检索结果-内蒙古大学图书馆

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

作者： Ashraf, Shaina Bezzaoui, Isabel Andone, Ionut Markowetz, Alexander Fegert, Jonas Flek, Lucie Department of Mathematics and Computer Science University of Marburg Germany FZI Forschungszentrum Informatik Germany Murmuras GmbH Germany Lab Germany University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (纸本)9782493814104

In today's rapidly evolving digital age, disinformation poses a significant threat to public sentiment and socio-political dynamics. To address this, we introduce a new dataset "DeFaktS", designed to understand and counter disinformation within German media. Distinctively curated across various news topics, DeFaktS offers an unparalleled insight into the diverse facets of disinformation. Our dataset, containing 105,855 posts with 20,008 meticulously labeled tweets, serves as a rich platform for in-depth exploration of disinformation's diverse characteristics. A key attribute that sets DeFaktS apart is, its fine-grain annotations based on polarized categories. Our annotation framework, grounded in the textual characteristics of news content, eliminates the need for external knowledge sources. Unlike most existing corpora that typically assign a singular global veracity value to news, our methodology seeks to annotate every structural component and semantic element of a news piece, ensuring a comprehensive and detailed understanding. In our experiments, we employed a mix of classical machine learning and advanced transformer-based models. The results underscored the potential of DeFaktS, with transformer models, especially the German variant of BERT, exhibiting pronounced effectiveness in both binary and fine-grained classifications. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

An Automatic Lung Nodule Classification System Based on Hybrid Transfer Learning Approach

引用

SN computer science 2022年第4期3卷 1-10页

作者： Saikia, Trishna Kumar, Ranjeet Kumar, Dilip Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jamshedpur 831014 India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Chennai 600127 India

Lung cancers are not only affecting the lung but also impair the respiratory mechanism. Worldwide, it becomes one of the high causes of mortality in humans. Several computer aided diagnosis (CAD) systems have been designed in recent years for diagnosis of several diseases. Early detection of lung cancer has become very important which can enhance survival chances among humans. The average rates of survival people with lung-cancer can rise from 14 to 49 percent if the diagnosed is taking place in time. Compare to X-ray, computed tomography (CT) is more operative as it involves multiple imaging approaches to support each other. In this study, we have used lung-patient CT scan images to classify the lung nodule into four classes, which are small-cell-carcinoma, adenocarcinoma, squamous-cell-carcinoma and large-cell-carcinoma. This study has proposed VGG networks integrated with support vector machine and random forest to create a hybrid algorithm that further helps in reducing computation complexity of classification. The lung nodules are classified using these hybrids algorithms with highest accuracy 98.70% and compared the results with existing methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Computed tomography Convolution neural network Random forest Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

引用

3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

Data collection of wireless sensor network based on trajectory optimization of laser-charged UAV

引用

High-Confidence Computing 2024年第2期4卷 116-128页

作者： Chuanwen Luo Jian Zhang Jin Qian Yi Hong Zhibo Chen Yunan Hou Xiujuan Zhang Yuqing Zhu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China College of Information Engineering Taizhou UniversityTaizhou 225300China School of Computer Science Qufu Normal UniversityRizhao 276826China Research Center of Applied Mathematics and Machine Intelligence Zhejiang LabHangzhou 311121China Department of Computer Science California State UniversityCA 90032USA

Unmanned Aerial Vehicle(UAV)can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks(WSNs),which is crucial for providing seamless services and improving the performance in the next generation wireless ***,since the UAV are powered by batteries with limited energy capacity,the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring *** overcome this problem,we study the Real-time Data Collection with Lasercharging UAV(RDCL)problem,where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors(LBDs).This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that realtime data performance is ensured It has been proven that the RDCL problem is *** address this,we initially focus on studying two sub-problems,the Trajectory Optimization of UAV for Data Collection(TODC)problem and the Charging Trajectory Optimization of UAV(CTO)problem,whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas,*** we propose an approximation algorithm to solve each of them with the constant ***,we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL ***,the proposed algorithm is verified by extensive simulations.

