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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Srijita Bandopadhyay Srimonti Dutta Imran Haider Bhavaraju Anuraag Jerry Zhu Saad Ahmed Bazaz Dept. of Electronics and Communication Engineering University of Engg. & Management Kolkata India School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering Vellore Institute of Technology Vellore India Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer & Emerging Sciences Islamabad Pakistan

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the β- VAE filtered approach employed in previous literature.

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Mobile Robot Manipulator Localization in Indoor Environments for Object Picking Tasks

Mobile Robot Manipulator Localization in Indoor Environments...

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International Conference on control and Robots (ICCR)

作者： Yunfan Xie Goragod Pongthanisorn Shin-ichiro Kaneko Genci Capi Dept. of Mechanical Eng. Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Electrical and Control Systems Engineering National Institute of Technology Toyama College Toyama Japan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

In dynamic and unpredictable work environments such as manufacturing, logistics, and automated warehouses, achieving high-precision self-localization estimation for efficient object picking are critical challenges for mobile robots. Ensuring accuracy and adaptability is essential for their successful operation. This paper presents a new method that integrates RGB cameras with Contrastive Learning (CL) for self-localization estimation and advanced object picking for mobile manipulators. The effectiveness of the proposed method for location estimation in object picking is verified in indoor environments.

关键词： Adaptation models Accuracy Robot vision systems Estimation Contrastive learning Predictive models Manipulators Robustness Indoor environment Mobile robots

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Meaning of the Clusters on Dimensionality Reduction by Word Clustering 12

Meaning of the Clusters on Dimensionality Reduction by Word ...

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12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Deguchi, Toshinori Seo, Sinsuke Ishii, Naohiro Dept. of Electrical and Computer Engineering Gifu College Gifu Japan Interdisciplinary Technology Development Gifu College Gifu Japan Information Systems Architecture Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

In text mining, Latent Semantic Analysis (LSA) is the popular method to reduce the dimension of document vectors. Since LSA produces a set of topics by statistical information, the meaning of each topic is not *** proposed a method to reduce the dimension by clustering the words in the documents. This method produces a set of clusters of words instead of topics. Using Word2vec to vectorize the words, the mean vector of the cluster is calculated, which shows the meaning of the *** this paper, we show the dimensionality reduction and the meaning of the generated clusters by word cloud, on document classification problem with a subset of BBC Dataset. © 2022 IEEE.

关键词： Classification (of information)

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Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification

Neuromorphic Camera Assisted High-Flow Imaging Cytometry for...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Ioannis Tsilikas Stavros Deligiannidis Aris Tsirigotis Georgios N. Tsigaridas Adonis Bogris Charis Mesaritakis Dept. Physics National Technical University of Athens Zografou Athens Greece Dept. Information and Communication Systems Engineering University of the Aegean Karlovasi Samos Greece Dept. Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9798350345995

In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach.

关键词： Biomedical imaging Cameras CMOS cameras Cytometry Machine learning Real time imaging

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The Impact of the Energy Storage System Operation Strategy on Power Grid Reliability

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Transactions of the Korean Institute of Electrical Engineers 2021年第12期70卷 1820-1826页

作者： Kim, Beopsoo Lee, Dayoung Rusetskii, Nikita Shusterzon, Konstantin Sidorov, Denis Kim, Insu Dept. of Electrical Engineering Inha University Korea Republic of Dept. of Electrical and Computer Engineering Inha University Korea Republic of School of Information Technology and Data Science Irkutsk National Research Technical University Russia Energy Systems Institute Siberian Branch Russian Academy of Sciences Russia

Based on the empirical data published by the Korea Electric Power Exchange and the Korea Meteorological Administration, we present a methodology that predicts the system marginal prices. For this purpose, this study uses the long-short term memory (LSTM) technique, which is commonly used in time-series prediction problems. As a result, we present a strategy for operating energy storage systems (ESSs) from the perspective of a prosumer rather than a system operator or conventional utilities. This study also examines the effects of the proposed prosumer actions on the system. The proposed operating strategy is established based on dynamic programming. In particular, this study takes the proposed operation strategy of ESSs the point of view of system reliability. © The Korean Institute of Electrical Engineers This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://***/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Reliability

