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检索条件"任意字段=23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022"

共 101 条记录，以下是31-40 订阅

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A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting 23rd

A Sequence to Sequence Long Short-Term Memory Network for Fo...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Santos, Luis Matos, Luis Miguel Ferreira, Luis Alves, Pedro Viana, Mario Pilastri, Andre Cortez, Paulo CCG ZGDV Inst EPMQ Guimaraes Portugal KYAIA SOLUCOES INFORMAT LDA Guimaraes Portugal OVERCUBE SA Guimaraes Portugal Univ Minho Dept Informat Syst LASI ALGORITMI Res Centre Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

Footwear sales forecasting is a critical task for supporting product managerial decisions, such as the management of footwear stocks and production levels. In this paper, we explore a recently proposed Sequence to Sequence (Seq2Seq) Long Short-Term Memory (LSTM) deep learning architecture for multi-step ahead footwear sales Time Series Forecasting (TSF). The analyzed Seq2Seq LSTM neural network is compared with two popular TSF methods, namely ARIMA and Prophet. Using real-world data from a Portuguese footwear company, several computational experiments were held. Focusing on daily sales, we analyze data recently collected during a 3-year period (2019-2021) and related with seven types of products (e.g., sandals). The evaluation assumed a robust and realistic rolling window scheme that considers 28 training and testing iterations, each related with one week of multi-step ahead predictions. Overall, competitive predictions were obtained by the proposed LSTM model, resulting in a weekly Normalized Mean Absolute Error (NMAE) that ranges from 5% to 11%.

关键词： Time series forecasting ARIMA Prophet Deep learning

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Multi-Scale Deep Ensemble learning for Melanoma Skin Cancer Detection 23

Multi-Scale Deep Ensemble Learning for Melanoma Skin Cancer ...

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23rd IEEE international conference on Information Reuse and Integration for data Science (IEEE IRI)

作者： Guergueb, Takfarines Akhloufi, Moulay A. Univ Moncton Dept Comp Sci Percept Robot & Intelligent Machines Res Grp PRIM Moncton NB Canada

ISBN: (纸本)9781665466035

Skin cancer is the most prevalent type of cancer in the world. Melanoma, specifically is among the deadliest variants of cancer, But if detected early, most melanomas can be cured with minor surgery. In this study we propose an innovative melanoma detection pipeline which utilizes ensemble learning to combine the predictive power of several deep convolutional neural network models. All experiments are performed using image data acquired from the Society for Imaging Informatics in Medicine and the international Skin Imaging Collaboration SIIM-ISIC 2020. The proposed approach achieved a high performance with an Area Under Curve (AUC) of 99.02% outperforming many state-of-the-art algorithms.

关键词： Pipelines Imaging Training data Surgery Melanoma Predictive models Prediction algorithms

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Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring 23rd

Synonym-Based Essay Generation and Augmentation for Robust A...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Tashu, Tsegaye Misikir Horvath, Tomas Eotvos Lorand Univ Fac Informat Dept Data Sci & Engn Telekom Innovat Labs Pazmany Peter Setany 1-C H-1117 Budapest Hungary Wollo Univ Kombolcha Inst Technol Coll Informat Kombolcha 208 Ethiopia Pavol Jozef Safarik Univ Fac Sci Inst Comp Sci Jesenna 5 Kosice 04001 Slovakia

ISBN: (纸本)9783031217524;9783031217531

Automatic essay scoring (AES) models based on neural networks (NN) have had a lot of success. However, research has shown that NN-based AES models have robustness issues, such that the output of a model changes easily with small changes in the input. We proposed to use keyword-based lexical substitution using BERT that generates new essays (adversarial samples) which are lexically similar to the original essay to evaluate the robustness of AES models trained on the original set. In order to evaluate the proposed approach, we implemented three NN-based scoring approaches and trained the scoring models using two stages. First, we trained each model using the original data and evaluate the performance using the original test and newly generated test set to see the impact of the adversarial sample of the model. Secondly, we trained the models by augmenting the generated adversarial essay with the original data to train a robust model against synonym-based adversarial attacks. The results of our experiments showed that extracting the most important words from the essay and replacing them with lexically similar words, as well as generating adversarial samples for augmentation, can significantly improve the generalization of NN-based AES models. Our experiments also demonstrated that the proposed defense is capable of not only defending against adversarial attacks, but also of improving the performance of NN-based AES models.

