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Proceedings of the 3rd international conference on Big data Research

作者： Iwao Fujino Christophe Claramunt Abdel-Ouahab Boudraa Tokai University Tokyo Japan Shanghai Maritime University & Naval Academy Research Institute Lanvéoc-Poulmic France Naval Academy Research Institute Lanvéoc-Poulmic France

ISBN: (纸本)9781450372015

AIS (Automatic Identification System) data received from moving vessels over an area of interest can be of very much interest for deriving maritime trajectory patterns. In this paper, a novel approach to extract course patterns from AIS data of vessels is presented. From machine learning and natural language processing principles, a topic model might be used for extracting implicit patterns underlying massive and unstructured collection of incoming data. To apply topic model to AIS data, PQk-means vector quantization to convert AIS data record to code documents is introduced. Then, a topic model is applied to extract course patterns from AIS data. In fact, courses, not only encompasses trajectory locations, but also headings and speeds, are recognized by the proposed algorithm. The performance of PQk-means is evaluated using the relative root mean square error and elapsed time. The potential of the approach is illustrated by a series of experimental results derived from practical AIS data set in a region of North West France.

关键词： Topic Model Course pattern Extraction PQk-means Automatic Identification System (AIS) Vector Quantization Maritime Big data

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Deep learning Envisioned Accident Detection System 6th

Deep Learning Envisioned Accident Detection System

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Alam, Intekhab Verma, Ayush Khari, Manju Jawaharlal Nehru University New Delhi110067 India

ISBN: (纸本)9783031530814

The conflict between computational overhead and detection accuracy affects nearly every Automated Accident Detection (AAD) system. Although the accuracy of detection and classification approaches has recently improved dramatically, the systems’ high computational resource requirements make them unsuitable for deployment in situations when immediate feedback is required. This paper suggests a strategy for developing an accurate, cost-efficient automatic collision identification system that can be deployed with a minimal amount of hardware. The AAD system is divided into three key phases—Detection, Tracking, and Classification and offers computationally reduced intensive algorithms for each of these stages. For the detection phase, YOLOv3, it uses a deep learning model that has been trained through knowledge distillation. Its accuracy is on par with that of YOLO (You-Only-Look-Once). On the basis of the information inference and data mining techniques COCO (Common Objects in Context) dataset, YOLOv3 surpasses all other identification techniques considering runtime complexity, averaging an astounding 30 frames/second on a low-end system. It attains an Average Precision (AP) mark of 44.8. For the tracking phase, the system makes use of SORT (Simple Online Real-time Tracking). Since a radial basis kernel with a support vector machine works efficiently, an area under the curve (or AUC) score of 0.92 is achieved. Alongside this, this paper examines a number of machine-learning techniques for the classification stage. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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AI*IA 2013: Advances in Artificial Intelligence: XIIIth international conference of the Italian Association for Artificial Intelligence, Turin, Italy, December 4-6, 2013. Proceedings 1

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丛书名： Lecture Notes in Computer Science

2013年

作者： Mario Alviano, Carmine Dodaro, Francesco Ricca (auth.), Matteo Baldoni, Cristina Baroglio, Guido Boella, Roberto Micalizio (eds.)

ISBN: (数字)9783319035246

ISBN: (纸本)9783319035239;9783319035246

This book constitutes the refereed proceedings of the 13th international conference of the Italian Association for Artificial Intelligence, AI*IA 2013, held in Turin, Italy, in December 2013. The 45 revised full papers were carefully reviewed and selected from 86 submissions. The conference covers broadly the many aspects of theoretical and applied Artificial Intelligence as follows: knowledge representation and reasoning, machine learning, natural language processing, planning, distributed AI: robotics and MAS, recommender systems and semantic Web and AI applications.

关键词：人工智能l。机器人学；数据采矿和知识发现；数理逻辑和正式语言；由抽象设备的计算；用户接口和人的计算机相互作用；模式识别

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Compare machine learning Models in Text Classification Using Steam User Reviews 2021

Compare Machine Learning Models in Text Classification Using...

