检索结果-内蒙古大学图书馆

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Chitra, K. Karthikeyan, M. Pradeep, M. Suriya, Prakash T D Viswanathan, T.J. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India Computer Applications Kongu Engineering College Erode India

ISBN: (纸本)9798350348217

Hate speech is a serious issue that can have harmful consequences for individuals and groups. In an effort to combat this issue, automated methods for identifying and categorising hate speech in text and other media have been developed using machine learning and deep learning techniques. These methods involve training machine learning algorithms on large datasets of labelled text, audio or visual data, and then using these algorithms to identify and classify new, unseen examples of hate speech. Machine learning approaches can be effective at detecting and classifying hate speech, but they also have limitations and can be prone to errors, particularly if the training data is biased or if the algorithms are not carefully designed and evaluated. It is important to thoughtfully examine the ethical and social consequences of using machine learning to identify hate speech and to ensure that these methods are transparent, fair, and accountable. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Optimizing Quality of Service in IoMT Through Fog Computing-Enabled Service Composition 13

Optimizing Quality of Service in IoMT Through Fog Computing-...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Anand, Dinesh Kaur, Avinash Singh, Parminder School of Computer Applications Lovely Professional University Punjab Phagwara India Guru Nanak Dev Engineering College Department of Computer Applications Punjab Ludhiana India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350380583

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, allowing for remote patient monitoring and personalized treatments. However, due to the resource limitations of medical devices, the vital nature of health- care data, and the necessity for real-time response, guaranteeing Quality of Service (QoS) in IoMT systems offers considerable issues. This research presents a technique for addressing these difficulties in IoMT by exploiting Fog Computing-Enabled Service Composition. The issue statement centers on the need to improve QoS in IoMT systems, which includes response time, reliability, and energy consumption. Existing solutions struggle to match the severe criteria of IoMT, where service deployment, orchestration, and data processing must be performed in a fast, energy-efficient, and reliable way. To address these concerns, we propose a set of techniques that combine Fog Computing with service composition to optimize QoS in IoMT. The framework is proposed to incorporate dynamics service orchestration, context ware decision-making and balance the load into the IoMT systems to allow service composition at network edge. The implementation of proposed technique is done and demonstrates how computing nodes are worked inside the IoMT system to enable dynamic service composition. Our results show improvements in the QoS parameters like response times, optimized energy consumption and enhanced reliability. © 2024 IEEE.

关键词： Fog Computing Internet of Medical Things Quality of Service Service Composition

来源：评论

学校读者我要写书评

暂无评论

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

引用

computer Systems science & engineering 2023年第5期45卷 1641-1654页

作者： K.Karthikeyan Liyakathunisa Eman Aljohani Thavavel Vaiyapuri Department of Computer Applications Anna UniversityRegional CampusMaduraiMaduraiIndia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Cloud data centers consume high volume of energy for processing and switching the servers among different *** Machine(VM)migration enhances the performance of cloud servers in terms of energy efficiency,internal failures and *** the other end,energy utilization can be minimized by decreasing the number of active,underutilized sources which conversely reduces the dependability of the *** VM migration process,the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the *** this view,the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization(IMFP-VMMO)model in cloud *** major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure *** accomplish this,IMFPVMMO model employs Gradient Boosting Decision Tree(GBDT)classification model at initial stage for effectual prediction of VM *** the same time,VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm(QO-AFSA)which in turn reduces the energy *** performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed *** comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

关键词： Cloud computing energy efficiency virtual machine migration failure prediction energy optimization metaheuristics

来源：评论

学校读者我要写书评

暂无评论

GD-STFA: Gradient Descent Sea Turtle Foraging Algorithm enabled Deep Q Network for Diabetic Retinopathy Detection

引用

Multimedia Tools and applications 2024年第18期83卷 53817-53836页

作者： Sabeena, A.S. Jeyakumar, M.K. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil - 629180 Tamilnadu Thuckalay India Department of Computer Applications Noorul Islam Centre for Higher Education Kumaracoil - 629180 Tamilnadu Thuckalay India

