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An Explainable AutoML-Driven Meta-Learning Scheme for Intrusion Prevention in Zero-Touch Networks Within Carbon Intelligent IIoT

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IEEE Internet of Things Journal 2025年

作者： Islam, Anik Karimipour, Hadis Gadekallu, Thippa Reddy University of Calgary Schulich School of Engineering Deptartment of Electrical and Software Engneering CalgaryT2N1N4 Canada Zhejiang A and F University The College of Mathematics and Computer Science Hangzhou311300 China Lovely Professional University Division of Research and Development Phagwara India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India

Carbon Intelligent Industrial Internet of Things (IIoT) systems are critical for achieving sustainable industrial automation but face challenges such as scalability, operational complexity, and security vulnerabilities. Zero-Touch Networks (ZTN), with their autonomous management capabilities, offer solutions to operational challenges but remain vulnerable to sophisticated cyber intrusions due to their high level of autonomy and interconnectedness. While Artificial Intelligence (AI), especially Deep Learning (DL), shows potential in intrusion detection, current approaches often encounter obstacles such as insufficient datasets, challenges in automated data preprocessing, and a lack of transparency. This paper introduces an AutoML-enabled Meta Learning-based Intrusion Prevention Scheme designed specifically for ZTN within Carbon Intelligent IIoT. The proposed framework integrates AutoML and meta-learning to streamline data preprocessing and improve model adaptability in dynamic and evolving threat environments. To ensure transparency, an Integrated Gradient-based Explainable AI (XAI) mechanism is employed, offering insights into the impact of individual features on model predictions, thereby addressing concerns related to trust and accountability in industrial applications. Experimental evaluations demonstrate the framework’s effectiveness in enhancing intrusion prevention, bolstering security, and improving transparency for ZTN in carbon intelligent IIoT, providing a comprehensive solution to prevailing challenges. © 2014 IEEE.

关键词： Network intrusion

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Fourier-Reflexive Partitions Induced by Poset Metric

Fourier-Reflexive Partitions Induced by Poset Metric

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作者： Xu, Yang Kan, Haibin Han, Guangyue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Fudan-Zhongan Joint Laboratory of Blockchain and Information Security Shanghai Engineering Research Center of Blockchain Fudan University Shanghai200433 China Department of Mathematics Faculty of Science The University of Hong Kong Hong Kong

Let H be the cartesian product of a family of finite abelian groups indexed by a finite set Ω. A given poset (i.e., partially ordered set) P = (Ω, ≤P) gives rise to a poset metric on H, which further leads to a partition Q(H, P) of H. We prove that if Q(H, P) is Fourier-reflexive, then its dual partition Λ coincides with the partition of Ĥ induced by P, the dual poset of P, and moreover, P is necessarily hierarchical. This result establishes a conjecture proposed by Gluesing-Luerssen in Gluesing-Luerssen, 2015. We also show that with some other assumptions, Λ is finer than the partition of Ĥ induced by P. In addition, we give some necessary and sufficient conditions for P to be hierarchical, and for the case that P is hierarchical, we give an explicit criterion for determining whether two codewords in Ĥ belong to the same block of Λ. We prove these results by relating the involved partitions with certain family of polynomials, a generalized version of which is also proposed and studied to generalize the aforementioned results © 1963-2012 IEEE.

关键词： Group theory

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ResNet-based Network for Recognizing Daily and Transitional Activities based on Smartphone Sensors 3

ResNet-based Network for Recognizing Daily and Transitional ...

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science North Bangkok Bangkok Thailand

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

In contemporary wearable computing contexts, sensor-based human activity recognition (HAR) has become a popular research topic. Investigators from the Health Applications research Institute presented promising discoveries to promote healthcare applications, including fall detection, athletic tracking and reporting, and a monitoring scheme for senior activities in intelligent homes. In these services, ordinary and transitory human actions are captured by smartphones' wearable sensors and analyzed as fundamental and complicated motions. Deep learning techniques demonstrated the usefulness and effectiveness of convolutional neural networks (CNNs) in extracting high-level features embedded in sensor data to develop reliable recognition models. CNN faces deterioration of gradient vanishing issues when networks require deeper convolution layers. To overcome the problem, we developed ResNet, a deep residual network for determining daily and transitory activities. Using a significant standard HAR dataset called the KU-HAR dataset that gathered smartphone sensor data of various human actions, we performed experiments to identify the most appropriate ResNet-based models. Experimental findings indicate that the ResNet-18 has the highest accuracy, at 93.54%. The acquired results surpass prior state-of-the-art models by 3.87% in terms of accuracy. © 2022 IEEE.

关键词： Smartphones

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Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network

Classification of Physical Exercise Activity from ECG, PPG a...

