检索结果-内蒙古大学图书馆

Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data Science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands eScienceCenter AmsterdamNL Department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands Department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

来源：评论

学校读者我要写书评

暂无评论

Effect of atomic ratio of ions on the particle diffusion and permeability of carbon nanotubes in reverse electrodialysis process using molecular dynamics simulation

引用

Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Ali, Ali B.M. Qader, Karwan Hussein Al-Zahiwat, Murtadha M. Sawaran Singh, Narinderjit Singh Salahshour, Soheil Mohammad Sajadi, S. Mokhtarian, Ali Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Computer Science Cihan University-Erbil Kurdistan Region Iraq Department of Chemical engineering College of Engineering University of Misan Amarah Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Faculty of Science and Letters Piri Reis University Tuzla Istanbul Turkey Department of Chemistry Payam e Noor University Saqqez Branch Kurdistan Saqqez Iran Department of Mechanical Engineering Khomeinishahr branch Islamic Azad University Khomeinishahr Iran

This study employed molecular dynamics simulations to investigate water transport through a carbon nanotube under an electric current, focusing on how varying ion atomic ratios influence key system parameters. These parameters include electric current intensity, fluid current intensity, maximum density, hydrogen bond count, and interaction energy as ion concentration changed. The research aimed to examine the effects of these changes on ion mobility, water permeability, and ion–carbon nanotube interactions. The study is conducted in two phases: equilibration, followed by the analysis of atomic transformations and the creation of various atomic ratios in samples. In the first phase, the kinetic energy of the atomic sample converges to 0.162 eV, and the potential energy reaches to 2.048 eV after 10 ns, indicating limited structural mobility and attractive forces among atoms. After equilibration, we achieved the atomic transformation process and created different atomic ratios. The results indicate that increasing ion ratios in the fluid led to a rise in electric current intensity, from 5.31 to 5.52 e/ns. Higher ion concentrations resulted in a greater density of charge carriers, enhancing ionic mobility and ion transport through the carbon nanotube. Moreover, higher ionic concentrations not only reduced the maximum density from 4.83 to 4.65 atoms/nm³ but also increases the number of broken hydrogen bonds, which could impact water transport and flow dynamics. Finally, according to the findings, there are 133 broken hydrogen bonds instead of 116, and the strength of the nanofluid flow, as well as the electric current, both increased when the ionic percentage of atoms rose to 5 %. © 2024 The Authors

关键词： Atomic ratio Carbon nanotube Channel geometry Electrodialysis Molecular dynamics simulation Reverse electrodialysis

来源：评论

学校读者我要写书评

暂无评论

Numerical study of thermal performance of silica-aerogel/paraffin nanostructure in the presence of CuO nanoparticles: A molecular dynamics approach

引用

Nano-Structures and Nano-Objects 2025年 41卷

作者： Ali, Ali B.M. Hussein, Rasha Abed Babadoust, Shahram Singh, Narinderjit Singh Sawaran Salahshour, Soheil Baghaei, Sh. Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Dentistry Al-Manara College for Medical Sciences Maysan Amarah Iraq Department of Medical Biochemical Analysis Cihan University-Erbil Kurdistan Region Erbil Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Faculty of Science and Letters Piri Reis University Tuzla Istanbul Turkey Department of Engineering Islamic Azad University Iran

The rise in air pollution and fuel costs increased the use of various renewable energy options. Currently, scientists face a significant challenge. Finding methods to store energy that can be easily converted is crucial. There is growing interest in using phase change materials for thermal energy storage systems. This interest stems from their ability to conserve energy and reduce air pollution. Silica aerogel effectively maintains the temperature of items over long periods. Phase change materials, recognized for storing thermal energy, are now favored for preserving both hot and cold temperatures. This study aimed to use computer simulations to understand the behavior of silica aerogel/PCM and CuO nanoparticles in a cube. The results show that the nanostructure can achieve a velocity of 0.0086 Å/fs and had a thermal conductivity of 1.85 W/m·K. These findings may have practical applications in heating and cooling systems, energy storage, and the aerospace industry. © 2025 Elsevier B.V.

