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

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A Comparative Analysis between Sentiment Analysis Methods in Stock Prediction of IoT-based companies 7

A Comparative Analysis between Sentiment Analysis Methods in...

引用

7th World Symposium on Communication engineering, WSCE 2024

作者： Pasoi, Georgia E. Talattinis, Kyriacos A. Memos, Vasileios A. Goudos, Sotirios K. Sarigiannidis, Panagiotis G. Ishibashi, Yutaka Psannis, Konstantinos E. University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Aristotle University of Thessaloniki Dept. of Physics Thessaloniki Greece University of Western Macedonia Dept. of Electrical and Computer Engineering Kozani Greece Aichi Sangyo University Faculty of Business Administration Dept. of Business Management Aichi444-0005 Japan

ISBN: (纸本)9798331542825

Predictions in financial markets have attracted much of scholars' interest. Since the stock market is generally defined as dynamic and volatile due to the affect of the well-known "Internet of Things"(IoT). It is a fact that IoT significantly impacts the financial market and stocks in various ways. Therefore, numerous researchers have attempted to detect patterns regarding the way financial markets react to various external factors. In the last decade, the integration of machine learning and sentiment analysis has appeared to be an effective approach for improving IoT-based stock price predictions in the financial field. This paper intends to propose an approach involving financial news as a source of information for forecasting the stock movement of noteworthy IoT-related companies, employing Big Data Analytics (BDA), Machine Learning, and Sentiment Analysis methods as well. A comparative analysis is conducted between lexicon-based and machine learning-based methods of sentiment analysis about their performance, particularly in the domain of IoT-related stock market prediction. © 2024 IEEE.

关键词： Decentralized finance

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Comparison of Fast Frequency Response Methods in the Low-Inertia Grid of Cyprus 3

Comparison of Fast Frequency Response Methods in the Low-Ine...

引用

3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Argyrou, Andreas Panagi, Savvas Aristidou, Petros Electricity Authority of Cyprus Distribution System Operator Nicosia Cyprus Cyprus University of Technology Dept. of Electrical & Computer Engineering & Informatics Limassol Cyprus

ISBN: (纸本)9798350375923

Modern electric power systems with high levels of penetration of renewable energy sources (RES) often present frequency security problems. The lack of inertia due to the reduced number of synchronous generators in these systems, in combination with the usual inability of RES to provide frequency support, leads to system operators having to curtail RES or rely heavily on Under Frequency Load Shedding schemes to ensure frequency security. Fast Frequency Reserves (FFR) have been proposed as a solution to strengthen the frequency support of the system and alleviate security problems. FFR allows mitigating Rate of Change of Frequency, Nadir, and post-fault frequency steady-state problems after an event. In this paper, we present, analyze, implement and compare five FFR controllers to alleviate frequency security problems in low-inertia grids. The low-inertia, islanded, Cyprus dynamic model is used to quantify the results and exhibit the impact on a real system. © 2024 IEEE.

关键词： Synchronous generators

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A comparative study for lung, colon and breast cancer diagnosis using different convolutional neural networks 7

A comparative study for lung, colon and breast cancer diagno...

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7th International Conference on Advances in biomedical engineering, ICABME 2023

作者： Akar, Hussein Diab, Jacqueline Abou Sbeity, Fatima Ali, Mohamad Abou Kassem, Abdallah Hamawy, Lara Lebanese International University Dept. of Biomedical Engineering Lebanon Lebanese University Dept. of Physics & Electronics Lebanon University of the Basque Country Dept.of Computer Science & Artificial Intelligence Spain Notre Dame University Dept. of Electrical &computer Engineering Lebanon

ISBN: (纸本)9798350325850

In modern times, the prevalence of diseases, particularly cancer, is rising rapidly. Diagnostic errors pose a critical safety concern in healthcare due to the complexity of medical decision-making, disease variability, and human cognitive limitations. This paper aims to compare different convolutional neural networks (CNNs) to find the most accurate model that can classify lung, colon, and breast cancer using histopathological images. A convolutional neural network is a subset of machine learning. It is one of the various types of artificial neural networks which are used for different applications and data types. A CNN is a kind of network architecture for deep learning algorithms and is specifically used for image recognition and tasks that involve the processing of pixel data. We created a baseline model that consists of a basic CNN architecture. It serves as a starting point for comparison with more complicated models. The baseline model's performance is then compared to the performance of various pretrained models (ResNet-50, ResNet-101, VGG-16, VGG-19, Inception V3, and DenseNet-121). The dataset used consists of 25,000 histopathological images of both the lung and colon and 277,000 histopathological of breast images. Results show that the ResNet-50 model is the most efficient among all the other models, scoring a validation accuracy of 98.3%. Additionally, ResNet-101, VGG-16, VGG-19, and DenseNet-121 also demonstrated strong performance, scoring 95.8%, 96.6%, 95.1%, and 92.4%, respectively. Meanwhile, Inception-V3displayed a fair result, scoring a validation accuracy of 73.2%. © 2023 IEEE.

