检索结果-内蒙古大学图书馆

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ramasamy, G. Kamalakkannan, S. Vels Institute of Science Technology & Advanced Studies Department of Computer Science School of Computing Sciences Pallavaram Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology School of Computing Sciences Pallavaram Chennai India

ISBN: (纸本)9798331509675

Container-based virtualisation has become a cornerstone of Cloud Computing (CC) due to its lightweight and scalable properties when compared to traditional virtual machines. However, it is still difficult to optimising resource allocation in containerised systems, and current approaches frequently fall short in addressing problems like high computational costs, scalability, cold-start scenarios, and Graphics Processing Unit (GPU) management. Container resource allocation is critical for effectively managing computing workloads in cloud settings, enabling scalability, performance optimization, and cost effectiveness. It is extensively used in a various industry, including e-commerce, healthcare, and finance, where dynamic workloads call for efficient resource management. Traditional resource allocation techniques, while their importance, frequently face difficulties such as unequal load distribution and resource underutilization. To address these constraints, this paper suggests a hybrid metaheuristic optimization algorithm that addresses limitations such as network distance, execution time, and migration costs to provide balanced load distribution. This study attempts to analyse the efficiency, time consumption and latency of resource allocation in dynamic cloud systems by addressing existing limitations. © 2025 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Detection of Brain Tumor using Transfer Learning using Conventional Autoencoder with Long Short Term Memory Method 6

Detection of Brain Tumor using Transfer Learning using Conve...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Agalya, D. Kamalakkannan, S. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798331522667

Brain tumor (BT) has generate a significant health challenge by putting pressure on healthy brain parts or spreading into other areas as well as blocking the flow of fluid around the brain. BT diagnosis is an extensive and time consumption process that depends primarily on radiologists experience and interpretive capabilities. For the area of medical imaging, automatic segmentation of images by means of Deep Learning (DL) approaches. The DL approaches assist in transforming the sector, resulted in improving precision and effectiveness of investigations. However, there are various feature extraction mechanisms available. Therefore, the Recurrent Neural Network (RNN) is utilized for feature extraction and image classification. In model execution, the data have trained in transforming the test image as well as data features for minimizing the domain shift is calculated through the Convolutional Autoencoder (CAE) for reconstruction loss. This research has concentrated in building a model with VGG16 as a single test that subjected at inference and existing method is adopted as neural networks for AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. The AE used to train from the source dataset and perform as the adaptors in optimizing during testing using a test subject for effective computation. Moreover, the Long Short Term Memory (LSTM) is utilized as RNN model with CAE for providing improved detection of BT in healthcare industries. Hence, the proposed CAE with LSTM is compared with AE with Convolutional Neural Network (CNN) for evaluating BT detection using MRI dataset with various BT type classifications. © 2025 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A Comparative Algorithmic Stratification to Identify Impact of Brain Tumor on Substantia Nigra in Humans

引用

SN computer science 2024年第7期5卷 901页

作者： Ganeshkumar, P.V. Prasanna, S. Vels Institute of Science Technology & amp Advanced Studies (VISTAS) Tamilnadu Chennai India Deptartment of Computer Application Vels Institute of Science Technology & amp Advanced Studies (VISTAS) Tamilnadu Chennai India

Brain is a pivotal part of the human body that requires accurate diagnosis and analysis on a periodical basis. The human brain is segmented into various regions, with the Substantia Nigra (SN) playing a decisive part in controlling the motor functions in the human body. The impact that brain tumors hold in terms of their size and location is an imperative attribute that determines the efficient functioning of substantia nigra. The existing research is distributed largely to comprehend the disfunctionalities in the brain, and analysis of motor inefficiencies, but fails to bridge the correlative breach that tumor impact has on substantia nigra. This research pivots on the understudied aspect of substantia nigra in relation to the density of the brain tumor through a juxtaposed algorithmic approach. The proposed indagation is effectuated with Binary Generalized Linear Model (GLM) Logistic Regression, Neural Network and an Optimizable Ensemble model effectuated by combining AdaBoost, Decision Tree, Neural Network, Binary GLM Logistic regression and Support vector machine. The simulations explicitly delineated that the optimizable ensemble delivered higher accuracy of classifying the tumors impacting Substantia Nigra, as compared to the other algorithmic approaches that explicated lower stratification accuracy. The simulations are implemented in MATLAB, and the results are successfully obtained. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Binary GLM logistic regression Brain tumor Neural network Optimizable ensemble model Substantia nigra

