检索结果-内蒙古大学图书馆

Soil Image Classification Using Transfer Learning Approach: MobileNetV2 with CNN

SN computer science 2024年第1期5卷 199页

作者： Banoth, Ravi Kumar Murthy, B. V. Ramana Department of Computer Science and Engineering University College of Engineering Osmania University Telangana Hyderabad India Department of Computer Science and Engineering Stanley College of Engineering and Technology for Women Abids Telangana Hyderabad India

This paper presents a novel study on soil image classification, leveraging the synergistic potential of transfer learning and convolutional neural networks (CNNs). The proposed approach combines the strengths of the MobileNetV2 architecture with a customized CNN model for accurate and efficient soil type recognition. The pre-trained MobileNetV2 is used to capture generic features before fine-tuning it with a dedicated soil image dataset comprising four distinct classes: red, clay, black, and yellow soils. To enhance the model’s capacity for discerning intricate soil textures, a specially designed CNN architecture is incorporated. The model’s performance is rigorously evaluated on a dataset of 108 images, each sized at 256 × 256 pixels, achieving an exceptional accuracy rate of 100% on the test dataset. The promising results demonstrate the efficacy of the proposed methodology in soil image classification tasks, offering potential applications in precision agriculture, environmental monitoring, and land management. While these findings showcase remarkable accuracy, further investigations are recommended to assess the model’s generalization across diverse environmental conditions and an expanded range of soil image datasets. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Black soil Clay soil Convolutional neural networks (CNNs) MobileNetV2 Red soil Soil image classification Yellow soil

来源：评论

学校读者我要写书评

暂无评论

Service Quality and Experience Based Service Selection Framework in Cloud 4

Service Quality and Experience Based Service Selection Frame...

引用

4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Yadav, Anurag Yadav, Neeraj Goraya, Major Singh Indian Institute of Technology Department of Computer Science and Engineering Roorkee India Bennett University Department of Computer Science and Engineering Greater Noida India Sant Longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur India

ISBN: (纸本)9798350395914

Selecting a cloud service provider (CSP) from a wide range of options with varying offerings poses a crucial challenge for service requesting consumers (SRCs). The decision criteria outlined in Service Level Agreement (SLA), which defines the Quality of Service (QoS) offered by the CSP, help in selecting a suitable provider. However, relying solely on the SLA for evaluating the merits of a CSP is insufficient. Therefore, the Quality of Experience (QoE) has gained significance in assessing the overall satisfaction of the (SRC). Additionally, monitoring consumer buying behavior is essential from a business perspective, as all aspects of a business transaction should be mutually beneficial for both the CSP and SRC. Therefore, a new framework is proposed to determine the relative rankings of CSPs and SRCs. Effectiveness of the framewo rk is established through experiments on the QWS cloud dataset, which reveal positive results in terms of implementation complexity and time required. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous information alignment and re-ranking for cross-modal pedestrian re-identification

引用

Multimedia Tools and Applications 2024年 1-24页

作者： Zhao, Tiezhu Liang, Xiaolun He, Kejing Yang, Qiuhong Ren, Ziliang School of Computer Science and Technology Dongguan University of Technology Guangdong Dongguan523808 China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510641 China School of Artificial Intelligence and mathematical Dongguan City University Guangdong Dongguan523419 China

Cross-modal pedestrian re-identification, encompassing visible and infrared images, presents a significant challenge owing to inherent disparities in imaging principles, resulting in substantial cross-modal discrepancies. Consequently, matching and extracting heterogeneous information becomes exceedingly arduous. To address these issues, this research introduces a novel cross-modal pedestrian re-identification methodology, leveraging a heterogeneous information alignment algorithm and a reranking strategy. Specifically, a novel algorithm for aligning heterogeneous local information is proposed, which dynamically aligns the diverse local details of a given pedestrian by identifying the shortest path within the distance matrix of the pedestrian’s heterogeneous local information. Furthermore, to tackle the predicament of extracting heterogeneous pedestrian information, an expanded k-nearest neighbor reranking strategy is introduced, which integrates the heterogeneous information of the same pedestrian by dynamically developing the k-nearest neighbor heterogeneous information derived from the query image. Extensive experimental evaluations substantiate the effectiveness of the proposed approach across diverse scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Framework for Data Loss Prevention and Detection

A Hybrid Framework for Data Loss Prevention and Detection

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Srivastava, Atul Srivastava, Priyam Pillai, Anuradha Sharma, Vijay Shankar AUUP Amity School of Engineering and Technology Lucknow India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Manipal University Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350385199