关键词： Wireless sensor networks Laser-charged UAV Trajectory optimization Data collection

来源：评论

学校读者我要写书评

暂无评论

Rice Leaves Disease Diagnose Empowered with Transfer Learning

引用

computer Systems science & Engineering 2022年第9期42卷 1001-1014页

作者： Nouh Sabri Elmitwally Maria Tariq Muhammad Adnan Khan Munir Ahmad Sagheer Abbas Fahad Mazaed Alotaibi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the agricultural industry,rice infections have resulted in significant productivity and economic *** infections must be recognized early on to regulate and mitigate the effects of the *** diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses,reduce pesticide use,and boost ta country’s *** the health of a rice plant through its leaves is usually done as a manual ocular *** this manuscript,three rice plant diseases:Bacterial leaf blight,Brown spot,and Leaf smut,were identified using the Alexnet *** research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply,lowering prices,and reducing production's environmental impact that affects the economy of any *** would be able to get more exact and faster results with this technology,allowing them to administer the most acceptable treatment *** Using Alex Net,the proposed approach achieved a 99.0%accuracy rate for diagnosing rice leaves disease.

关键词： Rice bacterial leaf blight brown spot leaf smut machine learning alexnet

来源：评论

学校读者我要写书评

暂无评论

Autoencoder-Assisted Feature-Type Distributed Clustering for Clinical and Biomedical Data Analysis

Autoencoder-Assisted Feature-Type Distributed Clustering for...

引用

IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Lenat Thomas Madhu S. Nair Saptarshi Bej Department of Computer Science Artificial Intelligence & Computer Vision Lab Cochin University of Science and Technology Kochi India Department of Data Science Indian Institute of Science Education and Research Thiruvananthapuram India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

The Uniform Manifold Projection and Approximation (UMAP) assisted Feature-type Distributed Clustering (FDC) workflow, contrary to the traditional UMAP algorithm, was found to be more informative for dimensionality reduction in tabular Clinical and Biomedical Routine Data (CBRD) due to the presence of diverse feature-types in such datasets. However, the prospect of using powerful neural network models for the Feature-type distributed dimensionality reduction task is yet to be explored. Considering the broader applicative potential of neural networks, we compared some autoencoder-based neural networks as an alternative to the UMAP algorithm for the FDC workflow. The study uses standard objective measures such as Silhouette Score and DB-Index to compare the quality of clusters and embeddings generated by the Autoencoder-assisted FDC (FDC-AE) approaches with the established UMAP-assisted FDC (FDC-UMAP) workflow. The evaluation involved two datasets in each size category from the medical field. The results indicate that for a dimensionality reduction and cluster identification task, the FDC-AE can be more effective compared to the FDC-UMAP, especially on larger datasets. Our research opens up the possibility of feature-distributed multi-label supervised dimensionality reduction, as well as the usage of pre-trained networks for such complex dimensionality reduction tasks.

关键词： Dimensionality reduction Manifolds Biological system modeling Neural networks Clustering algorithms Distributed databases Biomedical measurement Communications technology Bioinformatics Standards

来源：评论

学校读者我要写书评

暂无评论

Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance

Cross-View Meets Diffusion: Aerial Image Synthesis with Geom...

引用

IEEE Workshop on Applications of computer vision (WACV)

作者： Ahmad Arrabi Xiaohan Zhang Waqas Sultani Chen Chen Safwan Wshah Department of Computer Science Vermont Artificial Intelligence Lab University of Vermont Intelligent Machines Lab Information Technology University Center for Research in Computer Vision University of Central Florida

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Aerial imagery analysis is critical for many research fields. However, obtaining frequent high-quality aerial images is not always accessible due to its high effort and cost requirements. One solution is to use the Ground-to-Aerial (G2A) technique to synthesize aerial images from easily collectible ground images. However, G2A is rarely studied, because of its challenges, including but not limited to, the drastic view changes, occlusion, and range of visibil-ity. In this paper, we present a novel Geometric Preserving Ground-to-Aerial (G2A) image synthesis (GPG2A) model that can generate realistic aerial images from ground images. GPG2A consists of two stages. The first stage predicts the Bird's Eye View (BEV) segmentation (referred to as the BEV layout map) from the ground image. The second stage synthesizes the aerial image from the predicted BEV layout map and text descriptions of the ground image. To train our model, we present a new multimodal cross-view dataset, namely VIGORv2, built upon VIGOR [64] with newly collected aerial images, maps, and text descriptions. Our extensive experiments illustrate that GPG2A synthesizes better geometry-preserved aerial images than existing models. We also present two applications, data augmentation for cross-view geo-localization and sketch-based region search, to further verify the effectiveness of our GPG2A. The code and dataset are available at https://***/AhmadArrabi/GPG2A.

