检索结果-内蒙古大学图书馆

12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Ziqi Hu, Chengpeng Liu, Jialin Yao, Xin Department of Computer Science and Engineering Southern University of Science and Technology China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computing and Decision Sciences Lingnan University Hong Kong

Deep reinforcement learning has recently been successfully applied to online procedural content generation in which a policy determines promising game-level segments. However, existing methods can hardly discover diverse level patterns, while the lack of diversity makes the gameplay boring. This paper proposes an ensemble reinforcement learning approach that uses multiple negatively correlated sub-policies to generate different alternative level segments, and stochastically selects one of them following a dynamic selector policy. A novel policy regularisation technique is integrated into the approach to diversify the generated alternatives. In addition, we develop theorems to provide general methodologies for optimising policy regularisation in a Markov decision process. The proposed approach is compared with several state-of-the-art policy ensemble methods and classic methods on a well-known level generation benchmark, with two different reward functions expressing game-design goals from different perspectives. Results show that our approach boosts level diversity notably with competitive performance in terms of the reward. Furthermore, by varying the regularisation coefficient values, the trained generators form a well-spread Pareto front, allowing explicit trade-offs between diversity and rewards of generated levels. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

On the Spectral Efficiency of UAVs-assisted D2D Cooperative Communications Using NOMA

On the Spectral Efficiency of UAVs-assisted D2D Cooperative ...

引用

2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Belaoura, Widad Ghanem, Khalida Shakir, Muhammad Zeeshan Nedil, Mourad Boumerdes University Faculty of Technology Department Engineering of Electrical Systems Boumerdes Algeria Canada School of Computing Engineering and Physical Sciences University of the West of Scotland Scotland Paisley United Kingdom

ISBN: (纸本)9781665442282

The tomorrow's 6G wireless networks require high spectral efficiency to accommodate a massive number of devices over the limited available frequency spectrum. This paper aims to investigate a Device-to-Device (D2D) cooperative system for maximizing the spectrum efficiency in disaster management scenario, wherein multiple unmanned aerial vehicles (UAVs) act as relays to facilitate seamless communication between D2D transceivers. In this perspective, we integrate non-orthogonal-multiple-access (NOMA) technology to further enhance the transmission efficiency of the system. Simulation results demonstrate the superiority of the UAVs-assisted D2D cooperative solution when using NOMA in terms of spectral efficiency. © 2023 IEEE.

关键词： Spectrum efficiency

来源：评论

学校读者我要写书评

暂无评论

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

引用

Computer Modeling in engineering & Sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Feature Fool Exploitation for Lightweight Anomaly Detection in Respiratory Sound 10th

Feature Fool Exploitation for Lightweight Anomaly Detection...

引用

10th International Conference on Future Data and Security engineering, FDSE 2023

作者： Le, Kim-Ngoc T. Bang, Sammy Yap Xiang Le, Duc-Tai Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Seoul Korea Republic of College of Computing and Informatics Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819982950

Respiratory sound auscultation with digital stethoscopes is a common technique for identifying lung disorders, however, it requires a qualified medical expert to interpret the sounds, and inter-listener variability results from subjectivity in interpretations. To improve diagnostic accuracy and enhance patient treatment, there is a growing need for automated detection of lung diseases. Deep neural networks (DNNs) have demonstrated substantial potential in addressing such challenges. However, DNNs demand a significant amount of data, and the largest available respiratory dataset, ICBHI, comprises only 6898 breathing cycles, which is insufficient to train a satisfactory DNN model. To address the issue, we propose a robust and lightweight model that employs a feature fool exploitation technique to identify respiratory anomalies. Next, we deploy two evaluation approaches to evaluate its performance: random 60/40 splitting and 5-fold cross-validation, against state-of-the-art methods, using the ICBHI dataset. Remarkably, our scheme outperforms existing approaches up to 18.26%, achieving impressive accuracy rates of 72.36% and 89.46%, using the two respective train/test splitting methods. The results show a significant improvement from our method over existing approaches, suggesting its promise for future respiratory healthcare technology research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

引用

Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

来源：评论

学校读者我要写书评

暂无评论

Coral: Conversational What-If Process Analysis (Extended Abstract) 4

Coral: Conversational What-If Process Analysis (Extended Abs...

引用

Doctoral Consortium and the Tool Demonstration Track of the 4th International Conference on Process Mining, ICPM-D 2022

作者： Barón-Espitia, Daniel Dumas, Marlon González-Rojas, Oscar Systems and Computing Engineering Department Universidad de los Andes Colombia University of Tartu Tartu Estonia

Process simulation is used to estimate the performance of a process under hypothetical (what-if) scenarios, for example, to estimate what would be the cycle time if one of its tasks is automated. Despite the relevance of process simulation as a tool for planning business process improvements, the adoption of process simulation is hampered by the fact that the specification of what-if scenarios requires technical knowledge. This paper presents Coral, a chatbot that allows business users to specify what-if scenarios in a conversational manner. Coral takes as input a process simulation model, and enables users to interactively configure what-if scenarios in business terms. Coral also allows users to compare the performance of the process under the what-if scenario versus its performance under a baseline scenario. © 2022 Copyright for this paper by its authors.

