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检索条件"任意字段=2021 13th International Conference on Machine Learning and Computing, ICMLC 2021"

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Prediction of Restaurant Sales during High Demand States Using Population Statistical Data 13

Prediction of Restaurant Sales during High Demand States Usi...

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13th international conference on Mobile computing and Ubiquitous Network (ICMU)

作者： Shinoda, Kenji Yamada, Masato Takanashi, Motoki Tsuboi, Tetsuya Hasegawa, Daisuke Fukazawa, Yusuke Kimoto, Masatoshi DOCOMO InsightMkt INC Tokyo Japan NTT DOCOMO INC Tokyo Japan Saizeriya Co Ltd Chiba Japan DOCOMO Technol Inc Tokyo Japan

ISBN: (纸本)9784907626488

Predicting the future sales volume is a critical task in operating a restaurant as it helps to determine the number of staff and ingredients to have at any time and decide when to start preparing food. Future sales can typically be predicted according to the demand cycle;however, it is difficult to predict an immediate increase in demand because it is out of the demand cycle. To tackle this issue, this study proposes a method for predicting the next-hour future sales volume based on population statistical data, in addition to historical sales and current and historical weather data. the proposed method combines the results of two models, one predicting the sales volume, while the other determining whether the future demand will become high or low. the proposed method was evaluated using actual restaurant data collected in collaboration with a major Japanese restaurant company. the results demonstrate that the prediction accuracy can be improved by 1.45% compared to the prediction model of sales volume without combination of demand classification when the sales volume is high.

关键词： restaurant demand forecasting machine learning population statistics

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Long-term Cholesterol Risk Prediction using machine learning Techniques in ELSA Database 13th

Long-term Cholesterol Risk Prediction using Machine Learning...

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13th international Joint conference on Computational Intelligence (IJCCI) / 13th international conference on Evolutionary Computation theory and Applications (ECTA)

作者： Fazakis, Nikos Dritsas, Elias Kocsis, Otilia Fakotakis, Nikos Moustakas, Konstantinos Univ Patras Dept Elect & Comp Engn Rion 26504 Greece

ISBN: (纸本)9789897585340

Cholesterol is a crucial risk factor for cardiovascular diseases (CVDs) which in their turn are among the main causes of death worldwide and public health concern, with heart diseases being the most prevalent ones. For cholesterol control, the early prediction is considered one of the most effective ways. Utilizing the English Longitudinal Study of Ageing (ELSA), a large-scale database of ageing participants, a dataset is derived to evaluate the long-term cholesterol risk of elderly men and women using machine learning (ML) techniques. Several ML prediction models were assessed concerning Accuracy and Recall where the Logistic model tree was the best performer. the ultimate goal of this study is to identify individuals at risk and facilitate earlier intervention to prevent the future development of cholesterol.

关键词： Cholesterol Long-term Prediction machine learning

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SQL Injection Detection Using machine learning Techniques 8

SQL Injection Detection Using Machine Learning Techniques

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8th international conference on Soft computing & machine Intelligence (ISCMI)

作者： Hosam, Eman Hosny, Hagar Ashraf, Walaa Kaseb, Ahmed S. Cairo Univ Fac Engn Comp Engn Dept Cairo Egypt

ISBN: (纸本)9781728186832

SQL Injection is one of the most popular cyberattacks in which hackers can gain unauthorized access to sensitive data such as customer information, trade secrets, etc. SQL Injection works by injecting malicious SQL code into user input fields in order to illegitimately read, update, or delete database contents. Detecting SQL Injection is a binary classification problem in which it is required to decide whether a user input is malicious (i.e., includes an injected SQL code) or benign. SQL Injection can be detected using rule-based, machine learning, or deep learning techniques. this paper focuses on machine learning techniques for SQL Injection detection. the paper defines 13 relevant features that can be extracted from user inputs, and it presents and evaluates 6 different machine learning algorithms. A feature selection process is also conducted, and the models achieve an accuracy up to 99.6%. the presented models show clear abilities to generalize to unseen data and perform well with a limited training set size. Overall, Logistic Regression is concluded to be the most performing model.

关键词： cybersecurity cyberattacks SQL injection SQL injection detection machine learning feature selection

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Towards Intelligent Multi-Access Edge computing Using machine learning 13th

Towards Intelligent Multi-Access Edge Computing Using Machin...

