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9th International Conference on computer and Communications, ICCC 2023

作者： Tegene, Abebe Tamrat Liu, Qiao Gan, Yanglei Jimale, Elias Ayenew, Melak Leka, Habte University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Addis Ababa Science and Technology University Center of Excellence of Ai and Robotics Addis Ababa Ethiopia University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350317251

In many e-commerce recommendation systems, user profiles are mostly not available. In such cases, the recommendation task considers user behaviors like click sessions or browsing history. Traditional recommender methods often rely on users' past interactions for recommending user interests, which is difficult to apply to session-based recommender systems. However, these methods perform very poorly on this task. Recently, deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) showed outstanding performance in session-based recommendation problems. In this paper, we propose a hybrid CNN and gated recurrent unit (GRU) model that exploits the advantages of both CNN and GRU in modeling the rigid order requirement for interactions inside sessions and modeling the sequential long-term dependencies in session data, respectively. Our proposed hybrid model has two architectures: the first part consists of the CNN layers that take input from Item ID to learn local features and relationships in sessions, and the second part is a GRU layer that takes the output from the CNN layer to learn the long-term dependencies in that session. This hybrid technique enables the architecture for modeling prominent features in the session to provide accurate recommendations. Extensive experiments over a real-world dataset demonstrate that the proposed hybrid model performs well and outperforms those GRU and CNN-based models in the state-of-the-art recommendation task on both metrics used. © 2023 IEEE.

关键词： Recommender systems

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Attention Generative Adversarial Network with Transformer Encoder for Missing Sensor Time Series

Attention Generative Adversarial Network with Transformer En...

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2023 China Automation Congress, CAC 2023

作者： Xu, Zeng-Song Ma, Song Sun, Tao Sun, Xi-Ming School of Control Science and Engineering Dalian University of Technology Dalian116024 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

ISBN: (纸本)9798350303759

This study focuses on the imputation process of equipment sensor data with missing time-series values. To address this challenge, the main approach is to reconstruct the missing data in the sensor system by developing an attention generative adversarial imputation network learning model called ATGAIN. The model input is formatted by the sliding window method and the introduction of a mask matrix. Next, the Transformer and convolutional neural network architectures are integrated into ATGAIN's generator and discriminator, respectively, with careful design to effectively capture the temporal correlation and feature correlation within the remaining data. Finally, the proposed learning model is evaluated using an aircraft engine C-MAPSS dataset to demonstrate its effectiveness. © 2023 IEEE.

关键词： Time series

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More is Better: Channel-Robust Radio Frequency Fingerprinting with Random Overlay Augmentation

More is Better: Channel-Robust Radio Frequency Fingerprintin...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Wang, Yu Meneghello, Francesca Wang, Shufei Otsuki, Tomoaki Yuen, Chau Gui, Guan Wang, Xianbin College of Telecommunications and Information Engineering Njupt Nanjing China University of Padova Department of Information Engineering Padova Italy Keio University Faculty of Science and Technolozy Kanagawa Japan School of Electrical and Electronics Engineering Nanyang Technological University Singapore Western University Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350368369

Radio Frequency Fingerprinting (RFF) is a critical technology for enhancing physical-layer security by leveraging the unique RF characteristics of hardware, enabling authentication and anti-counterfeiting for wireless communication devices. In recent years, Deep Learning (DL) has been extensively applied in RFF, significantly improving identification accuracy and efficiency. However, DL- based RFF methods still encounter challenges regarding robustness, particularly in cross-channel scenarios. To address these challenges, we propose a channel-robust RFF method based on a Multi-Scale Convolutional Attention Network (MSCAN) with Random Overlay Augmentation (ROA). Specifically, MSCAN extracts and fuses features at different scales, allowing it to capture more comprehensive signal characteristics. Additionally, ROA is a combinatorial data augmentation (DA) strategy designed to simulate diverse characteristics of wireless propagation environments, thereby enhancing the adaptability and robustness of RFF in complex channel conditions. Experiments conducted on the ORACLE dataset demonstrate that our proposed method achieves over 92 % accuracy in cross-channel scenarios, outperforming the previously proposed DA strategy. The codes will be published in GitHub11https://***/BeechburgPieStar/SDG-for-Robust-SEI © 2025 IEEE.

关键词： Deep learning

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Adaptive Prescribed-Time Neural Control of Nonlinear Systems via Dynamic Surface Technique

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4948-4958页

作者： Wang, Ping Yu, Chengpu Lv, Maolong Zhao, Zilong Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Air Force Engineering University College of Air Traffic Control and Navigation Xi'an710051 China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

The adaptive practical prescribed-time (PPT) neural control is studied for multiinput multioutput (MIMO) nonlinear systems with unknown nonlinear functions and unknown input gain matrices. Unlike existing PPT design schemes based on backstepping, this study proposes a novel PPT control framework using the dynamic surface control (DSC) approach. First, a novel nonlinear filter (NLF) with an adaptive parameter estimator and a piecewise function is constructed to effectively compensate for filter errors and facilitate prescribed-time convergence. Based on this, a unified DSC-based adaptive PPT control algorithm, augmented with a neural networks (NNs) approximator, is developed, where NNs are used to approximate unknown nonlinear system functions. This algorithm not only addresses the inherent computational complexity explosion associated with traditional backstepping methods but also reduces the constraints on filter design parameters compared to the DSC algorithm that relies on linear filters. The simulation showcases the effectiveness and superiority of the devised scheme by employing a two-degree-of-freedom robot manipulator. © 2024 IEEE.

