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4th International Conference on Renewable Energies Offshore, RENEW 2020

作者： Chatterjee, Joyjit Dethlefs, Nina Department of Computer Science and Technology Big Data Analytics Research Group University of Hull Hull United Kingdom

ISBN: (纸本)9780367681319

Offshore wind farm operators need to make short-term decisions on planning vessel transfers to turbines for preventive or corrective maintenance. These decisions can play a pivotal role in ensuring maintenance actions are carried out in a timely and cost-effective manner. The present optimization of offshore vessel transfer uses mathematical models rather than learning decisions from historical data. In this paper, we design a simulated environment for an offshore wind farm based on Supervisory Control & Acquisition (SCADA) data and alarm logs of historical faults in an operational turbine. Firstly, we utilise a state-of-art decision tree model to predict fault types using SCADA features, and provide explainable decisions. Next, we apply deep reinforcement learning to automatically learn maintenance priorities corresponding to different fault types for ensuring prioritized vessel transfers for critical conditions, and deciding on optimal vessel fleet size. This can lead to significant savings in maintenance costs for the offshore wind industry. © 2021 Taylor & Francis Group, London.

关键词： Offshore wind farms

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Automated Testing for Service-Oriented Architecture: Leveraging Large Language Models for Enhanced Service Composition

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IEEE Access 2025年 13卷 89627-89640页

作者： Altin, Mahsun Mutlu, Behcet Kilinc, Deniz Cakir, Altan Istanbul Technical University Department of Computer Engineering Sariyer Istanbul34467 Turkey Wingie Enuygun Group Atasehir Istanbul34750 Turkey University of Bakircay Department of Computer Engineering Menemen Izmir35665 Turkey Istanbul Technical University Department of Data Science and Analytics Sariyer Istanbul34467 Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Sariyer Istanbul34467 Turkey

This article explores the application of Large Language Models (LLMs), including proprietary models such as OpenAI’s ChatGPT 4o and ChatGPT 4o-mini, Anthropic’s Claude 3.5 Sonnet and Claude 3.7 Sonnet, and Google’s Gemini 1.5 Pro, Gemini 2.0 Flash, and Gemini 2.0 Flash-Lite, as well as open-source alternatives including Qwen2.5-14B-Instruct-1M, and commercially accessed models such as DeepSeek R1 and DeepSeek V3, which were tested via APIs despite having open-source variants, to automate validation and verification in Application Programming Interface (API) testing within a Service-Oriented Architecture (SOA). Our system compares internal responses from the Enuygun Web Server against third-party API outputs in both JSON and XML formats, validating critical parameters such as flight prices, baggage allowances, and seat availability. We generated 100 diverse test scenarios across varying complexities (1–4 flight results) by randomly altering request and response parameters. Experimental results show that Google Gemini 2.0 Flash achieved high accuracy (up to 99.98%) with the lowest completion time (85.34 seconds), while Qwen2.5-14B-Instruct-1M exhibited limited capability in processing complex formats. Models such as OpenAI’s ChatGPT and Anthropic’s Claude Sonnet models also demonstrated strong performance in single-flight validation scenarios, making them suitable for low-latency, high-precision tasks. Our findings indicate that some open-source models can offer promising cost-effective alternatives, though performance significantly varies. This integration of LLMs reduced manual workload, improved test scalability, and enabled real-time validation across large-scale datasets. As LLM technologies mature, we anticipate further advances in automation, accuracy, and efficiency in software validation systems. © 2013 IEEE.

关键词： Service oriented architecture (SOA)

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Design and Implementation for Innovative Tourism Expert System based on Artificial Intelligence

Design and Implementation for Innovative Tourism Expert Syst...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R. Prasanth R. Anitha T. Sarathamani M. Manoj Department of Management Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Tamilnadu India Department of Computer Science and Engineering (AI) Brainware University Kolkata India Department of Computer Science with Data Analytics KPR College of Arts Science and Research Coimbatore Tamilnadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The tourism sector has long been looked at as a vital engine of economic expansion. The most recent developments in the tourism sector have benefitted governments, international organizations, common people and academia. For developing the tourism sector, it is important to analyze and understand tourist visitor patterns. Tourist forecasting helps maximize the tourism-based economy. The international tourist movement has grown at an unprecedented rate in recent decades, and many countries are concentrating on the tourism economy. While tourists visit a tourist place, spend a significant amount of money on their enjoyment, company, and hotel accommodations. ML algorithms are making significant contributions to forecasting system, and several researchers are working to improve the model’s accuracy. This research has proposed a machine learning approach for forecasting worldwide tourist arrivals that analyses prior quantitative information about visitors so that the governing body can arrange the necessary facilities for visitors accordingly. The proposed model is applied to a dataset compiled over a decade from all countries around the world. The detailed proposed approach and the various results of experiments are explained in this paper.

