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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Maria Mahardini Sakanti Viacheslav Siniaev Aurelia Amaris Win-Jet Luo Suhartono C. Bambang Dwi Kuncoro Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taichung Taiwan Institute of Applied Computer Science ITMO University St. Petersburg Russian Federation Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taichung Taiwan Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers.

关键词： Accuracy Microcontrollers Psychology Human factors Predictive models Boosting Prediction algorithms Real-time systems Sensors Wearable devices

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Illuminating the x-Axis by α-Floodlights 32

Illuminating the x-Axis by α-Floodlights

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32nd International Symposium on Algorithms and Computation, ISAAC 2021

作者： Nilsson, Bengt J. Orden, David Palios, Leonidas Seara, Carlos Żyliński, Pawel Department of Computer Science and Media Technology Malmö University Sweden Physics and Mathematics Department Universidad Alcalá Spain Department of Computer Science and Engineering University of Ioannina Greece Departament of Mathematics Universitat Politècnica de Catalunya Barcelona Spain Institute of Informatics University of Gdańsk Poland

ISBN: (纸本)9783959772143

Given a set S of regions with piece-wise linear boundary and a positive angle α ◦, we consider the problem of computing the locations and orientations of the minimum number of α-floodlights positioned at points in S which suffice to illuminate the entire x-axis. We show that the problem can be solved in O(n log n) time and O(n) space, where n is the number of vertices of the set S. © Bengt J. Nilsson, David Orden, Leonidas Palios, Carlos Seara, and Pawel Żyliński.

关键词： Floods

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Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches

arXiv

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arXiv 2024年

作者： Asimopoulos, Dimitris Siniosoglou, Ilias Argyriou, Vasileios Karamitsou, Thomai Fountoukidis, Eleftherios Goudos, Sotirios K. Moscholios, Ioannis D. Psannis, Konstantinos E. Sarigiannidis, Panagiotis R&D Department MetaMind Innovations P.C. Kozani Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom Sidroco Holdings Ltd. Nicosia Cyprus Physics Department Aristotle University of Thessaloniki Thessaloniki Greece Department of Informatics and Telecommunications Department University of Peloponnese Tripoli Greece Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece

In the realm of data privacy, the ability to effectively anonymise text is paramount. With the proliferation of deep learning and, in particular, transformer architectures, there is a burgeoning interest in leveraging these advanced models for text anonymisation tasks. This paper presents a comprehensive benchmarking study comparing the performance of transformer-based models and Large Language Models(LLM) against traditional architectures for text anonymisation. Utilising the CoNLL-2003 dataset, known for its robustness and diversity, we evaluate several models. Our results showcase the strengths and weaknesses of each approach, offering a clear perspective on the efficacy of modern versus traditional methods. Notably, while modern models exhibit advanced capabilities in capturing contextual nuances, certain traditional architectures still keep high performance. This work aims to guide researchers in selecting the most suitable model for their anonymisation needs, while also shedding light on potential paths for future advancements in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Pancreatic Tumor Detection by Convolutional Neural Networks

Pancreatic Tumor Detection by Convolutional Neural Networks

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Information Technology (ACIT)

作者： Muhammed Talha Zavalsız Sleiman Alhajj Kashfia Sailunaz Tansel Özyer Reda Alhajj Department of Computer Engineering Istanbul Medipol University Istanbul Turkey International School of Medicine Istanbul Medipol University Istanbul Turkey Department of Computer Science University of Calgary Alberta Canada Department of Computer Engineering Ankara Medipol University Ankara Turkey Department of Health Informatics University of Southern Denmark Odense Denmark

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging provides a way for the internal structure of the human body to be visible with various methods. With DL models, cancer detection, which is one of the most lethal diseases in the world, from medical images can be made possible with high accuracy. The main objective of this paper is to detect Pancreatic Cancer, which is one of the cancer types with the highest fatality rate, from a dataset of Computed Tomography (CT) images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. The designed DL model is integrated into the Flask application to develop a web application. With this application, early diagnosis of pancreatic cancer can be achieved, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started. Due to the abundance of medical images reviewed by medical professionals, this application can assist radiologists and other specialists in Pancreatic Tumor detection.

