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Mendel 2021年第1期27卷 9-17页

作者： Alquran, Hiam Alsleti, Mohammad Alsharif, Roaa Qasmieh, Isam Abu Alqudah, Ali Mohammad Harun, Nor Hazlyna Binti Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid21163 Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman11855 Jordan College of applied medical Sciences Radiological Science Program King Saud University Jeddah21435 Saudi Arabia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Kedah06010 Malaysia

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient;as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival. © 2021, Brno University of Technology. All rights reserved.

关键词： COVID-19

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Performance of the SegNet in the Segmentation of Breast Ultrasound Lesions

Performance of the SegNet in the Segmentation of Breast Ultr...

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Pan American Health Care Exchanges, PAHCE

作者： P. O. Vianna R. Farias W. C. A. Pereira Biomedical Engineering Program COPPE/Federal University of Rio de Janeiro RJ Brazil Systems and Computing Engineering Program COPPE/Federal University of Rio de Janeiro RJ Brazil

ISBN: (纸本)9781728170558

This work presents a performance analysis of a convolutional neural network (CNN), named SegNet, applied for automatic segmentation of breast ultrasound images. Defining the viability of CNN's could help with feature extraction and posterior classification of lesions, causing a decrease in the number of unnecessary biopsies. The breast ultrasound (BUS) dataset consists of 2054 images from 659 female patients, and each image was manually segmented by an experienced radiologist. The dataset is fed into the CNN without any filtering process applied to it and the test output is then compared with the radiologist's labels. We compared the results of different loss and activation functions, and the experiments reveal that the best performance was achieved using dice loss function, with dice coefficient of 81.1%.

关键词： Image segmentation Ultrasonic imaging Medical services Breast Feature extraction Generative adversarial networks Performance analysis

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Specialized reporting of article review - SRAR Method 4

Specialized reporting of article review - SRAR Method

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4th IEEE World Engineering Education Conference, EDUNINE 2020

作者： Rojas, Julio Ernesto Quispe Tuesta, Julio Enrique Quispe Universidad Nacional Pedro Ruiz Gallo Computing and Electronics Departament Lambayeque Peru Universidad Nacional Tecnologica de Lima sur Systems Engineering Program Lima Peru

ISBN: (纸本)9781728166384

The professional training of the engineering student must be innovated in order to meet the accreditation criterion and with the requirement of the university law that requires presenting an investigation to obtain the bachelor's degree and another to obtain the professional degree of engineer. In order to develop in students, the skills to search for scientific and technological articles, and the critical skills to evaluate documents and extract the main ideas from them in relation to a particular topic, the SRAR method or specialized report review method has been proposed. It has been applied during five academic semesters in the electronic engineering telephone systems course, with satisfactory results. The SRAR method has six stages:*** of the SRAR method and proposed topic, 2. Initial bibliographic search, 3. Autonomous training, 4. Selection of articles for review, 5. Study and evaluation of articles 6. Writing of specialized report for review of articles. © 2020 IEEE.

关键词： Students

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Work in Progress: Semi-Dual apprenticeship for the development of skill in professional training of the engineer 4

Work in Progress: Semi-Dual apprenticeship for the developme...

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4th IEEE World Engineering Education Conference, EDUNINE 2020

ISBN: (纸本)9781728166384

The development of competencies in the professional training of an engineer includes: knowledge, skills and attitudes. For the development of skills, laboratory practices with physical equipment, virtual systems and remote systems are alternatives;the practices in the place of work performance through technical visits or pre-professional practices in the factories and maintenance workshops. Dual learning includes the participation of students in theoretical sessions in the center of studies and practical sessions of the installation and maintenance of machines and equipment in factories and workshops. The semidual learning proposal considers 50% of the practical sessions in the laboratories of the study center and 50% in the facilities of the industrial company. The experience of the application in the course of electrical machines is reported, where the application of the Semi-dual learning method in five academic semesters was planned, from 2019 - 1 to 2021 - 1, to date it was applied in the semester 2019 1, with satisfactory results. © 2020 IEEE.

关键词： Professional aspects

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Clarifying Assumptions about Artificial Intelligence before Revolutionising Patent Law

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GRUR International 2022年第4期71卷 295-321页

作者： Kim, Daria Alber, Maximilian Kwok, Man Wai Mitrovic, Jelena Ramirez-Atencia, Cristian Perez, Jesus Alberto Rodriguez Zille, Heiner Max Planck Institute for Innovation and Competition Munich Germany Charite Institute of Pathology Berlin Germany Chinese University of Hong Kong Department of Physics (Formerly) Hong Kong BMBF Program for Women in AI Faculty of Computer Science and Mathematics Chair of Data Science University of Passau Germany Department of Computer Systems Universidad Politecnica de Madrid Spain School of Computing Science College of Science and Engineering University of Glasgow United Kingdom Faculty of Computer Science Otto von Guericke University Magdeburg Germany

This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of 'artificial ingenuity'. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where 'intelligent' systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited. © 2022 Published by OUP and CH Beck on behalf of GRUR e.V.

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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis

Learning Adaptation and Generalization from Human-Inspired M...

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Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Yi-Hsin You Chi-Wei Feng Yen-Min Chen Bo-Yi Kuo Academia Sinica Institute of Information Science Taipei Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Taiwan University Taipei Taiwan

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a human-inspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future.

