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Stock trend prediction based on multi-factor screening machine 24

Stock trend prediction based on multi-factor screening machi...

引用

5th international conference on computer science and Management technology, ICCSMT 2024

作者： Hu, Ruijie Faculty of Science and Technology Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798400709999

With the rapid development of financial markets, stock price prediction, as a highly complex and challenging topic, has attracted more and more attention from academia and industry. This study aims to use multiple machine learning algorithms to predict stock market prices, especially exploring the advantages of deep learning models in capturing market nonlinear and time series characteristics. In this article, a machine learning model is used for contribution analysis, analyzing multiple features of stocks, performing a weighted combination analysis of features, and then using a convolutional neural network to perform feature extraction and analysis of stocks, and combining the attention mechanism for deep feature extraction, and finally use the long short-term memory network (LSTM) model to output the model to analyze and predict future stock trends. The experimental results show that combining deep learning analysis technology and factor screening machine can significantly improve the prediction accuracy of the model. At the same time, this article also compared the performance of different models in different market environments, and concluded that in markets with higher volatility, models based on deep learning are more stable. Ultimately, this research provides a new technical means for risk management and investment decisions in financial markets. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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EEG-Based User Identification in Gaming: Comparative Analysis of Emotion Simulations 24

EEG-Based User Identification in Gaming: Comparative Analysi...

引用

8th international conference on computer science and Artificial Intelligence, CSAI 2024

作者： Li, Sukun Luo, Haitao Department of Mathematics and Computer Science Adelphi University Graden CityNY United States Department of Computer Science Pace University New York CityNY United States

ISBN: (纸本)9798400718182

EEG-based biometrics have gained attention with the quick development of Brain-computer interface technology and the increasing integration of biometric systems with various applications. Previous studies have explored the use of EEG signals for user identification, often focusing on resting-state EEG or task-specific signals. However, there remains a research gap on the impact of different emotional states. This study investigates the application of EEG biometrics within a gaming environment and evaluates four emotion simulation protocols. An EEG-based user identification method is proposed with DHM-t feature extraction algorithm. Results demonstrate that the emotion simulations reveals that EEG biometrics remain stable in games involving low-arousal negative emotion and low-arousal positive emotion with an overall classification rate of 97.21%. In contrast, high-arousal positive emotion provided recognition rates between 85.71% and 95.24%, while high-arousal negative emotion led to a decline in overall performance due to their stronger impact on mental activity. © 2024 Copyright held by the owner/author(s).

关键词： Feature extraction

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Correlation Analysis of Soil Moisture Content and Environmental Factors Based on LoRa-RSSI 24

Correlation Analysis of Soil Moisture Content and Environmen...

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8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Yang, Yi Zhang, Jie Zhou, Meijia Liu, Yinan Wan, Xuefen College of Information Science and Technology Donghua University Shanghai China College of Computer North China Institute of Science and Technology Hebei Langfang China

ISBN: (纸本)9798400710094

Soil moisture content (SMC) is influenced by a variety of environmental factors. Recent studies have demonstrated that integrating LoRa-RSSI data with environmental parameters can enhance the accuracy of soil moisture predictions while reducing dependence on soil moisture sensors. However, the diversity of these environmental factors can also increase sensor costs. To analyze the impact of various environmental parameters on soil moisture and to identify key factors that assist RSSI in accurately predicting soil moisture, this study collected data on soil moisture, RSSI, and relevant environmental parameters-including ambient temperature, humidity, light intensity, and wind speed-from a field site in the Songjiang District of Shanghai. Utilizing statistical analysis tools, the study investigated the correlations between LoRa-RSSI data, various environmental factors, and SMC. The findings provide a novel approach to data collection and analysis for employing deep learning techniques to predict soil moisture, offering valuable insights for soil moisture measurement in the Shanghai region. © 2024 Copyright held by the owner/author(s).

关键词： Soil moisture

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ElderEase AR: Enhancing Elderly Daily Living with the Multimodal Large Language Model and Augmented Reality 24

ElderEase AR: Enhancing Elderly Daily Living with the Multim...

引用

2024 international conference on Virtual Reality technology, ICVRT 2024

作者： Song, Tianyu Liu, Zhengyi Zhao, Ruibin Fu, Jie Department of Computer Science and Technology School of Computer Science and Technology Intelligent Perception Group Anhui University Anhui Hefei China Chuzhou University Anhui Chuzhou China Department of Computer Science and Technology School of Computer Science and Technology Artificial Intelligence Group Anhui University Anhui Hefei China Department of Computer Science and Technology School of Information Engineering Intelligent Perception Group Chuzhou University Anhui Chuzhou China

ISBN: (纸本)9798400710186

Elderly individuals often face challenges in independent living due to age-related cognitive and physical decline. To address these issues, we propose an innovative Augmented Reality (AR) system, "ElderEase AR", designed to assist elderly users in their daily lives by leveraging a Multimodal Large Language Model (MLLM). This system enables elderly users to capture images of their surroundings and ask related questions, providing context-aware feedback. We evaluated the system’s perceived ease-of-use and feasibility through a pilot study involving 30 elderly users, aiming to enhance their independence and quality of life. Our system integrates advanced AR technology with an intelligent agent trained on multimodal datasets. Through prompt engineering, the agent is tailored to respond in a manner that aligns with the speaking style of elderly users. Experimental results demonstrate high accuracy in object recognition and question answering, with positive feedback from user trials. Specifically, the system accurately identified objects in various environments and provided relevant answers to user queries. This study highlights the powerful potential of AR and AI technologies in creating support tools for the elderly. It suggests directions for future improvements and applications, such as enhancing the system’s adaptability to different user needs and expanding its functionality to cover more aspects of daily living. © 2024 Copyright held by the owner/author(s).

