检索结果-内蒙古大学图书馆

2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Ashraf, Humaira Khan, Fizza Ihsan, Uswa Al-Quayed, Fatima Jhanjhi, N.Z. Humayun, Mamoona International Islamic University Islamabad Department of Computer Science and Software Engineering Islamabad Pakistan Jouf University College of Computer and Information Sciences Department of Computer Science Sakakah Saudi Arabia Malaysia Applied Science Private University Applied Science Research Center Amman11937 Jordan Jouf University College of Computer and Information Sciences Department of Information Systems Sakakah Saudi Arabia

ISBN: (纸本)9798350335644

With the advancement and availability of the internet in the present age, everything is being wireless. Be it our home appliances or high defined monitoring systems, Wireless sensor networks play an important role. But with advancement comes a high probability of getting attacked. One of these attacks is the Blackhole attack (BHA), in which a node drains all the traffic towards itself and discards the data traffic without sending it towards the receiver. There exist multiple approaches that help to protect a network against BHA, but existing approaches have some drawbacks. Many proposed schemes make sure that the given technique uses less energy but failed to reduce the packet drop ratio. In the detection of BHA, the proposed scheme should not only detect defected nodes but also manages the efficiency. The proposed methodology used mobile agents with authentication of nodes and trust values to detect black hole nodes. The methodology is tested using different evaluation measures such as energy consumption, latency, network life, and packet delivery ratio. The proposed method uses a detection algorithm that increases energy consumption and hence networks lifetime. Packet delivery rate is increased by 19.51%, the energy consumption is reduced by 53.3%, and network life is increased by 43.3% as compared to the previous technique. © 2023 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Research on Home Energy Consumption Optimization Based on User Habit Analysis

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第3期8卷 839-855页

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Lin, Tao Kong, Lingping College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science VSB-Technical University of Ostrava Czech Republic

With the development of smart electricity technology and demand response, optimization of household electricity consumption behavior has become an important research element for energy saving in residential buildings. In the study of smart electricity consumption in households, the differences in users’ lifestyles and their preferences for the use of various appliances can have a great impact on the results. And many existing methods need to rely on users’ awareness, which does not meet the popular demand. In this paper, we propose a new method for residential load scheduling that takes into account the load characteristics of appliances and electricity consumption habits. By analyzing the household electricity consumption data set and mining the personalized needs and us-age preferences of this user for various appliances, we establish an optimization model for electricity consumption behavior that combines the minimization of electricity expenses and user comfort. Finally, an improved artificial bee colony algorithm is proposed for solving the optimization model and generating a personalized dispatching strategy combined with real-time electricity pricing (RTEP) tariff. The proposed improved artificial swarm algorithm is compared with other classical algorithms, including GA, PSO, ABC, and QABC, and the analysis of cases shows that the model can effectively reduce the electricity consumption cost and ensure the customer satisfaction, and the proposed improved ABC-based algorithm outperforms other algorithms in terms of cost and user comfort. © 2023, Journal of Network Intelligence.

关键词： Customer satisfaction

来源：评论

学校读者我要写书评

暂无评论

Acceleration Object Detection using FPGA platform based on Improved CNN 10

Acceleration Object Detection using FPGA platform based on I...

引用

10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Salehzadeh, Mohammad Hossein Monfared, Mohammad Hamidi Rostami, Vahid Salamati, Mahdi Department of Computer and Information Technology Engineering Science and Research Tehran Branch Islamic Azad University Tehran Iran Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran

ISBN: (纸本)9798350348873

Deep neural networks have emerged as a leading approach for object detection in recent years. However, their intensive computational requirements often make them unsuitable for embedded systems. The TinyYolo network is a widely-used deep neural network is designed to be simpler and smaller compared to its counterparts. Yet, it still presents challenges in FPGA implementations due to its large number of nodes. In this research, we focus on adapting the TinyYolo network for FPGA implementation. Our modified network comprises four deep neural sub-networks. One serves as the input, two as intermediate networks that extract both local and global features and the final one aggregates the extracted features. Based on these aggregated features, the target objects are detected. Our adaptation offers several advantages including Hardware-based implementation leading to increased computing power, Significant power savings compared to software implementations and Optimal parallel processing, power efficiency and memory usage making it suitable for embedded applications across various domains Our network implemented on the ZU3EG A484 chip achieved a processing speed of 58 FPS and consumed 4.105 W making it competitive with similar systems. ©2024 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Multiobjective Multiverse Optimizer for Multirobotic U-Shaped Disassembly Line Balancing Problems

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第2期5卷 882-894页

作者： Qin, Shujin Zhang, Shancheng Wang, Jiacun Liu, Shixin Guo, Xiwang Qi, Liang School of Economics and Management Shangqiu Normal University Shangqiu476000 China School of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China Monmouth University West Long Branch NJ07764 United States College of Information Science and Engineering Northeastern University Shenyang110819 China Department of Computer Science and Technology Shandong University of Science and Technology Qingdao266590 China

The development of technology accelerates the upgrade of products, which results in a significant number of obsolete products. This research aims to solve the multirobotic multiproduct U-shaped disassembly line balancing problem (M2UDP), in which different products are disassembled on a U-shaped model in a preset cycle time and assigned tasks to robots in each workstation reasonably. A linear mixed-integer model is established to maximize disassembly profit and minimize carbon emissions. An improved multiobjective multiverse optimizer (IMMO) that utilizes the sigmoid activation function in neural networks is proposed to find the optimal plan for the model. The improved algorithm is verified via a set of real-life instances and compared with three classical multiobjective optimization algorithms. The experimental results show that the proposed IMMO performs better than those peers in solving the M2UDP problems. © 2023 IEEE.