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2022 research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Human activity recognition (HAR) plays an increasingly vital role in several industrial applications, including medical services and rehabilitation surveillance. With the fast growth of information and communications technology, wearable technologies have recently triggered a new human-computer interaction. Wearable inertial sensors (IMUs) are commonly used in the area of HAR because this data source provides the most insightful motion signal data. Lately, HAR studies have examined the enhancement of activity recognition using bio-signals like Electrocardiogram (ECG) and Photoplethysmography (PPG). Nevertheless, current HAR research was constrained by machine learning techniques that relied on human-crafted feature extraction. This research proposed a deep learning technique to effectively identify physical activity behaviors using ECG, PPG, and IMU sensor data. ResNet-SE is a deep residual network that incorporates convolutional processes, shortcut connections, and squeeze-and-excitement. We trained and evaluated baseline deep learning models to assess the suggested network, including the proposed model, using the public HAR dataset called Wrist_PPG dataset. According to experimental findings, the suggested method earned the most fantastic accuracy of F1-score. In addition, our results indicate that the PPG data can be utilized to classify physical workouts. © 2022 IEEE.

关键词： Electrocardiograms

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A Deep Learning-based Model for Human Activity Recognition using Biosensors embedded into a Smart Knee Bandage 9th

A Deep Learning-based Model for Human Activity Recognition u...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

Today, biosensors are effectively utilized in illness detection, prevention, rehabilitation, patient medical monitoring, and personal health promotion. The advancement of biosensing technology offers rigorous methods for measuring an individual's motor function, which contributes significantly to restoring motor function. An smart knee bandage was recently designed to analyze and decode diverse biosignals recorded by knee bandage-integrated biosensors. This research aims to examine human activity recognition (HAR) using biosignals gathered by a smart knee bandage. To accomplish this, a deep residual neural network was established for biosensor-based HAR. For performance assessment, we utilized a significant public biosensor dataset known as the CSL-SHARE dataset, which gathers electromyography (EMG), electrogoniometer (EGM), and inertial measurement unit (IMU) signals of individuals completing 22 simplex and complicated everyday tasks. Five-fold cross-validation was used to train and assess deep learning models. According to our findings, integrating features derived from the EMG, EGM, and IMU signal produced the maximum accuracy of 91.60% and the highest F1-score of 92.13%. © 2022 The Authors. Published by Elsevier B.V.

关键词： Biosensors

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Experimental evidence of crystal-field, Zeeman-splitting, and spin-phonon excitations in the quantum supersolid Na2BaCo(PO4)2

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Physical Review B 2025年第15期111卷 155129-155129页

作者： Ghulam Hussain Jianbo Zhang Man Zhang Lalit Yadav Yang Ding Changcheng Zheng Sara Haravifard Xiawa Wang Division of Natural and Applied Sciences Duke Kunshan University Kunshan Jiangsu 215316 China Center for High-Pressure Science and Technology Advanced Research Beijing 100094 China Department of Physics Duke University Durham North Carolina 27708 USA Department of Mechanical Engineering and Materials Science Duke University Durham North Carolina 27708 USA

Drawing inspiration from the recent breakthroughs in the Na2BaCo(PO4)2 quantum magnet, renowned for its spin supersolidity phase and its potential for solid-state cooling applications, our study delves into the interplay among lattice, spin, and orbital degrees of freedom within this compound. Using temperature-, field-, and pressure-dependent Raman scattering techniques, we present experimental evidence revealing crystal-electric-field (CEF) excitations, alongside the interplay of CEF-phonon interactions. We performed density functional theory calculations for the phonon frequencies and compared them with the experimentally observed modes. In addition, our experiments elucidated electronic transitions from j1/2 to j3/2 and from j1/2 to j5/2, with energy levels closely aligned with theoretical predictions based on point-charge models. Moreover, the application of a magnetic field and pressure revealed Zeeman splittings characterized by Landé-g factors as well as the CEF-phonon resonances. The anomalous shift in the coupled peak at low temperatures originated from the hybridization of CEF and phonon excitations due to their close energy proximity and shared symmetry. These findings constitute a significant step towards unraveling the fundamental properties of this exotic quantum material for future research in fundamental physics or engineering application.

