检索结果-内蒙古大学图书馆

Automation and Computation (AUTOCOM), International Conference on

作者： Hemant Kumar Yadav Varsha Srivastava Sreenu Banoth Rohini Daksh Rawat Priyansha Rawat Artificial Intelligence & Data Science IIMT College of Engineering Greater Noida Uttar Pradesh India School of Computer Science and Engineering IILM University Greater Noida Uttar Pradesh Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

In this study, an automated system to detect tomato leaf diseases with Convolutional Neural Networks (CNNs) is introduced to aid farmers in early disease identification to avert crop losses and promote yield. The dataset is multiple classes of tomato leaf diseases, and the model was trained and tuned extensively for its performance. In particular, a high training accuracy of 95.73%, and validation accuracy of 89.85% was achieved by CNN. Furthermore, the system's precision, recall, and F1 metric were around 90%, indicating the balanced system performance. The proposed CNN model is shown to be more reliable and faster than the traditional image processing techniques and hence is an appropriate choice for real time agricultural applications. The integration of modern varieties technologies in AI based approaches in agriculture which will feed productivity while maintaining Sustainability. The results indicate that the proposed system could provide an effective means for disease detection for farmers and may alleviate some of the need for chemical treatments.

关键词： Training Productivity Plant diseases Accuracy Crops Predictive models Agriculture Convolutional neural networks Monitoring Diseases

来源：评论

学校读者我要写书评

暂无评论

Iterative Intraoperative Digital Tomosynthesis Image Reconstruction using a Prior as Initial Image 7

Iterative Intraoperative Digital Tomosynthesis Image Reconst...

引用

7th International Conference on Image Formation in X-Ray Computed Tomography

作者： Saad, Fatima Frysch, Robert Pfeiffer, Tim Saalfeld, Sylvia Schulz, Jessica Georgi, Jens-Christoph Nürnberger, Andreas Lauritsch, Guenter Rose, Georg Institute for Medical Engineering Otto-von-Guericke University Magdeburg Germany Forschungscampus STIMULATE Otto-von-Guericke University Magdeburg Germany Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany Siemens Healthcare GmbH Forchheim Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany

ISBN: (纸本)9781510656697

The efficacy of interventional treatments highly relies on an accurate identification of the target lesions and the interventional tools in the guidance images. Whereas X-ray radiography poses low doses to the patient, its weakness is in the superposition of the different image structures in a 2D image. Cone-beam computed tomography (CBCT) might look ideal providing exact 3D information, however this is at the cost of a higher radiation dose, longer imaging time, and more space requirements in the operating room. Introducing some depth information with relatively low dose, and requiring less space, digital tomosynthesis (DTS) is a potential candidate for guiding interventions. However, due to the few number of projections and to the limited angle acquisition, DTS has poor depth resolution. Since high quality patient-specific prior CT scans are usually performed prior to the intervention for diagnosis or to plan the intervention, and given that such images share a fair amount of information with the intraoperative DTS images, we propose in this work a prior-based iterative reconstruction framework to improve the intraoperative DTS image quality. The framework is based on registering the prior CT image to an intermediate low-quality intraoperative DTS image, then iteratively re-reconstructing the intraoperative DTS image using the coregistered prior CT as the starting image. We acquired prior CT and intraoperative CBCT data of a liver phantom and simulated some intraoperative DTS projection images using a spherical ellipse scan geometry. Our results show a great improvement in the DTS image quality with the proposed method and prove the importance of choosing a good starting point for the iterative DTS reconstruction. © 2022 SPIE.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Enhancing Kidney Stone Detection in CT scans with Image Processing and the DCNN-RELM Model

Enhancing Kidney Stone Detection in CT scans with Image Proc...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： Manjula Prabakaran Jesmin Zakaria S Kannan Santhiya P Rajasekar P M V Rathnamma Department of CSE- (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Electronics & Communication Engineering (ECE) NSHM Knowledge Campus Durgapur West Bengal India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Tamilnadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India Department of CSE KLM College of Engineering for Women Kadapa Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

