检索结果-内蒙古大学图书馆

A Framework for Malicious Domain Names Detection Using Feature Selection and Majority Voting Appr.a.h

Infor.a.ica (Slovenia) 2024年第3期48卷 419-438页

作者： Patil, Dhar.a.aj r. Department of Computer Engineering R.C.Patel Institute of Technology Shirpur India

As cyber.a.tacks become more sophisticated, identifying and mitigating bad domain names has become critical to assuring the security of online environments. This paper presents a framework for detecting malicious domain names using a feature selection strategy and a majority vote method. The suggested methodology begins with the extraction of impor.a.t features from domain names and their related characteristics, followed by a rigorous feature selection procedure to determine the most discriminating attributes. To accomplish feature selection, a variety of feature selection techniques are used, including chi-square statistics, infor.a.ion gain, gain ratio, and correlation-based feature selection, to analyse the value of each characteristic in distinguishing benign and malicious domain names. In addition, a majority voting strategy is utilised to improve the detection system’s overall accuracy and reliability by combining the predictions of different classifiers such as AdaBoost, logistic regression, k-nearest neighbours, naive bayes, and multilayer perceptron. The ensemble of classifiers is trained on the ideal features, yielding a complete and robust model capable of accurately recognising malicious domain names while minimising false positives. The proposed appr.a.h is evaluated against r.a.-world examples of harmful domain names. The suggested framework employing Chi-square feature selection and majority voting detects malicious domain names with an accuracy of 99.44%, precision of 99.44%, recall of 99.44%, and f-measure of 99.44%. The use of feature selection and a majority voting technique improves the system’s adaptability and resilience in the face emerging cyber thr.a.s. © 2024 Slovene Society Infor.a.ika. All rights reserved.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Explainable Emotion recognition in Social Networks with Transformers 24

Explainable Emotion Recognition in Social Networks with Tran...

引用

24th IEEE/ACIS Inter.a.ional Conference on computer and Infor.a.ion Science, ICIS 2024

作者： Perikos, Isidoros Pylarinou, Chara University of Patras Computer Technology Institute and Press 'Diophantus' Computer Engineering and Informatics Department Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350376647

Emotion recognition in text has become an essential research ar.a.within artificial intelligence and natural language processing due to its applications in sentiment analysis, human-computer interaction, and social media analysis. This paper introduces an appr.a.h for emotion recognition in social networks using transformer models enhanced with explainability techniques. By leveraging the advanced capabilities of transformers to analyze and classify emotions in written content, our finetuned models achieves high accuracy in identifying emotions according to the Ekman emotional model. The integration of LIME and SHAP provides transparency and interpretability to our model, making it more trustworthy for practical applications. Our findings indicate that models like XLNET report an exceptional perfor.a.ce in emotion recognition tasks. The explainability techniques illustrate the transformer model decision-making processes and the way that they utilize words and their continual infor.a.ion in sentences. © 2024 IEEE.

关键词： Distribution transformers

来源：评论

学校读者我要写书评

暂无评论

A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

引用

computers, Mater.a.s & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakar.a. Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to nor.a. brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the inter.a.s between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for.a. effective seizure alert *** granularity is essential for improving patient-specific interventions and developing pr.a.tive seizure management *** study addresses this gap by proposing a novel AI-based appr.a.h for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s perfor.a.ce,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of r.a.-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch nor.a.ization seizure classification stochastic gradient

来源：评论

学校读者我要写书评

暂无评论

EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

引用

Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnor.a.ities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and perfor.a.ce through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for.a.alysis. For classification, the PSNet + model categorizes medical images into nor.a. and abnor.a. cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MrI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior perfor.a.ce over existing appr.a.hes in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing perfor.a.ce. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Online Vehicle routing with Pickups and Deliveries Under Time-Dependent Travel-Time Constraints 24

Online Vehicle Routing with Pickups and Deliveries Under Tim...

引用

24th Symposium on Algorithmic Appr.a.hes for Transpor.a.ion Modelling, Optimization, and Systems, ATMOS 2024

作者： Kontogiannis, Spyros Paraskevopoulos, Andr.a. Zaroliagis, Christos Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press "Diophantus" Patras Greece Computer Technology Institute and Press "Diophantus" Greece

ISBN: (纸本)9783959773508

The Vehicle routing Problem with pickups, deliveries and spatiotemporal service constraints (VrPPDSTC) is a quite challenging algorithmic problem that can be dealt with in either.a. offline or.a. online fashion. In this work, we focus on a generalization, called VrPPDSTCtd, in which the travel-time metric is time-dependent: the traver.a.-time per r.a. segment (represented as a directed arc) is determined by some function of the departure-time from its tail towards its head. Time-dependence makes things much more complicated, even for the simpler problem of computing earliest-arrival-time paths which is a crucial subroutine to be solved (numerous times) by VrPPDSTCtd schedulers. We propose two online schedulers of requests to workers, one which is a time-dependent var.a.t of the classical Plain-Insertion heuristic, and an extension of it trying to digest some sort of forecasts for future demands for service. We enrich these two online schedulers with two additional heuristics, one targeting for distance-balanced assignments of work loads to the workers and another that makes local-search-improvements to the produced solutions. We conduct a careful experimental evaluation of the proposed algorithms on a r.a.-world instance, with or without these heuristics, and compare their quality with human-curated assignments provided by professional experts (human operators at actual pickup-and-delivery control centers), and also with feasible solutions constructed from a relaxed MILP formulation of VrPPDSTCtd, which is also introduced in this paper. Our findings are quite encouraging, demonstrating that the proposed algorithms produce solutions which (i) are significant improvements over the human-curated assignments, and (ii) have overall quality pretty close to that of the (extremely time-consuming) solutions provided by an exact solver for the MILP formulation. © Spyros Kontogiannis, Andr.a. Paraskevopoulos, and Christos Zaroliagis.

