检索结果-内蒙古大学图书馆

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Timande, Devanshi Agade, Nishant Gudadhe, Amit Faculty of Engineering & Technology Department of Artificial Intelligence and Data Science Wardha India Faculty of Engineering & Technology Wardha India

ISBN: (纸本)9798331505745

This paper presents an AI-driven framework for real-time smart grid optimization tailored specifically for solar energy integration. The proposed system features a multi-layered architecture that integrates data acquisition from solar irradiance sensors, smart meters, and meteorological sources with advanced pre-processing techniques to ensure data quality and fusion. Using advanced deep learning architectures, such as Long Short-Term Memory networks for forecasting solar irradiance and regression methodologies for predicting load, our proposed framework dynamically adjusts energy dispatch and demand response measures to address the inherent variability associated with solar power. Additionally, a combined strategy that incorporates reinforcement learning is implemented to improve real-time grid stability and operational efficiency, and explainable artificial intelligence methods are used to provide clarity in decision-making processes. Simulation studies conducted on an enhanced IEEE 24-bus test system have shown to reduce forecast error by up to 30% and prevent the occurrence of grid instability events by about 40% in contrast with traditional methods. These results are promising for AI-based analytics since it promises to enhance smart grid functionality and enable more sustainable use of renewable resources. Further research will develop this model with additional renewable energy sources and increase cybersecurity. © 2025 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

引用

Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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

Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

引用

IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Correction to: Supporting Coordination and Peer Editing in Students’ Online Collaborative Writing Processes (International Journal of Artificial Intelligence in Education, (2024), 10.1007/s40593-024-00450-w)

引用

International Journal of Artificial Intelligence in Education 2025年 1-2页

作者： Usher, Maya Roll, Ido Fuhrman, Orly Amir, Ofra Faculty of Education in Science and Technology Technion– Israel Institute of Technology Haifa Israel Faculty of Instructional Technologies Holon Institute of Technology Holon Israel Faculty of Data and Decision Sciences Technion– Israel Institute of Technology Holon Israel

One of the articles in the references list is cited with the wrong title. It is cited as: Usher, M., & Barak, M. (2024). Unpacking AI ethics in engineering higher education. International Journal of STEM Education, 11(35). The correct title of this article should be: Usher, M., & Barak, M. (2024). Unpacking the role of AI ethics online education for science and engineering students International Journal of STEM Education, 11(35). https://***/10.1186/s40594-024-00493-4. The original article has been corrected. © International Artificial Intelligence in Education Society 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Linked List Encryption Scheme for Image Steganography without Embedding

引用

Computer Modeling in Engineering & sciences 2024年第10期141卷 331-352页

作者： Pengbiao Zhao Qi Zhong Jingxue Chen Xiaopei Wang Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information *** enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover *** this paper,a novel encrypted communication scheme for image SWE is *** reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer *** employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be *** receiver can restore the secretmessage fromthe received image using only the list header position *** scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover *** boasts high concealment and security,along with a complete message restoration rate,making it resistant to ***,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of *** validate the proposed framework,practical tests and comparisons are conducted using multiple *** results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

关键词： Steganography encryption steganography without embedding coverless steganography

来源：评论

学校读者我要写书评

暂无评论

DCARES: deep convolutional neural network with neural-based optimization for image-based product recommender system

引用

Multimedia Tools and Applications 2025年 1-31页

作者： Hung, Bui Thanh Nhan, Nguyen Van Phuc Sy, Nguyen Tien Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh city Viet Nam

The development of recommendation systems represents a critical challenge in machine learning, particularly in today’s digital landscape. These systems play a pivotal role in delivering personalized product and service suggestions tailored to user preferences, thereby enhancing user experience and driving sales revenue. While methodologies such as collaborative filtering, content-based filtering, and hybrid recommender systems have been widely explored, creating a robust and effective recommendation system remains a complex and demanding task. This study introduces the DCARES model, a novel content-based filtering approach that leverages the similarity between product features. The DCARES model integrates a deep convolutional neural network (CNN) with machine learning techniques to form the core of the proposed recommendation system. CNNs are employed to extract rich and intricate features from product images, which are then combined with machine learning models to generate personalized recommendations based on user preferences. To optimize performance, the model is fine-tuned using various neural-based optimization algorithms, with the most effective one selected through rigorous evaluation. Experimental results demonstrate that the proposed model delivers highly accurate and effective recommendations in all metrics: Accuracy, RMSE, MAE and MAPE, showcasing its potential to outperform traditional approaches. By incorporating CNNs into recommendation systems, this study highlights a promising direction in the field of machine learning. The proposed approach not only enhances the quality and precision of recommendations but also opens new avenues for future research and applications, particularly in visually driven domains such as e-commerce and fashion. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