关键词： Geometry Image segmentation computer vision Costs Image synthesis Geology Computational modeling Layout Diffusion models Data augmentation

来源：评论

学校读者我要写书评

暂无评论

Personality Detection Using Context Based Emotions in Cognitive Agents

引用

computers, Materials & Continua 2022年第3期70卷 4947-4964页

作者： Nouh Sabri Elmitwally Asma Kanwal Sagheer Abbas Muhammad A.Khan Muhammad Adnan Khan Munir Ahmad Saad Alanazi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egyp t 3School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan Department of Computer Science GC UniversityLahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13120Korea

Detection of personality using emotions is a research domain in artificial *** present,some agents can keep the human’s profile for interaction and adapts themselves according to their ***,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the *** idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of *** our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual *** paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/*** proposed work consists of Jumbo Chatbot,which can chat with *** this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive ***,the Jumbo chatbot is using the BFI technique to interact with a *** accuracy of proposed work varies and improve through getting more experiences of interaction.

关键词： Emotions fuzzy personality detection contextual analysis semantic analysis

来源：评论

学校读者我要写书评

暂无评论

SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive Learning for Efficient Cross-Emotion Anomaly Detection

引用

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China

Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different emotional modalities. To the best of our knowledge, this topic has yet to be well studied, which could potentially benefit lots of valuable cognitive applications such as autistic children diagnosis and criminal deception detection. To this end, this paper proposes an efficient cross-emotion anomaly detection approach via semanticinconsistency reasoning and hybrid contrastive learning (SIR-HCL), which is the first attempt to detect the anomalous emotional pairs across the audio-visual emotions. First, the proposed framework utilizes dual-branch network to obtain the deep emotional features in each modality, and then employs the shared residual block to derive the semantically compatible features. Subsequently, an efficient hybrid contrastive learning approach is designed to enlarge the semantic-inconsistency among abnormal emotional pair with different affective classes, while enhancing the semantic-consistency and increasing the feature correlation between normal emotional pair from the same affective class. At the same time, an efficient bidirectional learning scheme is employed to significantly improve the data utilization and a two-component Beta Mixture Model is adaptively utilized to reason the anomalous emotion pairs. Extensive experiments evaluated on two benchmark datasets show that the proposed SIR-HCL method can well detect the anomalous emotional pairs across audio-visual emotional data, and brings substantial improvements over the state-of-the-art competing methods. © 2016 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Representation Matters for Mastering Chess: Improved Feature Representation in AlphaZero Outperforms Switching to Transformers 27

Representation Matters for Mastering Chess: Improved Feature...

引用

27th European Conference on Artificial intelligence, ECAI 2024

作者： Czech, Johannes Blüml, Jannis Kersting, Kristian Steingrimsson, Hedinn Artificial Intelligence and Machine Learning Lab TU Darmstadt Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Darmstadt Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States HoustonTX United States

ISBN: (纸本)9781643685489

While transformers have gained recognition as a versatile tool for artificial intelligence (AI), an unexplored challenge arises in the context of chess — a classical AI benchmark. Here, incorporating vision Transformers (ViTs) into AlphaZero is insufficient for chess mastery, mainly due to ViTs' computational limitations. The attempt to optimize their efficiency by combining MobileNet and NextViT outperformed AlphaZero by about 30 Elo. However, we propose a practical improvement that involves a simple change in the input representation and value loss functions. As a result, we achieve a significant performance boost of up to 180 Elo points beyond what is currently achievable with AlphaZero in chess. In addition to these improvements, our experimental results using the Integrated Gradient technique confirm the effectiveness of the newly introduced features. © 2024 The Authors.

关键词： Distribution transformers

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：