关键词： Flexible manipulators

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3-D Electrical Impedance Imaging of Lung Injury

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Progress In Electromagnetics Research B 2023年 103卷 19-36页

作者： Ma, Ming Hao, Zepeng Wang, Qi Li, Xiuyan Duan, Xiaojie Wang, Jianming Feng, Hui School of Life Science TianGong University Tianjin300382 China School of Electronic and Information Engineering TianGong University Tianjin300382 China School of Computer Science and Technology TianGong University Tianjin300382 China Department of Rehabilitation Medicine The Affiliated Jiangning Hospital of Nanjing Medical University China

Pulmonary edema assessment is a key factor in monitoring and guiding the treatment of critically ill patients. To date, the methods available at the bedside to estimate the physiological correlation of pulmonary edema and extravascular pulmonary uid are often unreliable or require invasive measurements. The aim of this article is to develop an imaging method of reliably assessing pulmonary edema by utilizing functional electrical impedance tomography. In this article, the Split- Bregman algorithm is used to solve the Total Variation (TV) minimization problem in EIT image reconstruction. A thorax model is constructed according to CT images of rats. Through simulation and experiment, the proposed method improves the quality of reconstructed image signifficantly compared with existing methods. A pulmonary edema experiment in rats is also carried out. The development of pulmonary edema is analyzed numerically through EIT images. © (2023). All Rights Reserved.

关键词： Rats

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MODELING AN INTELLIGENT FRAMEWORK FOR OPTIMIZING UAV PATH PLANNING AND ANTI-COLLISION IN AGRICULTURE

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Scalable Computing 2025年第2期26卷 503-516页

作者： RAO, VIJAYA CHANDRA V KARTHIKEYAN, M.P. SAVITA KUMAR, VENKATA SAIRAM N BHALLA, ANUBHAV SWARUP, LAKSHYA Computer Science and Engineering G. Pullaiah College Of Engineering and Technology India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Civil Engineering. R.V.R & J.C College of Engineering Guntur India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India

Unmanned aerial vehicles (UAV) are increasingly utilized for monitor expansive farming to their effectiveness in observation and data gathered. Efficient path planning and collision prevention are critical for optimizing UAV operation in such settings. The efficiency can be controlled by the quality and declaration of UAV sensed data, as well as the difficulty of real world ecological variables that could impact path optimization and collision prevention. This research introduce a novel technique, the customizable dung beetle search tuned random forest (CDBS-RF) method, designed to improve UAV route planning. The CDBSRF method integrated the dung beetle search (DBS) algorithm, known for its robust optimization capabilities, with random forest (RF) to enhance optimal path security and effectiveness. This method dynamically adjusts path safety and effectiveness. This approach dynamically adjusts path planning parameter to make sure optimal route selection and collision prevention. The suggested technique was evaluated utilizing UAV-sensed data and implement in python based virtual environment. Experimental consequences demonstrate that the CDBS-RF approach considerably enhances UAV path planning performance, provide safer and more efficient navigation for self and more efficient navigation for self-sufficient tarp monitoring. The performance evaluation techniques include the planning time (0.789) and path length (21.526). By utilize advanced optimization and anti-collision algorithms, this technique offer a promising solution for improving UAV operations in agricultural surveillance. © (2025), (West University of Timisoara). All rights reserved.

关键词： Random forests

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Forecasting Stock Market Volatility Using XGBoost: A Time Series Analysis 3

Forecasting Stock Market Volatility Using XGBoost: A Time Se...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Sharma, Tripti Prasad, Sanjeev Kumar Prasad, Shreeya Verma, Indu Sharma, Anupama Ghaziabad India School of Computer Science Engineering Galgotias University Greater Noida India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India University Delhi-NCR India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798331528201

Forecasting stock market volatility presents significant challenges and opportunities for both practitioners and researchers in the financial sector. This paper explores the application of eXtreme Gradient Boosting (XGBoost), an advanced ensemble machine learning technique, to predict stock market volatility using time series data from the National Stock Exchange of India (NSEI). Our methodology utilizes past price data containing open, high, low, and close prices, along with capacity and adjusted close values, to capture the underlying patterns indicative of future market movements. We preprocess this data using techniques like normalization and feature engineering to enhance the predictive capabilities of our model. The XGBoost model is upgraded through extensive hyperparameter tuning and is considered in contrast to customary measurements like Mean Squared Error (MSE), Root Mean Squared Blunder (RMSE), and the R2 Score to survey its precision and adequacy. Our results demonstrate that XGBoost can effectively capture the complexities and non-linear relationships inherent in stock price movements, outperforming standard benchmark models. The findings suggest that machine learning models, particularly those based on tree ensemble methods like XGBoost, provide a robust tool for financial analysts seeking to mitigate risks and capture opportunities in volatile markets. This paper contributes to the ongoing discourse in financial econometrics by confirming the viability of advanced machine learning techniques in forecasting market dynamics, thus offering valuable insights for both theoretical advancement and practical application in financial forecasting. © 2024 IEEE.