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Improving Gamma Imaging in Proton Therapy by Sanitizing Compton Camera Simulated Patient Data using Neural Networks through the BRIDE Pipeline

Improving Gamma Imaging in Proton Therapy by Sanitizing Comp...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Michael O. Hodge, Julian Jin, Peter L. Protz, Ella Wong, Elizabeth Obe, Ruth Shakeri, Ehsan Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Sharma, Vijay R. Mossahebi, Sina Ren, Lei Peterson, Stephen W. Polf, Jerimy C. Dartmouth College Departments of Mathematics United States U. of Maryland Dept. of Mathematics and Statistics United States James M. Bennett High School SalisburyMD United States Florida Atlantic University Department of Mathematics and Sciences United States Brookdale Community College Department of Mathematics United States U. of Houston Dept. of Computer Science and of Software Engineering Clear Lake United States U. of Maryland Dept. of Information Systems United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa M3D Inc. United States

ISBN: (纸本)9798350362480

Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with patient matter, the excited nuclei may emit prompt gamma ray interactions that can be captured by a Compton camera. The image reconstruction from this captured data faces the issue of mischaracterizing the sequences of incoming scattering events, leading to excessive background noise. To address this problem, several machine learning models such as Feedfoward Neural Networks (FNN) and Recurrent Neural Networks (RNN) were developed in PyTorch to properly characterize the scattering sequences on simulated datasets, including newly-created patient medium data, which were generated by using a pipeline comprised of the GEANT4 and Monte-Carlo Detector Effects (MCDE) softwares. These models were implemented using the novel 'Big-data REU Integrated Development and Experimentation' (BRIDE) platform, a modular pipeline that streamlines preprocessing, feature engineering, and model development and evaluation on parallelized GPU processors. Hyperparameter studies were done on the novel patient data as well as on water phantom datasets used during previous research. Patient data was more difficult than water phantom data to classify for both FNN and RNN models. FNN models had higher accuracy on patient medium data but lower accuracy on water phantom data when compared to RNN models. Previous results on several different datasets were reproduced on BRIDE and multiple new models achieved greater performance than in previous research. © 2024 IEEE.

关键词： Phantoms

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Switched-Capacitor Low-Pass Filter with Pole Frequency Tuning at Constant Frequency of Switching

Switched-Capacitor Low-Pass Filter with Pole Frequency Tunin...

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International Conference on Actual Problems of Electronic Instrument engineering (APEIE)

作者： Darya Y. Denisenko Irina A. Alferova Alexey E. Titov Dmitry V. Kuznetsov Dept. of Scientific Research Don State Technical University Dept. of Automatic Control Systems Southern Federal University Rostov-on-Don Russia Dept. of Scientific Research Dept. of Cyber Security of Information Systems Don State Technical University Rostov-on-Don Russia Dept. of Information Systems and Radio Engineering Don State Technical University Rostov-on-Don Russia

The circuitry of a switched-capacitor low-pass filter (SCF) is considered, in which it is possible to tune the pole frequency without changing the capacitances of the switched capacitors, for example, by selecting them of the same kind. To study the suggested SCF circuit, the analysis in the time domain was used, and the obtained characteristics of the SCF were further compared with the characteristics of a generalized second-order analog filter. It is shown that the parameters implemented by the suggested filter depend not only on the capacitance values of the switched capacitors, but also on the ratio of the resistances of two additional resistors. Moreover, the SCF pole frequency is determined by the switching frequency of the keys, the ratios of the capacitances of the switched capacitors, as well as the ratio of the resistances of additional resistors. Due to the goal-oriented selection of the resistances of these resistors, it is possible to lower the SCF pole frequency at constant and identical values of the capacitances of the switched capacitors.

