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A Dynamic and Deadline-Oriented Road Pricing Mechanism for Urban Traffic Management

Tsinghua science and technology 2022年第1期27卷 91-102页

作者： Jiahui Jin Xiaoxuan Zhu Biwei Wu Jinghui Zhang Yuxiang Wang the School of Computer Science and Engineering Southeast UniversityNanjing 211189China the Department of Computer Science and Engineering Hangzhou Dianzi UniversityHangzhou 310018China

Road pricing is an urban traffic management mechanism to reduce traffic ***,most of the road pricing systems based on predefined charging tolls fail to consider the dynamics of urban traffic flows and travelers’demands on the arrival *** this paper,we propose a method to dynamically adjust online road toll based on traffic conditions and travelers’demands to resolve the above-mentioned *** method,based on deep reinforcement learning,automatically allocates the optimal toll for each road during peak hours and guides vehicles to roads with lower toll ***,it further considers travelers’demands to ensure that more vehicles arrive at their destinations before their estimated arrival *** method can increase the traffic volume effectively,as compared to the existing static mechanisms.

关键词： road pricing traffic congestion al eviation deep reinforcement learning

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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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computer Modeling in engineering & sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah Sulaiman Abdullah Alateyah Department of Civil Engineering National Institute of Technology PatnaPatnaIndia Department of Computer Engineering and Applications GLA UniversityMathuraIndia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

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MmHand: Towards Pixel-Level-Accuracy Hand Localization Using A Single Commodity mmWave Device

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IEEE Internet of Things Journal 2025年第12期12卷 20800-20814页

作者： Zhang, Xiaoyu Li, Zhengxiong Xu, Chenhan Song, Luchuan Li, Huining Xue, Hongfei Wu, Yingxiao Xu, Wenyao State University of New York at Buffalo Department of Computer Science and Engineering AmherstNY14068 United States University of Colorado Denver Department of Computer Science and Engineering DenverCO80204 United States North Carolina State University Department of Computer Science RaleighNC27695 United States University of Rochester Department of Computer Science RochesterNY14627 United States University of North Carolina at Charlotte Department of Computer Science CharlotteNC28223 United States Hangzhou Dianzi University College of Computer Science and Technology Hangzhou Zhejiang310018 China

The hand localization problem has been a longstanding focus due to its many applications. The task involves modeling the hand as a singular point and determining its position within a defined coordinate system. However, due to data modality limitations, existing hand localization technologies face several challenges. For example, vision-based localization raises privacy concerns, while wearable-based methods compromise user comfort. In this paper, we introduce mmHand, a new device-free, privacy-preserving dynamic hand localization system with pixel-level accuracy, using a single commodity mmWave device. We first propose a mmImage generation tool to fully extract spatial information from raw mmWave data and introduce a novel 2D image-format representation of mmWave data. Next, we design a framework that provides a new quality evaluation method and pixel space labeling for the mmWave data. Finally, we present a cross-modality spatial feature-enhanced model with high spatial feature extraction capabilities, which can accurately localize hand positions at the pixel level in the mmWave radar U-V pixel coordinate system. We evaluate the system with experiments on 12 subjects in three scenarios, and the results across four metrics demonstrate the effectiveness of our hand localization system. © 2025 IEEE.

关键词： Data privacy

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Preventing 51% Attack by Using Consecutive Block Limits in Bitcoin

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IEEE Access 2024年 12卷 77852-77869页

作者： Mahmood Babur, Sohail Ur Rehman Khan, Shafiq Yang, Jing Chen, Yen-Lin Soon Ku, Chin Yee Por, Lip Government Murray College Department of Information Technology Sialkot51010 Pakistan Capital University of Science and Technology Department of Computer Science Islamabad44730 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

In permissionless blockchain systems, Proof of Work (PoW) is utilized to address the issues of double-spending and transaction starvation. When an attacker acquires more than 50% of the hash power of the entire network, they gain the ability to engage in double-spending activities, posing a significant threat to the PoW consensus algorithm. This research focuses on the consensus algorithm employed in the Bitcoin system, explaining how it operates and the security challenges it faces. The proposed modification to the PoW algorithm imposes a restriction on miners: they are not allowed to accept consecutive blocks from the same miner into the final local blockchain to prevent the 51% attack problem. This modification supports transactions that require six confirmations. In the event an attacker attempts a 51% attack with a private chain that consists of fewer than 6 blocks, it becomes easier to detect a double-spending attack before accepting the attacker's private chain. The modified algorithm introduces a 'Safe Mode Detection Algorithm' that scrutinizes incoming blocks for adjustments at the top of the local blockchain. If inconsistencies are identified, the consensus algorithm proceeds cautiously by comparing the UTXO dictionaries from the attacker's chain with those from the miner's own blockchain. This meticulous comparison aims to detect instances of double-spending. If such instances are detected, the miner rejects the attacker's chain, establishing a double-spend-free environment and thwarting 51% attacks. © 2013 IEEE.

