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检索条件"机构=Dept. of Computer and Electrical Engineering and Computer Science"

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Deep Feature Fusion Based Brain Tumor Classification From Imbalance MRI Dataset Using Weighted Loss Functions 2

Deep Feature Fusion Based Brain Tumor Classification From Im...

引用

2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Tasnim, Taieba Parvin, Farjana Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Deep learning using MRI images to classify brain tumours presents a significant challenge due to imbalanced training datasets, where one tumor type may have far more samples than others. The overrepresented class may perform worse in classifiers as a result of this imbalance. In order to tackle this issue, the paper examines how feature learning in CNNs is affected by weighted loss functions to manage data imbalance. The article propose deep feature fusion, where particular CNN model features that were trained using three distinct loss functions are fused and classified using softmax classifier. This approach lead to notable improvements in brain tumor classification compared to a CNN trained with standard cross-entropy loss. Notably, the proposed methods substantially reduce classification errors between uneven class samples. The proposed model achieved an impressive 94.5% classification accuracy, accompanied by an average Fl-score of 94.6%, average precision of 94.6 %, and average recall of 95%. These findings underscore the effectiveness of our methodology in improving brain tumor classification accuracy, offering a promising avenue for more reliable diagnosis and treatment planning in clinical settings, © 2024 IEEE.

关键词： Deep learning

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MeQANet: Deep Learning-based Framework for Classifying Medical Query Answering in Bengali

MeQANet: Deep Learning-based Framework for Classifying Medic...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Tasnia, Fairooz Hoque, Mohammed Moshiul Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798331529765

The automatic classification of medical QA pairs is crucial for improving healthcare efficiency, allocating specialized care, optimizing resource distribution, and facilitating robust data analysis. Bengali, the 7th most widely spoken language globally, has a growing volume of health-related queries. However, unlike English, there is no benchmark large-scale medical QA dataset, pre-trained language model, or human baseline score for medical QA in Bengali. This paper introduces a deep learning-based framework called MeQANet that leverages CNN and BiLSTM techniques for classifying medical QA texts in Bengali. It also presents a novel medical QA corpus in Bengali (BnMedQA), consisting of approximately 4500 medical QA pairs across eight domains: Orthopedic, ENT, Gastroenterology and Hepatology, Gynecology, Pediatrics, General Medicine, Dermatology, and Cardiology. Various techniques (including LR, SVM, RF, MNB, AdaBoost (AB), CNN, BiL-STM, and CNN+BiLSTM) are employed to perform the classification tasks. Experimental results indicate that the CNN+BiLSTM approach achieved the highest F1-score of 0.85 among all employed techniques. © 2024 IEEE.

关键词： Query languages

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Design of a Brain Controlled Robotic Arm: Initial Experimental Testing 21

Design of a Brain Controlled Robotic Arm: Initial Experiment...

引用

21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Aljedaani, Lama Taha Abdelhedi, Fatma Aldahasi, Renad Abdullah Batheeb, Amal Abdullah Engineering King Abdulaziz University Dept. of Electrical and Computer Jeddah Saudi Arabia

ISBN: (纸本)9798350374131

In recent years, a significant leap has been made in various techniques to control robotics to assist people with upper limb amputations. Individuals with limb amputations have been facing significant challenges in their daily lives, where the simple and daily tasks become much more complex and take longer time, or even impossible, without depending on other people. Furthermore, traditional assistive devices often require significant physical effort, and they may be unable to perform different tasks efficiently, thus restricting the amputee's independence. Consequently, there is a need for an advanced assistive device to provide greater control and functionality to improve upper limb amputee independence. The Brain-Controlled Robotic Arm project aims to develop an advanced assistive device that allows individuals with disabilities to control a robotic arm using their brain signals. The project applies brain-computer interface technology (BCI) that allows the human brain to interact with a digital computer through electroencephalogram (EEG) technology. In this project, the robotic arm utilizes an electroencephalogram (EEG) to capture brain signals and then process those signals to get the required signal in order to control the robotic arm. © 2024 IEEE.

关键词： Brain computer interface

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Radar Resolution Improvements Using Reference Target Sliding Method in RDM

Radar Resolution Improvements Using Reference Target Sliding...

