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2020 Index IEEE Transactions on Cognitive and Developmental Systems Vol. 12
2020
IEEE Transactions on Cognitive and Developmental Systems
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Wilaiprasitporn, T., +, TCDS Sept. 2020 486-496
An Associative-Memory-Based Reconfigurable Memristive Neuromorphic
System With Synchronous Weight Training. ...
., +, TCDS June 2020 311-322
Memory Mechanisms for Discriminative Visual Tracking Algorithms With
Deep Neural Networks. ...
Muscle Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection. Zhong, S., +, TCDS Sept. 2020 601-617 ...
doi:10.1109/tcds.2020.3044690
fatcat:yfo6c366aramfdltqegqyqphbq
Brain MRI ImageClassification for Cancer Detection using Deep Wavelet Autoencoder based Deep Neural Network
2019
IEEE Access
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Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with ...
The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods ...
[22] proposed a MRI fuzzy segmentation with neural network optimization for brain tumor detection. ...
doi:10.1109/access.2019.2902252
fatcat:7dthl7mb45h4fgsbezgsjfgxbu
Transduction Recursive Auto-Associative Memory: Learning Bilingual Compositional Distributed Vector Representations of Inversion Transduction Grammars
2014
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation
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We introduce TRAAM, or Transduction RAAM, a fully bilingual generalization of Pollack's (1990) monolingual Recursive Auto-Associative Memory neural network model, in which each distributed vector represents ...
Training of TRAAM drives both the autoencoder weights and the vector representations to evolve, such that similar bilingual constituents tend to have more similar vectors. ...
In RAAM, which stands for Recursive Auto-Associative Memory, using feature vectors to characterize constituents at every level of a parse tree has the advantages that (1) the entire context of all subtrees ...
doi:10.3115/v1/w14-4013
dblp:conf/ssst/AddankiW14
fatcat:3uy67qmcnneofmoroxbhxv77uq
Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks
2019
Sustainability
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An autoencoder was used to extract the main features underlying the time-series data in historical driving memory. ...
This paper focuses on the impact of driving memory on car-following behavior, particularly, historical driving memory represents certain types of driving regimes and drivers' maneuver in coordination with ...
Figure 4 . 4 Schematic diagram of the long short-term memory (LSTM) cell and LSTM's mechanism. ...
doi:10.3390/su11236755
fatcat:2o62mksoobdr7f6sfqibr4om4m
Deep Learning Anomaly Detection for Cellular IoT with Applications in Smart Logistics
2021
IEEE Access
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v
y (2k+1)
TABLE 1 . 1 ADM-EDGE memory resource utilization for the ADM-EDGE autoencoder with one hidden layer. ...
1452
TABLE 2 . 2 ADM-EDGE memory resource utilization for ADM-EDGE
autoencoders with different number of hidden layers (HL). ...
doi:10.1109/access.2021.3072916
fatcat:xz3zhvlyzzg23eju2ppo5uiace
Special Collection of Extended Selected Papers on "Novel Research Results Presented in The 12th International Conference on Information, Intelligence, Systems and Applications (IISA2021), 12–14 July 2021, Chania, Crete, Greecehttps://easyconferences.eu/iisa2021/"
2021
International Journal of Intelligent Decision Technologies
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Information and Multimedia Systems, with an increasing level of Intelligence, are constantly being developed that incorporate these advances. ...
Specifically, the fourth paper, by Konstantina Chrysafiadi and Evangelia-Aikaterini Tsichrintzi, is on "An Intelligent Fuzzy-based Emergency Alert Generation for People with Episodic Memory Decline Problems ...
Specifically, the system protects people with episodic memory decline problems or lapses of atten-tion from dangerous situations that may be due to their memory disorder and allows them to complete everyday ...
doi:10.3233/idt-210008
fatcat:isljnet4wje47mhxsvwvjh2nny
Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review
2021
Asian Journal of Research in Computer Science
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[26] used a hybrid deep autoencoder (with a Bayesian fuzzy clustering segmentation approach) to develop a brain tumor classification model. ...
A combination of wavelet with FCNN and autoencoder was found to be more successful for better tumor segmentation. ...
doi:10.9734/ajrcos/2021/v10i230239
fatcat:g5v6pxw375bozfimiekxbo4aya
An entropic associative memory
2021
Scientific Reports
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The system has been tested with a set of memory recognition and retrieval experiments with complete and severely occluded images. ...
AbstractNatural memories are associative, declarative and distributed, and memory retrieval is a constructive operation. ...
