检索结果-内蒙古大学图书馆

IAENG International Journal of computer science 2025年第4期52卷 931-952页

作者： Chinivar, Sneha Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Karnataka Bengaluru India

Online offensive behaviour continues to rise with the increasing popularity and use of social media. Various techniques have been used to address this issue. However, most existing studies consider offensive content identification as a binary or ternary problem, disregarding the potential for multi-class classification of derogatory content. In this work, we propose RoBERTa Embedding-based Graphical Approach Classifier (REGAC), which aims to designate the identified offensive content into varied classes,like age, gender, ethnicity, religion, and others to understand what exact qualities the bully usually targets in their victim so that pertinent measures can be taken to battle them. Additionally, the models’ efficiency is evaluated using an unbalanced dataset to identify types of offensive content. The word embeddings are generated for a balanced dataset using RoBERTa embedding model to bring out the best-fitting vectors. These vectors are then input into traditional Machine Learning algorithms (SVM, KNN, Logistic Regression, Random Forest, and XGBoost) and graph-based algorithms (Graph Convolution Network (GCN), GraphSAGE, and Graph Attention Network (GAT)) for fine-grained categorization of offensive textual content. The experimental results demonstrate the efficiency of combating social media’s offensive content of the proposed work with higher Precision, Recall, F1-Score, and Accuracy compared with most state-of-art approaches. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Graph embeddings

来源：评论

学校读者我要写书评

暂无评论

A user-adaptive privacy-preserving authentication of IoMT using zero knowledge proofs with ECC

引用

Multimedia Tools and Applications 2025年 1-32页

作者： Misra, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Utter Pradesh Kanpur India

The Internet of Things (IoT) has developed into a crucial component for meeting the connection needs of the current smart healthcare systems. The Internet of Medical Things (IoMT) consists of medical devices that are connected to patients in order to sense their medical parameters and exchange that information with healthcare professionals so they can deliver remote healthcare services. The need to protect the patient’s private information from being misused makes IoMT security an urgent issue that merits further study. Elliptic Curve cryptography (ECC), a popular substitute for traditional public key encryption like RSA, has several advantages. ECC is a prime contender for implementation on constrained devices with confined memory and speed, as well as wireless communication protocols that use low power like IoMT environment. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the IoMT that is based on elliptic curve encryption based zero-knowledge proof (ZKP) protocols. The proposed mechanism completes in order of milliseconds up to 300 ms for 160 bits. The research will include the comparison between computational delays in traditional protocols and ZKP-ECC protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP-ECC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

引用

Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

PS-CoT-Adapter: adapting plan-and-solve chain-of-thought for scienceQA

引用

science China(Information sciences) 2025年第1期68卷 392-393页

作者： Qun LI Haixin SUN Fu XIAO Yiming WANG Xinping GAO Bir BHANU School of Computer Science Nanjing University of Posts and Telecommunications Purple Mountain Laboratories Department of Electrical and Computer Engineering University of California at Riverside

Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A novel method for indian vehicle registration number plate detection and recognition using CNN

引用

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8563-8585页

作者： Pandey, Vibha Choubey, Siddhartha Patra, Jyotiprakash Mall, Shachi Choubey, Abha Department of Computer Science Engineering Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science Engineering Shri Shankaracharya Institute of Professional Management and Technology Chhattisgarh Raipur India School of Computer Science and Engineering Galgotias University Greater Noida India

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "preprocessing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. © 2024 – IOS Press. All rights re

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Ant lion optimization based inertia weight optimized KGMO for mobility Management in Heterogeneous LTE cellular networks

引用

Multimedia Tools and Applications 2024年第17期84卷 1-22页

作者： Venkata Narasimha Reddy, G. Jayudu, T Venkata Naga Komarolu, Janardhan Rajesh, Nichenametla Reddy, B Lakshmi Narayana Department of Computer Science and Engineering St. Johns College of Engineering and Technology Yerrakota Yemmiganur India Anantapur India Nandyal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Department of Computer Science and Engineering SV College of Engineering Kadapa India

This study proposes a hybrid optimization-based mobility management strategy employing Kinetic Gas Molecular Optimization (KGMO) and Ant Lion Optimization (ALO). Initially, KGMO calculates particle properties, such as gas molecule position and velocity, based on kinetic energy principles. While KGMO demonstrates expedited convergence in large-dimensional spaces, its limitations prompt the hybridization with ALO. ALO modifies the inertia weight of KGMO to mitigate these issues. The proposed KGMO-ALO approach is implemented and validated in a MATLAB environment, compared against other meta-heuristic techniques like Seagull and Monarch Butterfly Optimizations across various performance metrics including throughput, bit error rate, end-to-end delay, and HO. Existing techniques such as PSO, MBO, and SOA exhibit superior performance primarily in scenarios with high user counts. The underperformance of ALO is attributed to its reliance on the best antlion solution in each optimization round. By leveraging the hybrid KGMO-ALO approach, this study aims to overcome these limitations and advance mobility management strategies in heterogeneous LTE cellular networks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Regularized Multilayer Perceptron for Input Noise Immunity

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第1期5卷 115-126页

作者： Mondal, Rahul Pal, Tandra Dey, Prasenjit National Institute of Technology Durgapur Department of Computer Science and Engineering Durgapur713209 India Cooch Behar Government Engineering College Department of Computer Science and Engineering Cooch Behar736170 India

