检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2024

作者： De Jesus, Mervin Jommel T. Ballera, Melvin A. Graduate Program Technological Institute of the Philippines Manila Philippines Department of Computer Science Technological Institute of the Philippines Manila Philippines

ISBN: (纸本)9798331521219

Making decisions requires different information, especially when it is crucial. The traditional Analytic Hierarchy Process (AHP) is a great algorithm that will help make these decisions more quickly because it organizes the choices in a hierarchy. However, this algorithm has trouble with big datasets and changing decision settings because they depend on pairwise comparisons and subjective judgments when making decisions. Additionally, fuzzy and stochastic AHP algorithms were developed to improve the traditional ones. However, these algorithms are complex to use on a large scale and require many computers. This study presents a novel algorithm (Top-TC AHP) that integrates Support Vector Machine (SVM) and Top-K algorithms into the traditional AHP to help people make decisions faster and better. It uses SVM to sort data and get the probabilities of the criteria for the alternatives and Top-K to choose the top options. Time complexity, consistency ratio, and throughput were the metrics used to gauge the performance of the novel algorithm. As a result, when comparing Top-TC AHP to other ways, it gets better consistency, makes computing more manageable, and speeds things up. Because of this, it is beneficial for dealing with large amounts of data and situations that change quickly. In addition, the Top-TC AHP algorithm can help people make smart decisions in many areas, such as business and school planning. ©2024 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Predicting Cardiac Complications of Myocardial Infarction Patients Using Machine Learning

Predicting Cardiac Complications of Myocardial Infarction Pa...

引用

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Baidya, Shriyansh Gupta, Vibhuti Lovejoy High School LucasTX United States Meharry Medical College School of Applied Computational Sciences Department of Computer Science & Data Science NashvilleTN United States

ISBN: (纸本)9798350362480

In the United States, heart disease is the leading cause of death, killing about 695,000 people each year. Myocardial infarction (MI) is a cardiac complication which occurs when blood flow to a portion of the heart decreases or halts, leading to damage in the heart muscle. Heart failure and Atrial fibrillation (AF) are closely associated with MI. Heart failure is a common complication of MI and a risk factor for AF. Machine learning (ML) and deep learning techniques have shown potential in predicting cardiovascular conditions. However, developing a simplified predictive model, along with a thorough feature analysis, is challenging due to various factors, including lifestyle, age, family history, medical conditions, and clinical variables for cardiac complications prediction. This paper aims to develop simplified models with comprehensive feature analysis and data preprocessing for predicting cardiac complications, such as heart failure and atrial fibrillation linked with MI, using a publicly available dataset of myocardial infarction patients. This will help the students and health care professionals understand various factors responsible for cardiac complications through a simplified workflow. By prioritizing interpretability, this paper illustrates how simpler models, like decision trees and logistic regression, can provide transparent decision-making processes while still maintaining a balance with accuracy. Additionally, this paper examines how age-specific factors affect heart failure and atrial fibrillation conditions. Overall this research focuses on making machine learning accessible and interpretable. Its goal is to equip students and non-experts with practical tools to understand how ML can be applied in healthcare, particularly for the cardiac complications prediction for patients having MI. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

引用

Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

来源：评论

学校读者我要写书评

暂无评论

Three-Level BDDC for Virtual Elements 27th

Three-Level BDDC for Virtual Elements

引用

27th International Conference on Domain Decomposition Methods in science and Engineering, DD 2022

作者： Klawonn, Axel Lanser, Martin Wasiak, Adam Department of Mathematics and Computer Science Division of Mathematics University of Cologne Weyertal 86-90 Cologne50931 Germany Center for Data and Simulation Science University of Cologne Köln Germany

The Virtual Element Method (VEM) is a Galerkin-type method for the solution of partial differential equations which allows for the discretization with general polygonal/polyhedral meshes. © 2024, The Author(s), u...

ISBN: (纸本)9783031507687

关键词：

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

An Early-Stage Colorectal Cancer Detection from Colonoscopy Images Using Enhanced Res-UNET 1

引用

8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Mahanty, Mohan Vamsi, Bandi Dasari, Madhavi Appaji, Setti Vidya Sagar Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Computer science and Engineering Department Sridevi Women’s Engineering College Hyderabad India Baba Institute of Technology and Sciences Visakhapatanam India

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

The segmentation of the polyps during colonoscopy is one of the most crucial factors for successful colon cancer diagnosis. Due to the wide variety of sizes and shapes of these polyps, this process can be challenging. The introduction of deep learning models in biomedical image analysis has shown a great impact on disease detection and segmentation. Most of the deep learning models concentrated on larger size polyps only, smaller size polyps may not segmented accurately during the procedure and can cause cancer in 5–7 years. This can have a significant impact on the early-stage detection of colon cancer. This chapter proposes an enhanced Res-UNET-based deep learning–based model that can improve the performance of segmentation on small-size polyps. We evaluated the proposed model on ETIS-LaribPolypDB, CVC-ColonDB, CVC-ClinicDB, Kvasir-SEG colonoscopy image datasets. The results show that the proposed enhanced Res-UNET model achieves the top Dice segmentation accuracy and top Hausdorff distance over the recent state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Leveraging Support Vector Machines for Detection of Network Traffic Attacks 4

Leveraging Support Vector Machines for Detection of Network ...