关键词： Augmented reality

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Research on Teaching IoT Courses in the New Engineering Education Framework for Postgraduate Students 24

Research on Teaching IoT Courses in the New Engineering Educ...

引用

8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Yang, Yi Zhang, Jie Jiang, Xue-Qin College of Information Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798400710094

Internet of Things (IoT) is a crucial component of the new engineering. This paper addresses the need of new engineering education by exploring a three-stage teaching methodology focused on fundamental knowledge, cutting-edge knowledge, and practical engineering skills for postgraduate students. The results indicate that this teaching methodology is applicable to postgraduate students with diverse knowledge backgrounds and varying research interests. These explorations and practices will serve as valuable references for the advancement of new engineering education. We anticipate that the explorations will contribute significantly to curriculum framework design of new engineering education about IoT. © 2024 Copyright held by the owner/author(s).

关键词： Students

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How Artificial Intelligence Is Impacting on the STEM Education of Students with Disabilities: A Five Years Review 17

How Artificial Intelligence Is Impacting on the STEM Educati...

引用

17th international conference on computer Supported Education, CSEDU 2025

作者： Buzzi, Marina Pieriboni, Giuditta Leporini, Barbara IIT-CNR via Moruzzi 1 Pisa56124 Italy Department of Computer Science University of Pisa via Fibonacci Pisa Italy ISTI-CNR via Moruzzi 1 Pisa Italy

ISBN: (纸本)9789897587467

Artificial intelligence promises to revolutionize our life, bringing significant advances in any fields: health, education, work and leisure time. This paper analyzes the last 5-year literature concerning the use of AI for supporting people with disabilities in education. The aim is to investigate the current state of art of accessible applications in the STEM (science, technology, Engineering, Mathematics) field and understand if contents and tools are accessible for all, regardless of personal need and abilities. Personalization and adaptation emerge as fundamental factors when designing for people with disabilities. Privacy and ethics aspects often neglected are very relevant. The analysis suggests that the STEM field still suffers from accessibility gaps, and current tools need to evolve and be increased to be exploited by different disabilities and ensure the same opportunities for every student, engaging, motivating, and empowering them.. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Students

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Design and implementation of engineering integrated practical training management system under the background of engineering education 24

Design and implementation of engineering integrated practica...

引用

5th international conference on computer science and Management technology, ICCSMT 2024

作者： Xue, Tianbao Lan, Quanxiang School of Mathematics and Computer Science Panzhihua University Sichuan Panzhihua China

ISBN: (纸本)9798400709999

With the promotion of global economic integration, internationalised engineering education has become an important way for colleges and universities to improve their teaching quality and international competitiveness. In order to cultivate students' ability to solve complex engineering problems, many colleges and universities adopt the mode of integrated practical training projects. However, this model has the characteristics of long duration, multi-stage tasks, multi-dimensional assessment, etc. The use of traditional management methods will lead to management chaos, low teaching efficiency, and is difficult to adapt to modern teaching needs. For this reason, this paper designs and develops an intelligent, automation engineering integrated practical training management system based on B/S architecture. The system adopts SpringBoot, Vue, MySQL and Redis technologies to optimize the management process of the practical training project by automating the management of the project, marking scheme and grade analysis. In addition, the system has real-time performance analysis and feedback functions, which improves the efficiency of teaching management and provides strong support for universities to cultivate composite talents with innovative ability and comprehensive engineering literacy. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Efficiency

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Research on the Automatic Calculation of Surface Area Integrals 16

Research on the Automatic Calculation of Surface Area Integr...

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2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Mao, Ziyang Wang, Fangxiu An, Jiaqi Chen, Zunyu Chen, GuoXing School of Mathematics and Computer Science Wuhan Polytechnic University Hubei Wuhan China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

To address the issue that existing double integral calculators cannot calculate surface integrals, a method of converting surface integral input modes into double integral input modes is proposed. First, nine input modes for calculating double integrals are presented. Next, a method for converting surface integral input modes into double integral input modes is designed. Finally, a method for calculating surface integrals using a double integral calculator is designed. Experimental results show that the improved double integral calculator can calculate surface integrals. © 2025 SPIE.

关键词： Choquet integral

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DKDTrack: Dual-granularity Knowledge Distillation for RGBT Tracking 16

DKDTrack: Dual-granularity Knowledge Distillation for RGBT T...

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2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Hong, Fanghua Wen, Mai Lu, Andong Wang, Qunjing School of Electronic and Information Engineering Anhui University Hefei230601 China School of Artificial Intelligence Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

RGBT tracking is an important research direction in the field of computer vision and has received increasing attention. It aims to exploit the complementary advantages between RGB and TIR modalities to achieve robust object tracking. However, existing studies usually focus on the fusion and interaction between modalities, ignoring the importance of learning modal representations. To address this issue, we propose a novel Dual-granularity Knowledge Distillation RGBT tracker named DKDTrack. In particular, the method introduces an adaptive distillation strategy to achieve representation enhancement between modalities by online measurement of modality strength and weakness relationships. In addition, we design a dual-granularity distillation module for jointly guiding the learning of weaker modalities at the feature level and the attention level. Extensive experiments on three publicly available RGBT datasets demonstrate the effectiveness of DKDTrack and highlight the importance of modal representation learning. © 2025 SPIE.

关键词： RGB color model

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