检索结果-内蒙古大学图书馆

Exploring the determinants of university students’ contribution intention on crowdsourcing platforms: a value maximization perspective

引用

Interactive Learning Environments 2023年第5期31卷 2612-2634页

作者： Wang, Yu-Min Wang, Yi-Shun Wang, Yu-Yin Department of Information Management National Chi Nan University Puli Taiwan Department of Information Management National Changhua University of Education Changhua Taiwan Department of Computer Science and Information Management Providence University Taichung Taiwan

Crowdsourcing is an emerging web-enabled method to gain external expertise and ideas on tasks or projects. Mass contributions from the crowd are the key to the success and effectiveness of crowdsourcing. University students are important potential users and promising sources of contributions in the crowdsourcing context. However, little is known about the factors that shape university students’ intention to participate in this new web frontier. Thus, the current study investigates the determinants of university students’ contribution intention on crowdsourcing platforms. Based on the value-based adoption model (VAM) and crowdsourcing characteristics, this study develops a model to understand the determinants of university students’ intention to contribute ideas and solutions on crowdsourcing platforms. Data collected from 135 university students in Taiwan were tested against the research model using the partial least squares (PLS) method. The results indicate that perceived value and perceived behavioral control are key determinants of university students’ contribution intention on crowdsourcing platforms, and that reputation, perceived enjoyment, task autonomy, and trust are significant factors affecting perceived value. This study contributes to extend the knowledge regarding how to better promote crowdsourcing contributions, as well as how to foster and encourage university students to participate in crowdsourcing activities. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Crowdsourcing educational use of crowdsourcing motivation theory value maximization value-based adoption model

来源：评论

学校读者我要写书评

暂无评论

OntoMG: a unique and ontological-based intelligent framework for early identification of myasthenia gravis (MG)

引用

International Journal of Information Technology (Singapore) 2024年第6期16卷 3847-3853页

作者： Mahajan, Prerna Agarwal, Trapty Vekariya, Daxa Gupta, Rajesh Malviya, Ashwini Anandaraj, S.P. Jain, Garima Anand, D. Department of Computer Science and Information Technology Jain (Deemed to Be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Management Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science Engineering Presidency University Bengaluru India Department of Computer Science and Business Systems (CBS) Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Myasthenia Gravis (MG) is a neuromuscular disease causing extreme muscular fatigue, triggering problems with vision, swallowing, speech, mobility, dexterity, and breathing. However, early detection and prediction of MG is a crucial and quite challenging task for medical practitioners. So, in our proposed work, we are focused on introducing an ontology-based intelligent system for the prognosis of patients affected by this neuromuscular disease. The given paper presents a novel framework that employs the concepts of ontology, semantic web rules, and reasoner for deriving whether the patient is tested positive or negative. The suggested framework's effectiveness is assessed by comparing it to machine learning classifiers using parameters from the NIH repository for Myasthenia Gravis. The experimental results show that the ontological-based framework is able to achieve higher accuracy (80.85%), higher precision (78.37%), and higher recall (76.55%) than the existing classifiers used in the recent studies. © Bharati Vidyapeeth's Institute of computer Applications and management 2024.

关键词： Machine learning (ML) Myasthenia gravis disease Ontology Reasoners Semantic web rules language (SWRL)

来源：评论

学校读者我要写书评

暂无评论

Fortified Cuckoo Search Algorithm on training multi-layer perceptron for solving classification problems

引用

Personal and Ubiquitous Computing 2023年第3期27卷 1039-1049页

作者： Thirugnanasambandam, Kalaipriyan Prabu, U. Saravanan, D. Anguraj, Dinesh Kumar Raghav, R.S. Centre for Smart Grid Technologies School of Computer Science and Engineering Vellore Institute of Technology Tami Nadu Chennai Campus Chennai600127 India Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science Engineering VIT Bhopal University Bhopal-Indore Highway Madhya Pradesh Bhopal466114 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur522302 India School of Computing SASTRA Deemed University Tamil Nadu Thanjavur613007 India

