检索结果-内蒙古大学图书馆

2021 4th International Conference on Advanced Algorithms and Control engineering, ICAACE 2021

作者： Ge, Junwei Yu, Dehua Fang, Yiqiou School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

This paper proposed a multi-dimensional QoS cloud computing task scheduling algorithm based on improved ant colony algorithm, considering QoS demand of users and load balancing of cloud platform comprehensively. First, this paper defines a QoS model composed of the completion time and execution cost of tasks, and defines the cloud platform load balancing constraint function. Secondly, in view of the shortcomings of ant colony algorithm such as slow convergence speed and easy to fall into local optimum, the pheromone update method and expected heuristic function are improved, and the pheromone strength is dynamically changed. Finally, the simulation is carried out in cloudsim and compared with the ACS algorithm and the MMAS algorithm. Experimental results show that the algorithm in this paper is better than these two algorithms in terms of user satisfaction and cloud platform load. © Published under licence by IOP Publishing Ltd.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Multiscale-Temporal Feature Extraction and Boundary Confusion Alleviation for Vag-Based Fine-Grained Multi-Grade Osteoarthritis Deterioration Monitoring

SSRN

引用

SSRN 2023年

作者： Zhang, Yangwuyong Pan, Tongjie Ye, Yalan Wan, Zhengyi Liu, Benyuan Ding, Tan School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Force Medical University China

Background and Objective: Multi-grade osteoarthritis (OA) deterioration monitoring in dailyparadigm using Vibroarthrography (VAG) is very challenging due to two difficulties: 1) the composition of VAG signals is complex in the daily paradigm where friction is intensified because of weight-bearing movements. 2) VAG signal samples near the decision boundary of adjacent deterioration grades are easy to be misclassified. The majority of existing works only focus on binary classification of OA, providing inadequate assistance in instructing physicians to develop treatment plans based on the presence or absence of OA. Thus, we propose a novel framework for fine-grained multi-grade OA deterioration monitoring in daily ***: We propose an end-to-end deep learning framework termed Fine-grained Multi-grade OA Deterioration Monitor (FMOADM), which consists of Multiscale-temporal Feature Extraction (MTFE) and Confusion-Free Master-Slave (CF-MS) Classification. Specifically, MTFE is adopted to extract multiscale-temporal discriminative features from the complicated VAG signals. And center loss is introduced by CF-MS to alleviate confusion at the boundary of adjacent deterioration grades in the feature space. Meanwhile, master-slave structure is proposed for further fine-grained classification, where the master classifier integrates a channel attention mechanism and the slave classifier is designed to update MTFE parameters. As a result, the proposed method ensures fine-grained multi-grade OA monitoring performance via multiscale-temporal discriminative feature and boundary confusion ***: Experimental results on VAG-OA dataset demonstrate that our framework outperforms counterpart methods in the daily paradigm. The proposed framework achieved 78% in precision, obtaining an 8% improvement over the state-of-the-art ***: The proposed framework benefits efficient multi-grade OA deterioration monitoring, empowering physicians to develop treatm

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber Sensor: A Machine Learning Approach

Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber S...

引用

International Conference on Innovations in Science, engineering and technology (ICISET)

作者： Mahmudul Hoque Mahmud Ariful Hoque Maruf Mohammad Nasir Uddin Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Software Engineering Institute of Information Technology University of Dhaka Dhaka Bangladesh Department of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

This study introduces a novel approach to improving Photonic Crystal Fiber (PCF) Surface Plasmon Resonance (SPR) sensors by incorporating machine learning (ML) models for predicting core mode index. Traditionally, identifying core phenomena in PCF SPR sensors required manual visual inspection by experts, a process that is both time-consuming and prone to errors. To address this, a dual-core PCF model is developed and used Finite Element Method (FEM) simulations to create extensive training data for ML models. The proposed PCF model shows exceptional sensitivity to wavelength changes, with a recorded sensitivity of $\mathbf{6 0 0 0} \mathrm{nm} /$ RIU, making it highly effective for accurate mode prediction. The study evaluated nine ML models, including XGBoost, Random Forest, and Decision Tree, achieving accuracies of 0.9742,0.9645, and 0.9667, respectively. By integrating ML models, the core mode identification process is significantly faster and more efficient, offering major benefits for PCF SPR research. This work not only advances PCF sensor technology but also enhances efficiency and accuracy in various SPR-based applications.

