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arXiv 2025年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe Kim, Dong In Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and enhancing network security. In this paper, we propose a pioneering paradigm of mobile edge quantum computing (MEQC) that integrates quantum computing capabilities into classical edge computing servers that are proximate to mobile devices. To conceptualize the MEQC, we first design an MEQC system, where mobile devices can offload classical and quantum computation tasks to edge servers equipped with classical and quantum computers. We then formulate the hybrid classical-quantum computation offloading problem whose goal is to minimize system cost in terms of latency and energy consumption. To solve the offloading problem efficiently, we propose a hybrid discrete-continuous multi-agent reinforcement learning algorithm to learn long-term sustainable offloading and partitioning strategies. Finally, numerical results demonstrate that the proposed algorithm can reduce the MEQC system cost by up to 30% compared to existing baselines. © 2025, CC BY.

关键词： Qubits

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Blockchain based electronic educational document management with role-based access control using machine learning model

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Scientific Reports 2025年第1期15卷 1-19页

作者： P. Chinnasamy B. Subashini Ramesh Kumar Ayyasamy Ajmeera Kiran Binay Kumar Pandey Digvijay Pandey Mesfin Esayas Lelisho Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Engineering Srivilliputtur India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Uttrarakhand India Department of Technical Education (Government of Uttar Pradesh) Kanpur Uttar Pradesh India Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Ethiopia

The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways. Our study aims to propose a novel electronic educational document management technique using a blockchain-based fuzzy feed-forward convolutional temporal neural network that detects malicious users. Here, the training is carried out based on NLP analysis in document word weight indexing. This document management access control is based on role-based access with simulated remora swarm optimisation. In order to identify malicious users, this suggested system logs access requests on the blockchain and authenticated users. The findings demonstrate that this suggested architecture performs as intended in every case. The experimental analysis is based on a malicious user detection dataset regarding Prediction accuracy, Mean average precision, F-measure, Latency, QoS, Contract execution time, and Throughput. Based on dataset feature analysis, the proposed B-FCTNN_SRSO achieved a prediction accuracy of 98%, a mean average precision (MAP) of 95%, and an F1 score of 97%, with a latency of 96%. Additionally, based on blockchain security analysis, the B-FCTNN_SRSO attained a QoS of 97%, a precision of 94%, and a throughput of 96%.

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Focus on the Stability of Large Systems: Toward Automatic Prediction and Analysis of Vulnerability Threat Intelligence 6

Focus on the Stability of Large Systems: Toward Automatic Pr...

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6th IEEE International Conference on data science in Cyberspace, DSC 2021

作者： Song, Shiwen Wu, Qiong Zheng, Xin Wang, Peng Dou, Yuchen Li, Zhongwen Zhai, Lidong Institute Of Information Engineering Chinese Academy Of Sciences BeiJing China Bag Data Academy Zhongke Beijing China Institute Of Computing Technology Chinese Academy Of Sciences Cas Key Laboratory Of Network Data Science And Technology Beijing China

ISBN: (纸本)9781665418157

With the increase in the number of users and business volume, the business systems of Internet companies are becoming more and more complex, resulting in a surge in the number of alarms. A large number of dirty alarms add a huge workload to security operations, which indirectly pose a large number of threats to business systems. At present, most systems use the method of accessing third-party Threat Intelligence to assist operators to realize automatic handling of alarms. However, this method has lagging and accuracy problems, making this work always difficult to meet the requirements of fast and accurate. This article proposes a new method for gathering vulnerability Threat Intelligence, which can obtain vulnerability information in advance of security announcements issued by security vendors. By analyzing the vulnerability disclosure process, this method obtains vulnerability information from the original source submitted by open source mail group, of developers. We used NLP technology and XGBoost model to automatically analyze the vulnerability information, and finally generate FINTEL. The experimental result shows that this method has an accuracy of 93%, and can obtain vulnerability information 10h to 7 days before security vendors release. The scope of application covers all open source code repositories and some closed source repositories. © 2021 IEEE.

