检索结果-内蒙古大学图书馆

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Mahmud, Tanjim Hasan, Imran Aziz, Mohammad Tarek Rahman, Taohidur Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350327533

The rapid dissemination of information across social networks, online news portals, and digital platforms has given rise to a pervasive issue - fake news. This misinformation, deliberately propagated by groups with ulterior motives, has the potential to divert societies in alarming directions. In a digital landscape inundated with millions of daily stories, distinguishing fact from fabrication has become a formidable challenge, casting a dark cloud of uncertainty over the information age. In response to this critical issue, this paper presents an innovative approach for enhanced fake news detection through the fusion of deep learning and Repeat Vector representations. In an era where individuals of all ages increasingly rely on online news sources, the dissemination of misinformation has become an alarming norm. It is a challenge to discern deceptive narratives that aim to sow discord and create undue *** confront this challenge head-on, we propose a deep neural network model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures using the Repeat Vector method. This hybrid model excels in deciphering complex patterns within 1D data, effectively tackling the menace of fake news. Our rigorous evaluation demonstrates that our proposed model outperforms contemporary methods, achieving an impressive validation accuracy of up to 98.94% on our *** a world increasingly plagued by misinformation, our approach represents a significant stride toward safeguarding the integrity of information in the digital age. © 2024 IEEE.

关键词： Autoencoder Technique Convolutional Neural Network Deep Learning Long Short-Term Memory

来源：评论

学校读者我要写书评

暂无评论

Plant Disease Detection in Precision Agriculture: Deep Learning Approaches 2

Plant Disease Detection in Precision Agriculture: Deep Learn...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Dey, Puja Mahmud, Tanjim Nahar, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Department of Computer Science and Engineering Dhaka1212 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350327533

Plants affect economy, agriculture industry and climate of any country. Therefore it is very important to take care of plants. Like human being, plants are also infected by various disease that are resulted from virus, bacteria, and fungi. It is necessary to identify these diseases in time and cure them immediately to counteract the destruction of whole plant. In recent times, deep learning networks have attained tremendous improvement in classifying image and detecting object. Therefore, in our research, some pre-Trained Convolution Neural Networks (AlexNet, VGG16, VGG19) have been utilised along with transfer learning for detecting plant diseases. First of all, pre-processing technique has been applied to improve image quality and increase the accuracy. After the training process, testing has been done for validating the results. To train these models, PlantVillage dataset has been used which contains both diseased and healthy leaves. 80% data has been utilised in training step and other 20% has been used in testing. In this research, precision, recall and f1-score are also computed besides the accuracy. Using AlexNet, VGG16, and VGG19 96.63%, 95.05%, and 95.22% testing accuracy have been achieved respectively. As we can see that among all models the modified AlexNet performed best and got 96.63% as accuracy, 92 % as precision, 91 % as recall, and 91 % as f1-score. © 2024 IEEE.

关键词： Deep Learning Fully Connected Histogram equalization Pooling RELU

来源：评论

学校读者我要写书评

暂无评论

AutoSketch: Automatic Sketch-Oriented Compiler for Query-driven Network Telemetry 21

AutoSketch: Automatic Sketch-Oriented Compiler for Query-dri...

引用

21st USENIX Symposium on Networked Systems Design and Implementation, NSDI 2024

作者： Sun, Haifeng Huang, Qun Sun, Jinbo Wang, Wei Li, Jiaheng Li, Fuliang Bao, Yungang Yao, Xin Zhang, Gong National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Institute of Computing Technology CAS China Northeastern University China Huawei Theory Department

ISBN: (纸本)9781939133397

Recent network telemetry witnesses tremendous progress in two directions: query-driven telemetry that targets expressiveness as the primary goal, and sketch-based algorithms that address resource-accuracy trade-offs. In this paper, we propose AutoSketch that aims to integrate the advantages of both classes. In a nutshell, AutoSketch automatically compiles high-level operators into sketch instances that can be readily deployed with low resource usage and incur limited accuracy drop. However, there remains a gap between the expressiveness of high-level operators and the underlying realization of sketch algorithms. AutoSketch bridges this gap in three aspects. First, AutoSketch extends its interface derived from existing query-driven telemetry such that users can specify the desired telemetry accuracy. The specified accuracy intent will be utilized to guide the compiling procedure. Second, AutoSketch leverages various techniques, such as syntax analysis and performance estimation, to construct efficient sketch instances. Finally, AutoSketch automatically searches for the most suitable parameter configurations that fulfill the accuracy intent with minimum resource usage. Our experiments demonstrate that AutoSketch can achieve high expressiveness, high accuracy, and low resource usage compared to state-of-the-art telemetry solutions. © 2024 Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation, NSDI 2024. All rights reserved.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

Implementation of a Tunisian Coin Recognition Embedded System with Deep Learning

Implementation of a Tunisian Coin Recognition Embedded Syste...