CT of the urinary system, abdomen, and kidneys appears to improve kidney stone screening and identification. Segmentation and categorization are essential for kidney stone identification and management. This work developed a semi-automatic kidney stone detection system using geometric concepts and image processing. The main steps include model training, feature extraction, segmentation, embossing, and preprocessing. Preprocessing involves trimming the input image to remove unnecessary kidney regions to focus on critical CT scan locations. Relief embossing enhanced image details. Segmentation accurately classified veins and blood arteries to define kidney stone boundaries. CNN-RELM was our training model. Compared to CNN and ELM models, the recommended system performed better. Its 93.87% kidney stone detection and localization accuracy prove its reliability. The semi-automatic system's reliable kidney stone detection and segmentation method may improve clinical decision-making. Combining CNN-RELM modeling and enhanced image processing could lead to medical imaging applications.

关键词： Training Image segmentation Accuracy Three-dimensional displays Computed tomography Embossing data preprocessing Reliability Kidney Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation

arXiv

引用

arXiv 2024年

作者： Islam, Md. Farhadul Zabeen, Sarah Manab, Meem Arafat Mahin, Mohammad Rakibul Hasan Mondal, Joyanta Jyoti Reza, Md. Tanzim Hasan, Md Zahidul Haque, Munima Sadeque, Farig Noor, Jannatun Research Group Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Biotechnology Program Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer and Information Sciences University of Delaware United States School of Law and Government Dublin City University Ireland Gina Cody School of Engineering and Computer Science Concordia University Canada

The majority of medical images, especially those that resemble cells, have similar characteristics. These images, which occur in a variety of shapes, often show abnormalities in the organ or cell region. The convolution operation possesses a restricted capability to extract visual patterns across several spatial regions of an image. The involution process, which is the inverse operation of convolution, complements this inherent lack of spatial information extraction present in convolutions. In this study, we investigate how applying a single layer of involution prior to a convolutional neural network (CNN) architecture can significantly improve classification and segmentation performance, with a comparatively negligible amount of weight parameters. The study additionally shows how excessive use of involution layers might result in inaccurate predictions in a particular type of medical image. According to our findings from experiments, the strategy of adding only a single involution layer before a CNN-based model outperforms most of the previous *** Codes 68T45 © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Collaborative Hybrid Human AI Learning through Conceptual Exploration 3

Collaborative Hybrid Human AI Learning through Conceptual Ex...

引用

3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2024

作者： Ganter, Bernhard Hanika, Tom Hirth, Johannes Obiedkov, Sergei Ernst-Schröder-Zentrum Karolinenpl. 5 Darmstadt64289 Germany TU Dresden Dresden01062 Germany Intelligent Information Systems Universisty of Hildesheim Universitätsplatz 1 Hildesheim31141 Germany Knowledge & Data Engineering Group University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Faculty of Computer Science cfaed ScaDS.AI TU Dresden Dresden01062 Germany

Conceptual Exploration is a sophisticated method for the interactive and structured acquisition of knowledge from experts. It is therefore particularly suitable for the use in hybrid settings where both humans and AIs act as experts. This article provides a brief summary of how conceptual exploration can be used in the context of Hybrid Human AI systems, as discussed within a tutorial during the third HHAI conference in Malmö, Sweden. We will recapitulate two small experiments that were carried out with the participants of the tutorial and their results. Finally, we give some pointers on how this promising link can be further researched. © 2024 Copyright for this paper by its authors.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Invertibility of Discrete-Time Linear Systems with Sparse Inputs

Invertibility of Discrete-Time Linear Systems with Sparse In...

引用

IEEE Conference on Decision and Control

作者： Kyle Poe Enrique Mallada René Vidal Applied Mathematics and Computational Science Group University of Pennsylvania PA Mallada is with the Department of Electrical and Computer Engineering Johns Hopkins University MD Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control.

关键词： Linear systems Geometry Sufficient conditions Systematics Heuristic algorithms Standards

来源：评论

学校读者我要写书评

暂无评论

Analysis Method for Probabilistic Verification for Smart IoT Systems with Process Algebra 5

Analysis Method for Probabilistic Verification for Smart IoT...