Modern enterprises face a serious threat from data loss, or the unintentional or undesired disclosure of data. Modern Data Loss Protection (DLP) systems either discover anomalies in regular behavior (anomaly-based) or use signatures of known attack patterns. Though signature-based solutions are effective in accurately identifying known attacks and may thus be used to block them, they are unable to handle unexpected attacks or attackers who choose atypical attack vectors (such as those that are only known to insiders) to execute their attack. However, because they frequently produce a high false positive rate, anomaly-based solutions are not as useful for identifying suspicious activity. In contrast, they can identify undiscovered attacks. Our research presents a hybrid DLP system that integrates anomaly-based and signature-based approaches to facilitate both prevention and detection. Using an anomaly-based engine, the framework automatically builds a model of typical user behavior, enabling it to detect unusual transactions carried out by insiders. This is usually where anomaly-based solutions end. To prevent unwanted transactions from causing harm, our framework goes one step further by using an operator's feedback on alarms to automatically create and update attack signatures. We manage to improve time to detect a data breach was reduced by 67%, improving response efficiency. © 2024 IEEE.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Predicting Alzheimer's Onset through Neuroimaging 2

Predicting Alzheimer's Onset through Neuroimaging

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Kandula, Niravange Chakravarty, Sujata Kandula, Aksha Mohapatra, Sunil Kumar Centurion University of Technology & Management Computer Science & Engineering Vizianagaram India Centurion University of Technology & Management Computer Science & Engineering Bhubaneswar India Andhra University College of Engineering Computer Science & Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350370188

Alzheimer's disease is a progressive and lethal neurological condition epitomized by the accumulation of protein aggregates in the brain cells that are responsible for cognitive functions. It stands as the most prevalent cause of death, especially among the elderly. The resemblance between the neuropathology of various dementia types and the disease's irreversible nature presents a significant global healthcare challenge in terms of diagnosing and identifying it in its early stages. Traditional methods rely on cognitive assessments and subjective examination by clinicians which can be time-consuming and are prone to human errors. In contrast, a well-trained Deep Learning model can identify subtle structural and functional brain changes in Magnetic Resonance Imaging, that can serve as early indicators of neurodegenerative diseases, facilitating prompt and accurate therapies. The work examines image classification using Convolutional Neural Networks (CNN), Residual Networks (ResNet), VGG networks (VGGNET's), and Alex Net. The result shows that CNN and VGGNET outperformed in accuracy across non-demented, mildly demented, very mildly demented, and moderately demented stages with 98% and 99% accuracy, respectively. © 2024 IEEE.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Dual Attention Entropy Model for Efficient Neural Image Compression 21

Dual Attention Entropy Model for Efficient Neural Image Comp...

引用

21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Tianwen, Wu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611-731 China

ISBN: (纸本)9798331519254

In the field of neural image compression, it is essential to develop an efficient entropy model which predicts the probability distribution of the quantized latent representation. Previous CNN based or transformer based entropy models achieve suboptimal estimation because CNN based model cannot capture long-range dependencies, while transformer based model suffers from excessive memory and computational cost. To address this issue, we propose a novel Entropy model based on Dual Attention aggregation (EDA), which takes into account both global and local dependencies effectively and efficiently. Specifically, a Dual Attention Aggregation (DAA) block is proposed. Within the DAA block, Deformable Attention Transformer (DAT) is proposed to capture global dependencies efficiently by using sparse key-value pairs to filter out large amounts of irrelevant contexts, and then Neighborhood Attention Transformer (NAT) is used for learning the complementary local dependencies. Experiments demonstrate that our model (EDA) delivers the state-of-the-art rate-distortion performance while being time-efficient and memory-friendly. The source code can be accessed at https://***/TNTWEN/EDA. © 2024 IEEE.

关键词： Image compression

来源：评论

学校读者我要写书评

暂无评论

Machine learning advancements in traffic forecasting: Hybrid optimization ofls-svm for urban traffic management

Advances in Transportation Studies

引用

Advances in Transportation Studies 2024年 62卷 223-236页

作者： Jin, T. Zhang, Z. Liu, B. School of Information Engineering Yinchuan University of Science and Technology Ningxia Yinchuan750001 China

Economic development has led to a continuous increase in people's demand for transportation, which has led to an imbalance between the supply and demand of road facilities. Traffic congestion, traffic safety and other issues have caused the road traffic environment to become increasingly poor, thereby hindering the development of cities. Therefore, in response to the inability of traditional traffic flow prediction techniques to handle constantly changing traffic flow data, a machine learning traffic flowing predicting model on the foundation of least squares support vector machine is proposed. A hybrid optimizing algorithm based on particle swarm optimization and genetic algorithm is proposed to optimize the parameters of least squares support vector machines in response to the sensitivity of model parameters. Three factors of genetic algorithm are introduced into particle swarm optimization to optimize this model. These experiments confirm that the fitting accuracy between model's predicted values and the actual values is over 90%, and the residual fluctuation is relatively small. Its RMSE is approximately 33.52% to 75.76% lower than the other three algorithms, its MAE is approximately 27% to 67% lower than other algorithms, and its EC is approximately 1% to 5% higher than other algorithms. The proposed hybrid optimization algorithm can find a set ofoptimal parameters, making the least squares support vector machine have good stability and prediction accuracy in predicting traffic flow problems. This model can be used to predict traffic flow, congestion conditions and other traffic indicators, providing a reliable theoretical basis for urban traffic management. © 2024, Aracne Editrice. All rights reserved.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Plasma enhanced reduction method for synthesis of reduced graphene oxide fiber/Si anode with improved performance

