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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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The Effect of Cache Decision Policies on QoE in Multiple Video and Audio Streaming over ICN/CCN

The Effect of Cache Decision Policies on QoE in Multiple Vid...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Keisuke Kobayashi Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350309485

ISBN: (纸本)9798350309492

This paper considers video and audio transmission in ICN (Information-Centric Networking)/ CCN (ContentCentric Networking), where each intermediate node can cache content. We have proposed LCD with Prob, combining traditional policies: LCD (Leave Copy Down) and probabilistic caching. This paper assumes a case where multiple video and audio streams exist simultaneously. We assess application-level QoS (Quality of Service) and QoE (Quality of Experience) of video and audio transmission utilizing LCD with Prob. We also add a mechanism for further QoE enhancement called “LCD with Prob deletion.” We assess application-level QoS using a computer simulation with a tree network and QoE by means of a subjective experiment. From the assessment result, we show the effectiveness of LCD with Prob deletion against existing policies.

关键词： Network topology Scalability Redundancy Quality of service Streaming media Probabilistic logic Liquid crystal displays

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Implementation Graph Database Framework for Credit Card Fraud Detection 8

Implementation Graph Database Framework for Credit Card Frau...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Mauliddiah, Nurrahmi Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Credit card use is becoming more and more commonplace every day. Financial organizations and credit card customers lose a lot of money because of complicated illegal transactions. Fraudsters constantly stay on top of new technology to quickly perpetrate fraud against customer transaction patterns. We analyze credit card transaction networks and identify suspicious patterns, such as transactions connected to multiple accounts or unusual transaction patterns, transactions made at unusual times, and to monitor credit card transactions in real-time and quickly identify suspicious transactions. TigerGraph is used to analyze data, display results on a dashboard, and send notifications via email. One meth'\ Vc 1``13-od commonly used in anomaly detection is to compare data values against the standard deviation. In this research, we explain the use of TigerGraph as a platform for anomaly detection above the standard deviation, as well as the use of the Louvain algorithm in finding merchant communities used by fraudsters. The data used in this study comes from Sparkov simulation data obtained from Kaggle. Our results show that by using TigerGraph, we managed to achieve a very high accuracy rate of 99.77%, precision 82.84%, recall 72.38%, and f1-score 77,26% in predicting transaction fraud on Sparkov simulation data. This is much better than the results reported in a paper that uses the supervised machine learning method with the AdaBoost algorithm which achieves the highest accuracy of 77%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Alert Credit card fraud detection Dashboard TigerGraph

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MPEG-DASH Multi-View Video and Audio Transmission QoE Over HTTP/2 Stream

MPEG-DASH Multi-View Video and Audio Transmission QoE Over H...

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International Conference on Advanced Technologies for Communications, ATC

作者： Yamato Wakayama Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

In this paper, we consider multi-view video and audio streaming using MPEG-DASH, which enables to transmit video tailored to the network conditions over HTTP communication. This paper uses HTTP/2 instead of HTTP/1.1, which the authors previously used. HTTP/2 manages a series of request-response exchanges called a stream, which is assigned a unique stream ID. We perform a subjective experiment under various network conditions to evaluate application-level QoS and QoE. From the evaluation results, we investigate the effect of the HTTP/2 stream on the video and audio quality of the users in the multi-view video transmission scenario.

关键词： Transport protocols Bit rate Transform coding Quality of service Streaming media HTTP Quality assessment Delays Quality of experience Video recording

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QoE Comparison between Multi-User OFDMA and Reliable Groupcast for Wireless LAN Audiovisual Transmission

QoE Comparison between Multi-User OFDMA and Reliable Groupca...

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IEEE Region 10 Symposium

作者： Daiki Deguchi Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

This paper compares video and audio QoE of OFDMA multi-user transmission and reliable groupcast over wireless LAN. We assume video and audio transmission to several terminals simultaneously. As a reliable groupcast method, we employ Unsolicited Retry, a technique of IEEE 802.11aa GroupCast with Retries. We utilize IEEE 802.11ax for OFDMA transmission. We evaluate application-level QoS by computer simulation. We then assess QoE through a subjective experiment with video and audio streams generated by the output timing obtained from the simulation. We notice that each method has a situation to be applied appropriately.

