检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Thakkar, Nilam Kumar, Arvind Sukumar, P. Sarode, Deepika G. Patil, Harshal Vijayakumar, E. Ldrp - Institute of Technology & Research Ksv University Svkm Department of Information & Technology Gujarat India School of Computer Applications Mriirs Haryana Faridabad India Nandha Engineering College Department of Computer Science and Engineering Erode India Dr.D.Y.Patil Institute of Technology Department of Engineering Pune Pimpri India Iilm University Department of Computer Science and Engineering Greater Noida India KIT-Kalaignar Karunanidhi Institute of Technology Department of Mca Coimbatore India

ISBN: (纸本)9798350373110

In recent years, methods to detect bogus reviews have attracted the attention of numerous businesses and educational organizations. In order for reviews to accurately represent genuine user experiences and opinions, it is quite challenging to identify fake reviews. Supervised learning has been a popular approach to tackle the problem. The issue is that manually marking fake reviews with a degree of reliability that would be useful for training is very difficult, if not impossible. There are three phases to the suggested strategy preprocessing, feature extraction, and training the model. This proposed employs a series of preprocessing techniques to get the dataset's raw data ready for computational operations. Dimensionality reduction and the quantification of content-related and text-segment-related features comprise feature extraction. To train the model, a TPA-BiLSTM-CSSA was used. On average, the suggested method achieves a higher accuracy of 93.71% than BiLSTM and CSSA. © 2024 IEEE.

关键词： Fake detection

来源：评论

学校读者我要写书评

暂无评论

Effective Integration of Text Diffusion and Pre-Trained Language Models with Linguistic Easy-First Schedule 30

Effective Integration of Text Diffusion and Pre-Trained Lang...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ou, Yimin Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Diffusion models have become a powerful generative modeling paradigm, achieving great success in continuous data patterns. However, the discrete nature of text data results in compatibility issues between continuous diffusion models (CDMs) and pre-trained language models (PLMs). That is, the performance of diffusion models even degrades when combined with PLMs. To alleviate this issue, we propose to utilize a pre-trained decoder to convert the denoised embedding vectors into natural language instead of using the widely used rounding operation. In this way, CDMs can be more effectively combined with PLMs. Additionally, considering that existing noise schedules in text diffusion models do not take into account the linguistic differences among tokens, which violates the easy-first policy for text generation, we propose a linguistic easy-first schedule that incorporates the measure of word importance, conforming to easy-first-generation linguistic features and bringing about improved generation quality. Experiment results on the E2E dataset and five controllable tasks show that our approach can combine the merits of CDMs and PLMs, significantly outperforming other diffusion-based models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

Classification of Organic and Recyclable Waste Using a Deep Learning Approach 1

引用

2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Graceline Jasmine, S. Jagadish, Tarun Shabrez, Md. Febin Daya, J.L. School of Computer Science and Engineering Vellore Institute of Technology—Chennai Campus Chennai India Electric Vehicles Incubation Testing and Research Center Vellore Institute of Technology—Chennai Campus Chennai India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

In this paper, we propose an automated waste classification system that uses the help of deep learning and image processing methods. The main objective of our proposed system is to streamline the classification of waste into organic and recyclable. To achieve this, we used image processing techniques to improve the image quality of recyclable and organic waste from a publicly available dataset and trained our model using convolutional neural networks (CNN). The model achieved a high validation accuracy of 90.65%. We compared this model with four pre-trained models based on their accuracy and loss. Our proposed model for automating waste classification holds the potential to improve waste management practices and can also reduce the amount of waste that ends up in landfills. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Strategic Pricing and Routing to Maximize Profit in Congested Roads Considering Interactions With Travelers

引用

IEEE Transactions on Control of Network Systems 2025年第2期12卷 1638-1650页

作者： Kim, Youngseo Duan, Ning Zardini, Gioele Samaranayake, Samitha Wischik, Damon Cornell University Civil and Environmental Engineering IthacaNY United States Cornell University Operations Research and Information Engineering IthacaNY United States Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA United States University of Cambridge Department of Computer Science and Technology Cambridge United Kingdom

We introduce an innovative approach for analyzing strategic interactions in transportation networks featuring Mobility-on-Demand (MoD) services. This study focuses on achieving company-traveler equilibria, whereby a single company optimizes pricing and routing decisions to maximize profitability while considering travelers' mode choices, modeled via a multinomial logit model (MNL). Although profit maximization problems have been extensively studied in the field of revenue management across various domains, their application to transportation networks poses unique challenges, such as the influence of network topology and additional constraints (e.g., flow conservation, rebalancing, etc.). To address the inherent non-linear relationship arising from endogenous travel demand, we shift our domain space from price to market share. Subsequently, we derive prices using a direct one-toone correspondence within the MNL. This work is the first effort in leveraging such novel techniques in the context of transportation network analysis. Remarkably, the proposed reformulation results in an equivalent problem exhibiting convexity, offering computational efficiency and interpretability. By solving the Karush-Kuhn-Tucker (KKT) conditions, we characterize user equilibrium with the generalized route cost, which incorporates the operating cost by rebalancing and travelers' disutility caused by congestion. Our approach is empirically validated through a numerical analysis conducted on the widely recognized Sioux Falls network. The results underscore the effectiveness and practical applicability of our method in analyzing transportation networks featuring MoD services, and open the stage for important future investigations. © 2014 IEEE.

