Article
Computer Science, Artificial Intelligence
Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis
Summary: This article introduces a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA). The framework fully utilizes local information from network data by sampling a new type of contrastive instance pair, capturing the relationship between each node and its neighboring substructure in an unsupervised manner. A well-designed graph neural network (GNN)-based contrastive learning model is proposed to learn informative embeddings from high-dimensional attributes and local structures, measuring the agreement of each instance pair with its outputted scores.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Chenyu Wang, Ding Wang, Yi Tu, Guoai Xu, Huaxiong Wang
Summary: Despite years of research, designing a practical multi-factor user authentication scheme for wireless sensor networks is still challenging due to the security versus efficiency dilemma. Common security failures include smart card loss attacks and node capture attacks, with the latter receiving less attention compared to the former. This article systematically explores node capture attacks, providing insights for secure user authentication scheme design in WSNs.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Zhiwei Guo, Keping Yu, Zhihan Lv, Kim-Kwang Raymond Choo, Peng Shi, Joel J. P. C. Rodrigues
Summary: This paper presents a deep-federated-learning-based framework for securing POI microservices in cyber-physical systems. The system architecture isolates the cloud center from accessing user data on edge nodes and uses an interactive training mechanism. The proposed approach achieves optimal scheduling performance and demonstrates its practical utility.
IEEE WIRELESS COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaozheng Jin, Shaoyu Lu, Jiguo Yu
Summary: This article proposes a fault-tolerant consensus control scheme based on neural network and adaptive control techniques, which effectively addresses the issues of actuator faults, disturbed and faulty networks in nonlinear multiagent systems, and ensures bounded consensus of the system.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
A. S. Albahri, O. S. Albahri, A. A. Zaidan, Alhamzah Alnoor, H. A. Alsattar, Rawia Mohammed, A. H. Alamoodi, B. B. Zaidan, Uwe Aickelin, Mamoun Alazab, Salem Garfan, Ibraheem Y. Y. Ahmaro, M. A. Ahmed
Summary: Due to limitations of Pythagorean fuzzy and intuitionistic fuzzy sets, a new fuzzy set called q-rung orthopair fuzzy set (q-ROFS) has been developed to address restrictions in multicriteria decision making (MCDM). This study extended two MCDM methods under q-ROFS, resulting in q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) and q-rung orthopair fuzzy decision by opinion score method (q-ROFDOSM). The methodology involved two phases and was tested on a COVID-19 vaccine distribution case study, showing systematic ranking and sensitivity analysis results.
COMPUTER STANDARDS & INTERFACES
(2022)
Article
Computer Science, Artificial Intelligence
Grzegorz Dudek, Pawel Pelka, Slawek Smyl
Summary: The study presents a hybrid and hierarchical deep learning model for midterm load forecasting, combining ETS, LSTM, and ensembling. The model demonstrates high performance in load prediction and competes with both classical and state-of-the-art machine learning models.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Mohanad Sarhan, Siamak Layeghy, Marius Portmann
Summary: This paper proposes and evaluates standard NIDS feature sets based on the NetFlow network meta-data collection protocol and system to address the lack of standard feature sets in current NIDS datasets. The NetFlow-based NIDS feature set allows for a fair comparison of ML-based network traffic classifiers across different NIDS datasets, potentially bridging the gap between academic research and practical deployment of such systems.
MOBILE NETWORKS & APPLICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Liang Zhao, Hongmei Chai, Yuan Han, Keping Yu, Shahid Mumtaz
Summary: This article presents a collaborative vehicle data correction method for correcting V2X data errors to enhance driving safety. Experimental results demonstrate the effectiveness of the method in detecting and correcting erroneous data.
