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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection 7

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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7th International Congress on Ambient Intelligence, Software engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Jiménez-Delgado, E. Quesada-López, C. Méndez-Porras, A. Lara-Petitdemange, A. Alfaro-Velasco, J. Graduate Program in Computer Science and Informatics University of Costa Rica Costa Rica Technological Institute of Costa Rica Computer Engineering Department Costa Rica School of Computer Science and Informatics University of Costa Rica Costa Rica

ISBN: (纸本)9798350364538

In this study, two deep learning models for automatic tattoo detection were analyzed;a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures. © 2024 IEEE.

关键词： Convolution

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Model Decision Support System for Diagnosis COVID-19 Using Forward Chaining: A Case in Indonesia 8

Model Decision Support System for Diagnosis COVID-19 Using F...

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8th International Conference on Cyber and IT Service Management, CITSM 2020

作者： Henderi Maulana, Miftah Warnars, Harco Leslie Hendrie S. Setiyadi, Didik Qurrohman, Taufik University of Raharja Department of Informatics Engineering Tangerang Indonesia Postgraduate of Information Engineering University of Raharja Tangerang Indonesia BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Insani University Department of Informatics Engineering Bekasi Indonesia

ISBN: (纸本)9781728177014

The government in Indonesia and its staff work together to make tactical steps to prevent the spread of COVID-19 in the community. From the ministerial level to the heads of the provinces, regencies, and even the government. Therefore, this study aims to make a model decision support system to diagnose patients exposed to Covid-19, such as people in control, patients in oversight, and those who are positive for the Covid-19 Virus. Model decision support system development aims to provide information about the development of COVID- 19 and help the community in diagnosing themselves related to COVID-19 infection. In this study, the authors use the forward chaining method in application to get conclusions from the symptoms of the Covid-19. This research resulted in an application that patients exposed to the Covid-19, and it's also provided a solution for healing from patients. And this could be a reference for patients before consulting further with the doctor. © 2020 IEEE.

关键词： Decision support systems

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Semantic atomicity and multilinguality in the medical domain: Design considerations for the MORPHOSAURUS Subword Lexicon 5

Semantic atomicity and multilinguality in the medical domain...

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5th International Conference on Language Resources and Evaluation, LREC 2006

作者： Schulz, Stefan Markó, Kornél Daumke, Philipp Hahn, Udo Hanser, Susanne Nohama, Percy De Andrade, Roosewelt Leite Pacheco, Edson Romacker, Martin Department of Medical Informatics Freiburg University Hospital Freiburg Germany Health Informatics Laboratory Paraná Catholic University Curitiba Brazil Graduate Program in Electrical Engineering and Industrial Informatics CEFET-PR Curitiba Brazil Text Mining in Life Sciences Informatics Novartis Basel Switzerland Lab. Jena Germany

We present the lexico-semantic foundations underlying a multilingual lexicon the entries of which are constituted by so-called subwords. These subwords reflect semantic atomicity constraints in the medical domain which diverge from canonical lexicological understanding in NLP. We focus here on criteria to identify and delimit reasonable subword units, to group them into functionally adequate synonymy classes and to relate them by two types of lexical relations. The lexicon we implemented on the basis of these considerations forms the lexical backbone for MORPHOSAURUS, a cross-language document retrieval engine for the medical domain.

关键词： Semantics

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Initial explorations on using CRFs for turkish named entity recognition

Initial explorations on using CRFs for turkish named entity ...

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24th International Conference on Computational Linguistics, COLING 2012

作者： Şseker, Gökhan Akin Eryǐgit, Güļsen ITU Informatics Institute Graduate Program in Computer Science Istanbul Technical University Istanbul 34469 Turkey Department of Computer Engineering Istanbul Technical University Istanbul 34469 Turkey

This paper reports the highest results (95% in MUC and 92% in CoNLL metric) in the literature for Turkish named entity recognition;more specifically for the task of detecting person, location and organization entities in general news texts. We give an in depth analysis of the previous reported results and make comparisons with them whenever possible. We use conditional random fields (CRFs) as our statistical model. The paper presents initial explorations on the usage of rich morphological structure of the Turkish language as features to CRFs together with the use of some basic and generative gazetteers. © 2012 The COLING.

关键词： Random processes

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Development and demonstration of a prototype system for the early detection of drowning

Development and demonstration of a prototype system for the ...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Konishi, Nagato Ishigaki, Yo Nakada, Tsubasa Nemoto, Wataru Iinuma, Seizi Hoshino, Taisuke Ohkawara, Kazunori University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan Chief Executive Officer Plan I Corporation University of Electro-Communications Bioscience and Technology Program Department of Engineering Science Tokyo Japan

ISBN: (纸本)9781665402064

We have developed a small device that can measure 3-axis acceleration and pressure underwater, and collected data on drowning reactions by asking participants to reproduce the characteristic behavior of drowning. The data collected in this study is expected to aid the early detection of drowning reactions and reduce the number of accidents caused by drowning. © 2022 IEEE.