Diabetic Retinopathy (DR) refers to a retinal disease that happens due to high glucose levels in the blood, and it leads to blindness globally. In order to delay or avoid vision loss and degradation, early detection, and treatment are necessary. Hence, the conception of an automated technique for precise DR recognition is necessary. Thus, a Deep Q network (DQN) with a Gradient Descent-Sea Turtle Foraging Algorithm (GD-STFA) is proposed that inherits Gradient Descent (GD) in Sea Turtle Foraging Algorithm (STFA), to detect DR from images. This method involves various phases, like pre-processing, segmentation, as well as DR detection. Here, the DR image quality is enhanced in the pre-processing phase using a median filter. After that, the segmentation is done by utilizing Swin-UNet and it is trained using the proposed GD-STFA algorithm. Finally, the DQN trained with GD-STFA finds if a person has DR or normal from his retinal images. The experimentation is performed in detail and validated on the Indian Diabetic Retinopathy Image Dataset benchmark dataset. Ultimately, the experimental outcomes demonstrate that the model is better than traditional techniques regarding optimal accuracy of 0.98, sensitivity of 0.984 and specificity of 0.971. Also, regarding the segmentation accuracy, the proposed model is around 0.906 accurate with a Dice coefficient of 0.963. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2023.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

Enriched Model of Intuitionistic Fuzzy Adaptive Noise Filtering on MR Brain Image

引用

SN computer science 2023年第2期4卷 166页

作者： Srinivasan, Preethi Saroj Gurunathan, Pradeep Department of Computer Science and Engineering Anna University Chennai India Department of Computer Applications A.V.C. College of Engineering Mayiladuthurai India

MRI is one of the major modality in assisting the medical experts to diagnose and treat different brain diseases such as cancer, epilepsy and stroke. Poor light illumination of MRI scanners, electronic interferences and radiofrequency emissions cause noise in MR images. Handling such noises in MR brain images is a challenging task. To overcome the issue, this paper aims to develop an enhanced model that has the ability to handle three different types of noises namely salt and pepper noise, speckle noise and Gaussian noise. The proposed enhanced intuitionistic fuzzy adaptive filter (IFAF) model removes noises in MR brain images by categorizing pixels into membership and non-membership grade. IFAF preserves the edges and detailed information of the images by adapting contrast enhancement histogram equalization. Conventional denoising models face uncertainties in distinguishing normal and noise affected pixel of an image. The performance of IFAF is compared over different noises and the results show IFAF provides better results than the other conventional noise filtering models. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Contrast enhancement Denoising Gaussian Intuitionistic fuzzy MRI Salt and pepper Speckle noise

来源：评论

学校读者我要写书评

暂无评论

Blockchain Breakthrough: A Review of its Role in Modern Technology 2

Blockchain Breakthrough: A Review of its Role in Modern Tech...

引用

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

作者： Asthana, Sameer Chaturvedi, Ravi Prakash Mishra, Annu Nayyar, Poorva Shaliyar, Md. Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350368109

This article summarizes the Blockchain technology along with the artificial intelligence. The cutting edge technology is described in a elaborated manner along with its advantages, disadvantages and its impact with the artificial intelligence. There are various sectors in which it is used and the outcomes are discussed. The proficiency of blockchain has proved its reliability and threshold in different domains. © 2024 IEEE.

关键词： artificial intelligence blockchain ledger security

来源：评论

学校读者我要写书评

暂无评论

Osteoarthritis Prediction in Knee Joint Using Deep Learning Techniques 27

Osteoarthritis Prediction in Knee Joint Using Deep Learning ...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Singh, Shubham Kumar Chouhan, Kuldeep Agrawal, Arun Prakash Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350392319

Osteoarthritis is a disorder that transpires when the articular cartilage in the knee breaks down and changes to the underbelly of the cartilage. Normal joint fracture protection for the bones is provided by the slick substance known as articular cartilage. Early prediction can slow down the progression of the condition. Medical professionals' expertise is utilized to predict severity. In this research, we address the issue that arises when a model's validation accuracy is lower than its training accuracy. Specifically, we find that as dataset size increases, the validation accuracy of Osteoarthritis prediction falls. We used a Deep Convolutional Neural Network to develop an automatic osteoarthritis severity assessment system using the Kellgren and Lawrence (KL) grading system. With this method, X-ray images are used to predict the severity of osteoarthritis. This study's objective is to predict the severity stage of knee osteoarthritis in the form of prediction accuracy (in %) of osteoarthritis using a transfer learning and deep learning approach. As a result, EfficientNetB5 provides a prediction accuracy of over 97.45% as demonstrated in this work. © 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

Application of XGBoost algorithm and grid search hyperparameter tuning to study health effects among individuals in the industrial area