关键词： Degradation Wireless LAN Receivers Quality of service Streaming media Media Timing Reliability Quality of experience Streams

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QoE Enhancement of Multi-View Video Transmission Over ICN/CCN by Means of Cache Control Method

QoE Enhancement of Multi-View Video Transmission Over ICN/CC...

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IEEE Games Media Entertainment (GEM)

作者： Yoshiyuki Takada Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

This paper considers multi-view video and audio transmission on ICN (Information-Centric Networking)/CCN (Content-Centric Networking). Routers in ICN/CCN can cache content. Besides, the capacity of routers' caches is finite, so various cache control schemes have been proposed to improve cache efficiency. This paper presents and evaluates a new and suitable control scheme for multi-view video and audio transmission. For this purpose, we construct a network environment with Cefore. We assess application-level QoS (Quality of Service) and QoE (Quality of Experience). We then show the effectiveness of the proposed control scheme.

关键词： Information-centric networking Entertainment industry Quality of service Media Quality of experience Frequency control

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction 3

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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Exploring 3D Reconstruction with Drone Images: Advances and Challenges in Urban Environments 50

Exploring 3D Reconstruction with Drone Images: Advances and ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Miranda Baldivieso, Thiago João da Silva Batista, Taise Grazielle Chagas, Fabio Suim Rodrigues Velho, Luiz Carlos Pacheco Ferreira Rosa, Paulo Fernando Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea RJ Rio de Janeiro Brazil Department of Science and Industry systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9781665464543

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software-both commercial and open-source-and concluding with a qualitative evaluation of the results. © 2024 IEEE.

关键词： Aerial photography

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Mobile Based Brand and Car Type Detection Using Convolutional Neural Network 8

Mobile Based Brand and Car Type Detection Using Convolutiona...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Ramadhan, Cahaya Saputra, Hasta Ragil Dewi, Ratna Sari Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Although the parking lot is equipped with CCTV surveillance, car theft incidents still occur occasionally. These thieves exploit a loophole in the system that prevents the identification of the car's brand and model upon entering and exiting the parking lot using the same ticket. Therefore, the aim of this research is to develop a system capable of detecting the brand and model of cars. The study utilizes convolutional neural network technology and specifically focuses on the EfficientNet, MobileNet, and MobileNetV2 architectures due to their low resource requirements. Training images for the study are sourced from an online marketplace for used cars, with each of the 20 car brand and model variations consisting of 100 photographs. creates a model after being processed. For the prototype, the approach is put into practice through a mobile application. This study compares EfficientNet, MobileNetV1, and MobileNetV2 as the end result. EfficientNet scored 74%, MobileNetV1 74%, and MobileNetV2 66% on the test of accuracy. The accuracy increases to 80% after the EfficientNet has been tuned. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolutional Neural Network EfficientNet MobileNet V2 MobileNetV1 Parking system Transfer Learning

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Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecting the Ripeness of Oil Palm Fruit Bunch 10

Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecti...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Priyadi, Muhammad Reza Azhar Suharjito Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

Despite Indonesia's leading position in palm oil processing, the inaccurate assessment of oil palm fruit maturity poses challenges in determining the optimal harvest timing and maintaining product quality. With advancements in information technology, including image processing and object detection techniques like YOLO and EfficientDet, there is potential for automated and precise identification of palm fruit maturity. To explore this, the authors conducted research to identify the most suitable algorithm for detecting the ripeness of palm oil fruit. The study utilized a dataset consisting of 8299 images with six maturity levels, employing augmentation techniques to increase image variability. Results show that YOLO V8s outperformed EfficientDet Lite 4 in terms of object detection precision, achieving higher mean average precision (mAP) scores. YOLO V8s showed a 4% improvement in mAP@50 and a 16% improvement in mAP@50-95 compared to EfficientDet Lite 4. YOLO V8s achieved a mAP of 0.978 for mAP@50 and 0.765 for mAP@50-95, surpassing EfficientDet Lite 4. However, EfficientDet Lite 4 had an advantage in terms of class accuracy and bounding box placement, as indicated by smaller box loss and cIs- loss values. YOLO V8s also demonstrated better frames per second (FPS) performance, ranging from 10 to 15 FPS, compared to EfficientDet Lite 4. © 2023 IEEE.

关键词： Object detection

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