检索结果-内蒙古大学图书馆

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Al Masri Bassam Kasasbeh Raneem Qaddoura School of Computing and Informatics Al Hussein Technical University Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper investigates the application of classification models within the telecommunications industry, focusing on K-Nearest Neighbors (KNN), Random Forest (RF), and Naive Bayes (NB). Various machine learning models were applied to a dataset comprising 290,000 customer records to predict customer churn. While RF and NB models exhibited high performance, achieving precision and recall scores exceeding 99%, they also demonstrated significant overfitting. To address this, Learning curves, correlation heatmaps, and advanced feature normalization techniques, including quantile transformation, improved model generalization. The results revealed a notable reduction in overfitting, with cross-validation accuracy for KNN and NB improving by over 5%. Furthermore, hyperparameter tuning of the RF model reduced the gap between training and validation performance, achieving near-parity in scores. These findings contribute to the development of robust and reliable predictive models for machine learning applications, offering actionable insights for improving customer retention strategies in the telecommunications industry.

关键词： Radio frequency Training Computational modeling Nearest neighbor methods Predictive models Telecommunications Reliability Churn Tuning Overfitting

来源：评论

学校读者我要写书评

暂无评论

DR3DH: A DoS resistant extended triple Diffie–Hellman for mobile edge networks

引用

Computer Networks 2025年 265卷

作者： Altaweel, Ala Bouridane, Ahmed Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Mobile Edge Networks (MENs) are wireless networks that establish "close-to-end-users-clouds" to provide storage, computation, and communication services. MENs employ the Extended Triple Diffie–Hellman (X3DH) protocol to establish secret keys that provide end-to-end encryption among their nodes. X3DH relies on a cloud server that stores the public keys of the nodes to address the asynchronous scenarios when they operate offline. The cloud server in X3DH protocol represents a single point of failure (SPoF), as the exchange of secure keys is disrupted if the server fails or is compromised. Moreover, a malicious attacker, Eve, can launch various DoS attacks against the MEN nodes (e.g., packet dropping or packet modification) and wireless links (e.g., jamming) to disrupt the execution of X3DH during secure key exchange. This paper proposes the DoS-Resistant-X3DH (DR3DH) protocol to resolve the SPoF of the cloud server in X3DH and to address packet dropping, packet modification, and jamming DoS attacks. DR3DH employs Reed–Solomon Erasure Coding to encode the X3DH public keys into fragments that are distributed into a set of storage nodes. These storage nodes, which are determined by an Integer Linear Programming (ILP) solver, are optimum. That is, they have lowest probabilities to launch packet dropping/modification attacks and lowest probabilities that the wireless links toward them are jammed. The authors evaluated DR3DH through trace-driven simulations in MATLAB using two mobility traces and a proof-of-concept implementation in Java. The authors also compared DR3DH with two approaches that employ Random and Greedy policies when selecting the storage nodes for keys fragments. The results demonstrate the resistance of DR3DH to DoS attacks, achieving a high success rate that outperforms the Random and Greedy approaches by between 13.6% and 83.0%. Additionally, the results confirm the feasibility of DR3DH under different mobility models and node speeds, as demonstrated by

关键词： Packet loss

来源：评论

学校读者我要写书评

暂无评论

LSTM-QGAN: Scalable NISQ Generative Adversarial Network

LSTM-QGAN: Scalable NISQ Generative Adversarial Network

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Cheng Chu Aishwarya Hastak Fan Chen Luddy School of Informatics Computing and Engineering Indiana University Bloomington USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can diminish the QGAN’s effectiveness. Second, methods that segment inputs into smaller patches processed by multiple generators face scalability issues. In this work, we propose LSTM-QGAN, a QGAN architecture that eliminates PCA preprocessing and integrates quantum long short-term memory (QLSTM) to ensure scalable performance. Our experiments show that LSTM-QGAN significantly enhances both performance and scalability over state-of-the-art QGAN models, with visual data improvements, reduced Fréchet Inception Distance scores, and reductions of 5× in qubit counts, 5× in single-qubit gates, and 12× in two-qubit gates.

关键词： Visualization Scalability Qubit Logic gates Signal processing Generative adversarial networks Generators Speech processing Long short term memory Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Bridging Fashion and Technology: Synthetic Human Models for an Enhanced E-Commerce Experience 23rd

Bridging Fashion and Technology: Synthetic Human Models for...

