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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Tran, Trung Bach Kolmanovsky, Ilya Biberstein, Erik Makke, Omar Tharayil, Marina Gusikhin, Oleg The University of Michigan Department of Aerospace Engineering Ann ArborMI United States Ford Motor Company Global Data Insight & Analytics DearbornMI United States

ISBN: (纸本)9798350319958

The energy consumption of electric vehicles (EVs) depends on multiple factors. As it affects vehicle range, it must be accurately predicted. After a summary of the relevant literature, this paper focuses on the sensitivity of energy consumption to wind velocity and wind direction. The outcomes from model-based sensitivity analysis of the wind effects on energy consumption and the range of EVs over real-world routes are presented. data sources available for online and offline wind velocity and wind direction determination are discussed. Recognizing the interplay between range prediction and the route chosen, we consider a Markov Decision Process (MDP) based framework for battery-charge and travel-time aware route planning that accounts for the impact of the wind on optimal routing decisions including stops at the charging stations. Finally, we propose a framework that includes wind in the operation of EVs, which consists of learning the impact of wind, incorporating wind forecasting into range and energy prediction, and using that prediction to perform optimal routing. © 2023 IEEE.

关键词： Electric vehicles

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Analysis and Performance Evaluation of Credit Card Fraud by Multi-model ML 3

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

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Md Aminul Asif Imran, A.T.M. Rahman, Md Habibur Pabel, Md Amran Hossen Mishra, Bhupesh Kumar Basu, Kashinath University of Gloucestershire Data Science School of Engineering and Technology Cheltenham United Kingdom Dhaka Bangladesh University of Chittagong Department of Mathematics Chittagong Bangladesh Wright State University Master of Science in Business Analytics United States United Kingdom Oxford Brookes University Data Science School of Engineering Computing and Mathematics United Kingdom

ISBN: (纸本)9798350388282

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. © 2024 IEEE.

关键词： Decision trees

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Viseme Embeddings for Commonly Confused Words in Lip-Reading

Viseme Embeddings for Commonly Confused Words in Lip-Reading

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2023 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Fenghour, Souheil Chen, Daqing Guo, Kun Xiao, Perry South Bank University School of Engineering London London United Kingdom Xi'an Vanxum Electronics Technology Co. Ltd. Data Analytics Xi'an China

ISBN: (纸本)9798350322972

Automating lip reading has attracted a lot of research attention in the last several years and there are a wide variety of applications where automated lip reading could be implemented, including forensic closed-circuit television footage or to assist those with language impairment. An obstacle for both professional lip-readers and automated lip-reading systems is not only the situation of different-sounding words being confused because they produce identical lip movements, but also that word with similar lip movements can easily be confused because they have sequences of visemes that are similar. This paper proposes a library and corpus called Visemes2Vec for learning the embeddings of viseme sequences as a contribution and is to the best of our knowledge the first attempt at proposing library for visualising words that have similar lip movements. Visemes2Vec is analogous to Word2Vec which groups together that are semantically similar in meaning or Chars2Vec which groups together words that share common characters. © 2023 IEEE.

关键词： Embeddings

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KNAS-TP: Domain Knowledge-driven Automated Construction of Uplink and Downlink Throughput Predictor in 5G

KNAS-TP: Domain Knowledge-driven Automated Construction of U...

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International Conference on Communication Software and Networks, ICCSN

作者： Yuqian Yang Cong Zhao National Engineering Laboratory for Big Data Analytics Xi‘an Jiaotong University Xi‘an China

ISBN: (数字)9798331506322

ISBN: (纸本)9798331506339

The advent of 5G networks marks a revolutionary shift in wireless communications, offering unprecedented improvements in speed, latency, and connectivity. Accurate uplink and downlink throughput prediction is crucial for 5G network Operations and Maintenance (O&M) tasks, yet existing methods constructing intuitive predictors without structural optimization often fail to adapt to dynamic 5 G environments due to the lack of effective utilization of domain knowledge. To address this, we present KNAS-TP, the first domain knowledge-driven automated construction method for 5G throughput prediction based on Neural Architecture Search (NAS) and the Attention mechanism. KNAS-TP incorporates domain knowledge in NAS search space establishment and loss function definitions, which includes the match degree evaluation of the complexity of the searched neural network structure and corresponding knowledge. Additionally, KNAS-TP enhances model using an Attention mechanism to captures the uneven impacts of deep factors, thereby improving final model training and prediction accuracy. Extensive experimental results demonstrate that our model outperforms all comparative methods, achieving up to $8.5 \times$ lower throughput prediction MSE. The integration of domain knowledge is essential for robust and accurate throughput predictions, validating the effectiveness of our methodology in complex 5G scenarios.

