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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
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Xinxin Zhou et al.
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Amir Basati et al.
Summary: This paper presents a new and lightweight architecture for intrusion detection in IoT devices based on Parallel Deep Auto-Encoder (PDAE). By separating features using local and surrounding information, the accuracy of the model is improved while reducing the number of parameters and resource requirements. The effectiveness of the proposed model is evaluated and it outperforms state-of-the-art algorithms in terms of both accuracy and performance.
INFORMATION SCIENCES
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Yilong Ren et al.
Summary: Traffic prediction is crucial for traffic guidance and control, and it serves as a valuable decision-making tool for travelers to plan their routes and avoid congested roads. To tackle the challenge of short-term traffic prediction for special events, a traffic burst-sensitive model (TBSM) is proposed, which outperforms traditional machine learning and deep learning approaches according to experimental results.
KNOWLEDGE-BASED SYSTEMS
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Ya Li et al.
Summary: This study utilizes machine learning and artificial intelligence algorithms to design an intrusion detection system that uses an artificial neural network to detect abnormal behaviors in a medical IoT system. By employing the butterfly optimization algorithm to select key features of network traffic data, the accuracy reached 93.27%. The proposed algorithm outperformed other algorithms previously used for the same purpose.
WIRELESS PERSONAL COMMUNICATIONS
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Qiufen Ni et al.
Summary: Community partition is crucial in social networks, particularly in the face of network growth and increased data and applications. This study focuses on the community partition problem under the Linear Threshold (LT) model, aiming to maximize influence propagation within each community. The proposed continuous greedy algorithm, which exploits the properties of the relaxed function, is able to effectively improve influence spread and accuracy of the community partition. Theoretical analysis demonstrates a 1 - 1/e approximation ratio for the algorithm, while extensive experiments on real-world online social networks datasets validate its performance.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
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Nitish Chopra et al.
Summary: The Golden Jackal Optimization (GJO) algorithm, inspired by the hunting behavior of golden jackals, utilizes prey searching, enclosing, and pouncing steps mathematically to solve challenging engineering problems with unidentified search spaces.
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Kunrui Cao et al.
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IEEE INTERNET OF THINGS JOURNAL
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T. V. Ramana et al.
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COMPUTER COMMUNICATIONS
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IEEE TRANSACTIONS ON AUTOMATIC CONTROL
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Jing Chen et al.
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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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Bin Li et al.
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