引用

2024 IEEE International Symposium on Phased Array Systems and Technology, ARRAY 2024

作者： Shelton, Trent Joiner, Laurie Panagos, Adam University of Alabama in Huntsville Dept. of Electrical and Computer Engineering Huntsville United States

ISBN: (纸本)9798350392142

A novel reference target sliding method in the frequency domain of post-match-filtered radar data is presented and shown that target resolution can be improved to levels much finer than classical bandwidth-limited resolution metrics. The methods developed here have broad application and can be used to provide crossing target resolution, estimation parameter refinement, and beam-phase correction in the presence of overlapping targets. Additionally, the reduced requirement for bandwidth in target resolution will allow for low-bandwidth systems to perform on par with higher-bandwidth systems in several applications. The proposed methods are also well-suited to improve detection and tracking performance in lower cost arrays such as non-actively scanned arrays that employ digital beamforming. © 2024 IEEE.

关键词： Frequency domain analysis

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Data-Driven Estimation of Grid Voltage Phase Angle through di/dt Analysis for Hysteresis-Controlled Single Phase Inverter

Data-Driven Estimation of Grid Voltage Phase Angle through d...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Akuta, Hector Li, Yuan University of Pittsburgh Dept. of Electrical & Computer Engineering PittsburghPA United States

ISBN: (纸本)9798350376067

This paper proposes a data-driven method for estimating grid voltage phase angle in a hysteresis-controlled single-phase inverter through current derivative analysis, bypassing the need for voltage sensors and phase-locked loops (PLLs). Particularly beneficial for weak grid connections, this voltage-sensorless approach incorporates unique data processing and both linear and nonlinear curve fitting techniques. Its effectiveness is validated by simulations and experimental data, showcasing the potential for enhancing the resilience of grid-connected converters in renewable energy systems. © 2024 IEEE.

关键词： Hysteresis

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PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization 39

PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast M...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Mingjing Ju, Peizhong Liu, Jia Yang, Haibo Dept. of Computing and Information Sciences Ph.D. Rochester Institute of Technology United States Dept. of Computer Science University of Kentucky United States Dept. of Electrical and Computer Engineering The Ohio State University United States

ISBN: (纸本)157735897X

Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives. Despite the long history of MOO, recent years have witnessed a surge in interest within the ML community in the development of gradient manipulation algorithms for MOO, thanks to the availability of gradient information in many ML problems. However, existing gradient manipulation methods for MOO often suffer from long training times, primarily due to the need for computing dynamic weights by solving an additional optimization problem to determine a common descent direction that can decrease all objectives simultaneously. To address this challenge, we propose a new and efficient algorithm called Periodic Stochastic Multi-Gradient Descent (PSMGD) to accelerate MOO. PSMGD is motivated by the key observation that dynamic weights across objectives exhibit small changes under minor updates over short intervals during the optimization process. Consequently, our PSMGD algorithm is designed to periodically compute these dynamic weights and utilizes them repeatedly, thereby effectively reducing the computational overload. Theoretically, we prove that PSMGD can achieve state-of-the-art convergence rates for strongly-convex, general convex, and non-convex functions. Additionally, we introduce a new computational complexity measure, termed backpropagation complexity, and demonstrate that PSMGD could achieve an objective-independent backpropagation complexity. Through extensive experiments, we verify that PSMGD can provide comparable or superior performance to state-of-the-art MOO algorithms while significantly reducing training time. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multiobjective optimization

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AI-Based Effective Virtual Inertia Estimation in a Meshed Network of Inverters 50

AI-Based Effective Virtual Inertia Estimation in a Meshed Ne...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Alvand, Hamideh Zare, Alireza Shadmand, Mohammad B. Mazumder, Sudip K. Dept. of Electrical & Computer Engineering University of Illinois Chicago IL United States