关键词： Deep learning Computed tomography Computational modeling Biological system modeling Pancreatic cancer Cancer detection Convolutional neural networks

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A structure-activity relationship modelling of opioid compounds by using machine learning 15

A structure-activity relationship modelling of opioid compou...

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15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2023

作者： Sapundzhi, Fatima Lazarova, Meglena Dzimbova, Tatyana Georgiev, Slavi Ivanova, Antonina Department of Communication and Computer Engineering Faculty of Engineering South-West University Neofit Rilski 66 Ivan Myhailov Str. Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 St. Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Molecular Biology Bulgarian Academy of Sciences Acad. G.Bonchev Str bl. 21 Sofia1113 Bulgaria Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Str. bl. 8 Sofia1113 Bulgaria Department of Applied Mathematics and Statistics Faculty of Natural Sciences and Education University of Ruse 8 Studentska Str. Ruse7004 Bulgaria Department of Computer Science Faculty of International Economics and Administration Varna Free University Chernorizets Hrabar 84 Yanko Slavchev Str. Chaika Resort Varna9007 Bulgaria

Opiates are among the oldest drugs that are used to treat many medical problems. They are analgesic and sedative drugs that contain opium. The morphine is its most active ingredient and it is a widely used pain reliever despite its side effects. The main objective of this study is to construct a model which gives the structure-activity relationship among a series of mu-opioid ligands and molecular docking results. For this purpose, a model of mu-opioid receptors using machine learning is introduced. By obtaining a relationship between the docking results and the in vivo test, we could predict the biological effect of the newly synthesized ligands. © Published under licence by IOP Publishing Ltd.

关键词： Medical problems

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Lightweight Deep Learning for Resource-Constrained Environments: A Survey

arXiv

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arXiv 2024年

作者： Liu, Hou-I Galindo, Marco Xie, Hongxia Wong, Lai-Kuan Shuai, Hong-Han Li, Yung-Yui Cheng, Wen-Huang Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130000 China Faculty of Computing and Informatics Multimedia University Cyberjaya63100 Malaysia Hon Hai Research Institute Taipei114 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas. Copyright © 2024, The Authors. All rights reserved.

关键词： computer hardware

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A Proof of Kirchhoff's First Law for Hyperbolic Conservation Laws on Networks

arXiv

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arXiv 2022年

作者： Bayen, Alexandre M. Keimer, Alexander Müller, Nils Department of Electrical Engineering and Computer Sciences Department of Civil and Environmental Engineering University of California Berkeley United States Germany Max Planck Institute for Software Systems Saarland Informatics Campus Germany

Networks are essential models in many applications such as information technology, chemistry, power systems, transportation, neuroscience, and social sciences. In light of such broad applicability, a general theory of dynamical systems on networks may capture shared concepts, and provide a setting for deriving abstract properties. To this end, we develop a calculus for networks modeled as abstract metric spaces and derive an analog of Kirchhoff’s first law for hyperbolic conservation laws. In dynamical systems on networks, Kirchhoff’s first law connects the study of abstract global objects, and that of a computationally-beneficial edgewise-Euclidean perspective by stating its equivalence. In particular, our results show that hyperbolic conservation laws on networks can be stated without explicit Kirchhoff-type boundary *** Codes 35R02, 35C99 (Primary) 35L65, 00A71 (Secondary) © 2022, CC BY.

关键词： Dynamical systems

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Posit and Floating-Point Based Izhikevich Neuron: A Comparison of Arithmetic

SSRN

引用

SSRN 2023年

作者： Fernandez-Hart, T.J. Kight, James C. Kalganova, Tatiana Brunel University CEDPS Department of Electronic and Computer Engineering UxbridgeUB8 3PH United Kingdom University of Sussex School of Engineering and Informatics BrightonBN1 9QJ United Kingdom

