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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Salari, Soorena Rasoulian, Amirhossein Rivaz, Hassan Xiao, Yiming Concordia University Department of Computer Science and Software Engineering MontrealQC Canada Concordia University Department of Electrical and Computer Engineering MontrealQC Canada

ISBN: (纸本)9798350346459

Early surgical treatment of brain tumors is crucial in reducing patient mortality rates. However, brain tissue deformation (called brain shift) occurs during the surgery, rendering pre-operative images invalid. As a cost-effective and portable tool, intra-operative ultrasound (iUS) can track brain shift, and accurate MRI-iUS registration techniques can update pre-surgical plans and facilitate the interpretation of iUS. This can boost surgical safety and outcomes by maximizing tumor removal while avoiding eloquent regions. However, manual assessment of MRI-iUS registration results in real-time is difficult and prone to errors due to the 3D nature of the data. Automatic algorithms that can quantify the quality of inter-modal medical image registration outcomes can be highly beneficial. Therefore, we propose a novel deep-learning (DL) based framework with the Swin UNETR to automatically assess 3D-patch-wise dense error maps for MRI-iUS registration in iUS-guided brain tumor resection and show its performance with real clinical data for the first time. © 2023 IEEE.

关键词： Grading

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Online Distributed Optimization with Efficient Communication via Temporal Similarity 42

Online Distributed Optimization with Efficient Communication...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Juncheng Liang, Ben Dong, Min Boudreau, Gary Afana, Ali University of Toronto Department of Electrical and Computer Engineering Canada Ontario Tech University Department of Electrical Computer and Software Engineering Canada Ericsson Canada Canada

ISBN: (纸本)9798350334142

We consider online distributed optimization in a networked system, where multiple devices assisted by a server collaboratively minimize the accumulation of a sequence of global loss functions that can vary over time. To reduce the amount of communication, the devices send quantized and compressed local decisions to the server, resulting in noisy global decisions. Therefore, there exists a tradeoff between the optimization performance and the communication overhead. Existing works separately optimize computation and communication. In contrast, we jointly consider computation and communication over time, by encouraging temporal similarity in the decision sequence to control the communication overhead. We propose an efficient algorithm, termed Online Distributed Optimization with Temporal Similarity (ODOTS), where the local decisions are both computation- and communication-aware. Furthermore, ODOTS uses a novel tunable virtual queue, which completely removes the commonly assumed Slater's condition through a modified Lyapunov drift analysis. ODOTS delivers provable performance bounds on both the optimization objective and constraint violation. As an example application, we apply ODOTS to enable communication-efficient federated learning. Our experimental results based on real-world image classification demonstrate that ODOTS obtains higher classification accuracy and lower communication overhead compared with the current best alternatives for both convex and non-convex loss functions. © 2023 IEEE.

关键词： Computational efficiency

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Photovoltaic Maximum Power Point Tracking Based on Bayesian Optimization Neural Network 49

Photovoltaic Maximum Power Point Tracking Based on Bayesian ...

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

作者： Hu, Pengcheng Ukil, Abhisek Nair, Nirmal-Kumar C University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350331820

This paper uses the neural network to realize a photovoltaic (PV) system's maximum power point tracking. After Bayesian optimization of hyperparameters, the model can accurately determine the maximum power point voltage according to solar irradiance and temperature. The optimized PV system not only adapts to different operating conditions but also achieves the optimal power output under uniform irradiance, static irradiance shading, and dynamic shading, ensuring the maximum efficiency of the PV system. Moreover, the optimized model is superior to the unoptimized model and the traditional perturbation and observation method in terms of accuracy. The improved model enhances the practicality and reliability of the PV system, helping to improve PV efficiency and reduce energy loss. © 2023 IEEE.

关键词： Maximum power point trackers

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A Method to Evaluate User-Centered Self-Adaptive Systems for Smart Environments 10

A Method to Evaluate User-Centered Self-Adaptive Systems for...