关键词： Knowledge engineering Wireless communication Training Accuracy Attention mechanisms 5G mobile communication Predictive models Throughput Downlink Uplink

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LTL Model Checking for Verification of Electronic Medical Record (EMR) Design 8th

LTL Model Checking for Verification of Electronic Medical R...

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8th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2023

作者： Taryana, Acep Adzkiya, Dieky Mufid, Muhammad Syifa’ul Mukhlash, Imam Abate, Alessandro Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Engineering Faculty Universitas Jenderal Soedirman Purwokerto Indonesia Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9789819721351

This paper presents an in-depth investigation into the verification of a software design model, focusing on the context of Electronic Medical Record (EMR) systems. Verification plays a critical role in ensuring the correctness and reliability of such systems, making it a topic of paramount importance. In this study, we undertake the task of remodeling the EMR requirements using a finite transition system to facilitate rigorous analysis and verification. Additionally, we express the formal specification through Linear Temporal Logic (LTL), leveraging its expressive power and ability to capture temporal properties accurately. To assess the validity of the design model, we employ the widely recognized SPIN model checker, a tool known for its effectiveness in detecting errors and inconsistencies in software systems. Our analysis reveals several critical findings. Specifically, the model checker successfully identifies paths within the design model that fail to satisfy the specified LTL formula, shedding light on potential areas of concern. Moreover, the generation of a comprehensive counterexample provides valuable insights for further analysis and improvement of the software design. Overall, this research highlights the significance of verification techniques in software design and emphasizes the applicability of the finite transition system and LTL in this context. The findings underscore the importance of thorough verification and the potential benefits of employing tools like SPIN to enhance the reliability and robustness of EMR systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software design

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Acceleration Evaluation of a High-Speed Railway PC Box Girder Bridge with Slab Track 41st

Acceleration Evaluation of a High-Speed Railway PC Box Girde...

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41st IMAC, A Conference and Exposition on Structural Dynamics, 2023

作者： Yotsui, Haruki Matsuoka, Kodai Kaito, Kiyoyuki Department of Global Architecture Graduate School of Engineering Osaka University Osaka Suita-City Japan Data Analytics in Information and Communication Technology Division Railway Technical Research Institute Tokyo Kokubunji-City Japan

ISBN: (纸本)9783031366628

The resonance of railway bridges that occurs under high-speed trains passage is an important academic and practical issue. The bridge design code in Europe (Eurocode) sets an upper limit of 5 m/s2 on the bridge deck acceleration for a slab type track. However, the performance of high-speed railway bridges regarding train-induced deck acceleration specified in Eurocode has not been sufficiently verified. In this study, a slab track type PC box girder bridge in Japanese high-speed railway is targeted, and multipoint acceleration measurement is conducted. Based on the measurement results, the global and deck local modal characteristics are identified with ERA method, and the maximum deck acceleration is evaluated. As a result, the maximum deck acceleration of the bridge is much lower than the Eurocode limit value of 5 m/s2 even under the resonance condition. Unlike previous reports for European composite box girder bridges, the main deck vibration modes of the bridge are over 40 Hz, thus the deck local vibration did not affect the evaluation of deck acceleration. © 2024, The Society for Experimental Mechanics, Inc.

关键词： Acceleration

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Predicting Injury Severity in Construction Using Logistic Regression 7th

Predicting Injury Severity in Construction Using Logistic Re...

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7th International Conference on data Management, analytics and Innovation, ICDMAI 2023

作者： Maiti, Reetun Menon, Balagopal G. Centre for Computational and Data Sciences IIT Kharagpur Kharagpur India Department of Industrial and Systems Engineering IIT Kharagpur Kharagpur India Centre of Excellence in Safety Engineering and Analytics IIT Kharagpur Kharagpur India

ISBN: (纸本)9789819914135

Prediction of injury severity is an important task for articulating proper mitigation measures to reduce the loss of an accident resulting into injuries. In this study, using OSHA construction injury data, we developed a fatality prediction model using logistic regression and frequency weighted data transformation. The approach adopted here is useful for handling categorical predictors. The model is fairly fit to the data. The model could be used for prediction with the involvement of experienced domain experts. Interestingly, some of the predictors actuate an injury being fatal while other predictors inhibit it. Management should take appropriate mitigation measures to reduce the influence of the predictors contributing positively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Logistic regression

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Energy-Aware Blocking Hybrid Flow Shop Scheduling Problem with Sequence-depend Setup Times and Machine Speed Levels