关键词： conversational chatbot process simulation What-If process analysis

来源：评论

学校读者我要写书评

暂无评论

Automatic Multi-Class Non-Functional Software Requirements Classification Using Machine Learning Algorithms

Automatic Multi-Class Non-Functional Software Requirements C...

引用

International Research Conference on Smart computing and systems engineering (SCSE)

作者： M. A. F. Saroth P. M. A. K. Wijerathne B. T. G. S. Kumara Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Department of Software Engineering Faculty of Computing Sabaragamuwa University of Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

A successful software development process depends on software requirements. Those requirements are often classified into two categories: functional requirements (FR) and non-functional requirements (NFR). In software development, NFRs play a crucial role, but they are often forgotten by the developers. Therefore, it causes adverse consequences. As a result, an NFR classification method is required to assist software developers in classifying NFR from requirement documents. The majority of research in this area focuses on categorizing requirements as FR or NFR. However, NFR can be divided into more than two categories. As a result, a multi-label categorization strategy was required. This study presented an approach for categorizing NFR based on product, process, and external factors. To categorize the NFR, the authors developed four machine learning-based algorithms entitled Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K Nearest Neighbor Classifier (KNN) classifier, as well as one deep learning method named Long Short-Term Memory (LSTM) model. Ultimately, the authors examine the models and conclude that the LSTM model which gained an accuracy of 99.69% is appropriate for this type of classification.

关键词： Support vector machines Deep learning Logistic regression Machine learning algorithms Computational modeling Software Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Alzheimer's Disease Classification via Pre-trained Convolutional Neural Network Variants Utilizing Multi-level Thresholding based on Kapur's Entropy 2

Alzheimer's Disease Classification via Pre-trained Convoluti...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Mahendran, Nivedhitha Durai Raj Vincent, P.M. Srm Institute of Science and Technology School of Computing Technologies Department of Networking and Communications Kattankulathur Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372892

Neurological diseases are the disorders of brain and the nerves that run throughout the human body. There is a range of symptoms that arise as a result of chemical, structural, or electrical modifications in the regions of brain. One such neurological disease is the Alzheimer's Disease (AD), is considered as a slow and progressive disease that majorly affects the cognitive abilities of the individual. It is most common forms of dementia without any proper cure. However, early diagnosis and treatment might slow down the symptoms and improve the lifestyle of the individual. The most common techniques to identify the neurological disorders are imaging of the brain regions, namely, CT, MRI, PET, etc. The learning approaches, such as Deep Learning and Machine Learning are identified to be effective in analyzing these images to provide better accuracies in detecting the AD. In this study, four versions of Convolutional Neural Networks (CNN) are used to classify AD and Control cases. Further, the Kapur's thresholding technique is also implemented to segment the images for improving the classification accuracy of AD. From the results shown in the study, the inference shows that the VGG16 of CNN provides considerable improvement than the other deep learning models before and after the implementation of thresholding. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozone Prediction with Cost-Effective Edge Devices

arXiv

引用

arXiv 2025年

作者： Ken, Huam Ming Behjati, Mehran Rafsanjani, Ahmad Sahban Aslam, Saad Meng, Yap Kian Abdul Majeed, Anwar P.P. Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Malaysia Taicang Suzhou215400 China

The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system designed to predict ozone concentration in real-time. The system employs an Arduino Nano 33 BLE Sense microcontroller equipped with an MQ7 sensor for carbon monoxide (CO) detection and built-in sensors for temperature and pressure measurements. The data, sourced from a Kaggle dataset on air quality parameters from India, underwent thorough cleaning and preprocessing. Model training and evaluation were performed using Edge Impulse, considering various combinations of input parameters (CO, temperature, and pressure). The optimal model, incorporating all three variables, achieved a mean squared error (MSE) of 0.03 and an R-squared value of 0.95, indicating high predictive accuracy. The regression model was deployed on the microcontroller via the Arduino IDE, showcasing robust real-time performance. Sensitivity analysis identified CO levels as the most critical predictor of ozone concentration, followed by pressure and temperature. The system's low-cost and low-power design makes it suitable for widespread implementation, particularly in resource-constrained settings. This TinyML approach provides precise real-time predictions of ozone levels, enabling prompt responses to pollution events and enhancing public health protection. Copyright © 2025, The Authors. All rights reserved.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Estimation of Neurological Diseases and Age Using Brain MRI 4th

Estimation of Neurological Diseases and Age Using Brain MRI

引用

4th International Conference on Smart systems: Innovations in computing, SSIC 2023

作者： Ashokkumar, K. Dharshan, K. Ragupathi, T. Pritam, Anem Sagar, Amujuru Gnana Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Data Science and Business systems School of Computing SRM Institute of Science and Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819736898

Leveraging D-NN trained on neuroimaging data, we can effectively estimate the chronological ages of normal persons;this projected brain age has potential as a biomarker for identifying age-related disorders. The suggested technique employs a CNN cascade network from DL and the Support Vector Machine (SVM) algorithm from ML (SVM). These techniques have been employed to train MRI scans of the brain, classifying them into regular (unaffected by illness), Alzheimer’s disorder (AD), and neurocognitive disability. That we also have the ability to determine ages thanks to the age classifications on the photos. The brain MRI collection is trained using CNN and SVM, on which the classifications and age estimate will be carried out. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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：