Wafer surface reconstruction from top–down scanning electron microscope images

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.

Section: Applications Of Random Neural Networkmentioning

confidence: 99%

Random Neural Network Methods and Deep Learning

Yin¹

2019

“…Some of its other applications can be found in [1,3,4,9,12,25,26,89,[101][102][103]157,174] and several papers reviewing this subject can be found in the papers of the special issue in [49].…”

Section: The Random Neural Networkmentioning

confidence: 99%

“…The Random Neural Network was reported in Erol's earlier work and its theoretical foundations were developed in [44,47,48,78,79,97,132]. Some of its other applications can be found in [1,3,4,9,12,25,26,89,[101][102][103]157,174] and several papers reviewing this subject can be found in the papers of the special issue in [49].…”

Section: The Random Neural Networkmentioning

confidence: 99%

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

Caglayan¹

2016

We focus on Erol Gelenbe's scientific and technical contributions to probability models in the computer and information sciences, but limit our survey to the last fifteen years. We start with a brief overview of his work as a single author, as well as his work in collaboration with over 200 co-authors. We discuss some of his recent and innovative work regarding a new probability model that represents Intermittent Energy Sources for Computing and Communications, introducing Energy Packet Networks which are a probabilistic representation of the flow, storage and consumption of electrical energy at the microscopic level (in electronic chips), and at the macroscopic level (e.g. in buildings or data centers) and for its routing and dynamic usage by consuming units (such as computer elements, chips or machines). We next discuss his work on designing computer and communication systems that parsimoniously use energy in order to achieve a satisfactory level of quality of service (QoS). Trade-offs between system QoS and energy consumption are also considered. Then we turn to Prof. Gelenbe's pioneering work on Autonomic Communications and the design and implementation of CPN, the Cognitive Packet Network, and we also briefly review his spiking random neural network that was used in CPN. This is followed by a brief review of work that he conducted since 1999 on human evacuation from dangerous or catastrophic environments, and the design of technology driven Emergency Management Systems. His research since the late 2000s on Gene Regulatory Networks is then covered together with its application to the detecting possible disease from microarray data. Finally, we briefly discuss some novel analytical models that he developed in this period with publications appearing in journals of physics and applied mathematics.

“…The RNN is a probabilistic computational model which was inspired by the spiking behavior of neurons, and which has a well-developed mathematical theory [23,24,31] and efficient learning algorithms for recurrent networks [25,33,38]. The RNN has been successfully applied to several problems in engineering and information sciences, including pattern recognition [9,10,30,34], classification [32], image/video processing and compression [15][16][17]29,37], DoS attack detection [35,51,52], and others that can be found in numerous reviews on the subject [26,27,61].…”

Section: Introductionmentioning

confidence: 99%

Detecting Network-Unfriendly Mobiles With the Random Neural Network

Abdelrahman

2016

Mobile networks are universally used for personal communications, but also increasingly used in the Internet of Things and machine-to-machine applications in order to access and control critical services. However, they are particularly vulnerable to signaling storms, triggered by malfunctioning applications, malware or malicious behavior, which can cause disruption in the access to the infrastructure. Such storms differ from conventional denial of service attacks, since they overload the control plane rather than the data plane, rendering traditional detection techniques ineffective. Thus, in this paper we describe the manner in which storms happen and their causes, and propose a detection framework that utilizes traffic measurements and key performance indicators to identify in real-time misbehaving mobile devices. The detection algorithm is based on the random neural network which is a probabilistic computational model with efficient learning algorithms. Simulation results are provided to illustrate the effectiveness of the proposed scheme.