检索结果-内蒙古大学图书馆

2024 International Conference on Artificial Intelligence, computer, data sciences, and Applications, ACDSA 2024

作者： Soualihou, Soualihou Ngnamsie Kim, Jeong-Dong Zonyfar, Candra Lee, Hyun Lee, Taek Lee, Jung-Been Sun Moon University Dept. of Computer Science and Electronic Convergence Asan Korea Republic of Sun Moon University Dept. of Computer Science and Engineering Asan Korea Republic of Sun Moon University Division of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350394528

Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model's performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

An Extended Reality Platform Powered by Large Language Models: A Case Study on Teaching Dance Costumes 7

An Extended Reality Platform Powered by Large Language Model...

引用

7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Garzarella, Silvia Vallasciani, Giacomo Cascarano, Pasquale Hajahmadi, Shirin Cervellati, Elena Marfia, Gustavo University of Bologna Dept. of the Arts Bologna Italy University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331521578

In this paper, we present an Extended Reality (XR) platform powered by Large Language Models (LLMs), designed to support education in dance history and cultural heritage. The platform here applied to a case study on a dance costume, allows users to interact with 3D models, annotate them, and explore their historical and cultural contexts collaboratively. Using features like image-to-LLM queries, users can gain insights into costume details and performance history, and the annotation tools enable uploading multimedia resources, thereby simulating an archival research environment. By integrating LLMs, the platform provides tailored information and context on demand, enriching the user experience with detailed explanations about objects, such as costume construction, usage, and cultural significance. © 2025 IEEE.

关键词： Hosiery manufacture

来源：评论

学校读者我要写书评

暂无评论

Optimizing Potato Disease Detection in Pakistan with Machine Learning: A Comparative Analysis 3

Optimizing Potato Disease Detection in Pakistan with Machine...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Ahmed, Irfan Khalil, Manahal Ghulam, Safoora Mahmood, Jabar Batool, Khadija Khalid, Nibtal Arshad, Pakiza Dept of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science & Technology Zhejiang University Hangzhou China Dept of Computer Science Int. Institute of Science Art & Technology Gujranwala Pakistan

ISBN: (纸本)9798331504861

The agricultural sector of Pakistan depends heavily on the production of potatoes, however diseases like Bacterial Wilt, Late Blight, and Early Blight are posing a growing danger to this industry since they can negatively affect crop productivity and food security. Detecting these illnesses using traditional methods is time-consuming, labor-intensive, and sometimes it requires specialized knowledge which is not easy for farmers. The implementation of a machine learning (ML) framework for illness identification using the methods of K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) overcomes these challenges. This study utilizes data mining and preprocessing strategies to improve the accuracy of models and adaptability on a dataset of potato plants’ photos gathered across diverse areas in Pakistan. The models are assessed using performance criteria such computational efficiency, accuracy, precision, recall and F1-score. Random Forest model having 97% accuracy outperforms the other ML models in identifying plant disease. The suggested approach provides a practical, affordable, and effective way to identify diseases in a timely manner, allowing farmers to make informed decisions to lower crop losses. The aim of the study is to highlight how ML has the potential to transform Pakistani agriculture, enhancing food security and promoting long-term economic prosperity. ©2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Next Generation Intelligent Traffic Signal Control: Empowering Electronics Consumers with Edge-AIoT Capabilities

引用

IEEE Transactions on Consumer Electronics 2025年第1期71卷 1926-1934页

作者： Chavhan, Suresh Doswada, Rohit Gupta, Deepak Gunjal, Saymam Rodrigues, Joel J.P.C. School of Data Sciences Indian Institute of Science Education and Research Kerala Thiruvananthapuram India C-DoT Karnataka Bangalore India Dept. of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India Centre for Research Impact and Outcome Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering University of Visvesvaraya College of Engineering Karnataka Bangalore India Federal University of Piaui PI Teresina Brazil