Multi-layer perceptron (MLP) in artificial neural networks (ANN) is one among the trained neural models which can hold several layers as a hidden layer for intensive training to obtain optimal results. On the other hand, the classification problem has a high level of attraction towards researchers to increase the accuracy in classification. In ANN, feedforward neural network (FNN) is one model that possesses the art of solving classification and regression problems. When input data is given to FNN, it will apply the sum of product rule and the activation function to map the input with its appropriate output. In the sum of product rule, a term called weights is to be chosen appropriately to map between the input and output. In standard FNN, the weights are chosen in a random way which may lead to slower convergence towards the optimal choice of weight values. In this paper, an effective optimization model is proposed to optimize the weights of MLP of FNN for effective classification problems. Four different datasets were chosen, and the results are interpreted with statistical performance measures. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Advancements in E-commerce Supply Chain management in Forecasting and Optimization Strategies 4

Deep Learning Advancements in E-commerce Supply Chain Manage...

引用

4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Upreti, Kamal Saiyad, Fariyah Banu Jamaluddin Divakaran, Prakash Madhukar, Khadilkar Sujay School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science Ghaziabad India Bath spa university Rak Department of Commerce Ras Al-Khaimah United Arab Emirates Himalayan University Department of management Itanagar India KIT's Institute of Management Education and Research Kolhapur India

ISBN: (纸本)9798350383997

In this study, the influence of deep learning technologies on the optimization of supply chain management in the context of the e-commerce industry is examined. Using a dataset of historical data of sales, inventories, market fluctuations, and customer and supplier details, I investigate the efficiency of different deep learning models to predict demand and facilitate the optimal balance of inventories. Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and a model proposed by the authors are defined and applied, considering their accuracy, precision, recall, and F-1 score. The results show that the proposed model outperforms traditional products, achieving 97.5% of accuracy. In the context of the comparative analysis, the specific features of CNN, LSTM, and RNN are revealed, helping to understand the benefits and drawbacks of each recommendation. As a result, the proposed model proves that deep learning technologies have the power to change the approach to predictive analytics and supply chain management, allowing practitioners to focus on strengths and overcome the weaknesses of their structures. The impact of data preprocessing and hyperparameters is also considered along with the necessity to choose the most appropriate model evaluation technique. In the future, it is possible to implement other complex deep learning models, integrate additional data, and address the problem of data scaling and heterogeneity. In the era of modern technologies, e-commerce organizations should take these findings into consideration to discover the potential of deep learning, improve supply chain performance, reduce costs, and attract clients. This research contributes to the topic of using deep learning technologies in supply chain management, promoting innovation, and changes that may affect the industry drastically. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Fingerprint Security Using CNN for Robust Biometric Authentication and Spoof Detection 3

Enhancing Fingerprint Security Using CNN for Robust Biometri...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Kumar, Munish Tiwari, Ankita Choudhary, Shilpa Gulhane, Monali Kaliraman, Bhawna Verma, Rashmi Koneru Lakshmaiah Education Foundation Computer Science and Engineering Vaddeswaram India Koneru Lakshmaiah Education Foundation Department of Mathematics Vaddeswaram India Hyderabad India Department of Cse Pune India Dpg Institute of Technology and Management Department of Computer Science and Engineering Gurugram India