关键词： Virtual assistants Transforms Predictive models Market research Prediction algorithms Real-time systems Expert systems Forecasting Predictive analytics Meteorology

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Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey

arXiv

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arXiv 2023年

作者： Ghojogh, Benyamin Ghodsi, Ali Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada

This is a tutorial paper on Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), and their variants. We start with a dynamical system and backpropagation through time for RNN. Then, we discuss the problems of gradient vanishing and explosion in long-term dependencies. We explain close-to-identity weight matrix, long delays, leaky units, and echo state networks for solving this problem. Then, we introduce LSTM gates and cells, history and variants of LSTM, and Gated Recurrent Units (GRU). Finally, we introduce bidirectional RNN, bidirectional LSTM, and the Embeddings from Language Model (ELMo) network, for processing a sequence in both directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Machine Learning Techniques for Autism Spectrum Disorder: current trends and future directions

Machine Learning Techniques for Autism Spectrum Disorder: cu...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Kainat Khan Rahul Katarya Department of Computer Science & Engineering Big Data Analytics and Web Intelligence Laboratory Delhi Technological University New Delhi India

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD.

关键词： Autism Medical treatment Machine learning Market research Medical tests Biology data mining

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Extracting Biomedical Entities from Noisy Audio Transcripts 30

Extracting Biomedical Entities from Noisy Audio Transcripts

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ebadi, Nima Morgan, Kellen Tan, Adrian Linares, Billy Osborn, Sheri Majors, Emma Davis, Jeremy Rios, Anthony Department of Electrical and Computer Engineering The University of Texas San Antonio United States Department of Management Science and Statistics The University of Texas San Antonio United States Data Analytics The University of Texas San Antonio United States Department of Information Systems and Cyber Security The University of Texas San Antonio United States Department of Neurology Division of Neuropsychology UT Health San Antonio United States

ISBN: (纸本)9782493814104

Automatic Speech Recognition (ASR) technology is fundamental in transcribing spoken language into text, with considerable applications in the clinical realm, including streamlining medical transcription and integrating with Electronic Health Record (EHR) systems. Nevertheless, challenges persist, especially when transcriptions contain noise, leading to significant drops in performance when Natural Language Processing (NLP) models are applied. Named Entity Recognition (NER), an essential clinical task, is particularly affected by such noise, often termed the ASR-NLP gap. Prior works have primarily studied ASR's efficiency in clean recordings, leaving a research gap concerning the performance in noisy environments. This paper introduces a novel dataset, BioASR-NER, designed to bridge the ASR-NLP gap in the biomedical domain, focusing on extracting adverse drug reactions and mentions of entities from the Brief Test of Adult Cognition by Telephone (BTACT) exam. Our dataset offers a comprehensive collection of almost 2,000 clean and noisy recordings. In addressing the noise challenge, we present an innovative transcript-cleaning method using GPT4, investigating both zero-shot and few-shot methodologies. Our study further delves into an error analysis, shedding light on the types of errors in transcription software, corrections by GPT4, and the challenges GPT4 faces. This paper aims to foster improved understanding and potential solutions for the ASR-NLP gap, ultimately supporting enhanced healthcare documentation practices. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Error analysis

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Chronic Kidney Disease Classification Using Machine Learning Classifiers

Chronic Kidney Disease Classification Using Machine Learning...

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2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Alshakrani, Sara Taha, Ranyah Hewahi, Nabil Big Data Science and Analytics College of Science University of Bahrain Bahrain College of Information Technology University of Bahrain Department of Computer Science Bahrain

ISBN: (纸本)9781665440325

One of the important health problems is chronic kidney disease (CKD). It is increasing every day as a result of poor eating habits, insufficient water consumption, and lack of health awareness. CKD is a condition in which the kidneys are damaged over time due to a variety of causes, resulting in a progressive and irreversible loss of kidney function. Technological advancements, such as Machine Learning (ML), have a significant influence on the health sector by giving more accurate detection and successful treatment of many chronic diseases. This research paper explores a variety of ML techniques to predict kidney disease. The objective is to identify which ML classifier could be the best classifier for detecting and predicting CKD. In this work, seven ML approaches are used to predict kidney disease, namely, Gaussian Nave Bayes (GNB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Logistic Regression (LR), AdaBoost and Gradient Boosting. RF and GNB scored better than the other classifiers, with 100% accuracy. © 2021 IEEE.