引用

4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Tiwari, Anurag Indian Institute of Technology School of Artificial Intelligence & Data Science Jodhpur India Babu Banarasi das Institute of Technology& Management Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798350364729

Network security is crucial to ensure the protection of information systems in an increasingly connected world, and network traffic attack detection is an important aspect of maintaining system integrity. This study addresses this by investigating the use of a powerful and flexible machine learning algorithm, namely Support Vector Machines (SVM), to detect network traffic attacks. As a result, SVM has been successfully applied in different domains like network security, etc. In this study, SVM is applied for normal and malicious network traffic classification, including several attack types like Denial-of-Service (DoS), User-to-Root (U2R), and Remote-to-Local (R2L) attacks. To this end, the KDD Cup 99 dataset is employed for training and testing the model. These metrics are then used to evaluate the performance of the SVM-based intrusion detection system. The results demonstrate that SVM is able to recognize a high level of accurateness on network data traffic attacks as well as it has performed well in a difficult attack trends. In this case, the F1-scores for the Linear and RBF did result in 96.65/99.24, respectively, indicating that SVM also proved to be an effective algorithm choice in this example. Finally, the study either propose new algorithms or improve existing algorithms for SVM and demonstrate their effectiveness in the area of network security. In conclusion, this study reinforces the importance of SVM as an effective instrument in the detection of attacks on network traffic and strengthening cyber defense capabilities. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection

Sign of the Times: Evaluating the use of Large Language Mode...

引用

2024 Joint Workshop on Multiword Expressions and Universal Dependencies, MWE-UD 2024

作者： Phelps, Dylan Pickard, Thomas Mi, Maggie Gow-Smith, Edward Villavicencio, Aline Healthy Lifespan Institute The University of Sheffield United Kingdom Department of Computer Science The University of Sheffield United Kingdom Institute for Data Science and Artificial Intelligence The University of Exeter United Kingdom

ISBN: (纸本)9782493814203

Despite the recent ubiquity of large language models and their high zero-shot prompted performance across a wide range of tasks, it is still not known how well they perform on tasks which require processing of potentially idiomatic language. In particular, how well do such models perform in comparison to encoder-only models fine-tuned specifically for idiomaticity tasks? In this work, we attempt to answer this question by looking at the performance of a range of LLMs (both local and software-as-a-service models) on three idiomaticity datasets: SemEval 2022 Task 2a, FLUTE, and MAGPIE. Overall, we find that whilst these models do give competitive performance, they do not match the results of fine-tuned task-specific models, even at the largest scales (e.g. for GPT-4). Nevertheless, we do see consistent performance improvements across model scale. Additionally, we investigate prompting approaches to improve performance, and discuss the practicalities of using LLMs for these tasks. © European Language Resources Association: CC BY-NC 4.0.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

引用

Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

FasMe: Fast and Sample-efficient Meta Estimator for Precision Matrix Learning in Small Sample Settings 38

FasMe: Fast and Sample-efficient Meta Estimator for Precisio...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tan, Xiao Wang, Yiqin Shen, Yangyang Shen, Dian Wang, Meng Duan, Peibo Wang, Beilun School of Computer Science and Engineering Southeast University Nanjing China College of Design and Innovation Tongji University Shanghai China Department of Data Science & AI Monash University Melbourne Australia

Precision matrix estimation is a ubiquitous task featuring numerous applications such as rare disease diagnosis and neural connectivity exploration. However, this task becomes challenging in small sample settings, where the number of samples is significantly less than the number of dimensions, leading to unreliable estimates. Previous approaches either fail to perform well in small sample settings or suffer from inefficient estimation processes, even when incorporating meta-learning techniques. To this end, we propose a novel approach FasMe for Fast and Sample-efficient Meta Precision Matrix Learning, which first extracts meta-knowledge through a multi-task learning diagram. Then, meta-knowledge constraints are applied using a maximum determinant matrix completion algorithm for the novel task. As a result, we reduce the sample size requirements to O(log p/K) per meta-training task and O(log |G|) for the meta-testing task. Moreover, the hereby proposed model only needs O(p log ϵ−1) time and O(p) memory for converging to an ϵ-accurate solution. On multiple synthetic and biomedical datasets, FasMe is at least ten times faster than the four baselines while promoting prediction accuracy in small sample settings. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

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：