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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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A Comparative Study of DeepLabCut and Other Open-Source Pupillometry Data Analysis Algorithms – Which to Choose?

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Machine Graphics and Vision 2024年第2期33卷 77-90页

作者： Badkul, Amitesh Mishra, Sonakshi Kommajosyula, Srinivasa P. Department of Electrical & Electronics Engineering Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Department of Pharmacy Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Ph.D. Program in Computer Science The Graduate Center The City University of New York New YorkNY United States

Pupillometry measures pupil size, and several open-source algorithms are available to analyse pupillometry data. However, only a few studies compared these algorithms’ accuracy and computational resources. This study aims to compare the accuracy of computer vision-based algorithms (Swirski, Starburst, PuRe, ElSe, ExCuSe algorithms) and the machine learning algorithm, DeepLabCut, to the double-blinded human examiners (gold-standard). Training of DeepLabCut with different architectures and a variable number of markers (2-9 markers) was done on an open-source dataset. The duration of training was statistically longer for the ResNet152 model compared to the MobileNet model. The pupil diameters in computer vision-based software such as PuRe, Starburst, and Swirski were statistically different from human measurements. MobileNet 2 and 3 marker models were the closest to the human measurements. In conclusion, this work highlights the efficiency of lower marker models based on MobileNet architecture in DeepLabCut, which consumes fewer computational resources and is more accurate. © 2024 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Adversarial machine learning

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Traffic-Aware Beacon Interval for Position-Based Protocols in VANETs

Traffic-Aware Beacon Interval for Position-Based Protocols i...

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IEEE Latin-American Conference on Communications, LATINCOM

作者： Alvaro T. Amaya Alexandre A. P. Pohl Mauro S. Fonseca Ricardo Lüders Graduate Program in Electrical and Computer Engineering (CPGEI) Universidade Tecnológica Federal do Paraná (UTFPR) Curitiba Brazil

Position-based protocols in vehicular ad-hoc networks (VANETs) depend on the transmission of periodic beacon messages to keep the nodes informed about the current position of their neighbors. However, an excessive load of beacons can drastically deteriorate the network performance. This work presents a novel adaptive vehicular traffic-aware strategy to compute the beacon interval in a position-based protocol. The strategy relies on three mobility factors: vehicular density, the mean difference of speed among neighbors, and the spatial distribution of neighbors. Its effectiveness is proven through extensive simulations in an urban mobility scenario using the packet delivery ratio, the average end-to-end delay, overhead, and processing time as the performance metrics. The results show that our approach maintains a high delivery ratio and low End-To-End delay, overhead, and processing time regardless of the density of the surrounding vehicular traffic. The results also show that our method uses the available beacon interval range more efficiently than another traffic-aware approach.

关键词： Measurement Adaptation models Graphical models Computational modeling Collaboration Routing protocols Delays

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Preprocessing Algorithm for Seismic Signals Monitored by Fiber Optic Distributed Sensors

Preprocessing Algorithm for Seismic Signals Monitored by Fib...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Erik Tarlles Silveira Beatriz Brusamarello Uilian J. Dreyer Guilherme H. Weber Danilo F. Gomes Sidnei H. C. Teixeira Gilson A. Brunetto Luis F. P. Melegari Cicero Martelli Jean Carlos Cardozo da Silva Graduate Program in Electrical and Computer Engineering (CPGEI-UTFPR) Curitiba Parana Brazil Graduate Program in Energy Systems (PPGSE-UTFPR) Curitiba Parana Brazil Civil Engineering Studies Center (CESEC-UFPR) Curitiba Parana Brazil CPFL Energia Brasil Universidade Tecnológica Federal do Paraná Curitiba Paraná Brazil

Fiber optic-based sensors have experimented with significant technological advances in the last two decades. Distributed acoustic sensors represent their most recent iteration. This sensing technology become all the cable length into a sensor, a favorable condition for seismic studies. Data preprocessing is a crucial step in this study, in which the seismic signals captured by optical interrogators must be converted to the apparent velocity of particles that sensitized the fiber. Amongst the main conversion algorithms, the ones based on Fourier Transforms are more adopted at large-scale surveys. This paper presents an algorithm implementation for this conversion, based on the 2D Fourier Transform, in a small-scale laboratory investigation. The results are favorable and show that it is a possible use. It brings more definition to individualizing first break time arrivals, equalizing signal amplitude, and aligning oscillation peaks, essential parameters for applying reflections and refraction methods in seismic studies.

