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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Synergistic Integration of Photovoltaic and the Grid: An Intelligent ANFIS-Governed Interleaved Boost Converter Approach

Synergistic Integration of Photovoltaic and the Grid: An Int...

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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Balaji, V. Ganga, M. Barathi, K. Gouda, Pramod Kumar Prasad, Rajesh Venkatesh, Mammula MAI-NEFHI College of Engineering and Technology Department of Electrical and Computer Engineering Asmara Eritrea Hindusthan College of Engineering and Technology Department of Biomedical Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Eee Tamil Nadu India Aditya Institute of Technology and Management Dept. of Eee Andhra Pradesh Srikakulam India Stella Mary's College of Engineering Dept. of Eee Tamil Nadu India Avanthi's Research and Technological Academy Dept. of Eee A. P Vizianagaram531162 India

ISBN: (纸本)9798350387230

The transition towards Renewable Energy Sources (RES), mainly solar power is crucial in addressing the depletion of fossil fuel-based energy supplies and mitigating environmental concerns. This paper proposes an innovative approach to enhance efficiency of PhotoVoltaic (PV) systems by integrating a two-phase Interleaved Boost (IB) converter with an Adaptive Neuro Fuzzy Inference System (ANFIS) controller. The ANFIS controller optimizes the operation of the IB converter by adjusting the duty cycle based on two key input parameters: solar irradiation and temperature. This adaptive control method effectively transforms solar energy into electrical power, maximizing the PV system's power production. By using the ANFIS mechanism, the suggested system improves total output power, decreases overshoot, and produces a higher voltage output than conventional techniques, all of which lead to increased performance and dependability. The IB converter operates with an efficiency of 96.15% and minimized settling time. © 2024 IEEE.

关键词： Adaptive control systems

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern 13

Classification of Awareness in Disorders of Consciousness Us...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Lee, Chaewon Lee, Sunho Annen, Jitka Jeong, Ji-Hoon Massimini, Marcello Casarotto, Silvia Boly, Melanie Bodart, Olivier Thibaut, Aurore Rosanova, Mario Laureys, Steven Gosseries, Olivia Lee, Minji Dept. Data Science The Catholic University of Korea Bucheon Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent GhentB9000 Belgium Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical and Clinical Sciences "L. Sacco" University of Milan Milan Italy Wisconsin Institute for Sleep and Consciousness Dept. Psychiatry University of Wisconsin Madison United States Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (纸本)9798331521929

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future. © 2025 IEEE.

关键词： Electroencephalography

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Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons

arXiv

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arXiv 2024年

作者： Afentaki, Florentia Duarte, Paula Carolina Lozano Zervakis, Georgios Tahoori, Mehdi B. Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece Department of Computer Science Karlsruhe Institute of Technology Karlsruhe76131 Germany

Printed electronics technology offers a cost-effective and fully-customizable solution to computational needs beyond the capabilities of traditional silicon technologies, offering advantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printed electronics, which results in large feature sizes, poses a challenge for integrating complex designs like those of machine learning (ML) classification systems. Current literature optimizes only the Multilayer Perceptron (MLP) circuit within the classification system, while the cost of analog-to-digital converters (ADCs) is overlooked. Printed applications frequently require on-sensor processing, yet while the digital classifier has been extensively optimized, the analog-to-digital interfacing, specifically the ADCs, dominates the total area and energy consumption. In this work, we target digital printed MLP classifiers and we propose the design of customized ADCs per MLP’s input which involves minimizing the distinct represented numbers for each input, simplifying thus the ADC’s circuitry. Incorporating this ADC optimization in the MLP training, enables eliminating ADC levels and the respective comparators, while still maintaining high classification accuracy. Our approach achieves 11.2x lower ADC area for less than 5% accuracy drop across varying MLPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Comparator circuits

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Using clinical entity recognition for curating an interface terminology to aid fast skimming of EHRs

Using clinical entity recognition for curating an interface ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kollapally, Navya Martin Dehkordi, Mahshad Koohi H. Perl, Yehoshua Geller, James Deek, Fadi P. Liu, Hao Keloth, Vipina K. Elhanan, Gai Einstein, Andrew J. Zhou, Shuxin Kean University Department of Computer Science Union United States New Jersey Institue of Technology Department of Computer Science Newark United States New Jersey Institue of Technology Department of DataScience Newark United States New Jeresy Institue of Technology Department of Informatics Newark United States Montclair State University School of Computing Montclair United States Yale University Section of Biomedical Informatics and DataScience New Haven United States University of Nevada School of Medicine Nevada United States Columbia Medical Center Dept. of Medicine New York United States

ISBN: (纸本)9798350386226

Highlighting of Electronic Health Records (EHRs) involves marking essential content of EHR notes, corresponding to concepts of a clinical terminology. However, employing the best clinical terminology (SNOMED CT) for highlighting EHRs, captures only a portion of their crucial content. In this paper, we describe the curation of a Cardiology Interface Terminology (CIT) dedicated to the application of highlighting EHRs of cardiology patients. We utilize a Clinical-Named Entity Recognition (Clinical NER) approach for extracting phrases, of higher granularity than SNOMED CT concepts, from EHRs, for enriching CIT. For this purpose, we train a neural network model with BIOE-tagged (Beginning, Inside, End, and Outside) cardiology entities. Transfer Learning can be used to facilitate the curation of an interface terminology for highlighting EHRs for other specialties e.g. Nephrology. Large-scale highlighting enables overworked physicians and other healthcare providers to fast skim the dense volume of EHRs they regularly read. Secondary research and EHRs interoperability are other applications that can be supported by highlighting. © 2024 IEEE.

