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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Ensemble learning based Architecture Vulnerability Factor calculation using partial feature set in processors 11

Ensemble learning based Architecture Vulnerability Factor ca...

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11th international conference on Computer and Electrical engineering (ICCEE)

作者： Wang, Jiabin Jiao, Jiajia Fu, Yuzhuo Shanghai Maritime Univ Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

With the scaling technology, soft error induced bit upsets are increasingly threatening the processor reliability. Processor designers require effective tools or methodologies to estimate the often-used metric Architectural Vulnerability Factor (AVF). this paper presents an ensemble learning based AVF calculation methodology for fast reliability assessment. Instead of the entire feature set, only partial non-critical attributes are selected to build the predictive model so that many performance counters can be removed or shut down for saving memory space and power consumption. Millions of data collected from a cycle-accurate simulator sim-SODA, are trained by the latest learning methods in Tensorflow. the SPEC2000 results demonstrate the instanced ensemble learning-random forest and Ada-boost perform nearly perfect accuracy, better than linear regression, and neural network.

关键词： learning systems

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Quality of Wikipedia Articles: Analyzing Features and Building a Ground Truth for Supervised Classification 11

Quality of Wikipedia Articles: Analyzing Features and Buildi...

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11th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management (IC3K) / 11th international conference on Knowledge Discovery and Information Retrieval (KDIR)

作者： Bassani, Elias Viviani, Marco Univ Milano Bicocca Dept Informat Syst & Commun Edificio U14-Viale Sarca I-20126 Milan Italy Consorzio IT Trasferimento Tecnol C2T Milan Italy

ISBN: (纸本)9789897583827

Wikipedia is nowadays one of the biggest online resources on which users rely as a source of information. the amount of collaboratively generated content that is sent to the online encyclopedia every day can let to the possible creation of low-quality articles (and, consequently, misinformation) if not properly monitored and revised. For this reason, in this paper, the problem of automatically assessing the quality of Wikipedia articles is considered. In particular, the focus is (i) on the analysis of groups of hand-crafted features that can be employed by supervised machine learning techniques to classify Wikipedia articles on qualitative bases, and (ii) on the analysis of some issues behind the construction of a suitable ground truth. Evaluations are performed, on the analyzed features and on a specifically built labeled dataset, by implementing different supervised classifiers based on distinct machine learning algorithms, which produced promising results.

关键词： data Quality Wikipedia Supervised Classification Feature Analysis Ground Truth Building

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the Network Design of License Plate Recognition Based on the Convolutional Neural Network 11th

The Network Design of License Plate Recognition Based on the...

引用

11th international Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Tao, Xingzhen Gu, Fahui Xie, Shumin College of Information Engineering Jiangxi College of Applied Technology Ganzhou China Office of Academic Studies Guangdong Vocational and Technical College GuangzhouJiangxi341000 China

ISBN: (纸本)9789811555763

Due to the generation of data sets and the rapid improvement of GPU computing performance, in-depth learning has undergone qualitative changes and development in the past decade. Various excellent convolutional neural network models have been implemented and verified, which accelerates the application of the convolutional neural network in various fields. A license plate recognition based on the convolutional neural network is pro-posed for the application of large underground parking lots. An end-to-end identification network framework without segmentation characters is designed. At the same time, sequence information is added to the convolutional neural network for improving the license plate recognition rate. Compared with the existing step-by-step license plate detection and recognition method, the joint solution of a single network can avoid the error accumulation in the intermediate process. At the same time, it can improve the accuracy rate, save the recognition time, accelerate the vehicle entering and leaving time and avoid traffic congestion. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

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COVID-19 Prediction using ARIMA Model