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13th international conference on Interactive Mobile Communication, Technologies and learning (IMCL)

作者： Miladinovic, Igor Schefer-Wenzl, Sigrid Univ Appl Sci Campus Vienna Vienna Austria

ISBN: (纸本)9783030499327;9783030499310

Multi-Access Edge computing (MEC) has been introduced as a part of the 5G architecture to reduce network latency and traffic compared to cloud computing. Applications are running in MEC centers close to the end users improving user experience. Due to limited resources of a MEC center, only selected applications can be placed there. In this paper we introduce a machine learning-based approach to optimize network traffic by intelligent selection of applications running in a MEC center. Our approach is based on current and historical network traffic data to predict the potential for network traffic savings. We propose a smart city architecture for integrating this approach and discuss its benefits and challenges. Using smart waste management as a particular use case, we illustrate the application of our solutions.

关键词： IoT machine learning Smart cities Multi-Access Edge computing

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Study on machine learning and Prediction Model of Adverse Drug Reactions 13

Study on Machine Learning and Prediction Model of Adverse Dr...

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13th international conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Dong, LiHui Zhejiang Pharmaceut Coll Ningbo 315100 Peoples R China

ISBN: (纸本)9781665438926

Based on the free text data of virtual health community, this paper aims to construct a detection model of adverse drug reaction events by using machine learning. Firstly, this paper reviews the research status related to the theme, builds the overall framework of the detection model of adverse drug reaction events, and expounds the basic elements and logical relationship of each layer model. DIKW system is adopted as the model to design the logical framework. then, considering the dependence of output markers, the extraction model of adverse drug reactions based on BiLSTM and CRF is established. By analyzing the structural data of FAERS, we propose to classify the characteristics according to the differences of drug molecular structure, and construct a multi-core function pool. the simulation structure shows that the model can help doctors reduce the risk of potential adverse reactions when prescribing, and assist the adverse drug reaction monitoring institutions to detect the potential adverse drug reactions in advance.

关键词： ADR BiLSTM DIKW CRF machine learning

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Online Solution Based on machine learning for IT Project Management in Software Factory Companies 13

Online Solution Based on Machine Learning for IT Project Man...

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13th IEEE international conference on Computational Intelligence and Communication Networks, CICN 2021

作者： Marchinares, Augusto Hayashida Rodriguez, Ciro Rodriguez Universidad Nacional Mayor de San Marcos Facultad de Ingeniería de Sistemas e Informática Lima Peru

ISBN: (纸本)9781728176956

Project Portfolio Management is relevant for the growth of companies since it favors planning. Project Portfolio Management manages the resources to plan, control, and execute projects and obtain the strategic objectives of the organizations. In Project Portfolio Management, a large amount of data is forged, important for planning new projects in companies;therefore, the need arises to create models that help process and interpret the data. In this context, machine learning is presented as a technological enabler that allows a system, by itself and in an automated way, to learn to discover trends, patterns, and relationships between data;it is an engine of digital transformation of business and that organizations are embracing. therefore, this article aims to compile and review proposals made to implement machine learning in the management of the project portfolio and apply algorithms that allow the development of models that help in the management and evaluation of projects to be developed in a Software Factory. the CRISP-DM methodology is applied to process the data of costs, times, and types of Projects;the Python programming language is used, the dataset corresponds to a Software Factory. the results validate the models implemented using machine learning algorithms, such as regression and decision trees, and thereby obtain the best model for predictions, establishing the correlation between variables and the benefit to be achieved. It is concluded, the implementation of machine learning improves the IT Project Portfolio Management, helping to identify which projects are more profitable and beneficial. © 2021 IEEE.

关键词： Metadata

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A Query-Level Distributed Database Tuning System with machine learning 13

A Query-Level Distributed Database Tuning System with Machin...

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13th IEEE international conference on Joint Cloud computing (JCC)

作者： Fang, Xiang Zou, Yi Fang, Yange Tang, Zhen Li, Hui Wang, Wei Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China Univ Chinese Acad Sci Nanjing Coll Beijing Beijing Peoples R China

ISBN: (数字)9781665462853

ISBN: (纸本)9781665462853

Knob tuning is important to improve the performance of database management system. However, the traditional manual tuning method by DBA is time-consuming and error-prone, and can not meet the requirements of different database instances. In recent years, the research on automatic knob tuning using machine learning algorithm has gradually sprung up, but most of them only support workload-level knob tuning, and the studies on query-level tuning is still in the initial stage. Furthermore, few works are focus on the knob tuning for distributed database. In this paper, we propose a query-level tuning system for distribute database with the machine learning method. this system can efficiently recommend knobs according to the feature of the query. We deployed our techniques onto CockroachDB, a distribute database, and experimental results show that our system achieves higher performance under typical OLAP workload. For all categories of queries, our system reduces the latency by 9.2% on average, and for some categories of queries, this system reduces the latency by more than 60%.

关键词： query-level knob tuning distributed database machine learning

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SUpport for communication with deaf and dumb patients via few-shot machine learning 15

SUpport for communication with deaf and dumb patients via fe...

