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Motor data analysis of Parkinson’s disease patients

Motor data analysis of Parkinson’s disease patients

引用

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Vasiliki Fiska Nikolaos Giannakeas Nikolaos Katertsidis Alexandros T. Tzallas Konstantinos Kalafatakis Markos G. Tsipouras Univ. of Western Macedonia Kozani Greece Science and Technology Park of Epirus UNIVEYE IKE Ioannina Greece Univ. of Ioannina Arta Greece

ISBN: (数字)9781728195742

ISBN: (纸本)9781728195759

In this manuscript, a methodology for analysing motor signals from Parkinson's disease (PD) patients is presented. The signals are obtained from PD patients while wearing a glove device and sequentially performing standard motor tests. The signals are processed in order to detect the onset and offset from specific items (items 23-25) of the Unified Parkinson's Disease Rating Scale (UPDRS) and then the isolated signal parts are analysed in order to quantity the motor findings defined in UPDRS for these items, such as hesitation, movement amplitude and frequency, and rotation range. The obtained results indicate that the methodology can achieve accurate motor assessment (related to ground-truth UPDRS) for both “Off” and “On” stages.

关键词： Performance evaluation Signal processing algorithms Signal processing Sensors data mining Standards Diseases

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Enhance Content Selection for Multi-Document Summarization with Entailment Relation

Enhance Content Selection for Multi-Document Summarization w...

引用

international conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Yu-Yun Wang Jhen-Yi Wu Tzu-Hsuan Chou Ying-Jia Lin Hung-Yu Kao National Cheng Kung University Tainan Taiwan R.O.C

ISBN: (数字)9781665403801

ISBN: (纸本)9781665447379

Automatic text summarization is one of the common tasks in natural language processing. The main task is to generate a shorter version based on the original text and maintain relevant information. This paper studies multi-document summarization (MDS) that applies to news articles. MDS has two significant issues which are information overlap and information difference among multiple articles. Existing models mostly deal with MDS from the perspective of single document summarization (SDS). The models do not consider the relation between sentences in multiple news articles. Our proposed method deals with the issue and consists of two models. The sentence selector model selects representative sentences based on the entailment relation in different articles. The content is related to the event of the article extracted through the algorithm. The summary generator model generates a final summary to ensure that the summary contains no redundancy and maintains vital information. Experiment results show that our proposed model has effectively improved in the evaluation results. The main contribution of our approach is to use the entailment relation to obtain key content in multiple articles. Adding semantic comprehension can identify salient information clearly and improve the accuracy of MDS.

关键词： Training Redundancy Semantics Natural language processing Generators data mining Task analysis

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Simplified Approach for Predicting Emotions of Multi-Turn Textual Utterances

Simplified Approach for Predicting Emotions of Multi-Turn Te...

引用

international conference on Advances in ICT for Emerging Regions

作者： Jarsigan Vickneswaran Piruntha Navanesan Vahesan Vijayaratnam Uthayasanker Thayasivam University of Moratuwa Colombo Sri Lanka

ISBN: (数字)9781728186559

ISBN: (纸本)9781728186566

Sentiment analysis is a well-known topic in Natural Language processing (NLP). After a lot of research in this sector, the Emotion Analysis of Conversations prompted out as a trending topic. The main idea behind the Emotion Analysis of Conversations is to extract and classify the emotions expressed through a conversation. The scope of this research is adopted from "EmoContext" (SemEval 2019 - Task 3), a competition conducted by Codalab - Microsoft. There are some models that achieve a significant accuracy in this section but they are too much complicated to understand. There is a need for a model that achieves significant accuracy with a simplified approach for many uses. Here, we present a model that has achieved a microF1 score of 0.742 with our simple CNN-BiLSTM model and customized FastText word embedding, which comfortably betters the 3rd Quartile value of 0.7317 and stands up into the top quarter of the leader board of EmoContext competition.

关键词： Feature extraction data models Social networking (online) Blogs Analytical models Context modeling Bit error rate

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Image Colorization Using Generative Adversarial Networks and Transfer Learning

Image Colorization Using Generative Adversarial Networks and...

