocography Using Fuzzy Clustering and Adaptive Neuro -Fuzzy Inference System

Automatic Classification of Antepartum Cardiotocography Usin...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Yue Fei Xiaoqian Huang Qinqun Chen Jiamin Chen Li Li Jiaming Hong Zhifeng Hao Hang Wei School of Medicine Information Engineering Guangzhou University of Chinese Medicine Guangzhou China Tianhe District People’s Hospital First Affiliated Hospital of Jinan University Guangzhou Sunray Medical Apparatus Co. Ltd Guangzhou China School of Mathematics and Big Data Foshan University Foshan China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Antepartum cardiotocography (CTG) monitoring is a crucial screening tool widely utilized to evaluate fetal wellbeing. However, the complexity and non-linearity of CTG usually result in inter-observer and intra-observer variability in a visual CTG interpretation using clinical guidelines. In this paper, a fuzzy C-means clustering based adaptive neuro-fuzzy inference system (FCM-ANFIS) was proposed to automatically classify CTG for antenatal fetal monitoring. data visualization and spearman correlation analysis were implemented to select CTG features. Then, the fuzzy space was partitioned by using fuzzy Cmeans clustering algorithm, and the adjustment parameters were adjusted through the self-learning mechanism of neural networks and least squares algorithm. The experimental results show that the fuzzy space partition based on FCM clustering could improve the performance of ANFIS, and the proposed FCM-ANFIS model outperforms the state-of-the-art automatic classification of CTG models. In conclusion, the proposed FCM-ANIFIS model has promising learning ability and adaptability for the complexity and uncertainty of antenatal CTG interpretation.

关键词： Monitoring Machine learning Partitioning algorithms Uncertainty Training Feature extraction Clustering algorithms

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Planogram Design Analytics using Image processing

Planogram Design Analytics using Image Processing

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Computing, Analytics and Networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd international conference on

作者： Eesha Goel Kulbhushan Sharma Technology Team Denave India Private Limited Noida India

ISBN: (数字)9781728149998

ISBN: (纸本)9781728150000

A planogram is a tool for visual merchandising of retail stores. It displays a detailed view of the retail store with the major intent for product placement. The planogram is highly useful for examining the point of sale as it demonstrates the exact positioning of products. Two major benefits are there for building planograms while planning the store layout such as maximizing sales and space of the retail *** important part of Planogram management is to read planogram drawings and images and convert these to representational data in CSV or database *** this part, Contour analysis of image using OpenCV along with combination of text detection and recognition is performed using Connectionist Text Proposal Network and Convolutional Recurrent Neural Network deep learning models respectively. The proposed planogram design analytics system is implemented using real data. The customer had tested the system as per different cases. The feedback obtained from them confirms that the system meets the requirements to their satisfaction.

关键词： Text recognition Optical character recognition software Layout Feature extraction data mining Proposals Machine learning

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A Scalable Heterogeneous Big data Framework for e-Learning Systems

A Scalable Heterogeneous Big Data Framework for e-Learning S...

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international conference on Advances in Big data, Computing and data Communication Systems (icABCD)

作者： David Otoo-Arthur Terence L. van Zyl School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa

ISBN: (数字)9781728167701

ISBN: (纸本)9781728167718

The adoption of e-learning systems in higher education is a remarkable phenomenon that has redefined teaching and learning. Initially, it was proposed to allow people to learn for personal accomplishment without physically attending any traditional university or academic settings. While these systems continue to provide an efficient and flexible approach for teaching and learning, the rapid integration of ICTs and the expansion of data from these systems remain much concern to the education community. In this study, we propose a smart and secure data flow architectural framework for e-learning that uses a rich set of big data tools within a distributed and parallel analysis platform. Reference Model of Open Distributed processing (RM-ODP) reference model guided the development of this Big data framework for e-learning analytics. Using the RM-ODP model as a benchmark, we classify the educational institution's architecture in terms of existing elements, functions and processes to understand stakeholders' views for the development of the framework. Our approach uses an existing distributed computing environment and offers an adaptable standard framework to improve the data acquisition, storage, processing, analysis, security and virtualization for e-learning systems. We implement a scalable and adaptable big data framework for e-learning (BiDeL) and point out how big data concept could integrate into online learning systems to improve teaching and learning in higher education using Apache Spark as a case. The proposed framework was applied to both batch and streaming dataset of students online activities on moodle LMSs. BiDel framework performance shows improved data integration and data governance. This big data framework and the general view of the current state of the art in “big data” technologies will serve as a guide for the creation of e-learning systems, which create new value from existing but underused data. The acquired value will provide decision-makers in

关键词： Big data Electronic learning data models Analytical models Standards Business

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Electrothermal Model Parameters Extraction and Evaluation Based on BSIM-CMG for 7-nm Nanosheet Gate-All-Around Transistor

Electrothermal Model Parameters Extraction and Evaluation Ba...

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international conference on Solid-State and Integrated Circuit Technology

作者： Ren-Hua Liu Si-Qi Yang Xiao-Jin Li Ya-Bin Sun Yan-Ling Shi Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China

ISBN: (数字)9781728162355

ISBN: (纸本)9781728162362

In this paper, the BSIM-CMG model parameters are extracted for the nanosheet gate-alI-around (GAA) transistors with considering the temperature distribution and self-heating (SH) effect. The data used for parameter extraction is obtained from the 3-D TCAD simulation, which is calibrated with the experimental data to ensure the analysis accuracy. The extracted model and its parameter are validated from -40°C to 125°C. In order to evaluate our extracted model, the performance of the 6T-SRAM is investigated considering SH effect within different ambient temperature (TA). The results show that the read and write noise margins are all degenerated with increasing TA, and the static power increases as expected.