COVID-19 Prediction using ARIMA Model

引用

international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Venkatbharat Poleneni Jahnavi K Rao Syed Afshana Hidayathulla CMR University Main Campus Bangalore India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Real-time data has become a dominant aspect for understanding past, present, and future situations. Machine learning (ML) is one such subject that uses a variety of algorithms to understand the correlation between the given data, visualize the current scenario, and predict the future forecast which is the most crucial part. the entire world is currently undergoing a devastating situation due to the outbreak of novel coronavirus known as COVID-19. the COVID-19 at present has proved that it is a potential threat to human life. To contribute to control the spread and rising number of active cases in India, this study demonstrates the future forecasting of the total number of active cases in India in the upcoming 15 days. the future forecast is predicted using the ARIMA Model (Auto-regressive Integrated Moving Average) with the combination of Facebook Prophet which gives us the highest accuracy. the real-time data collection takes place from various resources after which the data pre-processing and data wrangling takes place. the data set is then split into the training set and testing set. Finally, the model is trained and tested for accuracy. With the completion of testing and training, the model is ready to predict future forecasts. the model also makes note of the predicted and actual values which helps it achieve higher accuracy in the future.

关键词： COVID-19 Training Social networking (online) data visualization Predictive models Real-time systems Testing

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Recognizing hand gestures for controlling home appliances with mobile sensors 11

Recognizing hand gestures for controlling home appliances wi...

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11th international conference on Knowledge and Systems engineering (KSE)

作者： Khanh Nguyen Trong Ha Bui Cuong Pham Posts & Telecommun Inst Technol Fac Informat & Technol Hanoi Vietnam Thuyloi Univ JEAI WARM UMMISCO IRD SU 175 Tay Son Hanoi Vietnam

ISBN: (纸本)9781728130033

Mobile and ambient sensors provide a scalable platform for the integration of computing devices and smart appliances for smart home. In which mobile devices, such as smart watches and smart-phone commonly embedded with actuators and sensors i.e., accelerometers and gyroscopes, have opened up chances for the user to easily control home appliances. this paper proposes an integrated method and system that utilize several deep models and mobile sensors for hand gestures applicable for smart homes. the system consists of three components of actual smart home configurations: (i) smart-watch worn on the user's wrist for capturing gesture patterns (ii) a recognition application that runs on the smart mobile phone and sends correspond commands to the home automation platform;and (iii) home automation platform with connected smart devices instrumented with ambient sensors. In addition, we define a simple yet easy-to-learn hand-gesture vocabulary composing of 18 gestures to the user. With the F-score of over 75%, our experiment on our self-collected data-set consisting of 18 gestures from 20 subjects, demonstrates that the feasibility of the gesture recognition for controlling home appliances.

关键词： Smart homes intelligent sensors Gesture recognition Vocabulary Home appliances Machine learning

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data Augmentation and Feature Fusion for Melanoma Detection with Content Based Image Classification 4th

引用

4th international conference on Advanced Machine learning Technologies and Applications, AMLTA 2019

作者： Das, Rik De, Sourav Bhattacharyya, Siddhartha Platos, Jan Snasel, Vaclav Hassanien, Aboul Ella Department of Information Technology Xavier Institute of Social Service RanchiJharkhand India Department of Computer Science and Engineering Cooch Behar Government Engineering College Cooch Behar India Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Ostrava Czech Republic Cairo University Giza Egypt

ISBN: (纸本)9783030141172

Computer aided diagnosis has leveraged a new horizon for accurate diagnosis of numerous fatal diseases. Melanoma is considered as one of the most lethal form of skin cancer which is increasingly affecting the population in recent times. the disease can be completely healed if diagnosed and addressed at an early stage. However, in most of the cases patients receive delayed care which results in fatal consequences. the authors have attempted to design an automated melanoma detection system in this work by means of content based image classification. Extraction of content based descriptors can nullify the requirement for manual annotation of the dermoscopic images which consumes considerable time and effort. the work has also undertaken a fusion based approach for feature combination for evaluating classification performances of hybrid architecture. the results have outclassed the state-of-the-art outcomes and have established significant performance improvement. © 2020, Springer Nature Switzerland AG.

关键词： Computer aided diagnosis

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Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine learning

Drug Recommendation System based on Sentiment Analysis of Dr...

