Industrial seedling quality assessment, such as attempting to find abnormal seedlings, is a challenging task where assessment methods must contend with the natural variability of seedlings, as well as the subjective n...
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Industrial seedling quality assessment, such as attempting to find abnormal seedlings, is a challenging task where assessment methods must contend with the natural variability of seedlings, as well as the subjective nature of expert judgements. Furthermore, obtaining expert judgements is expensive and time-consuming, so machine learning approaches which rely on fewer judgements would be useful in practice. We investigate autoencoders, operating on 3D point clouds obtained from 6732 seedlings to address this challenge, exploiting such systems' ability to work with partially labelled data. Point clouds from tomato seedlings are recorded using a 3D data capture platform, MARVIN'', and the quality of each seedling is determined by expert judgement. An existing system is used to establish baseline performance scores using a rule-based expert system and machine learning with handcrafted features. autoencoders are trained on the point clouds to learn representations for subsequent use in classification. We examine scenarios where large amounts of partially labelled data are available, and compare with the case where fully labelled data is available. To improve performance, we compare the architectural subcomponents based on PointNet and PointNet++, as well as the effect of different training strategies. We fad, with 13.6% of training data labelled, our model has correct classification rates of 97.7% and 82.7% for normals and abnormals respectively. With further improvements and fully labelled data, we find that correct classification rates of 97.6% and 96.1% can be reached. The results demonstrate that semi-supervised learning supported by partially labelled data has the potential to greatly reduce the cost of data curation, with minimal impact on overall accuracy.
Fiber-terahertz integrated communication system has emerged as a promising technology for 6G. In this paper, an end-to-end learning-based quadrature amplitude modulation (QAM) symbol-to-symbol autoencoder frame work i...
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As of more recently, deep learning-based models have demonstrated considerable potential, as they have outperformed all traditional practices. When data becomes high dimensional, extraction of features and compression...
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Wireless sensor networks (WSNs) are extensively deployed to gather and process data from monitoring environments. Due to their deployment in harsh and unattended conditions, sensor nodes are highly susceptible to faul...
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We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the privacy m...
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ISBN:
(数字)9781728186719
ISBN:
(纸本)9781728186719
We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the privacy mechanism provides a one-hot encoding of categorical features, representing exactly one class, while under data-type ignorant conditions the categorical variables are represented by a collection of scores, one for each class. We use a neural network architecture consisting of a generator and a discriminator, where the generator consists of an encoder-decoder pair, and the discriminator consists of an adversary and a utility provider. Unlike previous research considering this kind of architecture, which leverages autoencoders (AEs) without introducing any randomness, or variational autoencoders (VAEs) based on learning latent representations which are then forced into a Gaussian assumption, our proposed technique introduces randomness and removes the Gaussian assumption restriction on the latent variables, only focusing on the end-to-end stochastic mapping of the input to privatized data. We test our framework on different datasets: MNIST, FashionMNIST, UCI Adult, and US Census Demographic Data, providing a wide range of possible private and utility attributes. We use multiple adversaries simultaneously to test our privacy mechanism - some trained from the ground truth data and some trained from the perturbed data generated by our privacy mechanism. Through comparative analysis, our results demonstrate better privacy and utility guarantees than the existing works under similar, datatype ignorant conditions, even when the latter are considered under their original restrictive single-adversary model.
This paper presents a new method in the field of healthcare security that specifically targets cloud-based wireless sensor networks (WSNs). The suggested method integrates a goal-based artificial intelligent agent (GA...
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This paper presents a new method in the field of healthcare security that specifically targets cloud-based wireless sensor networks (WSNs). The suggested method integrates a goal-based artificial intelligent agent (GAIA) with an autoencoder (AE) architecture, yielding an autoencoder-based agent (AE-A). The main goal of this integrated system is to improve the efficiency of identifying botnet assaults, with a specific emphasis on the evolving security threats related to cloud computing. Our concept is around creating a meticulously calibrated, goal-driven AI agent tailored explicitly for healthcare applications. The agent meticulously analyses network data and proficiently integrates autoencoder-enhanced anomaly detection techniques to uncover intricate patterns indicative of botnet activities. The adaptability of the goal-based AI agent is improved by ongoing real-time learning, guaranteeing that its responses are in line with the primary goal of neutralizing threats. The autoencoder serves a vital role in the system by functioning as a tool for extracting features. This approach enables the AI Agent to navigate complex information and derive significant insights efficiently. Cloud computing resources greatly enhance the functionalities of a system, enabling scalability, real-time analysis, and improved responsiveness. Utilizing goal-driven AI and autoencoder together proves to be a successful strategy in safeguarding healthcare-oriented WSNs against botnet attacks. This technique takes a proactive stance in ensuring the security of sensitive medical data. The suggested model is evaluated against various models, including the bidirectional long short-term memory (BLSTM) method, the hybrid BLSTM with recurrent neural network (BLSTM-RNN) algorithm, and the Random Forest algorithm. The models are evaluated using metrics such as Matthews correlation coefficient (MCC), prediction rate, accuracy, recall, precision, and F1 score analysis. The investigation demonstrates tha
With the increasing use of computer networks and distributed systems, network security and data privacy are becoming major concerns for our society. In this paper, we present an approach based on an autoencoder traine...