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Iranian Machine Vision and Image processing (MVIP)

作者： Leila Kiani Masoud Saeed Hossein Nezamabadi-pour Computer Engineering Department Shahid Bahonar University of Kerman Kerman Iran Intelligent Data Processing Laboratory (IDPL) Shahid Bahonar University of Kerman Kerman Iran

ISBN: (数字)9781728168326

ISBN: (纸本)9781728168333

Automatic colorizing is one of the most interesting problems in computer graphics. During the colorization process, the gray one-dimensional images are converted to three-dimensional images with colored components. As a typical technique, Convolutional neural networks (CNNs) have been well studied and used for automatic coloring. In these networks, the information that is generalized over in the top layers is available in intermediate layers. Although the output of the last layer of CNNs is usually used in many applications, in this paper, we use a concept called "Hypercolumn" derived from neuroscience to exploit information at all levels to develop a fully automated image colorization system. There are not always millions of data available in the real world to train complex deep learning models. Therefore, the VGG19 model trained with the big data set of ImageNet is used as a pre-trained model in the generator network and the hypercolumn idea is implemented in it with DIV2K datasets. We train our model to predict each pixel's color texture. The results obtained indicate that the proposed method is superior to competing models.

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scSNVIndel. accurate and efficient calling of SNVs and indels from single cell sequencing using integrated Bi-LSTM

scSNVIndel. accurate and efficient calling of SNVs and indel...

引用

IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Lei Cai Yufeng Wu Jingyang Gao Beijing University of Chemical Technology Beijing China University of Connecticut Storrs CT US

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Single-cell data are sparse and have coverage fluctuations, making it difficult, in comparison with data obtained from next-generation sequencing (NGS), to call single nucleotide variants (SNVs) and indels. Furthermore, most existing sequencing methods are unable to effectively call whole-genome SNVs and indels from single cell sequencing (SCS) data. In this study, we propose a new method for the efficient identification of SNVs and indels from SCS data, called scSNVIndel. scSNVIndel uses bidirectional long short-term memory (Bi-LSTM) as its base and integrates new natural language processing (NLP) technology. It automatically extracts features and accurately calls SNVs and indels when using SCS data, which is characterized by uneven and discontinuous coverage. Moreover, scSNVIndel can call variants from the sequence directly, retaining valuable information from the SCS data, as it does not convert the sequence into an image like the DeepVariant method. The results show that scSNVIndel performs better in terms of accuracy and recall for calling variants, when compared with other existing methods. scSNVIndel is currently an open-source method, available at https://***/CSuperlei/scSNVIndel, and its usage methods are published on the following website: https://***/2020/06/18/scSNVIndel/.

关键词： Sequential analysis Feature extraction Task analysis Tensors Training data models data mining

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Prediction of Financial Big data Stock Trends Based on Attention Mechanism

Prediction of Financial Big Data Stock Trends Based on Atten...

引用

IEEE international conference on Big Knowledge (ICBK)

作者： Jiannan Chen Junping Du Zhe Xue Feifei Kou Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Stock trend prediction has always been the focus of research in the field of financial big data. Stock data is complex nonlinear data, while stock price is changing over time. Based on the characteristics of stock data, this paper proposes a financial big data stock trend prediction algorithm based on attention mechanism (STPA). We adopt Bidirectional Gated Recurrent Unit (BGRU) and attention mechanism to capture the long-term dependence of data on time series. The attention mechanism is used to analyze the weight of the impact of data from different time periods on the trend prediction results, thereby reducing the error of stock data change trend prediction and improving the accuracy of trend prediction. We select the daily closing price data of 10 stocks for model training and performance evaluation. Experimental results demonstrate that the proposed method STPA achieves higher precision, recall rate and F1-Score in predicting stock change trends than the other methods. Compared with mainstream methods, STPA improves the precision by 4%, improves recall by 2.5%, and improves F1-Score by 3.2%.

关键词： Market research Big data Communications technology Logic gates Prediction algorithms Hidden Markov models Time series analysis

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Social Media data Analysis for Intelligent Transportation Systems

Social Media Data Analysis for Intelligent Transportation Sy...

引用

Emerging Trends in Information Technology and Engineering (ic-ETITE), conference on

作者： B Mounica. K Lavanya. School of Computer Science and Engineering(SCOPE) VIT University Vellore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Transportation is the major tribulations in urban cities. Traditional transportation approach are not smart for traffic management so Intelligent transport systems works with automated system and gives better solutions for smart parking, electronic toll collection, traffic monitoring and planning etc by accessing data from different sources. Among passive data, Social media data is widely sourced for Intelligent Transportation Systems (ITS), users communicate and interact online by writing blogs through face book, twitter and other social media applications. This data from social media can be used for transportation analysis like traffic prediction, accident zones analysis etc. unfortunately the data sourced is highly unstructured and massive in volume leading to complex processing for data usage. Big data helps in processing, storing and analyzing this data. In this paper we summarize main research on review topics in the field of data preprocessing techniques for noisy data removal, data concentration methods for filtering the data, feature selection methods to extract the main features, abnormal behavior of data to identify the suspicious behavior accounts and road condition prediction for detecting the road patterns and traffic behavior by supervised learning and unsupervised machine learning algorithms, this paper discusses towards the end on future research directions in social media analysis in ITS.

