QRNN: $q$ -Generalized Random Neural Network.

The advancement in technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) has enabled the development of a variety of healthcare applications and services such as Remote Health Monitoring (RHM). Developing an RHM system requires integrating various medical sensors that generate a vast amount of data. The integration of AI and Machine Learning (ML) models in the RHM system for analysis of the medical data can enable the development of a decision support system that can reduce costs and save valuable time for clinicians and caregivers. This thesis is focused on leveraging ML and AI technique sto address the challenges in RHM with a specific emphasis on improving the performance of AI-based decision support systems. This is achieved by addressing limitations in current AI-based models and developing novel Random Neural Network (RNN)-based models for fall detection and epilepsy detection and prediction. Initially the thesis presents a literature review on state-of-the-art machine learning techniques used in remote healthcare solutions, automatic fall activity recognition for elderly populations, and epileptic seizure detection and prediction. Furthermore, the study presents a novel fall detection method based on RNN. The results obtained from the proposed model are compared with traditional classification algorithms such as Artificial Neural Networks, Convolutional Neural Networks, and state-of-the-art fall detection schemes. The proposed RNN-based method demonstrates significant improvements in accuracy compared to other methods. The proposed scheme achieved an overall accuracy of 98% in detecting falls. Additionally, several other parameters such as precision, recall, specificity, and F-measure show that the proposed algorithm has better generalization capabilities when compared with other traditional machine learning schemes. Furthermore, the proposed RNN is also compared with a recent scheme and the obtained results demonstrate the superiority of the proposed scheme. Additionally, the thesis presents two novel RNN-based classification schemes for detecting epileptic seizures. In the first scheme statistical features are extracted from raw EEG data, while the second method incorporates frequency analysis techniques such as Discrete Wavelet Transform (DWT) and Fast Fourier Transform. The performance of these models is evaluated and compared with other classification algorithms, demonstrating the effectiveness of RNN-based models for epilepsy detection. Finally, a novel RNN-based epilepsy prediction model is proposed for classification of EEG data into three classes i.e. ictal, pre-ictal, and inter-ictal. The model pre-processes the EEG data by decomposing the signals into five frequency sub-bands using DWT. After pre-processing, six statistical features are extracted from each frequency sub-band which a reused as an input vector to train the RNN model. The proposed scheme achieves an overall accuracy of 95.66% in seizure prediction, outperforming other state-of-the-art models.

Learning in the feed-forward random neural network: A critical review

In recent years, hydroforming has become the topic of a lot of active research. Researchers have been looking for better procedures and prediction tools to improve the quality of the product and reduce the prototyping cost. Similar to any other metal forming process, hydroforming leads to non-homogeneous plastic deformations of the workpiece. In this paper, a model is developed to predict the amount of deformation caused by hydroforming using random neural networks (RNNs). RNNs learn the behavior of a system from the provided input/output data in a manner similar to the way the human brain does. This is different from the usual connectionist neural network (NN) models which are based on simple functional analyses. Experimental data is collected and used in training as well as testing the RNNs. The RNN models have feedforward architectures and use a generalized learning algorithm in the training process. Multi-layer RNNs with as few as six neurons were used to capture the nonlinear correlations between the input and output data collected from an experimental setup. The RNN models were able to predict the center deflection, the thickness variation, as well as the deformed shape of circular plate specimens with good accuracy.

Since the early 1990s, Random Neural Networks (RNNs) have gained importance in the Neural Networks and Queueing Networks communities. RNNs are inspired by biological neural networks and they are also an extension of open Jackson's networks in Queueing Theory. In 1993, a learning algorithm of gradient type was introduced in order to use RNNs in supervised learning tasks. This method considers only the weight connections among the neurons as adjustable parameters. All other parameters are deemed fixed during the training process. The RNN model has been successfully utilized in several types of applications such as: supervised learning problems, pattern recognition, optimization, image processing, associative memory. In this contribution we present a modification of the classic model obtained by extending the set of adjustable parameters. The modification increases the potential of the RNN model in supervised learning tasks keeping the same network topology and the same time complexity of the algorithm. We describe the new equations implementing a gradient descent learning technique for the model.

Journal Article The Random Neural Network: A Survey Get access Stelios Timotheou Stelios Timotheou * Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London SW7 2BT, UK *Corresponding author: stelios.timotheou05@imperial.ac.uk Search for other works by this author on: Oxford Academic Google Scholar The Computer Journal, Volume 53, Issue 3, March 2010, Pages 251–267, https://doi.org/10.1093/comjnl/bxp032 Published: 30 April 2009 Article history Received: 01 November 2008 Revision received: 17 February 2009 Published: 30 April 2009

Deep Learning methods have proven to be very successful in classifying large data sets of high feature dimensionality. However, their success usually implies very long training times. In this paper we examine learning methods combining the Random Neural Network, a biologically inspired neural network and the Extreme Learning Machine that achieve state of the art classification performance while requiring much shorter training time. The Random Neural Network is a integrate and fire computational model of a neural network whose mathematical structure permits the efficient analysis of large ensembles of neurons. An activation function is derived from the RNN and used in an Extreme Learning Machine. We compare the performance of this combination against the ELM with various activation functions, we reduce the input dimensionality via PCA and compare its performance vs. autoencoder based versions of the RNN-ELM.