引用

2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Kessentini, Amina Ghanem, Rania Mouhamed, Tasnim Ben Masmoudi, Nouri National Engineering School of Sfax University of Sfax Laboratory of Electronics and Information Technologies Safx Tunisia Higher Institute of Computer Science and Multimedia Gabes Department of industrial computing Gabes Tunisia

ISBN: (纸本)9798350389838

Managing a large volume of coins poses a complex challenge in various fields, particularly within Tunisian administrations, specifically in terms of counting and classification. In this case, several coin recognition systems are distinguished, such as systems based on mechanical, electromagnetic, and image processing methods. However, these systems may suffer from accuracy and reliability shortcomings and are sensitive to deception. To avoid these problems, we have developed an intelligent system based on convolutional models (CNN) with the aim of automating coin detection, classification, and counting. Additionally, we have expanded our database by integrating a generative neural network model (GAN). The main goal of our paper is to develop a robust and precise solution capable of efficiently processing a large volume of coins quickly and presenting the results in a mobile application connected to the system. Our work combines the latest technologies to assist Tunisian administrations in better managing their finances. The accuracy result has achieved up to 97.8 % with real time detection. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Auto Encoding Neural Process for Multi-interest Recommendation 39

Auto Encoding Neural Process for Multi-interest Recommendati...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jiang, Yiheng Xu, Yuanbo Yang, Yongjian Yang, Funing Wang, Pengyang Li, Chaozhuo Lab of Mobile Intelligent Computing College of Computer Science and Technology Jilin University China Department of Computer and Information Science The State Key Laboratory of Internet of Things for Smart City University of Macau China Beijing University of Aeronautics and Astronautics China

ISBN: (纸本)157735897X

Multi-interest recommendation constantly aspires to an oracle individual preference modeling approach, one that satisfies the diverse and dynamic properties. Fueled by deep learning technologies, existing neural network (NN)-based recommender systems employ single-point or multi-point interest representation strategies for preference modeling, and boost the recommendation performance with remarkable margins. However, as the parameterized approximate function nature, NN-based methods remain deficiencies regarding the adaptability towards distinctive preference patterns cross different users, and the calibration over individual current intents. In this paper, we revisit multi-interest recommendation with the lens of stochastic process and Bayesian inference. Specifically, we propose to learn a distribution over functions to depict the individual diverse preferences rather than a unified function to approximate preference. Subsequently, the recommendation is equipped with the uncertainty estimation which conforms to the dynamic shifting intent. Along these lines, we establish the connection between multi-interest recommendation and neural processes by proposing NP-Rec, which realizes the flexible multiple interests modeling and uncertainty estimation, simultaneously. Empirical study on 4 real world datasets demonstrates that our NP-Rec attains superior recommendation performances to several state-of-the-art baselines, where the average improvement achieves up to 13.94%. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Instruction Tuning for Segment Anything Model 24

Deep Instruction Tuning for Segment Anything Model

引用

32nd ACM International Conference on multimedia, MM 2024

作者： Huang, Xiaorui Luo, Gen Zhu, Chaoyang Tong, Bo Zhou, Yiyi Sun, Xiaoshuai Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Fujian Xiamen China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400706868

Recently, Segment Anything Model (SAM) has become a research hotspot in the fields of multimedia and computer vision, which exhibits powerful yet versatile capabilities on various (un) conditional image segmentation tasks. Although SAM can support different types of segmentation prompts, we note that, compared to point- and box-guided segmentations, it performs much worse on text-instructed tasks, e.g., referring image segmentation (RIS). In this paper, we argue that deep text instruction tuning is key to mitigate such shortcoming caused by the shallow fusion scheme in its default light-weight mask decoder. To address this issue, we propose two simple yet effective deep instruction tuning (DIT) methods for SAM, one is end-to-end and the other is layer-wise. With minimal modifications, DITs can directly transform the image encoder of SAM as a stand-alone vision-language learner in contrast to building another deep fusion branch, maximizing the benefit of its superior segmentation capability. Extensive experiments on three highly competitive benchmark datasets of RIS show that a simple end-to-end DIT can improve SAM by a large margin, while the layer-wise DIT can further boost the performance to state-of-the-art with much less data and training expenditures. Our code is released at: https://***/wysnzzzz/DIT. © 2024 ACM.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

An Approach of Analyzing Classroom Student Engagement in Multimodal Environment by Using Deep Learning 9

An Approach of Analyzing Classroom Student Engagement in Mul...

