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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Kabdjou, Joelle Tagne, Elie Fute Rawat, Danda B. Acosta, Jaime Kamhoua, Charles University of Dschang Department of Mathematics and Computer Science Dschang Cameroon University of Buea Department of Computer Engineering Dschang Buea Cameroon Howard University Department of Electrical Engineering and Computer Science Washington United States Network Security Branch Devcom Army Research Laboratory Adelphi United States

ISBN: (纸本)9798350336092

The frequency of malicious activities by cyber attackers is on the rise, posing a significant challenge to counter cyber attacks in the Mobile Edge Computing (MEC) environment. The vulnerability of wireless networks, which serve as the primary communication media for MEC servers and IoT applications, is due to their open nature of communication. This work proposes a MEC architecture that utilizes Network Function Virtualization (NFV) and Service Function Chain (SFC) to combat cyber adversaries. Additionally, we evaluate the performance of a cyber deception system, which leverages a software-defined networking (SDN) controller and decision- maker to continuously monitor incoming MEC traffic and direct cyber adversaries to a deception MEC server. The deception MEC server collects information about cyber adversaries' intent, capabilities, and potential damage to the system, thus securing legitimate users from cyber attacks. The proposed approach was evaluated using Monte Carlo simulations, and numerous results were obtained which demonstrate the effectiveness of cyber deception in MEC. © 2023 IEEE.

关键词： Network function virtualization

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A Detailed Comparative Study of AI-Based Intrusion Detection System for Smart Cities

A Detailed Comparative Study of AI-Based Intrusion Detection...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Rakha, Muhammad Allah Akbar, Abdullah Chhabra, Gunjan Kaushik, Keshav Arshi, Oroos Khan, Inam Ullah Fast National University Of Computer And Emerging Sciences Department Of Computer Science Peshawar Pakistan National University Of Computer And Emerging Sciences Department Of Electrical Engineering Peshawar Pakistan Graphic Era Hill University Department Of Cse Dehradun India Graphic Era Deemed To Be University Uttarakhand Dehradun India School Of Computer Science University Of Petroleum And Energy Studies Dehradun India Isra University Department Of Electronic Engineering Islamabad Campus Pakistan

ISBN: (纸本)9798350366846

The proliferation of Internet of Things (IoT) devices poses potential challenges in the fast-developing field of smart cities, especially in cybersecurity. This work is an attempt to present an extensive comparative analysis of AI-based intrusion detection systems (IDS) designed for smart cities. The necessity of robust intrusion detection systems is highlighted early on in discussing the security threats associated with IoT and its importance in enhancing urban life. The study looks at current datasets that are frequently utilized to train IDS models, emphasizing both their shortcomings and the growing need for more diverse and representative datasets. This work explores the use of deep learning (DL) and machine learning (ML) approaches for intrusion detection in smart city environments, it looks at different classifiers and how well they identify cyber threats. This paper presents simulation methods and experimental results for demonstrating the accuracy, precision, recall, and F1-score performance of many classifiers using the complex datasets NF-BoT-IoT and IoTID20. This paper also touches upon the significance of continuing research in utilizing cutting-edge algorithms and novel datasets to improve smart cities' cybersecurity posture and, eventually, aid in creating more complex intrusion detection systems that can successfully handle the ever-changing threat landscape. It also emphasizes the necessity of ongoing innovation in this field and the potential for deep learning algorithms to improve cyberattack detection skills. © 2024 IEEE.

关键词： Smart city

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Early Detection of Fire in EV Battery Using Machine Learning Approach 2

Early Detection of Fire in EV Battery Using Machine Learning...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Kaur, Ishpreet Singh, Mukesh Kasana, Singara Singh Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India Thapar Institute of Engineering and Technology Electrical and Instrumentation Engineering Department Patiala India