关键词： Degrees of freedom (mechanics)

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Large tuning of the optical properties of nanoscale NdNiO3 via electron doping

arXiv

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arXiv 2024年

作者： Jin, Yeonghoon Qu, Teng Kumar, Siddharth Kubzdela, Nicola Tsai, Cheng-Chia De Li, Tai Ramanathan, Shriram Yu, Nanfang Kats, Mikhail A. Department of Electrical and Computer Engineering University of Wisconsin-Madison Wisconsin53706 United States Department of Applied Physics and Applied Mathematics Columbia University New York10027 United States Department of Electrical and Computer Engineering Rutgers The State University of New Jersey New Jersey08854 United States CUNY Graduate Center Advanced Science Research Center New York11201 United States Department of Material Science and Engineering University of Wisconsin-Madison Wisconsin53706 United States

We synthesized crystalline films of neodymium nickel oxide (NdNiO3), a perovskite quantum material, switched the films from a metal phase (intrinsic) into an insulator phase (electron-doped) by field-driven lithium-ion intercalation, and characterized their structural and optical properties. Time-of-flight secondary-ion mass spectrometry (ToF-SIMS) showed that the intercalation process resulted in a gradient of the dopant concentration along the thickness direction of the films, turning the films into insulator-metal bilayers. We used variable-angle spectroscopic ellipsometry to measure the complex refractive indices of the metallic and insulating phases of NdNiO3. The insulator phase has a refractive index of n ~ 2 and low absorption in the visible and near infrared, and analysis of the complex refractive indices indicated that the band gap of the insulating phase is roughly 3-4 eV. Electrical control of the optical band gap, with corresponding large changes to the optical refractive indices, creates new opportunities for tunable optics. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy gap

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A Deep Residual Network for Recognizing Transportation Vehicles using Smartphone Sensors 13

A Deep Residual Network for Recognizing Transportation Vehic...

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13th IEEE International Conference on Software engineering and Service science, ICSESS 2022

作者： Hnoohom, Narit Maitrichit, Nagorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand University of Phayao Department of Computer Engineering School of Information and Communication Technology Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The development of sensor technology has enabled the development of a variety of applications for human activity detection by wearable devices. Identifying transportation modes for contextual support in the execution of systems such as driver assistance and intelligent transportation planning is one of the advantageous applications of an intelligent transportation system (ITS). Due to the widespread use of smartphones in today's world, a mobile application-based solution is proposed that can significantly reduce the cost of implementing ITS. In this work, we recognized transportation vehicles using the accelerometer and gyroscope data collected by smartphones. To achieve the research goal, this work developed a deep residual network called DeepResNeXt that used convolutional kernels and residual connections for transportation vehicle recognition. We used a public benchmark dataset to evaluate the proposed deep residual network. Experimental results showed that DeepResNeXt was achieved better accuracy and F1-score than previous works. In addition, this work also investigated the effect of sensor types on recognition performance. The results showed that the deep residual network trained with accelerometers achieved higher accuracy and F1-score than the network trained with gyroscope data. © 2022 IEEE.

关键词： Accelerometers

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Badminton Activity Recognition and Player Assessment based on Motion Signals using Deep Residual Network 13

Badminton Activity Recognition and Player Assessment based o...

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13th IEEE International Conference on Software engineering and Service science, ICSESS 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

With the fast expansion of digital technologies and sporting events, interpreting sports data has become an immensely complicated endeavor. Internet-sourced sports big data exhibit a significant development trend. Big data in sports offer a wealth of information on sportspeople, coaching, athletics, swimming, and badminton. Today, various sports data are freely accessible, and incredible data analysis tools based on wearable sensors have been established, allowing us to investigate the usefulness of these data thoroughly. In this research, we investigate the detection of badminton action and player evaluation based on movement data captured by wearable sensors. Movement data captured by an accelerometer, gyroscope, and magnetometer are utilized for training and validating a classification model for badminton actions. In addition, the movement signals are used to train a player evaluation model employing a deep residual network. To assess our suggested technique, we utilized a publicly available benchmark dataset consisting of inertial measurement unit (IMU) sensors attached to every investigator's dominant wrist, palm, and both legs. The experimental findings indicate that the proposed deep residual network obtained good performance with a maximum accuracy of 98.00% for identifying badminton activities and 98.56% for evaluating badminton players. © 2022 IEEE.

关键词： Wearable sensors

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Multi-resolution CNN for Lower Limb Movement Recognition Based on Wearable Sensors 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Mekruksavanich, Sakorn Jitpattanakul, Anuchit Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakorn Pathom Thailand Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

Human activity recognition (HAR) remains a difficult challenge in human-computer interaction (HCI). The Internet of Healthcare Things (IoHT) and other technologies are expected to be used primarily in conjunction with HAR to support healthcare and elder care. In HAR research, lower limb movement recognition is a challenging research topic that can be applied to the daily care of the elderly, fragile, and disabled. Due to recent advances in deep learning, high-level autonomous feature extraction has become feasible, which is used to increase HAR efficiency. Deep learning approaches have also been used for sensor-based HAR in various domains. This study presents a novel method that uses convolutional neural networks (CNNs) with different kernel dimensions, referred to as multi-resolution CNNs, to detect high-level features at various resolutions. A publicly available benchmark dataset called HARTH was used to evaluate the recognition performance to collect acceleration data of the lower limb movements of 22 participants. The experimental results show that the proposed approach improves the F1 score and achieves a higher score of 94.76%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wearable sensors

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