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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Anekal, Latha Samanta, Akash Williamson, Sheldon Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350398069

To ensure effective and safer utilization of Lithium-Ion Batteries (LIB), accurate State Estimation particularly State of Health (SOH) is extremely essential, especially in automotive applications. Accurate information about SOH not only plays a crucial role in health-conscious battery management while at the same time helps to prognose battery fault and replacement. Battery SOH cannot be measured directly. Therefore, typically equivalent models and data-driven estimation techniques are used to obtain SOH through external measurements. Feature vectors required for SOH estimation can be very effectively extracted by Electrochemical Impedance Spectroscopy (EIS). Still, existing EIS-based methods have several limitations, issues, and challenges. Thus, a comprehensive review of state-of-the-art EIS-based SOH estimation techniques is presented in this paper to expedite the research effort toward developing a more advanced and practical EIS method for accurate and real-time SOH estimation. © 2022 IEEE.

关键词： Lithium-ion batteries

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FEW-SHOT LEARNING AS CLUSTER-INDUCED VORONOI DIAGRAMS: A GEOMETRIC APPROACH 10

FEW-SHOT LEARNING AS CLUSTER-INDUCED VORONOI DIAGRAMS: A GEO...

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10th International Conference on Learning Representations, ICLR 2022

作者： Ma, Chunwei Huang, Ziyun Gao, Mingchen Xu, Jinhui Department of Computer Science and Engineering University at Buffalo United States Computer Science and Software Engineering Penn State Erie United States

Few-shot learning (FSL) is the process of rapid generalization from abundant base samples to inadequate novel samples. Despite extensive research in recent years, FSL is still not yet able to generate satisfactory solutions for a wide range of real-world applications. To confront this challenge, we study the FSL problem from a geometric point of view in this paper. One observation is that the widely embraced ProtoNet model is essentially a Voronoi Diagram (VD) in the feature space. We retrofit it by making use of a recent advance in computational geometry called Cluster-induced Voronoi Diagram (CIVD). Starting from the simplest nearest neighbor model, CIVD gradually incorporates cluster-to-point and then cluster-to-cluster relationships for space subdivision, which is used to improve the accuracy and robustness at multiple stages of FSL. Specifically, we use CIVD (1) to integrate parametric and nonparametric few-shot classifiers;(2) to combine feature representation and surrogate representation;(3) and to leverage feature-level, transformation-level, and geometry-level heterogeneities for a better ensemble. Our CIVD-based workflow enables us to achieve new state-of-the-art results on mini-ImageNet, CUB, and tiered-ImagenNet datasets, with ∼2%-5% improvements upon the next best. To summarize, CIVD provides a mathematically elegant and geometrically interpretable framework that compensates for extreme data insufficiency, prevents overfitting, and allows for fast geometric ensemble for thousands of individual VD. These together make FSL stronger. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Computational geometry

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Surface EMG Signal Analysis using Hand-Crafted Features for Detection and Classification of GTC seizures 2

Surface EMG Signal Analysis using Hand-Crafted Features for ...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Naveed, Maryam Gul Khawaja, Sajid Usman Akram, Muhammad National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Epileptic seizures with the risk of sudden unexpected death in epilepsy affect the quality of life. Nearly, one-fourth of the individuals suffer from seizures that cannot be treated with medications. Due to the high-level possibility of injuries and complications, generalized tonic-clonic seizures have a considerable contribution to unexpected death. These generalized tonic-clonic seizures activity need to be detected and identified through brain and muscle activity, heart rates, and EMG signals. In this paper, we propose a framework for distinguishing normal activity from seizure activity along-with its categorization. Proposed framework focuses on extraction of multiple sEMG hand-crafted features with the time and frequency domain analysis. The proposed methodology for sEMG signals and for GTC class detection has been tested using multiple classifiers including KNN, SVM and ensembles. The obtained results have shown 10% improvement in classification over the state-of the-art approaches available in literature. © 2022 IEEE.

关键词： Feature extraction

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models 24

Explainable AI for Healthcare: An Approach Towards Interpret...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Zahoor, Kanwal Bawany, Narmeen Zakaria Ghani, Usman Jinnah University for Women Institute of Business Administration Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as 'normal' or 'pneumonia'. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness. © 2023 IEEE.