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ISBN:
(纸本)9781450392686
With the increasing use of computer networks and distributed systems, network security and data privacy are becoming major concerns for our society. In this paper, we present an approach based on an autoencoder trained with differential evolution for feature encoding of network data with the goal of improving security and reducing data transfers. One of the novel elements used in differential evolution for intrusion detection is the enhancements in the fitness function by adding the performance of a machine learning algorithm. We conducted an extensive evaluation of six machine learning algorithms for network intrusion detection using encoded data from well-known publicly available network datasets UNSW-NB15. The experiments clearly showed the supremacy of random forest, support vector machine, and K-nearest neighbors in terms of accuracy, and this was not affected to a high degree by reducing the number of features. Furthermore, the machine learning algorithm that was used during training (Linear Discriminant Analysis classifier) got a 14 percentage points increase in accuracy. Our results also showed clear improvements in execution times in addition to the obvious secure aspects of encoded data. Additionally, the performance of the proposed method outperformed one of the most commonly used feature reduction methods, Principal Component Analysis.
Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network *** existing machine learning-based IDSs learn patterns from the features extracted from network traf...
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Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network *** existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network *** having been used in the real world widely,the above methods are vulnerable to some types of *** this paper,we propose a novel attack framework,Anti-Intrusion Detection autoencoder(AIDAE),to generate features to disable the *** the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,***,a generative adversarial network is used to learn the flexible prior distribution of the latent *** correlation between continuous and discrete features can be kept by using the proposed training *** conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.
The composition of oxide glasses is characterized by high dimensionality and sparsity, making it challenging to establish high-precision predictive models. Therefore, feature extraction is essential. This study focuse...
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The composition of oxide glasses is characterized by high dimensionality and sparsity, making it challenging to establish high-precision predictive models. Therefore, feature extraction is essential. This study focuses on the optical properties of oxide glasses (refractive index and Abbe number), utilizing autoencoder (AE) and machine learning techniques to achieve automated feature extraction. The results indicate that compared to standalone neural networks (NN), AE-NN transforms unsupervised learning into supervised learning, reducing feature dimensions while improving model accuracy. Specifically, for the refractive index dataset, the dimensionality was reduced from 63 to 25, with a corresponding test set coefficient of determination (R2) of 0.95. For the Abbe number dataset, the dimensionality was reduced from 61 to 30, with a corresponding test set R2 of 0.97, demonstrating the effectiveness of the feature extraction method. Regarding interpretability, analyzing the encoder weight matrix of the AE-NN identified the importance of original features, with Co and Y being the most significant for both refractive index and Abbe number. Additionally, the application of the feature extraction method in machine learning models shows its generality in improving model performance, particularly for nonensemble models such as Support Vector Regression (SVR) or k-Nearest Neighbors (KNN), exhibiting significant accuracy enhancements. Finally, targeting lanthanide glasses, the established predictive model successfully identified novel optical glasses with high refractive index and Abbe number in the La2O3-Bi2O3 and La2O3-Ta2O5 systems, presenting new possibilities for optical component design.
Electrocardiograms can reveal irregular cardiac cycles, i.e., arrhythmia and detecting arrhythmia from its morphology is challenging. This article proposes a novel approach for arrhythmia detection using Gated Recurre...
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Electrocardiograms can reveal irregular cardiac cycles, i.e., arrhythmia and detecting arrhythmia from its morphology is challenging. This article proposes a novel approach for arrhythmia detection using Gated Recurrent Unit based autoencoder with an attention mechanism named AttentivECGRU, followed by fuzzy based threshold selection procedure. The proposed AttentivECGRU autoencoder has three segments namely AttentivECGRU-Encoder, AttentivECGRU-Latent-Attention Space Representation and AttentivECGRU-Decoder which are responsible respectively for generating latent space representation from the input signals, imposing relative score based on the importance of the features and reconstruct the input signals. The proposed method only requires the normal signals in the training phase;however, it can predict both normal and abnormal test signals effectively. Fuzzy based automated threshold selection procedure is also proposed to handle the overlapping nature of the reconstruction loss probability density function of normal and arrhythmia test signals. Results on ECG5000 dataset show that the proposed AttentivECGRU outperformed eleven other state-of-the-art methods, achieving the highest accuracy of 99.14% with Precision 0.9931, Recall 0.9682, and F1-score 0.9804. Confidence Interval tests confirm the statistical superiority of the proposed method over other compared techniques yielding the smallest error rate and error bound. Following the similar trend, the proposed method has produced very high predictive performance on TwoLeadECG dataset as well achieving 98.99% accuracy.
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