The immunity of multilayer perceptron (MLP) is less effective toward input noise. In this article, we have focused on the robustness of MLP with respect to input noise where noise can be additive or multiplicative. Here, we have proposed a DropConnect-based regularized MLP to reduce the coadaptation among the neurons of the hidden layer. At first, we have empirically and statistically shown that by reducing coadaptation among the hidden neurons, an MLP can achieve better noise immunity. We have also empirically shown that an MLP with input noise injection and $l_{2}$ regularizer is an effective approach to improve its noise immunity. However, the results indicate that it does not adjust the coadaptation among the hidden neurons. Therefore, for further improvement, we have proposed a hybrid regularized MLP (HRMLP), where DropConnect is combined with the noise injection and $l_{2}$ regularizer. In addition to input noise, we have also verified the robustness of HRMLP with respect to $\mathbf {20\%}$ outliers in the dataset. To justify the effectiveness of the proposed HRMLP, we have compared it with MLP, MLP with noise injection, MLP with $l_{2}$ regularizer, MLP with noise injection and $l_{2}$ regularizer, and MLP with DropConnect along with two state-of-the-art works based on $\mathbf {20}$ standard datasets. The experimental results for both noisy inputs and outliers confirm that the performance of HRMLP is significant compared to other methods. © 2020 IEEE.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

An innovative Telugu text summarization framework using the pointer network and optimized attention layer

引用

Multimedia Tools and Applications 2024年第37期83卷 84539-84564页

作者： M, Varaprasad Rao Chakma, Kunal Jamatia, Anupam Rudrapal, Dwijen Department of Computer Science & amp Engineering National Institute of Technology Agartala Tripura799046 India Department of Computer Science & amp Engineering CVR College of Engineering Telangana Hyderabad501510 India

Summarizing lengthy text involves distilling crucial information into a concise form by covering the key events in the source text. Previous researchers mostly explored the supervised approaches for the task, but due to its strong reliance on the quality of text features, the resulting summaries often lack precision and coherence. The performance of the state-of-the-art summarizers becomes poor when applied to various Indian languages due to several challenges. The current research paper proposes a summarization approach for Telugu text based on a Pointer Network with an Optimised Attention Layer (PN-OAL). In this approach, the attention layer’s weights are adjusted using a hybrid optimization method that combines the Fusion of Coyote Optimization and Squirrel Search method (FCO-SSA). Weight optimization is conducted in order to address the goal function, which involves the maximization of the cosine similarity between the source text and the summary text. A novel loss function is incorporated within the framework of the constructed network architecture to generate a robust summary. The proposed approach is experimented with alternative baseline approaches in order to validate its efficacy for summarizing Telugu documents. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

FIAC: fine-grained access control mechanism for cloud based IoT framework

引用

International Journal of Grid and Utility Computing 2025年第3期16卷 269-278页

作者： Chaudhury, Bhagwat Prasad Dhal, Kasturi Pattnaik, Srikanta Nayak, Ajit Kumar Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Computer Science and Engineering S‘O’A University Odisha Bhubaneswar India

Cloud computing technology provides various computing resources on demand to users on pay per use basis. The technology fails in terms of its usage due to confidentiality and privacy issues. Access control mechanisms are the tools to prevent unauthorized access to remotely stored data. Cipher Text Policy Attribute-based Encryption (CPABE) is a widely used tool for facilitating authorised users to access the remotely stored encrypted data with fine-grained access control. In the proposed model FIAC, access control mechanism is embedded in cloud-based application. The major contribution of this work is the design of three algorithms all of which are attribute based: Key Generation algorithm, and Encryption and Decryption algorithms. Only authorised users can view them to take appropriate action plans. To enhance security of the cloud-based monitoring system, we have embedded a security scheme, all of which are attribute based. Further, the computation time of the model is found to be encouraging so that it can be used in low power devices. The experimental outcomes establish the usability of our model. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

ElHBiAt: Electra pre-training network Hybrid of BiLSTM, the Attention layer to Aspect-Based Sentiment Analysis

引用

IEEE Access 2025年 13卷 88342-88370页

作者： Ghanee, Amin Jahanbin, Kia Yamchi, Abolfazl Razzaghi Shahid Beheshti University Department of Computer Engineering Tehran Iran Yazd University Department of Computer Engineering Yazd Iran Shiraz University Department of Computer Science Engineering and Information Technology Shiraz Iran

Aspect-based sentiment analysis is one of the famous and practical subjects in natural language processing. Traditional sentiment analysis assigns a polarity to the whole text or document and does not consider the aspects of the text, so it cannot correctly show the author’s opinion. Extracting aspects and determining their polarity has many challenges, such as the lack of rich datasets, negative transfer in cross-domain datasets, and fine-tuning of deep models of the ratio of texts with different contexts. In this research, a model has been proposed for aspect-based sentiment analysis with different contexts. This model uses the Electra as the backbone of the architecture, which is trained with different cross-domain datasets to transfer knowledge to the target domain. Also, in the proposed model of ElHBiAt, the resampling method is used to deal with imbalanced datasets, which causes the neural model to find less orientation than the polarities in the majority. In this model, two layers of attention are used to extract the essential aspects of the text, and the deep neural network BiLSMT is used to process the text in a two-way. The results show that the ElHBiAt model has been able to improve the efficiency by an average of 5.78% in the SemEval dataset, by 2% in the SEntFiN dataset, and by an average of 4.3% in the Drug dataset in terms of the F1-score. © 2013 IEEE.

关键词： Emotion Recognition

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：