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检索条件"任意字段=4th International Conference on Computer Science and Software Engineering, CSSE 2021"

共 6342 条记录，以下是31-40 订阅

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A Lithium-Ion Battery Cathode Material Literature Entity Recognition Method Based on Deep Learning 4th

A Lithium-Ion Battery Cathode Material Literature Entity Rec...

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4th international conference on Data and Information in Online Environments

作者： Yin, Ziyu Lei, Gang Xiao, Jianmao Qiu, Xinji Zhang, Qian Chang, Lei Chen, Haohui Wu, Musheng Zhao, Xushan Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China Jiangxi Normal Univ Dept Phys Nanchang 330022 Jiangxi Peoples R China Contemporary Amperex Technol Co Ltd Innovat Lab Ningde 352100 Fujian Peoples R China Jiangxi Prov Engn Res Ctr Blockchain Data Secur & Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9783031807121;9783031807138

With the continuous deepening of research on lithium-ion battery materials, the number of literature in related fields has shown explosive growth, posing a challenge to researchers in terms of literature reading and information screening. Named entity recognition technology can automatically mark the pre-defined entity content in the literature, helping researchers to quickly locate the key information of the articles, and improve the reading rate of the literature. In this paper, the classification system of lithium-ion battery materials literature is constructed through the combination of automation and manual analysis, and the named entity recognition operation is conducted. We adopt the named entity recognition model based on BiLSTM-CRF, combined with Bert word embedding technology, to automatically identify and extract entities in the literature, and develope an automatic document annotation tool. Our work provides strong support for researchers to quickly understand the literature content and key information, and lays a foundation for subsequent relationship extraction and the construction of a Knowledge graph.

关键词： Lithium-ion Battery Materials Information Extraction Classification System Named Entity Identification

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Mining analysis of traffic accident features based on fp-growth algorithm and apriori algorithm 4

Mining analysis of traffic accident features based on fp-gro...

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4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Bao, Chun Tang, Yang Yang, Bingheng Wang, Xiang Chen, Jianqiu Xiong, Huan Lou, Benxia Zhou, Shixuan Cao, Xiaoli Gu, Guobin Guangxi Key Laboratory of International Join for China-ASEAN Comprehensive Transportation Nanning University Nanning541699 China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In this paper, based on data mining technology, Fp-growth algorithm and Apriori algorithm are used to mine and analyze traffic accident features. the correlation between the causes and severity of traffic accidents is revealed through association rule mining. Apriori algorithm is less computationally efficient in mining frequent itemsets through the generation and pruning steps of candidate itemsets, though with high accuracy. In contrast, the Fp-growth algorithm avoids multiple database scans by constructing an Fp-tree, which saves memory resources and improves efficiency. the model construction and evaluation part uses a multi-type variable classification model to predict the accident severity level, and evaluates the model performance through a test set, which shows excellent performance in category 1. Ultimately, this study verifies the effectiveness of Fp-growth and Apriori algorithm in traffic accident factor mining and provides a decision support tool for traffic safety management. © 2025 SPIE.

关键词： Mining Roads Data mining Databases Safety Analytical research Transportation Performance modeling Mathematics Process modeling

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Sustainable development of solar power through the investigation of Partial Shading effect of solar module in terms of experimental set up and MATLAB simulation 4

Sustainable development of solar power through the investiga...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Khan, Mohiuddin Ferdous, Mahbub Chowdhury, Suman Department of Electrical and Electronic Engineering International University of Business Agriculture and Technology Dhaka1230 Bangladesh

the analysis on partial shading for a PV module is presented in this paper as a critical criterion for sustainable development in practical life. Nowadays it is an important challenge for the sustainable development in the energy sector where solar panel can take a vital role in this regard. A partial shading experiment on a PV module will be shown in this investigation, as well as comparisons of IV properties and PV panel efficiency with and without shade. the many causes of partial shade will be examined and there will be corrective ways to avoid partial shading. this project is developed into two stages namely, hardware and software implementation. For hardware implementation two 20W polycrystalline solar panel and different types of variable loads were used. In addition to this, the impact of partial shading of single and two different solar PV configurations are investigated: 1) Series 2) Parallel. the SIMULINK environment of MATLAB is utilized for simulation. Under partial shading conditions, the effect of two environmental elements, irradiance and temperature, will be seen in the simulated features. the bypass and blocking diodes are employed to increase the performance of PV arrays, and their influence can be observed in the simulation. From the practical observation it is seen that almost 86.2 % more power can be obtained from the solar module from 25 % shading to 0% shading on module. © 2025 the Authors. Published by Elsevier B.V.