关键词： Convolutional neural networks

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Dual-Branch Neural Networks for Predicting Shared Bikes 17

Dual-Branch Neural Networks for Predicting Shared Bikes

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Yang, Huigyu Raza, Syed M. Le, Duc-Tai Kim, Dongsoo S. Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Indiana Uiversity-Purdue University Dept. of Electrical and Computer Engineering Indianapolis United States Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Bike sharing is efficient urban mobility method as a user determines the rental period upon his/her demand. A dock-based sharing system designates places (i.e., bike docks or stations) where a user rents or returns a bike. Due to regionally and temporally erratic nature of urban mobility demand, bike imbalance among stations inevitably occurs and a service operator rebalances bikes to prevent service impairment. The precise prediction of imbalance for each station is essential to optimize the rebalancing schedule. This paper presents Dual-Branch Neural Networks (DBNNs) employing Long Short-Term Memory (LSTM) to classify the imbalance level of each station along with the prediction of bike counts. To address the dual objectives, the model trains by backpropagating both classification and prediction losses to the dual output branches individually, and jointly updates the trainable parameters of LSTM layers. The results showcase that the proposed model predicts bike counts within 8% average error for next 24 hours, and achieves 94% accuracy of classification for the same time period. © 2023 IEEE.

关键词： Forecasting

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Atlas-Based Automatic Internal Auditory Canal Localization with a Weakly-Supervised 3D U-Net

Atlas-Based Automatic Internal Auditory Canal Localization w...

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Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Mason, Hannah G. Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

ISBN: (纸本)9781510660373

Cochlear implants (CIs) are neural prosthetics which use an array of implanted electrodes to improve hearing in patients with severe-to-profound hearing loss. After implantation, the CI is programmed by audiologists who adjust various parameters to optimize hearing performance for the patient. Without knowing which auditory nerve fibers (ANFs) are being stimulated by each electrode, this process can require dozens of programming sessions and often does not lead to optimal programming. The internal auditory canal (IAC) houses the ANFs as they travel from the implantation site, the cochlea, to the brain. In this paper, we present a method for localizing the IAC in a CT image by deforming an atlas IAC mesh to a CT image using a 3D U-Net. Our results suggest this method is more accurate than an active shape model-based method when tested on a test set of 20 images with ground truth. This IAC segmentation can be used to infer the position of the invisible ANFs to assist with patient-specific CI programming. © 2023 SPIE.

关键词： Cochlear implants

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Longitudinal Analysis of Cerebellum Volume Changes in a Multiple Sclerosis Cohort

Longitudinal Analysis of Cerebellum Volume Changes in a Mult...

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Medical Imaging 2025: Image Processing

作者： Wang, Shimeng Calabresi, Peter A. Saidha, Shiv Carass, Aaron Prince, Jerry L. Dept. of Biomedical Engineering Johns Hopkins University BaltimoreMD21218 United States Dept. of Radiology Johns Hopkins School of Medicine BaltimoreMD21287 United States Image Analysis and Communications Laboratory Dept. of Electrical and Computer Engineering Johns Hopkins University MD21218 United States

ISBN: (纸本)9781510685901

Multiple sclerosis (MS) is a degenerative disease of the central nervous system that affects millions worldwide. The cerebellum, crucial for motion coordination, is affected by this disease. In this study, we employed a modified deep-learning-based cerebellum parcellation framework, ACAPULCO, to investigate the longitudinal patterns of cerebellar volume in cohorts with relapsing-remitting MS (RRMS) and secondary progressive MS (SPMS). We identified and corrected ACAPULCO errors in our cohort with a post-processing step. Statistical analysis was performed with a linear mixed-effect model on the volumetrics of the parcellated regions. Our findings indicate that Lobules IV, V, and VI are the most affected by disease progression, agreeing with previous clinical research. © 2025 SPIE.

关键词： Image segmentation

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Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label

Self-supervised Registration and Segmentation of the Ossicle...

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Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Zhang, Yike Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

ISBN: (纸本)9781510660373

AI-assisted surgeries have drawn the attention of the medical image research community due to their real-world impact on improving surgery success rates. For image-guided surgeries, such as Cochlear Implants (CIs), accurate object segmentation can provide useful information for surgeons before an operation. Recently published image segmentation methods that leverage machine learning usually rely on a large number of manually annotated ground truth labels. However, it is a laborious and time-consuming task to prepare the dataset. This paper presents a novel technique using a self-supervised 3D-UNet that produces a dense deformation field between an atlas and a target image that can be used for atlas-based segmentation of the ossicles. Our results show that our method outperforms traditional image segmentation methods and generates a more accurate boundary around the ossicles based on Dice similarity coefficient and point-to-point error comparisons. The mean Dice coefficient is improved by 8.51% with our proposed method. © 2023 SPIE.