关键词： Adversarial attack data augmentation Automatic essay scoring

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Enhancing Road Infrastructure Maintenance Using Deep learning Approach 23th

Enhancing Road Infrastructure Maintenance Using Deep Learnin...

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23rd international conference on intelligent Systems Design and Applications, ISDA 2023

作者： Vinothkumar, S. Dhanushya, S. Guhan, S. Krisvanth, P. Kongu Engineering College TamilNadu Erode638060 India

ISBN: (纸本)9783031648359

The physical gathering of info on road damage can be time-consuming and dangerous to people's safety, yet preserving the transportation network is essential for providing secure and lasting transport systems. To address these challenges, this paper presents a novel approach that leverages Unmanned Aerial Vehicles (UAVs) and deep learning approaches can greatly improve the effectiveness and precision of road damage identification. The proposed approach employs the YOLOv8 algorithm, cutting-edge object detection and localization model, for detecting road damage in UAV images. The YOLOv8 model offers improved accuracy compared to previous versions, namely YOLOv4, YOLOv5, and YOLOv7, by using UAVs equipped with high-resolution cameras, info on road damage can be captured efficiently and remotely. On a synthesis of the Indian roadway data and the rdD2022 dataset, this method was trained and tested. The trained YOLOv8 model was used to identify and locate different types of road degradation such cracks, potholes, and surface degradation. The combination of UAV imagery and deep learning algorithms has shown great potential to detect automated road deterioration. This research highlights the advantages of using YOLOv8 for improved accuracy in identifying road damages. This work lays the path for further developments in the field, enabling safer transport systems and more effective road maintenance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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Building a Model with AutoML in Machine Faults Detection 23th

Building a Model with AutoML in Machine Faults Detection

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23rd international conference on intelligent Systems Design and Applications, ISDA 2023

作者： Duymaz, Şeyma Department of Industrial Engineering Faculty of Engineering and Natural Sciences Istanbul Rumeli University Istanbul Turkey

ISBN: (纸本)9783031647758

Technological advancements have provided numerous benefits for production activities. The primary aim of companies is to minimize costs and avoid unforeseen expenses. Machine failures may result in excessive and unexpected costs for firms. As a result, it is of paramount importance to anticipate and prevent machine breakdowns in advance. Predictive maintenance evaluations can be valuable in recognizing equipment and machine failures. The objective of this study is to utilize automated machine learning (AutoML) in order to achieve optimal outcomes via the adjustment of parameters on machine learning (ML) algorithms aimed at detecting machine faults. The dataset comprises 77,004 rows and 13 features extracted from Google data center operators. Five different classes of machine operating status are identified, namely: evict, fail, finish, kill and lost. Among these, fail, finish and evict classes are deemed failure states. AutoML can be accessed through various libraries. This study specifically uses the Auto-Keras, Auto-Sklearn and Lazy predict libraries, all of which are open-source Python libraries. In this study, hyperparameter optimization was conducted on the random forest (RF) algorithm, which was the third best algorithm obtained through the Lazy Predict library. The code was written using Anaconda. It is important to note that while the results of these libraries are valuable, manual modifications may be necessary for optimal outcomes. Notably, the Decision Tree Classifier algorithm achieved an accuracy of 97%, which was the highest among the evaluated options. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Deep learning Transforming the Manufacturing Industry: A Case Study 23

Deep Learning Transforming the Manufacturing Industry: A Cas...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ahmad, Hafiz Mughees Rahimi, Afshin Hayat, Khizer University of Windsor Mechanical Automotive and Materials Engineering WindsorON Canada Ifiveo Canada INC. WindsorON Canada

ISBN: (纸本)9781665494571

The traditional manufacturing industry uses decades-old technology and manual processes that are expensive, laborious, redundant, and a slight human error can cost millions of dollars. With the industrial revolution 4.0, industries are adopting intelligent additions and modifications into their systems involving robotics and internet of things (IoT) based technology, which can help in their operations. Artificial intelligence and machine learning have revolutionized every field, and the manufacturing industry has also started to reap its benefits. In this case study, we have explained the applications of deep learning-based approaches contributing to the automation of surveillance of production lines. Several cameras are installed to collect live feeds on the floor lines where the manually operated cranes and different tasks it performs are detected and tracked by state-of-the-art Convolutional Neural Network-based YOLOv3, an object detection model, and DeepSORT tracking algorithm. The post-processing is done on the matrices generated. It varies based on the use case and helps the manufacturers with automated inspection, quality control, and labor productivity. This process will save millions of dollars per year just in the crane maintenance operation for our industry partner's client. The developed technology can be replicated in almost every manufacturing industry using traditional methods and provide them better key insights into their production lines. © 2021 IEEE.

