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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

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Boosted-3R: Towards a Novel Framework for Inferring ABAC Policies 4

Boosted-3R: Towards a Novel Framework for Inferring ABAC Pol...

引用

4th intelligent Cybersecurity conference, ICSC 2024

作者： Aboukadri, Sara Ouaddah, Aafaf Mezrioui, Abdellatif Institut National des Postes et des Telecommunications STRS Lab/CEDOC 2TI Rabat Morocco

ISBN: (纸本)9798350354775

In the realm of Identity and Access Management, access control mechanisms are established to prevent unauthorized entry, ensuring that only authorized entities receive permission to access resources. Among access control models, AttributeBased Access Control (ABAC) stands out by considering diverse attributes of users, resources, and the environment, enabling finegrained control and dynamic policy implementation. However, implementing and migrating to ABAC model poses challenges, particularly in large organizational contexts. this paper introduces Boosted-3R, an innovative approach that employs federated machine learning and CatBoost, an advanced algorithm tailored for categorical feature handling, to derive ABAC rules from access logs using association Rule mining, Reliability verification and Redundancy deletion. Utilizing pattern mining and categorical feature classification, our framework extracts reliable rules from access logs, enabling the prediction of access decisions for new requests. the proposed approach is evaluated using a real dataset from Amazon, and experimental results showcase an accuracy score of 96.7%. © 2024 IEEE.

关键词： Federated learning

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Deep learning for Enhanced Detection and Characterization of Pulmonary Nodules 4

Deep Learning for Enhanced Detection and Characterization of...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Lavanya, G. Muthulakshmi, M. Latha, Manohar Keerthinathan, A. Vishal Krishh, P. Sulthana, Sheba Meghana, Meka Kavya Uma Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350349900

this study aims to develop, an effective customized Convolutional Neural Network model that can accurately identify areas on CT scans that have lung nodules and those that do not. the study utilizes two datasets, the LUNA-16 dataset for training and the IQ-Oth/NCCD dataset for testing. Additionally, we employ a U-Net model for precise lung nodule segmentation. this segmentation approach aimed to achieve a comprehensive characterization of nodules based on their spatial characteristics. the testing phase involves evaluating the performance of our trained models on the IQ-Oth/NCCD dataset, particularly focusing on localizing nodules in malignant cases. Overall, the research highlights the effectiveness of deep learning in early detection and diagnosis of lung cancer. this work contributes to improving the accuracy and reliability of automated pulmonary nodule detection systems, with a focus on identifying nodules in patients with suspected malignancies. © 2024 IEEE.

关键词： Lung cancer

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Detection of IoT Cyberattacks in Smart Cities: A Comparative Analysis of Deep learning and Ensemble learning Methods 4th

Detection of IoT Cyberattacks in Smart Cities: A Comparative...

引用

4th International conference on Novel and intelligent Digital Systems

作者： Kebede, Zeru Kifle Hajek, Petr Univ Pardubice Fac Econ & Adm Studentska 84 Pardubice 53210 Czech Republic

ISBN: (纸本)9783031733437;9783031733444

In this study, we embarked on a comparative investigation of Deep learning (DL) techniques and ensemble learning approaches for enhancing IoT security. Specifically, we scrutinized the performance of Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Random Forest (RF), and AdaBoost models evaluated to both binary and specific attack vector classifications. the imbalanced and voluminous datasets of UNSW-NB15 and CICIDS2017 were employed for evaluation. the empirical evidence gleaned from our experiments suggests that RF exhibits superior efficacy over its counterparts, with accuracy and F1-score in the range of 99.68% to 99.90%. Within the DL paradigm, the MLP model achieved the highest F1-score (99.17%) and the lowest False Positive Rate (FPR) of 0.0037 using UNSW-NB15, among DL models. Overall, the proposed models exhibit commendable performance in binary classification tasks. However, this does not indicate their suitability for the detection of all types of attacks, as the individual attack detection result shows. Furthermore, models employed in our work demonstrated superior results as compared to existing models that used smaller sample sizes of these datasets.

关键词： IoT Cyberattack Smart City Intrusion Detection Deep learning Ensemble learning

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Improved strategy based sparrow search algorithm for UAV 2D path optimization 4

Improved strategy based sparrow search algorithm for UAV 2D ...

