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共 16682 条记录，以下是171-180 订阅

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Multi-Scale Fusion and Saliency Suppression Network for Fine-Grained Visual Classification 13

Multi-Scale Fusion and Saliency Suppression Network for Fine...

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13th International conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Guo, Tong Wei, Zhuozhen Pang, Cheng Lan, Rushi Huang, Chaoyi Li, Jiahao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Nanning Research Institute Guilin University of Electronic Technology Nanning China

ISBN: (纸本)9798331516147

Fine-grained visual classification (FGVC) relies on accurately capturing subtle differences within similar categories, making it a challenging task in computer vision. Current methods often enhance classification by focusing on salient regions or by fusing multi-level features, but they encounter two main issues: (1) excessive reliance on the most prominent regions while overlooking other informative areas, and (2) limited flexibility in integrating features across different scales. To address these challenges, we propose the Multi-Scale Fusion and Saliency Suppression Network (MSFSN), a framework comprising two core modules that can be seamlessly integrated into convolution neural networks. First, we introduce a Multi-Scale Feature Fusion Module (MSFFM) that performs cross-scale feature integration to better capture contextual semantics and fine details. Second, we implement an Attention Suppression Module (ASM) to downplay the most salient features, encouraging the model to explore other potential discriminative regions. Our method does not require additional annotations, such as bounding boxes or key-points, and is designed for end-to-end training. Experimental evaluations on multiple fine-grained benchmarks demonstrate that our method achieves state-of-the-art performance. © 2025 IEEE.

关键词： Convolutional neural networks

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A Non-deep Approach to Classifying Movie Genres Based on Multimodal Data 13th

A Non-deep Approach to Classifying Movie Genres Based on M...

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13th International conference on Image Processing and Communications, IP and C 2023, 13th International conference on computer Recognition Systems, CORES 2023

作者： Niedziólka, Pawel Zyblewski, Pawel Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

ISBN: (纸本)9783031416293

Multimodal data processing has recently become popular due to technological advances and easier access to real video, audio, images, or text data. this data type is often processed using deep neural networks associated with high time and computational complexity. the present work addresses the problem of classifying a multimodal MM-IMDb dataset, representing the problem of recognizing a movie genre based on a poster and a brief description of the plot. For experiments, 20 binary subsets were separated, from which features were then extracted. Features from the text were obtained using the tf-idf method, while the posters were reduced to a single color. computer experiments on the resulting tabular data were conducted separately on both modalities, as in the concatenated feature space. the results confirmed that classical approaches to feature extraction could allow satisfactory quality classification of multimodal data to be obtained even when using relatively simple pattern recognition algorithms. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Network Model Based on the theory of the Strong Product Graphs 13

Network Model Based on the Theory of the Strong Product Grap...

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13th International conference on Electronics, Communications and networks, CECNet 2023

作者： Yin, Haoran Li, Feng Qian, Weimin College of Computer Science Qinghai Normal University Xi'ning810000 China

ISBN: (纸本)9781643684802

Strong product is an important and effective method for designing a large-scale interconnection network by using several small networks. the large network contains these small networks as its subnetworks and retains many good properties of these small networks. the fact that graphs are used to represent the topological structure of interconnection networks has been widely accepted and applied by computer science and engineering. Some topological structure parameters and properties of the large network constructed through strong product are closely related to that of factor networks. the main focus of this paper is to investigatethe topological structure of the strong product graph factor graph, and deduce the topological structure of the strong product graph. © 2024 the authors and IOS Press.

关键词： Directed graphs

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13th International conference on Mobile Computing, Applications, and Services, MobiCASE 2022

13th International Conference on Mobile Computing, Applicati...

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13th International conference on Mobile Computing, Applications, and Services, MobiCASE 2022

ISBN: (纸本)9783031318900

the proceedings contain 9 papers. the special focus in this conference is on Mobile Computing, Applications, and Services. the topics include: Centralized Multicasting AODV Routing Protocol Optimized for Intermittent Cognitive Radio Ad Hoc networks;youBrush: Leveraging Edge-Based Machine Learning in Oral Care;construction and Evaluation of a Return Prediction Model for One-Way Car Sharing;a Bi-directional Attribute Synchronization Mechanism for Access Control in IoT Environments;fault Tolerance in Cloud Manufacturing: An Overview;investigating the Adoption of Mobile Government from Citizen’s Perspectives in Saudi Arabia;Search by Pattern in GPS Trajectories.