关键词： Copper Oxide Molecular Dynamics Simulation Nanocomposite Nanoparticles Phase Change Materials Silica Aerogel

来源：评论

学校读者我要写书评

暂无评论

Unlocking the Future: A Decade of Insights into Short-Term Stock Market Trends with Machine Learning and the S&P 500 Index 4

Unlocking the Future: A Decade of Insights into Short-Term S...

引用

4th International Conference on Innovative Research in Applied science, Engineering and technology, IRASET 2024

作者： Tajbel, Salma Elassad, Zouhair Elamrani Abou Atlas, Abdelghafour Marrakech Morocco Faculty Of Science And Information Technology Data Science Lab Birulia Bangladesh

ISBN: (纸本)9798350309508

This research paper presents a comprehensive exploration of short-term stock market trend prediction using state-of-the-art machine learning techniques, anchored in a decade-long analysis of the S&P 500 Index. Leveraging a rich dataset spanning ten years, our study investigates the performance of various machine learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest, and Gated Recurrent Unit (GRU) models. These models were evaluated based on their ability to harness financial indicators and trading volume data, providing a deep dive into the complex factors influencing stock market movements. The GRU model, in particular, demonstrated exceptional performance, underscoring its potential as a superior predictive tool due to its efficient handling of temporal dependencies. This comparative analysis not only highlights the importance of selecting the appropriate model to match specific data characteristics but also contributes significantly to the broader understanding of how machine learning can enhance the prediction of stock market dynamics. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuned BERT Model for Hate Speech Detection in Political Discourse 22

Fine-Tuned BERT Model for Hate Speech Detection in Political...

引用

22nd International Conference on ICT and Knowledge Engineering, ICT and KE 2024

作者： Nwaiwu, Steve Jongsawat, Nipat Tungkasthan, Anucha Thaloey, Jarinya RMUTT Data and Information Science Faculty of Science and Technology Pathum Thani Thailand RMUTT Big Data Analytics and Management Faculty of Science and Technology Pathum Thani Thailand

ISBN: (纸本)9798350391411

This research tackles the challenge of detecting hate speech and offensive content in political discussions on social media. By employing natural language processing (NLP) techniques, the study aims to contribute to creating healthier online communities. The researchers started with a manually labeled dataset of Reddit comments centered on political discourse. To address the common issue of class imbalance in classification tasks, they used an oversampling strategy to ensure equal representation of each class. Additionally, they applied data augmentation, specifically synonym replacement, to increase the diversity of the training data, which helps improve the model's generalization. For text preprocessing, the Bidirectional Encoder Represen- tations from Transformers (BERT) tokenizer was used. This tokenizer converts text into subwords, which is especially useful for handling out-of-vocabulary terms. The model used in this study is a fine-tuned version of BERT base, trained with specific hyperparameters: a learning rate of 5e-5, a batch size of 24, and a weight decay of 0.01. Training was carried out over seven epochs using the Hugging Face Transformers library and NVIDIA Tesla P100 GPUs available on Kaggle. The model's performance was assessed using 5-fold cross- validation, resulting in impressive metrics: an average accuracy of 99.34 percent, precision of 99.36 percent, recall of 99.34 percent, F1-score of 99.34 percent, and an ROC-AUC score of 99.96 percent. These results highlight the model's effectiveness in identifying hate speech and offensive content across different data splits. Compared to earlier research, this approach shows significant improvement in performance metrics. It demonstrates the potential of combining BERT with data augmentation and oversampling techniques for detecting hate speech in political discourse on Reddit. This study lays the groundwork for future research into real-time detection systems and cross-lingual models. © 2024 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Ethnobotanical survey of medicinal and aromatic plants used by the population of Settat Province, Morocco