引用

Multimedia Tools and applications 2025年 1-44页

作者： Johnson, Susymary Perumalsamy, Deepalakshmi Department of Computer Applications Kalasalingam Academy of Research and Education Virudhunagar District Tamil Nadu Krishnankoil626126 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar District Tamil Nadu Krishnankoil626126 India

This research focuses on utilizing data-driven machine learning techniques to study the impact of air pollution on the health of residents in industrial regions. The objective is to develop an efficient machine learning model that can accurately identify communities with a higher prevalence of health issues and diseases. A population-based survey was conducted in four geographical areas, collecting data on sociodemographic characteristics, health effects, perceived health status, and annoyance due to industries. The proposed model, XGBoost_BLR_GridCV, achieved high accuracy (ROC_AUC: 0.9215), precision of 92%, recall of 91% and f1-score of 91% in predicting the proximity of residents with chronic diseases. The results highlight the significant association between living in high proximity to industrial regions and increased rates of asthma, respiratory illness, and heart-related health problems. Comparative analysis with other tree-based models further demonstrates the effectiveness of the proposed model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Forestry

来源：评论

学校读者我要写书评

暂无评论

Intelligent Analysis and Identification of Palm Gestures using ML Algorithms

Intelligent Analysis and Identification of Palm Gestures usi...

引用

2024 International Conference on computer, Electrical and Communication engineering, ICCECE 2024

作者： Roy, Debraj Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar De, Utpal Chandra Tripathy, Abinash School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications Kiit Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350386479

The area of human-computer interaction (HCI) has made significant strides in recent years, thanks in large part to the development of innovative technologies that aim to improve the way in which people and machines work together. One of these areas is the use of hand gestures to control gadgets and apps, which has the potential to create a form of interaction that is more intuitive and natural. The introduction of a palm gesture detection system that is both dependable and effective has the potential to completely alter the ways in which we interact with various electronic gadgets and software programmes. The objective is to develop a palm gesture recognition system that is capable of precise detection and classification of hand gestures in real time. The computer vision methods is applied to collect and interpret the hand movements. Following this, machine learning algorithms will be used to train the system on a huge dataset consisting of hand gestures. The system will be able to identify the hand gestures in real time and will categorise them according to predetermined categories. The experimental results are compared against other gesture recognition systems to study the accuracy of the system. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

SLDERCLC: Improving Smart Learning Frameworks Through Deep Learning Enhanced Recommender Systems Using an Efficient Cross-Layer Collaborative Approach

引用

SN computer science 2025年第2期6卷 1-18页

作者： Kaur, Harjinder Kaur, Tarandeep Kumar, Mukesh Bhardwaj, Vivek School of Computer Applications Lovely Professional University Punjab Phagwara 144411 India Department of Computer Applications Chandigarh School of Business Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India School of Computer Science and Engineering Manipal University Jaipur Jaipur 303007 India

The exponentially scaling domain of a smart learning frameworks necessitates advanced recommender systems capable of providing precise and relevant educational content to learners. Traditional recommender systems often grapple with challenges such as data sparsity, cold start problems, and the inability to capture complex user-item interactions & scenarios. This work introduces a novel approach to recommender systems in smart learning frameworks, addressing these limitations by leveraging deep learning techniques. Our proposed model amalgamates Deep Q Learning with Multilayer Graph Neural Networks (GNNs) for Cross Layer Collaborative Filtering and integrates Autoencoders with Capsule Networks for enhanced recommendation accuracy levels. The use of Deep Q Learning facilitates efficient decision-making in dynamic environments, while Multilayer GNNs adeptly handle relational data, improving the recommendation process by capturing the intricate connections within the educational contents. Furthermore, the incorporation of Autoencoders with Capsule Networks offers a sophisticated mechanism for understanding hierarchical relationships in data, which is crucial for personalized learning paths. The effectiveness of our model is substantiated through rigorous testing on the ASSISTments and EdNet datasets. The results are compelling, showcasing a 4.9% increase in precision, 5.5% improvement in accuracy, 3.5% higher recall, 2.9% greater AUC (area under the curve), 3.4% increased specificity, and a notable 4.5% reduction in delay for smart learning recommendations in comparison with SBBR, DRL and ROME. These improvements highlight the model’s proficiency in delivering timely and relevant educational content, thereby enhancing the learning experience of students as well as the teaching strategies of faculty. The framework attains significant improvements in the efficiency and effectiveness of educational content recommendations thereby increasing the retention rate of students w

关键词： Autoencoders Deep learning Graph Neural Networks Recommender systems Smart learning

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：