关键词： Profitability

来源：评论

学校读者我要写书评

暂无评论

Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

引用

computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

来源：评论

学校读者我要写书评

暂无评论

Prediction of the Pore-Pressure Built-Up and Temperature of Fire-Loaded Concrete with Pix2Pix

引用

computers, Materials & Continua 2024年第5期79卷 2907-2922页

作者： Xueya Wang Yiming Zhang Qi Liu Huanran Wang Mechanics and Materials Science Research Center Ningbo UniversityNingbo315211China Jinyun Institute Zhejiang Sci-Tech UniversityLishui321400China School of Civil Engineering and Architecture Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China

Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.

关键词： Fire loaded concrete spalling risk pore pressure generative adversarial network(GAN) Pix2Pix

来源：评论

学校读者我要写书评

暂无评论

Improving Implicit Discourse Relation Recognition with Semantics Confrontation 30

Improving Implicit Discourse Relation Recognition with Seman...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Cai, Mingyang Yang, Zhen Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Implicit Discourse Relation Recognition (IDRR), which infers discourse logical relations without explicit connectives, is one of the most challenging tasks in natural language processing (NLP). Recently, pre-trained language models (PLMs) have yielded impressive results across numerous NLP tasks, but their performance still remains unsatisfactory in IDRR. We argue that prior studies have not fully harnessed the potential of PLMs, thereby resulting in a mixture of logical semantics, which determine the logical relations between discourse arguments, and general semantics, which encapsulate the non-logical contextual aspects (detailed in Sec.1). Such a mixture would inevitably compromise the logic reasoning ability of PLMs. Therefore, we propose a novel method that trains the PLMs through two semantics enhancers to implicitly differentiate logical and general semantics, ultimately achieving logical semantics enhancement. Due to the characteristic of PLM in word representation learning, these two semantics enhancers will inherently confront with each other, facilitating an augmentation of logical semantics by disentangling them from general semantics. The experimental results on PDTB 2.0 dataset show that the confrontation approach exceeds our baseline by 3.81% F1 score, and the effectiveness of the semantics confrontation method is validated by comprehensive ablation experiments. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Empowering Semantic Segmentation with Selective Frequency Enhancement and Attention Mechanism for Tampering Detection

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3270-3283页

作者： Xu, Xu Lv, Wenrui Wang, Wei Zhang, Yushu Chen, Junxin Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China

Nowadays, massive amounts of multimedia contents are exchanged in our daily life, while tampered images are also flooding the social networks. Tampering detection is therefore becoming increasingly important for multimedia integrity, and it is generally realized by designing specific convolutional neural networks. From a new perspective, this article proposes two pluggable modules for empowering existing semantic segmentation models for tampering detection. First, a selective frequency enhancement (SFE) module is developed to suppress the semantic information and selectively enhance the tamper information. Second, a boundary enhanced attention (BEA) module is designed to highlight the edge information of tempered area. Our SFE and BEA modules are combined with five mainstream semantic segmentation networks for performance evaluation. The experiment results demonstrate that our modules are able to empower the semantic segmentation networks for tampering detection, and their combinations even perform better than state-of-the-art algorithms in certain datasets. © 2020 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design 13

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Hong, Wenjing Chen, Cheng Zhu, Zexuan Tang, Ke Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design. © 2024 IEEE.

关键词： Vaccines

来源：评论

学校读者我要写书评

暂无评论

Accurate Classification of Cervical Cancer Based on Multi-layer Perceptron Hunger Games search Optimization technique 21

Accurate Classification of Cervical Cancer Based on Multi-la...

引用

21st International Learning and technology Conference, L and T 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. Youssef, Rasha Y. The Higher Institute for Management and Information Technology Minya Egypt Nahda University Faculty of Computer Science Computer Science Department Beni Suef Egypt Effat University College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Cancer is distinguished by the presence of abnormal cellular proliferation and growth, both of which serve as signs and symptoms for this kind of illness. computer vision, deep learning, and metaheuristics optimization techniques are increasingly important for solving complex medical Artificial Intelligence (AI) problems such as cancer detection. This paper introduces a new methodology for training the Multi-Layer Perceptron (MLP) using the optimization algorithm known as Hunger Games search Optimization technique (HGO) and apply this method to classify the cervical cancer. The main goal of this method is to reduce the error and enhance the classification rate of cervical cancer. The outcomes show that the MLP with HGO algorithm performed better than other algorithms in terms of classification efficacy and accuracy rate. Simulation outcomes indicate that the proposed strategy performs better than previously published research in terms of effectiveness for the classification optimization methods. © 2024 IEEE.

关键词： Diseases

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：