检索结果-内蒙古大学图书馆

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 IEEE.

关键词： Application programs

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Deep Learning Methods for Automated Diagnosis of Pulmonary Diseases from Chest X-Ray Images: A Study Based on ICD-10 2

Comparative Analysis of Deep Learning Methods for Automated ...

引用

2nd International Conference on Data, Information and computing Science, CDICS 2024

作者： Wijayakusuma, I.G.N. Lanang Sudarma, Made Putra, I. Ketut Gede Darma Sudana, Oka Engineering Study Program Universitas Udayana Bukit Jimbaran Indonesia Electrical Engineering Study Program Universitas Indonesia Bukit Jimbaran Indonesia Information Technology Study Program Universitas Udayana Bukit Jimbaran Indonesia

ISBN: (纸本)9781643685762

This research delves into the comparative assessment of diverse deep learning architectures for the automated identification of pulmonary diseases in chest X-ray images, aligning with the diagnostic framework of the International Classification of Deseases, 10th Revision (ICD-10). The evaluated architectures encompass Convolutional Neural Network (CNN), MobileNetV2, DenseNet, VGG16, and InceptionV3. Through an extensive investigation conducted on a comprehensive dataset comprising 108,948 chest X-ray images, we scrutinize the performance metrics and diagnostic accuracy of each architecture. Notably, our findings reveal MobileNetV2 as the frontrunner, exhibiting an outstanding accuracy rate of 97% for Emphysema disease. This remarkable accuracy underscores the robustness and efficiency of MobileNetV2, even amidst large-scale diasets, positioning it as a promising solution for the automated detection of pulmonary ailments from medical imaging. Additionally, we analyze the computational efficiency and resource requirements of each architecture, providing insights into their practical applicability in real-world clinical settings. The implications of our study extend beyond technical benchmarks, offering valuable insights for healthcare practitioners, researchers, and developers working towards enhancing diagnostic processes in pulmonary healthcare. By elucidating the comparative strengths and limitations of various deep learning architectures, this research contributes to the advancement of computer-aided diagnosis systems, paving the way for more accurate and efficient detection of lung diseases from chest X-ray imaging data. © 2025 The Authors.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

DC programMING AND ALGORITHM FOR NONCONVEX LOG TOTAL VARIATION IMAGE RECONSTRUCTION

Journal of Applied and Numerical Optimization

引用

Journal of Applied and Numerical Optimization 2025年第1期7卷 41-54页

作者： Zhang, Benxin Xue, Lei Sun, Shaohua Li, Haixia Zhu, Zhibin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

In this paper, a nonconvex variational model with log total variation is proposed to investi-gate robust image reconstruction from a certain number of measurements. Due to the form of the new model, the difference of convex functions (DC) programming is presented and a DC algorithm (DCA) is adopted. The specific DCA subproblem can be solved by the alternating direction method of multipliers (ADMM). Theoretically, we prove the sequence generated by the DCA converges to a stationary point. The experimental results demonstrate that the new method outperforms the competitive methods when the measurement is limited or the noise level is significant. © 2025 Journal of Applied and Numerical Optimization.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

A Reinforcement Learning-assisted Evolutionary computing Approach to Capacitated Vehicle Routing with Time Windows

A Reinforcement Learning-assisted Evolutionary Computing App...

引用

2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Irtiza, Muhammad Kalaria, Rudri Kayes, A.S.M. School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia

ISBN: (纸本)9798400704956

This study proposes an innovative approach to address the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) by integrating Reinforcement Learning (RL) into Evolutionary Algorithms (EAs), forming the Reinforcement Learning-assisted EA (RL-EA). While traditional EAs struggle with scalability and convergence speed, RL offers promise in dynamic decision-making. By combining the strengths of both paradigms, RL-EAs aim to enhance the state-of-the-art solutions for CVRPTW. The proposed approach seeks to optimise fleet routes while adhering to capacity and time constraints, crucial for logistics and transportation sectors. This study contributes to the advancement of optimisation techniques in complex transport scenarios, offering potential improvements in efficiency and cost-effectiveness. © 2024 Copyright held by the owner/author(s).

关键词： Vehicle routing

来源：评论

学校读者我要写书评

暂无评论

Scheduling Policies in Edge computing for Real-Time Video Analytics 3

Scheduling Policies in Edge Computing for Real-Time Video An...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shreevatsan, S.S. Annambhotla, Yasasree Lasya Reji, Anaswara Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This study compares different scheduling algorithms used in edge computing for real-time video processing. Instead of sending video data to cloud servers, the video is processed directly at the edge, reducing delays and saving bandwidth. The focus is on how different scheduling methods manage resources, prioritize tasks, and reduce the time it takes to analyze video. The algorithms are tested in simulated environments to understand how they impact system efficiency and performance. The goal of this work is to find the best scheduling policies for real-time video processing at the edge, improving overall system performance and scalability. © 2025 IEEE.

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

Defending the Digital Frontier: URL-Based Phishing Detection Extension 7th

Defending the Digital Frontier: URL-Based Phishing Detection...