来源：评论

学校读者我要写书评

暂无评论

Investigating the Applications and Importance of Artificial Intelligence in Orthodontic Treatment Planning: A Systematic Review 14

Investigating the Applications and Importance of Artificial ...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Ramyaalakshmi, A. Poonguzhali, S. Vels Institute of Science Technology & Advanced Studies Chennai India Department of Computer Applications Vels Institute of Science Technology & Advanced Studies Chennai India

In the current scenario, the evolution of orthodontics and dental based applications have been raised with a strong impact of Artificial Intelligence. The main objective of the proposed systematic review is to study the various applications and importance of AI in orthodontic treatment planning. Electronic databases like Scopus, PubMed, Google Scholar, Embase, Cochrane, Medline, and Web of science have been thoroughly searched. Included were studies that employed AI to plan orthodontic treatments. Data were retrieved and examined to determine the clinical significance of applying AI to orthodontic treatment planning. This PRISMA model-based systematic review includes a total of 73 papers. The investigations demonstrated that artificial intelligence (AI) may be applied in orthodontics for evaluation and importance of ML and AI in Orthodontic Treatment Planning, AI-based software can provide accurate and efficient analysis of cephalometric images, reducing the time and effort required for manual analysis. AI-Enabled cephalometric landmark detection for orthodontic analysis and treatment planning, investigating the application of artificial intelligence on the dentofacial orthopedics and the specialty of orthodontics, application of automated orthodontic extraction indication detection using AI, investigating the application of artificial intelligence on maxillofacial radiology and specialty of oral diagnostics, investigating the importance of artificial intelligence in therapeutic dentistry, AI-powered prediction of post-orthognathic surgery facial aesthetics, investigating the major application of artificial intelligence on the forensic odontology, investigating the major application of artificial intelligence on the maxillofacial surgery and specialty in oral. The systematic evaluation shows that AI provides promising potential for enhancing orthodontic treatment planning. The use of AI can result in more precise diagnosis, treatment planning, and individualized ort

关键词： Dentistry

来源：评论

学校读者我要写书评

暂无评论

Malicious Webpage Detection Based on Feature Fusion Using Natural Language Processing and Machine Learning 2

Malicious Webpage Detection Based on Feature Fusion Using Na...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Pradeepa, G. Devi, R. Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai India

ISBN: (纸本)9798350347579

Malicious websites are purposefully designed to deceive internet users to steal sensitive personal information, infect the victim's system with malware, cause financial losses, and damage the victim's reputation. Finding these pages or links is hard for internet users. Such websites are discovered using detection tools. The majority of detection techniques use blacklisting or whitelisting strategies to find and prevent malicious websites. However, compiling such a sizable list of website links is a time-consuming job that is challenging to update regularly. Therefore, the researchers employ machine learning-based methods to identify these fraudulent connections. These methods are based on the features taken from URLs or web pages. Additionally, features such as DNS details, webpage reputation, and visual similarity data are used. However, these features are few and do not fully utilize the URLs or website contents. This work focuses on merging URL lexical features and content-based features for malicious webpage detection in order to fully exploit the dataset's potential. Natural language processing methods like Hashing, Count, and Term Frequency - Inverse Document Frequency (TF-IDF) vectorizers are employed to extract features from the content of Web pages. The suggested approach's efficiency is evaluated by using the most well-known machine learning methods. The outcome shows that the Count vectorizer with Random Forest achieves a higher accuracy of 91.17% with 500 features. © 2023 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Backpropagation Shuffled Leaping Neural Network Implementation in Classification of DDoS Packet Flow Traffic in Data Mining

引用

SN computer science 2024年第8期5卷 1131页

作者： Umamaheswari, N. Kumutha, K. Kalaivani, M.S. Ramya, R. Tagore College of Arts and Science Tamilnadu Chennai India Department of Computer Application- PG Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Computer science and Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India

All web applications remember the ultimate goal of storage services, Distributed Denial of Service (DDoS), to achieve high security from various attacks. The client-server application reduces fees and runs the elite registration gadget, while the notable part assumes that it is manufactured using a grant application. Analysis of the traceback data is difficult in the previous system, and then it containsa common DDoS attack *** DDoS attacks involving different network layers such as (SIDDoS (SQL (Structured Query Language) injection), HTTP (HyperText Transfer Protocol) flood, TCP (Transmission Control Protocol) are recommended here as there are signs that new datasets of common datasets are not being collected in the proposed system. The DDoS attack classification consists of three main steps: preprocessing, trace out the source IP address, Backpropagation Shuffled Leaping Neural Network (BSLNN) based on maintaining the specifications’ integrity. The preprocessing is the first step based on the Gabor Filter used to remove the data’s noise and the second Adaboost Random Optimal Selection method to analyzethe packet flow’s relative frequency. The third step is the Backpropagation Shuffled Leaping Neural Network (BSLNN) classification of traceback data maintaining the specifications analysis’s integrity. The proposed system achieves good periodic study of packet flow accuracy and time complexity. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaboost random optimal selection Backpropagation Shuffled Leaping Neural Network (BSLNN) Gabor filter Preprocessing Trace out the source IP address

