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A semantic software development based on random probability distribution model

International Journal of Technology Intelligence and Planning 2021年第1期13卷 84-102页

作者： Mei, Qiang Huang, Yanmei Anandhan, Prathik Parthasarathy, R. College of Blockchain Jiangxi University of Engineering Xinyu338029 China Center for Big Data Analytics Jiangxi University of Engineering Xinyu338029 China VIT University Tamil Nadu Vellore India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College India

Machine learning strategies to automate data processing are urgently needed with the increasing accumulation of biological datasets. In recent years, software development has received considerable attention because of its interpretation of the model topics originating from natural language processing. In this article, the application and development of semantic software development emphasise the understanding of semantic software development. Furthermore, literature was reviewed and evaluated in detail based on semantic software development models to the clustering-based random probability distribution model (CRPDM). However, the related studies offered an outlook for the use of topical models to create semantic software applications according to the types of models based on the random probability entity. The simulation of CRPDM improves the accuracy and performance of analysing semantic software applications. Furthermore, the theme models are a promising method in semantic software development research for different applications that have been firmly researched in this paper. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Semantics

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Estimation of ship operational performance degradation using deep-learning-based fuel oil consumption prediction models

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International Journal of Naval Architecture and Ocean engineering 2025年 17卷

作者： Park, Donghyun Jung, Jae-Yoon Park, Beom Jin Department of Big Data Analytics Kyung Hee University Gyeonggi-do Korea Republic of Department of Industrial and Management Systems Engineering Kyung Hee University Gyeonggi-do Korea Republic of Advanced-Intelligent Ship Research Division Korea Research Institute of Ships and Ocean Engineering Daejeon Korea Republic of

This paper proposes a novel method for estimating ship operational performance degradation (SOPD) using a fuel oil consumption (FOC) prediction model based on deep neural networks with shortcut connections. The model leverages operational and environmental data from a crude oil tanker over a 21-month period to predict FOC and assess SOPD. A cumulative anchoring effect is introduced as a new feature of the FOC prediction model, capturing the impact of biofouling caused by prolonged anchoring in warm waters. In this study, SOPD is considered the additional fuel rate required for a journey leg due to degradation, which is estimated by comparing predicted FOC with and without the cumulative anchoring effect. The SOPD estimation is illustrated according to increasing journey legs based on the FOC prediction models. The proposed SOPD estimation method provides valuable insights for shipping companies to optimize operational schedules and improve fuel efficiency. © 2025 The Society of Naval Architects of Korea

关键词： Deep neural networks

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Analysis and Performance Evaluation of Credit Card Fraud by Multi-model ML

Analysis and Performance Evaluation of Credit Card Fraud by ...

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International Conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Md Aminul Islam A. T. M. Asif Imran Md Habibur Rahman Md Amran Hossen Pabel Bhupesh Kumar Mishra Kashinath Basu Data Science School of Engineering and Technology Cheltenham University of Gloucestershire UK Masters in ICT Bangladesh University of Professionals(BUP) Dhaka Department of Mathematics University of Chittagong Chittagong Bangladesh Master of Science in Business Analytics Wright State University Data Science AI & Modelling Centre (DAIM) University of Hull Data Science School of Engineering Computing and Mathematics Oxford Brookes University UK

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Using credit cards is a convenient and efficient payment mechanism. Credit card fraud significantly impacts financial loss, mental health, and the reputation of financial institutions. This study would incorporate an analysis of the preceding statement about eliminating several obstacles associated with the availability of public data, the presence of unbalanced data, the dynamic nature of fraud tendencies, and the prevalence of false alarms. The authors discuss various machine-learning techniques used to detect credit card fraud. Extreme Learning, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, and XG Boost are among these techniques on the European card benchmark dataset. A comparative evaluation of the effectiveness of machine learning has also been conducted, and precision is increased by incorporating multiple layers. The study’s findings indicate significant improvements in several crucial metrics, including accuracy, f1-score, precision, and AUC curves. XGBoost model achieves optimized values of $99.9 \%$ accuracy. The proposed model outperforms contemporary machine learning approaches in credit card fraud detection. It has been observed that the combination of data balancing techniques significantly reduces the occurrence of false negatives. This study has substantial potential for credit card fraud detection applications in the real world, providing an effective and efficient method for addressing this ongoing critical issue.

