检索结果-内蒙古大学图书馆

4th International Conference on Educational technology, ICET 2024

作者： Guo, Tianhang Yan, Jiaxuan Cao, Can Xiong, Kang Chen, Peiwen Yang, Huali Wuhan Textile University School of Computer Science and Artificial Intelligence Wu Han China

ISBN: (纸本)9798350376944

This thesis explores the growing mental health problems of today's students, with a special focus on depression among college students. By analyzing relevant data and literature, the possibility of using games as an innovative psychological calming tool to alleviate students' psychological stress is proposed. The paper first introduces the background and severity of students' mental health problems, as well as the limitations of traditional psychological assessment and medication. Then, it elaborates on the game-based psychological assessment technology and the application of decision tree and random forest modeling in analyzing college students' psychological data. Subsequently, the thesis designs and realizes a driving game based on classical music and natural scenery, and verifies the effectiveness of the game in relieving stress through experiments. Finally, the importance and potential of the game for students' mental health are summarized, and future research directions are envisioned. The thesis provides new ideas and methods to improve students' mental health and provide better psychological support and assistance to students. © 2024 IEEE.

关键词： Game design

来源：评论

学校读者我要写书评

暂无评论

Wearable Location Tracker During Disaster using AI and IoT 3

Wearable Location Tracker During Disaster using AI and IoT

引用

3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Praveen, P. Kumar, K. Pranay Sr University School of Computer Science & Artificial Intelligence Telangana State Warangal India

ISBN: (纸本)9798350375190

The combination of artificial intelligence (AI) and the Internet of Things (IoT) has improved the capabilities of location trackers, especially in disaster management. This brief explores the collaboration between AI and the Internet of Things to improve the efficiency and effectiveness of tracking devices in the field in the event of an incident. Wearable location trackers equipped with IoT sensors collect real-time data on the wearer's movements, environmental conditions, and vital signs. AI algorithms process this data, enabling the device to distinguish between normal activities and emergency situations. Machine learning algorithms enable the device to learn from historical data, improving its predictive capabilities and reducing false alarms. This design of GPS location tracking is through a satellite. It includes AI and IoT for receiving the data in case of disaster. To view the location, the person can use any device connected to the Internet. The parameters we can find through this are the scalable map of the surrounding area, latitude, longitude, speed, and altitude. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Multi-View Collaborative Learning Network for Speech Deepfake Detection 39

Multi-View Collaborative Learning Network for Speech Deepfak...

引用

39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Zhang, Kuiyuan Hua, Zhongyun Lan, Rushi Guo, Yifang Zhang, Yushu Xu, Guoai School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Information Security Guilin University of Electronic Technology China Alibaba Group China School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics China

ISBN: (纸本)157735897X

As deep learning techniques advance rapidly, deepfake speech synthesized through text-to-speech or voice conversion networks is becoming increasingly realistic, posing significant challenges for detection and raising potential threats to social security. This growing realism has prompted extensive research in speech deepfake detection. However, current detection methods primarily focus on extracting features from either the raw waveform or the spectrogram, often overlooking the valuable correspondences between these two modalities that could enhance the detection of previously unseen types of deepfakes. In this work, we propose a multi-view collaborative learning network for speech deepfake detection, which jointly learns robust speech representations from both raw waveforms and spectrograms. Specifically, we first design a Dual-Branch Contrastive Learning (DBCL) framework for learning different view features. DBCL consists of two branches that learn representations from the raw waveform or the spectrogram and utilizes contrastive learning to enhance inter- and inner-view correlations. Additionally, we introduce a Waveform-Spectrogram Fusion Module (WSFM) to exchange multi-view information for collaborative learning. In the feature learning process, WSFM converts features between views and merges them adaptively using waveform-spectrogram cross-attention. The final detection is conducted based on the concatenation of the waveform and spectrogram features. We conduct extensive experiments on four benchmark deepfake speech detection datasets, and the experimental results demonstrate that our method can achieve better detection performance than current state-of-the-art detection methods. © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Taming Unleashed Large Language Models With Blockchain for Massive Personalized Reliable Healthcare