引用

23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Duarte, Ana Rita Conceição, Luís Department of Informatics Engineering School of Engineering Polytechnic of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development School of Engineering Polytechnic of Porto Porto Portugal

ISBN: (纸本)9783031734960

The fashion industry is undergoing a significant transformation, driven by advancements in digitalization and artificial intelligence (AI). This paper explores the integration of Stable Diffusion Models (SDMs) and AI to create high-quality images of synthetic models wearing a target cloth in multiple poses, enhancing the users’ experience on e-commerce websites and addressing the fast-paced demands of fashion trends. The proposed pipeline includes multiple steps: face generation, pose estimation, cloth warping, human synthesis, and refinement networks, each designed to enhance the realism and quality of the final images for e-commerce platforms. Experimental results on a small VITON-HD dataset demonstrate this approach’s overall success. BRISQUE, NIQE, and entropy were used for objective evaluation, scoring 14.1449, 4.1354, and 7.1238 respectively, indicating a high level of detail, naturalness, and complexity on the generated images. Future work should focus on enhancing the representation of clothing with complex patterns and lower and full garments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clothes

来源：评论

学校读者我要写书评

暂无评论

Deep-Source EEG Generation with Deep Learning from Different Configurations of Surface EEG

Deep-Source EEG Generation with Deep Learning from Different...

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Jiadao Zou Qingxue Zhang Luddy School of Informatics Computing and Engineering Indiana University Department of Computer Science and Department of Biomedical Engineering Indiana University School of Informatics Computing and Engineering Indiana University Luddy Indianapolis

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Brain dynamics big data is of increasing promise for many applications like epilepsy detection and cognitive understanding, with the advancements of consumer technology. However, the deep-source brain measurement is difficult. In this study, we target the brain electroencephalogram (EEG) application, and investigate the deep-source EEG generation from surface EEG towards convenient big data. The deep learning algorithm has been developed to mine different configurations of the surface EEG streams, including the single-channel and multi-channel cases, for deep-source EEG generation. Promising experiments on the epilepsy application have been conducted, demonstrating the great promise of deep-learning-empowered deep-source EEG generation. This study will greatly advance brain dynamics mining towards smart consumer technology.

关键词： Deep learning Heuristic algorithms Smart healthcare Epilepsy Big Data Electroencephalography Streams Consumer electronics

来源：评论

学校读者我要写书评

暂无评论

A novel multi-agent architecture based on decomposition and learning automata to hybridize multi-objective metaheuristics

引用

Memetic computing 2025年第2期17卷 1-28页

作者： Fernandes, Islame F. C. Goldbarg, Elizabeth F. G. Maia, Silvia M. D. M. Institute of Computing Federal University of Bahia (UFBA) Ondina Salvador Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte (UFRN) Campus Universitário Lagoa Nova Natal Brazil

Hybrid metaheuristics can effectively tackle multi-objective optimization problems. Recently, researchers gained interest in procedures, referred to as architectures, that can provide generic functionalities and features for hybridizing arbitrary metaheuristics. Although a previously proposed multi-agent architecture, MO-MAHM, achieved high-quality solutions for bi-objective problems, its application for more than two objectives requires further discussions. To this end, MO-MAHME, a MO-MAHM extension for handling two or more objectives, is proposed in this paper. MO-MAHME maintains concepts from particle swarm optimization and multi-agent paradigms, including particle movement and agent intelligence. Further, it uses a decomposition-based velocity operator prescinding from aggregation functions and reinforcement learning automata scheme for supporting the decisions of the agents. This paper shows that these algorithmic components can significantly improve the architectural performance. We apply MO-MAHME to the quadratic assignment problem with up to four objectives. Hybridization combines three evolutionary algorithms and a local search. A comparison of the experimental results of MO-MAHME and ten algorithms (including hybrid approaches, hyper-heuristics, and algorithms from the quadratic assignment problem literature) is presented. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Decomposition Hybridization of metaheuristics Learning automata Multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal control for unreliable manufacturing and remanufacturing system under environmental subsidy and overrun penalty mechanism

引用

IEEE Transactions on Engineering Management 2025年 72卷 1983-2002页

作者： Turki, Sadok Jemmali, Mahdi Hidri, Lotfi University of Lorraine Department of Logistic and Maintenance UFR MIM at Metz Metz17 France University of Sharjah Computer Science Department College of Computing and Informatics Sharjah United Arab Emirates King Saud University Industrial Engineering Department College of Engineering Riyadh11421 Saudi Arabia

Because of environmental concerns, remanufacturing has become an integral process for many production companies. Most published papers dealing with manufacturing and remanufacturing systems (MRSs) overlook some industrial features. These include machine unreliability, the distinction between newly manufactured and remanufactured products, and uncertainty about the returned used products (RUPs). As a result, these models do not reflect the reality of industrial systems. This study aims to bridge the gap in research on MRSs by elaborating an optimal and dynamic control of manufacturing, remanufacturing, and recovery of RUPs. The goal is to develop a model based on manufacturing remanufacturing emissions hedging point control policy (MREHP) to adjust the production of new and remanufactured products, and recovery of RUPs according to economic and environmental thresholds. To reduce carbon emissions, a new method is proposed based on the environmental subsidy and overrun penalty mechanism (ESOPM), and that is combined with MREHP to control in parallel the MRS and generated emissions. A robust optimization algorithm is developed to determine the optimal economic and environmental thresholds in MREHP and ESOPM. A comparative study is provided to evaluate our proposed ESOPM in comparison with traditional reduction policies: carbon cap and trade (CCT) and carbon tax regulation (CTR). The obtained results reveal that machines' reliability, rate of RUPs, emissions subsidy, and carbon emissions limit have notable impacts on economic and environmental thresholds. Additionally, results show that the proposed ESOPM performs higher profit than CTR and CCT while reducing emissions when the carbon price is below a certain value. © 1988-2012 IEEE.