关键词： Accuracy Sensitivity Photonic crystal fibers Predictive models Sensor phenomena and characterization Surface plasmon resonance Data models Sensors Finite element analysis Numerical models

来源：评论

学校读者我要写书评

暂无评论

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

引用

arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

来源：评论

学校读者我要写书评

暂无评论

A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： K. Malathi M. Ramkumar E D Kanmani Ruby G.B. Santhi Vivek Korde Hamzeh J Aljawawdeh Department of CSE Saveetha School of Engineering Saveetha Institute of Science and Technology (SIMATS) Saveetha University Chennai Department of ECE Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Chennai Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77 % ), and F1-Score (92.88 % ) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention.

关键词： Deep learning Support vector machines Biological system modeling Autoencoders Predictive models Biomarkers Convolutional neural networks Long short term memory Immune system Diseases

来源：评论

学校读者我要写书评

暂无评论

Vote Bank Forecast Based on Social Media Sentiment Analysis

Vote Bank Forecast Based on Social Media Sentiment Analysis

引用

International Conference on Open Source Systems and Technologies (ICOSST)

作者： Zubair Zaland Sibghat Ullah Bazai Muhammad Imran Ghafoor Saira Akram Muhammad Waseem Muhammad Qaseem Dept. of Software Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept.of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Engineering Pakistan Television Corporation Lahore Pakistan

Sentiment Analysis of human behavior from social media revolutionized many fields, such as health, business, and election prediction. Sentiment analysis is the process of analyzing an individual’s opinions, attitudes, views, sentiments, and emotions. In order to predict sentiments, predicting the polarity of words and categorizing them as positive or negative are the major goals. A quick and effective way to determine what the public thinks about one’s own political party, the opposition party, and political figures is through Twitter. prior sentiment analysis techniques have primarily struggled with classification accuracy because biases in the training data cause a sizable portion of tweets to be incorrectly classified. As a remedy, we set out to collect real-time data in order to forecast Pakistan’s next election results. Users can quickly share real-time updates on a variety of activities and events on Twitter, promoting the rapid diffusion of information and news. To do this, we accessed the Twitter API through the “Tweepy’’ Python package and retrieved real-time data from the service. Through keyword searches, we were able to find more than 5,000 tweets. We attributed feelings to particular tweets using Python’s “TextBlob’’ package. We are able to generate prediction graphics by determining the frequency of positive attitudes with respect to a particular candidate.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

SSRN

引用

SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

TransDeeplabv3: Multi-Prior Segmentor for Medical Image Segmentation

TransDeeplabv3: Multi-Prior Segmentor for Medical Image Segm...

引用

2021 China Automation Congress, CAC 2021

作者： Yue, Yi Tian, Zhiqiang Qiao, Yanan School of Software Engineering Xi'an Jiao Tong University Xi'an China School of Computer Science and Technology Xi'an Jiao Tong University Xi'an China

ISBN: (纸本)9781665426473

At present, medical image segmentation is a challenging and developing task in medical image field, in which semantic segmentation algorithms play an important role in many clinical applications. The atrous spatial pyramid pooling (ASPP) module of Deeplab-V3 can well balance the diversity, robustness, and connectivity of global feature map extraction under different scenarios. However, there is still a large improving prospect for medical image segmentation. Therefore, we introduce mechanisms of multi-group parallel processing, mass guided loss focusing on irregular shape, and k-nearest attention architecture, respectively, to achieve higher robustness and stronger global connectivity with more sufficient receptive field compared to original methods. The proposed model effectively alleviates the deficiencies of local semantic receptive fields, improves the ability of generalization of our model, and overcomes the agnosticism of feature spatial distribution. Under the premise of maintaining feature diversity and enhancing heterogeneous information correlation, we obtain better segmentation performance in our experiments by enriching global feature representations. We conduct a series of segmentation experiments using the proposed model and on medical image datasets: STARE, CHASE, DRIVE and HRF. Compared with Deeplab-V3 baseline, the performance is significantly improved in terms of several metrics, which verifies the effectiveness of our method. © 2021 IEEE

