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2nd International Conference on Big Data, Information and computer Network, BDICN 2023

作者： Bian, Ning Wang, Li University of Science and Technology Liaoning College of Computer and Software Engineering Anshan China

ISBN: (纸本)9798350321364

Collecting a lot of feature data can enhance the prediction impact for the recommender system's CTR prediction task. The second-order feature cross factorization machine model, which is the most popular, has the ability to automatically learn implicit features based on existing features. The factorization machine model, however, has the following issues: (1) The encoding is fixed, and the same feature represents the same in various contexts. (2) The encoding type is single, and the feature is represented by a single weight vector, which cannot completely express the information of the feature. To harvest possible feature information, recommender systems employ neural networks. A novel method of feature intersection is created by the Compressed Interaction Network (CIN) component suggested in xDeepFM. In order to address the issue of encoding fixation in the factorization machine, the original feature encoding is reconstructed in this paper using xDeepFM in conjunction with the feature refinement network (FRNet). Additionally, different feature representations are expressed in various contexts for the same feature. To address the issue of a single encoding type in factorization machines, the field-aware interactive machine (FIM) component provides feature information at the domain and feature levels, respectively. The model is known as the Field-aware Feature Refinement Depth Factorization Machine (FRDFFM). On the Criteo dataset and the Avazu dataset, respectively, the suggested model was evaluated, and it produced promising results. © 2023 IEEE.

关键词： Forecasting

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An Unified PSO and Fuzzy Satisfaction Framework for Power Flow Optimization

An Unified PSO and Fuzzy Satisfaction Framework for Power Fl...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Chatterjee, Vedatrayee Roy, Dhiman Maitra, Arnab Ghosh, Soubhik Wania, Rumsha Waqia Asansol Engineering College Computer Science & Engineering Asansol India Bengal College of Engineering & Technology Computer Science & Engineering Durgapur India

ISBN: (纸本)9798350390759

This study introduces a new method for multi-objective optimization (MOO) in electrical power systems, aiming to minimise emission release, power losses and fuel costs concurrently. Diverging from traditional techniques that primarily target cost reduction, the proposed Power Flow Optimization (PFO) framework combines Fuzzy Satisfaction Maximization strategy with Particle Swarm Optimization (PSO), acknowledging the vagueness in decision-makers' objectives. By formulating the optimization problem as a fuzzy satisfaction maximization problem, the method aims to find optimal trade-off solutions that balance conflicting objectives. Testing the PFO on a standard IEEE 30-bus system with six generators demonstrates its effectiveness in providing solutions that optimize both actual and imaginary powers while considering fuel cost, emission reduction, and power loss, thereby offering valuable insights for efficient power system operation. © 2024 IEEE.

关键词： Load flow optimization

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Hybrid Fuzzy Data Aggregation and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第20期48卷 95-106页

作者： Alhijaj, Asaad A. Marzook, Ali K. Hussein, Dheyaa Mezaal Alkenani, Jawad Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq University of Basrah College of Engineering Department of Electrical Engineering Basrah Iraq University of Technology Office of the President of the University Baghdad Iraq Department of Computer Science Shatt Al-Arab University College Basra Iraq

Wireless sensor networks (WSNs) are networks with many sensor nodes that are utilized for various purposes, including the military and medical. In hazardous circumstances, precise data aggregation and routing are essential, and the energy consumption of the sensors needs to be closely controlled. Nonetheless, there is a significant chance of redundant data because of external factors and nearby sensors. A multitude of information can be found in large datasets, some of it unnecessary and others useful. This redundancy negatively impacts performance in terms of redundant transmission and computing costs. However, data aggregation might help a network get rid of unnecessary data. In this work, we present a hybrid protocol called fuzzy data aggregation with fuzzy spider monkey optimization routing protocol (FDA-FSMORP) that represents an intelligent approach to collecting sensor data in HWSNs considering energy consumption. The results indicated that the suggested method beat in minimizing data latency our approach reduced energy consumption by 73% using energy more effectively when compared to our simulated outcomes. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Sensor nodes

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A method for determining the spatial pattern of forest trees based on the uniformity theory

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Journal of Forestry Research 2024年第6期35卷 148-159页

作者： Yeqiong Shi Xiulong Gao Chunling Lang Chuanwen Luo College of Computer Science and Information Engineering Harbin Normal UniversityHarbin 150040People’s Republic of China College of Vocation and Technical Education Guangxi Science and Technology Normal UniversityLaibin 546199People’s Republic of China College of Mathematics and Computer Science Guangxi Science and Technology Normal UniversityLaibin 546199People’s Republic of China College of Forestry Northeast Forestry UniversityHarbin 150040People’s Republic of China

