International Conference on Engineering Applications of Neural Networks最新文献

Support Vector Based Anomaly Detection in Federated Learning 联盟学习中基于支持向量的异常检测

International Conference on Engineering Applications of Neural Networks Pub Date : 2024-07-04 DOI: 10.1007/978-3-031-62495-7_21

Massimo Frasson, Dario Malchiodi

引用次数: 0

Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch snn的框架:数据科学导向软件的回顾和SpykeTorch的扩展

International Conference on Engineering Applications of Neural Networks Pub Date : 2023-02-15 DOI: 10.48550/arXiv.2302.07624

Davide L. Manna, Alex Vicente-Sola, Paul Kirkland, Trevor J. Bihl, G. D. Caterina

引用次数: 3

A Critical Analysis of Classifier Selection in Learned Bloom Filters 学习布隆过滤器中分类器选择的关键分析

International Conference on Engineering Applications of Neural Networks Pub Date : 2022-11-28 DOI: 10.48550/arXiv.2211.15565

D. Malchiodi, Davide Raimondi, G. Fumagalli, R. Giancarlo, Marco Frasca

{"title":"A Critical Analysis of Classifier Selection in Learned Bloom Filters","authors":"D. Malchiodi, Davide Raimondi, G. Fumagalli, R. Giancarlo, Marco Frasca","doi":"10.48550/arXiv.2211.15565","DOIUrl":"https://doi.org/10.48550/arXiv.2211.15565","url":null,"abstract":"Learned Bloom Filters, i.e., models induced from data via machine learning techniques and solving the approximate set membership problem, have recently been introduced with the aim of enhancing the performance of standard Bloom Filters, with special focus on space occupancy. Unlike in the classical case, the\"complexity\"of the data used to build the filter might heavily impact on its performance. Therefore, here we propose the first in-depth analysis, to the best of our knowledge, for the performance assessment of a given Learned Bloom Filter, in conjunction with a given classifier, on a dataset of a given classification complexity. Indeed, we propose a novel methodology, supported by software, for designing, analyzing and implementing Learned Bloom Filters in function of specific constraints on their multi-criteria nature (that is, constraints involving space efficiency, false positive rate, and reject time). Our experiments show that the proposed methodology and the supporting software are valid and useful: we find out that only two classifiers have desirable properties in relation to problems with different data complexity, and, interestingly, none of them has been considered so far in the literature. We also experimentally show that the Sandwiched variant of Learned Bloom filters is the most robust to data complexity and classifier performance variability, as well as those usually having smaller reject times. The software can be readily used to test new Learned Bloom Filter proposals, which can be compared with the best ones identified here.","PeriodicalId":202517,"journal":{"name":"International Conference on Engineering Applications of Neural Networks","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116366148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark 交通事故伤害严重程度预测:一个新的不平衡数据集和基准

International Conference on Engineering Applications of Neural Networks Pub Date : 2022-05-20 DOI: 10.48550/arXiv.2205.10441

Paschalis Lagias, G. Magoulas, Y. Prifti, A. Provetti

引用次数: 0

On the Suitability of Neural Networks as Building Blocks for The Design of Efficient Learned Indexes 神经网络在高效学习指标设计中的适用性研究

International Conference on Engineering Applications of Neural Networks Pub Date : 2022-02-21 DOI: 10.48550/arXiv.2203.14777

Domenico Amato, Giosuè Lo Bosco, Raffaele Giancarlo

{"title":"On the Suitability of Neural Networks as Building Blocks for The Design of Efficient Learned Indexes","authors":"Domenico Amato, Giosuè Lo Bosco, Raffaele Giancarlo","doi":"10.48550/arXiv.2203.14777","DOIUrl":"https://doi.org/10.48550/arXiv.2203.14777","url":null,"abstract":"With the aim of obtaining time/space improvements in classic Data Structures, an emerging trend is to combine Machine Learning techniques with the ones proper of Data Structures. This new area goes under the name of Learned Data Structures. The motivation for its study is a perceived change of paradigm in Computer Architectures that would favour the use of Graphics Processing Units and Tensor Processing Units over conventional Central Processing Units. In turn, that would favour the use of Neural Networks as building blocks of Classic Data Structures. Indeed, Learned Bloom Filters, which are one of the main pillars of Learned Data Structures, make extensive use of Neural Networks to improve the performance of classic Filters. However, no use of Neural Networks is reported in the realm of Learned Indexes, which is another main pillar of that new area. In this contribution, we provide the first, and much needed, comparative experimental analysis regarding the use of Neural Networks as building blocks of Learned Indexes. The results reported here highlight the need for the design of very specialized Neural Networks tailored to Learned Indexes and it establishes a solid ground for those developments. Our findings, methodologically important, are of interest to both Scientists and Engineers working in Neural Networks Design and Implementation, in view also of the importance of the application areas involved, e.g., Computer Networks and Data Bases.","PeriodicalId":202517,"journal":{"name":"International Conference on Engineering Applications of Neural Networks","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123564191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Deep Learning of Brain Asymmetry Images and Transfer Learning for Early Diagnosis of Dementia 脑不对称图像的深度学习和迁移学习在痴呆早期诊断中的应用

International Conference on Engineering Applications of Neural Networks Pub Date : 2021-06-25 DOI: 10.1007/978-3-030-80568-5_5

Nitsa J. Herzog, G. Magoulas

引用次数: 2

Large-Scale Geospatial Data Analysis: Geographic Object-Based Scene Classification in Remote Sensing Images by GIS and Deep Residual Learning 大规模地理空间数据分析:基于GIS和深度残差学习的遥感图像地理对象场景分类

International Conference on Engineering Applications of Neural Networks Pub Date : 2020-06-05 DOI: 10.1007/978-3-030-48791-1_21

Konstantinos Demertzis, L. Iliadis, E. Pimenidis

引用次数: 6

Semantic Segmentation of Vineyard Images Using Convolutional Neural Networks 基于卷积神经网络的葡萄园图像语义分割

International Conference on Engineering Applications of Neural Networks Pub Date : 2020-06-05 DOI: 10.1007/978-3-030-48791-1_22

T. Kalampokas, K. Tziridis, A. Nikolaou, E. Vrochidou, G. Papakostas, T. Pachidis, V. Kaburlasos

引用次数: 7

A Genetic Programming Method for Scale-Invariant Texture Classification 尺度不变纹理分类的遗传规划方法

International Conference on Engineering Applications of Neural Networks Pub Date : 2020-06-05 DOI: 10.1007/978-3-030-48791-1_47

Haythem Ghazouani, W. Barhoumi, Y. Antit

引用次数: 5

Efficient Implementation of a Self-sufficient Solar-Powered Real-Time Deep Learning-Based System 自给自足的太阳能实时深度学习系统的高效实现

International Conference on Engineering Applications of Neural Networks Pub Date : 2020-06-05 DOI: 10.1007/978-3-030-48791-1_7

Sorin Liviu Jurj, Raul Rotar, Flavius Opritoiu, M. Vladutiu

引用次数: 5