关键词： Profitability

来源：评论

学校读者我要写书评

暂无评论

Arabic Speech Recognition of zero-resourced Languages: A Case of Shehri (Jibbali) Language 6

Arabic Speech Recognition of zero-resourced Languages: A Cas...

引用

6th Workshop on Open-Source Arabic Corpora and Processing Tools, OSACT 2024 with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation

作者： Alrashoudi, Norah Alshahri, Omar Said Al-Khalifa, Hend Department of Information Technology College of Computer and Information Science King Saud University Riyadh Saudi Arabia Islamic Sciences Institute Diwan of the Royal Court Salalah Oman

ISBN: (纸本)9782493814364

Many under-resourced languages face data scarcity issues due to a lack of standardized writing systems, making ASR training more challenging and costly. However, there's a growing need to adapt ASR for indigenous languages to support language documentation, preservation, and the development of learning materials for these communities. Shehri or Jibbali, a spoken language in Oman, lacks extensive annotated speech data. This paper aims to investigate transfer learning techniques to develop an ASR model for this under-resourced language. We collected a Shehri (Jibbali) speech corpus and utilized transfer learning by fine-tuning pre-trained ASR models on this dataset, including Wav2Vec2.0, HuBERT and Whisper. Evaluation using word error rate (WER) and character error rate (CER) showed that the Whisper model, fine-tuned on the Shehri (Jibbali) dataset, significantly outperformed other models, with the best results from Whisper-medium achieving 3.5% WER. This demonstrates the effectiveness of transfer learning for resource-constrained tasks, showing high zero-shot performance of pre-trained models. © 2024 ELRA Language Resource Association.

关键词： Speech processing

来源：评论

学校读者我要写书评

暂无评论

Likelihood Ratio Based Voice Comparison Using Cepstral Coefficients and GAN 2

Likelihood Ratio Based Voice Comparison Using Cepstral Coeff...

引用

2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kruthika, S.G. Nagavi, Trisiladevi C. Mahesha, P. Department of Computer Science and Engineering S.J. College of Engineering JSS Science & Technology University Karnataka Mysore India

In order to identify suspects in digital forensics, Forensic Voice Comparison (FVC) entails comparing trace voices with known suspect voice samples. The FVC uses semi-automatic approaches to identify speakers using acoustic features. Two Australian datasets such as forensic evaluation dataset 1 & English dataset 2 were used. The current research on FVC samples is noise-reduced using a stationary noise reduction algorithm within a likelihood ratio-based framework. To demonstrate the power of knowledge representation, acoustic features like Mel Frequency Cepstral Coefficients(MFCC) and Linear Predictive Cepstral Coefficients (LPCC) were extracted. The suspect is then identified by classifying the model using the Generative Adversarial Network(GAN) and Artificial Neural Network (ANN). The accuracy is 80% & 90% for MFCC for dataset 1 & 2. Similarly, accuracy is 20% & 40% for LPCC for dataset 1 & 2. The overall comparison of acoustic features with two different datasets the MFCC performs better accurate results for Australian English dataset 2 with varying sample sizes. © 2024 Elsevier B.V.. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

MMAT: Multi-scale Multi-attention Transformer for Fine-Grained Wild Fungi Visual Classification 21st

MMAT: Multi-scale Multi-attention Transformer for Fine-Grai...

引用

21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Dai, Qinyan Lu, Yuxiang Wang, Chunlin Lu, Hongtao Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Information Science and Technology Chuxiong Normal University Yunnan China

ISBN: (纸本)9789819601219

Fine-Grained Visual Classification (FGVC) is a computer vision task that involves classifying subtle differences in images. While the Vision Transformer (ViT) is excellent at capturing long-range dependencies in sequences, it may not be as effective in extracting local image features that are crucial for fine-grained tasks. To address this issue, we propose a multi-scale multi-attention transformer model (MMAT), which consists of two parts. Firstly, a method for fine-grained feature fusion is used to aggregate and compress feature maps from the last ViT layer, which serves as input for the classifier. We evaluated various pooling strategies to determine their impact on classification accuracy. Secondly, an attention-based Local Feature Extraction Module leverages attention weights from ViT’s multi-head self-attention mechanism to identify key local regions for further feature extraction and classification via part-CNNs. Experiments were conducted on the public dataset CUB-200-2011, as well as a self-built wild fungi dataset. The results of the experiments demonstrate the model’s superior performance and wide applicability in the field of FGVC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： FGVC Multi-Attention Part-CNN Vision Transformer

来源：评论

学校读者我要写书评

暂无评论

Integrating Speech Input in Educational Immersive Virtual Reality Applications: A Systematic Review 12

Integrating Speech Input in Educational Immersive Virtual Re...