关键词： Support vector machines Performance evaluation Logistic regression Accuracy Benchmark testing Credit cards Fraud

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Identifying Micro-influencers on Social Media using User Graph Construction Approach 13

Identifying Micro-influencers on Social Media using User Gra...

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13th International Conference Knowledge and Systems engineering, KSE 2021

作者： Ngo, Doan-Thinh Cao, Cam-Nhung Hoang, Phuong-Linh Ngo, Xuan-Bach Nguyen, Tien-Dat Tran, Duc-Anh Nguyen, Trang M. Tran Ngoc, Linh Nguyen Tran, Mai-Vu Vnu University of Engineering and Technology Hanoi Viet Nam Viettel Big Data Analytics Center Viettel Group Viettel Telecommunication Company Viet Nam

ISBN: (纸本)9781665499750

Nowadays Influencers are responsible for setting trends and influencing public opinion on social media. As a result, identifying and uncovering influencers can assist organizations in having a good impact on their users. We investigated influencer recognition techniques utilizing a graph method in this research, with the goal of categorizing influencers into different sectors. The purpose of this paper is to concentrate the report on creating a graph in the social networking platform Facebook in order to locate influencers. A collection of edges and vertices represent this graph. To discover actual influencers, we create graphs and compare the application of two graph ranking algorithms, PageRank and HITS. © 2021 IEEE.

关键词： Social aspects

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Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery

arXiv

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arXiv 2024年

作者： Wang, Jiaxin Zhang, Lingling Liu, Jun Guo, Tianlin Wu, Wenjun The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an710049 China

We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. GRD aims to identify unlabeled instances in existing pre-defined relations or discover novel relations by assigning instances to clusters as well as providing specific meanings for these clusters. The key challenges of GRD are how to mitigate the serious model biases caused by labeled pre-defined relations to learn effective relational representations and how to determine the specific semantics of novel relations during classifying or clustering unlabeled instances. We then propose a novel framework, SFGRD, for this task to solve the above issues by learning from semi-factuals in two stages. The first stage is semi-factual generation implemented by a tri-view debiased relation representation module, in which we take each original sentence as the main view and design two debiased views to generate semi-factual examples for this sentence. The second stage is semi-factual thinking executed by a dual-space tri-view collaborative relation learning module, where we design a cluster-semantic space and a class-index space to learn relational semantics and relation label indices, respectively. In addition, we devise alignment and selection strategies to integrate two spaces and establish a self-supervised learning loop for unlabeled data by doing semi-factual thinking across three views. Extensive experimental results show that SFGRD surpasses state-of-the-art models in terms of accuracy by 2.36% ∼5.78% and cosine similarity by 32.19%∼ 84.45% for relation label index and relation semantic quality, respectively. To the best of our knowledge, we are the first to exploit the efficacy of semi-factuals in relation extraction. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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A Review on AI Algorithms for Energy Management in E-Mobility Services

arXiv

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arXiv 2023年

作者： Yan, Sen Shah, Maqsood Hussain Li, Ji O’Connor, Noel Liu, Mingming The School of Electronic Engineering SFI Insight Centre for Data Analytics Dublin City University Ireland The Nanjing University of Aeronautics and Astronautics China The Department of Mechanical Engineering University of Birmingham United Kingdom

E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns in the transportation sector. The depletion of fossil fuels, escalating greenhouse gas emissions, and the imperative to combat climate change underscore the significance of transitioning to electric vehicles (EVs). This paper seeks to explore the potential of artificial intelligence (AI) in addressing various challenges related to effective energy management in e-mobility systems (EMS). These challenges encompass critical factors such as range anxiety, charge rate optimization, and the longevity of energy storage in EVs. By analyzing existing literature, we delve into the role that AI can play in tackling these challenges and enabling efficient energy management in EMS. Our objectives are twofold: to provide an overview of the current state-of-the-art in this research domain and propose effective avenues for future investigations. Through this analysis, we aim to contribute to the advancement of sustainable and efficient e-mobility solutions, shaping a greener and more sustainable future for transportation. © 2023, CC BY.