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10th International Conference on eDemocracy and eGovernment, ICEDEG 2024

作者： Avila, Marcelo Quito, Christian Valdez, Wilson Cardenas-Delgado, Paul Cedillo, Priscila Universidad de Cuenca Computer Science Department Cuenca Ecuador University of Szeged Software Engineering Department Szeged Hungary

ISBN: (纸本)9798350365535

With the advent of Industry 4.0, Ambient Intelligence, and other related technologies, self-Adaptation has been seen in modern systems. In this sense, Human-computer Interaction (HCI) requires an approach focused on the user and the environment, which facilitates and enhances the use of the systems. In this context, the quality of Self-Adaptive Systems (SAS) is essential for their adoption, dissemination, and service. Thus, this study introduces a detailed evaluation method to analyze the quality of HCI in SAS, providing a solution applicable to various contexts aligned with the standard ISO/IEC 25040. This study also presents a quality model aligned with the ISO/IEC 25010 standard. The quality evaluation method allows the objective definition for the measurement application through specific metrics that determine the degree of functional adequacy and interaction capability. Finally, this study demonstrates how to perform a detailed evaluation of the quality of HCI in SAS, using as an example the evaluation of the Spotify system as a case study, applying metrics to evaluate functional adequacy and interaction capacity aligned with ISO/IEC 25040 and 25010 standards. © 2024 IEEE.

关键词： ISO Standards

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Development of a Directional Element for Solar Inverter-Rich Distribution Networks

Development of a Directional Element for Solar Inverter-Rich...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： The Hoang, Tran Nair, Nirmal-Kumar C. Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand

ISBN: (纸本)9798350396782

The high penetration of electronic-coupled distributed energy sources, especially photovoltaic systems into distribution networks has created new challenges for traditional non-directional protection systems. Fault currents can now come from multiple sources, making protection location and direction detection difficult. Although conventional negative-sequence directional elements have been proposed as alternative solutions for detecting unbalanced fault direction, the unusual waveforms of current and voltage contributed by photovoltaic systems can adversely affect their performance. This paper proposes a new directional approach that utilizes prefault voltage and during-fault negative-sequence current component to determine the direction of unbalanced faults in photovoltaic system-dominated distribution networks while considering the dynamic voltage support of photovoltaic systems as imposed by new grid code requirement. The proposed method is validated through numerical analysis and EMT simulations in the software environment PowerFactory/DIgSILENT. © 2023 IEEE.

关键词： Directional element distribution network EMT simulation photovoltaic system unbalanced faults

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Dm-Health App:Diabetes Diagnosis Using Machine Learning with Smartphone

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computers, Materials & Continua 2022年第7期72卷 1713-1746页

作者： Elias Hossain Mohammed Alshehri Sultan Almakdi Hanan Halawani Md.Mizanur Rahman Wahidur Rahman Sabila Al Jannat Nadim Kaysar Shishir Mia Department of Software Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science Najran UniversityNajran55461Saudi Arabia Department of Computer Science and Engineering Rajshahi University of Engineering and TechnologyRajshahi6204Bangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh Department of Computer Science and Engineering BRAC UniversityDhaka1212Bangladesh Department of Computer Science and Engineering World University BangladeshDhaka1230Bangladesh

Diabetes Mellitus is one of the most severe diseases,and many studies have been conducted to anticipate *** research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic,pre-diabetic,or non-diabetic without the assistance of any physician or medical *** study’s methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture *** Diabetes Prediction Approach uses a novel approach,Light Gradient Boosting Machine(LightGBM),to ensure a faster *** Proposed System ArchitectureDesign has been combined into sevenmodules;the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to *** Doctor Consultation Module ensures free treatment related to *** this research,90%accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor’s algorithm(KNN)&*** evaluate the model’s performance,Receiver Operating Characteristics(ROC)Curve and Area under the ROC Curve(AUC)were applied with a value of 0.948 and 0.936,*** manuscript presents some exploratory data analysis,including a correlation matrix and survey ***,the proposed solution can be adjustable in the daily activities of a diabetic patient.

