Neural Networks Based Feature Selection in Biological Data Analysis

Jacak, Witold; Pröll, Karin; Winkler, Stephan

doi:10.1007/978-3-319-01436-4_5

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The third method is the filter method, which selects features based on general characteristics. Statistical criteria are used to rank features, and the highest-ranking features are chosen [15,16]. Consistency based feature selection (CNF) [16] and Correlation based feature selection (CFS) [17][18][19][20][21][22][23] are the filtering methods employed.…”

Section: Related Workmentioning

confidence: 99%

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

Rameshkumar¹,

Ganesan²,

Merline³

2023

Computer Systems Science and Engineering

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In The Wireless Multimedia Sensor Network (WNSMs) have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets. By utilising portable technologies, it achieves solid and significant results in wireless communication, media transfer, and digital transmission. Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature, moisture content, and other environmental conditions in recent decades. WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appropriate audio and video information. Many video sensor network studies focus on lowering power consumption and increasing transmission capacity, but the main demand is data reliability. Because of the obstacles in the sensor nodes, WMSN is subjected to a variety of attacks, including Denial of Service (DoS) attacks. Deep Convolutional Neural Network is designed with the state-action relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart Agriculture. The Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the network. It reduces the attacker detection delay and improves the detection accuracy. In order to protect the network against DoS assaults, an improved machine learning technique must be offered. An efficient Deep Neural Network approach is provided for detecting DoS in WMSN. The required parameters are selected using an adaptive particle swarm optimization technique. The ratio of packet transmission, energy consumption, latency, network length, and throughput will be used to evaluate the approach's efficiency.

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Section: Related Workmentioning

confidence: 99%

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

Rameshkumar¹,

Ganesan²,

Merline³

2023

Computer Systems Science and Engineering

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Integrating machine learning with QFD for selecting functional requirements in construction automation

Tamayo

Khan

Al‐Hussein

et al. 2020

Int. J. Industrialized Constr.

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An important aspect of the conceptual design is at the customer requirement definition stage, where an optimal number of functional requirements are specified with the application of quality function deployment. To facilitate a systematic specification of functional requirements, state-of-the-art unsupervised machine learning techniques will be introduced in the feature selection of functional requirements. However, the scarcity of references on unsupervised feature selection in the literature reflects the difficulty associated with this topic. At the customer requirement definition phase, three techniques will be proposed for selecting functional requirements, namely: (a) principal component analysis, (b) forward orthogonal search, and (c) Kohonen self-organizing map neural network. These machine learning feature selection techniques address the limitations of current approaches in systematically determining the minimum functional requirements from the mapping of customer requirements in quality function deployment. When applied to the conceptual design of the transportable automated wood wall framing machine that is under development at the University of Alberta, the proposed feature selection techniques have been observed to be: (i) fast, (ii) amenable to small quality function deployment dataset, and (iii) adequate in realizing design objectives. The results presented in this paper can be easily extended to online determination of customer requirements and functional requirements, project management, contract management, and marketing.

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Neural Networks Based Feature Selection in Biological Data Analysis

Cited by 2 publications

References 14 publications

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

Integrating machine learning with QFD for selecting functional requirements in construction automation

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