Article
Computer Science, Artificial Intelligence
Weixiang Zhou, Jun Fu, Huaicheng Yan, Xin Du, Yueying Wang, Hua Zhou
Summary: This article investigates the problem of path following for underactuated unmanned surface vehicles (USVs) subject to state constraints. A control algorithm is proposed that combines the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The algorithm consists of three modules: guidance law, dynamic controller, and event triggering. By introducing the guidance-based path-following (GBPF) principle and using a critic neural network (NN) to approximate the cost function, the proposed approach can handle the "singularity" problem and guarantee approximate optimal performance. The simulation results and experimental validation demonstrate the effectiveness of the approach.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Dinh C Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia Dobre, Won-Joo Hwang
Summary: Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have enabled the use of artificial intelligence (AI) in smart healthcare. Federated Learning (FL), as a distributed collaborative AI paradigm, is particularly attractive for smart healthcare due to its ability to train AI models without sharing raw data. This survey provides a comprehensive overview of the recent advances in FL, its motivations, requirements, and applications in key healthcare domains.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Yun Li, Shichao Xia, Mengyan Zheng, Bin Cao, Qilie Liu
Summary: In order to improve mobile users' service experience, careful design of offloading policy is necessary in mobile cloud computing. This paper investigates the offloading policy in heterogeneous wireless networks by formulating the mobile users' workload offloading problem and proposing optimization frameworks and methods. Experimental results show the effectiveness of the proposed methods for deterministic and random WiFi connections.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Review
Computer Science, Theory & Methods
N. Deepa, Quoc-Viet Pham, Dinh C. Nguyen, Sweta Bhattacharya, B. Prabadevi, Fang Fang, Pubudu N. Pathirana, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta
Summary: This article provides a comprehensive survey on the application of blockchain in the field of big data. It covers an overview of blockchain and big data, their integration motivation, and various blockchain services for big data acquisition, storage, analytics, and privacy preservation. The article also reviews state-of-the-art studies on the use of blockchain in big data applications in different domains and analyzes representative projects. Furthermore, challenges and future directions are discussed.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Thanh Thi Nguyen, Vijay Janapa Reddi
Summary: This article presents a survey of DRL approaches developed for cyber security, including vital aspects such as DRL-based security methods for cyber-physical systems and autonomous intrusion detection techniques. It also discusses multiagent DRL-based game theory simulations for defense strategies against cyberattacks. Future research directions and extensive discussions on DRL-based cyber security are provided.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jinchao Chen, Fuyuan Ling, Ying Zhang, Tao You, Yifan Liu, Xiaoyan Du
Summary: This study focuses on the coverage path planning problem of heterogeneous UAVs. By building models and proposing an algorithm, it achieves good enough path planning and efficient coverage of multiple separated regions.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Theory & Methods
Ying Zhao, Jinjun Chen
Summary: This article summarizes and analyzes the application of differential privacy solutions in protecting unstructured data, including various privacy models and mechanisms, as well as the challenges they face. It also discusses the privacy guarantees of these methods against AI attacks and utility losses, and proposes several possible directions for future research.
ACM COMPUTING SURVEYS
(2022)
Article
Computer Science, Artificial Intelligence
Yuxiang Zhang, Wei Li, Mengmeng Zhang, Shuai Wang, Ran Tao, Qian Du
Summary: This study proposes a new domain adaptation method that combines few-shot learning with domain alignment based on graph information aggregation to address the issue of reduced classification performance in the presence of new classes in target data. By training on source data with all label samples and target data with a few label samples, two key blocks are designed to extract and compare features and distributions across different domains, and cross-domain graph alignment methods are used to mitigate the impact of domain shift on few-shot learning. Experimental results demonstrate the superiority of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Muhammad Attique Khan, Muhammad Imran Sharif, Mudassar Raza, Almas Anjum, Tanzila Saba, Shafqat Ali Shad
Summary: This research proposes a skin lesion diagnosis method using deep learning and color features. By utilizing optimized color features for segmentation and deep learning models for classification, the proposed method achieves promising results.