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

JavaBench: A Benchmark of Object-Oriented Code Generation for Evaluating Large Language Models

arXiv

引用

arXiv 2024年

作者： Cao, Jialun Chen, Zhiyong Wu, Jiarong Cheung, Shing-Chi Xu, Chang Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Code generation benchmarks such as HumanEval are widely adopted to evaluate LLMs’ capabilities. However, after consolidating the latest 24 benchmarks, we noticed three significant imbalances. First, imbalanced programming language. 95.8% of benchmarks involve Python, while only 5 benchmarks involve Java, resulting in an insufficient understanding of LLMs’ capability to generate Java code. Second, imbalanced code granularity. Function-/statement-level benchmarks account for over 83.3% of benchmarks. Only a mere handful extends to class-/project-levels, and all are limited to Python. Third, lacking advanced features. Existing benchmarks primarily assess basic coding skills (e.g., variables, operators, and control structures), while overlooking advanced Object-Oriented Programming (OOP) features (i.e., encapsulation, inheritance, and polymorphism). Considering the prevalence of these advanced features in real-world Java project development, constructing benchmarks to test LLMs on handling OOP features is necessary. To fill these gaps, we propose JavaBench, a project-level Java benchmark that exercises OOP features. It comprises four Java projects with 389 methods in 106 Java classes. The test coverage is up to 92%, and JavaBench is attested by 282 undergraduate students, reaching a 90.93/100 average score (i.e., pass rate against the test suite), ensuring the quality of documentation, code skeleton, and tests. To better evaluate LLM’s capability against JavaBench, we introduce a systematic evaluation design covering three context settings and five synthesis strategies at two granularities using three hierarchical metrics. Our extensive experiment yields several interesting findings. First, we noticed that regarding project-level Java programming, LLMs are far behind undergraduate students (no project can be correctly completed by any studied LLMs, and at most 41.17% Pass@5 in a more relaxed evaluation). Second, using method signature as prompt context may strike an ide

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis on Students’ Evaluation of Higher Educational Institutions

Sentiment Analysis on Students’ Evaluation of Higher Educat...

引用

International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Toçoğlu, Mansur Alp Onan, Aytuğ Department of Software Engineering Faculty of Technology Manisa Celal Bayar University Manisa45400 Turkey Department of Computer Engineering Faculty of Engineering and Architecture İzmir Katip Çelebi University İzmir35620 Turkey

ISBN: (纸本)9783030511555

Sentiment analysis is the method of identifying and classifying the views of users from text documents into different sentiments, such as positive, negative or neutral. Sentiment analysis can be employed to extract structured and informative knowledge from unstructured text pieces. This knowledge can serve as an important source of information for decision support systems and individual decision makers. Sentiment analysis plays an important role in many fields like education, where student input is crucial for assessing the effectiveness of educational institutions. In this paper, we present a machine learning based approach to sentiment analysis on students’ evaluation of higher educational institutions. We analyze a corpus containing approximately 700 student reviews written in Turkish, with the use of conventional text representation schemes and machine learning classifiers. In the experimental analysis, three conventional text representation schemes (i.e., term-presence, term-frequency, TF-IDF scheme) and three N-gram models (1-gram, 2-gram and 3-gram) have been considered in conjunction with four classifiers (i.e., support vector machines, Naïve Bayes, logistic regression, and random forest algorithm). The predictive performance of four ensemble learners (i.e., AdaBoost, Bagging and Random Subspace and voting algorithm) have been also evaluated. The empirical results indicate that machine learning based approach yields promising results on students’ evaluation of higher educational institutions. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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：