IEEE TRANSACTIONS ON RELIABILITY
(2022)
Article
Computer Science, Artificial Intelligence
Shengliang Peng, Shujun Sun, Yu-Dong Yao
Summary: This article provides a comprehensive survey of state-of-the-art deep learning-based modulation classification algorithms, focusing on the techniques of signal representation and data preprocessing utilized in these algorithms. The algorithms can be categorized into four groups based on how the received signal is represented, and the advantages as well as disadvantages of each representation method are summarized and discussed.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jianhui Wang, Hongkang Zhang, Kemao Ma, Zhi Liu, C. L. Philip Chen
Summary: This paper addresses the issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis. A new control approach is proposed, which effectively compensates for the hysteresis effect and bounds the tracking error.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Nitin Rathi, Kaushik Roy
Summary: This article proposes a low-latency deep spiking network called DIET-SNN, which optimizes the membrane leak and firing threshold to reduce latency while maintaining competitive accuracy. Through evaluation and comparative experiments, DIET-SNN shows excellent performance in image classification tasks with efficient computational capabilities.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Luo, Zhigang Liu, Long Jin, Yue Zhou, MengChu Zhou
Summary: This study achieves highly accurate community detectors by adjusting the scaling factor in the SNMF model, leading to significant accuracy gains in community detection over the state-of-the-art detectors.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Guoqiang Tan, Zhanshan Wang
Summary: This paper proposes a method for estimating the reachable set of delayed Markovian jump neural networks with bounded disturbances. By using an improved inequality and an augmented Lyapunov-Krasovskii functional, an accurate ellipsoidal description of the reachable set is obtained, and the effectiveness of the method is demonstrated through simulation results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Benjamin Cramer, Yannik Stradmann, Johannes Schemmel, Friedemann Zenke
Summary: Spiking neural networks serve as the basis for versatile and power-efficient information processing in the brain, and optimization techniques allow for complex functional networks to be instantiated in-silico. To compare the computational performance of these networks, spike-based classification data sets have been introduced, highlighting the importance of utilizing spike timing information for accurate classification.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Zhiwei Guo, Keping Yu, Ali Kashif Bashir, Di Zhang, Yasser D. Al-Otaibi, Mohsen Guizani
Summary: This article presents a deep information fusion-based point-of-interest (POI) scheduling system in the mobile social network (MSN) environment, implemented through an edge-cloud deep hybrid sensing (PS-MSN) framework. The system integrates multisource information, improves feature expression abilities, and demonstrates excellent performance in experiments.
Article
Computer Science, Artificial Intelligence
Zhipeng Tan, Jing Chen, Qi Kang, MengChu Zhou, Abdullah Abusorrah, Khaled Sedraoui
Summary: The study proposes a dynamic embedding projection-gated convolutional neural network (DEP-CNN) for multi-class and multi-label text classification. Using the dynamic embedding projection gate (DEPG) allows better capture of word information and control over the integration of context information.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ali Kazemy, James Lam, Xian-Ming Zhang
Summary: This article investigates the problem of event-triggered synchronization of master-slave neural networks, assuming stochastic deception attacks on the communication channels. Two discrete event-triggered mechanisms and static output feedback are introduced to reduce data transmission. By using the Lyapunov-Krasovskii functional method, sufficient conditions for synchronization are derived in terms of linear matrix inequalities, facilitating the design of suitable controllers. A numerical example is presented to demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ce Li, Chunyu Xie, Baochang Zhang, Jungong Han, Xiantong Zhen, Jie Chen
Summary: A new method named memory attention networks (MANs) is proposed to address the complex variations of skeleton joints in 3-D spatiotemporal space for action recognition. By using the temporal attention recalibration module (TARM) and spatiotemporal convolution module (STCM), and introducing the collaborative memory fusion module (CMFM), the performance is significantly improved.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Fanrong Qu, Engang Tian, Xia Zhao
Summary: This article investigates the chance-constrained $H_{infinity}$ state estimation problem for a class of time-varying neural networks subject to measurements degradation and randomly occurring deception attacks. A novel energy-constrained deception attack model is proposed, and the explicit expression of the estimator gains is derived by solving a minimization problem. Numerical examples and a practical three-tank system are used to demonstrate the correctness and effectiveness of the proposed estimation scheme.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Faliang Huang, Xuelong Li, Changan Yuan, Shichao Zhang, Jilian Zhang, Shaojie Qiao
Summary: This paper proposes a novel model named AEC-LSTM to improve LSTM networks by integrating emotional intelligence (EI) and attention mechanism, aiming to enhance the learning of text sentiment features and improve sentiment classification performance effectively.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