Article
Computer Science, Artificial Intelligence
Wenfeng Zheng, Lirong Yin
Summary: This paper proposes a joint optimization method based on multi-layer semantics to explore the influence of sentence representation and reasoning models on reasoning performance. The experiments show that this method outperforms existing methods. The optimization of sentence representation and reasoning models have different impacts on reasoning results and there is a mutual constraint between them.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Habiba Arshad, Muhammad Attique Khan, Muhammad Irfan Sharif, Mussarat Yasmin, Joao Manuel R. S. Tavares, Yu-Dong Zhang, Suresh Chandra Satapathy
Summary: Human gait recognition is important in video surveillance, but traditional methods are sensitive to changes in clothes and viewing angles. This article proposes an integrated framework using deep neural network and fuzzy entropy controlled skewness. Experimental results on well-known datasets show promising performance.
Article
Computer Science, Information Systems
Tausif Diwan, G. Anirudh, Jitendra Tembhurne
Summary: Object detection is a significant problem in computer vision, and deep learning has greatly improved its performance. Object detectors can be categorized into two stage and single stage detectors, with two stage detectors typically achieving higher accuracy and single stage detectors having faster inference time. YOLO, a widely adopted single stage object detection algorithm, has the advantage of faster inference speed. This paper provides a comprehensive review of single stage object detectors, particularly YOLO, and compares them with two stage detectors. It also summarizes different versions of YOLO and their applications, as well as future research directions.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Honghao Gao, Junsheng Xiao, Yuyu Yin, Tong Liu, Jiangang Shi
Summary: In this article, a mutually supervised few-shot segmentation network is proposed to address the limited samples problem. The network utilizes feature fusion, graph attention network, and prior information to enhance the performance of few-shot segmentation. Experimental results on two datasets demonstrate the effectiveness and superior performance compared to baseline methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shuangming Yang, Jiang Wang, Bin Deng, Mostafa Rahimi Azghadi, Bernabe Linares-Barranco
Summary: This study introduces a scalable hardware framework for fault-tolerant context-dependent learning in neuromorphic computing, demonstrating an improvement in network throughput. The proposed system can be utilized for real-time decision-making, brain-machine integration, and research on brain cognition during learning.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Qingchen Zhang, Laurence T. Yang, Zhikui Chen, Peng Li
Summary: The study introduced a high-order PCM algorithm (HOPCM) for big data clustering, along with a distributed method based on MapReduce. Additionally, a privacy-preserving HOPCM algorithm (PPHOPCM) was devised for protecting private data in the cloud.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Review
Computer Science, Interdisciplinary Applications
Hui Wen Loh, Chui Ping Ooi, Silvia Seoni, Prabal Datta Barua, Filippo Molinari, U. Rajendra Acharya
Summary: This study focuses on the application areas of XAI technology in healthcare, emphasizing the need for more attention from the XAI research community in detecting abnormalities in 1D biosignals and identifying key text in clinical notes.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Licheng Jiao, Ruohan Zhang, Fang Liu, Shuyuan Yang, Biao Hou, Lingling Li, Xu Tang
Summary: Video object detection is a rapidly evolving task in computer vision, with deep learning methods achieving excellent results but facing challenges from duplicate and spatiotemporal information in video data. Scholars have made significant progress in researching deep learning detection algorithms in the context of video data in recent years.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Anmin Fu, Shui Yu, Yuqing Zhang, Huaqun Wang, Chanying Huang
Summary: This paper proposes a new privacy-aware public auditing mechanism for shared cloud data by constructing a homomorphic verifiable group signature. It ensures the integrity and security of the data.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Theory & Methods
Tan Guo, Keping Yu, Moayad Aloqaily, Shaohua Wan
Summary: The Artificial Intelligence Internet of Things (AIoT) is an emerging concept that aims to connect intelligent things for efficient intercommunication. This paper presents a prior-dependent graph (PDG) construction method to model and discover complex relations in data, enabling high-efficiency data clustering and dimensionality reduction. Experimental results demonstrate that the PDG model achieves substantial performance compared to existing graph learning models.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Hardware & Architecture
Mina Javanmard Goldanloo, Farhad Soleimanian Gharehchopogh
Summary: The paper proposes two improved firefly algorithms, one suitable for small and medium dimensions, and the other for increasing dimensions. Experimental results show that these two methods perform better in mathematical benchmarking functions.
JOURNAL OF SUPERCOMPUTING
(2022)
