检索结果-内蒙古大学图书馆

Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

引用

Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Correction to: Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions (International Journal of Intelligent Robotics and Applications, (2024), 10.1007/s41315-024-00333-2)

引用

International Journal of Intelligent Robotics and Applications 2024年第1期9卷 365-373页

作者： Stefanopoulou, Aliki Raptis, Emmanuel K. Apostolidis, Savvas D. Gkelios, Socratis Kapoutsis, Athanasios Ch. Chatzichristofis, Savvas A. Vrochidis, Stefanos Kosmatopoulos, Elias B. Information Technologies Institute The Centre for Research & ampTechnology Hellas 6th km Harilaou - Thermis Thessaloniki57001 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Kimmeria Campus Xanthi67100 Greece Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos 2 Danais Avenue PafosCY 8042 Cyprus

In the original publication, the author has found few errors in the figures 2, 4, 11 and 12 which significantly impact both the paper's quality and the reputation of the journal. These issues undermine the clarity of the methodology and the novelty of the research. 1. Each column represents a different case in the experiment, corresponding to the number of UAVs in both the obstacle-free and obstacle areas. However, in Fig. 2, the first row contains identical subfigures across all columns, while the subfigures in the remaining rows are identical to each other The incorrect Fig. 2 appears as below: (Figure presented.) However the corrected Fig. 2 should appear as below. (Figure presented.) 2. In Fig. 4, every row showcase the average QoF for the testing scenarios (obstacle-free and obstacle area). However the first row has the exact same figures with the second row. The incorrect Fig. 4 appears as below: (Figure presented.) However the corrected Fig. 4 should appear as below. (Figure presented.) 3. Figures 11 and 12 showcase the distribution of each Quality of Flight (QoF) metric value for every UAV in the conducted trials during the testing scenarios. However, Fig. 11 is the same with Fig. 12. The incorrect Figs. 11 and 12 appears as below: (Figure presented.) However the corrected Figs. 11 and 12 should appear as below. (Figure presented.) Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle-free area Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle area The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Powered Edge Analytics for IoT-based Sensor Networks 2

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Subramani, Ponnusamy Selvam, M. Panneer Sworna Kokila, M.L. Vijayalakshmi, R. Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Physics Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India School of Computing SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Design and Analysis of various Computational Control Algorithms for a Multivariable Process 3

Design and Analysis of various Computational Control Algorit...

引用

3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Zrigan, Ahmed F. Janani, R. Olwane, Mahmood M. Shanab, Walid T. Libyan Academy for Postgraduate Studies Department of Electrical & Computer Engineering Tripoli Libya Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Department of Electronics and Instrumentation Engineering Tamilnadu Kanchipuram India Higher Institute of Engineering Technologies Department of Electronics Technology Tripoli Libya

ISBN: (纸本)9798350319897

This Paper focuses on the application of Particle Swarm Optimization (PSO) in analyzing, controlling, and comparing two models of multivariable PID controllers in the context of distillation columns. The PSO algorithm, frequently used in PID implementation, is based on heuristic approach and experience to deliver superior performance in the industry. However, for stability and interaction analysis, the Relative Gain Array and Niederlinski index are utilized respectively, for the multivariable "system first order plus time delay"systems. To prevent unfavorable interactions, a decoupled system and full matrix controller are created for one of these systems. The ultimate goal is to achieve the optimum outcome possible through the use of PSO and other analysis methods. © 2023 IEEE.

关键词： Distillation columns

来源：评论

学校读者我要写书评

暂无评论

Cryptanalysis of DBST, a lightweight block cipher

引用

Frontiers of computer Science 2024年第4期18卷 219-221页

作者： Sadegh SADEGHI Nasour BAGHERI Department of Mathematics Institute for Advanced Studies in Basic Sciences(IASBS)Zanjan 45137-66731Iran Research Center for Basic Sciences and Modern Technologies(RBST) Institute for Advanced Studies in Basic Sciences(IASBS)Zanjan 45137-66731Iran Electrical Engineering Department Shahid Rajaee Teacher Training UniversityTehran 16788-15811Iran School of Computer Science(SCS) Institute for Research in Fundamental Sciences(IPM)Tehran 19395-5531Iran

