检索结果-内蒙古大学图书馆

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Noh, Kyoungrae Kim, San Kim, Jaekwang Sungkyunkwan University Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Convergence Program for Social Innovation Suwon Korea Republic of

ISBN: (纸本)9798350373332

In real-world image classification tasks, neural networks often encounter significant challenges due to unexpected or deceptive correlations and biases inherent in the dataset. These biases can emerge from disproportionate data distributions, causing models to generalize poorly to new, unseen data. Such data distribution issues are particularly problematic compared to more balanced datasets because they lead the model to rely on bias attributes rather than intrinsic attributes. Ideally, the model should classify based on intrinsic attributes, but due to the influence of bias attributes, frequent misclassification occurs. Such biases compromise the fairness and accuracy of the model, especially in critical scenarios such as medical diagnosis, autonomous driving, or criminal justice, where misclassification can have significant consequences. To address these challenges, we propose an innovative two-stage approach aimed at mitigating bias more effectively and efficiently. © 2024 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Cuisine Image Recognition and Recommender System using Convolutional Neural Network 5

Cuisine Image Recognition and Recommender System using Convo...

引用

5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Kuan-Yun Kao, Hao-Xiang Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan Chung Hua University Program of Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

The fusion of technology and culinary exploration has allowed for the emergence of advanced online customer service systems. We developed a novel approach to enhance the dining experience. We used a Cuisine Image Recognition and Recommender System (CIRRS) powered by Convolutional Neural Network (CNN) to identify and suggest diverse cuisines based on visual inputs. CIRRS swiftly identified diverse cuisines from user-captured images, offering information on origin, ingredients, and variations by crawling websites. The system enhanced dining experiences by suggesting personalized menus based on individual preferences and past selections. Extensively tested across various culinary genres, CIRRS consistently demonstrated accuracy and adaptability. User feedback validated its potential to simplify dining choices and elevate satisfaction. This innovative system enriched dining experiences as a valuable tool for culinary enthusiasts, chefs, and restaurateurs, bridging the gap between technology and gastronomy, It also offers a unique way to explore Taiwanese cuisines. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system web crawler

来源：评论

学校读者我要写书评

暂无评论

General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

引用

engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

来源：评论

学校读者我要写书评

暂无评论

Optimal Multi-Keyword Ranked Search Matching in Public Cloud with Enhanced Secure Search Scheme Over Encrypted Data 9

Optimal Multi-Keyword Ranked Search Matching in Public Cloud...

引用

9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Radha, B. Jaya Bharathi, R. Vidhya Muthulakshmi, R. Vidhya, K. Reddy, R. Vijaya Kumar Rondon, Ruchika Sri Krishna Arts And Science College Department of Ict And Cognitive Systems Tamil Nadu Coimbatore641008 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600 123 India Kpr Institute of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Arasur641407 India Lakshmaiah Education Foundation Department of Computer Science Engineering Andhra Pradesh Vaddeswaram522502 India Graphic Era Deemed To Be University Department of Petroleum Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350365092

In this research work, the authors present an improved secure search algorithm that will allow for optimum multi-keyword ranked search matching in public cloud storage that uses encrypted data. The goal of the plan is to provide a method that is both safe and effective for searching for and retrieving important data stored in cloud settings, all while preserving the data's privacy and confidentiality. Experiments and simulations are carried out in order to assess the viability of the suggested plan, and mathematical expressions are used to depict the algorithms and assessment criteria. According to the findings of the research, the strategy that was presented is capable of achieving a high level of accuracy, recall, and F1-score while simultaneously reducing the number of false positives and false negatives. The research underlines how important it is to address security problems in cloud computing settings, and it gives significant insights that can be used to design cloud computing services that are both safe and efficient. In the future, possible topics of study might include further refining and optimizing of the scheme that was described, as well as the application of the method in real cloud computing systems. © 2024 IEEE.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

The Utilization of Fuzzy Logic and Bollinger Bands to Enhance Trading Decision-Making During the Bitcoin Halving Phase

引用

Procedia computer science 2024年 245卷 272-281页

作者： Oscar Adam Darmawan Yaya Heryadi Lukas Lili Ayu Wulandhari Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

The volatile behavior of Bitcoin's price, especially during its halving periods, poses considerable obstacles for forecasting and decision-making in cryptocurrency trading. This paper presents a novel method that combines application fuzzy logic with Bollinger Bands to improve trading decision-making in times of high market volatility. This study conducted an experiment utilizing three fuzzy logic controllers and Bollinger Bands (BB) to determine the strength of buy, hold, and sell signals. This dataset includes the initial and final prices that are used to calculate the BB. The raw and computed values serve as the precise input parameters for the Fuzzy Inference System (FIS). The membership functions were categorized into four levels: very low, low, high, and very high, based on the input default settings utilized by traders. Rulesets were created using fuzzy logic to produce signals that indicate the level of strength of a trading advice. This study evaluate the effectiveness of this hybrid method in comparison to the traditional utilization of the Bollinger Band only indicator and Moving Average Convergence Divergence (MACD) indicator, which is widely favored by traders to identify possible market fluctuations. This methodology involves creating a trading simulation that is based on past Bitcoin halving events. The objective is to assess the efficacy of these strategies in managing heightened volatility. The application of fuzzy logic with the Bollinger Bands model yielded a success rate of 92.47% while analyzing 93 daily data points from the previous Bitcoin halving event on May 11, 2020.