引用

5th IEEE International Conference on Smart Internet of Things, SmartIoT 2021

作者： Lee, Sunghyeon Song, Junsup Karagiannis, Dimitri Lee, Moonkun Department of Computer Science and Engineering Jeonbuk National University Jeonju Korea Republic of Research Group Knowledge Engineering University of Vienna Vienna Austria

ISBN: (纸本)9781665445115

Process algebra is one of the most suitable formal methods to model Smart IoT Systems to specify their probabilistic behavior, since process algebra may include probability property for choice operation as shown in PAROMA and PACSR, which, however, do not have enough analytic methods to control the degree of nondeterminism for the operation. In order to overcome the limitation, the paper presents a new formal method, called dTP-Calculus, and an analytic method to control the nondeterminism in order to satisfy safety and security requirements of the IoT systems. To prove the concept of the approach in the paper, the SAVE tool suite has been developed on the ADOxx and is used to demonstrate its applicability with a Smart EMS (Emergence Medical Service) Example. © 2021 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Book Recommendation System Using Hybrid Content and Collaborative Filtering Techniques

Book Recommendation System Using Hybrid Content and Collabor...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Urvi Gupta Tripti Singh Vidyush Singh Umang Rastogi Sushil Kumar Computer Science and Engineering KIET Group of Institutions Delhi-NCR Ghaziabad UP India CSE Department KIET Group of Institutions Delhi-NCR Ghaziabad India CSE Department Data Science & Deep Learning Lab KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In this study, the process of producing a book recommendation system outlines the process of finding books to provide personalized suggestions according to user preference and behavior. The system increases user satisfaction by tailoring recommendations to their tastes, improving the overall reading experience, and enhancing engagement and retention on plat-forms. It tracks users with collaborative filtering. interactions, content-based filtering to study book characteristics such as genre and author, and mixed methods to combine both techniques that will help ensure accuracy and circumvent similar limitations of cold start problems. Machine learning models-, pre-processing of data algorithms, and accuracy and recall measures of analysis ensure the effectiveness of the system in providing individualized and relevant recommendations that are 85% accurate. The findings reveal test the system's capacity to generate good suggestions, improving user experience and engagement. Improvements in the future could include real-time recommendations and broader feedback integration towards further optimization in accuracy and usability.

关键词： Measurement Accuracy Filtering Collaborative filtering User experience Real-time systems User preference Usability Recommender systems Optimization

来源：评论

学校读者我要写书评

暂无评论

Delayed Memory Unit: Modeling Temporal Dependency Through Delay Gate

引用

IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10808-10818页

作者： Pengfei Sun Jibin Wu Malu Zhang Paul Devos Dick Botteldooren Department of Information Technology WAVES Research Group Ghent University Ghent Belgium Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong SAR China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Recurrent neural networks (RNNs) are widely recognized for their proficiency in modeling temporal dependencies, making them highly prevalent in sequential data processing applications. Nevertheless, vanilla RNNs are confronted with the well-known issue of gradient vanishing and exploding, posing a significant challenge for learning and establishing long-range dependencies. Additionally, gated RNNs tend to be over-parameterized, resulting in poor computational efficiency and network generalization. To address these challenges, this article proposes a novel delayed memory unit (DMU). The DMU incorporates a delay line structure along with delay gates into vanilla RNN, thereby enhancing temporal interaction and facilitating temporal credit assignment. Specifically, the DMU is designed to directly distribute the input information to the optimal time instant in the future, rather than aggregating and redistributing it over time through intricate network dynamics. Our proposed DMU demonstrates superior temporal modeling capabilities across a broad range of sequential modeling tasks, utilizing considerably fewer parameters than other state-of-the-art gated RNN models in applications such as speech recognition, radar gesture recognition, ECG waveform segmentation, and permuted sequential (PS) image classification.

关键词： Logic gates Delays Delay lines Recurrent neural networks Long short term memory Computational modeling Computational efficiency Transformers Sun Speech recognition

来源：评论

学校读者我要写书评

暂无评论

IKGN: Intention-aware knowledge Graph Network for POI Recommendation

IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

引用

IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is available at https://***/Jungle123456/IKGN.

关键词：

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：