引用

Heliyon 2025年第3期11卷 e42323页

作者： Lahyaoui, Manal Dakka, Nadia El Menyiy, Naoual Sobeh, Mansour Razi, Pakhrur Goh, Khang Wen Al-Worafi, Yaser Mohammed Farrukh, Muhammad Junaid Bouyahya, Abdelhakim Laboratory of Human Pathologies Biology Department of Biology Faculty of Sciences Mohammed V University in Rabat Rabat 10106 Morocco Laboratory of Pharmacology National Agency of Medicinal and Aromatic Plants Taounate 34025 Morocco AgroBioSciences Mohammed VI Polytechnic University Lot 660–Hay MoulayRachid Ben-Guerir 43150 Morocco Center of Disaster Monitoring and Earth Observation Physics Department Universitas Negeri Padang Padang Indonesia Faculty of Data Science and Information Technology INTI International University Malaysia College of Medical Sciences Azal University for Human Development Sana'a Yemen College of Pharmacy University of Science and Technology of Fujairah Fujairah United Arab Emirates Faculty of Pharmaceutical Sciences UCSI University Cheras Kuala Lumpur Malaysia

To promote Morocco's medicinal and aromatic plants, an ethnobotanical study was conducted in the Settat Province, part of the Casablanca-Settat region, Morocco. The methodology employed in this study involved a direct, oral survey conducted with 30 herbalists from various villages in the Settat province, after obtaining their informed consent. Additionally, an online survey was distributed, to which 212 individuals from the local population, representing different age groups, responded. The sample size was chosen to ensure a confidence level of 88 %. The results provided a database on the modes of use, plant parts utilized, treated pathologies, and recommended dosages for 51 medicinal and aromatic plants from the Settat province and enabled us to calculate their Familiarity Index (FI). The data collected showed that the Lamiaceae, Apiaceae, and Fabaceae families are the most represented. The calculated Familiarity Index indicated that the most commonly used species were Verbena, Pimpinella anisum L., and Origanum vulgare, with Familiarity Index of 0.22, 0.22, and 0.18, respectively. The most frequently utilized plant part was the leaves, and 70.59 % of respondents preferred drying the plants before preparing them as infusions or decoctions. The results highlighted the primary ailments treated with these plants, such as diabetes, gastrointestinal disorders, cardiovascular diseases, oral conditions, cancers, insomnia, and stress. Additionally, the study referenced traditional medicinal uses from various national studies and international scientific research that validated the therapeutic properties of each plant. © 2025 The Authors

关键词： Ethnobotanical survey Medicinal and aromatic plants Morocco Settat Traditional medicine

来源：评论

学校读者我要写书评

暂无评论

Orbit Weighting Scheme in the Context of Vector Space information Retrieval

引用

Computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah Department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates Information Security Department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan Department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

来源：评论

学校读者我要写书评

暂无评论

Traceability and Identity Protection in Smart Agricultural IoT System Framework Based on Blockchains

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Xie, Mingyue Liu, Jun Chen, Shuyu Lin, Mingwei Xu, Guangxia Xu, Zeshui Chang, Zheng University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China Chongqing University School of Big Data and Software Engineering Chongqing400065 China Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China Sichuan University Business School Chengdu610000 China University of Jyväskylä Faculty of Information Technology P.O. Box 35 JyväskyläFIN-40014 Finland