来源：评论

学校读者我要写书评

暂无评论

Generalizations across filler-gap dependencies in neural language models 28

Generalizations across filler-gap dependencies in neural lan...

引用

28th Conference on Computational Natural Language Learning, CoNLL 2024

作者： Howitt, Katherine Nair, Sathvik Dods, Allison Hopkins, Robert Melvin Department of Linguistics University of Maryland United States Department of Computer Science University of Maryland United States University of Maryland Institute for Advanced Computer Studies United States

ISBN: (纸本)9798891761780

Humans develop their grammars by making structural generalizations from finite input. We ask how filler-gap dependencies, which share a structural generalization despite diverse surface forms, might arise from the input. We explicitly control the input to a neural language model (NLM) to uncover whether the model posits a shared representation for filler-gap dependencies. We show that while NLMs do have success differentiating grammatical from ungrammatical filler-gap dependencies, they rely on superficial properties of the input, rather than on a shared generalization. Our work highlights the need for specific linguistic inductive biases to model language acquisition. © 2024 Association for Computational Linguistics.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Logic-based Man-in-the-Middle Attack Detection and Improving Routing Efficiency in the IoT Network 1

Fuzzy Logic-based Man-in-the-Middle Attack Detection and Imp...

引用

1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Sivasankari, N. Kamalakannan, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India

ISBN: (纸本)9798350323795

The Internet of Things (IoT) is a remarkable technology for industrial development, smart homes, intelligent transportation, and more. Typically, in an IoT network, a Man in the Middle attack creates an intrusion node that can, either arbitrarily discard data packets or steal the data, thereby disturbing network performance and reducing network efficiency. While there have been numerous approaches using machine learning techniques were proposed to detect the intrusion nodes, they have often failed to address issues related to throughput and packet loss ratios. Additionally, the conventional approach does not concentrate on routing efficiency. A Fuzzy Logic based Man-in-the-Middle attack detection and Cuckoo Search Algorithm (FLCSA) is proposed to solve this issue for improving routing efficiency in the IoT Network. In this proposed work, the fuzzy logic system is used to efficiently detect intrusion nodes using the inputs such as node degree, node energy, and node delay and outputs that isolate intrusion nodes in the network. The cuckoo search (CS) algorithm is used to form the optimal route from sender to receiver in the network, selecting efficient relays based on node energy, link lifetime, and node bandwidth. The simulation results show that the proposed method has better throughput, increased detection ratio, and minimized network delay. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Privacy Preserving Q-Pragmatic-Sensitive Feature Analysis based Blockchain Security Using Provable Partition Fold Padding Encryption

Privacy Preserving Q-Pragmatic-Sensitive Feature Analysis ba...

引用

2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Punithavathi, S. Jeyalaksshmi, S. Vels Institute of Science Technology & Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai India

ISBN: (纸本)9798350398465

Information security is important factor in decentralized cloud environment because of sharing personalized information among the Internet need privacy preserving to protect the data. In recent days, the Blockchain security is a developing technique to protect the healthcare sensitive information in outsourced cloud environment. But increasing malicious activity and cryptography ley leakages leads security failures. Most security considering the privacy information as common to protect information publicly. This creates a chance of sensitive information attacks to attain the data. To resolve this security problem, we propose a privacy preserving Q-Pragmatic-Sensitive Feature Analysis (PP-QPSFA) based Blockchain security using provable partition fold padding encryption (PP-FPE) to improve the healthcare transaction security. Initially the Term Frequency Behavior Analysis (TFBA) estimate the privacy concerns of user accessibility. The Q-pragmatic-sensitive feature analysis (PP-QPSFA) is attained to find the sensitive privacy feature terms. Then to make secure data using Provable Partition Fold Padding Encryption (PP-FPE) and verified through Role Based Access Verification (RBA V) to make authentication. The proposed system proves higher security concern in blockchain healthcare to improve the privacy preserving in cloud environment. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

引用

Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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：