关键词： Deep learning Explainable AI Grad-CAM Healthcare LIME SHAP Transfer learning

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A Data-Driven Analysis of software Testing Automation Challenges Using Structural Equation Modeling (SEM) Approach

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IEEE Access 2025年 13卷 96159-96181页

作者： Abrar, Muhammad Faisal Majeed, Muhammad Faran Saqib, Muhammad Alferaidi, Ali Uddin, Raza University of ha'Il College of Computer Science and Engineering 55476 Saudi Arabia Kohsar University Department of Computer Science Punjab Muree Pakistan University of Engineering & Technology Department of Software Engineering Peshawar25000 Pakistan

The adoption of automation in software testing presents challenges that can hinder its effectiveness and scalability. This study systematically investigates these challenges using a multi-phase research approach. First, a Systematic Literature Review (SLR) was conducted to identify 14 Critical Challenges (CCs) in automation adoption. Second, a questionnaire survey of 50 industry experts validated these challenges and examined their interrelationships. Finally, Structural Equation Modeling (SEM) a mathematical and statistical approach was employed to analyze correlations and uncover structural dependencies among the challenges. The SEM analysis identified five latent variables: Human Resource Constraints (HRC), Technological & Process Challenges (TPC), Financial & Resource Constraints (FRC), Security & Reliability Issues (SRI), and Future Adaptability & Scalability (FAS) that significantly influence automation adoption. Hypothesis testing revealed that HRC (-0.32), TPC (-0.45), FRC (-0.41), and SRI (-0.38) negatively impact automation success, whereas FAS (+0.51) plays a pivotal role in enabling successful adoption. Model validation through Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA) confirmed strong construct reliability and fit indices (RMSEA = 0.043, CFI = 0.95, TLI = 0.92, SRMR = 0.037). The study highlights the need for workforce training, standardized automation processes, cost-effective solutions, and security enhancements. By providing an empirically validated framework, this research contributes to both academia and industry, guiding decision-makers in optimizing automation strategies and improving software testing efficiency. © 2013 IEEE.

关键词： software testing

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Improving students' cognitive abilities in online environment based on neurofeedback 14

Improving students' cognitive abilities in online environmen...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Jamil, Nuraini Lakas, Abderrahmane Belkacem, Abdelkader Nasreddine College of Information Technology United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates College of Information Technology United Arab Emirates University Department of Computer and Network Engineering United Arab Emirates

ISBN: (纸本)9798350399431

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style. © 2023 IEEE.

关键词： Brain

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An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

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computers, Materials & Continua 2022年第8期72卷 2597-2615页

作者： Yazeed Yasin Ghadi Nida Khalid Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Human Activity Recognition(HAR)plays an important role in life care and health monitoring since it involves examining various activities of patients at homes,hospitals,or ***,the proposed system integrates Human-Human Interaction(HHI)and Human-Object Interaction(HOI)recognition to provide in-depth monitoring of the daily routine of *** propose a robust system comprising both RGB(red,green,blue)and depth *** particular,humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency *** track the movement of humans,we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray(LBG)algorithm for vector ***,the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network(ANN)while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for *** are two publicly available datasets used for experimentation on HHI recognition:Stony Brook University(SBU)Kinect interaction and the University of Lincoln’s(UoL)3D social activity ***,two publicly available datasets are used for experimentation on HOI recognition:Nanyang Technological University(NTU)RGB-D and Sun Yat-Sen University(SYSU)3D HOI *** results proved the validity of the proposed system.

关键词： Artificial neural network human-human interaction humanobject interaction k-ary tree hashing machine learning

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Comparative Study On Breast Cancer Classification Using Multiple Convolution Neural Network Architectures 3

