检索结果-内蒙古大学图书馆

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Revathi, A. Poonguzhali, S. School of Computing Sciences Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350386578

Rice is the second-most significant cereal plant, next to wheat, and the most commonly cultivated staple food in India. Rice production has declined in recent years due to the spread of several rice diseases, changes in weather conditions, and soil quality. Rice blast, also known as RB, is the most devastating of all rice plant diseases and causes a vast amount of annual loss. The key challenge is to minimize crop loss due to rice blast disease while also increasing agricultural output. It is critical to predict the disease ahead of time so that the required safeguards can be taken. As manual prediction of rice blast disease requires a huge amount of labor and time, it is a must to implement an automated system using machine learning (ML) techniques, which are widely used artificial intelligence (AI) methods for prediction models. In the proposed system, the sample data set of 15,400 has been fed as an input to the ML algorithms via Nïve Bayes, random forest, KNN, and voting classifiers for prediction. The results show that the ensemble method of combining the machine learning algorithm-based prediction technique offers improved accuracy than the random forest algorithm, with an accuracy of 99.5%. The proposed methodology will greatly benefit agriculturalists with increased profits and safeguard food for the country. © 2024 IEEE.

关键词： Agriculture Artificial Intelligence Disease Prediction Machine Learning Supervised Learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Detection of Blood Cancer from Microscopic Images: A Comparative Study 9

Machine Learning-Based Detection of Blood Cancer from Micros...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Manoj, Milan Sreekumar, Vishnu Reghuram, Surya Anjali, T. Ramlal, Nandakishor Prabhu Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Altair Engineering Ab Stockholm Sweden

ISBN: (纸本)9798350377972

Early and accurate detection of blood cancer is critical for improving patient outcomes. This article is proposed to develop and evaluate the effectiveness of using machine learning techniques for automation in blood cancer detection based on microscopic images of blood cells. A comparison will be conducted using three of the most used models: Support Vector Machine, Decision Tree, and Random Forest on their capability in the classification process of distinguishing blood cells from benign and malig-nant groups. The methodology is divided into the following steps: image preprocessing, extraction of color histogram and texture features through the Gray-Level Co-occurrence Matrix (GLCM), and optimization of hyperparameters for model training. Evaluation metrics used are accuracy, precision, recall, and F1-score. Random Forest has been seen to perform with a high accuracy of 99.08% with all the categories showing robust classification. It was followed by SVM with an accuracy of 96.15 % and Decision Tree with 94.92%. The results present Random Forest as a valuable resource for the automated diagnosis of blood cancer. This paper reiterates the promise that machine learning holds in boosting diagnostic efficiency and encourages future studies into more advanced approaches such as deep learning for further improvement in clinical applications. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Graph Contrastive Learning for Truth Inference 40

Graph Contrastive Learning for Truth Inference

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Liu, Hao Liu, Jiacheng Tang, Feilong Li, Peng Chen, Long Yu, Jiadi Zhu, Yanmin Gao, Min Yang, Yanqin Hou, Xiaofeng Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong East China Normal University School of Data Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Aizu School of Computer Science and Engineering Japan Simon Fraser University School of Computing Science Canada

ISBN: (纸本)9798350317152

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques. © 2024 IEEE.

关键词： Crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

Rumor detection using dual embeddings and text-based graph convolutional network

引用

Discover Artificial Intelligence 2024年第1期4卷 1-15页

作者： Pattanaik, Barsha Mandal, Sourav Tripathy, Rudra M. Sekh, Arif Ahmed School of Computer Science and Engineering XIM University Odisha Bhubaneswar752050 India Department of Computer Science UiT The Arctic University Tromsø9017 Norway

Social media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumors. Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. Their success is due to their ability to identify structural patterns in rumors and effectively use neighborhood information. We present a novel rumor detection model using TextGCN, which utilizes a word-document graph to represent rumor texts. This model uses dual embedding from two pre-trained transformer models: generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). These embeddings serve as node representations within the graph, enhancing rumor detection. Combining these deep neural networks effectively extracts significant contextual features from rumors. This graph undergoes convolution, and through graph-based learning, the model detects a rumor. We evaluated our model using publicly available rumor datasets, such as PHEME, Twitter15, and Twitter16. It achieved 88.64% accuracy on the PHEME dataset, surpassing similar models, and performed well on Twitter15 and Twitter16 with accuracies of 81.98% and 83.41%, respectively. © The Author(s) 2024.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks 10

Accurate and Optimized Labelling of Fashion Products Through...