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4th IEEE International conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Guo, Wanli Xinjiang University Urumqi China

ISBN: (纸本)9798350361643

In recent years, swarm intelligence optimization algorithms have demonstrated excellent performance in optimization, achieving favorable results in solving complex practical problems. Sparrow Search Algorithm (SSA) is an intelligent algorithm proposed in recent years. this paper focuses on the two-dimensional path optimization of Unmanned Aerial Vehicles (UAVs). Addressing issues such as the decline in population diversity and susceptibility to local optima in the later iterations of the Sparrow Search Algorithm, a multi-strategy hybrid improved Sparrow Search Algorithm (ISSA) is proposed. ISSA is applied to UAV two-dimensional path optimization planning to find the optimal path for obstacle avoidance. this paper utilizes Piecewise Linear Chaotic Mapping (PWLCM) to optimize the initial settings of the population, enhances population diversity, and effectively controls the balance between global search and local optimization using a nonlinear weight factor. this strengthens the algorithm's global optimization ability and avoids the algorithm from being trapped in local optima. Perturbation is introduced through longitudinal and transverse crossover strategies, introducing new genetic variations among population individuals to increase diversity, helping the algorithm escape local optima and find better solutions. through multi-strategy improvements, a hybrid improved ISSA Sparrow Search Algorithm is formed. Simulation experiments demonstrate that ISSA outperforms five other algorithms including GA, SSA, PSO, DE, and GWO in terms of path optimization performance. © 2024 IEEE.

关键词： Global optimization

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Balancing Human-robot Collaborative Disassembly Line by Using Dingo optimization Algorithm 4

Balancing Human-robot Collaborative Disassembly Line by Usin...

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4th IEEE International conference on Human-Machine Systems, ICHMS 2024

作者： Guo, Xiwang Zhou, Liangbo Zhou, Mengchu Wang, Weitian Wang, Jiacun Qi, Liang Liaoning Petrochemical University Information And Control Engineering Fushun China Liaoning Petrochemical University Information And Control Fushun China New Jersey Institute Of Technology Electrical And Computer Engineering NewarkNJ United States Montclair State University School Of Computing Montclair United States Monmouth University Computer Science And Software Eng. West Long BranchNJ United States Shandong University Of Sci. And Tech. Computer Intelligent Sci. And Tech. Qingdao China

ISBN: (纸本)9798350315790

Disassembling and recycling scrapped products play important roles in effectively reducing environmental pollution and improving resource sustainability. A multi-product human-robot collaborative disassembly-line-balancing problem (MHDP) arises from doing so and represents an important yet to be solved in the remanufacturing field. this work explores how to use optimization algorithms to schedule tasks between humans and robots to maximize remanufacturing profit in human-robot collaboration contexts. Specifically, we investigate MHDP and develop a mixed integer programming model considering various system constraints. We propose a dingo optimization algorithm that simulates wild dogs' social hierarchy and chasing processes to solve MHDP. We compare the proposed algorithm with an exact solution finder, i.e., IBM CPLEX and a well-known intelligent optimization method, i.e., Genetic Algorithm to show its competitive performance in terms of solution accuracy and efficiency. © 2024 IEEE.

关键词： Genetic algorithms

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Self-supervised representation learning with artificial fish swarm optimization for machine anomalous sound detection 4

Self-supervised representation learning with artificial fish...

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4th International conference on Internet of things and Smart City, IoTSC 2024

作者： Chen, Shengbing Wang, Jiajun Wang, Junjie Xu, Zhiqi School of Artificial Intelligence and Big Data Hefei University Hefei China

ISBN: (纸本)9781510681767

Machine Anomalous Sound Detection is crucial for artificial intelligence automation in the context of the fourth industrial revolution. Recent approaches employ self-supervised representation learning, which combines representations extracted through training models on normal and pseudo-anomaly data in the upstream phase with anomaly detection algorithms in the downstream phase. Although effective in extracting representations and demonstrating some robustness, this approach overlooks the complexity and intrinsic relevance of the individual tasks in the upstream models. To address these challenges, this paper introduces the Self-Supervised Multi-Task Representation learning with Artificial Fish Swarm optimization. this network integrates two task classifiers into the upstream model, specifically designed to distinguish the sound origin of the target machine and discern attribute information related to the machines. We assign weights to individual tasks and optimize these weights using the artificial fish swarm algorithm. Our approach was evaluated on the DCASE2022 Task 2 evaluation dataset, where the method outperformed integrated models by an average of 6.8% in terms of AUC and pAUC. © 2024 SPIE.

关键词： Self-supervised learning

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Machine learning Driven Problem Detection for Production optimization - Well Cease to Flow Prediction 4

Machine Learning Driven Problem Detection for Production Opt...

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4th EAGE Digitalization conference and Exhibition

作者： Gönczi, D. Reddicharla, N. Ramarao Rachapudi Venkata, S. Toumi, O.T. Abdalla, R.A. Nikolaev, D. Datagration Solutions Inc. United States ADNOC Onshore United Arab Emirates

ISBN: (纸本)9789462824850

Early detection of the onset of a well ceasing to flow was always a challenging task. Liquid loading or well cease to flow problem is the inability of a well to remove liquids that are produced from the wellbore. the produced liquid accumulates in the well creating a static column of liquid, therefore creating a back pressure against formation pressure while resulting production decline until the well ceases production. the proposed solution exhibits the capacity to discern prospective opportunities with greater efficiency compared to the conventional multidisciplinary team assessment, achieving this outcome within a matter of hours as opposed to the protracted durations of weeks or months. the utilisation of automation and machine learning problem detection significantly expedites the reassessment and identification of optimal intervention candidates, thereby mitigating the risk of failure and consequently enhancing the overall economic success rate. the entirety of the workflow processes is automated, undergoing updates upon the availability of new datasets in accordance with a predetermined scheduling system. through this comprehensive approach, the solution harmonizes domain knowledge and human expertise, resulting in a substantial reduction in both time and cost associated with the evaluation of intervention candidates. © 4th EAGE Digitalization conference and Exhibition 2024.