Reduced precision number formats are gaining popularity in many areas of computational science, due to their potential to improve energy efficiency, silicon use, and speed. However, this is often at the expense of introducing arithmetic errors which affect the accuracy of a system. The optimal balance must be struck, judiciously choosing a number format using as few bits as possible, while minimising accuracy loss. In this study, we examine one such format, posit arithmetic as a replacement for floating-point when conducting spiking neuron simulations, specifically using the Izhikevich neuron model. This model is capable of simulating complex neural firing behaviours, 20 of which were originally identified by Izhikevich and are used in this study. We compare the accuracy, spike count, and spike timing of the two arithmetic systems at different bit-depths against a 64-bit floating-point gold-standard. Additionally, we test a rescaled set of Izhikevich equations to mitigate against arithmetic errors by taking advantage of posit arithmetic’s tapered *** findings indicate that there is no difference in performance between 32-bit posit, 32-bit floating-point, and our reference standard for 95% of the tested firing types. However, at 16-bit, both arithmetic systems diverge from the 64-bit reference, albeit non-uniformly. For instance, the posit implementation demonstrates an accumulated spike timing error of 0.5ms over a 1000ms simulation compared to 9ms for floating-point – an 18x improvement using posit arithmetic for regular (tonic) spiking. This finding holds particular importance given the prevalence of this particular firing type in specific regions of the brain. Furthermore, when we rescale the neuron equations, this error is eliminated altogether. Hence, our results demonstrate that posit arithmetic is not only a viable replacement for 64-bit floating-point in these simulations, it can do so while using 4× fewer bits. As a Posit Arithmetic Unit has simila

关键词： Digital arithmetic

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Leukocoria Identification: A 5-Fold Cross Validation CNN and Adaboost Hybrid Approach

Leukocoria Identification: A 5-Fold Cross Validation CNN and...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Indah Purnama Sari Al-Khowarizmi Fanny Ramadhani Andy Satria Oris Krianto Sulaiman Department of Information Technology Universitas Muhammadiyah Sumatera Utara Medan Indonesia Department of Computer Science Universitas Negeri Medan Medan Indonesia Department of Information Technology Universitas Dharmawangsa Medan Indonesia Department of Informatics Engineering Universitas Islam Sumatera Utara Medan Indonesia

Leukocoria, which is distinguished by an unusual white reflection in the pupil, is an important sign of several eye conditions. Early detection and correct alleviation of leukocoria are critical for prompt diagnosis and therapy. In this article, we propose a hybrid method for leukocoria classification utilizing Convolutional Neural Networks (CNN) and Adaptive Boosting (AdaBoost). The performance of the suggested strategy is assessed using the 5-Fold Cross Validation method in the proposed work. In order to increase classification accuracy, CNN is utilized for feature extraction and AdaBoost is employed as a boosting technique. The experimental results reveal that the suggested combined strategy is more effective in relieving leukocoria than the CNN or AdaBoost single models. In comparison to the conventional CNN technique, the performance results of the model utilizing the CNN + Adaboost method with 5-Fold Cross Validation offer better and superior outcomes in diagnosing leukocoria. Results from the CNN + Adaboost model using 5-Fold Cross Validation show very high accuracy values of 95%, precision values of 95%, recall values of 95%, and Fl scores of 94%.

关键词：

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AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data Science Training Platform to Increase Researcher Diversity and Advance Health Equity 11

AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data...

引用

11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Ahmed, Toufeeq Hoyal, Aidan Stinson, Katie Johnson, Jay Latif, Zainab Burge, Legand L. Libin, Alexander Gordon Gao, Guodong Shara, Nawar Vishwanatha, Jamboor K. Vanderbilt University Medical Center Department of Biomedical Informatics Nashville United States Howard University Department of Electrical Engineering and Computer Science WashingtonDC United States Georgetown University MedStar Health Research Institute WashingtonDC United States Johns Hopkins University Carey Business School Baltimore United States University of North Texas Health Science Center at Fort Worth Institute for Health Disparities Fort Worth United States

ISBN: (纸本)9798350302639

AIM-AHEAD is an NIH-funded consortium whose goal is to advance health equity and researcher diversity via artificial intelligence and machine learning. In our first year of operations, we launched AIM-AHEAD Connect, a centralized platform for the consortium stakeholders containing mentoring, networking, and professional development resources. This paper describes the resources available in AIM-AHEAD Connect as well as collaborative efforts to provide tools and training for the entire consortium. © 2023 IEEE.

关键词： Artificial intelligence

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