ISBN: (纸本)9781665464543

Mass penetration of renewable energy sources (RES) has drastically triggered the integration of clusters of VSG-based grid-forming inverters (GFMIs) to enhance grid stability and facilitates the transition towards power electronics dominated grid (PEDG). Although high number of GFMIs provides a smoother frequency response due to higher emulated virtual inertia, considering the complex dynamic of the PEDG, estimating the emulated virtual inertia by the primary control layer of the GFMIs introduces extensive mathematical modeling and computational burden. To address the shortcomings of conventional model-based approaches, this paper proposes a data-driven methodology based on convolution neural network (CNN) to estimate effective virtual inertia by analyzing the explanatory frequency response of a cluster of GFMIs. The neural network (NN) is trained offline and evaluated on a sample cluster of 100% GFMIs through multiple case studies to validate the effectiveness of the proposed methodology. The application of the proposed data-driven tool can further facilitate network analysis by considering the corresponding aggregated model to enhance the stability analysis of the PEDG. © 2024 IEEE.

关键词： Frequency response

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Optimized Guard Band Allocation for TN and NTN Coexistence Using Reinforcement Learning 15

Optimized Guard Band Allocation for TN and NTN Coexistence U...

引用

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

作者： Jang, Jiseok Jung, Soyi Ajou University Dept. Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350364637

Efficient interference management is crucial for the coexistence of terrestrial networks and non-terrestrial networks. This paper proposes an approach to optimize frequency band allocation by determining the optimal guard bandwidth, reducing interference, and improving coexistence between terrestrial networks and non-terrestrial networks. Simulation results offer guidelines for setting appropriate guard bandwidth, ensuring effective terrestrial networks and non-terrestrial networks coexistence. © 2024 IEEE.

关键词： Deep reinforcement learning

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Multi-Objective Phase Shift Control Optimization for Dual Active Bridge Converters

Multi-Objective Phase Shift Control Optimization for Dual Ac...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Davila, Antonella Haro Hasan, Syed Imam Uzum, Alper Elrayyah, Ali Sozer, Yilmaz The University of Akron Dept. of Electrical and Computer Engineering AkronOH United States

ISBN: (纸本)9798350376067

This comprehensive analysis evaluates multiobjective optimization of the phase shift modulation techniques to evaluate the optimized phase shift at various operating points for a Dual Active Bridge converter. By simulating scenarios to understand the behavior of a system under various conditions for different modulation techniques, this study provides insights into different outcomes to optimize the system and identify the most effective phase-shift combinations. The system's performance is assessed based on two key metrics: the inductor RMS current and Zero Voltage Switching (ZVS) utilization. The analysis focuses on predicting the RMS inductor current for various phase-shift modulations, including Single-Phase Shift, Dual Phase Shift, Extended Phase Shift, and Triple Phase Shift modulation. Besides, peak current is crucial for assessing ZVS conditions, facilitating the optimization of the final set of phase-shift modulations. © 2024 IEEE.

关键词： Zero voltage switching

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Binary Classification of Breast Tumours using CBAM-Enhanced Collaborative Network 3

Binary Classification of Breast Tumours using CBAM-Enhanced ...

引用

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

作者： Das, Soumit Nivel, Mst Fabia Akter Rajshahi University of Engineering & Technology Dept. of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350388282

For women breast cancer is a life-threatening problem, making the importance of early detection crucial. Expert pathologists assess cell shapes in breast histopathology images at various magnification levels for accurate diagnosis. Unfortunately, a shortage of pathologists in many countries heightens the risk of human error in disease identification and treatment. To address this challenge, this study proposed a CBAM-Enhanced Collaborative Network for classifying benign and malignant tumors in breast cancer histopathological images, offering a potential solution to enhance accuracy and efficiency in diagnosis. The model included a transfer learning branch using a pretrained DenseNet121 structure with integrated CBAM (Convolutional Block Attention Module). Simultaneously, a collaborative branch employed Inception-ResNet-v2 with CBAM for feature extraction from histopathological images. The feature fusion module then optimized information from both branches. The proposed method achieved impressive binary classification accuracies on the BreaKHis dataset: 98.31% overall, with 98.33%, 98.08%, 99.67%, and 97.80% for magnification factors of 40x, 100x, 200x, and 400x, respectively. CBAM-Enhanced Collaborative Network also exhibits strong generalization capabilities, as evidenced by its superior performance on the ICIAR2018 BACH Challenge dataset. © 2024 IEEE.

关键词： Image classification

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