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检索条件"任意字段=24th International Conference on Engineering Applications of Neural Networks, EANN 2023"

共 298 条记录，以下是1-10 订阅

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24th international conference on engineering applications of neural networks, eann 2023

24th International Conference on Engineering Applications of...

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24th international conference on engineering applications of neural networks, eann 2023

ISBN: (纸本)9783031342035

the proceedings contain 49 papers. the special focus in this conference is on engineering applications of neural networks. the topics include: Towards Improved User Experience for Artificial Intelligence Systems;forecasting Functional Time Series Using Federated Learning;group-Personalized Federated Learning for Human Activity Recognition through Cluster Eccentricity Analysis;modeling Others as a Player in Non-cooperative Game for Multi-agent Coordination;neural Network Bootstrap Forecast Distributions with Extreme Learning Machines;subsampled Dataset Challenges and Machine Learning Techniques in Table Tennis;towards Explaining Shortcut Learning through Attention Visualization and Adversarial Attacks;a Novel neural Network-Based Recommender System for Drug Recommendation;DACL: A Domain-Adapted Contrastive Learning Approach to Low Resource Language Representations for Document Clustering Tasks;evaluating the Extraction of Toxicological Properties with Extractive Question Answering;semi-automated System for Pothole Detection and Location;a Critical Analysis of Classifier Selection in Learned Bloom Filters: the Essentials;balancing High-Dimensional Datasets with Complex Layers;botDroid: Permission-Based Android Botnet Detection Using neural networks;classification of Time Signals Using Machine Learning Techniques;conductivity Classification Using Machine Learning Algorithms in the "Bramianon" Dam;preface;a Machine Learning Approach for Seismic Vulnerability Ranking;generation of Bases for Classification in the Bio-inspired Layered networks;load-Shedding Management in a Smart Grid Architecture through Smart Metering;MiniAnDE: A Reduced AnDE Ensemble to Deal with Microarray Data;multi-view Semi-supervised Learning Using Privileged Information;a Multi-relationship Language Acquisition Model for Predicting Child Vocabulary Growth;knowledge Graph of Urban Firefighting with Rule-Based Entity Extraction;optimal Traffic Flow Distributions on Dynamic networks;Streamlined Traini

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Toward an Epidermal Patch for Voice Prosthesis and Diseases Prediction by a Fuzzy-Neuro Paradigm 1

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24th international conference on engineering applications of neural networks (eann)

作者： Malcangi, Mario Felisati, Giovanni Saibene, Alberto Alfonsi, Enrico Fresia, Mauro Cambiaghi, Pasquale Univ Milan Dept Comp Sci Milan Italy Univ Milan Dept Heath Sci Milan Italy IRCCS Mondino Fdn Pavia Italy MRS Srl Cazzaniga Bergamo Italy

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Voice rehabilitation and diseases prediction is required today because neural degeneration or neurological injury alters the motor component of the speech system in the phonation area of the brain. A novel approach to voice rehabilitation consists in predicting the phonetic control by the EMG. In a previous work we demonstrated that the voice-production apparatus (tongue muscle) generates a specific EMG signal that identify the phoneme emitted. the inference paradigm is EFuNN (Evolving Fuzzy neural Network) trained by the sampled EMG (Electro Myo Gram) signal at phonation-. Time. A phoneme-to-speech non-invasive epidermal patch is to be designed with energy harvestimg and MEMS loudspeakers.

关键词： EFuNN Evolving Fuzzy neural Network voice dysarthria Voice rehabilitation Myoelectric signal Patch

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BotDroid: Permission-Based Android Botnet Detection Using neural networks 1

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24th international conference on engineering applications of neural networks (eann)

作者： Seraj, Saeed Pimenidis, Elias Pavlidis, Michalis Kapetanakis, Stelios Trovati, Marcello Polatidis, Nikolaos Univ Brighton Sch Architecture Technol & Engn Brighton BN2 4GJ E Sussex England Univ West England Dept Comp Sci & Creat Technol Bristol BS16 1QY Avon England Distributed Analyt Solut 17 Fawe St London 14 6FD England Edge Hill Univ Dept Comp Sci Ormskirk L39 4QP England

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Android devices can now offer a wide range of services. they support a variety of applications, including those for banking, business, health, and entertainment. the popularity and functionality of Android devices, along with the open-source nature of the Android operating system, have made them a prime target for attackers. One of the most dangerous malwares is an Android botnet, which an attacker known as a botmaster can remotely control to launch destructive attacks. this paper investigates Android botnets by using static analysis to extract features from reverse-engineered applications. Furthermore, this article delivers a new dataset of Android apps, including botnet or benign, and an optimized multi-layer perceptron neural network (MLP) for detecting botnets infected by malware based on the permissions of the apps. Experimental results show that the proposed methodology is both practical and effective while outperforming other standard classifiers in various evaluation metrics.

