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检索条件"机构=dept. of Computer Computer Systems Engineering"

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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Federated Learning Model for Early Detection of Dementia Usi...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Elsersy, Mohamed Sherif, Ahmed Imam, Ahmad Abdel-Aliem Khan Mamun, Mohammad M. R. Khalil, Kasem Haitham, Mohamed Higher Colleges of Technology Computer and Information Systems Dept. Abu Dhabi United Arab Emirates University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States William Carey University College of Osteopathic Medicine Mississippi MS United States Tennessee Technological University Electrical and Computer Engineering Department TN United States University of Mississippi Electrical and Computer Engineering Department MS United States Cairo Egypt

ISBN: (纸本)9798350322347

Alzheimer's disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Question Answering System for Healthcare Information based on BERT and GPT 8

Question Answering System for Healthcare Information based o...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Jeong, Seung Won Kim, Cheong Ghil Whangbo, Taeg Keun Gachon University Dept. of IT Convergence Engineering Gyeonggi-Do13120 Korea Republic of Namseoul University Dept. of Computer Scicence Choongnam 31020 Korea Republic of

ISBN: (纸本)9798350396546

Recently, demand for telemedicine counseling has been on the rise. Although telemedicine is legally prohibited in Korea, the number of healthcare-related questions on online question and answer platforms is steadily increasing. However, doctors have to manually write answers to similar questions, which is time-consuming and inefficient. This study implemented a healthcare information provision question and answer system using an algorithm that combines BERT and GPT-2 structures. Intellectual Q&A pairs were used as training data, and the BERT-GPT algorithm experimented with creating sentences that provide healthcare information such as related medical subjects and suspected diseases as answers to healthcare-related questions. As a result of BERT-GPT2 algorithm gets worse PPL score and loss than the baseline model, but better score in the qualitative assessment. This result means BERT-GPT2 understands the conversation intention better. © 2023 IEEE.

关键词： Telemedicine

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Waste Generation Forecasting Using Mobility Data During COVID-19 Pandemic 4

Waste Generation Forecasting Using Mobility Data During COVI...

引用

4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Fathabadi, Alireza Toghroli Abdeljaber, Abdulrahman Abdallah, Mohamed Talib, Manar Abu Maraqa, Munjed Dept. of Civil and Environmental Engineering University of Sharjah Sharjah United Arab Emirates Dept. of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

Municipal solid waste (MSW) generation forecasting serves as the basis for future waste management strategic plans. However, rapid socio-economic and environmental changes lead to fluctuations in the MSW generation patterns. This is particularly exacerbated during unforeseen events, such as the COVID-19 pandemic. This study investigated the effect of COVID-19 and mobility conditions on the behavior of MSW generation predictors. Multiple artificial intelligence models were employed, including generalized linear models decision trees (DTs), support vector machines, deep learning, random forests (RFs), and gradient-boosted trees (GBTs). The models were tested around a 5-year historical MSW generation, incorporated with various socio-economic, seasonal, and climatic factors, in Abu Dhabi, United Arab Emirates. Multiple data split ratios were examined: 1) 60/40, 70/30, and 49/51 to simulate the COVID-19 pandemic. The feature importance analysis indicated that socioeconomic had the highest impact on generation rates, with a Pearson correlation coefficient of 0.30-0.71, followed by seasonal and climatic factors, with 0.12-0.40 and 0.15, respectively. The forecasting results indicated that training the models with 70% of the dataset resulted in higher accuracy compared to using a 60% training split. GBTs and RFs demonstrated superior performance, with root mean squared error (RMSE) of 0.029 and 0.031, respectively. On the other hand, DT achieved the lowest accuracy of 0.057 RMSE. The analysis revealed a significant improvement in the models' predictive capabilities upon the inclusion of COVID-19 and mobility conditions by 17-41%. The findings of this study emphatically highlight the importance of incorporating contextual variables into MSW generation prediction methodologies, affirming their significant impact on enhancing forecasting accuracy in rapidly changing environments. © The Institution of engineering & Technology 2023.