Blockchain-based IoT applications in agriculture have drawn extensive attention in recent years, allowing the implementation of smart agriculture solutions. By transmitting collected relevant data to a control center through the blockchain, corresponding regulation can be realized in agricultural production management systems. However, existing efforts for directly adopting the technique to data transmission are obstructed by several issues. The traceability of agricultural data stored in the blockchain leads to the exposure of the identity of the data collecting devices. And the tracing difficulty of completely invisible data for identity protection also exists in the smart agricultural system. To tackle these limitations, we propose a novel blockchain-based smart agricultural IoT system framework for regulating the agricultural production environment through trusted data. First, the elliptic curve integrated encryption scheme (ECIES) and the group signature scheme are integrated to guarantee the traceability and identity protection of the data and equipment, respectively. Then, to enhance the security of session key transmission in the ECIES scheme, we further design an on-chain-off-chain key agreement protocol (ECIES-OOKA). In addition, we propose a novel group manager selection method based on probabilistic linguistic term sets (PLTSs) for the group signature implementation. Finally, a practical example is provided to demonstrate the group manager selection process and verify the feasibility of the proposed method. The security and performance analysis for the system framework are also presented. © 2004-2012 IEEE.

关键词： Crop protection

来源：评论

学校读者我要写书评

暂无评论

Improving brain tumor classification: An approach integrating pre-trained CNN models and machine learning algorithms

引用

Heliyon 2025年第10期11卷

作者： Shoaib, Mohamed R. Zhao, Jun Emara, Heba M. Mubarak, Ahmed S. Omer, Osama A. Abd El-Samie, Fathi E. Esmaiel, Hamada College of Computing and Data Science (CCDS) Nanyang Technological University (NTU) Singapore 639798 Singapore Department of Electronics and Electrical Communications Engineering Ministry of Higher Education Pyramids Higher Institute (PHI) for Engineering and Technology 6th of October City 3220412 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh 84428 Saudi Arabia Electrical Engineering Department Aswan Faculty of Engineering Aswan University Aswan 81542 Egypt Department of Electronics and Communication Engineering College of Engineering A'Sharqiyah University Ibra 400 Oman Faculty of Engineering Aswan University Aswan 81542 Egypt

Accurate detection of brain tumors is crucial for enhancing patient outcomes, yet the interpretation of Magnetic Resonance Imaging (MRI) scans poses significant challenges. This study introduces a novel approach to brain tumor classification by exploring three pre-trained convolutional neural network (CNN) models: DenseNet201, EfficientNetB5, and InceptionResNetV2, combined with softmax activation for feature extraction. These features are then subjected to Principal Component Analysis (PCA) for dimensionality reduction. Subsequently, three machine learning models—Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Gaussian Naive Bayes (GNB)—are employed for classification. The results reveal that DenseNet201, when combined with SVM and MLP, outperforms the other models in terms of accuracy, recall, and precision. Specifically, DenseNet201 achieves 100% accuracy, recall, and precision on dataset-I and 98% accuracy, recall, and precision on dataset-II when paired with SVM and MLP. This study provides valuable insights into the interplay between CNN models, feature extraction techniques, and machine learning algorithms for brain tumor classification, highlighting the efficacy of DenseNet201 combined with SVM and MLP. © 2024 The Author(s)

关键词： Brain tumor classification Convolutional neural networks Feature extraction Image classification Machine learning Pre-processing Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual data Selection

Generalised Unsupervised Domain Adaptation of Neural Machine...

引用

2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021

作者： Vu, Thuy-Trang He, Xuanli Phung, Dinh Haffari, Gholamreza Department of Data Science AI Faculty of Information Technology Monash University Australia

ISBN: (纸本)9781955917094

This paper considers the unsupervised domain adaptation problem for neural machine translation (NMT), where we assume the access to only monolingual text in either the source or target language in the new domain. We propose a cross-lingual data selection method to extract in-domain sentences in the missing language side from a large generic monolingual corpus. Our proposed method trains an adaptive layer on top of multilingual BERT by contrastive learning to align the representation between the source and target language. This then enables the transferability of the domain classifier between the languages in a zero-shot manner. Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks. We evaluate our cross-lingual data selection method on NMT across five diverse domains in three language pairs, as well as a real-world scenario of translation for COVID-19. The results show that our proposed method outperforms other selection baselines up to +1.5 BLEU score. © 2021 Association for Computational Linguistics

关键词： Neural machine translation

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：