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2021 3rd international conference on Software Engineering and Development (ICSED)

作者： Youchen Miao Zeyu Jin Yumeng Zhang Yuchen Chen Junren Lai David R. Cheriton School of Computer Science University of Waterloo Canada The Fu Foundation School of Engineering and Applied Science Columbia University USA University of North Carolina at Chapel Hill USA Michigan State University USA James Cook University Singapore

ISBN: (纸本)9781450385213

Text Classification and Sentiment Analysis of game reviews are viewed as important parts in not only academic fields but also in game studies. In this paper, with more than 400 thousand game reviews on Steam platform, we preprocess the data using different libraries (sklearn, nltk, and spaCy) and use them as inputs to build three sentiment classification models based on different algorithms (Naive Bayes, SVM, and Random Forest). In contrast to previous studies that only focus on different sentiment analysis models, our paper also highlights the use of different APIs to preprocess the data and their corresponding model performance. The results show that no matter which API we choose, Random Forest models always perform the best. However, in terms of training time, Naive Bayes is the fastest. This work can be used to apply grid search for researchers to automatically find the optimum API before conducting sentiment analysis in the future.

关键词： text mining sentiment classification models sentiment analysis sklearn nltk spaCy

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Assessment on Structural Integrity of In-service machine Using De-noised Vibrational Modal data and Artificial Neural Network

Assessment on Structural Integrity of In-service Machine Usi...

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2018 3rd international conference on Design, Mechanical and Material Engineering (D2ME 2018)

作者： Zhi Chao Ong Ee Teng Yap Zubaidah Ismail Shin Yee Khoo Department of Mechanical Engineering Faculty of EngineeringUniversity of Malaya Department of Civil Engineering Faculty of EngineeringUniversity of Malaya

The recent oil price drop creates a demand for swift action within oil and gas industry to shift focus from increasing daily production rates,to optimizing existing assets in achieving *** machinery,one of the industry's key asset many times failed due to high amplitude vibration that contributes to accelerated wear and tear and subsequently results in high cycle fatigue *** such there is a need to develop a structural integrity assessment for in-service machinery for continuous and safe ***-based method such as Experimental Modal Analysis(EMA) is widely used for damage detection on civil and piping system under stationary ***,in industrial applications,system shutdown is very *** is also undesirable in this case due to the dominant ambient and system disturbances on the in-service *** alternative method called Impact-Synchronous Modal Analysis(ISMA) is developed to perform modal analysis under noisy *** the ISMA technique in de-noising the non-synchronous disturbances at upstream could generate a cleaner and static-like modal data downstream for *** Neuron Networks(ANN) is then applied extensively in structural damage identification purposes based on changes in modal data due to its excellent pattern recognition *** leveraging on the latest technologies,*** and ANN as proposed,it allows real-time monitoring of assets,in this case,the machines,as well as the ability to transform continuous streams of data into useful information to predict damages.

关键词： Assessment on Structural Integrity of In-service machine Using De-noised Vibrational Modal data and Artificial Neural Network Modal service

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DVC in Open Source ML-development: The Action and the Reaction

DVC in Open Source ML-development: The Action and the Reacti...

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AI Engineering - Software Engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Lorena Barreto Simedo Pacheco Musfiqur Rahman Fazle Rabbi Pouya Fathollahzadeh Ahmad Abdellatif Emad Shihab Tse-Hsun Peter Chen Jinqiu Yang Ying Zou Concordia University Montréal Canada Queen’s University Kingston Canada University of Calgary Calgary Canada

ISBN: (数字)9798400705915

ISBN: (纸本)9798350352191

machine learning (ML) systems are gaining popularity, reshaping various domains ranging from customer services to software engineering. The effectiveness of ML systems is dependent on the quality of their training data. Therefore, practitioners invest substantial time experimenting with different data, parameters, and models to guarantee the quality of the end system. Prior work highlighted unique challenges of developing ML systems, particularly concerning versioning data and models. Recently, various tools such as DVC and MLFlow have emerged to aid developers in the storage and tracking of data. Despite their growing popularity, very little is known about their usage patterns and impact on open-source software (OSS) systems. To address this gap, we conducted an empirical study on 56 GitHub OSS projects that use DVC to understand the DVC usage pattern and the impact of using DVC on the software development process. We found that Versioning and tracking is the most adopted DVC feature, being utilized by all 56 projects and being the only adopted feature in 85.7% of them. Furthermore, we found that DVC has a significant impact on the software development process indicators such as the number of created pull requests (PRs), and the number of bug-fix commits. For instance, our findings showed that DVC causes a peak in the number of commits and PRs at the moment of the adoption, followed by a long-term decrease. We believe that our findings can assist practitioners in tailoring tools to better meet user requirements and help organizations realize potential outcomes of adopting such *** CONCEPTS• Software and its engineering → Software configuration management and version control systems.