关键词： MATLAB

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of things (IIoT). the deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. this presents a variety of well-known and increasing issues related to the industrial system. the IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. the reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. this helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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Application of Hyperledger Blockchain Technology to Logistics Supply Chain with IoT 4

Application of Hyperledger Blockchain Technology to Logistic...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sayar, Ahmet Kurtbas, Muhammet Cuneyd Sancaktar, Caglayan Kabak, Rana Dudu Cakmak, Sukru Department of Computer Engineering Kocaeli University Kabaoglu Baki Komsuoglu Avenue No:515 Umuttepe Kocaeli İzmit41010 Turkey Atez Software Technologies R&D Center Skyland B Blok No: 4B İstanbul Turkey

Product movement tracking stands as a pivotal aspect of logistics operations. the ever-expanding application of IoT technologies holds significant potential to enhance product tracking in logistics. However, the rapid expansion of IoT usage has also introduced several challenges, particularly concerning data privacy, security, and transparency. Blockchain technology, through distributed ledger systems, offers a secure and transparent means to manage transactions within a network. In this study, the Hyperledger Fabric blockchain network, chosen for its private and permissioned nature, leverages authentication features to ensure the confidentiality, integrity, and security of data from IoT devices. By integrating the widely adopted MQTT protocol, IoT devices involved in logistics processes communicate effectively with the blockchain network. this study presents a system where IoT devices enable the secure and automated tracking of data through blockchain's registration and distribution mechanisms, all of which are demonstrated within a simulation environment. Developed specifically for the logistics sector, this system holds promising potential to address challenges across various fields. © 2025 the Authors. Published by Elsevier B.V.

关键词： Distributed ledger

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Applying Machine Learning to Improve Weather Forecasting in a Mediterranean Area: Preliminary Experiments 4th

Applying Machine Learning to Improve Weather Forecasting in...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Yousaf, Umair De Rango, Alessio Furnari, Luca D’Ambrosio, Donato Senatore, Alfonso Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (纸本)9783031812439

We investigate the effectiveness of Machine Learning (ML) as a complementary tool in enhancing the accuracy of precipitation forecasting in the Calabria region (southern Italy), a Mediterranean area frequently affected by extreme rainfall events, with significant material damage and potential loss of human lives. the operational weather forecasting system developed at the University of Calabria, based on the Weather Research and Forecasting (WRF) model, is considered to this end. We test two ML models, namely an artificial neural network and a random forest, to improve the accuracy of WRF forecasting. We provide several WRF output variables to the ML models. After a proper calibration (learning and test phases) over a two-year-long extended dataset, we assess the results to the original WRF outcomes (validation phase). Preliminary experiments resulted in a significant improvement by the WRF-ML coupled systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Application of Deep Learning for Wildfire Risk Management: Preliminary Results 4th

Application of Deep Learning for Wildfire Risk Management:...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： De Rango, Alessio D’Ambrosio, Donato Mendicino, Giuseppe Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (纸本)9783031812439

Due to ongoing climate change and the related increased risk of drought, wildfires in the Mediterranean are expected to become more frequent and intense. this note introduces the use of Deep Learning to produce susceptibility maps for wildfire risk assessment to tackle this scenario. Indeed, thanks to the improved computational capabilities of modern GPUs, recent literature has pointed out that it is possible to train Deep Learning models on big datasets by outperforming standard approaches in several application domains, including the one object of this study. An Artificial Neural Network (ANN) model has been developed and applied to the Calabria region (southern Italy) study area. the network has been trained and validated over ten years of data by considering both meteorological and geomorphological data, and human factors. Preliminary results are promising, evidencing a higher network’s accuracy to the operational state-of-the-art Fire Weather Index (FWI) method, currently applied for forecasting and mitigation purposes in the study area. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Elderly Health Care Data Integration Framework: Design and Implementation 4th

Elderly Health Care Data Integration Framework: Design and I...