关键词： Open source software

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Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions

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Computers, Materials & Continua 2022年第10期73卷 1799-1815页

作者： Muhammad Hamza Azam Mohd Hilmi Hasan Azlinda A Malik Saima Hassan Said Jadid Abdulkadir Centre of Research in Data Science Computerand Information Sciences DepartmentUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Petroleum Engineering Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Institute of Computing Kohat University of Science and TechnologyKohat26000K.P.KPakistan

Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future *** price forecasting have been a critical input to energy corporations’strategic decision-making systems over the last 15 *** strategies have been utilized for price forecasting in the past,however Artificial Intelligence Techniques(Fuzzy Logic and ANN)have proven to be more efficient than traditional techniques(Regression and Time Series).Fuzzy logic is an approach that uses membership functions(MF)and fuzzy inference model to forecast future electricity *** c-means(FCM)is one of the popular clustering approach for generating fuzzy membership ***,the fuzzy c-means algorithm is limited to producing only one type of MFs,Gaussian *** generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem *** a result,for the best and most improved results for electricity price forecasting,an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is ***,the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM *** approach is used to compute and improve forecasting accuracy for electricity prices,where Australian Energy Market Operator(AEMO)data is *** results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted.

关键词： Fuzzy logic fuzzy C-means type-1 fuzzy membership function electricity price forecasting

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CeTrivium:A Stream Cipher Based on Cellular Automata for Securing Real-Time Multimedia Transmission

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Computer Systems science & Engineering 2023年第12期47卷 2895-2920页

作者： Osama S.Younes Abdulmohsen Alharbi Ali Yasseen Faisal Alshareef Faisal Albalawi Umar A.Albalawi Faculty of Computer and Information Technology University of TabukTabuk71491Saudi Arabia Faculty of Computers and Information Menoufia UniversityMenoufia13829Egypt School of Computing&Data Science Wentworth Institute of TechnologyBoston02115USA

Due to their significant correlation and redundancy,conventional block cipher cryptosystems are not efficient in encryptingmultimedia *** based onCellularAutomata(CA)can provide amore effective *** CA have recently gained recognition as a robust cryptographic primitive,being used as pseudorandom number generators in hash functions,block ciphers and stream *** have the ability to perform parallel transformations,resulting in high throughput ***,they exhibit a natural tendency to resist fault *** stream cipher schemes based on CA have been proposed in the ***,their encryption/decryption throughput is relatively low,which makes them unsuitable formultimedia *** and Grain are efficient stream ciphers that were selected as finalists in the eSTREAM project,but they have proven to be vulnerable to differential fault *** work introduces a novel and scalable stream cipher named CeTrivium,whose design is based on *** is a 5-neighborhood CA-based streamcipher inspired by the designs of Trivium and *** is constructed using three building blocks:the Trivium(Tr)block,the Nonlinear-CA(NCA)block,and the Nonlinear Mixing(NM)*** NCA block is a 64-bit nonlinear hybrid 5-neighborhood CA,while the Tr block has the same structure as the Trivium stream *** NM block is a nonlinear,balanced,and reversible Boolean function that mixes the outputs of the Tr and NCA blocks to produce a *** of CeTrivium has indicated that it can resist various attacks,including correlation,algebraic,fault,cube,Meier and Staffelbach,and side channel ***,the scheme is evaluated using histogramand spectrogramanalysis,aswell as several differentmeasurements,including the correlation coefficient,number of samples change rate,signal-to-noise ratio,entropy,and peak signal-to-noise *** performance of CeTrivium is evaluated and compared with other state-of-

关键词： Stream ciphers cellular automata securing real-time streaming cryptography CeTrivium

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A Machine Learning-Based Multi-Model Approach for Predicting Post-Deep Brain Stimulation Outcomes in Alzheimer’s disease Patients

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Recent Patents on Engineering 2025年

作者： Thakur, Gopal Kumar Kulkarni, Shridhar Raguru, Jaya Krishna Tayubi, Iftikhar Aslam Rao, P Srinivasa Mudarakola, Lakshmi Prasad Reddy, P Chandra Shaker Department of Data Sciences Harrisburg University of Science & Technology Harrisburg PA United States Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Faculty of Computing and Information Technology Rabigh (FCITR) King Abdulaziz University Jeddah Saudi Arabia School of CSE Department of ISE Jain University Karnataka Bengaluru India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Warangal India