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Compressing Deep Neural networks Using Explainable AI 13

Compressing Deep Neural Networks Using Explainable AI

引用

13th International conference on computer and Knowledge Engineering, ICCKE 2023

作者： Soroush, Kimia Raji, Mohsen Ghavami, Behnam Shiraz University School of Electrical and Computer Engineering Shiraz Iran Shahid Bahonar University of Kerman Department of Computer Engineering Kerman Iran

ISBN: (纸本)9798350330151

Deep neural networks (DNNs) have demonstrated remarkable performance in many tasks but it often comes at a high computational cost and memory usage. Methods to reduce data size, such as pruning and quantization are applied to reduce the memory footprint of DNNs and make it possible to accommodate them on resource-constrained edge devices. Recently, explainable artificial intelligence (XAI) methods have been introduced with the purpose of understanding and explaining AI methods. XAI can be applied to understand the internal operations of Deep Neural networks (DNNs), including the significance of various neurons and features in shaping the overall performance of DNNs. In this paper, a novel DNN compression approach using XAI is proposed to efficiently reduce the DNN model size with negligible accuracy loss. In the proposed approach, the importance score of DNN parameters (i.e. weights) are computed using a gradient-based XAI technique called Layer-wise Relevance Propagation (LRP). then, the scores are used to compress the DNN as follows: 1) the parameters with the negative or zero importance scores are pruned and removed from the model, 2) mixed-precision quantization is applied to quantize the weights with higher/lower score with higher/lower number of bits. the experimental results show that, the proposed compression approach reduces the model size by 64% while the accuracy is improved by 42% compared to the state-of-the-art XAI-based compression method. © 2023 IEEE.

关键词： Deep neural networks

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Security of Cloud-Assisted BANs Using Digital Twin 13

Security of Cloud-Assisted BANs Using Digital Twin

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13th International conference on Information Technology in Asia, CITA 2023

作者： Sama, Najm Us Zen, Kartinah Ud Din, Aziz Azim, Nazia Ur Rahman, Atiq Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Sarawak Kota Samarahan94300 Malaysia University of Peshawar Shaikh Zayed Islamic Center Peshawar Pakistan Abdul Wali Khan University Mardan Pakistan Higher Colleges of Technology Faculty of Computer Information Science United Arab Emirates

ISBN: (纸本)9798350326208

Wireless Sensor networks (WSNs) that monitor individuals' physical and medical conditions are called Body Area networks (BANs). this technology uses wireless sensors to monitor vital signs from the human body. In order to overcome processing and storage limitations, the BAN is often connected to the cloud. Wireless Body Area Network (WBAN), however, opens up the possibility of various security threats when using the cloud. the digital twin (DT) can provide users with hundreds of thousands of interpretations of the physical device without interfering with its normal functionality. It provides an accurate depiction of a cyberattack before time, which maximizes efficiency by reflecting and simulating the physical devices and their relationships with the environment. this paper proposes an integrated security framework for cloud-assisted BANs (CBANs), which will use DT to detect cyberattacks. By combining digital and physical models, cyber-physical security (CPS) can be optimized in advance at low risk and cost. the results of the study show that designing and evaluating techniques for the protection of CBAN using digital twin will help security experts to use an optimized solution without physical testing that will also save time and resources. © 2023 IEEE.

关键词： Wireless local area networks (WLAN)

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Application of ML in Agriculture Field With Its Enhancement Methods 13

Application of ML in Agriculture Field With Its Enhancement ...

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13th IEEE International conference on Communication Systems and Network Technologies, CSNT 2024

作者： Agrawal, Shobit Bhardwaj, Sonika Sharma, Nandini Mishra, Varun Gl Bajaj Group of Institutions Department of Computer Science and Engineering Uttar Pradesh Mathura India Gla University Department of Computer Engineering and Application Uttar Pradesh Mathura India

ISBN: (纸本)9798350305463

Food is essential for humanity and is primarily produced through agriculture. While addressing fundamental human needs, agriculture also serves as a global source of employment. Recent advancements in machine learning (ML) research have not only enhanced agricultural gains but also brought about additional benefits, including resource optimizations. ML, functioning as an advanced technology, provides farmers with detailed advice and insights into crop yield and resources optimization, aiding in the reduction of farming losses. this article comprehensively examines recent ML applications and trends in agriculture, showcasing its potential for more accurate and effective farming, yielding high-quality results with reduced human labor. the findings emphasize the considerable space for innovation in meeting the demands of businesses seeking sustainable and optimized agricultural practices. these technologies, can be crucially helpful in agriculture in automation Furthermore, the paper serves as a potential milestone for future research in various directions of agriculture. © 2024 IEEE.