关键词： Blockchain

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Clinical deep model to analyse medical multivariate time-series data for health diagnosis

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Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

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Mechanical and high temperature tribological performances of CrHfNbTaTiCxNy high entropy coatings: Experimental and theoretical study

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Ceramics International 2025年第17期51卷 23140-23156页

作者： Yin, Mengjuan Liang, Wenping Miao, Qiang Yu, Haiyang Gao, Jiumei Xu, Yuehao Blackwood, Daniel J. Jin, Hongmei College of Materials Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Materials Science & Engineering National University of Singapore 9 Engineering Drive 1 Singapore 117575 Singapore Agency for Science Technology and Research 1 Fusionopolis Way Singapore 138632 Singapore Wuxi Research Institute Nanjing University of Aeronautics and Astronautics China

The present paper was focused on understanding of relationships between structure, composition, mechanical and high temperature tribological performances of the CrHfNbTaTiCxNy high entropy coatings. The CrHfNbTaTiCxNy coatings were produced using a novel double-glow plasma surface alloying method, which revealed a graded microstructure and composition, strongly affected adhesion strength and tribological performance. It was found that the CrHfNbTaTiCN coating demonstrated excellent high temperature wear resistance among these coatings. The observed unique properties included that the varied trend of the calculated Vickers hardness values of the coatings was consistent well with the experimental ones, and the CrHfNbTaTiCN exhibits relatively good thermal stability at high temperature. In addition, the insights into tribological properties showed that the high-entropy carbonitrides coating always show lowest wear rate, ∼13 times and ∼32 times lower than that of Ti substrate at 600 °C and 700 °C, due to its highest H/E∗ and H3/E∗2 values, low coefficient of thermal expansion α and G values obtained from the theoretical calculation, as well as the formation of dense metal-oxidation layer. The wear rate values of CrHfNbTaTiCxNy coating at 700 °C were consistently lower than that at 600 °C, owing to the formation of the stable wear-resistant glaze contributing to the lower wear rate at the higher temperature. © 2025

关键词： Thermolysis

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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Towards Interpretable Machine-Learning-Based DDoS Detection

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SN computer science 2024年第1期5卷 1-15页

作者： Zhou, Qianru Li, Rongzhen Xu, Lei Nallanathan, Arumugam Yang, Jian Fu, Anmin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Electronic Engineering and Computer Science Queen Mary University of London London E1 4NS United Kingdom

The Internet is the most complex machine humankind has ever built, and thus, it is difficult to defend it from attacks. The most common attack to the Internet is DDoS attacks. With the growing popularity for QUIC protocol, DDoS detection tasks are increasingly rely on machine learning (ML), which is based on black-box model and cannot explain its decision. A interpretable and transparent ML model is the foundation of a trustworthy ML-based DDoS attack detection. Current ML interpretation methodologies in cyber intrusion detection are heuristic, which is neither accurate nor sufficient. This paper proposed a rigorous interpretable ML-driven omnipotent DDoS detection approach, based on knowledge compilation technologies. Details of rigorous interpretation calculation process for the ML model are presented, which include an accelerated prime implicant calculation method driven by knowledge compilation for the DDoS detection ML model, and a map, combine, and merge (M &M) algorithm to discretize continuous features into Boolean expression. The proposed Prime implicant reasons calculation algorithm has been tested on a DDoS LOIC and HOIC attack detection ML model with 100% accuracy, trained with real-life DDoS data. An exhaust list of explanations are given in detail as rules for the omnipotent DDoS intrusion detection learnt by the ML model used. As the ML interpretation method is an SAT problem-solving process, the explanations are rigorous and sufficient reasons for the ML model for DDoS attack detection, and are believed to shade light on DDoS detection research work in cybersecurity community. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DDoS attack Explainable artificial intelligence Intrusion detection system Knowledge compilation Prime implicate

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Introduction to the Special Issue on the Bottleneck of Blockchain Techniques Scalability,Security and Privacy Protection

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computer Modeling in engineering & sciences 2024年第12期141卷 1933-1937页

作者： Shen Su Daojing He Neeraj Kumar Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen518055China Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004India

Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising *** the success of existing blockchain architectures like Bitcoin,Ethereum,Filecoin,Hyperledger Fabric,BCOS,and BCS,current blockchain applications are still quite *** struggles with scenarios requiring high-speed transactions(e.g.,online markets)or large data storage(e.g.,video services)due to consensus efficiency *** restrictions pose risks to investors in blockchain-based economic systems(e.g.,DeFi),deterring current and potential *** protection challenges make it difficult to involve sensitive data in blockchain applications.

关键词： services Ether limitations

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