The spatial pattern of trees is an important feature of forests,and different spatial patterns of trees exhibit different ecological *** has confirmed that natural forests with random patterns have higher biodiversity and stronger resistance to unstable factors such as pests and *** if they are disturbed or destroyed by unstable factors such as pests and diseases,they can still recover and rescue themselves;while artificial forests with uniform and clustered patterns have lower biodiversity and are susceptible to unstable factors such as pests and *** once pests and diseases occur,it’s more difficult for them to *** order to promote the healthy and stable develop-ment of the forestry industry and protect the diversity of the biological environment,it is necessary to protect the random pattern of natural forests from being destroyed in the process of forest management,while effectively transforming the spatial pattern of artificial forests into a random ***,in order to ensure the convenient and accurate determination of the type of forest spatial pattern,research on methods for determining forest spatial pattern has become particularly *** on the theory of uniformity,this study proposes definitions and related theories of included exclusive sphere,included exclusive body,included random pattern,and included *** the guidance of the definition of inclusion uniformity and related theories,and by using mathematical method,it is proved that the uniformity of inclusion(CL)is asymptotically subject to the Eq.18,Therefore,the relationship between the included uniformity(CL)and the number of trees in the sample plot was established,and the corresponding relationship formula was obtained,and then the determination of the spatial pattern type of trees was completed by using the corresponding relationship *** rigorous reasoning and case verification,the determination method of forest spat

关键词： Uniformity Forest spatial pattern Contained exclusive sphere Co correlation coefficient Included Uniformity

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Deep Reinforcement Learning for Solving the Vehicle Routing Problem in Practical Logistics 13

Deep Reinforcement Learning for Solving the Vehicle Routing ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Cai, Junchuang Zhang, Xinzhi Lin, Qiuzhen Dong, Lisha Chen, Weineng Ming, Zhong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Institute of Information Technology College of Sino-German Robotics Shenzhen China SSouth China University of Technology College of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350308365

The vehicle routing problem with simultaneous pickup-delivery and time windows (VRPPDTW) is applicable to a wide range of practical scenarios within the domains of transportation and logistics. When addressing this complex problem, conventional heuristic algorithms have exhibited limitations in effectiveness, primarily due to the need for expert knowledge in heuristic design, which often prioritizes immediate improvements and leads to convergence towards local optima. To overcome this challenge, we propose a deep Q-network-based iterated local search algorithm (DQN-ILS). This algorithm employs a deep Q-learning network to automatically learn a policy for selecting heuristics to improve the current solution. Simultaneously, an adaptive perturbation strategy is designed to avoid becoming trapped in local optima. In this way, our algorithm can better balance the trade-off between exploration and exploitation when solving VRPPDTW. Experimental studies conducted on a real test suite of large-scale VRPPDTW instances validate the superiority of the proposed DQN-ILS over several recently proposed methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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How Does Knowledge Coupling Affect Innovation Performance? The Mediation Role of Exploratory and Exploitative Innovation

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Journal of the Knowledge Economy 2025年 1-23页

作者： Zhao, Yating Liang, Yongquan Zhang, Yang Zhou, Yanping College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Economics and Management Shandong University of Science and Technology Qingdao China

The primary objective of this study is to investigate the impact of different types of knowledge coupling, complementary knowledge coupling, and alternative knowledge coupling, on the innovation performance of knowledge-intensive enterprises. The research further explores the mediating role of dual innovation, encompassing both exploratory innovation and exploitative innovation, in the relationship between knowledge coupling and enterprises innovation performance during the process of knowledge creation. Additionally, the study analyzes the moderating effect of environmental uncertainty on the link between dual innovation and innovation performance. The research sample is derived from survey data collected from 315 knowledge-intensive enterprises in China. The analytical approach employed is a hierarchical regression analysis model to test the proposed model. Results indicate that both knowledge coupling and dual innovation exert positive effects on innovation performance. Notably, exploratory innovation and exploitative innovation assume distinct mediating roles in the relationship between knowledge coupling and innovation performance, thereby constituting crucial factors in the practical realm of enterprise innovation. Regarding environmental uncertainty, the study reveals a significant moderating effect on the relationship between dual innovation and enterprises innovation performance, with varying degrees of influence. Environmental uncertainty positively regulates exploratory innovation and innovation performance, whereas it exerts a negative regulatory effect on exploitative innovation and firm innovation performance. The empirical findings of this study hold substantial significance for decision-makers and regulators within knowledge-intensive enterprises, offering insights into the consideration of both exploratory and exploitative innovation when pursuing innovation performance. Furthermore, the study contributes a fresh perspective to the fields of knowled