Objectives and Background: Deep-brain stimulation (DBS) is a demonstrated rehabilitation for Alzheimer's Disease (AD), regularly resulting in an upgrade of engine work. In any case, some unfortunate incidental upshots can occur past DBS, which can deteriorate the patient's satisfaction. In this way, the medical group must painstakingly choose patients over whom to perform DBS. In the past, there have been a few endeavors to associate pre-usable information and DBS experimental results, generally centered on engine symptomatology. Objectives & Methodology: A machine learning (ML)-based strategy called a multimodel pipeline alluded to as FlowModel is designed and implemented to anticipate an enormous number of DBS medical results for AD. It is composed of a patented Artificial Neural Network (ANN) for processing medical data, using a conventional image handling technique to extract morphological biomarkers from T1 imaging, and employing a Support Vector Machine (SVM) to perform regressions. We authenticated it in 256 AD patients who underwent DBS. Results: The FlowModel demonstrated high predictive precision, achieving a correlation coefficient of as high as 0.82 and successfully predicting 63 out of 84 clinical outcome scores. Importantly, FlowModel outperformed traditional linear models, demonstrating the capability to learn effectively from preoperative data alone. Conclusion: The uniqueness of this study lies in its ability to predict multiple clinical outcomes across different modalities, independent of stimulation parameters. The introduction of this ML pipeline contributes to the precision medicine landscape by providing a robust, data-driven tool to select patients for DBS, potentially improving clinical decision-making and patient outcomes. © 2025 The Author(s). Published by Bentham science Publishers.

关键词： AD clinical prediction DBS machine learning neural networks support vector machine

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COVID-19 mRNA Vaccine Degradation Prediction Using LR and LGBM Algorithms

COVID-19 mRNA Vaccine Degradation Prediction Using LR and LG...

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2021 Asian Conference on Intelligent computing and data sciences, ACIDS 2021

作者： Ing, Soon Hwai Abdullah, Azian Azamimi Harun, Nor Hazlyna Kanaya, Shigehiko Perlis Malaysia Perlis Malaysia Data Science Research Lab School of Computing School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia Computational Systems Biology Lab Graduate School of Information Science Nara Institute of Science and Technology IkomaNara Japan

The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvellous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19. Two machine learning algorithms, which are, Linear Regression (LR) and Light Gradient Boosting Machine (LGBM) are proposed for models development using Python language. dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that LGBM (0.2447) performs better than LR (0.3957) for this study when evaluated with the RMSE metric. © Published under licence by IOP Publishing Ltd.

关键词： Vaccines

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Blockchain-Enabled Quality Assurance and Traceability in the Indian Confectionery Market

Blockchain-Enabled Quality Assurance and Traceability in the...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Subashini, B. Shanthi, P. Venkatesh, K. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamilnadu kattankulathur India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai119 India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu kattankulathur India

ISBN: (纸本)9798331543617

India, whose population growth is its greatest asset cannot afford to let its people eat confectionery that is either tainted or somehow polluted, since this would lead to extensive malnutrition. Traditional food supply chains are characterized by centralization and the problems associated with it such as having a single point of failure and producing inconsistent products while compromising the quality, along with data loss. With the need for daily reports in all regions of India regarding food fraud, contamination, and adulteration, a clearly advanced decentralized model is sought after for the creation of a supply chain. This paper introduces the supply chain network for the Indian confectionery market based on blockchain to better address quality claims, contamination, and transparency. This enables real-time traceability within the ecosystem, by using IoT strategies along with the Ethereum blockchain leading to better product authentication and resulting in higher consumer confidence. Blockchain creates a secure data tracking function, while smart contracts can be used to define the process - such as IoT monitoring of temperature. The united method enables verified data to be available to the consumer, energy efficiency, and trust across the supply chain. © 2024 IEEE.

关键词： Smart contract

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Deep Graph Reinforcement Learning for UAV-Enabled Multi-User Secure Communications

arXiv

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arXiv 2025年

作者： Tang, Xiao Zhao, Kexin Shen, Chao Du, Qinghe Wang, Yichen Niyato, Dusit Han, Zhu School of Information and Communication Engineering Xi’an Jiaotong University Xi’an710049 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518063 China School of Electronics and Information Northwestern Polytechinical University Xi’an710072 China Faculty of Electronics and Information Engineering Xi’an Jiaotong University Xi’an710049 China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering University of Houston Houston77004 United States

While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rather challenging. The conventional approaches extended from optimization perspectives are usually quite involved, especially when jointly considering factors in different scales such as deployment and transmission in UAV-related scenarios. In this paper, we address the UAV-enabled multi-user secure communications by proposing a deep graph reinforcement learning framework. Specifically, we reinterpret the security beamforming as a graph neural network (GNN) learning task, where mutual interference among users is managed through the message-passing mechanism. Then, the UAV deployment is obtained through soft actor-critic reinforcement learning, where the GNN-based security beamforming is exploited to guide the deployment strategy update. Simulation results demonstrate that the proposed approach achieves near-optimal security performance and significantly enhances the efficiency of strategy determination. Moreover, the deep graph reinforcement learning framework offers a scalable solution, adaptable to various network scenarios and configurations, establishing a robust basis for information security in UAV-enabled communications. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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