1 Introduction Conventional cryptographic primitives are not suitable for resource-constrained ***,numerous lightweight primitives have been proposed over the past decade,specifically tailored for such ***,such ciphers would not be used in a real-world application unless they were thoroughly tested for security against existing *** this direction,DBST[1]cipher has been recently proposed by Yan et *** balance security,cost,and efficiency,DBST has implemented a novel 4-branch generalized Feistel variant *** this structure,a 2-branch XOR operation has been incorporated in the middle section,establishing interconnections between all *** the other hand,by leveraging the bit-slice technique and subkeys,the algorithm introduces an innovative approach to constructing a dynamic S-box *** designers of DBST cipher have extensively investigated its resistance to known attacks such as linear and differential cryptanalysis,as well as impossible differential cryptanalysis,among *** them,the objective of differential cryptanalysis[2]is to identify high-probability characteristics in the plaintext that are transmitted to the ciphertext,known as distinguishers.

关键词： structure. primitive branch

来源：评论

学校读者我要写书评

暂无评论

Soft Sensors for Industrial Applications: Comparison of Variables Selection Methods and Regression Models

Soft Sensors for Industrial Applications: Comparison of Vari...

引用

2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Leiria, Joana Salles, Rodrigo Mendes, Jerome Sousa, Pedro University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal Oncontrol Technologies Rua Cidade Poitiers Coimbra3000-108 Portugal

ISBN: (纸本)9798350347074

The digitalization of industrial environments has enabled the development of tools that make the production process more efficient and safer. In this sense, the Soft Sensor (SS) plays a fundamental role. Through historical data and indirect measurements, it is possible to estimate the value of important variables that are difficult to measure. This paper presents the SS development process: data collection and pre-processing, variable selection, model selection for SS implementation, model training and testing, and performance evaluation. The selection of variables was made with the help of Pearson Correlation, Mutual Information, and fastTracker algorithm techniques. For the implementation of SS have been tested several models: Multiple Linear Regression, Ridge Regression, Least Absolute Shrinkage and Selection Operator, Elastic Net, Support Vector Regression and Gaussian Mixture Models. Four datasets were used to test the development of the SS. © 2023 IEEE.

关键词： Correlation methods

来源：评论

学校读者我要写书评

暂无评论

Rural Ecosystem Monitoring in Food Security Analysis Based on Sustainable Agriculture: Artificial Intelligence Application

引用

Remote Sensing in Earth Systems Sciences 2024年第1期8卷 56-64页

作者： M, Mohideen AbdulKader Kumaran, M. Senthil Keerthika, Vijay Reddy, Polu Srinivasa Rajendra, Alla R, Subbulakshmi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India Department of Artificial Intelligence and Machine Learning (AI & amp ML) MLR Institute of Technology Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad 500043 India Department of Civil Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore 641105 India

The ecological security patterns (ESPs) in the Qinling Mountains are facing significant challenges due to the worldwide issues of harsh climate and urbanization. Achieving sustainable development in China requires an understanding of the features of ESPs in the Qinling Mountains, an important ecological barrier. With a Yangxian focus, this work makes use of techniques like geographic information systems (GISs), remote sensing, and machine learning (ML). This research proposed a novel technique in rural region ecosystem monitoring-based food security based on sustainable agriculture using machine learning in artificial intelligence application. Here, input is collected as food security analysis for rural region ecosystem monitoring as well as processed for noise removal and normalization. Then, this data features are extracted and classified using reinforcement radial Gaussian encoder with adversarial Boltzmann temporal neural networks. Experimental analysis is carried out for parameters like training accuracy, random precision, sensitivity, AUC, and F-1 score. The proposed method obtained 98% of training accuracy, 96% of sensitivity, 95% of F-1 score, 97% of AUC, and 93% of random precision. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial intelligence application Ecosystem monitoring Food security Machine learning Rural region Sustainable agriculture

来源：评论

学校读者我要写书评

暂无评论

A Large-scale AI-generated Image Inpainting Benchmark

arXiv

引用

arXiv 2025年

作者： Giakoumoglou, Paschalis Karageorgiou, Dimitrios Papadopoulos, Symeon Petrantonakis, Panagiotis C. Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece Information Technologies Institute CERTH Greece