ISBN: (纸本)9798350300802

In recent years, Electric Vehicles (EVs) have revolutionized the automobile industry. Nowadays, EVs are the preferred means of transportation for people because they are easy to drive, convenient, and make less noise. EVs use Lithium-Ion Batteries (LIB) to power the vehicle. However, in past years, there have been multiple instances of EVs catching fire and becoming one of the most urgent problems in EVs. The leading causes of such mishaps include faulty Battery Management System (BMS), no proper venting mechanism for overheated cells to release heat, poor quality cells, and short-circuiting. Many researchers proposed fire handling systems that consider sensor values, such as temperature, voltage, etc. while some focus on battery health management. However, there is a need for a detection mechanism to detect fire in advance to save people's life and property from damage. Some researchers have proposed fire detection strategies considering only a few sensor readings that cannot detect the extent of a fire early and inform users about the situation. Therefore, in the proposed work, an approach is designed considering both environmental and battery data using multi-sensors for developing an intelligent early fire detection system. The proposed approach first performs exploratory data analysis on real dataset to get an insight into data for fault diagnosis, leading to early thermal runaway. Further, Machine Learning (ML) and ensemble methods are used to check the potentiality of the fire or detect fire in advance. Finally, the system is evaluated concerning accuracy, recall, precision, and Fl-Score. © 2023 IEEE.

关键词： Battery Management System Electric Vehicles Fire Detection Lithium-Ion Battery Machine Learning

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Shape Organization for Enhanced Design Optimization in Generative Systems Using Clustering Technology 6

Shape Organization for Enhanced Design Optimization in Gener...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Krishna, Raji Suganya, R. Yaganti, Mounika Sai Mohamed Buhari, Seyed Vellore Institute of Technology School of Electrical Engineering Chennai Campus Chennai India Vellore Institute of Technology School of Computer Science Engineering Chennai Campus Chennai India University Brunei Darussalam Department of Computer Science Gadong Brunei

ISBN: (纸本)9798350394702

The current research in design space articulation tries to solve the problem that comes up because computer generative systems are becoming more complex, and there are more design options. This study makes a contribution to the existing body of research by introducing a technique for form clustering that utilizes K-medoids, a Machine Learning approach. The suggested method uses a clustering method to group design shapes that are similar and then pick a representative body for each cluster in a 2D-Grid Based representation. The efficacy of the process has been empirically validated by applying it to a novel test case and comparing it with other clustering evaluation methods. The accuracy of the results of this evaluation is about the same as that seen in an outside study. This gives us essential information about how to judge machine learning techniques. © 2023 IEEE.

关键词： Hamming distance

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Security, Trust, and Privacy in Cyber-physical Systems (CPS) 2

Security, Trust, and Privacy in Cyber-physical Systems (CPS)

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2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Busari, Wareez A. Bello, Abeeb A. Department of Cyber Security Science Federal University of Technology Niger State Minna Nigeria Department of Electrical and Computer Engineering Mahindra University Hyderabad India

ISBN: (纸本)9798350388664

This paper explores the convergence of physical and computational components in Cyber-Physical Systems (CPS). Security, trust, and privacy are paramount for reliable and resilient operation of these interconnected systems across diverse sectors like healthcare and smart cities. The paper analyses the complex interplay of these factors, identifying practical challenges and proposing solutions to enhance system robustness. By analysing trends, research gaps, and offering actionable recommendations, the study aims to empower stakeholders in fortifying CPS against evolving cyber threats. It emphasizes the need for comprehensive strategies to mitigate risks, protect sensitive information, and ensure data integrity and system reliability. Through a holistic approach integrating security, trust, and privacy mechanisms, the research contributes to advancing effective security measures in CPS, ultimately fostering a culture of trust and resilience in the digital era. © 2024 IEEE.

关键词： Differential privacy

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Internet of Medical Things (IoMT) for Premature Estimate of Epileptic Seizures 16

Internet of Medical Things (IoMT) for Premature Estimate of ...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Alrawi, Ali Amer Ahmed Mashhadany, Yousif Al Najeeb, Mushtaq Hadi, Sura A. Algburi, Sameer Graovac, Stevica University of Anbar Department of Electrical Engineering Anbar Iraq University Science Malaysia School of Computer Science Malaysia Al-Kitab University College of Engineering Kirkuk Iraq University of Belgrade Faculty of Electrical Engineering Belgrade Serbia Faculty of Electrical Engineering Montenegro