引用

international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Satvik Garg Jaypee University of Information Technology Solan India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Since coronavirus has shown up, inaccessibility of legitimate clinical resources is at its peak, like the shortage of specialists and healthcare workers, lack of proper equipment and medicines etc. the entire medical fraternity is in distress, which results in numerous individual's demise. Due to unavailability, individuals started taking medication independently without appropriate consultation, making the health condition worse than usual. As of late, machine learning has been valuable in numerous applications, and there is an increase in innovative work for automation. this paper intends to present a drug recommender system that can drastically reduce specialists heap. In this research, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes like Bow, TF-IDF, Word2Vec, and Manual Feature Analysis, which can help recommend the top drug for a given disease by different classification algorithms. the predicted sentiments were evaluated by precision, recall, f1score, accuracy, and AUC score. the results show that classifier LinearSVC using TF-IDF vectorization outperforms all other models with 93% accuracy.

关键词： Drugs Sentiment analysis Stochastic processes Static VAr compensators Manuals Recommender systems Random forests

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Meaningful data Sampling for a Faithful Local Explanation Method 1

引用

20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Rasouli, Peyman Yu, Ingrid Chieh Univ Oslo Dept Informat Oslo Norway

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

data sampling has an important role in the majority of local explanation methods. Generating neighborhood samples using either the Gaussian distribution or the distribution of training data is a widely-used procedure in the tabular data case. Generally, this approach has several weaknesses: first, it produces a uniform data which may not represent the actual distribution of samples;second, disregarding the interaction between features tends to create unlikely samples;and third, it may fail to define a compact and diverse locality for the sample being explained. In this paper, we propose a sampling methodology based on observation-level feature importance to derive more meaningful perturbed samples. To evaluate the efficiency of the proposed approach we applied it to the LIME explanation method. the conducted experiments demonstrate considerable improvements in terms of fidelity and explainability.

关键词： Lime

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Study on Machine learning Techniques with Conventional Tools for Payment Fraud Detection 11

Study on Machine Learning Techniques with Conventional Tools...

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11th international conference on Information Technology and Electrical engineering (ICITEE)

作者： Vidanelag, Harindu Mudunkotuwa Mudunkotuwe Hitiwadi Tasnavijitvong, Treepatchara Suwimonsatein, Panit Meesad, Phayung King Mongkuts Univ Technol North Bangkok Informat & Data Sci Program Bangkok Thailand King Mongkuts Univ Technol North Bangkok Fac Informat Technol Bangkok Thailand

ISBN: (纸本)9781728140551

Payment fraud is intentional deception with the purpose of obtaining financial gain or causing loss by implicit or explicit trick, committed by many parties in order to gain significant financial benefits. that had been a major reason for personal financial losses that account over a billion losses a year. At the same time, fraud detection has been improved and currently is embraced by the cutting-edge information technology "Machine learning". However, majority of the available studies have been studying with the deep high-end techniques with various costly technologies, and focusing on accuracy and time of the model. they have also been limited to past fraud histories. this study is conducted with multiple machine learning techniques with the use of synthesized dataset, which is not limited to the history, and our study is performed by using the conventional open source data science tools. However, the results seem to be above the expectation.

关键词： fraud detection supervised learning classification

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Predicting Customers Intending To Cancel Credit Card Subscriptions Using Machine learning Algorithms: A Case Study 11

Predicting Customers Intending To Cancel Credit Card Subscri...

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11th international conference on Electrical and Electronics engineering (ELECO)

作者： Altinisik, Fehim Yilmaz, Hasan Huseyin Turkiye Vakiflar Bankasi TAO EBIS Directorate R&D & Digital Transformat Istanbul Turkey

ISBN: (纸本)9786050112757

Following paper introduces analysis of machine learning algorithms implemented in order to predict customers of commercial bank who may be in risk of cancelling credit card subscriptions by following three months after a year or less activity. An analysis of various data preprocessing, sampling and structuring procedures using a feature set made up of 106 variables -describing customers' transaction activity, demographics, overall contentment and relative information to consumer experience- also shared. Study also includes performance comparison of Deep Neural Networks against other generic machine learning algorithms on two different cases. Deep Neural Networks were the point of interest of this study and it turns out, them to perform better than generic machine learning algorithms.

关键词： Deep neural networks

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