关键词： Stoping

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D2D Resource Allocation optimization Algorithm Based on Deep Reinforcement learning 4

D2D Resource Allocation Optimization Algorithm Based on Deep...

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4th International conference on Electronic Materials and Information Engineering (EMIE)

作者： Zheng, Kaijin Wang, Yijun Zhuang, Dawei Zhou, Jialu Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun Peoples R China

ISBN: (纸本)9798350390230;9798350390223

In the cellular network framework, the resource allocation problem for Device-to-Device (D2D) communication technology is particularly critical. the core issue is how to efficiently allocate channel resources to D2D users while minimizing interference to cellular users (CU), ensuring the overall performance and Quality of Service (QoS) of the network. To address this challenge, this letter has proposed a resource allocation strategy based on deep reinforcement learning algorithms, aimed at dynamically adjusting channel allocation and transmission power in D2D communication. Firstly, an in-depth study and analysis of the conflicts and interference issues between D2D communication and cellular users in the cellular network environment regarding channel reuse are conducted. By quantitatively analyzing the impact of signal interference on communication quality, key factors requiring optimization are identified. Secondly, Double-Actor Critic (D-AC) algorithm is developed for dynamically allocating communication channels and adjusting transmission power. this algorithm iteratively optimizes D2D channel reuse and power control strategies, aiming to balance the efficient use of channel resources with the minimization of signal interference. thirdly, the D-AC algorithm is implemented and evaluated for its effectiveness in enhancing system throughput, reducing collision probabilities, and maintaining quality of service standards. By comparing with existing resource management schemes, the advantages of the D-AC algorithm are demonstrated.

关键词： Device to Device (D2D) Deep Reinforcement learning (DRL) power control resource allocation

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Joint optimization of Service Caching and Task Scheduling in Energy-Efficient Mobile Edge Computing 4

Joint Optimization of Service Caching and Task Scheduling in...

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4th International conference on intelligent Technology and Embedded Systems, ICITES 2024

作者： Chen, Bin Huang, Wenxuan Wang, Linhui Zhang, Yi Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798331506292

this paper studies an energy-efficient task scheduling problem that takes into account the cooperation among service caching-enabled mobile edge computing (MEC) servers. We consider a MEC system consisting of multiple sub-networks of MEC nodes and formulate the studied problem as a constrained optimization model that makes decisions on energy-efficient assignment of task requests. To cope with the formulated optimization problem, we propose a metaheuristic approach that is built upon the searching mechanism of the squirrel search algorithm (SSA) but involves improved operators and name the metaheuristic approach ISSA. By using an offloading sequence (i.e., a permutation of all computation tasks) to represent the scheduling solution, ISSA uses a cooperative task assignment heuristic for evaluating the fitness value of each candidate solution. this heuristic is responsible for dispatching each task in the sequence to a certain MEC node within the sub-network or outsourcing the task to the cloud in case of insufficient resources, such that the total energy consumption can be calculated. In addition, when updating the squirrel positions to facilitate the metaheuristic search, we employ a new updating rule to enhance ISSA's capability in exploring the solution space. We perform extensive simulations to demonstrate the advantage of our proposed ISSA over other metaheuristic approaches in reducing the total energy consumption. © 2024 IEEE.

关键词： Constrained optimization

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Optimizing intelligent Hotel Room Resource Allocation Using Deep learning Techniques 4

Optimizing Intelligent Hotel Room Resource Allocation Using ...

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4th International conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Gao, Sheng Yang, Yulin Li, Jiaqi Wu, Yaxuan Zhang, Jingbao Wang, Danzhi Beijing University of Posts and Telecommunications Century College Beijing China

ISBN: (纸本)9798331531225

In light of the accelerated advancement of intelligent hospitality establishments, the effective distribution of room resources has emerged as a pivotal research area, with the objective of enhancing operational efficacy and customer satisfaction. To address this issue, this paper proposes a novel room resource allocation optimisation model based on deep learning, which builds upon the existing Transformer architecture. In particular, an enhanced Transformer model has been devised, which incorporates the time embedding module into the network architecture in order to capture the temporal dynamic characteristics of customer booking behaviour. Furthermore, the ability to identify key features has been enhanced by introducing a weighted self-attention mechanism. Furthermore, the model output layer incorporates a multi-objective optimisation strategy, which serves to balance the relationship between customer satisfaction and hotel revenue. the experimental results demonstrate that the proposed model exhibits superior performance in terms of room utilisation, resource allocation efficiency and customer satisfaction when compared to the traditional method. © 2024 IEEE.

关键词： Hotels

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