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15th International Conference on Emerging Technologies, ICET 2019

作者： Waqar, Muhammad Inam, Shehla Ur Rehman, Muhammad Azeem Ishaq, Muhammad Afzal, Mafia Tariq, Nimra Amin, Faisal Qurat-Ul-Ain National University of Science and Technology Dept. of Biomedical Engineering and Sciences Pakistan Riphah International University Dept. of Biomedical Engineering Islamabad Pakistan Dept. of Electrical Engineering National University of Science and Technology Islamabad Pakistan Bahria University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781728154039

The medical device used for auscultation is known as a stethoscope. An ordinary acoustics-based stethoscope is limited in its ability to provide high-quality sound in noisy environments. To deal with this problem digital stethoscopes have been developed. A digital stethoscope enables the use of advanced noise cancellation techniques resulting in high- quality sound compared to a conventional stethoscope. Currently, conventional stethoscopes are commonly used by physicians due to their low cost compared to the digital ones. This paper presents an Arduino DUE based cost-effective digital stethoscope with real-time realization of cardiac sounds over a graphic display and logging capabilities for post-processing in powerful numerical software such as MATLAB. The proposed design is considerably low-cost compared to the commercially available digital stethoscopes while providing 2.375 \pm 0.222 dB better signal-to-noise ratio. © 2019 IEEE.

关键词： MATLAB

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Fast Spatial Autocorrelation

Fast Spatial Autocorrelation

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IEEE International Conference on Data Mining (ICDM)

作者： Anar Amgalan LR Mujica-Parodi Steven S. Skiena Dept. of Physics and Astronomy Stony Brook University Dept. of Biomedical Engineering Stony Brook University Dept. of Computer Science Stony Brook University

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Physical or geographic location proves to be an important feature in many data science models, because many diverse natural and social phenomenon have a spatial component. Spatial autocorrelation measures the extent to which locally adjacent observations of the same phenomenon are correlated. Although statistics like Moran's I and Geary's C are widely used to measure spatial autocorrelation, they are slow: all popular methods run in Ω(n 2 ) time, rendering them unusable for large data sets, or long time-courses with moderate numbers of points. We propose a new S A statistic based on the notion that the variance observed when merging pairs of nearby clusters should increase slowly for spatially autocorrelated variables. We give a linear-time algorithm to calculate S A for a variable with an input agglomeration order (available at https://***/aamgalan/spatial_autocorrelation). For a typical dataset of n ≈ 63,000 points, our S A autocorrelation measure can be computed in 1 second, versus 2 hours or more for Moran's I and Geary's C. Through simulation studies, we demonstrate that S A identifies spatial correlations in variables generated with spatially-dependent model half an order of magnitude earlier than either Moran's I or Geary's C. Finally, we prove several theoretical properties of S A : namely that it behaves as a true correlation statistic, and is invariant under addition or multiplication by a constant.

关键词： Correlation Computational modeling Time series analysis Transforms Time measurement Spatial databases Standards

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Employing Method for a Method: Design and Evaluation of the Proposed PDEduGame Process Method using Design science Research Method 20

Employing Method for a Method: Design and Evaluation of the ...

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6th International Conference on computer and Technology Applications, ICCTA 2020

作者： Ismail, Rozana Ibrahim, Roslina Ya'acob, Suraya Bahari, Rokiah Kamal, Syed Ardi Syed Yahya Azami, Husna Hafiza R. Hamid, Siti Zamilah Abd. Universiti Teknologi Malaysia UTM Kuala Lumpur Razak Faculty of Technology and Informatics School Lumpur Malaysia Dept. of Computer Science and Services UTMSPACE UTM Kuala Lumpur Lumpur Malaysia UTMSPACE UTM Kuala Lumpur Lumpur Malaysia

ISBN: (纸本)9781450377492

Identifying the process of participatory design (PD) approach to cater for students' mutual needs in recognizing their educational game design aspirations for schools and higher education and applying their game knowledge to the educational game prototype setting, is very challenging. Previously, we suggested an effective method for students to develop their educational games based on a particular course. The PD process method is needed to identify the contributions of the design artifact. Since PD is widely used in the design of the game prototype and is also placed in the Information System (IS) field, the typical design science research (DSR) approach is likely the most suitable. This paper discusses the details of how DSR is utilized for the intended purpose. This paper is divided into four parts, i.e., 1) the application of DSR in the IS fields;2) DSR and the proposed PD process method;3) the application of the DSR methodology to PD research;and 4) the conclusion, implications, and future research. The findings of the study may contribute to an enhanced understanding of the evaluation of DSR artifacts such as the mentioned PD process and the contribution of a new extended PD process in the game design process, particularly in a developed country such as Malaysia. © 2020 ACM.