引用

12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Alghamdi, Nuha Cristea, Alexandra I. Durham Univeristy Department of Computer Science United Kingdom University of Jeddah Information Systems and Technology Department Saudi Arabia

ISBN: (纸本)9798350350982

The topic of immersive virtual reality (IVR) in education has gained increasing attention in recent years, due to its potential to enhance learner outcomes and to mitigate learning costs. As we can capture a multitude of information from speech and generate valuable information from it, there has been an interest in exploring this source of data in such environments. Additionally, speech is being used in new and different ways in such environments. However, its specific usage in IVR-based education has not been reviewed yet. Thus, this systematic review seeks to examine, for the first time, the current state of research, specifically on using speech input - and, related to this, Natural Language Processing (NLP) - in educational IVR environments. We conducted a comprehensive search of the popular Web of science and Scopus databases, to identify relevant papers. To properly reflect the state-of-the-art, English peer-reviewed articles published in the last 5 years (between 2020 - 2024), were included in the review, based on keywords search. 595 articles were identified and processed via the established PRISMA procedure, to exclude all duplicate or irrelevant papers, rendering 23 articles as relevant. For these, we identified the target educational subjects and the purpose of using speech as a data source. We also investigated speech recognition models and NLP models used. This systematic review provides evidence supporting the use of speech input as a valuable data source in educational IVR applications. We also propose the first, to the best of our knowledge, taxonomy for speech and NLP for IVR in education, as well as identify potential further research directions, all of which can help researchers and educators, when considering incorporating speech and NLP into educational IVR applications, to enrich the teaching and learning experience. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Density-Aware and Semantic-Guided Fusion for 3-D Object Detection Using LiDAR-Camera Sensors

引用

IEEE Sensors Journal 2023年第18期23卷 22051-22063页

作者： Jhong, Sin-Ye Chen, Yung-Yao Hsia, Chih-Hsien Wang, Yu-Quan Lai, Chin-Feng Department of Engineering Science National Cheng Kung University Tainan701 Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan260 Taiwan

Three-dimensional object detection plays a key role in autonomous driving systems. The performance of light detection and ranging (LiDAR)-based models is limited because of the sparsity and inhomogeneity of point clouds. Researchers have developed numerous camera-LiDAR-based fusion methods to address the limitations of LiDAR-based models. Still, because of the major differences in representations between pixel and point cloud, these fusion methods cannot appropriately leverage the benefits of both types of sensors. In this study, we developed a 3-D object detection model based on LiDAR-camera fusion. The model first augments raw points with possibility features extracted from a camera stream and subsequently uses possibility-guided farthest point sampling (P-FPS) for high-quality feature extraction in the 3-D backbone. In addition, we designed a detection head for density-aware artificial point sampling and fusion that can aggregate geometric and semantic features by using high-resolution artificial points and shift the position of an artificial point according to the density of points in a region of interest (ROI) to obtain more representative point features. We conducted massive experiments by using the public KITTI dataset. Our method outperforms previously developed state-of-the-art 3-D object detection methods, especially in detecting small and distant objects. © 2023 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Analyses of Tabular AlphaZero on Strongly-Solved Stochastic Games

引用

IEEE Access 2023年 11卷 18157-18182页

作者： Hsueh, Chu-Hsuan Ikeda, Kokolo Wu, I-Chen Chen, Jr-Chang Hsu, Tsan-Sheng Japan Advanced Institute of Science and Technology School of Information Science Ishikawa Nomi923-1211 Japan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City23741 Taiwan Academia Sinica Institute of Information Science Taipei11529 Taiwan

The AlphaZero algorithm achieved superhuman levels of play in chess, shogi, and Go by learning without domain-specific knowledge except for game rules. This paper targets stochastic games and investigates whether AlphaZero can learn theoretical values and optimal play. Since the theoretical values of stochastic games are expected win rates, not a simple win, loss, or draw, it is worth investigating the ability of AlphaZero to approximate expected win rates of positions. This paper also thoroughly studies how AlphaZero is influenced by hyper-parameters and some implementation details. The analyses are mainly based on AlphaZero learning with lookup tables. Deep neural networks (DNNs) like the ones in the original AlphaZero are also experimented and compared. The tested stochastic games include reduced and strongly-solved variants of Chinese dark chess and EinStein würfelt nicht!. The experiments showed that AlphaZero could learn policies that play almost optimally against the optimal player and could learn values accurately. In more detail, such good results were achieved by different hyper-parameter settings in a wide range, though it was observed that games on larger scales tended to have a little narrower range of proper hyper-parameters. In addition, the results of learning with DNNs were similar to lookup tables. © 2013 IEEE.

关键词： Reinforcement learning

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：