引用

10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Vithyatharshana, N. Thrinayani, Yedhoti Kulkarni, Yogeshwar Nitin Singh, Rimjhim Padam Kanchan, Sneha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. This paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. This data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A deep learning approach for image and text classification using neutrosophy

引用

International Journal of Information Technology (Singapore) 2024年第2期16卷 853-859页

作者： Wajid, Mohd Anas Zafar, Aasim Wajid, Mohammad Saif School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science Aligarh Muslim University Aligarh India School of Engineering and Sciences Tecnológico de Monterrey Monterrey Mexico

The proliferation of data on the web and on personal computers is a direct result of the proliferation of new technologies and gadgets. Most of these pieces of information are collected through a number of different methods (text, image, video, etc.). This type of information is also vital for e-commerce websites. The products on these websites feature both images and descriptions in text form, making them multimodal in nature. Earlier classification and information retrieval algorithms focused largely on a single modality. This study leverages multimodal data for categorization utilising neutrosophic fuzzy sets for uncertainty management for information retrieval tasks. This work employs image and text data and, inspired by prior ways of embedding text over an image, seeks to classify the images using neutrosophic classification algorithms. Neurosophic convolutional neural networks (NCNNs) are used to learn feature representations of the generated images for classification tasks. We demonstrate how a pipeline based on NCNN may be applied to learn representations of the unique fusion method. Traditional convolutional neural networks are subject to unexpected noisy conditions in the test phase, and as a result, their performance for the categorization of noisy data degrades. Comparing our technique against individual sources on multi-modal classification dataset provides good results. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Convolutional neutral network Early fusion Fuzzy logic Information retrieval Late fusion Multimodal data Neutrosophic logic

来源：评论

学校读者我要写书评

暂无评论

Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays 3

Explainable AI-based Detection and Interpretation of Abnorma...

引用

3rd International Conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Muthukumar, Sai Smrithi Gandham, Bhavika Vijayekkumaran, Trisha Jyotsna, C. Aiswariya Milan, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523893

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes. © 2025 IEEE.

关键词： CheXpert Explainable AI Grad-CAM ResNet X-ray masks X-rays

来源：评论

学校读者我要写书评

暂无评论

Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN) 6

Multi-Controller Architecture Design and Features in Softwar...

引用

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

作者： Prabhu, V. Balamurugan, P. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798331522667

Networking paradigm known as "Software-Defined Networking" (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can’t be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done. © 2025 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Automatic Skull Shape Completion of Defective Skulls Using Transformers for Cranial Implant Design 2

Automatic Skull Shape Completion of Defective Skulls Using T...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Resmi, S. Singh, Rimjhimpadam Palaniappan, Kannappan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electrical Engineering and Computer Science University of Missouri MO United States

Cranioplasty is a surgical method that restores the aesthetic and protecting function of a damaged skull by implanting material into the damaged *** and accurate design of patient specific cranial implants is very much required in the process of *** time consumption for designing and manufacturing of patient specific cranial implant has become an obstruction for cranioplasty procedures. Hence, a fully automatic and fast design of cranial implant becomes very important. The cranial implant design processmainly comprises of two steps. The former step concentrates on the automatic skull shape completion of defective skulls to fill the gaps and the cracks created in the skull. While the second step computes the difference between defective input and the completed skull for generating the implant. Currently computer aided design is used for the skull shape completion task which is a time consuming process. The application of deep learning techniques may result to faster and accurate skull shape completionwhich can be used for the design of patient specific cranial implants. This work proposes a novel approach combining 3D U-Net with Transformers for the automatic skull shape completion *** are using a vision transformer in the encoder section of the 3D U-Net architecture to consider the volumetric skull reconstruction as sequence- to-sequence prediction problem and to efficaciously grasp the global contextual information. The work also compares its performance with the famous variants of 3D U-Net deep network model namely, 3D U-Net and 3D U-Net with attention. From the values resulted for the dice coefficient metric it is clear that the proposed 3D U-Net with transformer approach performs better than the other two models on test images. © 2024 Elsevier B.V.. All rights reserved.

关键词： computer aided design

来源：评论

学校读者我要写书评

暂无评论

Pneumonia Disease Assessment with Full Diagnostics Evaluation 2

Pneumonia Disease Assessment with Full Diagnostics Evaluatio...

引用

2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Nithin, Rayala Nagarajan, G. Sai Prathap, Thammishetti Venkat Pradeep, Srikonda Sampreeth, Itharaju School of Computing Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Anand Nagar Krishnankoil Virudhnagar India

ISBN: (纸本)9798350384277

Pneumonia remains a critical global health issue, causing significant illness and mortality, especially in vulnerable populations. Early and precise diagnosis is essential for effective management and timely the intervention. This research paper introduces a novel integrated approach for the prediction and comprehensive diagnostics of pneumonia, harnessing cutting-edge advancements in medical imaging, machine learning, and clinical data analysis. The proposed framework merges three fundamental components: (1) Chest X-ray Image Analysis, (2) Clinical Data Integration, and (3) Machine Learning-Based Prediction. The chest X-ray image analysis employs state-of-the-art deep learning techniques to automatically identify and classify pneumonia-related anomalies, offering detailed insights into pulmonary conditions. Simultaneously, clinical data, encompassing patient history, demographics, and laboratory results, are integrated into the predictive model. A hybrid machine learning algorithm, combining Convolutional Neural Networks (CNN) and Gradient Boosting, is utilized to ensure robust and precise disease prediction. The research demonstrates the efficacy of this integrated approach through a thorough evaluation on a substantial dataset of pneumonia cases. Findings indicate a notable enhancement in pneumonia prediction accuracy compared to conventional diagnostic methods. Moreover, the model's interpretability is enhanced, allowing healthcare professionals to scrutinize the rationale behind each prediction, promoting trust and transparency in the decision-making process. © 2024 IEEE.

关键词： Support vector machines

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：