ISBN: (纸本)9798350342338

Fake fingerprint recognition needs to pose a significant problem in the quickly developing field of biometric security. The creation of creative and effective spoof detection systems has become crucial as weaknesses in traditional authentication methods have been revealed. To solve this problem, this research employs Convolutional Neural Networks (CNNs) for next-generation biometric security, a ground-breaking approach. The proposed CNN-based fingerprint spoof detection approach utilizes deep learning inherent capabilities to extract complex patterns and information from fingerprint photos. The CNN model trains on a broad dataset containing multiple spoofing attempts to distinguish precisely between authentic and fake fingerprints. There are two significant contributions from this study. First, in order to fully train and assess the CNN model, we provide two datasets covering a wide range of fingerprint spoofing scenarios. Second, by successfully adjusting to the always-changing environment of spoofing tactics, our custom-built architecture outperforms traditional approaches and establishes new standards in detection accuracy. These results highlight CNNs' better capacity to discriminate between authentic and false fingerprints, placing them as a key component of future biometric security systems. This study highlights the value of utilizing cutting-edge technologies, like CNNs, to strengthen biometric security against new threats. By combining cutting-edge deep learning algorithms with the complexities of fingerprint spoof detection, we enter a new age of next-generation biometric security that promises enhanced accuracy and resilience in the face of shifting challenges. Comparing the suggested system to earlier methods, accuracy is 98.99%. © 2023 IEEE.

关键词： Biometrics CNN Fingerprint Security Spoofing

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Feature-Selection Method Based on mRMR and Binary Peacock Optimization Algorithm 2

A Hybrid Feature-Selection Method Based on mRMR and Binary P...

引用

2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Sahu, Bibhuprasad Panigrahi, Amrutanshu Pati, Abhilash Mishra, Rajalaxmi Senapati, Sudhir Kumar Sahu, Kashyapa Kanti Department of Information Technology Hyderabad India Department of Cse Odisha Bhubaneswar India College of It and Management Education Department of Csa Odisha Bhubaneswar India Pj College of Management and Technology Department of Computer Science Odisha Bhubaneswar India

ISBN: (纸本)9798350364699

An essential aspect of developing effective biomarkers that facilitate disease diagnosis and drug discovery is identifying the most critical characteristics from microarray data. Since it is expensive to validate biomarkers, the number of biomarkers must be reduced while retaining their effectiveness. A common challenge with microarray data is that it results in a large number of properties, known as the curse of dimensionality. Almost all the feature selection (FS) techniques developed up-to-date do not possess the ability to result in lower dimensionality, ensure sound classification, easy algorithms and exhaustive search of the parameter space. This study describes a hybrid FS approach by combining the mRMR and binary variant of the peacock algorithm (Binary POA) algorithm. It introduces mRMR by modifying its quantization functions to the continuous nature of the microarray data attribute and uses mRMR as a first step to normalize both datasets and select an initial feature subset. In the following step, a binary POA algorithm is deployed to optimize the selected feature subset. The performance of the proposed mRMR-BPOA ML model is evaluated using benchmark classifier Support Vector Machine. The Result analysis proves that the performance of mRMR-BPOA is superior to the existing counterparts. © 2024 IEEE.

关键词： Microarrays

来源：评论

学校读者我要写书评

暂无评论

Interoperability and Standardization in the Internet of Things 9

Interoperability and Standardization in the Internet of Thin...

引用

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

作者： Saidala, Ravi Kumar Haseeb, V.V. Padmapriya, G. Jayabalan, S. Priya, V. Kalai Shaikh, Anwar Ahamed Alliance University Department of Computer Science Engineering Karnataka Bangalore560076 India Nam College Department of Computer Science Kerala Tamil Nadu Kallikkandy 670693 India School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India Panimalar Engineering College Department of Computer Science And Business System Tamil Nadu Chennai600123 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Puduvoyal601206 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350365092

This research highlights the importance of standards and interoperability in the IoT. An essential component of IoT ecosystems is interoperability, which refers to the ability for various systems and devices to converse and share data. Formats for data, methods of communication, and safeguards are all outlined by standards. Widespread adoption and scalability are made possible through interoperability and standards, which allow devices from many manufacturers to communicate with one another. This research investigates the alliances, regulatory authorities, and commercial associations that cooperate in the creation and advancement of open standards that can be embraced by other entities. The main objective of the research is to showcase the construction of an Internet of Things device capable of communicating with various embedded peripheral protocols utilizing Plug and Play (PnP) technology. Moreover, the research examines the impact of regulatory frameworks on the IoT ecosystem and their role in fostering interoperability. An IoT ecosystem necessitates a standardized and open environment to provide optimal performance in terms of connectivity, data exchange, and innovation. This research contributes to the progress of harmonizing and achieving interoperability among World Wide Web Things. © 2024 IEEE.