ISBN: (纸本)9798350381344

This paper proposes an Internet of Medical Things (IoMT) Seizure Detection Algorithm that uses smartphone acceleration sensors to detect early seizures. The propose algorithm used based on MATLAB Mobile, which seamlessly accesses MATLAB capabilities and uses MATLAB Drive for cloud-based data storage and analysis. Time-domain and frequency-domain analysis turn acceleration impulses into meaningful feature vectors. Then, machine learning classifiers like SVMs or deep learning models are used to train the system on a seizure and non-seizure dataset. The algorithm is calibrated to distinguish s suraabdlattif@*** eizure occurrences from regular activity. The IoMT technique uses real-time streaming to send sensor data to the cloud-based MATLAB Drive for continuous monitoring. The MATLAB Mobile app allows smartphone users to remotely monitor seizure activity using the seizure detection algorithm. The proposed IoMT Seizure Detection Algorithm is tested utilizing a broad dataset of seizure episodes and physical activities. Accuracy, sensitivity, specificity, and the ROC curve are evaluated. The algorithm's robustness and effectiveness in detecting seizure occurrences highlight its potential as a noninvasive, cost-effective early seizure detection system. MATLAB Mobile and MATLAB Drive integration shows that IoMT-based healthcare solutions implemented in real-world circumstances, improving patient care and medical insights. The auto-correlation between the patient's motion and the [] data helped clinicians decide if the patient's motion was typical or not. Up to 85% consistency was found while using our proposed method for detecting epilepsy. © 2023 IEEE.

关键词： Smartphones

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A Tensor-based t-SVD-LSTM Remaining Useful Life Prediction Model for Industrial Intelligence

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IEEE Transactions on Industrial Informatics 2022年 1-12页

作者： Wang, Xiaokang Yang, Laurence T. Cao, Enyuan Guo, Luyang Ren, Lei Deen, M. Jamal School of Computer Science and Technology Hainan University Haikou China School of Artificial Intelligence University of the Chinese Academy of Sciences Beijing China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Electrical and Computer Engineering McMaster University Hamilton Canada

The Industrial Internet of Things (IIoT) integrates smart sensors and actuators for the widespread digitization and enhancement of industrial and manufacturing processes. Smart equipment is used to improve the industrial intelligence and make industrial production more flexible, safer and more efficient. For complex equipment, product life-cycle management (PLM) including remaining useful life (RUL) is one of the essential issues for industrial intelligence. In this paper, a tensor-based remaining useful life prediction model is proposed to facilitate the life-cycle management, which combines features from time domain and frequency domain. For the characteristics of continuous generation of industrial data streaming, tensor singular value decomposition (t-SVD) is combined with long shortterm memory network (LSTM) method to predict the RUL of devices from high-order and high-noise time series data. Finally, experiments are carried out on three different data sets including the battery charge and discharge data set, the bearing acceleration life cycle data set, and the turbofan data set to measure the performance of the proposed model. IEEE

关键词： Time domain analysis

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Generating Models of Human Gait in Patients with Parkinson's Disease using Genetic Programming 21

Generating Models of Human Gait in Patients with Parkinson's...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Navikevicius, Tristan Nóbrega, Lígia Reis Houghten, Sheridan De Oliveira Andrade, Adriano Pereira, Adriano Alves Brock University Department of Computer Science St. CatharinesON Canada Federal University of Uberlândia Faculty of Electrical Engineering Uberlândia Brazil

ISBN: (纸本)9798350356632

Parkinson's disease is an extremely debilitating condition where the brain is not producing enough dopamine to accurately coordinate movement. One symptom of Parkinson's disease, freezing of gait, prevents the affected person from either starting to walk or continuing walking. We use a dataset that provides time-series data of volunteers' gait while performing four different tasks of varying complexity. Symbolic regression through genetic programming is applied to the dataset to create models of the gait of volunteers with and without Parkinson's disease, including those who may be experiencing freezing of gait, while also factoring in their medication status (ON or OFF). Comparing the gait of volunteers in the different groups, the methodology produced models that were most accurate for the group of PD patients with freezing of gait when off their medication applied to other volunteers in the same group. It was also found that for some volunteers it was not possible to distinguish between ON and OFF medication states, providing a possible indication of issues with their medication. © 2024 IEEE.

关键词： Genetic programming

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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