引用

Chinese Chemical Letters 2024年第11期35卷 505-511页

作者： Yihong Li Zhong Qiu Lei Huang Shenghui Shen Ping Liu Haomiao Zhang Feng Cao Xinping He Jun Zhang Yang Xia Xinqi Liang Chen Wang Wangjun Wan Yongqi Zhang Minghua Chen Wenkui Zhang Hui Huang Yongping Gan Xinhui Xia School of Materials Science&Engineering Zhejiang UniversityHangzhou 310027China College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014China School of Materials Science and&Engineering Zhejiang Sci-Tech UniversityHangzhou 310018China Department of Engineering Technology Huzhou CollegeHuzhou 313000China Institute of Fundamental and Frontier Science University of Electronic Science and Technology of ChinaChengdu 611371China Key Laboratory of Engineering Dielectric and Applications(Ministry of Education) School of Electrical and Electronic EngineeringHarbin University of Science and TechnologyHarbin 150080China Zhejiang Academy of Science and Technology for Inspection&Quarantine Hangzhou 311215China State Key Laboratory of Photocatalysis on Energy and Environment Fuzhou UniversityFuzhou 350116China

Silicon(Si)is considered as one of the most promising anode materials for advanced lithium-ion batteries due to its high theoretical capacity,environmental friendliness,and widespread ***,great challenges such as volumetric expansion,limited ionic/electronic conductivity properties and complex manufacturing processes hinder its practical ***,a novel plasma-enhanced reduced graphene oxide fibers/Si(PrGOFs/Si)composite anode is first proposed by using wet-spinning technology followed by plasma-enhanced reduction *** PrGOFs provide large space to accommodate the volume expansion of Si nanoparticles(SiNPs)by forming a flexible 3D conductive *** to the conventional thermally reduced graphene oxide fibers/Si(TrGOFs/Si)sample,the PrGOFs/Si anodes demonstrate higher conductivity,specific surface area,and superior fabrication ***,the Pr GOFs/Si anodes exhibit a reversible capacity of 698.3 mA h/g,and maintain a specific capacity of 602.5m Ah/g at a current density of 200 m A/g after 100 cycles,superior to conventional Tr GOFs/Si *** research presents a novel strategy for the preparation of high-performance Si/carbon anodes for energy storage applications.

关键词： Li ion batteries Si anode Plasma Graphene fibers Carbon

来源：评论

学校读者我要写书评

暂无评论

Prediction and Management of Building Energy Consumption Based on Building Environment Simulation Design Platform DeST and Meteorological Data Analysis Algorithm

引用

Strategic Planning for Energy and the Environment 2024年第2期43卷 357-380页

作者： Bai, Chaoqin Liu, Junrui School of Civil Engineering and Architecture Henan University of Science and Technology Luoyang471000 China

Currently, the carbon emissions of building energy consumption account for a significant portion of all carbon emissions. How to reduce carbon emissions to achieve carbon neutrality is an important current research direction. Therefore this research builds a predictive algorithm model for analyzing energy consumption data of meteorological buildings using DeST platform for energy saving and emission reduction to achieve carbon neutrality. The new model uses Internet of Things and cloud platform technology to build a simulation building platform, and uses the support vector machine algorithm in the analysis algorithm to vectorize building energy consumption data, which can achieve normalization processing of building energy consumption and meteorological data. By processing building energy consumption data, prediction of building energy consumption at the next moment can be achieved. The experimental results show that the precision and accuracy of the new algorithm are higher than genetic algorithm 1 and 0.15 respectively, and 0.6 and 0.07 higher than clustering analysis algorithm respectively. Therefore, applying this algorithm model to building energy consumption prediction can significantly improve the accuracy and precision of the algorithm. © 2024 River Publishers.

关键词： Carbon sequestration

来源：评论

学校读者我要写书评

暂无评论

Construction of evaluation index system of architecture education based on vector analysis

引用

International Journal of Wireless and Mobile Computing 2024年第3期27卷 256-266页

作者： Chu, Kaifeng Tan, Hui Zhang, Siyao School of Civil Engineering and Architecture Anhui University of Science and Technology Anhui Huainan China

In order to further improve the quality of architecture education, an architectural teaching evaluation model is constructed based on vector analysis and dynamic memory network. Among them, emotional analysis is carried out using students’ comments on the teaching process as evaluation data, and the analysis results are used as the basis for teaching evaluation. The experimental results show that the designed aspect-level sentiment analysis model integrating prior knowledge (API-DMN), has a high accuracy in the sentiment classification and sentiment analysis, and its accuracy is stable at more than 80%. Therefore, the designed model can be applied to the evaluation of architecture education and teaching, and the credibility of the evaluation results is high. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Data accuracy

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：