引用

IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4498-4511页

作者： Sun, Lianshan Liu, Diandong Wang, Maoxue Han, Yongyi Zhang, Yanqing Zhou, Biwei Ren, Yi Zhu, Peng Shaanxi University of Science and Technology College of Electronic Information and Artificial Intelligence Xi'an710021 China Shaanxi Joint Laboratory of Artificial Intelligence Xi'an710021 China The Affiliated Drum Tower Hospital of Nanjing University Medical School Department of Radiology Nanjing210008 China Xidian University Laboratory of Artificial Intelligence Hangzhou Institute of Technology Xi'an710032 China Nanjing University of Science and Technology School of Economics and Management Nanjing210094 China

The digital health field's pursuit of massive, personalized healthcare continuously faces constraints from doctors' resources and capacity limitations. Recently, the emergence of large language models (LLMs), with their remarkable comprehension and processing abilities, has revolutionized digital health and enhanced massive, personalized healthcare. Although these LLMs have achieved significant advancements, they have also introduced inevitable hallucinations, which impact patient safety when used in massive applications. To address these challenges, this study proposes a digital hospital for a massive, personalized, reliable healthcare service named the Chat Chain-Brain-based Doctor (CHATCBD). In addition, this study transforms the LLM-based diagnostic process into a digital hospital architecture, designs a controllable AI agents framework, and develops a self-audit mechanism to enhance their reliability. The proposed CHATCBD uses blockchain technology to decentralize external regulation of the LLMs' personalized diagnoses. It introduces a blockchain-based personalized routing management mechanism to improve patient-centered decision-making and designs a blockchain-based audit framework based on a proposed mathematical model that ensures both the professionalism and honesty of audits, serving as a safety net for addressing LLM hallucinations. The results of extensive experiments conducted on 13 datasets from multiple perspectives demonstrate that the proposed CHATCBD system can significantly enhance the capabilities of LLMs in personalized healthcare. © 2013 IEEE.

关键词： Chatbots

来源：评论

学校读者我要写书评

暂无评论

A New Method for Evaluating the Coverage Performance of LEO Satellite Constellations

A New Method for Evaluating the Coverage Performance of LEO ...

引用

2024 International Conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： Li, Jianlin Zhang, XianFeng Lu, Yong Foshan University School of Computer Science and Artificial Intelligence Foshan China Guangzhou College of Technology and Business School of Engineering Guangzhou China

ISBN: (纸本)9798350366204

The paper proposes a new method to evaluate the coverage performance of Low Earth Orbit (LEO) constellations. Based on the Fibonacci grid, this method introduces the standard deviation of coverage multiplicity as a metric of global coverage uniformity. Moreover, according to the coverage demand deviation, this paper optimizes the allocation of satellite resources of different regions by considering the global population distribution. The simulations on some existing constellations including OneWeb, Kuiper, and Starlink demonstrate that the proposed method can effectively improves the coverage performance analysis of satellite constellation on coverage uniformity and resource utilization efficiency. © 2024 IEEE.

关键词： Population statistics

来源：评论

学校读者我要写书评

暂无评论

Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

引用

SN computer science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

来源：评论

学校读者我要写书评

暂无评论

Exploring information hiding in images – problems and measures: a survey

引用

Multimedia Tools and Applications 2025年 1-63页

作者： Khan, Ahmed Sarfaraz, Aaliya School of Computer Science Taylor’s University Subang Jaya Malaysia School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

The widespread adoption of digital communication technologies across various domains has led to a significant shift towards digitalization. However, this evolution has also introduced vulnerabilities, including unauthorized access, data manipulation, and fabrication by malicious entities, which severely impact the confidentiality and integrity of information. The proliferation of digital images on the internet has facilitated the use of these media for embedding confidential data in a way that prevents detection or extraction by unauthorized third parties. Despite numerous efforts, existing image steganography and watermarking techniques often lack robustness. This paper proposes several novel strategies to enhance the dynamism and imperceptibility of these techniques, integrating cryptographic methods by manipulating the entropy of encrypted images using minimal pixel value differences and applying unique image transformations. Additionally, the study highlights that implementing multi-layered security mechanisms can protect confidential information from modern statistical steganalysis attacks, thereby preserving both the integrity of hidden data and image quality. A comprehensive review of information hiding, specifically focusing on image watermarking and steganography, is provided. Furthermore, this survey outlines key problem statements and offers quantitative metrics for future research in this area. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

R-DTI: Drug Target Interaction Prediction Based on Second-Order Relevance Exploration 39

R-DTI: Drug Target Interaction Prediction Based on Second-Or...

