Article
Medicine, General & Internal
Olfat M. Mirza, Hana Mujlid, Hariprasath Manoharan, Shitharth Selvarajan, Gautam Srivastava, Muhammad Attique Khan
Summary: The goal of this study is to develop a wearable device that uses IoT to identify infections in remote regions quickly and accurately. It operates with a multi-objective framework and utilizes different mathematical approaches to improve detection quality. The proposed method outperforms current state-of-the-art methods in all case studies.
Article
Computer Science, Information Systems
Chen Zhao, Zhipeng Gao, Yang Yang, Qian Wang, Zijia Mo, Xinlei Yu
Summary: In this article, a novel federated unsupervised learning method for image classification is proposed, which can be performed without any ground truth annotations. To address the issue of non-independent and identically distributed (non-IID) decentralized data in IoT scenarios, a dynamic update mechanism based on weights divergence is further proposed. Extensive experiments show that our method outperforms all baseline methods in terms of classification accuracy.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Darius Nahavandi, Roohallah Alizadehsani, Abbas Khosravi, U. Rajendra Acharya
Summary: Wearable technologies have brought new and rapidly developing tools to the field of personal devices, with the potential to integrate with artificial intelligence methods. This paper reviews the recent applications of wearable devices that leverage AI to achieve their objectives, including specific examples in domains such as medical, industrial, and sport.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Information Systems
Yuming Feng, Weizhe Zhang, Shujun Yin, Hao Tang, Yang Xiang, Yu Zhang
Summary: This article proposes a novel reinforcement learning-based collaborative DDoS detection method and utilizes a lightweight unsupervised classifier for network traffic analysis. The dynamic changes in the IoT environment are handled using the soft actor-critic model and a collaborative aggregation module, ensuring excellent detection performance for different types of IoT devices.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Xinyan Zhou, Jiaqi Pan, Zenan Zhang, Xiaoyu Ji, Haiming Chen
Summary: Verifying the user identity of wearable devices is important for system security, and a promising solution is PPG-based two-factor authentication using widely deployed PPG sensors. This article presents the design of G-PPG, a gesture-related PPG-based authentication mechanism that can validate the user's identity nonintrusively. G-PPG achieves high accuracy through a gesture detection module, specific feature extraction, user-defined security level, and adaptive update scheme, with over 90% accuracy in experimental scenarios.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Civil
Mauro A. A. da Cruz, Lucas R. Abbade, Pascal Lorenz, Samuel B. Mafra, Joel J. P. C. Rodrigues
Summary: The rapid development and widespread adoption of IoT have resulted in an increase in attacks targeting IoT environments. This paper proposes a solution to detect replication attacks in IoT by analyzing abnormal network traffic through machine learning.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Daojing He, Ziming Zhao, Sammy Chan, Mohsen Guizani
Summary: This article proposes an identity authentication protocol between embedded devices and servers using elliptic curve encryption and timestamp security attributes. The protocol ensures device anonymity and prevents replay attacks. The security and performance of the protocol are proven through formal verification and experimental comparison with existing protocols.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Shichen Zhang, Pedram Kheirkhah Sangdeh, Hossein Pirayesh, Huacheng Zeng, Qiben Yan, Kai Zeng
Summary: This article introduces a learning-based authentication scheme for wireless IoT devices without input interfaces, which can recognize passwords when users hold the device and write the password over the air, and works in scenarios with nonlinear antenna arrays. Test results show a high recognition accuracy.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Jing Huey Khor, Michail Sidorov, Ming Tze Ong, Shen Yik Chua
Summary: This article proposes a data protection protocol that ensures data integrity, reduces transaction fees, and prolongs battery life for IoT devices used with public blockchain networks. It presents a proof of concept using an ESP32S2 device to evaluate the performance of the proposed data storage protocol. The evaluation results demonstrate that data integrity can be achieved for low-power sensor nodes connecting to public blockchains via Wi-Fi network.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Aaisha Makkar, Sahil (GE) Garg, Neeraj Kumar, M. Shamim Hossain, Ahmed Ghoneim, Mubarak Alrashoud
Summary: The Internet of Things (IoT) consists of millions of devices connected through wired or wireless channels for data transmission, with data volume expected to grow rapidly in the coming years. Machine learning algorithms play a key role in enhancing security and usability of IoT systems, while also being exploited by attackers to target vulnerabilities. This article proposes a spam detection method using machine learning for IoT devices, which has been validated and proven effective compared to existing schemes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Zuchao Ma, Liang Liu, Weizhi Meng, Xiapu Luo, Lisong Wang, Wenjuan Li
Summary: With the rise of cyber attacks, NIDS has become an essential tool for protecting IoT environments. However, the effectiveness of the detection model is crucial for NIDS performance, and it can be influenced by the learning mechanism and training data. To address these challenges, we propose a collaborative learning-based framework called ADCL, which leverages multiple models trained in similar environments to improve detection performance in IoT networks.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Foivos Michelinakis, Anas Saeed Al-Selwi, Martina Capuzzo, Andrea Zanella, Kashif Mahmood, Ahmed Elmokashfi
Summary: Proper configuration of parameters is crucial as it impacts the energy consumption of NB-IoT devices. Simple modifications to default settings can lead to significant energy savings based on empirical measurements and analysis.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Liling Zhang, Xinyu Lei, Yichun Shi, Hongyu Huang, Chao Chen
Summary: Federated learning is a distributed machine learning technique that enables IoT devices to train an ML model jointly using a centralized server. Local data of IoT devices is protected as it never leaves the devices. To address the poor generalization performance of models trained over multisource domains, we propose federated adversarial domain generalization (FedADG) to enhance FL with domain generalization capability.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Wanli Ni, Jingheng Zheng, Hui Tian
Summary: We propose a semi-federated learning (SemiFL) framework to address the challenges faced by existing federated learning in massive IoT networks. This framework seamlessly integrates centralized and federated paradigms and shows high scalability even with computing-limited sensors. Compared to traditional learning methods, SemiFL utilizes distributed data and computing resources more effectively through collaborative model training between edge servers and local devices. Simulation results demonstrate the effectiveness of our SemiFL framework for massive IoT networks.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Di Wu, Rehmat Ullah, Paul Harvey, Peter Kilpatrick, Ivor Spence, Blesson Varghese
Summary: This article introduces an adaptive offloading FL framework called FedAdapt for applying federated learning on IoT devices. By offloading the layers of deep neural networks to servers, it speeds up local training on computationally constrained devices and uses reinforcement learning for adaptive optimization and clustering to address challenges of computational heterogeneity and changing network bandwidth.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)