关键词： Prediction models

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Bulk Sample Analysis Using Associated Alpha Particle Neutron Generator and Artificial Neural Network 14

Bulk Sample Analysis Using Associated Alpha Particle Neutron...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Shahabinejad, Hadi Sudac, Davorin Alamaniotis, Miltiadis Nad, Karlo Objodas, Jasmina Institute Ruder Boskovic Dept. of Experimental Physics Zagreb Croatia University of Texas at San Antonio Dept. of Electrical and Computer Engineering Texas United States

ISBN: (纸本)9798350318067

Container inspection using x-rays and neutrons is in great demand at borders nowadays. While x-ray inspection has been extensively developed, neutron inspection is still under development. This work investigates the possibility of utilizing an artificial neural network (ANN) to analyze prompt-gamma spectra generated by tagged fast neutrons in an experimental setup. A simple multi-layer perceptron neural network is trained to map 10 Regions of Interest (ROIs) of 14 MeV neutron-induced prompt gamma-ray (PG) spectra, which are measured using a 3x3 inch NaI detector, to the weight percentages of 4 key constituents. The key constituents consist of one liter of Graphite, Water, Quartz sand, and Melamine. The selected inputfeatures are the integrated counts under major peaks of the PG spectra of the key constituents. The required dataset for training and testing the network is generated using the fundamental spectra of key constituents. Finally, the trained network is evaluated using a mixed sample measured over a period of 2 hours. The obtained primary results are quite satisfactory. The errors in determining the weight percentages of key constituents are less than 1 percent when the sample itself is a key constituent, and the correlation coefficient between target values and predicted values of ANN for both the train and test data is almost one. © 2023 IEEE.

关键词： Neutrons

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Composition of Motion from Video Animation Through Learning Local Transformations 48

Composition of Motion from Video Animation Through Learning ...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Vrigkas, Michalis Tagka, Virginia Plissiti, Marina E. Nikou, Christophoros University of Western Macedonia Dept. Communication and Digital Media Kastoria52100 Greece University of Ioannina Dept. Computer Science and Engineering Ioannina45110 Greece

ISBN: (纸本)9781728163277

In this work, we solve the problem of motion representation in videos, according to local transformations applied to specific keypoints extracted from static the images. First, we compute the co-ordinates of the keypoints of the body or face through a pre-trained model, and then we introduce a convolutional neural network to estimate a dense motion field through optical flow. Next, we train a generative adversarial network that exploits the previous information to generate new images that resemble as much as possible the target frames. To reduce trembling and extract smooth movements, our model incorporates a low-pass spatio-temporal Gaussian filter. Results indicate that our method provides high performance and the movement of objects is accurate and robust. © 2023 IEEE.

关键词： Animation Dense motion GANs Gaussian filter Video-synthesis

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An Evaluation of MFCC and ZCR Feature Extraction with CNN Model of Speaker Identification System 13

An Evaluation of MFCC and ZCR Feature Extraction with CNN Mo...

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13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Kaladharan, N. Arunkumar, R. Dept. of Computer Engineering Government Polytechnic College Theni India Department of Computer Science and Engineering FEAT Annamalai University India

ISBN: (纸本)9781713857938

The speech is the important medium to transfer our feeling to the neighbors. Speaker recognition is the process to recognize our speech in our owned database. Speaker recognition has divided in to speaker identification and speaker verification. Speaker identification is different mode which is text dependent, independent and prompted. Identifying the speaker in our database is the major task in our process. Identifying data or speech is the main aim to handle in ourdatabase. Speaker identification skills arebroadly useful in security and investigation, voice confirmation, electronic voice snooping and classify verification. Various features extraction ideas are used in identification as well as verification such as mfcc, zcr, pncc and so on. MFCC are cepstral coefficients resultant on a warped frequency scale centered on human hearing observation. In the computation of MFCC, the first thing is windowing the speech signal to split the speech signal into frames. The zero-crossing rate is the proportion at which the signal modifications from positive to zero to negative or from negative to zero to positive. Power Normalized Cepstral Coefficients is well identified for the high accuracy of automatic speech recognition systems even in high-noise *** paper presents the comparison of mfcc and zcr with the CNN modeling system. CNN is the top modeling idea in the recent decades. © Grenze Scientific Society, 2022.

关键词： Extraction

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Internet of Things and Machine Learning-based Diabetes Management System

Internet of Things and Machine Learning-based Diabetes Manag...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Khan, Mohammad Ahmed Nauman, Zainab Farooq, Mahad Zafar, Saima International School Lahore Dept. Isl Advanced Level Lahore Pakistan National University of Computer and Emerging Sciences Dept. of Electrical Engineering Lahore Pakistan