关键词： Interoperability

来源：评论

学校读者我要写书评

暂无评论

Editorial: The Second International Workshop on Artificial Intelligence Systems in education - AIxEDU 2

Editorial: The Second International Workshop on Artificial I...

引用

2nd International Workshop on Artificial INtelligent Systems in education, AIxEDU 2024

作者： Casalino, Gabriella Limongelli, Carla Schicchi, Daniele Taibi, Davide Temperini, Marco Department of Computer Science University of Bari Aldo Moro Bari Italy Department of Civil Computer Science and Aeronautical Technologies Engineering Roma Tre University Rome Italy Institute for Educational Technology Italian National Research Council CNR-ITD Palermo Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

The Second International Workshop on Artificial Intelligence Systems in education (AIxEDU) marks its 2024 edition in conjunction with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA), held from 25-28 November 2024 in Bolzano, Italy. This workshop brings together Artificial Intelligence and education experts to explore the latest innovations and best practices in applying AI to educational contexts. Participants discuss recent trends, research initiatives, and emerging developments in AI technologies that aim to enhance teaching methodologies, support personalized learning, and address pressing educational challenges. The workshop emphasizes a multidisciplinary approach to understanding how AI systems can promote inclusion, improve learning outcomes, and empower educators while addressing ethical considerations and ensuring equitable access to education aligned with global sustainable development goals. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

The Role of Ensemble Learning in Advancing Child Fetal Health: A Focus on Voting and Bagging Techniques 1

The Role of Ensemble Learning in Advancing Child Fetal Healt...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Swain, Satyaprakash Patra, Kumar Janardan Jayasingh, Suvendra Kumar Mohanty, Mihir Narayan Pattanayak, Binod Kumar Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Odisha Bhubaneswar India Odisha University of Technology and Research Department of Computer Science and Engineering Odisha Bhubaneswar India Institute of Management and Information Technology BPUT Department of Computer Science and Engineering Odisha Cuttack India Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Electronics and Communication Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350361780

The field of healthcare has witnessed significant advancements in leveraging technology for in-depth analysis of fetal health data, offering profound insights into child development. We meticulously gathered an extensive dataset comprising 1500 instances of child fetal health data sourced from New Born and Pediatric Health Centre, Cuttack, Odisha. We proposed a robust analytical pipeline for effectively classifying and predicting fetal health outcomes. The process initiated with meticulous data preprocessing and refinement, ensuring data integrity and quality. We use clustering techniques unveiled inherent patterns and groupings within the dataset, setting the stage for in-depth analysis and feature extraction. Further enriching the analysis, Principal Component Analysis (PCA) is deployed to streamline data dimensionality while retaining essential information. For prediction we use ensemble learning techniques. To predict the condition, we used voting ensemble method encompassing Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB), after predictive models are successfully constructed, showcasing notable accuracies of 92.28%, 83.89% and 82.22%. These results underscore the effectiveness of these models in predicting fetal health outcomes based on the comprehensive dataset. To further refine model performance, a meticulous GridSearchCV approach exhaustively explored optimal parameter values within predefined parameter grids, significantly enhancing predictive capabilities. Additionally, leveraging a bagging method, particularly Random Forest (RF), Logistic Regression (LR), and Gradient Boosting, further bolstered predictive accuracies. These bagging models demonstrated exceptional achieving accuracy of 94.02%, 93.22% and 92.28%. The systematic approach involving clustering, dimensionality reduction, ensemble learning, and parameter optimization resulted in highly accurate predictive models for fetal health assessment. Our research not only highligh

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

引用

ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated 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：