关键词： Electronic health record

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Animated Virtual Therapist in Virtual Reality Environment for People with Parkinson's Disease

Animated Virtual Therapist in Virtual Reality Environment fo...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Wenting Li Ruth Agada Jie Yan Biomedical Engineering Dept. Washington University St. Louis USA Department of Computer Science Bowie State University Bowie USA

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

This study investigates the effectiveness of virtual rehabilitation using a 3D animated virtual therapist delivering the LSVT-BIG program for people with Parkinson's disease (PwPD). Integrating an animated virtual therapist in a VR environment aims to overcome accessibility barriers, especially for individuals in remote areas. This VR-enhanced system provides immersive experiences, real-time feedback, and posture correction through computer vision, along with progress-tracking via personalized reports. This approach offers potential improvements in accessibility, engagement, and motivation, which may contribute to enhanced quality of life for individuals with Parkinson's disease.

关键词： computer vision Three-dimensional displays Authoring systems Parkinson's disease Medical treatment Virtual reality Real-time systems Artificial intelligence

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Brain Tumor Detection using Convolutional Neural Networks (CNNs)

Brain Tumor Detection using Convolutional Neural Networks (C...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sasimekala M M Siva V Shanmuga Shyam B Veeramalai Sankaradass C Selvaganesan R. Ponnusamy Department of Biomedical Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The brain's unchecked and fast cell development fuels a tumor. It may prove lethal if left untreated in the early stages. Accurate segmentation and classification remain difficult despite many noteworthy efforts and promising results. The differences in the tumor size, form, and proportions make brain tumor detection extremely difficult. The primary goal of this study is to provide researchers with a thorough literature review on the use of magnetic resonance (MR) imaging in diagnosing brain malignancies. This study suggested multiple methods for detecting tumors and brain cancer using statistical image processing and artificial intelligence. An assessment matrix for a certain system employing particular systems and dataset kinds is also displayed in this study. The anatomy of brain tumors, available data sets, augmentation techniques, component extraction, and classification between machine learning (ML) and deep learning (DL) models are also explained in this work. Lastly, the study gathers all pertinent information for identifying and comprehending tumors, including their advantages, disadvantages, developments, and future trends. In this study, an attempt is made to classify the tumors using CNNs.

关键词： Deep learning Image segmentation Magnetic resonance Imaging Market research Data models Fuels Data mining Systematic literature review Image classification

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Performance Comparison of 15-Level Multilevel Inverter Topologies 15

Performance Comparison of 15-Level Multilevel Inverter Topol...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Anusha, G. Arora, Krishan Sharma, Himanshu Thota, Surya Prakash Verma, Chaman Raboaca, Maria Simona Filote, Constantin Lovely Professional University Dept. of Eee Punjab India Gnits Dept. of Eee Hyderabad India Eötvös Loránd University Faculty of Informatics Dept. of Media and Educational Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava Romania

ISBN: (纸本)9798350321388

Multilevel inverters (MLI) have gained a significant emphasis in the area of renewable energy applications in recent years. The popularity of MLIs is due to their ability to efficiently convert the sustainable sources of energy, such as wind and solar power, into usable electrical energy. One key advantage of using reduced multilevel inverter configurations is that it can help to reduce the system overall cost by decreasing number of components required. The reduction of components in the inverter circuit results in simplification of design and lower expenditure. This paper proposes a fifteen-level modified multilevel inverter with cascaded h-bridge with a minimized switch count. The simulation results of the proposed inverter demonstrate that it is able to effectively attenuate harmonics and with reduced number of components required while still providing good performance. The suggested inverter design is also evaluated on its harmonic reduction characteristics, providing a cleaner output waveform and less distortion. © 2023 IEEE.

关键词： Solar energy

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Microaneurysm Detection for Early DR Diagnosis Using Advanced Image Processing and a Lightweight Feature Pyramid Network

Microaneurysm Detection for Early DR Diagnosis Using Advance...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Muhammad Zeeshan Tahir Muhammad Nasir Tufail Muhammad Sanyuan Zhang Muhammad Ahsan Qureshi Dept. of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China School of Biomedical Engineering University of Science and Technology of China Hefei Anhui China Department of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Diabetic Retinopathy (DR) is a diabetes-related condition that can lead to vision loss and even permanent blindness. The growing number of diabetic patients combined with a shortage of ophthalmologists underscores the need for automated screening tools to enable early detection. Microaneurysms (MAs), the earliest signs of DR, yet identifying them in fundus images is challenging because of their tint size and subtle features. Furthermore, low contrast, noise, and lighting variations in fundus images, including glare and shadows, add complexity to the detection process. To address these challenges, we applied image enhancement techniques such as green channel extraction, gamma correction, and median filtering to improve image quality. Additionally, to boost the performance of the MA detection model, we integrated a lightweight Feature Pyramid Network (FPN) with a pre-trained ResNet34 backbone, allowing for multiscale feature capture. Our method was evaluated on the E-ophtha dataset, achieving a sensitivity of 0.601, precision of 0.697 and F1 score of 0.651. The experimental findings indicate that the proposed method surpasses current approaches.

关键词： Image quality Diabetic retinopathy Sensitivity Filtering Noise Lighting Blindness Feature extraction Complexity theory Image enhancement

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