关键词： Intelligent Transportation Systems Social Media data Machine Learning Intelligent transportation systems Social Media transport traffic behavior Artificial Intelligence Machine Learning

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Regularization of Neural Network Using DropCoadapt

Regularization of Neural Network Using DropCoadapt

引用

international econference on Computer and Knowledge Engineering (ICCKE)

作者： Hojjat Moayed Eghbal G. Mansoori School of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (数字)9781728185668

ISBN: (纸本)9781728185675

Complex co-adaption between hidden neurons prevents fine tuning of all parameters by overfitting, especially in deep neural models. Dropout has played an essential role to tackle this problem. However, dropout suppress neurons blindly. In this paper, we present a technique to assign dropout rate according to co-adaption pressure, called DropCoadapt. At first, it identifies co-adaption clusters on hidden units. We define the term co-adaption pressure as the density of co-adaption in each co-adaption clusters. Then, the dropout rate updates for each neuron according to the obtained co-adaption pressure. Experimental results on MNIST and CIFAR-10 datasets confirm that DropCoadapt obtains better performance and outperforms other methods.

关键词： Neurons Training Standards Adaptation models Information processing data models Convolutional neural networks

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Genetic Algorithm For Fitting Cardiac Cell Biophysical Model Formulations

Genetic Algorithm For Fitting Cardiac Cell Biophysical Model...

引用

Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Akwasi Darkwah Akwaboah Pascal Yamlome Jacqueline A. Treat Jonathan M. Cordeiro Makarand Deo Department of Engineering Norfolk State University Norfolk VA Department of Experimental Cardiology Masonic Medical Research Institute Utica NY

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Modeling cardiac cell electrophysiology relies on fitting model equations to experimental data obtained under voltage/current clamping conditions. The fitting procedure for these often-nonlinear ionic current equations are mostly executed by trial-and-error by hand or by gradient-based optimization approaches. These methods, though sometimes sufficient at converging at optimal solutions is based on the premise that the characteristic objective function is convex, which often does not apply to cardiac model equations. Meta-heuristic methods, such as evolutionary algorithms and particle swarm algorithms, have proven resilient against early convergence to local optima and saddle-point parameter solutions. This work presents a genetic algorithm-based approach for fitting the adult cardiomyocyte biophysical model formulations to the experimental data obtained in human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM). Specifically, whole-cell patch clamp ionic current data of rapid delayed rectifier potassium current, I Kr , transient outward potassium current, I to and hyperpolarization-activated current, I f , was used for fitting. Using a two-point crossover scheme along with initial population and mutation constraints randomly selected from a uniformly distributed constrained parameter space, near-optimal fitting was achieved with R 2 values (n = 5) of 0.9960±0.0007, 0.9995±0.0002, and 0.9974±0.0014 for I Kr , I to and I f respectively.

关键词： Mathematical model Indium tin oxide Fitting Biological system modeling data models Genetic algorithms Optimization

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Deep Graph Library: Overview, Updates, and Future Developments [GrAPL 2020 Keynote Speaker]

Deep Graph Library: Overview, Updates, and Future Developmen...

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IEEE international Symposium on Parallel and Distributed processing Workshops and Phd Forum (IPDPSW)

作者： George Karypis (AWS Deep Learning Science University of Minnesota)

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, ecommerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. In the last few years, Graph Neural Networks (GNNs) have emerged as a promising new supervised learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. Deep Graph Library (DGL) is an open source development framework for writing and training GNN-based models. It is designed to simplify the development of such models by using graphbased abstractions while at the same time achieving high computational efficiency and scalability by relying on optimized sparse matrix operations and existing highly optimized standard deep learning frameworks (e.g., MXNet, PyTorch, and TensorFlow). This talk provides an overview of DGL, describes some recent developments related to high-performance multi-GPU, multi-core, and distributed training, and describes our future development roadmap.

关键词： data mining conferences Bioinformatics Libraries Machine learning Social network services

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