关键词： Time series

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Design and Implementation of Side Channel Attack Based on Deep Learning LSTM

Design and Implementation of Side Channel Attack Based on De...

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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Center for Cyber Security Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9781665482585

Encryption algorithms and encryption devices both play a key role in ensuring the safety of data that has been encrypted. Various types of attacks, such as energy analysis, can be used to assess the reliability of the encryption devices. Since it was originally introduced, side channel attacks' deep learning-based methodology has drawn plenty of attention. This is one of several different attack strategies. In this paper, a side channel attack method based on the LSTM deep learning network is suggested. The method use Correlation Power Analysis (CPA) to find the relevant information in the side channel power consumption data. The choice of a suitable interest interval to utilize as the feature vector in the creation of the neural network model is then guided by the position of the interest points. The trials' findings show that the LSTM model outperforms both MLP and CNN in terms of how well it executes side channel attacks. © 2023 IEEE.

关键词： Side channel attack

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Exploring the Dark Side: A Systematic Review of Generative AI’s Role in Network Attacks and Breaches

Exploring the Dark Side: A Systematic Review of Generative A...

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Conference of Recent Trends and Applications of Soft Computing in engineering, RTASCE 2024

作者： Kurtović, Hamza Šabanović, Esma Almisreb, Ali Abd Saleh, Mohammed A. Ismail, Nurlaila Faculty of Computer Science and Engineering International University of Sarajevo Sarajevo Bosnia and Herzegovina Department of Cybersecurity International Information Technology University Almaty Kazakhstan College of Engineering Universiti Teknologi MARA Selangor Shah Alam Malaysia

ISBN: (纸本)9783031828805

Generative artificial intelligence has a paradoxical nature, which on the one hand brings innovative breakthroughs, and on the other hand brings with it a series of growing challenges. Processing of natural language, computer vision and generating creative content has been significantly changed and redefined, and therefore its darker background emerges through exploitation in cybercrime, which increases the complexity of protecting network systems and infrastructure. The paper explores the conflicting characteristics of generative artificial intelligence in the field of cybersecurity, focusing on the troubling issues it creates. With the advancement of generative artificial intelligence, powerful tools have been discovered to orchestrate new types of cyberattacks, and such attacks threaten not only the confidentiality and integrity of data, but also pose significant threats to business viability and national security. The research conducts a thorough analysis of the literature to explore the role of generative artificial intelligence in these attacks, highlighting its dual function as an enabler of technological progress and a facilitator of cyber risk. An in-depth examination of the vulnerabilities present in network systems and applications that are particularly vulnerable to generative attacks driven by artificial intelligence are sought to be addressed in the paper. Additionally, it assesses future defense methods and technological breakthroughs with the potential to reduce the dangers associated with generative artificial intelligence in the digital world, and aims to provide a special perspective on the dynamic interaction between generative artificial intelligence and cybersecurity issues by analyzing and studying previous research and emphasizing the need to create strong and effective solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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MQTT-Based Android Chat Application for IoT

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SN computer science 2022年第5期3卷 402页

作者： Anne, Lahari Nandan, Tanmayi Kunj, Mehta Kumar, S. Ananda Mahesh, G. Sangeetha, R. School of Computer Science and Engineering VIT Vellore India BMSIT Bangalore India School of Information Technology and Engineering VIT Vellore India

In recent times there is an increase in the number of Android chat applications in the digital market. Most of these applications are based on IoT protocols like AMQP, XMPP, etc. These protocols are optimal in networks with stable connections, equipped nodes and high power devices. Through our work we propose a chat platform that is based on MQTT protocol with the aim to reduce the load on the devices with this lightweight protocol. The protocol is implemented using Eclipse MQTT server that has plugins and modules to support MQTT implementation on Android platform. Android Studio is used for the simulation and Mosquitto broker is used for publish/subscribe model implementation. The performance of the proposed model is compared with existing chat applications and the MQTT protocol features are compared with other application layer protocols. The stimulated model is tested and verified on android mobiles with version higher than 4.0. It is found that MQTT offers certain features which are better suitable in networks with constrained devices, less bandwidth capacity, or poor connection in remote locations. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Android chat Eclipse Server Mosquitto MQTT Publish/subscribe

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