关键词： Android Malware detection Botnets neural networks New dataset

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Hierarchical Prediction in Incomplete Submetering Systems Using a CNN 1

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24th international conference on engineering applications of neural networks (eann)

作者： Alonso, Serafin Moran, Antonio Perez, Daniel Prada, Miguel A. Fuertes, Juan J. Dominguez, Manuel Univ Leon Grp Invest Supervis Control & Automatizac Proc In Escuela Ing Ind Informat & Aeroespacial Campus Vegazana S-N Leon 24007 Spain

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Energy market liberalization brings new opportunities, since large consumers have direct access to energy trading to buy energy for the next day. However, that requires a good estimation of the expected amount of energy and its hourly distribution in advance. On the other hand, smart energy meters are being installed in many facilities with the aim of achieving holistic submetering systems. these systems consist of a set of meters structured in several levels, so that there are hierarchical relations among upstream and downstream meters. this information could be exploited for achieving accurate one-day-ahead energy predictions. However, submetering systems might be incomplete due to unavailable meters or lost energy. In this paper, we propose a hierarchical prediction method for incomplete submetering systems that is based on 2D convolutional neural network (2D CNN) and is able to perform day ahead prediction of power consumption. this method exploits the hierarchical relations among meters and considers periodicity in order to forecast the power consumption for the next day. the proposed hierarchical method has proved to be more accurate and fast to forecast power consumption in incomplete submetering systems than using an individual predictions.

关键词： Hierarchical prediction Power consumption Submetering systems Convolutional neural networks

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Object Detection for Functional Assessment applications 1

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24th international conference on engineering applications of neural networks (eann)

作者： Melino-Carrero, Alessandro Nieva Suarez, Alvaro Losada-Gutierrez, Cristina Marron-Romera, Marta Guardiola Luna, Irene Baeza-Mas, Javier Univ Alcala Dept Elect Edificio PolitecnCtra Madrid Barcelona KM 33600 Alcala De Henares 28805 Spain

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

this paper presents a proposal for object detection as a first stage for the analysis of Human-Object Interaction (HOI) in the context of automated functional assessment. the proposed system is based in a two-step strategy, thus, in the first stage there are detected the people in the scene, as well as large objects (table, chairs, etc.) using a pre-trained YOLOv8. then, there is defined a ROI around each person that is processed using a custom YOLO to detect small elements (forks, plates, spoons, etc.). Since there are no large image datasets that include all the objects of interest, there has also been compiled a new dataset including images from different sets, and improving the available labels. the proposal has been evaluated in the novel dataset, and in different images acquired in the area in which the functional assessment is performed, obtaining promising results.

关键词： Image processing Object detection Occupational therapy Functional assessment Human-Object Interaction

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Pruning for Power: Optimizing Energy Efficiency in IoT with neural Network Pruning 1

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24th international conference on engineering applications of neural networks (eann)

作者： Widmann, thomas Merkle, Florian Nocker, Martin Schoettle, Pascal MCI Management Ctr Innsbruck Innsbruck Austria

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

the Internet of things (IoT) has rapidly emerged as a crucial driver of the digital economy, generating massive amounts of data. Machine learning (ML) is an important technology to extract insights from the data generated by IoT devices. Deploying ML on low-power devices such as microcontroller units (MCUs) improves data protection, reduces bandwidth, and enables on-device data processing. However, the requirements of ML algorithms exceed the processing power, memory, and energy consumption capabilities of these devices. One solution to adapt ML networks to the limited capacities of MCUs is network pruning, the process of removing unnecessary connections or neurons from a neural network. In this work, we investigate the effect of unstructured and structured pruning methods on energy consumption. A series of experiments is conducted using a Raspberry Pi Pico to classify the FashionMNIST dataset with a LeNet-5-like convolutional neural network while applying unstructured magnitude and structured APoZ pruning approaches with various model compression rates from two to 64. We find that unstructured pruning out of the box has no effect on energy consumption, while structured pruning reduces energy consumption with increasing model compression. When structured pruning is applied to remove 75% of the model parameters, inference consumes 59.06% less energy, while the accuracy declines by 3.01 %. We further develop an adaption of the TensorFlow Lite framework that realizes the theoretical improvements for unstructured pruning, reducing the energy consumption by 37.59% with a decrease of only 1.58% in accuracy when 75% of the parameters are removed. Our results show that both approaches are feasible to significantly reduce the energy consumption of MCUs, leading to various possible sweet spots within the trade-off between accuracy and energy consumption.