关键词： Travel time

来源：评论

学校读者我要写书评

暂无评论

rEGAC: Multi-Class Offensive Content Identification Using Graphical Appr.a.h

IAENG International Journal of Computer Science

引用

IAENG Inter.a.ional Jour.a. 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 incr.a.ing 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 ter.a.y problem, disregarding the potential for multi-class classification of derogatory content. In this work, we propose roBEr.a.Embedding-based Graphical Appr.a.h 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 roBEr.a.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 appr.a.hes. © (2025.0), (Inter.a.ional 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

r.a.-TIME GLUCOSE MONITOrING SYSTEM AND DIETArY rECOMMENDATIONS FOr DIABETES MELLITUS USING FOG COMPUTING

引用

Telecommunications and radio engineering (English translation of Elektrosvyaz and radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, r. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune r.a.tion causes the pancr.a. to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific nor.a. blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropr.a.e. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Enhancement of patient's health prediction system in a graphical representation using digital twin technology

引用

Multimedia Tools and Applications 2025年第17期84卷 18625-18649页

作者： Sobhana, M. Ch, Smitha Chowdary Koneru, Sowmya Mohan, G. Krishna Kumar, K. Kranthi Department of Computer Science and Engineering V.R. Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering Dhanekula Institute of Engineering & Technology Andhra Pradesh Vijayawada521139 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of IT Vasireddy Venkatadri Institute of Technology Andhra Pradesh Guntur522508 India

The patient health prediction system is the most critical study in medical research. Several prediction models exist to predict the patient's health condition. However, a relevant result was not attained because of poor quality. The IoT-sensed data contains more noise content, which maximizes the complexity of the health prediction. These demerits resulted in low prediction and perfor.a.ce scores. So the proposed work aims to develop a novel Coati-based recurrent Digital Twin Framework (CbrDTF) to predict the patients' health conditions. The novelty of this research lies in the combined function of the coati optimization and recurrent network with the digital twin for health prediction. Initially, the IoT-sensed data was imported, the data were preprocessed, and meaningful features were selected. Then predict the health condition of the patients and classify the conditions. Here the incorporated coati function at the classification layer extracted the relevant features from the sensed medical data for the robust prediction and also modified the parameters of the recurrent digital twin to improve the prediction and classification accuracy. Finally, the perfor.a.ce was measured;the presented model attained a high exactness score of 99.81% in the prediction accuracy, recall, f-value, and precision;it also, validated the computation time of 611.81 s. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

r.a.-Time Challenges and Opportunities for.a. Effective resource Management in Multi-cloud Environment

引用

SN computer Science 2024年第2期5卷 1-8页

作者： Basha, H. Anwar.a.ilkumar, B.H. Swetha, G. reddy, raghavendra Manoli, Sunil Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India R R Institute of Technology Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India

As the usage of multi-cloud setups grows, resource management will become a major concern. Because of the dynamic nature of these environments, as well as fluctuating workloads and service-level targets, an effective algorithm for r.a.-time resource allocation and optimisation is required. The Adaptive Multi-Cloud resource Management Algorithm (AMCr.a. is proposed in this study to handle the obstacles and capitalise on the benefits of resource management in a multi-cloud context. To ensure optimal resource utilisation, cost effectiveness, and service perfor.a.ce, AMCr.a.incorporates r.a.-time monitoring, workload prediction, and intelligent resource allocation. Experiment results show that AMCr.a.is successful in adapting to changing situations and managing resources in r.a. time, resulting in enhanced resource utilisation and overall perfor.a.ce in multi-cloud systems. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AMCrMA Cloud federation Cloud orchestration Data management Intelligent resource allocation resource management

来源：评论

学校读者我要写书评

暂无评论

Model-to-Circuit Cross-Approximation for Printed Machine Learning Classifiers

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 3532-3544页

作者： Armeniakos, Giorgos Zer.a.is, Georgios Soudris, Dimitrios Tahoori, Mehdi B. Henkel, Jorg Karlsruhe Institute of Technology Karlsruhe76131 Germany National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece University of Patras Computer Engineering and Informatics Department Patras26504 Greece Karlsruhe Institute of Technology Department of Computer Science Karlsruhe76131 Germany

Printed electronics (PEs) promises on-demand fabrication, low nonrecurring engineering costs, and subcent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit the r.a.ization of complex ML models in PE, even with bespoke architectures. In this work, we present an automated, cross-layer.a.proximation framework tailored to bespoke architectures that enable complex ML models, such as multilayer perceptrons (MLPs) and support vector machines (SVMs), in PE. Our framework adopts cooperatively a hardware-driven coefficient approximation of the ML model at algorithmic level, a netlist pruning at logic level, and a voltage overscaling at the circuit level. Extensive experimental evaluation on 12 MLPs and 12 SVMs and more than 6000 approximate and exact designs demonstrates that our model-to-circuit cross-approximation delivers power.a.d ar.a.optimal designs that, compared to the state-of-the-art exact designs, feature on average 51% and 66% ar.a.and power reduction, respectively, for less than 5% accuracy loss. Finally, we demonstrate that our framework enables 80% of the examined classifiers to be battery-powered with almost identical accuracy with the exact designs, paving thus the way toward smart complex printed applications. © 1982-2012 IEEE.

关键词： Timing circuits

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：