The two primary measures of land mine detection performance are the probability of detection P/sub d/ and the probability of false alarm P/sub fa/. These two measures are highly inter-dependent and must be evaluated together. The relationship between the two probabilities directly affects the overall performance of the sensor in the field. In this paper we introduce a novel false alarm non-parametric filter based on the random neural network (RNN) model and the /spl delta/-technique. The study is based on mine detection using electromagnetic induction (EMI) sensors. The minefield data are pre-processed via the /spl delta/-technique before applying it to the RNN. The RNN has a predefined structure that tries to implement a mapping close enough in some precise sense to the discrimination function between non-mine and mine patterns. Limited numbers of non-mine and mine patterns, extracted from a small calibration area for a certain minefield provided by DARPA, are used for training the RNN. We show that the RNN gives effective decisions on patterns measured on other locations using different EMI sensor. The results show that the RNN produces a probability of detection up to 100 per cent with a substantial reduction of false alarms over the /spl delta/-technique (up to 40 per cent false alarm filtering).

Artificial Neural Networks (ANNs)-based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large-scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running the bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network, a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of being a spiking neural network. This is demonstrated on a number of real-world classification datasets.

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

Biological nervous systems consist of networks of diverse, sophisticated information processors in the form of neurons of different classes. In most artificial neural networks (ANNs), neural computation is abstracted to an activation function that is usually shared between all neurons within a layer or even the whole network; training of ANNs focuses on synaptic optimization. In this paper, we propose the optimization of neuro-centric parameters to attain a set of diverse neurons that can perform complex computations. Demonstrating the promise of the approach, we show that evolving neural parameters alone allows agents to solve various reinforcement learning tasks without optimizing any synaptic weights. While not aiming to be an accurate biological model, parameterizing neurons to a larger degree than the current common practice, allows us to ask questions about the computational abilities afforded by neural diversity in random neural networks. The presented results open up interesting future research directions, such as combining evolved neural diversity with activity-dependent plasticity.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

Critical infrastructure now faces greater vulnerabilities and a higher risk of cyberattacks as a result of the (IIoT) quick expansion. The security and dependability of industrial systems must be ensured by identifying and thwarting these threats. In this paper, we use a hybrid approach of deep learning and RNN called hybrid deep random neural network (HDRNN) to offer a novel method of identifying cyber-attacks in the IIoT.The proposed HDRNN model combines the benefits of random neural networks with deep learning to improve the detection of IIoT cyberattacks. The deep learning component makes use of deep neural networks' capacity to extract intricate features from unstructured data, while the random neural network component offers robustness and adaptability to manage changing attack patterns.Realistic threats and benchmark datasets such as UNSW-NB15and DS2OS are used in experimental evaluations. High accuracy, precision, and recall rates are attained by the model, which successfully detects a variety of assaults including infiltration, data manipulation, and denial of service.The suggested HDRNN model offers a promising approach for improving the security of IIoT systems by precisely identifying cyber-attacks in real-time. The model's hybrid nature enables enhanced detection capabilities, adaptability to changing attack patterns, and a reduction in false positives, enabling efficient threat mitigation and protecting crucial infrastructure in the IIoT context.

Recent work demonstrated the value of multi clusters of spiking Random Neural Networks (RNN) with dense soma-to-soma interactions in deep learning. In this paper we go back to the original simpler structure and we investigate the power of single RNN cells for deep learning. First, we consider three approaches with the single cells, twin cells and multi-cell clusters. This first part shows that RNNs with only positive parameter can conduct convolution operations similar to those of the convolutional neural network. We then develop a multi-layer architecture of single cell RNNs (MLSRNN), and show that this architecture achieves comparable or better classification at lower computation cost than conventional deep-learning methods.

The random neural network (RNN) is a mathematical model for an “integrate and fire” spiking network that closely resembles the stochastic behavior of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods.

In recent years, neural networks have achieved remarkable progress in various fields and have also drawn much attention in applying them on scientific problems. A line of methods involving neural networks for solving partial differential equations (PDEs), such as Physics-Informed Neural Networks (PINNs) and the Deep Ritz Method (DRM), has emerged. Although these methods outperform classical numerical methods in certain cases, the optimization problems involving neural networks are typically non-convex and non-smooth, which can result in unsatisfactory solutions for PDEs. In contrast to deterministic neural networks, the hidden weights of random neural networks are sampled from some prior distribution and only the output weights participate in training. This makes training much simpler, but it remains unclear how to select the prior distribution. In this paper, we focus on Barron type functions and approximate them under Sobolev norms by random neural networks with clear prior distribution. In addition to the approximation error, we also derive bounds for the optimization and generalization errors of random neural networks for solving PDEs when the solutions are Barron type functions.