引用

39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Hua, Yang Xu, Tianyang Song, Xiaoning Feng, Zhenhua Wang, Rui Zhang, Wenjie Wu, Xiaojun School of Artifcial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi China Sino-UK Joint Laboratory on Artificial Intelligence Ministry of Science and Technology China International Joint Laboratory on Artificial Intelligence Ministry of Education China

ISBN: (纸本)157735897X

Drug Target Interaction (DTI) prediction has witnessed promising performance boosts accompanied by advanced multimodal feature extraction. However, existing approaches suffer from two main difficulties. First, the complex protein structures cannot be well represented by current protein-sequence-based feature extractors. Second, the gap between protein and drug features increases the vulnerability of the obtained classifier thus degrading the prediction robustness. To address these issues, we propose a novel R-DTI method by exploring the second-order relevance in both protein structural feature extraction and DTI prediction phases. Specifically, we construct a pre-trained structural feature extractor that mines the atomic relevance of each amino acid. Then, an inter-feature structure-preserved Riemannian network is designed to expand the existing protein extraction patterns. To improve the prediction robustness, we also develop a Riemannian classifier that uses the second-order protein-drug relevance with a unified feature space. Extensive experimental results prove the merits and superiority of our R-DTI against the state-of-the-art, achieving 1.4% and 1.9% higher AUC-ROC on the BindingDB and DrugBank datasets, respectively. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Titanic Survival Predictor Using Machine Learning Algorithms 4

Titanic Survival Predictor Using Machine Learning Algorithms

引用

4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Ali Shaik, Mohammed Sairam, P. Rahul, P. Sadhvik, T. Nithin, P. Sr University School of Computer Science & Artificial Intelligence Telangana State Warangal India

ISBN: (纸本)9798350354218

Presumably, one of the most notorious and catastrophic catastrophes in history is the sinking of the RMS Titanic. Unfortunately, the Titanic sank during her inaugural voyage on April 15, 1912, early in the morning after slamming into an iceberg. Out of 2224 passengers and crew, approximately 1502 perished. Social stratification is a situation where people within a society are separated into various ranking on a hierarchical basis. These groups, also known as social classes are ranked as a social order that is made up of a person's education level, income level Next similar models based on machine learning will be chosen to come to a conclusion whether some groups of individuals are candidates to survive the long space travel. The results of application of the distinct machine learning models were illustrated down one after the other and compared through a general viewpoint of precision. Initially, to get a better feeling of how the data is distributed and what the traits are, the datasets were first examined and prepared. Following that, we ran a variety of machine learning models including Logistic regression, decision trees, random forests, naive Bayes classifier and k nearest neighbors (kNN) for prediction. While conducting our research, we benchmarked the performance of each algorithm using strict assessment techniques like evaluation of variables like the required computational effort, accuracy, precision, recall, and F1 score. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

The Application of the Teaching Model of 'Promoting Learning Through Competition, Promoting Teaching Through Competition' in the Course of data Analysis and Mining 24

The Application of the Teaching Model of 'Promoting Learning...

引用

24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wang, Zhaojing Li, Li Wang, Bangchao Luo, Ruiqi Ye, Luyao School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan430200 China

ISBN: (纸本)9798350365658

data Analysis and Mining is a core compulsory course for data science majors in higher education. At present, the course faces problems such as single teaching methods, limited resources, outdated experimental projects, poor applicability, and unreasonable evaluation methods, which affect the teaching quality of the course and hinder the development of talents in the field of data science. Introducing the teaching model of 'Promoting Learning through Competition, Promoting Teaching through Competition' into the teaching of the data Analysis and Mining course, integrating competition activities into the classroom, formulating practical and feasible implementation plans, actively updating teaching content and methods, improving the teaching effectiveness and quality of the data Analysis and Miningcourse through teaching reform, and providing reference for relevant course teaching reforms. This study has been applied in practical teaching and has proven the effectiveness of this teaching model from multiple aspects. © 2024 IEEE.

关键词： data quality

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：