关键词： Machine learning diabetes-prediction support vector machine(SVM) LightGBM eHealth ROC-AUC

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Improving Breast Cancer Detection Accuracy Through Random Forest Machine Learning Algorithm

Improving Breast Cancer Detection Accuracy Through Random Fo...

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Agrawal, Atul Khanday, Akib MohiUd Din Alazzawi, Esraa Mohammed Bhushan, Bharat Baniya, Pashupati Jamil, Akhtar Department of Computer Science and Engineering ITS Engineering College Greater Noida India Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031709234

Breast cancer continues to be a significant global health issue that greatly affects the well-being of people worldwide. Detecting breast cancer early is vital for improving the outcomes of patients. One promising method for breast cancer detection is the use of the Random Forest machine learning algorithm. In a recent research paper, we investigated how Random Forest can be used to predict breast cancer by employing explainable AI techniques. We analyzed the specific features that the algorithm relies on to classify breast cancer and highlighted the advantages of Random Forest compared to other machine learning algorithms in diagnosing breast cancer. Our model offers a well-thought-out, efficient, and easily understandable approach to predicting breast cancer using explainable machine learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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A New Wave of Texture Feature: Enhancing Deepfake Detection via Image Waveform 15

A New Wave of Texture Feature: Enhancing Deepfake Detection ...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Jhun, Ga San Hong, Pyo Min Lee, Keun Ahn, Jong Hyun Kim, Young Seon Kang, Dongwoo Lee, Youn Kyu Hongik University Department of Computer Engineering Seoul Korea Republic of Hongik University Department of Software and Communications Engineering Seoul Korea Republic of Hongik University Department of Electronic & Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

To address the security threats posed by highly realistic deepfakes, various deepfake detection methods have been proposed. In particular, texture features resulting from the post-processing steps in deepfake generation, such as color correction and edge smoothing have been widely utilized in deepfake detection methods. However their detection method is limited against advanced image synthesis mechanisms. In this paper, we propose a novel deepfake detection method based on texture features, an Image Waveform. Our proposed method converts the pixel values of an image into an Image Waveform to enhance texture features and utilizes two-stream CNN to maximize deepfake detection performance by effectively leveraging the features of the original image. Experimental results of real-world deepfake dataset, confirmed that our proposed method achieves higher detection accuracy compared to existing texture feature-based detection methods. © 2024 IEEE.

关键词： Image texture

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Generation of hyperspectral point clouds: Mapping, compression and rendering

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computers and Graphics (Pergamon) 2022年 106卷 267-276页

作者： López, Alfonso Jurado, Juan M. Jiménez-Pérez, J. Roberto Feito, Francisco R. Department of Computer Science University of Jaén Spain Department of Software Engineering University of Granada Spain

Hyperspectral data are being increasingly used for the characterization and understanding of real-world scenarios. In this field, UAV-based sensors bring the opportunity to collect multiple samples from different viewpoints. Thus, light-material interactions of real objects may be observed in outdoor scenarios with a significant spatial resolution (5 cm/pixel). Nevertheless, the generation of hyperspectral 3D data still poses challenges in post-processing due to the high geometric deformation of images. Most of the current solutions use both LiDAR (Light Detection and Ranging) and hyperspectral sensors, which are integrated into the same acquisition system. However, these present several limitations due to errors derived from inertial measurements for data fusion and the spatial resolution according to the LiDAR capabilities. In this work, a method is proposed for the generation of hyperspectral point clouds. Input data are formed by push-broom hyperspectral images and 3D point clouds. On the one hand, the point clouds may be obtained by applying a typical photogrammetric workflow or LiDAR technology. Then, hyperspectral images are geometrically corrected and aligned with the RGB orthomosaic. Accordingly, hyperspectral data are ready to be mapped on the 3D point cloud. This procedure is implemented on the GPU by testing which points are visible for each pixel of the hyperspectral imagery. This work also provides a novel solution to generate, compress and render 3D hyperspectral point clouds, enabling the study of geometry and the hyperspectral response of natural and artificial materials in the real world. © 2022 Elsevier Ltd

关键词： Data fusion

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