关键词： Alternative knowledge coupling Complementary knowledge coupling Environmental uncertainty Exploitative innovation Exploratory innovation Innovation performance

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SGAN-RL: Fusion classification method for network intrusion detection based on data augmentation

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Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 569-583页

作者： Li, Lining Liu, Shuai Ren, Rong Wu, Lianhai Wang, Qian Zhang, Bing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information Science and Engineering Yanshan University P.R. Qinhuangdao China The Key Laboratory of Software Engineering Yanshan University Qinhuangdao China

Although traditional research methods for intrusion detection can effectively prevent and mitigate issues such as data leaks to avoid severe consequences, existing intrusion detection technologies encounter limitations, including low classification accuracy and high false positive rates, particularly when dealing with high-dimensional, complex network anomaly traffic data. Additionally, the common problem of class imbalance in intrusion detection datasets exacerbates these challenges. This paper introduces a novel approach to network intrusion detection classification, termed SGAN-RL, which integrates ResNet and LSTM models based on the semi-supervised generative adversarial network (SGAN) framework. This method utilizes SGAN to generate synthetic data resembling real intrusion detection data and continually trains the discriminator to produce high-quality data, thereby enhancing data distribution balance and quality. Moreover, a fusion intrusion detection classification model is presented, leveraging the strengths of ResNet and LSTM architectures to capture spatial and temporal features, respectively. This synergistic fusion enhances the model’s capacity for comprehensive data representation, resulting in improved performance in intrusion detection data classification tasks. Experiments conducted on intrusion detection datasets NSL-KDD, UNSW-NB15, and CIC-IDS2017 demonstrate that the proposed model enhances accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, respectively, showcasing its robust performance and reliability in network intrusion detection classification. © 2025 – IOS Press. All rights reserved.

关键词： Network intrusion

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Multiclass Classification for Cyber Threats Detection on Twitter

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computers, Materials & Continua 2023年第12期77卷 3853-3866页

作者： Adnan Hussein Abdulwahab Ali Almazroi College of Computer Science and Engineering Department of Computer ScienceAL-Ahgaff UniversityMukallaYemen College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddahSaudi Arabia

The advances in technology increase the number of internet systems *** a result,cybersecurity issues have become more *** threats are one of the main problems in the area of ***,detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its *** study aims to analyze Twitter data to detect cyber threats using a multiclass classification *** data is passed through different tasks to prepare it for the *** Frequency and Inverse Document Frequency(TFIDF)features are extracted to vectorize the cleaned data and several machine learning algorithms are used to classify the Twitter posts into multiple classes of cyber *** results are evaluated using different metrics including precision,recall,F-score,and *** work contributes to the cyber security research *** experiments revealed the promised results of the analysis using the Random Forest(RF)algorithm with(F-score=81%).This result outperformed the existing studies in the field of cyber threat detection and showed the importance of detecting cyber threats in social media *** is a need for more investigation in the field of multiclass classification to achieve more accurate *** the future,this study suggests applying different data representations for the feature extraction other than TF-IDF such as Word2Vec,and adding a new phase for feature selection to select the optimum features subset to achieve higher accuracy of the detection process.

关键词： Cybersecurity cyber threat detection artificial intelligence machine learning Twitter

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Evolutionary Reinforcement Learning with Parameterized Action Primitives for Diverse Manipulation Tasks 39

Evolutionary Reinforcement Learning with Parameterized Actio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qiu, Xianxu Huang, Haiming Chen, Weiwei Lin, Qiuzhen Chen, Wei-Neng Sun, Fuchun College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)157735897X

Reinforcement learning (RL) has shown promising performance in tackling robotic manipulation tasks (RMTs), which require learning a prolonged sequence of manipulation actions to control robots efficiently. However, most RL algorithms often suffer from two problems when solving RMTs: inefficient exploration due to the extremely large action space and catastrophic forgetting due to the poor sampling efficiency. To alleviate these problems, this paper introduces an Evolutionary Reinforcement Learning algorithm with parameterized Action Primitives, called ERLAP, which combines the advantages of an evolutionary algorithm (EA) and hierarchical RL (HRL) to solve diverse RMTs. A library of heterogeneous action primitives is constructed in HRL to enhance the exploration efficiency of robots and dual populations with new evolutionary operators are run in EA to optimize these primitive sequences, which can diversify the distribution of replay buffer and avoid catastrophic forgetting. The experiments show that ERLAP outperforms four state-of-the-art RL algorithms in simulated RMTs with dense rewards and can effectively avoid catastrophic forgetting in a set of more challenging simulated RMTs with sparse rewards. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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