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IEEE Transactions on Intelligent Vehicles 2023年 1-10页

作者： Lin, Jingmin Yu, Chunhui Wang, Ling Liu, Guanjun Wang, Jiacun Ma, Wanjing Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai P.R. China Department of Computer Science and Technology Tongji University Shanghai P.R. China Computer Science and Software Engineering Department Monmouth University USA

Lane closure due to events such as accidents creates bottlenecks on expressways. The mandatory lane changes of merging vehicles lead to congestion. As the cyber-physical system (CPS) develops, mixed traffic consisting of connected vehicles (CVs) and human-driven vehicles (HVs) has emerged. CVs can receive lane-changing (LC) advisories and complete merging to alleviate congestion. Most existing studies assume CVs can timely and exactly follow LC advisories, which is not realistic with human drivers. This study develops an LC advisory model for CVs, whose response time and compliance degrees are considered, at an expressway bottleneck with lane closure under mixed traffic of CVs and HVs. The LC strategies are optimized for CVs to minimize the total delay of CVs and HVs approaching the bottleneck. The constraints include the domains of decision variables, vehicle passing states at the bottleneck, vehicle kinematics, implementation of LC advisories, LC safety, the maximum number of LC manoeuvres, the minimum time interval between LC advisories, the potential LC CVs, and the evolution of vehicle states. The simulation-based method is applied to predict vehicle delay, which is formulated as an implicit function of the LC strategies for CVs. Genetic Algorithm (GA) is designed for solutions. The numerical studies validate the advantages of the proposed model. The sensitivity analysis shows that: 1) the critical CV penetration rate is 60%, below which the marginal benefits are significant with increasing CV penetration rates;and 2) the consideration of the response time and the compliance degree of CVs makes a great difference. IEEE

关键词： Vehicles

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Emotion and Mentality Monitoring Assistant (EMMA) 8

Emotion and Mentality Monitoring Assistant (EMMA)

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kiridena, Ishini Marasinghe, Dayan Karunarathne, Ruvini Wijethunga, Kavindu Fernando, Harinda Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350396638

Human emotions, psychology, and social well-being are all parts of mental health. It has an impact on how people feel, think, and act. It aids in figuring out how individuals act under pressure, interact with each other, and arrive at decisions. Every period of life, from childhood and adolescence to maturity, is vital for mental health. There are several elements that affect mental health issues, such as experiences with trauma or abuse in life, work-life stress, relationship difficulties, etc. Help is available for many of the mental health issues that are identified within a person. The proposed solution is implementing a friendly chatbot that is capable of capturing human emotional states (happy, angry, sad, etc.) and mental health states (depression, anxiety, stress, etc.) using human computer interaction and health record management. The proposed solution has been developed by considering both regular users and mental health practitioners. The user can communicate with the proposed assistant using text or voice to identify their emotions and mental health based on keywords, particular characteristics of voice and facial recognition used during the conversation. Once the mental health and emotion statuses are analyzed the electronic health records will be readily available on the blockchain as blockchain tokens, this could be shared with the practitioners securely. © 2023 IEEE.

关键词： Blockchain

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A Novel Comparative Method for Evaluating Compensation Networks in IPT Systems 49

A Novel Comparative Method for Evaluating Compensation Netwo...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Dong, Weihao Madawala, Udaya Baguley, Craig The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Auckland University of Technology Department of Engineering Computer & Mathematical Sciences Auckland New Zealand

ISBN: (纸本)9798350331820

A novel method for evaluating compensation networks in IPT systems is proposed for a fair comparison. An optimal control strategy is adopted in the comparison to ensure operation under optimal conditions. Results indicate efficiency boundary can be used as a powerful tool for intuitive evaluation of the compensation networks. © 2023 IEEE.

关键词： Efficiency

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Dysarthric Speech Recognition using Depthwise Separable Convolutions: Preliminary Study 12

Dysarthric Speech Recognition using Depthwise Separable Conv...

