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

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23rd international conference on intelligent data engineering and automated learning, ideal 2022

23rd International Conference on Intelligent Data Engineerin...

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23rd international conference on intelligent data engineering and automated learning, ideal 2022

ISBN: (纸本)9783031217524

The proceedings contain 52 papers. The special focus in this conference is on intelligent data engineering and automated learning. The topics include: Benchmarking data Augmentation Techniques for Tabular data;deep learning Based Predictive Analytics for Decentralized Content Caching in Hierarchical Edge Networks;explanations of Performance Differences in Segment Lining for Tunnel Boring Machines;On Autonomous Drone Navigation Using Deep learning and an intelligent Rainbow DQN Agent;an intelligent Decision Support System for Road Freight Transport;endowing intelligent Vehicles with the Ability to Learn User’s Habits and Preferences with Machine learning Methods;duplication Scheduling with Bottom-Up Top-Down Recursive Neural Network;towards a Low-Cost Companion Robot for Helping Elderly Well-Being;zero-Shot Knowledge Graph Completion for Recommendation System;synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring;the Covid-19 Influence on the Desire to Stay at Home: A Big data Architecture;distance-Based Delays in Echo State Networks;eduBot: A Proof-of-Concept for a High School Motivational Agent;A Simulation Model for Predicting the Spread of COVID-19 Virus;ICU Mortality Prediction Using Long Short-Term Memory Networks;intelligent learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers;how Image Retrieval and Matching Can Improve Object Localisation on Offshore Platforms;Ethereum Investment Based on LSTM and GRU Forecast;Generating a European Portuguese BERT Based Model Using Content from *** Archive;a Vision Transformer Enhanced with Patch Encoding for Malware Classification;characterizing Cardiovascular Risk Through Unsupervised and Interpretable Techniques;association Rules Mining for Reducing Items from Emotion Regulation Questionnaires;explainable Artificial Intelligence for Improved Modeling of Processes;efficient Sensor Selection for Individualized Prediction Based on Biosignals;understanding the Classes

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28th international conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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28th international conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference is on Image Processing, Computer Vision, and Pattern Recognition. The topics include: Image-Based Seal Recognition: Approaches and Challenges in Current automated Systems;Deep learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light Image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch Images;residential Real Estate Image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color Images;lalitha: A Hand Gesture-Based Computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity data, Image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and Image Processing Techniques;using Linkage Context for automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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Face ReID Method via Deep learning 23rd

Face ReID Method via Deep Learning

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

作者： Lima Romero, Yves Augusto da Rocha Neto, Ajalmar Rego IFCE Fed Inst Ceara Dept Comp Sci Fortaleza Ceara Brazil

ISBN: (纸本)9783031217524;9783031217531

Recenlty, many face reid strategies have been developped. These strategies create vectorial spaces capable of representing data on reduced dimensions. Such representations are produced by deep learning models that learn to maximize the intra-class similarity and minimize the inter-class similarity. The method described in this article proposes a new face reid strategy based on the Facenet model. It consists of training support vector machines on the euclidean distances calculated between the embeddings of the Facenet model, notably reducing the false positive rate.

关键词： Face ReID Machine learning Deep learning

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On Studying the Effect of data Quality on Classification Performances 23rd

On Studying the Effect of Data Quality on Classification Per...

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

作者： Jouseau, Roxane Salva, Sebastien Samir, Chafik Univ Clermont Auvergne Clermont Ferrand France

ISBN: (纸本)9783031217524;9783031217531

During the last decade, data have played a key role for learning and decision making models. Unfortunately, the quality of data has been ignored or partially investigated as a pre-processing step. Motivated by applications in various fields, we propose to study data quality and its impact on the performance of several learning models. In this work, we first study the difficulty of repairing errors by introducing a list of elementary repairing tasks ranging from easy to complex with an increasing level. Then, we form categories from the state-of-the-art cleaning and repairing methods. We also investigate if it is always efficient to repair data. By including standard classifications models and public dataset, our work enables their use in different contexts and can be extended to other machine learning applications.

关键词： data quality data engineering data cleaning data repairing Classification Machine learning

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intelligent learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers 23rd

Intelligent Learning Rate Distribution to Reduce Catastrophi...

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

作者： Kenneweg, Philip Schulz, Alexander Schroeder, Sarah Hammer, Barbara Bielefeld Univ Inspirat 1 D-33615 Bielefeld Germany

ISBN: (纸本)9783031217524;9783031217531

Pretraining language models on large text corpora is a common practice in natural language processing. Fine-tuning of these models is then performed to achieve the best results on a variety of tasks. In this paper, we investigate the problem of catastrophic forgetting in transformer neural networks and question the common practice of fine-tuning with a flat learning rate for the entire network in this context. We perform a hyperparameter optimization process to find learning rate distributions that are better than a flat learning rate. We combine the learning rate distributions thus found and show that they generalize to better performance with respect to the problem of catastrophic forgetting. We validate these learning rate distributions with a variety of NLP benchmarks from the GLUE dataset. The source code is open-source and free software, available at https://***/TheMody/NAS-CatastrophicForgetting.

关键词： Natural language processing BERT learning rate Transformer

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Using GANs to Improve the Accuracy of Machine learning Models for Malware Detection 23rd

Using GANs to Improve the Accuracy of Machine Learning Model...

