The Random Neural Network in a neurocomputing application for Web search

Serrano, Will; Gelenbe, Erol

doi:10.1016/j.neucom.2017.08.075

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…User relevance is learned iteratively from user feedback, independently using either gradient descent to reorder the results to the minimum distance to the user Relevant center point or reinforcement learning that rewards the relevant dimensions and "punishes" the less relevant ones (Figure 13). The result relevance is calculated by applying an innovative cost function based on the division of a query into a multidimensional vector that weights its dimension terms with different relevance parameters [177]. User relevance is learned iteratively from user feedback, independently using either gradient descent to reorder the results to the minimum distance to the user Relevant center point or reinforcement learning that rewards the relevant dimensions and "punishes" the less relevant ones (Figure 13).…”

Section: The Random Neural Network In Web Searchmentioning

confidence: 99%

Neural Networks in Big Data and Web Search

Serrano

2018

Data

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As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.

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Section: The Random Neural Network In Web Searchmentioning

confidence: 99%

Neural Networks in Big Data and Web Search

Serrano

2018

Data

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“…The positive signals are called excitatory , while the negative signals are called inhibitory . It has been used in many applications, including image and video compression [ 30 , 31 ], the recognition of tumours from Magnetic Resonance Images of the human brain [ 32 ], toxicity prediction of chemical compounds [ 33 ], web search [ 34 ], and network routing and cloud management [ 35 , 36 , 37 , 38 , 39 ]. It is a special instance of the family of stochastic networks known as G-Networks [ 40 , 41 , 42 , 43 , 44 , 45 ] which have many different areas of application.…”

Section: Reinforcement Learning and The Random Neural Networkmentioning

confidence: 99%

Interest Forwarding in Named Data Networking Using Reinforcement Learning

Akinwande

2018

Sensors

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In-network caching is one of the key features of information-centric networks (ICN), where forwarding entities in a network are equipped with memory with which they can temporarily store contents and satisfy en route requests. Exploiting in-network caching, therefore, presents the challenge of efficiently coordinating the forwarding of requests with the volatile cache states at the routers. In this paper, we address information-centric networks and consider in-network caching specifically for Named Data Networking (NDN) architectures. Our proposal departs from the forwarding algorithms which primarily use links that have been selected by the routing protocol for probing and forwarding. We propose a novel adaptive forwarding strategy using reinforcement learning with the random neural network (NDNFS-RLRNN), which leverages the routing information and actively seeks new delivery paths in a controlled way. Our simulations show that NDNFS-RLRNN achieves better delivery performance than a strategy that uses fixed paths from the routing layer and a more efficient performance than a strategy that retrieves contents from the nearest caches by flooding requests.

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“…Neurocomputing originally referred to hardware that mimics neuroscience structures to create models of the nervous system [1]. This concept is further extended to computing systems that operate using bioinspired computing models, including neural networks [2] and deep-learning networks [3]. In recent years, widespread research on neurocomputing technology has been driven by the rapid development of cognitive learning applications and the limited computing power of the Von Neumann architecture.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, research has not explained the current situations and positions of specific technological fields on a technology map or identified technology hotspots from a comprehensive perspective. Furthermore, development of neurocomputing technology involves numerous technological fields [2,14,15], and such technology has limitless development potential. A hotspot refers to a study repeatedly cited in other different studies [16].…”

Section: Introductionmentioning

confidence: 99%

Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends

Chang¹,

Fan²

2020

Sustainability

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In recent years, development in the fields of big data and artificial intelligence has given rise to interest among scholars in neurocomputing-related applications. Neurocomputing has relatively widespread applications because it is a critical technology in numerous fields. However, most studies on neurocomputing have focused on improving related algorithms or application fields; they have failed to highlight the main technology hotspots and development trends from a comprehensive viewpoint. To fill the research gap, this study adopts a new viewpoint and employs technological fields as its main subject. Neurocomputing patents are subjected to network analysis to construct a neurocomputing technology hotspot. The results reveal that the neurocomputing technology hotspots are algorithms, methods or devices for reading or recognizing printed or written characters or patterns, and digital storage characterized by the use of particular electric or magnetic storage elements. Furthermore, the technology hotspots are discovered to not be clustered around particular fields but, rather, are multidisciplinary. The applications that combine neurocomputing with digital storage are currently undergoing the most extensive development. Finally, patentee analysis reveal that neurocomputing technology is mainly being developed by information technology corporations, thereby indicating the market development potential of neurocomputing technology. This study constructs a technology hotspot network model to elucidate the trend in development of neurocomputing technology, and the findings may serve as a reference for industries planning to promote emerging technologies.

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The Random Neural Network in a neurocomputing application for Web search

Cited by 11 publications

References 15 publications

Neural Networks in Big Data and Web Search

Neural Networks in Big Data and Web Search

Interest Forwarding in Named Data Networking Using Reinforcement Learning

Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends

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