关键词： Support vector machines

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Time Series Analysis by Bi-GRU for Forecasting Bitcoin Trends based on Sentiment Analysis 13

Time Series Analysis by Bi-GRU for Forecasting Bitcoin Trend...

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13th International conference on computer and Knowledge Engineering, ICCKE 2023

作者： Saadatmand, Fatemeh Zare Chahoki, Mohammad Ali Yazd University Department of Computer Engineering Iran

ISBN: (纸本)9798350330151

In the last few years, the cryptocurrency market, especially Bitcoin, has attracted many people, including machine learning engineers. they have heavy competition in predicting the price or the rise and fall of the price in the future. To achieve this goal, they used various types of approaches like Linear Regression, SVM, and deep learning methods like Neural networks, Recurrent neural networks like RNN, LSTM, and GRU, Bidirectional neural networks, and a combination of methods. M.L and D.L engineers used various types of information to feed their models especially emotional analysis of people. Emotions that people express on social networks, especially Twitter. the purpose of this paper is to introduce a new approach to Bitcoin trend prediction using deep learning algorithms. By sentiment analysis of extracted data from Twitter and tracking the previous price. the data collected for this research is between January 2012 and December 2020. this article compares LSTM, Bi-LSTM, GRU, and Bi-GRU algorithms to predict the trend of Bitcoin price changes. the Bi-GRU algorithm better performance by registering a record of 72% accuracy in predicting the trend of Bitcoin price changes and improving 20% the speed of the learning process. © 2023 IEEE.

关键词： Sentiment analysis

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T-CacheNet: Transformer-based Deep Reinforcement Learning for Next-Generation Internet Content Caching 24

T-CacheNet: Transformer-based Deep Reinforcement Learning fo...

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13th International conference on networks, Communication and Computing, ICNCC 2024

作者： Kim, Hakho Sun, Teh-Jen Huh, Eui-Nam Department of Artificial Intelligence Kyung Hee University Yongin Korea Republic of Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of

ISBN: (纸本)9798400717352

the rapid growth of internet users and mobile data traffic has led to increasing demand for efficient content caching strategies. Traditional caching algorithms such as Least Recently Used (LRU) and Least Frequently Used (LFU) struggle to adapt to dynamic environments, resulting in frequent cache misses, inefficient content retrieval, and degraded user experience. A key challenge in content caching is deciding both whether to cache newly requested content and which cached content to replace when capacity is limited. Reinforcement learning (RL)-based approaches offer a dynamic solution by continuously optimizing these decisions based on real-time content demand. In this paper, we propose T-CacheNet, a Transformer-based RL model for content caching, leveraging the self-attention mechanism to effectively manage long-term dependencies in content requests. We introduce two techniques - Miss-based delayed hit reward learning and partial information learning - to address the computational challenges of Transformer models while maintaining high efficiency in making caching and replacement decisions. T-CacheNet significantly improves adaptability in dynamic content delivery networks by increasing cache hit rates and preventing inefficient resource utilization. © 2024 Copyright held by the owner/author(s).

关键词： Deep reinforcement learning

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Predicting Early Possibilities of Breast Cancer Detection Using Machine Learning Techniques 13

Predicting Early Possibilities of Breast Cancer Detection Us...

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13th International conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Khan, Rubina Liyakat Quraishi, Suhail Javed Singh, Anupam George, Remya P. Ahmad, Nazia Hussain, Sajithunisa Imam Abdulrahman Bin Faisal University Applied College Dammam Saudi Arabia Mriirs Computer Applications Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350380583

Breast cancer is one of the imperative health concerns affecting women globally, and early diagnosis can help to enhance the prognosis for the cases and patients. the main concern of the current study is to use machine learning algorithms to analyze the dataset of features of breast cancer and predict whether or not they are malignant. In the pre-processing step, we implemented basic steps and on classification steps, we applied several classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and kNearest Neighbor. Among all the classifiers, the Random Forest classifier had the highest estimated accuracy. the paper also brings into prominence the features which are selected and the affect of different sorts of machine learning etc in the diagnosis of the disease. © 2024 IEEE.

关键词： Adversarial machine learning

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