关键词： Game design

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Enhancing Classification with Semi-Supervised Deep Learning Using Distance-Based Sample Weights

arXiv

引用

arXiv 2025年

作者： Abedinia, Aydin Tabakhi, Shima Seydi, Vahid Dept. of Computer Science Islamic Azad University South-Tehran Branch Tehran Iran School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale United States School of Ocean Sciences Bangor University Menai Bridge United Kingdom

Recent advancements in semi-supervised deep learning have introduced effective strategies for leveraging both labeled and unlabeled data to improve classification performance. This work proposes a semi-supervised framework that utilizes a distance-based weighting mechanism to prioritize critical training samples based on their proximity to test data. By focusing on the most informative examples, the method enhances model generalization and robustness, particularly in challenging scenarios with noisy or imbalanced datasets. Building on techniques such as uncertainty consistency and graph-based representations, the approach addresses key challenges of limited labeled data while maintaining scalability. Experiments on twelve benchmark datasets demonstrate significant improvements across key metrics, including accuracy, precision, and recall, consistently outperforming existing methods. This framework provides a robust and practical solution for semi-supervised learning, with potential applications in domains such as healthcare and security where data limitations pose significant *** Codes 68T05, 62H30 © 2025, CC BY.

关键词： Semi-supervised learning

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HPTMT: Operator-based architecture for scalable high-performance data-intensive frameworks

arXiv

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arXiv 2021年

作者： Kamburugamuve, Supun Widanage, Chathura Perera, Niranda Abeykoon, Vibhatha Uyar, Ahmet Kanewala, Thejaka Amila von Laszewski, Gregor Fox, Geoffrey Luddy School of Informatics Computing and Engineering BloomingtonIN47408 United States Digital Science Center BloomingtonIN47408 United States Indiana University Alumni IN47408 United States Biocomplexity Institute Initiative and Computer Science Dept. University of Virginia

Data-intensive applications impact many domains, and their steadily increasing size and complexity demands high-performance, highly usable environments. We integrate a set of ideas developed in various data science and data engineering frameworks. They employ a set of operators on specific data abstractions that include vectors, matrices, tensors, graphs, and tables. Our key concepts are inspired from systems like MPI, HPF (High-Performance Fortran), NumPy, Pandas, Spark, Modin, PyTorch, TensorFlow, RAPIDS(NVIDIA), and OneAPI (Intel). Further, it is crucial to support different languages in everyday use in the Big Data arena, including Python, R, C++, and Java. We note the importance of Apache Arrow and Parquet for enabling language agnostic high performance and interoperability. In this paper, we propose High-Performance Tensors, Matrices and Tables (HPTMT), an operator-based architecture for data-intensive applications, and identify the fundamental principles needed for performance and usability success. We illustrate these principles by a discussion of examples using our software environments, Cylon and Twister2 that embody HPTMT. © 2021, CC BY.

关键词： Matrix algebra

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Pore Detection from Human Skin Image using U-Net 9

Pore Detection from Human Skin Image using U-Net

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9th International Congress on Advanced Applied informatics, IIAI-AAI 2020

作者： Kachi, Keisuke Iwahori, Yuji Usami, Hiroyasu Fukui, Shinji Wang, Aili Bhuyan, M.K. Chubu University Dept. of Computer Science Kasugai487-8501 Japan Aichi University of Education Informatics and Communication Tech. Kariya448-8542 Japan Harbin University of Science and Technology Higher Education Key Lab. Harbin150006 China Indian Institute of Technology Guwahati Dept. of Electronics and Electrical Eng. Guwahati781039 India

ISBN: (纸本)9781728173979

There are medical hair removal and esthetic's hair removal in the current hair removal approach. Hair loss damages the tissues from which hair grows (hair cells, dermal papilla). Esthetic's hair removal has more constraints than the medical hair removal without a resident medical doctor and the effectiveness of the hair removal is lower. Therefore, pore detection is important to monitor the effect of hair removal. This paper proposes pore detection approach using U-Net [1] which is used in medical image segmentation and shows that proposed method detects pores from human skin images with high accuracy. © 2020 IEEE.

关键词： Image segmentation

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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning

Constructing a Cryptocurrency-Price Prediction Model Using D...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Toai Kim Tran Thanh Thi Tuyet Le Thinh Tien Bui Vang Quang Dang Roman Senkerik Faculty of Economics Ho Chi Minh City University of Technology and Education No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Vietnam Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. Listopadu 2172/15 Ostrava-Poruba Czech Repulic Faculty of Applied Informatics Tomas Bata University in Zlin T. G. Masaryka 5555 Zlin Czech Republic

ISBN: (纸本)9781665491075

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies.