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12th International Conference on Speech technology and Human-computer Dialogue, SpeD 2023

作者： Shahamiri, Seyed Reza Mandal, Krishnendu Sarkar, Sudeshna The University of Auckland Faulty of Engineering Department of Electrical Computer and Software Engineering Auckland New Zealand Indian Institute of Technology Computer Science & Engineering Department Kharagpur India

ISBN: (纸本)9798350327977

As a neurological disability that affects muscles involved in articulation, dysarthria is a speech impairment that leads to reduced speech intelligibility. In severe cases, these individuals could also be handicapped and unable to interact with digital devices. For such individuals, Automatic Speech Recognition (ASR) technologies could be life changing by enabling them to communicate with others as well as computing devices via voice commands. Nonetheless, ASR systems designed to recognize healthy speech have shown very poor performance to transcribe dysarthric speech, signaling the need to design ASR specifically tailored for dysarthria. Dysarthric Speech Recognition (DRS) research has progressed gradually because of the challenges the research community faces such as the scarcity of dysarthric speech that does not allow the researchers to design deeper acoustic models needed to better learn dysarthric speech variations. In this paper we report on our preliminary findings to improve our previous DSR called Speech Vision and study the effects of Separable Convolutional neurons to improve its acoustic model. Speech Vision is a novel Dysarthric Speech Recognition system that learns to recognize the shape of the words uttered by dysarthric speakers instead of recognizing phone sequences and then mapping them to words. Experiments conducted on the utterances provided by all UA-Speech dysarthric speakers indicate the proposed Depthwise separable architecture provided better word recognition accuracies compared to the original Speech Vision's architecture across all dysarthric speech intelligibility classes. © 2023 IEEE.

关键词： Convolution

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A Cost-Efficient IPT System with an Uncompensated Foil Primary Coil 49

A Cost-Efficient IPT System with an Uncompensated Foil Prima...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Lin, Zhongzheng Madawala, Udaya Baguley, Craig Computer and Software Engineering The University of Auckland Department of Electrical Auckland New Zealand Computer & Mathematical Sciences Auckland University of Technology Department of Engineering Auckland New Zealand

ISBN: (纸本)9798350331820

This paper proposes a low-cost inductive power transfer (IPT) system, comprising an uncompensated foil primary coil and a compensated vertical Litz-wire secondary coil structure, for stationary electric vehicle (EV) charging applications. A mathematical model is presented for the proposed system, and a 2 kW case study shows comparable performance to a dual-compensated IPT system. © 2023 IEEE.

关键词： Reactive power

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Baby Bump: A Monitoring System for Pregnant Mothers and Babies 9

Baby Bump: A Monitoring System for Pregnant Mothers and Babi...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Balasooriya, Dhanuka Perera, Sasini Wijendra, Dinuka R. Rathnayaka, Sahani Kishan, Rajendra Rajendran, Karthiga Thirukumar, Markandu Sri Lankan Institute of Information Technology Department of Computer Systems And Software Engineering Malabe Sri Lanka Teaching Hospital Department of Obstetrics & Gynaecology Batticaloa Sri Lanka

ISBN: (纸本)9798350394474

In contemporary times, nations like Sri Lanka are actively enhancing their efforts to improve the life expectancy of their citizens, with a strong focus on public health. The relationship between health and life expectancy is pivotal. Poor health conditions tend to diminish life expectancy, while robust health measures tend to extend it. Within the context of health, birth and death events are of paramount importance. Birth represents the genesis of life, enabling individuals to contribute to future generations and the overall population's expansion. It's the cornerstone of life on our planet. However, modern times have witnessed challenges in pregnancy and childbirth, with some infants not surviving the prenatal period or being born with health complications. Consequently, there is a pressing need to present a solution that can contribute to the betterment of maternal and infant health, ultimately augmenting Sri Lanka's life expectancy. The proposed solution is the development of a mobile application, encompassing features such as a Nutrition Predictor, Medicine Effect Predictor, AI chatbot, and Baby Status Predictor. This innovative application aims to address the complex issues surrounding pregnancy and childbirth, with the overarching goal of improving the nation's life expectancy. © 2024 IEEE.