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14th international conference on ICT, Society, and Human Beings, ICT 2021, 18th international conference on Web Based Communities and Social Media, WBC 2021 and 13th international conference on e-Health, EH 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021

作者： Shovkoplias, Grigorii Tkachenko, Mark Asadulaev, Arip Alekseeva, Olga Dobrenko, Natalia Kazantsev, Daniil Vatian, Alexandra Shalyto, Anatoly Gusarova, Natalia ITMO University Saint Petersburg197101 Russia

ISBN: (纸本)9789898704306

Improving healthcare quality and patient safety for patients with disabilities is one of the most important goals of e-Health. A large percentage of these patients are persons with disabling hearing loss, i.e. partially or completely deaf and dumb. In our work, we discuss the opportunity of fast Sign Language processing having only a small number of examples. We investigated the possibility of classifying datasets with an extremely small number of samples of electromyograms of deaf and dumb gestures using few-shots learning methods. Several such methods have been considered - Matching Networks, Model-Agnostic Meta-learning, and Prototypical Networks. the developed methodology makes it possible to train electromyogram classifiers using an extremely small amount of data for other deaf and dumb sign languages. To do this, it is enough to collect a small dataset for re- or additional training of these models for the classification of another language, which is easy to accomplish in practice by means of a small session of recording the gestures of several deaf-mute people - speakers of a particular sign language. In our study, we selected threemain few-shot learning approaches and compared them on small sign language dataset. Based on our results, one can choose the best models and their modifications to adapt to the practice task. © 14th international conference on ICT, Society, and Human Beings, ICT 2021, 18th international conference on Web Based Communities and Social Media, WBC 2021 and 13th international conference on e-Health, EH 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.

关键词： machine learning

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Fake News Detection via English-to-Spanish Translation: Is It Really Useful? 13th

Fake News Detection via English-to-Spanish Translation: Is I...

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13th international conference on Social computing and Social Media (SCSM) part of the 23rd international conference on Human-Computer Interaction (HCI)

作者： Ruiz, Sebastian Providel, Eliana Mendoza, Marcelo Univ Valparaiso Escuela Ingn Civil Informat Valparaiso Chile Univ Tecn Federico Santa Maria Dept Informat Santiago Chile

ISBN: (纸本)9783030776251;9783030776268

Social networks are used every day to report daily events, although the information published in them many times correspond to fake news. Detecting these fake news has become a research topic that can be approached using deep learning. However, most of the current research on the topic is available only for the English language. When working on fake news detection in other languages, such as Spanish, one of the barriers is the low quantity of labeled datasets available in Spanish. Hence, we explore if it is convenient to translate an English dataset to Spanish using Statistical machine Translation. We use the translated dataset to evaluate the accuracy of several deep learning architectures and compare the results from the translated dataset and the original dataset in fake news classification. Our results suggest that the approach is feasible, although it requires high-quality translation techniques, such as those found in the translation's neural-based models.

关键词： Fake news English-to-Spanish translation Statistical machine Translation Deep learning Twitter

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Applications of machine learning Techniques in Genetic Circuit Design 2021

Applications of Machine Learning Techniques in Genetic Circu...

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2021 13th international conference on machine learning and computing, icmlc 2021

作者： Zhu, Jiajie Zhang, Qi Forouraghi, Babak Wang, Xiao Saint Joseph's University Department of Computer Science PhiladelphiaPA United States Arizona State University School of Biological and Health Systems Engineering TempeAZ United States

ISBN: (纸本)9781450389310

Construction of mathematical models to investigate genetic circuit design is a powerful technique in synthetic biology with real-world applications in biomanufacturing and biosensing. the challenge of building such models is to accurately discover flow of information in simple as well as complex biological systems. However, building synthetic biological models is often a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. the primary goal of this study was to investigate the utility of various machine learning (ML) techniques to accurately construct mathematical models for predicting gene expressions in genetic circuit designs. Specifically, classification and regressions models were built using Random Forrest (RF), Support Vector machines (SVM), and Artificial Neural Networks (ANN). the obtained accuracy of the regression model using RF and ANN yielded R2 scores of 0.97 and 0.95, respectively, compared to the best score of 0.63 obtained in an earlier study. Furthermore, a classifier model was built using the green fluorescent protein (GFP) measurements obtained from the experiments conducted in this work. Biologists use GFP as an indicator of gene expression, enabling easy measurement of its protein level in the living cells. the measured GFP values were predicted with 100% accuracy by both RF and ANN classifier models while identifying various synthetic gene circuit patterns. the paper also highlights importance of the relevant data preparation techniques to ensure high accuracy is obtained by the utilized ML models. © 2021 ACM.

关键词： Gene expression

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