Comparative Study On Breast Cancer Classification Using Mult...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Hossinq, Md. Murad Molla, Shourav Javed Mehedi Shamrat, F.M. Samia, Mahsuba Meherunnessa Saha, Rakhi Moni Khater, Ankit Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh Jadavpur University Department of Computer Science and Engineering India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Cancer of the breast is one of the primary causes of mortality of women across the globe. Breast abnormalities may often be diagnosed and classified with the use of ultrasound imaging. To better pinpoint health issues, radiologist diagnosis have been supported by a variety of CAD technologies created in recent decades. Using Automatic Breast Ultrasound (ABUS) scans, this research employed CNN models to differentiate between benign tumor, malignant tumor, and Normal breast tissue. Pre-processing the data is crucial in data classification since it helps the learning model function more effectively. The implemented dataset contains unprocessed image data where classes have data imbalance issue. So, it is important to resolve this imbalance problem. To resolve this issue data augmentation approach is applied on the minority class. Before the learning models are employed to the preprocessed data, the data is first partitioned into two subsets: a training set and a test set. This study's deep learning models for breast ultrasound classification are MobileNetV2, VGG16, AlexNet, ResNet, Inception V3, and Xception. Each model is trained and then evaluated at different epochs to keep track of its progress. The study found that as the epochs go higher, data loss lowers and accuracy rises. The accuracy of MobileNetV2, VGG16, AlexNet, ResNet, Inception V3, and Xception is 98.82%, 94.70%, 95.96%, 93.50%, 92.72%, and 93.65%, respectively. MobileNetV2 outperforms with 98.82% accuracy rate the comparative to other applied models. Finally, breast cancer is classified using the MobileNetV2 model with significant accuracy and precision. © 2022 IEEE.

关键词： Ultrasonic imaging

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Dispatching Shots Among Multiple Quantum computers: An Architectural Proposal 4

Dispatching Shots Among Multiple Quantum Computers: An Archi...

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Bisicchia, Giuseppe Garcia-Alonso, Jose Murillo, Juan M. Brogi, Antonio University of Pisa Department of Computer Science Pisa Italy University of Extremadura Quercus Software Engineering Group Cáceres Spain

ISBN: (纸本)9798350343236

Quantum Computing is continuously evolving and expanding. As time goes by, more and more Quantum computer implementations become available, each of them with their own features. In such a scenario, it can be difficult for developers to identify which Quantum computer is the most suitable for their needs. In this paper, different from current works presenting strategies to select only one Quantum computer, we propose a change of perspective. Indeed, due to the probabilistic nature of Quantum Mechanics, performing a computation in a Quantum computer usually requires iterating the same execution many times (called shots), to eventually end with a distribution of the final results. Leveraging this need, our architecture enables selecting many Quantum computers for the same circuit and spreading the shots among them. Such a mechanism offers also the possibility for developers to access the partial distributions obtained from the output of a subset of the selected computers. Finally, our architecture proposes to decouple the decision process from the actual execution of such a decision, by enabling developers to encode their specific custom policies. © 2023 IEEE.

关键词： software engineering

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GPT-4 enhanced multimodal grounding for autonomous driving:Leveraging cross-modal attention with large language models

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Communications in Transportation Research 2024年第1期4卷 5-23页

作者： Haicheng Liao Huanming Shen Zhenning Li Chengyue Wang Guofa Li Yiming Bie Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of MacaoMacao SAR999078China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610000China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering and Computer and Information Science University of MacaoMacao SAR999078China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of MacaoMacao SAR999078China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Transportation Jilin UniversityChangchun130000China

In the field of autonomous vehicles(AVs),accurately discerning commander intent and executing linguistic commands within a visual context presents a significant *** paper introduces a sophisticated encoder-decoder framework,developed to address visual grounding in *** Context-Aware Visual Grounding(CAVG)model is an advanced system that integrates five core encoders—Text,Emotion,Image,Context,and Cross-Modal—with a multimodal *** integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features,augmented by state-of-the-art Large Language Models(LLMs)including *** architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic(RSD)layer for attention *** architectural design enables the model to efficiently process and interpret a range of cross-modal inputs,yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual *** evaluations on the Talk2Car dataset,a real-world benchmark,demonstrate that CAVG establishes new standards in prediction accuracy and operational ***,the model exhibits exceptional performance even with limited training data,ranging from 50%to 75%of the full *** feature highlights its effectiveness and potential for deployment in practical AV ***,CAVG has shown remarkable robustness and adaptability in challenging scenarios,including long-text command interpretation,low-light conditions,ambiguous command contexts,inclement weather conditions,and densely populated urban environments.

关键词： Autonomous driving Visual grounding Cross-modal attention Large language models Human-machine interaction

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