关键词： Deep learning Recurrent neural networks Bitcoin Predictive models Prediction algorithms Market research Convolutional neural networks

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License plate detection and recognition: A study of review

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AIP Conference Proceedings 2023年第1期2834卷

作者： Naaman Omar Subhi R. M. Zeebaree Mohammed A. M. Sadeeq Rizgar R. Zebari Hanan M. Shukur Ahmed Alkhayyat Lailan M. Haji Shakir Fattah Kak Technical College of Administration Information Technology Management Dept. Duhok Polytechnic University Duhok-Iraq Technical College of Engineering Energy Dept. Duhok Polytechnic University Duhok-Iraq Computer Science Department Nawroz University Duhok –Iraq Computer Engineering Dept. Al-Kitab University Kirkuk-Iraq Technical Engineering Dept. Islamic University Najaf Najaf-Iraq Computer Science Dept. University Of Zakho Duhok-Iraq Technical College of Informatics Information Technology Dept. Duhok Polytechnic University Duhok-Iraq

The detection and recognition of automatic license plates (ALPs) is an important task for traffic surveillance and parking management systems, as well as for sustaining the flow of modern civic life. It’s been suggested that there are a variety of ways to detect and recognize ALPs thus far Image processing and machine learning techniques are typically used in these approaches. For object detection and license plate identification, this article reviews most of the approaches. In order to improve the suitability of the input pictures for subsequent processing, many pre-processing approaches including Gaussian filtering and adaptive image contrast augmentation were examined. Deep semantic segmentation networks and deep learning techniques are also utilized to find the license plate areas in the input picture… For example, deep encoder-decoder network architecture and convolutional neural network (CNN) models used to recognize license plate.

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Microfluidic high-throughput cell pairing and retrieval for studying breast cancer-MSC engulfment 21

Microfluidic high-throughput cell pairing and retrieval for ...

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21st International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2017

作者： Chen, Yu-Chih Gonzalez, Maria E. Hiziroglu, Ayse Buke Zhao, Xintao Brien, Riley Cheng, Yu-Heng Zhang, Zhixiong Kleer, Celina G. Yoon, Euisik Dept. of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Comprehensive Cancer Center University of Michigan Medical School Ann ArborMI United States Dept. of Pathology University of Michigan Medical School Ann ArborMI United States Dept. of Biomedical Engineering University of Michigan Ann ArborMI United States

ISBN: (纸本)9780692941836

Metastasis is the major cause of death in breast cancer patients. The interaction with mesenchymal stem/stromal cells promotes breast cancer metastasis. To investigate cellular engulfment in vitro, we have developed a high-throughput cell pairing and retrieval platform, which enables: (1) integration of 3,000 cell-pairing micro-chambers, allowing for contact of paired cancer cells and MSCs in each chamber, (2) automatic image collection and analysis to quantify engulfing rates, and (3) retrieval of target engulfing cells for downstream analysis. © 17CBMS-0001.

关键词： Microfluidics

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Modeling Lung Functionality in Volume-Controlled Ventilation for Critical Care Patients

Modeling Lung Functionality in Volume-Controlled Ventilation...

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2020 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020

作者： Al-Hetari, Husam Y. Alginahi, Yasser Kabir, Muhammad Nomani Al-Naggar, Noman Q. Al-Rumaima, Mahmoud A. Hasan, Md Munirul University Malaysia Pahang Malaysia Faculty of Computing Gambang Kuantan26300 Malaysia Dept. of Biomedical Engineering Faculty of Engineering University of Science and Technology Yemen University of Windsor Dept. of Electrical and Computer Engineering 401 Sunset Ave WindsorONN9B 3P4 Canada

ISBN: (纸本)9781728169460

Mechanical ventilators are the instruments that assist breathing of the patients having respiratory diseases e.g., pneumonia and coronavirus disease 2019 (COVID-19). This paper presents a modified lung model under volume-controlled ventilation to describe the lung volume and air flow in terms of air pressure signal from the ventilator. A negative feedback is incorporated in the model to balance the lung volume that is influenced by a lung parameter called positive end expiration pressure. We partially solved the lung model equation which takes the form of a first-order differential equation and then unknown parameters associated with the model were computed using a nonlinear least-squares method. Experimental data required for parameter identification and validation of the lung model were obtained by running a volume-controlled ventilator connected to a reference device and an artificial lung. The proposed model considering negative feedback achieves a better accuracy than that without feedback as demonstrated by test results. The developed model can be used in intensive care units (ICU) to evaluate mechanical ventilation performance and lung functionality in real-time. © 2020 IEEE.

关键词： COVID-19

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