关键词： Accidents

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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Training Swinging Door Trending Compression Algorithm for IoT devices 10

Training Swinging Door Trending Compression Algorithm for Io...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Correa, Juan David Arias Pinto, Alex Sandro Roschildt Montez, Carlos Post-graduate Program of Automation Systems Engineering Florianópolis Brazil Dept. of Informatics and Statistic Dept. of Automation and Systems Engineering Florianópolis Brazil Dept. of Automation and Systems Engineering Florianópolis Brazil

ISBN: (纸本)9798350373011

The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having limited energy and computational capacities. Transmission of redundant or irrelevant samples frequently wastes the device's resources. Furthermore, Storing a large amount of redundant data could consume storage space and not offer any benefit. Data Compression (DC) methods are a potential solution. DC could reduce communication usage and storage demand. This research proposes the Training Swing Door Trending (TSDT) for being implemented in IoT Devices. TSDT is a new algorithm that improves the classic Swing Door Trending (STD). They represent the data by trend lines and have a constant computational complexity. TSDT has a training step for the automatic configuration of its parameters. This article additionally presents the Compression Factor (C-Score), a new quality metric to analyze the compression results in lossy DC methods. C-Score takes as a basis the F-Score, a measure of predictive performance. The C-Score uses the Compression and Error metrics to evaluate the compression performance in Lossy Algorithms. © 2024 IEEE.

关键词： Data compression ratio

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Study of initial guess influence on the quality of solutions on binary genetic algorithm in job shop scheduling problem 14

Study of initial guess influence on the quality of solutions...

引用

14th International Symposium on Operational Research, SOR 2017

作者： Ferreira da Cruz, Valmir Pereira, Fabio Henrique Universidade Nove de Julho Industrial Engineering Graduate Program Francisco Matarazzo Av. 612 São Paulo Brazil Universidade Nove de Julho Informatics and knowledge management Graduate Program Francisco Matarazzo Av. 612 São Paulo Brazil

ISBN: (纸本)9789616165501

The purpose of this study is to evaluate the influence of the initial guess to generate the Genetic Algorithm population of solutions of scheduling problems in relation to the quality and feasibility of the solutions. The scheduling problem is defined as to find the sequence of operations on the machines that optimize some performance measure as, for example, the use of resources and the total processing time (makespan). It is common to treat such problems with the use of metaheuristics as genetic algorithm mainly due to its computational complexity. This work carried out experiments with a set of literature instances, varying the sequencing rule used in the generation of initial solutions. Usually, rules from the literature have been tested and identified a hybrid rule that generates a smaller number of non-feasible solutions and the number of instances that have reached the optimal makespan. © 2017 Slovenian Society Informatika. All Rights Reserved.

关键词： Genetic algorithms

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On the Evaluation of Access-point Handovers for UAVs in Long-distance Missions

On the Evaluation of Access-point Handovers for UAVs in Long...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Fagundes, Leonardo A. Souza, Vitor Barbosa Brandao, Alexandre S. Núcleo de Especialização em Robótica Federal University of Viçosa Department of Electrical Engineering and Graduate Program on Computer Science Viçosa Brazil Federal University of Viçosa Graduate Program on Computer Science Department of Informatics Viçosa Brazil

ISBN: (纸本)9780738131153

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications either to interconnect the UAVs with each other or to connect them with Ground Control Stations (GCS). Due to the complexity and difficulty of performing long distance experiments with these robots, simulation becomes a viable and necessary alternative. However, focusing either on the modeling of UAV operations or communication dynamics, available simulation tools are not able to capture the complex interdependencies between these two aspects, especially in applications where extensive communications are required. The main contribution of this work is the modeling and implementation of strategies for enhancing connectivity of a UAV performing long-distance navigation tasks, taking into account applications' requirements. The proposed strategies aim at assessing the impact of frequent handovers between wireless access points in terms of packet loss and available bandwidth. The first strategy prioritizes maintaining a connection until the signal-to-noise ratio (SNR) observed by the robot reaches a predefined threshold. For this, it is assumed that the data to be transmitted is small enough so that the robot may change its modulation to obtain smaller bandwidths while improving connection time. The second one prioritizes bandwidth availability, thus, it always searches for a network that is able to offer data rates higher than a minimum threshold. A hybrid model is validated by considering the characteristics of each individual strategy. The capabilities of the simulation tool designed for this work are illustrated through some case-study scenarios. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Feasibility study of using social networks platform for learning support: An example of Facebook

Feasibility study of using social networks platform for lear...

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20th International Conference on Computers in Education, ICCE 2012

作者： Chan, Chien-Lung Fu, Wen-Erh Lai, K. Robert Tseng, Shu-Fen Department of Information Management Yuan Ze University Taiwan Department of Computer Science and Engineering Yuan Ze University Taiwan Graduate Program in Social Informatics Yuan Ze University Taiwan

ISBN: (纸本)9789810746490

In this study, we explored the students' computer-supported collaborative learning behavior based on the Facebook platform. Sixty two senior college students major in Information Management took Decision Support System (DSS) class. Besides the lectures and class discussion, the students participated in the DSS Facebook for collaborative learning. We found that students' characteristics (e.g., gender and mindset of learning) are important factors to affect their Facebook usage behavior and learning performance. The students using DSS Facebook more often get better performance in their final projects, learning satisfaction and the online communication behavior survey. We also found that gender affects the usage of social networks platform. For instance, male students use social networks platform several times per week and get better performance in online communication, learning satisfaction and creativity self-efficacy.

关键词： Decision support systems

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