Deep Smart Contract Intent Detection - IEEE Xplore
Abstract—In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of
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Abstract—In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of
To address this gap, we propose SMARTINTENTNN (Smart. Contract Intent Neural Network a deep learning model designed to automatically detect development
A deep-learning algorithm based reconstructive model has been studied for detecting DDoS attacks in the com- munication networks of smart grid. Using the class
Schmidhuber [34] reviews Deep. Learning in neural networks. 1.2. Summary of contributions. This paper utilizes the Intelligent Internet Search Assistant. (ISA)
Keywords: under-display camera (UDC organic light-emitting diode (OLED signal-to-noise ratio (SNR modulation transfer function (MTF deep neural network
Smart manufacturing refers to a new manufacturing paradigm where manufacturing machines are fully connected through wire- less networks, monitored by sensors
10 Sep 2026 For analytical clarity, the contributions can be organized into four interrelated thematic areas: Smart forecasting beyond finance; deep
For effective farm management, a variety of techniques are utilized, including IoT, cloud computing, AI, machine learning, deep learning, big data, etc. Deep
15 Mar 2023 The aim of this paper was to propose a deep learning approach for the recognition of ambulation and fitness activities using smart insoles
The performance of our developed deep reinforcement learning controller was evaluated against a traditional controller that employed an outdoor air temperature
1 Jul 2024 Presently, the application of cutting-edge artificial intelligence (AI) technology is changing many aspects of how industrial organizations
DeepFusion is a deep learning-based vulnerability detection tool for Ethereum smart contracts, which adopts a supervised learning paradigm. It can detect five
Leveraging Deep Learning for smart sample alignment. A.V. Luebben1, J. Luebben1. 1Bruker AXS, Ă–stliche RheinbrĂĽckenstraĂźe 49, 76187. Karlsruhe Anna.Luebben
24 Feb 2022 This idea brings about a generalization of the Smart Grid concept to Smart Multi. Energy Grids [22] that lies on the interaction between
Deep Air – A Smart City AI Synthetic ta Digital Twin lving the alability ta oblems. Esteve ALMIRALL a,1, Davide CALLEGARO a, Peter BRUINS a Mar SANTAMARÍA b
Keywords: Bi-LSTM, Smart Home, Deep Learning, Class Imbalance, Focal Loss, Activity Recognition. Posted Date: March 20th, 2026. DOI: https doi.org/
31 Mar 2025 IoT networks' security vulnerabilities expose smart city infrastructure to cyber-attacks. A hybrid deep learning (DL) model introduced in
FastFlow: Accelerating Deep Learning Model Training with Smart. Offloading of Input Data Pipeline. Taegeon Um Samsung Research [email protected]
Indonesia. Cite as: Radja Leba SM, Nggaruaka T, Butarbutar R. Smart Teaching in Rural Indonesia: Harnessing AI-Assisted Deep Learning for Teacher
9 Apr 2025 Consequently, the Industrial Control Systems (ICS) of smart grids have become more exposed to cyber risks due to enhanced network integration