Traffic congestion has become a major issue that is being faced by the majority of road users. The increasing vehicle usage, and the lack of space and funds to construct new transport infrastructure, further complicates the issue. In this scenario, it is important to come up with an intelligent and economical solution that improves the quality of road users' service. The problem with the traffic handling framework is signal timings are fixed which is not adaptive to the density of vehicles. To address this issue we propose an Edge-Augument Artificial Intelligence of Things (AIoT) road user cooperation for traffic management. The proposed system efficiently utilizes electronic devices to learn and adapt to changing traffic conditions in real-time. By optimizing the traffic signal timings based on the actual traffic conditions, adaptive systems reduce delay, improve traffic flow, reduce fuel consumption and pollution, and improve the electronics consumers' and road users' experiences. The proposed system has been tested with real-time experiments by integrating Electronic devices like cameras, smartphones, and AGX Xavier (edge device) with Cloud (ThingSpeak). The proposed system is verified by simulating the proposed system in the SUMO traffic simulator and its reliability is concluded by comparing the waiting time, depart delay, running, and halt time with the existing traditional method. © 2025 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Audio Content Processing System for Automatic Music Classification using Mask R-CNN

Audio Content Processing System for Automatic Music Classifi...

引用

2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Putta, Narsaiah Srinivas, R. Ravi Prasad, Dasu Vaman Rawoof, S.M. Suvarna Kumari, T. Dept. of Computer Science & Engg. Vasavi College of Engineering Telangana Hyderabad India Dept. of Computer Science & Engg. Mahatma Gandhi Institute of Technology TS Hyderabad India Dept. of Computer Science & Engg. Anurag University Telangana Hyderabad India Anwarul Uloom College Telangana Hyderabad India Dept. of Computer Science & Engg. Chaitanya Bharathi Institute of Technology TS Hyderabad India

ISBN: (纸本)9798331533137

The definition of a music genre is a subjective category that aids in identifying audio tracks within a shared culture or set of conventions. These genres are necessary for classifying various music-related metadata and tracks into domains that share similar musical qualities. The categorization of audio data according to their genre holds significant importance in the field of information retrieval of music. The current retrieval of various musical databases over computer networks necessitates the development of specialized methods for searching and organizing these data sets. The paper presents a model for genre classification, developed and automated using deep neural networks and the Mask R-CNN method. We performed a dimensional reduction of the model's target labels to enhance the previously mentioned method. Lastly, we will predict the behavior of the classification task through a qualitative analysis of the results using Keras. We conducted experiments using various pre-existing models such as FCN, Timbre CNN, end-to-end, and CNN. We conducted experiments using both raw amplitude data and Mel spectrograms from the raw audio data, which yielded more accurate results. At 86% and 53%, respectively, the suggested model's recall rates for hip-hop and rock music demonstrate a noteworthy level of accuracy. ©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

Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness 4

Efficient MILP Decomposition in Quantum Computing for ReLU N...

引用

4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023

作者： Franco, Nicola Wollschläger, Tom Poggel, Benedikt Günnemann, Stephan Lorenz, Jeanette Miriam Fraunhofer Institute for Cognitive Systems Iks Munich Germany Technical Univ. of Munich Dept. of Computer Science Munich Data Science Institute Germany

ISBN: (纸本)9798350343236

Emerging quantum computing technologies, such as Noisy Intermediate-Scale Quantum (NISQ) devices, offer potential advancements in solving mathematical optimization problems. However, limitations in qubit availability, noise, and errors pose challenges for practical implementation. In this study, we examine two decomposition methods for Mixed-Integer Linear Programming (MILP) designed to reduce the original problem size and utilize available NISQ devices more efficiently. We concentrate on breaking down the original problem into smaller subproblems, which are then solved iteratively using a combined quantum-classical hardware approach. We conduct a detailed analysis for the decomposition of MILP with Benders and Dantzig-Wolfe methods. In our analysis, we show that the number of qubits required to solve Benders is exponentially large in the worst-case, while remains constant for Dantzig-Wolfe. Additionally, we leverage Dantzig-Wolfe decomposition on the use-case of certifying the robustness of ReLU networks. Our experimental results demonstrate that this approach can save up to 90% of qubits compared to existing methods on quantum annealing and gate-based quantum computers. © 2023 IEEE.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning Approach for Prioritized Heterogeneous Object Tracking in UAV Swarms

Deep Reinforcement Learning Approach for Prioritized Heterog...