引用

9th IEEE International Women in Engineering (WIE) Conference on Electrical and computer Engineering, WIECON-ECE 2023

作者： Nuha, Nishat Sultana Mahmud, Tanjim Rezaoana, Noortaz Hossain, Mohammad Shahadat Andersson, Karl University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Sweden

ISBN: (纸本)9798350319651

Student engagement refers to the level of involve-ment, interest, attention, and active participation that students demonstrate during their learning experiences. As the engage-ment rate increases the learning rate, teachers always try to mea-sure the engagement rate in the classroom in various ways. This paper introduces a multimodal approach for detecting student engagement, a vital aspect of effective learning. Leveraging Convolutional Neural Networks (CNNs), video and audio data are integrated to capture comprehensive insights into engagement levels. The objective is to classify student engagement into three categories: highly engaged, engaged, and not engaged. Separate unimodal models are employed for speech and video data., achieving notable accuracies of 92.87 % and 94.77 %, respectively, in recognizing engagement levels. The combined model, achieved through feature-level fusion, yields a competitive accuracy of 69.36%, benefiting from the strengths of both modalities. Al-though the combined model exhibits slightly lower test accuracy compared to the individual speech and video models, its enhanced interpretability and comprehensive insights make it a promising avenue for future research. © 2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Learning Order Forest for Qualitative-Attribute Data Clustering 27

Learning Order Forest for Qualitative-Attribute Data Cluster...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Mingjie Feng, Sen Zhang, Yiqun Li, Mengke Lu, Yang Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781643685489

Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit cluster distributions reflected by qualitative attribute values, e.g., the nominal values of attributes like symptoms, marital status, etc. This paper, therefore, discovered a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values. That is, treating a value as the vertex of the tree allows to capture rich order relationships among the vertex value and the others. To obtain the trees in a clustering-friendly form, a joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters. It turns out that the latent distance space of the whole dataset can be well-represented by a forest consisting of the learned trees. Extensive experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results. Comparisons of 10 counterparts on 12 real benchmark datasets with significance tests verify the superiority of the proposed method. Source code of the proposed method is available at [39]. © 2024 The Authors.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Explainable AI for Tomato Leaf Disease Detection: Insights into Model Interpretability 26

Explainable AI for Tomato Leaf Disease Detection: Insights i...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Mahmud, Tanjim Barua, Koushick Barua, Anik Basnin, Nanziba Das, Sudhakar Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Leeds Beckett University Department of Computer Science and Engineering United Kingdom University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå971 87 Sweden

ISBN: (纸本)9798350359015

Plant diseases pose a significant threat to global food security by limiting access to safe and abundant food sources while impacting agricultural productivity and food safety. To address this challenge, innovative disease detection techniques are crucial. This paper presents a novel approach to tomato leaf disease detection grounded in deep transfer learning. We employ convolutional neural networks (CNNs) to recognize and categorize diseases, leveraging pre-trained models such as EfficientNetB3, Xception, and mobileNetV2. Our experimental results highlight the efficacy of these models, with the CNN achieving an accuracy of 0.92, mobileNetV2 at 0.93, Xception at 0.94 and EfficientNetB3 at 0.993. By combining these machine learning techniques with image processing methods, we create a system capable of diagnosing tomato leaf diseases promptly. The system employs image processing techniques to facilitate disease recognition, enabling automated disease identification as soon as symptoms manifest on leaves or plants. This empowers farmers with timely information and appropriate treatment options, ultimately leading to precise disease management. Furthermore, our research delves into model interpretability by employing Saliency maps and Grad-CAM techniques, providing valuable insights into the decision-making process of our deep learning models. This transparency enhances the trustworthiness of the AI-driven disease detection system, fostering its practical application in agriculture. © 2023 IEEE.

关键词： Convolutional neural network Image diagnosis Image processing Machine learning techniques

来源：评论

学校读者我要写书评

暂无评论

CDGNet: A Cross-Time Dynamic Graph-Based Deep Learning Model for Vehicle-Based Traffic Speed Forecasting

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Yuchen Luo, Haiyong Zhao, Fang Sun, Poly Z.H. Qin, Yanjun Zeng, Liang Hui, Bo Wang, Chenxing School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Industrial Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Tsinghua University Beijing China University of Tulsa Tulsa USA

Vehicle-based traffic speed forecasting aims to predict the average speed of vehicles on the road in the future, which is an essential side information in intelligent vehicles and beneficial to safe autonomous driving, yet is very challenging due to the complex and dynamic spatio-temporal dependencies in real-world vehicle-based traffic. To extract intricate correlations among multiple vehicle-based speed time series, previous methods have used graph convolution networks. However, the conventional static and dynamic graphs fail to reflect the traffic evolution and the hysteresis spatial influence caused by vehicle movement. To address this issue, we propose a novel cross-time dynamic graph-based deep learning model, named CDGNet, for vehicle-based traffic speed forecasting. The model is able to effectively capture hysteresis spatial dependencies between each time slice and its historical time slices through the cross-time dynamic graph-based GCN. Meanwhile, a gating mechanism is integrated into our cross-time dynamic graph, which conforms to the sparse correlation in the real world. Besides, GCNs are incorporated into a novel encoder-decoder architecture to forecast multi-step speed. Experimental results on three real-world vehicle-based traffic speed datasets demonstrate the superiority of our CDGNet over various state-of-the-art spatio-temporal forecasting methods and the effectiveness of each component. We additionally provide a visualization of our cross-time dynamic graph to show the capability of assisting intelligent vehicles to avoid congestion. IEEE

关键词： Forecasting

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：