Learning a Robust Fuzzy Cognitive Map Based on Bubble Entropy Fusion With SCAD Regularization for Solar Power Generation

引用

IEEE Transactions on Sustainable Energy 2025年第3期16卷 1837-1848页

作者： Li, Shoujiang Wang, Jianzhou Zhang, Hui Liang, Yong Macau University of Science and Technology Department of Engineering Science Faculty of Innovation Engineering Taipa Macau999078 China Shaanxi University of Science and Technology School of Mathematics and Data Science Xi'an710021 China Peng Cheng Laboratory Shenzhen518005 China

Accurate and reliable solar photovoltaic (PV) power forecasting are crucial for cost-effective resource planning and stable operation of smart grids. However, current methods are affected by the intermittent, non-stationary and stochastic nature of solar energy and thus cannot satisfy the requirement of high-precision forecasting. To this end, we propose a fuzzy cognitive map (FCM) forecasting method based on bubble entropy and smoothly clipped absolute deviation (SCAD) regularization, called BesFCM. This method first utilizes bubble entropy to fuse two mode decomposition methods to improve the representation of PV data to capture effective features with significant stability and discriminative ability, then employs a FCM with a combination of fuzzy logic, neural networks, and expert systems to model solar PV power generation, and finally develops a high order FCM learning method based on SCAD regularization to alleviate the overfitting problem, enhancing the robustness and generalization ability of forecasting. Experimental results demonstrate that the BesFCM achieves the best overall performance on PV power datasets from multiple sampling intervals in multiple regions of Belgium compared to multiple state-of-the-art baselines, validating the effectiveness for solar power generation forecasting, providing support and reference for improving the quality of smart grid dispatch and reducing spare capacity reserves. © 2010-2012 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Semantic-Based Detection of Ponzi Smart Contracts: Enhancing Bytecode Analysis with Source Code 4

Semantic-Based Detection of Ponzi Smart Contracts: Enhancing...

引用

4th International Conference on Electronic Information Engineering and Computer technology, EIECT 2024

作者： Weng, Bingjie Ma, Jianhui University of Science and Technology of China Faculty of Artificial Intelligence and Data Science Anhui Hefei China University of Science and Technology of China Faculty of Computer Science and Technology Anhui Hefei China

ISBN: (纸本)9798331528850

The rapid development of blockchain technology has led to many innovations, with the widespread use of smart contracts being particularly notable. However, this trend has spawned diverse blockchain scams, including the Ponzi scheme, which takes the form of Ponzi contracts. Due to blockchain users' misunderstanding of blockchain transparency, this scam can often result in significant financial losses. To complicate matters further, the vast majority of smart contracts deployed on blockchain networks are not open source, and users can only see the contract's bytecode. This situation makes Ponzi contracts even more challenging to identify, making it difficult for users to judge the security and legitimacy of a contract beforehand, thus increasing the risk of being defrauded. Most existing work uses only bytecode for detection and cannot fully utilize the corresponding source code information. In this paper, we propose a novel bimodal learning-fusion framework for enhancing Ponzi contract detection only in the presence of bytecode. Firstly, we employ the semantic-aware neural network to extract semantic feature from source code and bytecode. Then, we propose a modal transformation strategy to learn the relationship between source code and bytecode. Finally, a modal attention mechanism is introduced to construct a fusion representation to detect the contract even in the absence of source code modality. Experimental results show that the method performs better than existing methods that use machine learning for detection of Ponzi contracts. © 2024 IEEE.

关键词： Blockchain

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：