In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potent...
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Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, wh...
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In power distribution systems, common issues such as voltage fluctuations, voltage instability, current harmonics, and power imbalances often arise, negatively impacting the stability and power quality (PQ) of the pow...
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In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness ...
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In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The
This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neut...
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To tackle the energy crisis and climate change,wind farms are being heavily invested in across the *** China's coastal areas,there are abundant wind resources and numerous offshore wind farms are being *** secure ...
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To tackle the energy crisis and climate change,wind farms are being heavily invested in across the *** China's coastal areas,there are abundant wind resources and numerous offshore wind farms are being *** secure operation of these wind farms may suffer from typhoons,and researchers have studied power system operation and resilience enhancement in typhoon ***,the intricate movement of a typhoon makes it challenging to evaluate its spatial-temporal *** published papers only consider predefined typhoon trajectories neglecting *** address this challenge,this study proposes a stochastic unit commitment model that incorporates high-penetration offshore wind power generation in typhoon *** adopts a data-driven method to describe the uncertainties of typhoon trajectories and considers the realistic anti-typhoon mode in offshore wind farms.A two-stage stochastic unit commitment model is designed to enhance power system resilience in typhoon *** formulate the model into a mixed-integer linear programming problem and then solve it based on the computationally-efficient progressive hedging algorithm(PHA).Finally,numerical experiments validate the effectiveness of the proposed method.
Public speaking plays an important role in various domains and most of the people struggle with it due to lack of experience and fear. To address this issue, we propose to develop a comprehensive public speaking model...
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This paper proposes a method for estimating the State of Charge (SOC) of lithium iron phosphate (LFP) batteries based on an experimental thermal model that considers internal battery temperatures. The proposed algorit...
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Driven by the surge in code generation using large language models (LLMs), numerous benchmarks have emerged to evaluate these LLMs capabilities. We conducted a large-scale human evaluation of HumanEval and MBPP, two p...
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The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse ...
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