12,056 research outputs found
Library of model components for process simulation relevant to production activities, Prototype 1 versions
Production Economics,
Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition
Handwritten digits recognition has been treated as a multi-class classification problem in the machine learning context, where each of the ten digits (0-9) is viewed as a class and the machine learning task is essentially to train a classifier that can effectively discriminate the ten classes. In practice, it is very usual that the performance of a single classifier trained by using a standard learning algorithm is varied on different data sets, which indicates that the same learning algorithm may train strong classifiers on some data sets but weak classifiers may be trained on other data sets. It is also possible that the same classifier shows different performance on different test sets, especially when considering the case that image instances can be highly diverse due to the different handwriting styles of different people on the same digits. In order to address the above issue, development of ensemble learning approaches have been very necessary to improve the overall performance and make the performance more stable on different data sets. In this paper, we propose a framework that involves CNN based feature extraction from the MINST data set and algebraic fusion of multiple classifiers trained on different feature sets, which are prepared through feature selection applied to the original feature set extracted using CNN. The experimental results show that the classifiers fusion can achieve the classification accuracy of â„ 98%
netFound: Foundation Model for Network Security
In ML for network security, traditional workflows rely on high-quality
labeled data and manual feature engineering, but limited datasets and human
expertise hinder feature selection, leading to models struggling to capture
crucial relationships and generalize effectively. Inspired by recent
advancements in ML application domains like GPT-4 and Vision Transformers, we
have developed netFound, a foundational model for network security. This model
undergoes pre-training using self-supervised algorithms applied to readily
available unlabeled network packet traces. netFound's design incorporates
hierarchical and multi-modal attributes of network traffic, effectively
capturing hidden networking contexts, including application logic,
communication protocols, and network conditions.
With this pre-trained foundation in place, we can fine-tune netFound for a
wide array of downstream tasks, even when dealing with low-quality, limited,
and noisy labeled data. Our experiments demonstrate netFound's superiority over
existing state-of-the-art ML-based solutions across three distinct network
downstream tasks: traffic classification, network intrusion detection, and APT
detection. Furthermore, we emphasize netFound's robustness against noisy and
missing labels, as well as its ability to generalize across temporal variations
and diverse network environments. Finally, through a series of ablation
studies, we provide comprehensive insights into how our design choices enable
netFound to more effectively capture hidden networking contexts, further
solidifying its performance and utility in network security applications
Open source environment to define constraints in route planning for GIS-T
Route planning for transportation systems is strongly related to shortest path algorithms, an optimization problem extensively studied in the literature. To find the shortest path in a network one usually assigns weights to each branch to represent the difficulty of taking such branch. The weights construct a linear preference function ordering the variety of alternatives from the most to the least attractive.Postprint (published version
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