关键词： Nutrition

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in Engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Money Laundering Detection and Mitigation Using Decision Tree Ensembles

Money Laundering Detection and Mitigation Using Decision Tre...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Ebenezer, Adeoti Babajide Olutayo, Boyinbode Olumide O, Obe Akintola, K.G. Emmanuel, Mgbeahuruike Oluwayemisi Boye, Fatade Babcock University Software Engineering Department Ogun state Ilisan Remo Nigeria Federal University of Technology Akure Department of Computer Science Ondo state Akure Nigeria

ISBN: (纸本)9798350376838

Money laundering is an unlawful activity that has caused underdevelopment to Africa, it's a ravaging vices that cripples economic growth, increases criminality and leads to economic sabotage. Many works previously done on money laundering detection, it was observed that it takes times before fraudulent transactions can be detected, it is also less effective, meaning that is can easily be circumvents and the data sampling methods used in previous researches makes the model to suffer from overfitting and loss of information, however this research adopts SMOTE (Synthetic Minority Oversampling Techniques) algorithm method to balance the dataset so as to have a fair representation of the two classes in order for machine learning model to accurately evaluate the models. The Objective of this research is to adapt and implement decision tree ensemble learning algorithms to detect money laundering activities, also design and implement an Anti Money Laundering (AML) query system to mitigate money laundering activities based on detection results and finally to carry out the performance evaluation of decision tree ensemble algorithms using standard performance metrics. © 2024 IEEE.

关键词： Laundering

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A Reluctance Model for a New IPT Coil Design 49

A Reluctance Model for a New IPT Coil Design

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Hu, Meilin Madawala, Udaya K. Baguley, Craig University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Auckland University of Technology Department of Engineering Computer and Mathematical Sciences Auckland New Zealand

ISBN: (纸本)9798350331820

Dynamic inductive power transfer (IPT) can be used to charge moving electric vehicles (EVs) through the magnetic coupling between primary coils in a road with secondary coils onboard an EV. However, consistency in charging requires stability of power transfer, which is significantly affected by variations in the alignment between conventional primary and secondary coils in relative motion. This paper proposes a new coil structure for dynamic IPT systems with a high degree of tolerance to coil misalignment. The reluctance model is presented for the proposed coil to further investigate the influence of coil widths on the mutual inductance (M). Results indicate that a trade-off exists between improving M and tolerance to coil misalignment. © 2023 IEEE.

关键词： Alignment

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AID4I:An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

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computers, Materials & Continua 2023年第8期76卷 2121-2143页

作者： Anil Sezgin Aytug Boyacı ATASAREN National Defence UniversityIstanbul34334Turkey Software in Motion Department Siemens AdvantaSiemens Corporate TechnologyIstanbul34870Turkey Department of Computer Engineering National Defence University Air Force AcademyIstanbul34149Turkey

By identifying and responding to any malicious behavior that could endanger the system,the Intrusion Detection System(IDS)is crucial for preserving the security of the Industrial Internet of Things(IIoT)*** benefit of anomaly-based IDS is that they are able to recognize zeroday attacks due to the fact that they do not rely on a signature database to identify abnormal *** order to improve control over datasets and the process,this study proposes using an automated machine learning(AutoML)technique to automate the machine learning processes for *** groundbreaking architecture,known as AID4I,makes use of automatic machine learning methods for intrusion *** automation of preprocessing,feature selection,model selection,and hyperparameter tuning,the objective is to identify an appropriate machine learning model for intrusion *** studies demonstrate that the AID4I framework successfully proposes a *** integrity,security,and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening *** a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security,AID4I is a powerful and effective instrument for intrusion *** preprocessing module,three distinct imputation methods are utilized to handle missing data,ensuring the robustness of the intrusion detection system in the presence of incomplete *** selection module adopts a hybrid approach that combines Shapley values and genetic *** Parameter Optimization module encompasses a diverse set of 14 classification methods,allowing for thorough exploration and optimization of the parameters associated with each *** carefully tuning these parameters,the framework enhances its adaptability and accuracy in identifying potential

关键词： Automated machine learning intrusion detection system industrial internet of things parameter optimization

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