关键词： Image segmentation

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Kalman filter estimation method for battery cell parameters in Reconfigurable Cascaded Multilevel Converter

Kalman filter estimation method for battery cell parameters ...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Tresca, Giulia Formentini, Andrea Granata, Samuele Magrini, Carlo Zanchetta, Pericle University of Genoa El. Elec. and Telec. Engineering and Naval Architecture Dept. Genova Italy University of Pavia Electrical Computer and Biomedical Engineering Dept. Pavia Italy University of Nottingham Electrical and Electronics Engineering Dept. Nottingham United Kingdom

ISBN: (纸本)9798350316445

This paper presents an online estimation technique Kalman filter-based able to evaluate both the open circuit voltage and the internal resistance of the battery cells installed within the Reconfigurable Cascaded Multilevel Converter. This technique fills the gap for the complex reconfigurable structures, allowing the estimation of the parameters with only the knowledge of the output current and voltage and instantaneous active battery cells. The estimation technique is combined with a sorting algorithm to address the specific topic of multilevel converter controlled with digital modulation. Specifically, the estimation algorithm here presented ensures a reduced convergence time, demonstrated by the low mean error computed. Moreover, the estimation technique is simulated during both discharging and charging processes, showing how different initial conditions may affect the convergence time depending on the power flow. © 2023 IEEE.

关键词： Battery cells Estimation Technique Internal resistance Kalman Filter Multilevel Converter Online Estimation Open circuit voltage Reconfigurable Cascaded Multilevel Converter Sorting algorithm

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A Dynamic Security Assessment Tool for Modern Power Systems with Massive RES Penetration 3

A Dynamic Security Assessment Tool for Modern Power Systems ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Therapontos, Phivos Charalambous, Charalambos A. Aristidou, Petros Electricity Authority of Cyprus Distribution System Operator Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Cyprus University of Technology Dept. of Electrical & Computer Engineering & Informatics Limassol Cyprus

ISBN: (纸本)9798350375923

Modern power systems are facing significant challenges due to the massive penetration of renewable energy sources (RES). Recently, issues related to frequency security, system strength, and excessive fault levels have been experienced in some power systems. Classical dynamic security assessment (DSA) tools have not been developed to identify and analyze these new challenges that are critical in low-inertia grids. In this paper, a DSA tool has been developed that evaluates frequency security, system strength, and maximum fault levels to allow for secure system planning and operation. Furthermore, to increase the accuracy of the DSA tool, inverter-based resources' (IBRs') protections and capabilities have been incorporated in the DSA tool. A real-time operation and an operational planning application of the DSA tool are showcased using the islanded Cyprus Power System. © 2024 IEEE.

关键词： Dynamic security assessment frequency security IBR modeling low-inertia grids system strength

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Monolithic 3D Printed Micro-Optofluidic Device for Two-phase Flow Monitoring and Bioapplications 13

Monolithic 3D Printed Micro-Optofluidic Device for Two-phase...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Saitta, Lorena Cutuli, Emanuela Celano, Giovanni Tosto, G Claudio Guarino, Francesca Cicala, Gianluca Bucolo, Maide University of Catania Dept.of Civil Engineering and Architecture Catania95125 Italy University of Catania Dept. of Electrical Electronic and Computer Science Engineering Catania95125 Italy University of Catania Dept. of Biomedical and Biotechnological Science Catania95125 Italy

ISBN: (纸本)9798350356199

In this work, a biocompatible 3D printed micro-optofluidic (MoF) device designed for monitoring two-phase flows is presented. It was manufactured using the Projection Micro-Stereolithography (PμSL) 3D printing technique combined with a biocompatible resin. This device successfully monitored both an air-water slug-flow and a two-phase mixture of micrometric eukaryotic yeast cells (Saccharomyces cerevisiae) suspended in a phosphate-buffered saline (PBS) solution. Its working principle relies on the absorption phenomenon, since different light transmissions are correlated to either the fluid or the cells interfering with a laser beam in a microchannel section. To accomplish this, the MoF device was designed with two micrometric slots for the insertion of optical fibers to capture the light signal. Both the micro-optical and microfluidic components, each 200 μm in size, were integrated into the MoF device as a monolithic structure. A wide experimental campaign was performed on the MoF device investigating different flow rates and cells concentrations. The optimal operative condition for achieving the most robust working ability were determined using a Design of Experiments (DoE) approach. © 2024 IEEE.

关键词： Two phase flow

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