ISBN: (纸本)9798350315974

Effective diabetes management is critical for diabetics which involves continuous glucose level monitoring, generally carried out invasively which is costly, uncomfortable, and inconvenient. Lately, non-invasive approaches have been proposed for low-cost and high-accuracy glucose level monitoring using optical and transdermal methods. This paper presents the design, implementation, and testing of a diabetes management system based on Internet of Things (IOT), machine learning, cloud storage, and mobile application development. The system involves the use of non-invasive glucose level monitoring techniques that use the near infrared spectroscopy (NIRS) and bio-impedance spectroscopy (BIS). It is a painless procedure that involves passing a tiny-amplitude electrical current with a specific frequency through cables connected to electrodes put on the human body. To capture the lowest impedance point, the BIS system executes a frequency sweep. The NIR system employs two distinct light emitting diodes (LEDs) with two distinct wavelengths, each of which has a peak absorption for glucose molecules and is less responsive to other blood components. The machine learning linear regression model is used to estimate the glucose level by using the observed parameters of the two techniques as features, along with some other physical attributes. After estimating the glucose level, the system transmits the information over a Wi-Fi module to a cloud service from where it is accessed for display via a mobile application. The system was tested in terms of the mean absolute error (MAE), mean squared error (MSE), and root-mean squared error (RMSE) and all values are minimal. © 2023 IEEE.

关键词： Internet of things

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Database Matching Service for Stable Marriage Problem 25

Database Matching Service for Stable Marriage Problem

引用

25th International Electronics Symposium, IES 2023

作者： Alimudin, Akhmad Ishida, Yoshiteru Politeknik Elektronika Negeri Surabaya Dept. of Multimedia Creative Technology Surabaya Indonesia Toyohashi University of Technology Dept. of Computer Science and Engineering Toyohashi Japan

ISBN: (纸本)9798350314731

This study revolves around delving into the realm of dynamic preferences within the context of the Stable Marriage Problem (SMP). While the Gale-Shapley algorithm necessitates a time complexity of O(n^{2}) to solve an SMP with static preferences, the pursuit of identifying stable matchings under dynamic preferences becomes significantly more time-intensive, entailing a time complexity of O(k\cdot n!\cdot n^{2}). The majority of the process involved in discovering stable matchings under dynamic preferences revolves around iterative searching. This circumstance motivates us to develop a database comprising SMP instances and their corresponding stable matchings. This database facilitates a reduction in the time required to uncover static stable matchings from O(n^{2}) to O(n). Additionally, our application incorporates several features that enhance users' comprehension of stable matching structures within SMP instances, including visualization through a lattice structure. © 2023 IEEE.

关键词： Database systems

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Parametric Optimization of WEDM Process for Machining ANSI Steel Using Soft-Computing Methods

Parametric Optimization of WEDM Process for Machining ANSI S...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Yi-Zhen Chiang, Hsin-Han Liao, Jo-An Chen, Yen-Lin Li, Hsiao-Chi National Taipei University of Technology Dept. of Vehicle Engineering Taipei Taiwan National Taipei University of Technology Dept. of Computer Science and Information Engineering Taipei Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

This paper introduces a parametric method for optimizing the wire electric discharge machining (WEDM) process applied to steel machining. The proposed approach employs soft-computing methods to determine the optimal process parameters for achieving maximum material removal rate and minimum surface roughness. This work conducts an analytical investigation to evaluate the feasibility of the proposed approach. © 2023 IEEE.

关键词： Soft computing

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Utilizing Anomaly Detection Methods for Identifying Fraudulent Activities in Credit Card Transactions 1

Utilizing Anomaly Detection Methods for Identifying Fraudule...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Prasad, N. Siva Rama Krishna Gangopadhyay, Amit Prabakaran, R. Mishra, Nilamadhab Prasad, D. Shyam Kapoor, Sneha Saveetha School of Engineering Dept. of CSE Chennai India Mohan Babu University Dept. of ECE Tirupati India Arignar Anna Institute of Science and Technology Dept. of Computer Application India VIT Bhopal University Dept. of CSE Bhopal India CVR College of Engineering Dept. of Electronics and Instrumentation Engineering Hyderabad India Swami Vivekanand Subharti University Dept of CSE Meerut India

ISBN: (纸本)9798350356816

Anomaly detection involves identifying unusual occurrences that could signal potential issues, such as security breaches, system failures, financial fraud, structural defects, or medical errors. In the context of digital finance, credit card fraud has become a significant concern, with fraudulent transactions often designed to appear legitimate. To address this, we employ the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset during sampling, and use the ARIMA model for time series forecasting to ensure accurate results. This research explores various fraud detection techniques and models, including K-Nearest Neighbors (KNN), Random Forest, Support Vector Classifier (SVC), and Decision Trees. We evaluate and compare the performance of these models to determine which offers the highest accuracy in detecting fraud. © 2024 IEEE.

关键词： Anomaly detection

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