Recent advances in generative models enable highly realistic image manipulations, creating an urgent need for robust forgery detection methods. Current datasets for training and evaluating these methods are limited in scale and diversity. To address this, we propose a methodology for creating high-quality inpainting datasets and apply it to create DiQuID, comprising over 95,000 inpainted images generated from 78,000 original images sourced from MS-COCO, RAISE, and OpenImages. Our methodology consists of three components: (1) Semantically Aligned Object Replacement (SAOR) that identifies suitable objects through instance segmentation and generates contextually appropriate prompts, (2) Multiple Model Image Inpainting (MMII) that employs various state-of-the-art inpainting pipelines primarily based on diffusion models to create diverse manipulations, and (3) Uncertainty-Guided Deceptiveness Assessment (UGDA) that evaluates image realism through comparative analysis with originals. The resulting dataset surpasses existing ones in diversity, aesthetic quality, and technical quality. We provide comprehensive benchmarking results using state-of-the-art forgery detection methods, demonstrating the dataset’s effectiveness in evaluating and improving detection algorithms. Through a human study with 42 participants on 1,000 images, we show that while humans struggle with images classified as deceiving by our methodology, models trained on our dataset maintain high performance on these challenging cases. Code and dataset are available at https://***/mever-team/DiQuID. © 2025, CC BY.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

引用

Recent Advances in computer Science and Communications 2024年第4期17卷 1-19页

作者： Govardanan, Chemmalar Selvi Murugan, Ramalingam Yenduri, Gokul Gurrammagari, Deepti Raj Bhulakshmi, Dasari Kandati, Dasaradharami Reddy Supriya, Yarradoddi Gadekallu, Thippa Reddy Rathore, Rajkumar Singh Jhaveri, Rutvij School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science Cardiff School of Technologies Cardiff Metropolitan University Llandaff Campus Western Avenue CardiffCF5 2YB United Kingdom Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382007 India Lovely Professional University Phagwara India College of Information Science and Engineering Jiaxing University 314001 China Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Zhongda Group Zhejiang Province HaiyanCounty Jiaxing City314312 China

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems. From peripheral devices that monitor vital signs to remote patient monitoring systems and smart hospitals, IoMT provides a vast array of applications that empower healthcare professionals. However, the integration of IoMT presents numerous obstacles, such as data security, privacy concerns, interoperability, scalability, and ethical considerations. For the successful integration and deployment of IoMT, addressing these obstacles is essential. Federated Learning (FL) permits collaborative model training while maintaining data privacy in distributed environments like IoMT. By incorporating Explainable Artificial Intelligence (XAI) techniques, the resulting models become more interpretable and transparent, enabling healthcare professionals to comprehend the underlying decision-making processes. This integration not only improves the credibility of Artificial Intelligence models but also facilitates the detection of biases, errors, and peculiar patterns in the data. The combination of FL and XAI contributes to the development of more privacy-preserving, trustworthy, and explainable AI systems, which are essential for the development of dependable and ethically sound IoMT applications. Hence, the aim of this paper is to conduct a literature review on the amalgamation of FL and XAI for IoMT. © 2024 Bentham Science Publishers.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Enhancing Mobile Multimedia Trustworthiness through Federated AI-based Content Authentication: Enhancing Mobile Multimedia

引用

Journal of Mobile Multimedia 2023年第6期19卷 1415-1438页

作者： Rajesh, M. Vengatesan, K. Sitharthan, R. Dhanabalan, Shanmuga Sundar Gawali, Mahendra Bhatu Department of Computer Engineering Sanjivani College of Engineering India Centre for Smart Grid Technologies School of Electrical Engineering Vellore Institute of Technology Chennai India Functional Materials and Microsystems Research Group RMIT University Melbourne 3001 VIC Australia Department of Information Technology Sanjivani College of Engineering India

The rapid proliferation of mobile devices and multimedia content has led to an increased need for ensuring trustworthiness and authentication of the shared data. Traditional centralized methods have proven to be insufficient in maintaining privacy and addressing scalability issues. This paper presents a novel approach to enhancing mobile multimedia trustworthiness through the application of Federated AI-based content authentication techniques. By leveraging the benefits of distributed machine learning and edge computing, our proposed framework efficiently authenticates multimedia data while preserving user privacy and reducing latency. Our system employs a federated learning model that trains AI algorithms on local devices, allowing them to collaboratively build a robust and accurate authentication model. Additionally, this research introduces a blockchain-based decentralized trust management system to further enhance the integrity and traceability of the authentication process. Through extensive evaluations, this research demonstrate that our proposed framework significantly improves the trustworthiness of mobile multimedia content while minimizing the overhead and resource consumption associated with traditional centralized approaches. © 2023 River Publishers.

关键词： content authentication decentralized trust management Federated AI mobile multimedia trustworthiness

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：