Further work by Ritter and collaborators on Morphological Associative Memory 15, 16 and Implicative Fuzzy Associative Memories 17, 18 provided the basis for practical applications. ...
doi:10.1038/s41598-021-86270-7
pmid:33767252
fatcat:mhebiwhstvahfklqpv6w32f2la
Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems
2022
Computers Materials & Continua
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The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies. ...
With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. ...
In [36] , the integration of fuzzy logic with cross-correlation was proposed to improve the detection precision. ...
doi:10.32604/cmc.2022.018773
fatcat:itxfgifm5rgrhiqpqqjc5ckfmu
Driving Maneuver Classification Using Domain Specific Knowledge and Transfer Learning
2021
IEEE Access
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Hence, it is unable to measure the driving risk associated with a particular maneuver. ...
Besides, we have a plan to add a neural attention mechanism with the proposed model to focus on a subset of features of the time series dataset. ...
doi:10.1109/access.2021.3089660
fatcat:6lkr7bg5lfdnlfcxzl2aya5egu
Autoencoder-based Unsupervised Intrusion Detection using Multi-Scale Convolutional Recurrent Networks
[article]
2022
arXiv
pre-print
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Short-Term Memory (LSTM) based Autoencoder Network to process the temporal features. ...
To address this, we propose a unified Autoencoder based on combining multi-scale convolutional neural network and long short-term memory (MSCNN-LSTM-AE) for anomaly detection in network traffic. ...
Our IDS uses a novel Autoencoder combining multiscale Convolutional Neural Network with Long Short-Term Memory (MSCNN-LSTM) in the Autoencoder architecture to capture spatial-temporal dependencies in traffic ...
arXiv:2204.03779v1
fatcat:rvxxtx5mmjdhvifwojwbqojhkm
Fundamentals of Neural Networks
2021
International Journal for Research in Applied Science and Engineering Technology
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A brief introduction to Neuro-Fuzzy and its applications with a comprehensive review of NN technological advances is provided. ...
The purpose of this study is to familiarise the reader with the foundations of neural networks. ...
Neuro-fuzzy systems are systems that combine fuzzy logic with neural networks. To perform better, these hybrid systems might combine the benefits of neural networks with fuzzy logic. ...
doi:10.22214/ijraset.2021.37362
fatcat:2ebyvnxsj5djbewbd4ii4ubl4y
Deep Learning Anomaly Detection for Cellular IoT with Applications in Smart Logistics
[article]
2021
arXiv
pre-print
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Among several approaches to cope with these challenges, data-based methods rooted in deep learning (DL) are receiving an increased interest. ...
The proposed architecture embeds autoencoder based anomaly detection modules both at the IoT devices (ADM-EDGE) and in the mobile core network (ADM-FOG), thereby balancing between the system responsiveness ...
TABLE I I ADM-EDGE MEMORY RESOURCE UTILIZATION FOR THE ADM-EDGE AUTOENCODER WITH ONE HIDDEN LAYER. ...
arXiv:2102.08936v2
fatcat:sqxdaz2dujbvvohxqgj4aqpdfi
Single-Cell Transcriptome Data Clustering via Multinomial Modeling and Adaptive Fuzzy K-Means Algorithm
2020
Frontiers in Genetics
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In the learned low-dimensional latent space, we proposed an adaptive fuzzy k-means algorithm with entropy regularization to perform soft clustering. ...
In this paper, we combined the deep autoencoder technique with statistical modeling and developed a novel and effective clustering method, scDMFK, for single-cell transcriptome UMI count data. ...
Therefore, instead of designing a specific non-linear association expression or bayesian priors, like mixture dirichlet distribution, we utilized deep autoencoders to approximate the underlying manifold ...
doi:10.3389/fgene.2020.00295
pmid:32362908
pmcid:PMC7180207
fatcat:osiaxajdpzfvncckxg6x435rlq
Eye movement behavior identification for Alzheimer's disease diagnosis
2018
Journal of Integrative Neuroscience
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With this information a set of denoising sparse-autoencoders are trained and a deep neural network is built using the trained autoencoders and a softmax classifier that identifies subjects with Alzheimer's ...
disease with 89.78% accuracy. ...
Initially, people experience memory loss and confusion, which may be mistaken for the kinds of memory changes that are sometimes associated with normal aging [1] . ...
doi:10.31083/j.jin.2018.04.0416
fatcat:2rl7wpmpfffn7d7rrijr65gbuq
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