关键词： Customer services Training data Organizations machine learning Control systems data models Distance measurement

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learning Classifier System Improvement Based on Probability Driven and Neural Network Driven Approaches

Learning Classifier System Improvement Based on Probability ...

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Eastern European Regional conference on the Engineering of Computer Based Systems

作者： Ladislav Clementis Institute of Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Ilkovi?ova 3 842 16 Bratislava Slovakia

ISBN: (纸本)9781479908530

Rule-based systems like learning Classifier System are widely used in areas where data mining, data classification and pattern recognition tasks are essential. It is often difficult to address the knowledge base of these complex classifier systems, which is usually a set of classifiers. Therefore we use evolutionary processes like genetic algorithms to develop their knowledge *** provide modified learning Classifier System enriched by probability model to help build an appropriate knowledge base more effectively. We included a neural network into the action selection process and therefore action can be determined accordingly with a probability model. We provide simulation results which demonstrate efficiency of learning processes to compare these approaches.

关键词： learning classifier system the battleship game neural network problem probability model decision making

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Arabic Dialect Identification in Social Media

Arabic Dialect Identification in Social Media

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international conference on Computer Applications & Information Security (ICCAIS)

作者： Reem AlYami Rabeah AlZaidy Information and Computer Science Department King Fahd University of Petroleum & Minerals

ISBN: (数字)9781728142135

ISBN: (纸本)9781728142142

Although the Arabic language is spoken in twenty-two countries by more than 250 million speakers, it is still considered by Natural Language Processing NLP practitioners as a low resource language. Formal sources of Arabic texts are typically written in Modern Standard (or Written) Arabic (MSA), which is a form that is used in formal writing and taught in schools to Arabic speakers. However, informal communication among Arabic speakers is through informal local diglossic dialects. A diglossic language is one where the speakers of the same language have varying dialects. In Arabic, there are multiple dialects in different regions of the Arab world: Gulf, Levantine and North Africa. Users commonly communicate in social media using their local dialect rather than the formal MSA. This introduces a core NLP problem for Arabic, which is dialect identification. It is essential to identify the specific dialect prior to performing tasks such as parsing, tokenizing and other downstream tasks such as semantic inferences. Processing massive amounts of data written in these local dialects requires this identification step to improve accuracies, especially for automatic text comprehension tasks. Although Arabic dialects share a majority of common words, it is not uncommon for the same word to have different meanings across dialects. In addition to improving NLP task accuracies, Arabic Dialect Identification ADI enables a finer-grained demographic identification for mining texts related to consumer reports, health forums, entertainment and tourism reviews, and many others which ultimately lead to improved services for each demographic. The problem of ADI has been addressed by several studies such as (Al-Walaie & Khan, 2017), and (Harrat et al., 2019). Some works focus mainly on curating data sets for the problem such as the Sham dataset proposed by (Abu Kwaik et al., 2018). In this work we focus on both tasks: we curate an Arabic dialect dataset for two variants of Arabic (Saudi A

关键词： Task analysis Support vector machines Natural language processing machine learning Training Computational modeling data models

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Enhancing the Predictive Accuracy of Lithium-Ion Battery Health in EV Using GBM-XGBoost

Enhancing the Predictive Accuracy of Lithium-Ion Battery Hea...