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Estimation of Soil Moisture with Meteorological Variables in Supervised Machine Learning Models

Estimation of Soil Moisture with Meteorological Variables in...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Mariam Hussain Nusrat Sharmin Sumayea Binte Shafiul Dept. of Climate and Energy Systems Engineering Ewha Womans University Seoul South Korea Dept. of Computer Science and Engineering University of Science and Technology Chittagong Bangladesh Dept. of Epidemiology Bangladesh University of Health Sciences Dhaka Bangladesh

Water cycles, climate-related hazards, and agroirrigation are strongly controlled by soil moisture (SM) content. For water resource management, prediction is a key to mitigate and regulate expected economic losses and property damages. This paper compares two supervised machine learning (ML) techniques: support vector regression (SVR) and random forest (RF) to predict SM. In RStudio, various meteorological variables: temperature, relative humidity, wind speed, and rainfall are trained to estimate SM. For eight divisions, SM and weather variables are obtained from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER). The experiments include daily observations for 39 (1982 to 2020) to develop SVR and RF models. To estimate SM from the predictive model, datasets from diverse regions: Rajshahi, Mymensingh, Chittagong, and Sylhet are utilized in training (60%) and Rangpur, Barisal, Khulna, and Dhaka are segregated for validation (40%) resulting in accuracy of 88 to 95.8%. This model further is applied to forecast daily SM for each city including two districts (Bogra and Jessore) and found slightly higher model performance for SVR (90.7%) than RF (90.1%) on average (Year: 2021). For agricultural, industrial and urban water supplies as well as drought, landslides, and river erosions can be mitigated by an accurate estimation of soil moisture. The investigations encourage for providing SM budget to public with supervised ML techniques mostly among data-sparse regions.

关键词： Radio frequency Support vector machines Training Biological system modeling Soil moisture Urban areas Weather forecasting

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A Distributed Ensemble of Diverse Deep Learning Models for Predicting COVID-19 Cases 20

A Distributed Ensemble of Diverse Deep Learning Models for P...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Hassan, Mohammad Mehedi Alrakhami, Mabrook S. Elhendi, Ahmed Zohier Alqahtani, Salman A. King Saud University Information Systems Department Ccis Riadh 11543 Saudi Arabia King Saud University Science Technology and Innovation Dept. Riadh 11543 Saudi Arabia King Saud University Computer Engineering Department Ccis Riadh 11543 Saudi Arabia

ISBN: (纸本)9781665463973

The outbreak of the COVID-19 pandemic has resulted in a significant impact on global health and economy. Forecasting the spread of COVID-19 cases is essential for policymakers to make informed decisions and allocate resources accordingly. Deep learning has shown promising results in predicting the spread of infectious diseases, including COVID-19. However, selecting the most appropriate model for a given forecasting task can be challenging, as no single model is guaranteed to perform well for all scenarios. In this paper, we propose a distributed ensemble approach that combines the predictions of different deep learning models, including Autoregressive Integrated Moving Average (Auto-ARIMA), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), to improve the accuracy of COVID-19 case forecasts. Specifically, we train each deep learning model on a subset of the data and aggregate their predictions to obtain an ensemble forecast. To achieve a distributed ensemble, we use an edge computing framework to train the deep learning models on multiple nodes in a parallel and distributed manner. We evaluate our approach on publicly available COVID-19 datasets, and our results show that the proposed ensemble model outperforms the individual models in terms of forecasting accuracy. Furthermore, our distributed ensemble approach significantly reduces the training and prediction time compared to training each model separately. © 2023 IEEE.

关键词： COVID-19

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Analysis and optimization of clinical pathways using timed continuous Petri nets.

Analysis and optimization of clinical pathways using timed c...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： César Arzola Cristian Mahulea Jorge Júlvez Dept. of Computer Science and Systems Engineering Universidad de Zaragoza Spain Aragón Institute for Engineering Research Universidad de Zaragoza Spain

This paper presents a novel approach to analyze and optimize healthcare systems based on clinical pathways, using timed continuous Petri nets (TCPNs) under infinite server semantics. TCPNs allow for an efficient continuous-time analysis of the patient flow and resource utilization dynamics of these types of systems. We demonstrate the feasibility and effectiveness of our method through a case study of a hip fracture pathway at the "Lozano Blesa" University Clinical Hospital, in Zaragoza Spain. Additionally, we propose a method to optimize the behavior of the modeled system by taking a control theory approach, which enables the system to achieve maximum throughput more efficiently. Finally, we provide simulation results to demonstrate the effectiveness of the proposed controller in practical settings.

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