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Procedia Computer Science 2025年 253卷 1134-1143页

作者： Ahmed Missaoui Cemalettin Ozturk Barry O’Sullivan Insight Centre for Data Analytics University College Cork Ireland Process Energy and Transport Engineering Munster Technological University Cork Ireland

The industry sector has the second largest energy demand after electricity generation. Given the high trend of energy prices, scarcity of its supply due to political instability and dependency on fossil fuels for its production makes energy the primary challenge for the manufacturing sector to stay competitive. Therefore, in addition to novel equipment technologies that use less energy, energy-efficient scheduling has also been a priority for manufacturing companies. In this study, the Hybrid Flowshop Scheduling Problem with Blocking Constraints and Sequence-Depend Setup Times (BHFS-SDST) is investigated for the minimization of makespan and total energy consumption (TEC). First, a novel bi-objective Mixed Integer Linear Programming (MILP) model is formulated and solved through augmented epsilon constraints. Then, a novel multi-objective approach based on Iterated Greedy meta-heuristic is developed for larger instances. Efficiency and the scalability of the proposed approaches are tested with small, medium, and large instances. Computational experiments show the effectiveness of developed methods in solving the BHFS-SDST problem.

关键词： Hybrid flowshop blocking constraint Sequence-depend setup times Makespan Energy consumption Mixed Integer Linear Programming Iterated greedy

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Predicting Public Sentiment to Proposed Laws: E-waste Management Laws as a Use Case

Predicting Public Sentiment to Proposed Laws: E-waste Manage...

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2024 International Symposium on Networks, Computers and Communications, ISNCC 2024

作者： Cox, Thomas Badawi, Ibrahim Sharkh, Mohamed Abu Ferris State University Data Science and Analytics Grand Rapids United States Seattle University Seattle United States Ferris State University Digital Media Software Engineering Grand Rapids United States

ISBN: (纸本)9798350364910

Machine learning has been successfully used to predict sentiment for general-purpose short-text sentences with relative accuracy. However, challenges around predicting legislation acceptance call for deeper analysis of longer, more interconnected text paragraphs. The E-waste management domain has a pressing need for insights into public discourse on legislation. This work presents a comprehensive approach to analyzing public discourse surrounding e-waste legislation by integrating social media data from Twitter and legislative data from LegiScan. Graph Machine Learning (GraphML) is utilized to build the prediction engine. A heterogeneous graph is constructed to capture the relationships between tweets and legislative bills using API-based data collection, preprocessing techniques, and pre-trained sentiment models. The ML pipeline includes robust preprocessing of text data, feature extraction using RoBERTa embeddings, dimensionality reduction via PCA, and the use of various Graph Neural Network (GNN) architectures (Graph Convolutional Network (GCN), Graph Attention Network (GAT), and GraphSAGE) for model training and evaluation. The final model achieved a test accuracy of 0.777, a test F1-score of 0.745, and a test ROC AUC of 0.819, demonstrating the efficacy of graph-based sentiment analysis in this context. This work's findings have significant implications for policymakers, stakeholders, and researchers in the e-waste management domain. Policymakers can use the model's sentiment analysis insights to gauge public support for specific legislative actions, providing informed and responsive decision-making. E-waste management Stakeholders can understand public opinion around e-waste legislation and develop effective communication strategies. Additionally, the proposed methodology provides an end-to-end framework for researchers to explore further applications of graph-based sentiment analysis. © 2024 IEEE.

关键词： Dimensionality reduction

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Automated Detection of Type 2 Diabetes with Imbalanced and Machine Learning Methods 3rd

Automated Detection of Type 2 Diabetes with Imbalanced and M...

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3rd International Conference on Machine Learning, Image Processing, Network Security and data Sciences, MIND 2021

作者： Anirudh, G. Talukdar, Upasana Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India

ISBN: (纸本)9789811958670

Diabetes mellitus is an abominable disease, if left untreated, may turn fatal. This paper presents rigorous experimentation to ascertain a machine learning model that can implement an accurate diagnosis of diabetes mellitus. While many research efforts have been publicized till date to detect diabetes, none of them addresses the class imbalance problem. This paper carries out the experiments on Pima Indian Diabetes in three categories a. experiments with traditional machine learning algorithms, b. experiments with ensemble machine learning methods, and c. experiments with machine learning algorithms after imbalanced data pre-processing techniques. The performance of the solutions has been evaluated using F1-score, Precision, Recall, Area Under Precision-Recall curve, Area Under Receiver Operating Characteristic curve, and Classification Accuracy. The experimental results show improved performance with the proposed approach as compared to the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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