关键词： Energy utilization

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An Autonomous Self-learning and Self-adversarial Training neural Architecture for Intelligent and Resilient Cyber Security Systems 1

引用

24th international conference on engineering applications of neural networks (eann)

作者： Demertzis, Konstantinos Iliadis, Lazaros Hellen Open Univ Sch Sci & Technol Informat Studies Patras Greece Democritus Univ Thrace Sch Engn Fac Math Programming & Gen Courses Dept Civil Engn Xanthi Greece

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Cybersecurity systems have become increasingly important as businesses and individuals rely more on technology. However, the increasing complexity of these systems and the evolving nature of cyber threats require innovative solutions to protect against cyber attacks. One promising approach is the idea of autonomous self-learning and auto-training neural architectures. Autonomous self-learning refers to the ability of the system to adapt to new threats and learn from past experiences without human intervention. Auto-training, on the other hand, refers to the ability of the system to improve its performance over time by automatically adjusting its parameters and algorithms. this research proposes an autonomous Self-Learning and Self-Adversarial Training (SLSAT) neural architecture for intelligent and resilient cyber security systems. It is an extension of the next-generation Continuous-Time Reservoir Computing (CTRC) that was proposed by the authors recently. the CTRC is a time-series anomaly detection system controlled by time-varying differential equations. It uses Reinforcement Learning (RL) to dynamically fine-tune the reservoir computing parameters in order to identify the aberrant changes in the data. the proposed method in this research improves the CTRC's architecture by including a Conditional Tabular Generative Adversarial Network (CTGAN). Specifically, including CTGAN allows the SLSAT architecture to generate synthetic data based on the identified abnormalities to improve the model's performance and adapt to new and evolving threats without manual intervention. this, as proved experimentally, helps the model identify aberrant changes in the data and fend off poison and zero-day attacks.

关键词： Reservoir Computing Continuous-Time Reservoir Computing Cyber Defense Time Series Analysis

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A Multi-relationship Language Acquisition Model for Predicting Child Vocabulary Growth 1

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24th international conference on engineering applications of neural networks (eann)

作者： Roxburgh, Andrew Grasso, Floriana Payne, Terry R. Univ Liverpool Dept Comp Sci Liverpool L69 3BX Merseyside England

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

If we can predict the words a child is likely to learn next, it may lay the foundations for developing a tool to assist child language acquisition, especially for children experiencing language delay. Previous studies have demonstrated vocabulary predictions using neural network techniques and graph models;however, individually these models do not fully capture the complexities of language learning in infants. In this paper, we describe a multi-relationship-layer predictive model, based on a graph neural network. Our model combines vocabulary development over time with quantified connections between words calculated from fifteen different norms, incorporating an ensemble output stage to combine the predictions from each layer. We present results from each relationship layer and the most effective ensemble arrangement.

关键词： graph neural networks language acquisition

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Accelerating Bayesian neural networks on Low-Power Edge RISC-V Processors 24

Accelerating Bayesian Neural Networks on Low-Power Edge RISC...

引用

IEEE 24th international conference on Nanotechnology (NANO)

作者： Perez, Samuel Resano, Javier Suarez Gracia, Darfo Univ Zaragoza Aragon Inst Engn Res I3A Dept Comp Sci & Syst Engn DIIS Zaragoza Spain

ISBN: (纸本)9798350386257;9798350386240

neural networks (NNs) are a very popular solution for classification tasks. As the combination of Internet of things (IoT) with Machine Learning (ML), also known as TinyML, grows in popularity, more NN are being executed on low-end edge systems. the reliability of the predictions is crucial for safety-critical applications. Bayesian neural networks (BNNs) address this issue by calculating uncertainty metrics with their predictions at the cost of increasing computing requirements. this work addresses the challenges of executing BNNs inference on low-end systems. BNNs require multiple forward passes in which the weights are sampled from distributions. this sampling process can take up to 85,13% of execution time. this work optimizes the weight sampling and integrates it within a low cost custom extension for a RISC-V CPU, improving speedup up to x8,10 and similar energy savings.

关键词： neural networks

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A Novel neural Network-Based Recommender System for Drug Recommendation 1

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24th international conference on engineering applications of neural networks (eann)

作者： Al Mubasher, Hadi Doughan, Ziad Sliman, Layth Haidar, Ali Beirut Arab Univ Fac Engn Debbieh Lebanon Ecole Ingn Gen Numer Paris France

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

With the advancement of Machine Learning, recommender systems have emerged with the aim of improving the user experience in a world where data and available alternatives are tremendously growing. Employing Natural Language Processing with such systems can provide them with a sense of empowerment, given that most of the users' opinions are reflected through reviews. Artificial neural networks, the core of Deep Learning, have sparked a lot of interest in many research fields, owing to the appealing property of learning feature representations out of nowhere. To that end, this paper presents a novel hybrid recommender system that is based on Natural Language Processing and Artificial neural networks. the proposed model is evaluated and compared with a similar model, where the advantages of the proposed model are clearly presented. the paper is concluded by highlighting research opportunities that can be done in the future.

关键词： Machine Learning Recommender Systems Natural Language Processing Deep Learning Artificial neural networks Drug Recommendation

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