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8th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2023

作者： Xin, Yutong Peng, Jingyi Lu, Zejian Lee, Yong Yang, Yang China Industrial Control Systems Cyber Emergency Response Team 100029 China China Academic of Electronics and Information Technology 100041 China School of Artificial Intelligence Beijing University of Posts & Telecommunications 100876 China Collage of Electrical and Computer Engineering Sungkyunkwan University 16419 Korea Republic of

ISBN: (纸本)9798350317923

This paper focuses on studying the deep learning (DL) application of neural networks to solve the reception of single-antenna OFDM signals. Specifically, in multi-antenna scenarios, the channel model is more complex compared to single-antenna cases. By leveraging the characteristics of DL, such as automatic learning of parameters using deep neural networks, we treat the reception process of MIMOOFDM signals as a black box and utilize neural networks to accomplish the signal reception task. Moreover, we propose the data-driven multi-pilot convolution neural network for MIMO-OFDM receiver (DMCNet). By incorporating complex convolution and complex fully connected structures, we design a receiver network to recover the transmitted signals from the received signals. We validate the accuracy and robustness of DMCNet under different channel conditions, comparing the bit error rates with different schemes. Additionally, we discuss the factors influencing various channel effects. Experimental results demonstrate that the DL-based reception scheme exhibits promising feasibility in terms of accuracy and interference resistance when compared to traditional approaches. © 2023 IEEE.

关键词： MIMO systems

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Transparent AI for Adaptive Fraud Detection 19

Transparent AI for Adaptive Fraud Detection

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Uzoma, Agomuo Kingsley Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions;however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an F1-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for real-time fraud detection, which is essential for safeguarding the integrity of modern financial systems. © 2025 IEEE.

关键词： Decentralized finance

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Protection of Access Pattern 7

Protection of Access Pattern

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7th International Conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Gao, Biao Jia, Shijie Yin, Peng Zhang, Xueying State Key Laboratory of Information Security Institute of Information Engineering Cas School of Cyber Security University of Chinese Academy of Sciences Beijing China Defence Industry Secrecy Examination and Certification Center School of Cyber Security University of Chinese Academy of Sciences Beijing China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400708688

Encryption is insufficient to ensure the system security because the access patterns of user will still reflect the information about the data and serve as an indicator for adversary to infer the sensitive information. We conclude the related work on the access pattern protection to provide a thorough literature review of history independence and oblivious random access machine, which address the issues of static and dynamic access pattern observations respectively. © 2023 Owner/Author.

关键词： Cryptography

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A New RISC-V CPU for Safety-Critical systems

A New RISC-V CPU for Safety-Critical Systems

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Mediterranean Conference on Embedded Computing (MECO)

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Bratislava Slovakia

In this paper, we propose a novel hardening technique against Single Event Effects, which enables high-frequency operation and does not cause large power consumption and area overhead. The protection is based on redundancy, separation of the pipeline into two main sections, and is fully applicable at Register Transfer Level. It provides fast detection of faults, simple recovery by a flush of the pipeline, and allows a large prediction unit to be unprotected. We present the protection scheme at the core with the RISC-V instruction set. Simulations confirm that the protection can handle the injected faults. Synthesis shows that protection lowers the maximum frequency by only 3.9%. The area increase is only 108% and can be even lower. Total power consumption increased by 119%.

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Hardened Processor Architecture

Hardened Processor Architecture

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International Conference on Mixed Design of Integrated Circuits and systems (MIXDES)

Processors for terrestrial applications are protected by Earth's atmosphere and magnetic field, so the probability of single event effects (SEE) is significantly lower. Shrinking of the technology nodes allows higher operational frequency and performance, but their susceptibility to SEE is increasing. Nowadays satellites and safety critical automotive applications require more and more computational power. The hardening by design has been implemented mainly by redundancy of the whole cores. The consequences are lower frequency, larger area, and higher power consumption. We propose a novel hardening technique, which enables high frequency operation and doesn't cause large power consumption and area overhead. The protection is based redundancy and separation of the pipeline into two main sections. It provides a fast detection of faults, simple recovery by flush of the pipeline, and allows a large prediction unit to be unprotected. The whole protection scheme can be implemented at the Register Transfer Level. We present the protection scheme at the core with RISC-V instruction set. Simulations confirm the protection can handle the injected faults. Synthesis shows, the protection lowers the maximum frequency by only about 4.7%. The area increase is only about 118% and can be even lower.

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Framework for Improving the Effectiveness of Discussions at English-Language Articles Analysis 5

Framework for Improving the Effectiveness of Discussions at ...