引用

7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Vamsi, P. Muthaiah, U. Roshan Vardhan, C.H. Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9783031699856

The internet’s rapid expansion has led to an increase in cyberattacks and phishing schemes that rely on uniform resource location (URL) links. In today’s digital landscape, it’s vital to identify these harmful links for safety. Traditional methods struggle to adapt to new website URLs, so machine learning is being used to detect malicious URLs. Previous studies have explored various machine learning techniques for URL detection, but a recurring issue is the need to preprocess website links before analysis. To address this problem, a new neural network method has been introduced. This innovative approach enables the creation of a model capable of identifying malicious URLs without the need for repetitive feature extraction steps. This groundbreaking technique has shown impressive results in detecting dangerous web addresses, operating swiftly and accurately with a detection rate of 99.61%. This represents a significant advancement in the field of cybersecurity, offering an effective solution to the ever-evolving threat landscape posed by malicious URLs on the internet. © IFIP International Federation for Information Processing 2024.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

An Empirical Comparative Study on Pruning and Quantization Algorithms for Model Compression 2

An Empirical Comparative Study on Pruning and Quantization A...

引用

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

作者： Mukesh, Narravula Kumar, Mallisetty Rathna Vikas, Ambati Sai Jeyakumar, Gurusamy Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798331505745

In a world where Neural networks and Deep learning have become of major importance, the need for methods to reduce their memory storage and usage has become of major importance. Hence the need for quantization and pruning has become huge and important. Also, the hardware used also decides the necessity of memory reduction due to the hardware's compatibility and memory computation constraints. This study compares different pruning and quantization models and reports about which are the best methods among the different methods considered in this study. The proposed comparison is carried out using the metrics of prediction accuracy and the time taken for training. This study identifies that magnitude-based pruning is the best among all the CPU-based systems with an accuracy of 0.998, whereas structured pruning has the highest accuracy of 0.985. The focus is on the hardware used, which are mainly the GPU-based systems and the CPU-based systems. The study reveals that the pruning method is better in the faceoff between pruning and quantization methods in terms of accuracy and computational time. When compared between the CPU and the GPU, concerning accuracy CPU holds the edge, whereas concerning the computational time the GPU holds the edge. Overall, concerning the balance between accuracy and computational time, the GPU-based systems hold the upper hand, making them more suitable for the optimization task. © 2025 IEEE.

关键词： Central Processing Unit Graphical Processing Unit Neural Networks Pruning Quantization

来源：评论

学校读者我要写书评

暂无评论

Agri Assist: An AI Integrated Farmer Assistant 3

Agri Assist: An AI Integrated Farmer Assistant

引用

3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Reddy, Pennabadi Devendra Sampath Reddy, K. Satya Jayanth, P. Kakarla, Bhanu Prakash Balakrishnan, Roshni M. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

In this paper, an AI Agriculture Assistant integrating a crop recommendation system and an agricultural query chatbot is developed to provide guidance on crop cultivation. Based on this, the crop recommendation system uses a stack ensemble model comprising Random Forest and Gradient Boosting, and an accuracy of 99.32% and F1-Score of 99.26%. Here, word embeddings are performed through FastText which allows for a quick response time (0.0244 seconds) and a cosine similarity of 0.88629 for the chatbot. With RSA encryption for securing user data, we ensure secure communication. It uses the critical elements of the soil composition, weather and crop performance in formulating tailored agricultural guidance. Back end communication is done through Flask, and the entire system was deployed in this manner with a web interface to drive usage and for real time data responses. This entire application helps the farmers in decision making regarding crops cultivation and management through accurate and secure assistance. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Effective Hazard Categorization of Near-Earth Objects with Random Forest Techniques 6

Effective Hazard Categorization of Near-Earth Objects with R...

引用

6th International Conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2024

作者： Nair, Rekha R. Babu, Tina Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

Near-Earth Objects (NEOs) have long been a subject of interest for astronomers and planetary defense experts due to their potential impact risk. Hence, Random Forest techniques were utilized to classify NEOs as hazardous or non-hazardous using NASA's comprehensive dataset of 338,199 NEO observations from NASA, spanning from 1910 to 2024. The data underwent preprocessing, including standard scaling and the removal of outliers. Various visualization methods were applied to elucidate patterns within the dataset. The Random Forest model achieved a 95% accuracy rate in distinguishing between hazardous and non-hazardous NEOs after outlier removal. The model demonstrated precision values of 0.95 and 0.70 for non-hazardous and hazardous classes respectively, with corresponding recall values of 0.97 and 0.58. This research underscores the efficacy of Random Forest algorithms in assessing NEO hazards and discusses the implications for prioritizing observational resources and enhancing early warning systems for potential NEO threats. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Vulnerability Scanning by CPE-CVE Matching 15

Vulnerability Scanning by CPE-CVE Matching

引用

15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Adithya, Amaravadi Vyas, Vansh Mohan, Madan Aaswin, V.A. Lanka, Sanjana Ganeshan, R. School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh 466114 India

ISBN: (纸本)9798331300579

Today organizations and institutions face a crucial challenge while managing their software vulnerabilities. The dynamic nature of security threats and constant software updates makes it difficult to have an efficient system to monitor and ensure software security by regularly checking for and installing the latest updates to fix potential vulnerabilities. All information regarding these updates and vulnerabilities is present in the official National Vulnerabilities Database (NVD). The challenge lies in efficiently locating software identifiers and updating the software inventory without constantly modifying the core algorithm. Through this Project, we aim to develop a CPE-CVE matching tool that aims to match CPE (Common Platform Enumeration) Identifiers to their respective CVE (Common Vulnerabilities and Exposures) Identifiers. This enables us to assess a software's vulnerability to known threats and helps us build the bridge between specific vulnerabilities and affected software configurations. We have developed an algorithm to match CVEs based on CPE entries or identifiers dynamically. This reduces any human error that may occur while constantly updating the core algorithm and ensures the adaptability and effectiveness of the organization's software ecosystem in managing vulnerabilities. © Grenze Scientific Society, 2024.

关键词： program diagnostics

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

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

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：