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Real Time Chest X-Ray Interpretation Applying Vision Transformer for Tuberculosis Detection

Real Time Chest X-Ray Interpretation Applying Vision Transfo...

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International Conference on computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Muhammad Irhamsyah Roslidar Yunidar M. Al Lail Qadrillah Nasaruddin Fitri Arnia Khairul Munadi Post Graduate Program in Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical and Computer Engineering Faculty of Engineering Universitas

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

Tuberculosis (TB) is an infectious disease that can attack the lungs and other organs. In the diagnosis of TB, the interpretation of X-ray radiological images requires special skills from medical specialists. However, the lack of experience of medical personnel and focus on X-ray examinations can be a challenge for radiologists because it has the potential to cause diagnostic errors in image interpretation. Therefore, we proposed a web-based application of real-time chest X-ray interpreter by integrating the deep learning model of Vision Transformer (ViT) to interpret chest X-ray images. The inference model was trained and tested on 652 normal and 652 tuberculosis X-ray images. The data preprocessing of Contrast Adaptive Histogram Equalization (CLAHE) was implemented to increase chest X-ray image contrast as well as inference model performance. The proposed real time system can inteprete the tested chest x-ray images with an accuracy of 95% and with simulation duration time of 4.7 seconds.

关键词： Adaptation models computer vision Accuracy Sensitivity Tuberculosis Data preprocessing Transformers Real-time systems X-ray imaging Medical diagnostic imaging

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Plasmonic BICs as Biosensors with Better Detectivity

Plasmonic BICs as Biosensors with Better Detectivity

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Li, Zhichao Prasad, Ciril S. Wang, Xielin Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

Plasmonic sensors exhibit high sensitivity due to enhanced local fields. But, their detectivity is poor because of their poor Q-factors. Using a plasmonic BIC, we experimentally demonstrate enhanced Q-factors in a pla... 详细信息

ISBN: (纸本)9781957171258

关键词： Q factor measurement

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Amico: Message Exchange Service for Elderly People 11

Amico: Message Exchange Service for Elderly People

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Isozaki, Atsushi Fukae, Kazuki Arai, Kenichi Kobayashi, Toru Nakamura, Masahide Nagasaki University Computer and Information Science Program Graduate School of Engineering Nagasaki Japan Nagasaki University Division of Electrical Engineering and Computer Science Graduate School of Engineering Nagasaki Japan Kobe University Center of Mathematical and Data Sciences Hyogo Japan

ISBN: (纸本)9781665492324

This study is related to a system that enables elderly people to communicate interactively with young people who use existing message exchange services by simply speaking to an avatar on a tablet PC, without having to perform complicated operations of the tablet PC. On the other hand, the young people side can bring in elderly people who could not previously participate in existing message exchange services groups. The effectiveness of the system was verified through demonstration experiments with two households consisting of elderly people in their 80s. © 2022 IEEE.