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4th international conference on Data and Information in Online Environments

作者： Zhong, Zhean Liu, Chenyu Zhao, Jing Xiao, Jianmao Gao, Qinghang Gao, Chao Ouyang, Chuying Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China Jiangxi Prov Engn Res Ctr Blockchain Data Secur & Nanchang 330022 Jiangxi Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China Jiangxi Normal Univ Dept Phys Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9783031807121;9783031807138

the application of Internet of things (IoT) detection devices in the field of elderly healthy generates a large amount of detection data, which faces problems such as diversified data sources and non-uniform parsing methods. the fragmentation and confusion of the process from data subscription to data analysis have become important challenges affecting the development of the healthy aging field. In this paper, we designed and proposed an elderly health care framework with a model-as-a-service orientation. the framework automates the integration of health data for the elderly, including subscription services. It handles Model Training Analysis through a workflow-based system. Users can configure workflows using a low-code method provided by the platform. First, we abstracted and formalized the integration problem and workflow and defined a workflow description language. then, we built an healthy aging unified modeling framework based on the problem model and provided a general introduction to the framework architecture. the module for analyzing is responsible for model training, persistence, and data analysis, enabling users to customize the selection of machine learning models and corresponding parametric. Ultimately, we built a model library and an external RESTful interface based on the automation option of Automated machine learning (AutoML). the experiments demonstrate this paper's proposed framework's feasibility and effectiveness.

关键词： Data curation Elderly health care Model-as-a-Service Machine learning

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Preparation and Properties of Pluronic F-127 Based Alumina Inks for Additive Manufacturing 4th

Preparation and Properties of Pluronic F-127 Based Alumina ...

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4th international conference on Recent Advancements in Mechanical engineering, ICRAME 2023

作者： Majumder, Debabrata Karuna Purnapu Rupa, P. Department of Material Science and Engineering Tripura University Tripura Suryamaninagar India

ISBN: (纸本)9789819741663

Alumina is a widely available low-cost engineering ceramic used in different rugged and harsh environments. Most of the alumina parts are produced by conventional powder metallurgy techniques. But the fabrication of complex alumina parts in traditional processes is difficult due to the complexity of the manufacturing process. Direct ink writing is one of the additive manufacturing techniques that build an object in a layer-by-layer manner according to the computer-aided design. To prepare alumina inks, Pluronic F-127 was used to form a hydrogel. the resultant ink consisted of 65 wt.% of alumina (solid loading), and the rest 35 wt.% was Pluronic F-127 hydrogel. Rheological observations showed that the prepared ink is shear thinning in nature which enables the ink to flow smoothly from the nozzle. From the differential scanning calorimetry (DSC) studies, it is observed that all of the organic binder is removed before 500 °C. Subsequently, the alumina sample was sintered at 1300 °C for 90 min in a muffle furnace. the scanning electron microscopy (SEM) image of the fracture surface of sintered sample at 1300 °C showed the partial sintering with open porosities. the resultant structure shows that direct ink writing can be used to obtain a porous alumina microstructure. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sintering

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Fusion of Multiscale Convolution and LSTM for Stock Price Prediction 4th

Fusion of Multiscale Convolution and LSTM for Stock Price Pr...

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4th international conference on Data and Information in Online Environments

作者： Sheng, Hui Hu, Jiyong Wang, Menglin Liu, Min Zhang, Haoming Huang, Longjun Jiangxi Normal Univ Sch Software Nanchang Jiangxi Peoples R China Jiangxi Technol Business Polytech Nanchang Jiangxi Peoples R China

ISBN: (纸本)9783031807121;9783031807138

the stock market has long been a topic of great interest in the financial sector. the job of predicting stock prices has also proven challenging. It is challenging to capture such intricate correlations in time-series data because traditional approaches usually assume constant data, which does not account for the time-varying, dynamic, and extremely noisy nature of time-series data. Data obtained from the stock market is fundamentally a multi-scale, non-smooth, non-linear time series. To address the aforementioned issues, a stock prediction model (MCA_LSTMNet) that combines multi-scale convolutional attention (MCA) and a Long Short-Term Memory network (LSTM) is proposed, which is able to better capture the long-term dependence and complicated feature connections of stock data. the daily closing price is used as the target sequence, and the remaining characteristics are used as the exogenous sequence in the model's first division of the many features of the stock time series into two sequences. the encoder then uses time convolution at various scales to extract the feature from the exogenous sequence's many time scales. the hidden layer state and multi-scale features of the decoder are then weighted and fused using the attention mechanism (AM) to produce the context vector at the corresponding moment, combine it with the value of the target sequence at each moment as the decoder's input, and finally acquire the prediction outcome. the MCA_LSTMNet model is tested on three datasets in this study: SSE50, SZSE Component Index, and CSI300. the experimental findings demonstrate that the MCA_LSTMNet model performs better in terms of prediction and generalization than other benchmark approaches.

关键词： Multi-scale convolution Attention mechanism LSTM Stock price forecasting

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