关键词： Adaptive boosting

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DISCO: Internal Evaluation of Density-Based Clustering

arXiv

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arXiv 2025年

作者： Beer, Anna Krieger, Lena Weber, Pascal Ritzert, Martin Assent, Ira Plant, Claudia Faculty of Computer Science University of Vienna Vienna Austria IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich Jülich Germany UniVie Doctoral School Computer Science University of Vienna Vienna Austria Data Science @ Uni Vienna University of Vienna Vienna Austria Institute of Computer Science and Campus Institute Data Science University of Göttingen Göttingen Germany Department of Computer Science Aarhus University Aarhus Denmark

In density-based clustering, clusters are areas of high object density separated by lower object density areas. This notion supports arbitrarily shaped clusters and automatic detection of noise points that do not belong to any cluster. However, it is challenging to adequately evaluate the quality of density-based clustering results. Even though some existing cluster validity indices (CVIs) target arbitrarily shaped clusters, none of them captures the quality of the labeled noise. In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels. DISCO reliably evaluates density-based clusters of arbitrary shape by assessing compactness and separation. It also introduces a direct assessment of noise labels for any given clustering. Our experiments show that DISCO evaluates density-based clusterings more consistently than its competitors. It is additionally the first method to evaluate the complete labeling of density-based clustering methods, including noise labels. © 2025, CC BY.

关键词： Cluster analysis

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Parameter Based Literature Survey of COVID-19 Mortality Dynamics Using Machine Learning Techniques 6

Parameter Based Literature Survey of COVID-19 Mortality Dyna...

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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Sewariya, Manish Katarya, Rahul Delhi Technical University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science New Delhi India

ISBN: (纸本)9781665435598

The pandemic COVID-19 (SARS-CoV-2) known as the coronavirus is spreading at a very alarming rate in the world which is quite a threatening situation. In terms of disease presentation, the global epidemic COVID-19 can be traced to the ancestral influenza viruses. COVID-19 outbreak is a very perturbing situation for the high rate of mortality as reported in recent studies. The mortality and morbidity rates have always been a concerning issue in the determination of the scale of epidemics. These mortality rate predictions subsume an interesting factor within them i.e. variance of mortality rates across regions. The advancement in machine learning-based tools is a vital component corresponding to addressing the 'heterogeneity in fatality rates'. This review envelopes the recent developments in mortality rate predictions for COVID-19. The review helps in gaining an insight into Spatio-temporal mortality dynamics which will be fruitful with reference to future control-strategy implementation. By observing the Spatio-temporal pattern of mortality dynamics, the inter-dependence of various factors can be explored, and hence, mortality rates can be reduced in the upcoming time. © 2021 IEEE.

关键词： COVID-19

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Automated Machine Learning (AutoML): Transforming data science Workflows in Big data analytics

Automated Machine Learning (AutoML): Transforming Data Scien...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammed I. Nofal Banan Mohammad Alfalah Fayiz Momani Muaadh Mukred Omar Zughoul Fathye Mohammed Department of Business Intelligence & Data Analytics Department University of Petra Amman Jordan Amman Jordan Department of Banking and Financial Science University of Petra Amman Jordan Amman Jordan Department of Business Intelligence & Data Analytics University of Petra Amman Jordan Amman Jordan Department of Business Analytics Sunway Business School- Sunway University Petaling Jaya Bandar Sunway Selangor Malaysia Department of Computer information system and computer science Ahmad bin Mohammad Military College (ABMMC) Shahaniya Qatar Department of Business Analytics Sunway Business School University Petaling Jaya Bandar Sunway Selangor Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The introduction of Automated Machine Learning (AutoML) can be considered a game-changing development in the field of data science and more specifically, in the area of big data analytics. AutoML brings an expert-level approach to non-experts, allowing them to conduct intricate data analysis because it simplifies complicated tasks such as data preprocessing, feature engineering, model testing, hyper parameter adjustment of the models, and finally deploying the trained models. This advancement lessens reliance on qualified personnel, improves efficiency and helps cut down any associated lag in time from when an organization begins to utilize large amounts of data. This paper illustrates AutoML facilitates progress in multiple components for modern times: evolution, accessibility, integration into numerous sectors, and decision making. Respecting AutoML's various roles, fostering innovation while promoting inclusivity comes with certain challenges such as ethical considerations and algorithmic transparency. To summarize, AutoML will most likely disrupt analytical maturity and strengthen competitive advantages deemed sustainable today.

关键词： Analytical models Technological innovation Decision making Big data data science Automated machine learning Market research data models Tuning Testing

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