关键词： Transistors Logic gates Gallium arsenide data models Solid modeling Semiconductor process modeling Fitting

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MOBDroid: An Intelligent Malware Detection System for Improved data Security in Mobile Cloud Computing Environments

MOBDroid: An Intelligent Malware Detection System for Improv...

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Australasian Telecommunication Networks and Applications conference, ATNAC

作者： Noah Oghenefego Ogwara Krassie Petrova Mee Loong Bobby Yang School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand

ISBN: (数字)9781728188270

ISBN: (纸本)9781728188287

We propose an intelligent malware detection system (MOBDroid) that aims to protect the end-user's mobile device (MD) in mobile cloud computing (MCC) environment. MOBDroid utilizes the Android Operating System (OS) permission-based security system. The APK files of 28,306 benign and malicious applications (apps) collected from the AndroZoo and RmvDroid malware repositories were used in the system development process. The apps were decompiled in order to extract their manifest files and construct a dataset comprising the permissions requested by each of the apps. We identified some unique permissions that could be used to distinguish between malicious and benign apps and performed a series of experiments using a machine learning (ML) model; the model drew on the *** library and was implemented in C#.net. In the experiments conducted, we obtained classification accuracy of 96.89%, a detection rate of 98.65%, and false negative rate of 1.35%. The results indicate that our model compares very favorably to other models reported in the extant literature.

关键词： Malware Classification algorithms Feature extraction data models Cloud computing Mobile applications Communications technology

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Precise Cerebrovascular Segmentation

Precise Cerebrovascular Segmentation

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IEEE international conference on Image processing

作者： F. Taher A. Soliman H. Kandil A. Mahmoud A. Shalaby G. Gimel’farb A. El-Baz College of Technological Innovation Zayed University Dubai UAE BioImaging Lab University of Louisville Louisville KY USA Department of Computer Science The Unviersity of Auckland Auckland New Zealand

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Analyzing cerebrovascular changes using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images can detect the presence of serious diseases and track their progress, e.g., hypertension. Such analysis requires accurate segmentation of the vasculature from the surroundings, which motivated us to propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that capture the appearance of macro and micro-vessels. The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets, while the current appearance is represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) whose parameters are estimated by a modified Expectation-Maximization (EM) algorithm. The proposed approach was validated on 190 data sets using three metrics, which revealed high accuracy compared to existing approaches.

关键词： Feature extraction data models Solid modeling Brain modeling Three-dimensional displays Blood vessels Measurement

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Myanmar Text-to-Speech System based on Tacotron (End-to-End Generative Model)

Myanmar Text-to-Speech System based on Tacotron (End-to-End ...

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international conference on Information and Communication Technology Convergence (ICTC)

作者： Yuzana Win Htoo Pyae Lwin Tomonari Masada Yangon Technological University Yangon Myanmar Artificial Intelligence and Science Rikkyo University Tokyo Japan

ISBN: (数字)9781728167589

ISBN: (纸本)9781728199016

The main motivation of this paper is to improve the naturalness of Myanmar text-to-speech system using an end-to-end generative model called Tacotron. We introduce the open-source implementation for Myanmar text-to-speech system with very high natural-sounding. In this paper, there are four main parts: speech corpus creation, data pre-processing, applying end-to-end generative model, and speech synthesis. Firstly, we develop a speech corpus of 8k sentences from a large set of news articles, novel books, daily usages and travel-related expressions for corpus creation. Secondly, we use a syllable segmenter and text normalizer for data pre-processing. Thirdly, we apply end-to-end generative model called Tacotron that synthesizes speech directly from the sequence of text characters. Finally, we use Griffin-Lim algorithm to convert the corresponding text into the output speech. For the subjective evaluation, we compare our synthesized speech output with the original recording speech in both intelligibility and naturalness by using mean opinion score (MOS). The experimental results show that we can obtain the synthesized speech comparable to the similar state-of-the-art synthsizers for other languages.

关键词： data models Information and communication technology Speech synthesis Open source software Spectrogram Convergence

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Student Performance Prediction from E-mail Assessments Using Tiny Neural Networks

Student Performance Prediction from E-mail Assessments Using...

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IEEE Integrated STEM Education conference (ISEC)

作者： Nikhil Yadav Kajal Srivastava Division of Computer Science Mathematics and Science St. John’s University Queens New York USA Business Communication Jaipuria Institute of Management Lucknow India

ISBN: (数字)9781728175201

ISBN: (纸本)9781728175218

Predicting student performance using e-mail assessments can help in early interventions to better assist students sooner, rather than later, in STEM courses. In this paper, we propose CorC-Net, a tiny artificial neural network (ANN) that operates on limited data comprised of features scored from student assessments based on writing e-mails. ANNs are typically built using large scale data sets to truly realize their full potential; however, tiny neural networks overcome this problem by utilizing smaller batches of data making them easier to train. COrC-Net uses scored e-mails for content, organization, and clarity and classifies how students will perform. Formative instructor feedback provided between the assessments implies that CorC-Net is a more logical fit to simulate the “learning” process when human reaction to feedback and corrective action is involved. This is true especially in sequential course assessment tasks. In this paper, we show that COrC-Net outperforms other multiclass classification algorithms like decision trees, support vector machines, Gaussian Naive Bayes, and K-nearest neighbors. CorC-Net's success in classifying student performance shows great potential in courses where longterm temporal assessment data is not available.

关键词： Support vector machines Artificial neural networks Predictive models Feature extraction Natural language processing data models Decision trees

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