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

作者： Simion, Ciprian-Alin Balan, Gheorghe Gavrilut, Dragos Teodor Alexandru Ioan Cuza Univ Fac Comp Sci Iasi Romania Bitdefender Lab Iasi Romania

ISBN: (纸本)9783031217524;9783031217531

The increase of cyber-attacks and new malware in the last decade led to the usage of various machine learning techniques in security products. While these techniques are designed to improve accuracy, some practical constraints (such as lowering the false positive rate) often influence the selected model. This paper focuses on how various generative adversarial networks can be used to improve the average detection rate and reduce the false positives for a given neural network, by altering the training set. The result of this paper is a technique that can be used to reduce the number of false positives while preserving or in some cases increasing the detection rate.

关键词： Malware Threats Adversarial machine learning False positives Detection GAN

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An Approach to Authenticity Speech Validation Through Facial Recognition and Artificial Intelligence Techniques 23rd

An Approach to Authenticity Speech Validation Through Facial...

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

作者： Faria, Hugo Rodrigues, Manuel Novais, Paulo Univ Minho Braga Portugal

ISBN: (纸本)9783031217524;9783031217531

Since all times, humans tend to adapt their speech, in terms of authenticity, according to the moments specific needs, making some statements or claims about something that do not correspond to reality, in short, lying about some matter. Identifying such moments has always been a challenging task, not at all times successful, and requiring external artefacts, with scarce availability and inducing stressful situations. With recent advances in hardware technology, making enormous computational power available in our hands through smartphones and fast network technologies, and with Artificial Intelligence evolution, namely Deep learning, it seems possible to achieve the goal of validating speech authenticity, with smartphones, using facial recognition to detect signs of untruthful speech. This paper presents a framework to achieve this goal.

关键词： Artificial intelligence Deep learning Facial recognition Deception detection

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Effective Prevention of Semantic Drift in Continual Deep learning 23rd

Effective Prevention of Semantic Drift in Continual Deep Lea...

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

作者： Saadi, Khouloud Khan, Muhammad Taimoor Natl Univ Comp & Emerging Sci Islamabad Pakistan

ISBN: (纸本)9783031217524;9783031217531

Lifelong machine learning or continual learning models attempt to learn incrementally by accumulating knowledge across a sequence of tasks. Therefore, these models learn better and faster. They are used in various intelligent systems that have to interact with humans or any dynamic environment. Dynamically expandable networks are continual deep learning models that allow its architecture to expand with a sequence of tasks. The model retains knowledge from the previous tasks that results in high performance on newer tasks. The existing models use Minkowski distance measures to separate nodes of the current network, resulting in higher catastrophic forgetting. These measures are susceptible to high dimensional sparse vectors, resulting in sub-optimum performance. We propose ang-DEN, as a dynamically expanding continual learning architecture that use angular distance metric. It addresses semantic drift through better separation of nodes achieving 97% average accuracy with an improvement of 1.3% across all tasks on MNIST variant datasets.

关键词： Continual deep learning Catastrophic forgetting Semantic drift ang-DEN

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Deep learning Based Predictive Analytics for Decentralized Content Caching in Hierarchical Edge Networks 23rd

Deep Learning Based Predictive Analytics for Decentralized C...

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

作者： Chakraborty, Dhruba Rabbi, Mahima Hossain, Maisha Khaled, Saraf Noor Oishi, Maria Khanom Alam, Md Golam Rabiul Brac Univ 66 Mohakhali Dhaka 1212 Bangladesh

ISBN: (纸本)9783031217524;9783031217531

The content-centric network is a state-of-the-art networking architecture for content distribution and content caching. However, it is inefficient to cache every content in each network device. The modern edge computing technology opens the door for content caching on the edge of the network. However, still, we have to decide which content we should cache and which content we should replace from the cache. Deep learning-based predictive analytics can play an important role in selecting content for caching purposes. In this research, we will use LSTM-based Recurrent Neural Network(RNN) for decentralized content caching at the hierarchical edge of the network.

关键词： Predictive analytics Content Caching Edge networking Deep learning Recurrent Neural Network(RNN) Long short-term memory(LSTM) Decentralized Hierarchical

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Efficient Sensor Selection for Individualized Prediction Based on Biosignals 23rd

Efficient Sensor Selection for Individualized Prediction Bas...

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

作者： Vieth, Markus Grimmelsmann, Nils Schneider, Axel Hammer, Barbara Bielefeld Univ Bielefeld Germany Bielefeld Univ Appl Sci Bielefeld Germany

ISBN: (纸本)9783031217524;9783031217531

Soft sensors combine a hardware component with an intelligent algorithmic processing of the raw sensor signals. While individualization of software components according to a person's specific needs is comparably cheap, individualization of the sensor hardware itself is usually impossible in mass production. At the same time, the number of raw sensors should be minimum to reduce production costs. In this contribution, we propose to model this challenge as a feature selection problem, which optimizes a feature set simultaneously with respect to a family of functions corresponding to individualized post-processing of sensor signals. This concept is integrated into a number of different classical feature selection schemes, and evaluated in the context of the placement of pressure sensors as part of a shoe insole. It turns out that feature selection respecting the class of functions is superior to both placement based on anatomical considerations and classical feature selection methods.

关键词： intelligent wearables Feature selection Model individualization

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