关键词： Energy utilization

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Addressing the Challenge of Missing Medical data in Healthcare analytics: A Focus on Machine Learning Predictions for ICU Length of Stay

Addressing the Challenge of Missing Medical Data in Healthca...

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Intermountain engineering, Technology and Computing (IETC)

作者： Mahmad Isaq Karankot Max Marceau Ethan M. Glenn Rylan P. Fowers David M. Hedges Bonnie Sheehey Bradley M. Whitaker Electrical and Computer Engineering Montana State University Bozeman Montana USA Analytics/Data Science Department SelectHealth Murray Utah USA History and Philosophy Montana State University Bozeman Montana USA

ISBN: (数字)9798350373073

ISBN: (纸本)9798350373080

This research investigates the impact of missing data on the performance of machine learning algorithms, with a particular focus on the MIMIC-IV dataset. This project aims to investigate the extent to which missing data negatively impacts the training of machine learning algorithms, and whether demographic groups with a higher proportion of missing data (i.e.,ethnicity) have lower predictive accuracy. Using advanced machine learning and data analysis techniques, our results highlight important considerations related to missing data in medical datasets and provide useful insights for improving predictive modeling and decision support systems in clinical practice offers.

关键词： Training Decision support systems Machine learning algorithms MIMICs Machine learning Medical services Predictive models

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A Rationale-Centric Framework for Human-in-the-loop Machine Learning 60

A Rationale-Centric Framework for Human-in-the-loop Machine ...

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60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Lu, Jinghui Yang, Linyi Namee, Brian Mac Zhang, Yue The Insight Centre for Data Analytics University College Dublin Ireland School of Computer Science University College Dublin Ireland School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China SenseTime Research

ISBN: (纸本)9781955917216

We present a novel rationale-centric framework with human-in-the-loop - Rationales-centric Double-robustness Learning (RDL) - to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks-especially for few-shot learning scenarios. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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An incremental ensemble learning system for Vietnamese e-commerce product classification 13

An incremental ensemble learning system for Vietnamese e-com...

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13th International Conference Knowledge and Systems engineering, KSE 2021

作者： Linh, Nguyen Tran Ngoc Quan, Vu Hong Ngan, Le Hoang Phu, Tran Duy Le, Hoang-Quynh Data Analytics Center Viettel Group Viettel Telecom Hanoi Viet Nam University of Engineering and Technology Vietnam National University Hanoi Viet Nam

ISBN: (纸本)9781665499750

With the booming of e-commerce platforms, text classification models play an increasingly important role in businesses. Major challenges that businesses would face include dataset imbalance, continuously added data, language specificity and product specificity. In this paper, we propose a scalable incremental machine learning system for industrial-scale deployment in real-world business. The system also includes steps to optimize e-commerce product specifics. The proposal tactics including keyword dictionary mapping, sampling technique and ensemble learning delivered better performance when compared to models without them. Our experiments also showed that minibatch SVM produced good results and might be considerable in a lighter system. © 2021 IEEE.

关键词： Support vector machines

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UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model 20

UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive M...

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20th Workshop on Biomedical Language Processing, BioNLP 2021

作者： Can, Duy-Cat Nguyen, Quoc-An Duong, Quoc-Hung Nguyen, Minh-Quang Nguyen, Huy-Son Ngoc, Linh Nguyen Tran Ha, Quang-Thuy Tran, Mai-Vu VNU University of Engineering and Technology Hanoi Viet Nam Viettel Big Data Analytics Center Viettel Telecommunication Company Viettel Group Viet Nam

ISBN: (纸本)9781954085404

This paper describes a system developed to summarize multiple answers challenge in the MEDIQA 2021 shared task collocated with the BioNLP 2021 Workshop. We propose an extractive summarization architecture based on several scores and state-of-the-art techniques. We also present our novel prosper-thy-neighbour (PtN) strategies to improve performance. Our model has been proven to be effective with the best ROUGE-1/ROUGE-L scores, being the shared task runner-up by ROUGE-2 F1 score (over 13 participated teams). © 2021 Association for Computational Linguistics

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