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Advancement in Technology (ICONAT), international conference for

作者： Jaidev Kumar N Saikumari Soumya Mishra P. Sukumar S Vinayagam Anil Kumar Lamba Department of Chemistry Hariom Saraswati P. G. College Dhanauri Haridwar Uttarakhand India Department of S&H R.M.K. College of Engineering and Technology Chennai Tamilnadu India Quality Assurance Cell KIIT Deemed University Bhubaneswar Odisha India Department of Computer Science and Engineering Nandha Engineering College (Autonomous) Erode Tamilnadu India Department of Bioscience Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India School of Computer Science and Engineering Geeta University Delhi NCR Panipat Haryana India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

The growing recognition of electric vehicles (EVs) requires dependable and precise techniques for forecasting the condition of lithium-ion batteries, a crucial element that impacts vehicle efficiency and lifespan. Conventional techniques, such as Linear Regression and Random Forest Regression, frequently lack the necessary predictive accuracy. This study aims to improve the accuracy of battery health measurements by proposing a solution that utilizes Gradient Boosting machines and XGBoost (GBM-XGBoost). The proposed approach utilizes sophisticated machine learning methods to analyze extensive datasets, manage incomplete data points, and capture complex non-linear associations among variables. Thorough data preprocessing, which includes conducting correlation analysis to pick features, guarantees that only the most relevant predictors are utilized. The GBM-XGBoost model has been carefully trained and optimized, showing exceptional performance with a Mean Absolute Error (MAE) of 1.23, Mean Squared Error (MSE) of 2.45, Root Mean Squared Error (RMSE) of 1.56, a Coefficient of Determination ($\mathrm{R}^{machine}$ Score) of ${0 . 9 8}$, and a Mean Absolute Percentage Error (MAPE) of ${2.10\%}$. The suggested approach offers substantial improvements over current methodologies by dramatically reducing error rates and enhancing predictive power. This leads to more efficient battery management systems and eventually extends the operational lifespan of electric vehicle (EV) batteries.

关键词： Lithium-ion batteries Electric potential Analytical models Accuracy Error analysis Linear regression Battery management systems Predictive models data models Random forests

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Ontology-based venous thromboembolism risk assessment model developing from medical records

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2019年第4-Sup期19卷 151-151页

作者： Yang, Yuqing Wang, Xin Huang, Yu Chen, Ning Shi, Juhong Chen, Ting Tsinghua Univ State Key Lab Intelligent Technol & Syst Inst Artificial Intelligence Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Tsinghua Fuzhou Inst Digital Technol Beijing 100084 Peoples R China Peking Union Med Coll Hosp Dept Ultrasound Beijing Peoples R China Peking Union Med Coll Beijing Peoples R China Peking Union Med Coll Hosp Dept Resp Beijing Peoples R China

Background Padua linear model is widely used for the risk assessment of venous thromboembolism (VTE), a common but preventable complication for inpatients. However, genetic and environmental differences between Western and Chinese population limit the validity of Padua model in Chinese patients. Medical records which contain rich information about disease progression, are useful in mining new risk factors related to Chinese VTE patients. Furthermore, machine learning (ML) methods provide new opportunities to build precise risk prediction model by automatic selection of risk factors based on original medical records. Methods Medical records of 3,106 inpatients including 224 VTE patients were collected and various types of ontologies were integrated to parse unstructured text. A workflow of ontology-based VTE risk prediction model, that combines natural language processing (NLP) and machine learning (ML) technologies, was proposed. Firstly ontology terms were extracted from medical records, then sorted according to their calculated weights. Next importance of each term in the unit of section was evaluated and finally a ML model was built based on a subset of terms. Four ML methods were tested, and the best model was decided by comparing area under the receiver operating characteristic curve (AUROC). Results Medical records were first split into different sections and subsequently, terms from each section were sorted by their weights calculated by multiple types of information. Greedy selection algorithm was used to obtain significant sections and terms. Top terms in each section were selected to construct patients' distributed representations by word embedding technique. Using top 300 terms of two important sections, namely the 'Progress Note' section and 'Admitting Diagnosis' section, the model showed relatively better predictive performance. Then ML model which utilizes a subset of terms from two sections, about 110 terms, achieved the best AUC score, of 0.973 +/- 0

关键词： Medical record Venous thromboembolism (VTE) Natural language processing (NLP) Risk assessment machine learning (ML)

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