关键词： fuzzy logic crypto price prediction bitcoin bollinger bands macd cryptocurrency

来源：评论

学校读者我要写书评

暂无评论

Using the CNN architecture based on the EfficientNetB4 model to efficiently detect Deepfake images 1

Using the CNN architecture based on the EfficientNetB4 model...

引用

IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Zhalgasbayev, Arman Aiteni, Tolegen Khaimuldin, Nursultan Sofrware Engineering Student Astana IT University Astana Kazakhstan Computer Science Student Astana IT University Astana Kazakhstan Senior lecturer of Computer Engineering Educational Program Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

This study evaluates the effectiveness of using the EfficientNetB4 model architecture for deepfake image detection and identifies the main challenges in developing an accurate model for deepfake image detection. Deepfake image detection is a complex task that requires lots of data, advanced models and new approaches for handling features in face images which shows that image is real or fake. This research is based on the Deepfake Detection Challenge (DFDC), which took place in 2019-2020, where world experts showed new approaches to solving this problem. During the research and development of the model, the best practices and experience of experts were used to identify the deepfake images. ©2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

引用

植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

来源：评论

学校读者我要写书评

暂无评论

Giant phonon-skyrmion coupling in ferromagnet/heavy metal heterostructures

引用

Physical Review B 2024年第6期110卷 064423-064423页

作者： Egor Savostin Vitaliy Lomakin Program in Materials Science and Engineering Center for Memory and Recording Research Department of Electrical and Computer Engineering

The strong impact of the strain-induced Dzyaloshinskii-Moriya interaction (SIDMI) on the magnetization dynamics of skyrmions in nanomagnetic structures is demonstrated. The effects of SIDMI are characterized by skyrmion equations (SEs) of motion and magnetoelastic (ME) equations. The study is performed on a model system of MgO/CoFe/Pt stacked on a piezoelectric substrate. The results demonstrate a major nonlinear amplification in both the first- and higher-harmonic magnitudes of the skyrmion breathing mode due to SIDMI. Remarkably, this enhancement can trigger a skyrmion collapse, enabling its deletion with ultraweak strain-induced excitations. The SIDMI effect is shown to be much more significant than the conventional ME effect. These findings open different avenues for the efficient manipulation of nanomagnetic structures through strain.

关键词： Exchange interaction Magnetic interactions Magnetization dynamics Magnetoacoustic effect Micromagnetism Spin dynamics

来源：评论

学校读者我要写书评

暂无评论

YOLOS-MCI: Automating Microservices Coupling Index Calculation using Vision Transformers

引用

Procedia computer science 2024年 245卷 924-933页

作者： Gintoro Yaya Heryadi Lukas Lili Ayu Wulandhari Ilvico Sonata Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

Microservice Architectures (MSA) provide flexibility and scalability in software development. However, accurately measuring the level of interdependence among Microservices continues to be a difficult task. Precisely evaluating this connection is essential for efficient MSA design, maintenance, and future development. Conventional techniques for assessing Microservice coupling are frequently done by hand, require a significant amount of time, and are susceptible to mistakes. This impedes the capacity to make well-informed judgments regarding the integration and adjustment of services. This study introduces a new method for automating the computation of the Microservice Coupling Index (MCI) by utilizing the You Only Look at One Sequence (YOLOS) object identification technique in combination with Vision Transformer (ViTs) technology. YOLOS is utilized for identifying constituents within Unified Modeling Language (UML) Component Diagrams, facilitating precise classification and effective assessment of coupling. The model exhibits varying performance over multiple Intersection over Union (IoU) thresholds and object sizes, with an average precision (AP) of 0.406 over IoU values ranging from 0.50 to 0.95. The maximum precision is achieved at an IoU of 0.50, with an AP of 0.709. The model demonstrates good performance in identifying smaller components, especially when evaluated at a 0.75 IoU threshold. However, it faces challenges in detecting small items, suggesting potential areas for improvement in future iterations. Initial results indicate that this automation greatly decreases the need for manual, labor-intensive tasks and enhances the precision of measuring coupling in MSA, hence facilitating effective decision-making in service integration and modification. Automating the computation of the coupling index has the potential to significantly influence the design and management of durable and readily controllable microservice architectures.

关键词： Coupling Microservices Microservices Coupling Index UML Component Diagrams Vision Transformer YOLO

来源：评论

学校读者我要写书评

暂无评论

Multi-Resolution Location-Based Training for Multi-Channel Continuous Speech Separation 48

Multi-Resolution Location-Based Training for Multi-Channel C...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Taherian, Hassan Wang, DeLiang The Ohio State University Department of Computer Science and Engineering United States The Ohio State University Center for Cognitive and Brain Sciences United States

ISBN: (纸本)9781728163277

The performance of automatic speech recognition (ASR) systems severely degrades when multi-talker speech overlap occurs. In meeting environments, speech separation is typically performed to improve the robustness of ASR systems. Recently, location-based training (LBT) was proposed as a new training criterion for multi-channel talker-independent speaker separation. Assuming fixed array geometry, LBT outperforms widely-used permutation-invariant training in fully overlapped utterances and matched reverberant conditions. This paper extends LBT to conversational multi-channel speaker separation. We introduce multi-resolution LBT to estimate the complex spectrograms from low to high time and frequency resolutions. With multi-resolution LBT, convolutional kernels are assigned consistently based on speaker locations in physical space. Evaluation results show that multi-resolution LBT consistently outperforms other competitive methods on the recorded LibriCSS corpus. © 2023 IEEE.

关键词： Source separation

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：