关键词： Manufacturing industries Deep learning Cranes Tracking Surveillance Object detection Maintenance engineering

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Performance/Resources Comparison of Hardware Implementations on Fully Connected Network Inference 23rd

Performance/Resources Comparison of Hardware Implementations...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Lozada, Randy Ruiz, Jorge Gonzalez, Manuel L. Sedano, Javier Villar, Jose R. Garcia-Vico, Angel M. Skibinsky-Gitlin, E. S. Inst Tecnol Castilla & Leon Burgos Spain Univ Oviedo Oviedo Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada 18071 Spain

ISBN: (纸本)9783031217524;9783031217531

Fully Connected Network inference is a complex algorithm that can be accelerated using edge devices like Field Programmable Gate Array (FPGA). One commonly known performance improvement for Fully Connected Network inference is quantization. This technique replaces the floating points weights of the network by integers. Frameworks like Open Neural Network Exchange (ONNX) and Tensorflow Lite provide solutions for this procedure. However, these frameworks have different inference algorithms with different operations and data types. In this article inference algorithms of common Fully Connected Networks in ONNX and Tensorflow Lite have been analysed. A performance and resource usage comparison is tested on Xilinx((R)) Zynq UltraScale+(TM) PSoC. Results show that to achieve lower latency is better to avoid floating point operations in the inference algorithm. In terms of FPGA resource usage, an increase is observed when the neural network becomes more complex regardless of the algorithm. This growth in resource usage is framework-dependent.

关键词： FPGA Fully connected network Quantization

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The Black-Box Syndrome: Embracing Randomness in Machine learning Models 23rd

The Black-Box Syndrome: Embracing Randomness in Machine Lear...

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23rd international conference on Artificial Intelligence in Education (AIED)

作者： Anthis, Z. UCL Gower St London WC1 6BT England

ISBN: (纸本)9783031116476;9783031116469

Acknowledging the 'curse' of dimensionality, the educational sector has reasonably turned to automated (in some cases autonomous) solutions, in the process of extracting and communicating patterns in data, to promote innovative teaching and learning experiences. As a result, learning Analytics (LA) and Educational dataMining (EDM) have both been relying on various Machine learning (ML) techniques to project novel and meaningful predictions. This inclusion has led to the need for developing new professional skills in the teaching community, that go beyond digital competence and data literacy. This paper seeks to address the issue of ML adoption in educational settings by using an interactive Exploratory learning Environment (ELE) to test the potential impact of randomness on explainability. The goal is to investigate how misconceptions about stochasticity can lead to distorted projections or expectations, and potentially expose any lack of transparency (to teachers), indirectly affecting the overall trust in artificially intelligent tools.

关键词： learning Analytics (LA) Educational data Mining (EDM) Machine learning (ML) Exploratory learning Environment (ELE)

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification 23

Domain Adaptation Random Vector Functional Link for White Bl...

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23rd international conference on Machine learning and Cybernetics, ICMLC 2024

作者： Wang, Qin Wang, Xi-Zhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells. © 2024 IEEE.

关键词： Deep learning

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intelligent Question Answering System Based on Knowledge Graph 23

Intelligent Question Answering System Based on Knowledge Gra...

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作者： Feng, Xijin Liu, Qi Liu, Xiaodong School of Computer and Software Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Nanjing China School of Computing Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9781665494571

In order to build a smart city and pursue more efficient city management, various industries have introduced intelligent question answering into process management. The intelligent question answering system based on the knowledge graph is dedicated to feeding back the results of natural language questions to users, which greatly simplifies the urban management process. From the four methods of template-based, semantic parsing, sorting and retrieval, and deep learning, the intelligent question answer system based on the knowledge graph is summarized, and combined with the current application status, it summarizes and introduces the question answering system based on the knowledge graph that helps various industries in the construction of smart cities. It helps to provide new ideas for more researchers who are committed to urban management and knowledge graph question answering. © 2021 IEEE.

关键词： Industries Deep learning Smart cities Semantics Natural languages Sorting

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