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A novel strong duality-based reformulation for trilevel infrastructure models in energy systems development

arXiv

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arXiv 2023年

作者： Herrala, Olli Gabriel, Steven A. Oliveira, Fabricio Ekholm, Tommi Aalto University Department of Mathematics and Systems Analysis Espoo Finland University of Maryland Department of Mechanical Engineering/Applied Mathematics Statistics & Scientific Computing Program College ParkMD United States Norwegian University of Science and Technology Department of Industrial Economics and Technology Management Trondheim Norway Finnish Meteorological Institute Helsinki Finland

We explore the class of trilevel equilibrium problems with a focus on energy-environmental applications and present a novel single-level reformulation for such problems, based on strong duality. To the best of our knowledge, only one alternative single-level reformulation for trilevel problems exists. This reformulation uses a representation of the bottom-level solution set, whereas we propose a reformulation based on strong duality. Our novel reformulation is compared to this existing formulation, discussing both model sizes and computational performance. In particular, we apply this trilevel framework to a power market model, exploring the possibilities of an international policymaker in reducing emissions of the system. Using the proposed methods, we are able to obtain globally optimal solutions for a five-node case study representing the Nordic countries and assess the impact of a carbon tax on the electricity production portfolio. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy resources

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HAEE: Question Classification Using Hierarchical Intra-Attention Enhancement Encoder

HAEE: Question Classification Using Hierarchical Intra-Atten...

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Jen-Wei Wang Kai-Hsiang Chen Jen-Wei Huang National Cheng Kung University Tainan Taiwan Graduate Program for Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

ISBN: (纸本)9781665408264

With the development of E-commerce, an Automated Question-Answering system takes a crucial part in customer service. Question classification, which assigns labels to questions according to the answer types, is one of the tasks in question answering. Previous methods usually used handcraft features like named entity recognition, but it needs the predefined dictionary or tools. The machine learning approaches are recently applied to this task and achieve high accuracy. In this paper, we proposed HAEE, a Hierarchical intra-Attention Enhancement Encoder which composed of bidirectional GRUs and intra-attentions. In addition, we adopt the character input to address the issue of the OOV (Out-Of-Vocabulary) problem and create multiple intra-attentions to simulate the certain relationships between characters (Chinese) or words (English) to enhance the influence of tokens on the sentence. We evaluate the HAEE model in an actual corporate setting and several datasets. As shown in the experimental results, our HAEE model outperforms the existing state-of-the-art models on question classification tasks, especially for the Chinese corpus.

关键词： Dictionaries Customer services Machine learning Predictive models Electronic commerce Task analysis

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The last course of coarse coding: Hemispheric similarities in associative and categorical semantic processing

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BRAIN AND LANGUAGE 2022年 229卷 105123页

作者： Mech, Emily N. Kandhadai, Padmapriya Federmeier, Kara D. Department of Psychology University of Illinois at Urbana-Champaign United States Department of Computing Studies and Information Systems Douglas College Canada Program in Neuroscience University of Illinois at Urbana-Champaign United States The Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign United States

To test theories that posit differences in how semantic information is represented in the cerebral hemispheres, we assessed semantic priming for associatively and categorically related prime-target pairs that were graded in relatedness strength. Visual half-field presentation was used to bias processing to the right or left hemisphere, and event-related potential (ERP) and behavioral responses were measured while participants completed a semantic relatedness judgement task. Contrary to theories positing representational differences across the cerebral hemispheres, in two experiments using (1) centralized prime presentation and lateralized targets and (2) lateralized primes and targets, we found similar priming patterns across the two hemispheres at the level of semantic access (N400), on later measures of explicit processing (late positive complex;LPC), and in behavioral response speeds and accuracy. We argue that hemispheric differences, when they arise, are more likely due to differences in task demands than in how the hemispheres fundamentally represent semantic information.

关键词： Hemispheric asymmetries Event-related potentials (ERPs) N400 LPC Associative priming Categorical priming Coarse coding hypothesis

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Recent advances in wearable electrochemical biosensors towards technological and material aspects

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Biosensors and Bioelectronics: X 2024年 19卷

作者： Hosseinzadeh Fakhr, Mahan Lopez Carrasco, Ivan Belyaev, Dmitry Kang, Jihun Shin, YeHyeon Yeo, Jong-Souk Koh, Won-Gun Ham, Jeongwoo Michaelis, Alexander Opitz, Joerg Beshchasna, Natalia Fraunhofer Institute for Ceramic Technologies and Systems IKTS Maria-Reiche-Straße 2 Dresden01109 Germany Faculty of Mechanical Science and Engineering Dresden University of Technology Dresden Germany School of Integrated Technology College of Computing Yonsei University Seoul Korea Republic of BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul Korea Republic of Department of Chemical and Biomolecular Engineering Yonsei University Seoul Korea Republic of

The next generation of wearable biosensors comes with the latest advancements in biosensor technology. Soft and stretchable electrode materials like hydrogels with the similar functionalities of human tissue including stretchability, self-healability, and responsiveness to different stimuli have emerged as the most versatile materials in wearable electronics. The incorporation of conductive nanofillers is found to enhance the sensitivity of the electrochemical biosensors significantly. Microfluidic technology has reduced the volume of samples and reagents required for the analysis, allowing continuous biomedical monitoring from a drop of biofluid. In this paper, the most advanced progress in electrochemical wearable platforms that can noninvasively and continuously monitor the biochemical markers in body fluids for the diagnosis and health management is reviewed. Innovation in microelectronics, modification, fabrication technologies, and detection methods are the main focus of the discussion. In particular, hydrogel-based sensors and microfluidic systems as the latest technology trends in wearable detection are discussed in detail. Integration of miniaturized electrochemical wearable biosensors with wireless technology as a great promise for real-time healthcare monitoring and point-of-care (POC) diagnostics is also summarized. Finally, we outline the most advanced wearable biosensors with optimized material and design as well as key challenges that need to be addressed to improve sensing performance (accuracy, sensitivity, selectivity, stability), portability (miniaturized size and light weight), and flexibility of the wearable biosensors. © 2024 The Authors

关键词： Body fluids

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