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5th IEEE International Conference on Advanced information and Communication Technologies, AICT 2023

作者： Zdebskyi, Petro Berko, Andrii Vysotska, Victoria Voloshyn, Serhii Chyrun, Sofia Schuchmann, Vadim Information Systems and Networks Lviv Polytechnic National University Lviv Ukraine Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine Osnabrück University Osnabrück Germany Lviv Polytechnic National University Telecommunication Department Lviv Ukraine West Ukrainian National University Software Engineering Department Ternopil Ukraine

This paper proposes a discussion system that should be more suitable for this kind of activity than social networks. It is also proposed to use the existing pretrained model called GPT-4, but after training it on data... 详细信息

ISBN: (纸本)9798350372571

关键词： Semantics

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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A power model for monitoring environmental parameters on the edge

A power model for monitoring environmental parameters on the...

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2021 IEEE International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2021

作者： Hurbungs, V. Fowdur, T.P. Bassoo, V. University of Mauritius Faculty of Information Communication Digital Technologies Software and Information Systems Department University of Mauritius Faculty of Engineering Electrical and Electronic Department

ISBN: (纸本)9781665412629

In the Internet of Things, connected devices capture real-time data which is sent to the Fog or Cloud layer for processing. The transfer of large data volumes is subject to latency and variable transfer rates. Edge computing is an emerging trend that aims at processing data nearer to its source thereby reducing data transfer and the need for continuous connectivity. The main contribution of this paper is to investigate the power requirements of a battery-powered Raspberry Pi which acts as an Edge node. The latter monitors essential environmental parameters such as air temperature, air humidity, soil moisture and light intensity. The sensors are connected to an Arduino interfaced with the Raspberry Pi. A software-based power model relative to CPU utilization is proposed to measure the power consumption of the Raspberry Pi. A Java program is used to capture and save the sensor values in a database for further analysis. Experiments demonstrate that the power usage increases linearly with CPU utilization. The proposed power model has a root mean squared error of 0.023 and 0.036 respectively when one and two Arduinos are connected. The power requirement of a single Raspberry Pi with four Arduinos is estimated to be 2.58 Watt when the Java process is running. It is also projected that a 6, 500 mAh battery can power this type of environmental setup that monitors plant growth for 12 hours. © 2021 IEEE.

关键词： Data transfer

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N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages 22

N-Gram-Based Machine Learning Approach for Bot or Human Dete...

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6th International Conference on Intelligent systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Kavadi, Durga Prasad Sanaboina, Chandra Sekhar Patan, Rizwan Gandomi, Amir Department Of Information Technology B V Raju Institute Of Technology Narsapur Telangana India Department Of Computer Science And Engineering University College Of Engineering Kakinada Jawaharlal Nehru Technological University Kakinada India Decentralized Science Lab College Of Computing And Software Engineering Kennesaw State University MariettaGA30060 United States Faculty Of Engineering And Information Systems University Of Technology Sydney UltimoNSW Australia

ISBN: (纸本)9781450396288

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection. © 2022 ACM.

关键词： Feature Selection

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Highly Accurate Crow Search Optimized CNN Classifier for Cyclone Prediction

Highly Accurate Crow Search Optimized CNN Classifier for Cyc...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ramya Kavin K. S R. Anuja A. T. R. Krishna Priya D. Karthikeyan Kiruba Thangam Raja Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Tamilnadu India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural Networks (CNNs) with the optimization capabilities of the Crow Search Algorithm (CSA). The resulting model, called Crow Search Optimized CNN (CSO-CNN), harnesses the feature extraction and classification strengths of CNNs, while the CSA is used to optimize the hyperparameters of the CNN architecture for optimal performance. The model is trained and validated on a comprehensive dataset that includes satellite imagery and meteorological data from a wide range of cyclone events. The CSO-CNN achieves remarkable classification accuracy, surpassing traditional CNN models and other leading techniques. Its ability to quickly and accurately assess the likelihood of cyclone formation provides crucial decision support for disaster management authorities, enabling timely and effective response strategies. By integrating the CSA optimization technique with the CNN framework, this study presents a novel approach to cyclone prediction. It highlights the potential of combining advanced Machine Learning (ML) algorithms to tackle complex environmental challenges and deepen our understanding of intricate atmospheric phenomena.

关键词： Accuracy Atmospheric modeling Disaster management Predictive models Prediction algorithms Feature extraction Cyclones Satellite images Convolutional neural networks Optimization

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