关键词： Fabrication

来源：评论

学校读者我要写书评

暂无评论

Towards Robust Federated Learning: Investigating Poisoning Attacks Under Clients Data Heterogeneity 19

Towards Robust Federated Learning: Investigating Poisoning A...

引用

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

作者： Soubih, Abdenour Lahmer, Seyyid Ahmed Abuhamad, Mohammed Abuhmed, Tamer College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Information Engineering University of Padova Padua Italy Department of Computer Science Loyola University Chicago Chicago United States

ISBN: (纸本)9798331507817

Federated Learning (FL) offers a privacy-preserving solution by enabling multiple clients to train a shared model collaboratively without centralizing data. However, the decentralized nature of FL presents challenges, particularly regarding security and performance under adversarial conditions. This paper investigates the effects of poisoning attacks under data heterogeneity. Our experiments evaluate the impact of varying malicious client fractions and poison concentration levels on the accuracy of the model. We explore the effects of poisoning attacks on FedAvg and FedNova models using medical imaging tasks. Our findings reveal that increasing data heterogeneity exacerbates the effects of poisoning, with FedNova demonstrating greater resilience compared to FedAvg. We found that the number of malicious clients plays a more significant role in degrading performance than the ratio of poisoning samples shared by each malicious client, suggesting that even modest levels of poisoning can be tolerated by most algorithms. The study highlights the importance of developing robust defense mechanisms to maintain model performance under adversarial conditions. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Driven QoT Prediction for Enhanced Optical Networks in DWDM System

引用

IEEE Access 2025年 13卷 80445-80460页

作者： Sakthivel, Sudha Belgaum, Mohammad Riyaz Sajak, Aznida Abu Bakar Alam, Muhammad Mansoor Su'ud, Mazliham Mohd Universiti Kuala Lumpur Kuala Lumpur54100 Malaysia University of Technology and Applied Sciences Department of Information Technology Muscat133 Oman Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Arab Open University Faculty of Computer Studies Aali 18211 Bahrain Riphah International University Faculty of Computing Islamabad46000 Pakistan

This research demonstrated the machine learning (ML) classifiers with regression learning to improve an optical system’s quality of transmission (QoT). In Optical Communication, the data can be communicated from source to destination through the established lightpaths. However, as the signal traverses the optical links and devices, its QoT may deteriorate due to various impairments. The QoT is an essential component that determines the connectivity of an optical network. Therefore, ensuring a QoT guarantee is necessary to establish a successful lightpath. Predicting the QoT before establishing lightpaths can guide the routing and allocation of resources required for the lightpaths. In this research, using ML models an appropriate QoT analytical prediction model is developed computationally. Simulations were conducted at a 10 Gbps data rate per channel for 64-channel DWDM systems. The proposed model significantly improves in detecting fiber nonlinearity, and performance was studied using Q-factor, BER, and noise power. The results indicate that the SVM-based classifier with regression learning performs better than any other classifiers discussed in this research. This study assesses the efficiency of the proposed ML models in predicting the QoT for established lightpaths. Results indicate that all the ML classifiers with Regression models can accurately predict the transmission quality for over 90% of lightpaths. However, the proposed SVM-based classifier with a regression model demonstrates superior generalization, with a nearly perfect QoT prediction rate of around 99% for the established lightpaths. In the network planning stage, residual margins are added to compensate for inaccuracies, which ensures accurate signal reception. The proposed ML model achieved a lightpath residual margin with a 0.7dB error. © 2013 IEEE.

关键词： Fiber to the x

来源：评论

学校读者我要写书评

暂无评论

Roll for Robot: A Tabletop Role-Playing Game for Designing Socially Assistive Robots for Depression Management 16th

Roll for Robot: A Tabletop Role-Playing Game for Designing ...

引用

16th International Conference on Social Robotics, ICSR + AI 2024

作者： Collins, Sawyer Henkel, Kenna Baugus Henkel, Zachary Śabanović, Selma Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States College of Computer Science and Engineering Mississippi State University Mississippi State StarkvilleMS United States

ISBN: (纸本)9789819635214

Imagining and creating a future with a robot tailored to an individual’s needs and wants provides a unique challenge to both designers and perspective users. Using both an existing base zoomorphic robot, TherabotTM in a specially designed tabletop role-playing game, we have explored what it would look like for someone living with depression to go through a "day in the life" with their newly designed robot companion. Inspired by this collaborative co-design technique, eight participants explained their own relationship with depression, identified a character that they connected with, and chose the physical form, sensors, and abilities for a socially assistive robot. Using this character and robot, participants described how they might overcome depression symptoms and identified uses when managing depression symptoms for the robot. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine design

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：