关键词： Personal computers

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Quality Analytics for Latin American and Brazilian Robotics Symposiums

Quality Analytics for Latin American and Brazilian Robotics ...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： Luiz Marcos Garcia Gonçalves Electrical and Computer Engineering Graduate Program Universidade Federal do Rio Grande do Norte Natal Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

We bring historical findings related to the most important Latin American and Brazilian Robotics Symposiums including their quality analytics regarding all papers published since their creation. For this quality analysis, we first provide a little of history and then we apply a methodology based on a systematic mapping of the literature using Scopus (from Elsevier) and Web of Science. The quality analysis includes variables as citations metrics, and also other historical variables as participation per countries, per states, per universities, and contributed areas among others. We use metadata and tools available in the most commonly used digital libraries, for a fair comparison. To the end, with basis on the results of our methodological approach, we point out that the community has followed the right path for establishing a high quality event, mainly regarding the decision of publication of the proceedings at IEEE Xplore, and recently in the Brazilian computer Society Library (Sol-SBC), all together. Also, our analysis corroborate to verify the modi operandi that have been used in the events execution, including joining in the Workshop of Robotics in Education (WRE) and the competitions (RCB, OBR, MNR, CBR, LARC). Besides happening jointly, each event keeps their original event structure and characteristics.

关键词： Measurement Systematics Conferences Education Metadata Libraries History Robots

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The use of long short-term memory models to estimate soybean pricing: a regional climate data evaluation from Brazil

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IEEE Access 2025年 13卷 101869-101884页

作者： Oliva, Jefferson Tales Martelli, Otavio Pigozzo Barbosa, Marco Antonio De Castro Molin, Viviane Dal Casanova, Dalcimar Teixeira, Marcelo Federal University of Technology-Paraná Graduate Program in Electrical and Computer Engineering Pato Branco Brazil Federal University of Technology-Paraná Department of Academic Informatics Pato Branco Brazil Federal University of Technology-Paraná Graduate Program in Production and Systems Engineering Pato Branco Brazil

This work uses machine learning methods to analyze the influence of the Brazilian climate on international soybean price variability. For this purpose, climatic data, historical series of the extended national consumer price index, dollar price historical series, soybean harvest historical series, and international soybean price historical series are collected from different data sources. The data is then preprocessed and grouped by state. Afterward, data from the five most productive Brazilian states, Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS), which together correspond to 74.9% of total production, are chosen. Then, predictive models were built using the long short-term memory (LSTM) method, which is applied to each state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. These models are evaluated using 10-fold sequential split cross-validation, whose predictive results are used for performance comparison through statistical hypothesis testing. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest average values of root mean squared error (RMSE) and mean absolute error (MAE), which are 20.90 and 16.72, respectively. Regarding predictors trained without climate data, the RS and PR obtained the lowest averaged values of RMSE (20.84) and MAE (16.81), respectively. After performing statistical hypothesis tests, no significant differences were found between the models, stating, with certainty 95%, that the Brazilian climatic data did not influence soybean prices in our study. © 2013 IEEE.

关键词： Brazilian Climatic Data Long-Short Term Memory Machine Learning Soybean Time Series

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An Expanded Latent Space Autoencoder for Land Cover Classification of Remote Sensing Images from EuroSAT

An Expanded Latent Space Autoencoder for Land Cover Classifi...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： Emerson Vilar Oliveira Luiz Marcos Garcia Gonçalves Graduate Program in Electrical and Computer Engineering Universidade Federal do Rio Grande do Norte Natal Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

Image classification has been instrumental in the interpretation and labeling of images in the field of remote sensing, computer vision, and in robotics applications. Machine learning and artificial intelligence algorithms, particularly artificial neural networks, are extensively utilized for this purpose. In this work we propose the Expanded Latent Space Autoencoder (ELSA) with a case use application to classify land cover data. The main idea on the ELSA network structure is to utilize the latent spaces of multiple internal autoencoders in order to create an expanded latent space. This expanded latent space extracts more information from the input data, and serves as input features for a more simpler classifier network. In order to evaluate the proposed network's ability to extract features and classify complex and multispectral images we employed it to the EuroSAT dataset. The results demonstrate a remarkable capacity for feature extraction using the ELSA network, with lower complexity, trained with a reduced number of images. The classifier network achieved a final accuracy of 98.7%, matching or exceeding the performance of more complex state-of-the-art models.

关键词： Machine learning algorithms Instruments Autoencoders Land surface Machine learning Feature extraction Robot sensing systems Labeling Remote sensing Image classification

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