引用

2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Nithya, B. Subash, Neethu Vignesh, Hari National Institute of Technology Dept. of Computer Science and Engg Tiruchirappalli India St.Joseph's College Department of Data Science Tiruchirappalli India

ISBN: (纸本)9798350314946

Deep Reinforcement learning(DRL) has recently showcased its boundless potential in Multi-agent collaboration. There exist DRL-based models that strive to unravel the Unmanned aerial vehicle (UAV) Multi-target tracking predicaments, however, the conundrum of tracing desired targets persists as most research endeavors solely focus on the agents' policies while neglecting the dynamic environment, which harbors elusive and unpredictable targets. The proposed model executes Prioritized Heterogeneous Object Tracking (PHOT) using DRL, to furnish a solution for preferred Multi-target tracking by upholding the policy of UAV swarms. Tracking preferred targets assumes paramount importance as these targets necessitate meticulous monitoring within the ever-changing environmental backdrop, particularly during surveillance missions employing UAV swarms, which can harness the full potential of the UAV swarms in real-time. A YOLO v4-based algorithm is put forth, which can trail prioritized targets in a tracking by detection paradigm. To maintain the policy of UAV swarms, a dueling double deep Q-learning algorithm is deployed, complemented by a reward function that stimulates UAVs to track the preferred targets instantaneously. The UAVDT dataset is employed for the simulations, and the results evince that PHOT exhibits robustness, suitable for deployment in a real-time environment, amassing a highly commendable average cumulative reward over time, and enabling actions predicated on unidentical prioritized targets. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security in Communication Networks through Quantum Key Distribution 2

Enhancing Security in Communication Networks through Quantum...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Purushottam, Vaishnavi V Meena, S. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bengaluru India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Quantum Key Distribution or QKD revolutionizes secure communication using quantum mechanics. Unlike traditional cryptographic methods vulnerable to advances in computing power, QKD guarantees unconditional security by detecting any eavesdropping attempt. Quantum Key Distribution (QKD) constitutes a through development in the realm of secure communications by utilizing the ideas of quantum mechanics to provide potentially unbreakable encryption. This study examines the most current advancements in QKD, including the recently announced Measurement-Device-Independent QKD (MDI-QKD) and protocols like BB84 and E91. We analyze the real-world implementation, including satellite-based QKD and quantum repeaters. Furthermore, we discuss the challenges and opportunities of integrating QKD into present communication infrastructure. The significance of QKD in attaining secure communication networks for the next generation is underscored by our findings, which also have ramifications for forthcoming research and applications. Ultimately, this research aims to contribute to the development of robust and impenetrable communication networks in an increasingly interconnected world. © 2024 IEEE.

关键词： Quantum communication

来源：评论

学校读者我要写书评

暂无评论

LAN: Learning to Adapt Noise for Image Denoising

LAN: Learning to Adapt Noise for Image Denoising

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Changjin Kim Tae Hyun Kim Sungyong Baik Dept. of Computer Science Hanyang University Dept. of Data Science Hanyang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Removing noise from images, a.k.a image denoising, can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments. While there have been striking improvements in image Denoising with the emergence of advanced deep learning architectures and real-world datasets, recent denoising net-works struggle to maintain performance on images with noise that has not been seen during training. One typical approach to address the challenge would be to adapt a Denoising network to new noise distribution. Instead, in this work, we shift our focus to adapting the input noise itself, rather than adapting a network. Thus, we keep a pretrained network frozen, and adapt an input noise to capture the fine-grained deviations. As such, we propose a new denoising algorithm, dubbed Learning-to-Adapt-Noise (LAN), where a learnable noise offset is directly added to a given noisy image to bring a given input noise closer towards the noise distribution a denoising network is trained to handle. Consequently, the proposed framework exhibits performance improvement on images with unseen noise, displaying the potential of the proposed research direction.

关键词： Training Adaptation models computer vision Noise Noise reduction Deep architecture Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

引用

2024 IEEE International Conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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：