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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是4811-4820 订阅

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A Novel Approach Combining Decoupling Training with Prototype-based Contrastive learning for Tracking Long-Tailed Recording in Optical Aerial Imagery 2

A Novel Approach Combining Decoupling Training with Prototyp...

引用

2nd IEEE international conference on Signal, Information and data Processing, ICSIDP 2024

作者： Xie, Jianlin Wang, Guanqun Zhang, Tong Fan, Shilong Zhuang, Yin Chen, He Beijing China Peking University National Key Laboratory for Multimedia Information Processing Beijing China

ISBN: (纸本)9798331515669

Scene classification is a classical task in optical aerial imagery. In practical applications, it is difficult to obtain a balanced training samples supporting model training. Therefore, the dataset usually follow the Long-Tailed distribution, which is challenge for classification task, especially for multi-categories scene classification. It would make model intend to learn the representation of head classes with majority samples, which lead to under-represent middle and tail classes with minority samples and affect a better classifier construction for long-tailed classification. In this article, a novel Prototypical Supervised Contrastive learning Based on Decoupling Framework called PSCL-DF is proposed for Long-Tailed Classification in Optical Remote Sensing Imagery to make a better feature learning and better classifier leaning. Finally, extensive tests are performed on the two artificially generated long-tailed datasets, demonstrating the robustness and effectiveness of the proposed methods. Our results show that decoupling feature extraction and classification achieve significant performance gains. © 2024 IEEE.

关键词： Contrastive learning

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Collaborative Resource Allocation for Blockchain-Enabled Internet of Things with Multi-Agent Deep Reinforcement learning

Collaborative Resource Allocation for Blockchain-Enabled Int...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Xinyu Wan, Jianxiong Li, Leixiao Liu, Chuyi Si, Xiaowei School of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Ho-hhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Taiwan

ISBN: (纸本)9781665410205

Mobile Edge computing (MEC) reduces service latency and enhances Quality of Service (QoS) by offloading tasks to the wireless network edge. However, the rapid growth of task offloading and the associated data transmission security challenges deserve further investigation. This study proposes a blockchain-MEC hybrid solution where mobile devices process tasks and engage in block mining to boost system utility. The objective is to maximize the accumulated reward in the blockchain-MEC system by optimizing off loading decisions, channel selection, transmission power, computing resources, and block intervals. A Markov Decision Process is formulated to model the optimization problem which is solved via a multi-agent deep reinforcement learning (MADRL) algorithm. The results of the simulation demonstrate that our approach is more effective than the baseline method. © 2024 IEEE.

关键词： Quality of service

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Innovative Deep learning Techniques for Solving Complex Computer Vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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2024 international conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jayachitra, T. Sathya, S. Ramesh, P.S. Jothikumar, R. Gouri, M.S. Hussain, Mohammed Waheeduddin Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruttani India Vellore Institute of Technology Department of Smart Computing Vellore India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Baselios Mathews - Ii College of Engineering Department of Computer Science and Engineering Sasthamcotta Kollam India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India

ISBN: (纸本)9798350375442

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications. © 2024 IEEE.

关键词： Stochastic systems

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Multimodal Neuro-Fuzzy Approach for Emergency Healthcare Virtual Infrastructure 15

Multimodal Neuro-Fuzzy Approach for Emergency Healthcare Vir...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Bhatia, Madhulika Singh, Sandeep Kumar Souza, Jose Neuman Universidade Federal do Ceará Brazil Function Up India Federal University of Ceará Computer Science Department Brazil

ISBN: (纸本)9798350370249

The Multimodal Neuro-Fuzzy Approach (MNFA) is a novel approach to providing healthcare services through virtual infrastructure. This method combines Artificial Intelligence (artificial neural networks) techniques with fuzzy logic, to provide a robust and reliable virtual healthcare infrastructure in cases of emergency. The MNFA approach enables healthcare professionals to have access to up-to-date medical information and treatment guidelines quickly and accurately even in remote or rural areas. Therefore, this approach has the potential for providing efficient and effective healthcare services in remote locations which otherwise would not be accessible due to geographical constraints. Additionally, with the help of MNFA approach, healthcare organizations can save time and money by reducing manual labor costs associated with responding to emergencies. This allows for better automation of clinical decisions in emergency situations. The MNF model is already trained with help azure of Microsoft and gets accuracies in matlab (ANFIS). It enables faster and more accurate diagnosis, improved patient care coordination, and a better understanding of the dynamics between health care providers and patients. This approach can also be used to design intelligent systems that are capable of accurately predicting patient outcomes based on past data. As created a framework with Django and MySQL which is deploy on cloud platform to easy access of framework and collect more data on which will store in cloud database and present in cloud public domain. As such, it provides an invaluable tool for improving healthcare services in times of crisis. Healthcare professionals to effectively monitor, analyze, and take necessary action in an emergency situation. In this framework use cloud platform to fast and easy access of virtual healthcare. This experimental investigation demonstrates that, when used to solve real-world problems, our suggested approach outperforms alternative methods. © 2024

关键词： AI ANN CDT Emergency machine learning Neuro-Fuzzy approach Virtual Healthcare

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An Information Completion Method Based on Security Knowledge Graph with Fusing Neighborhood Information 2

An Information Completion Method Based on Security Knowledge...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Wang, Zhengxian Zhang, Wenbo Dai, Jiaxi Shenyang Ligong University School of Information Science and Engineering Shenyang China Shenyang Urban Construction University Shenyang China

ISBN: (纸本)9798350368888

In the research of knowledge graph completion, the issue of missing information in open-source network security knowledge repositories has persisted due to challenges such as difficulty in coordinating heterogeneous information and maintaining historical data. To address the problem of insufficient feature learning from different domains in existing information completion methods, a information completion method based on security knowledge graph with fusing neighborhood information (KGC-N), is proposed. To capture neighborhood information, this method constructs a security knowledge graph associated with the Structured Threat Information Expression (STIX 2.1) open-source network security knowledge repository. During the data preprocessing phase, Global Vectors for Word Representation (GloVe) is used to enhance the model's training effectiveness. Leveraging graph traversal techniques, it captures neighborhood information with various relationships within a multi-hop range through the knowledge graph. The captured neighborhood features are then learned using a Graph Attention Network to enhance the model's predictive performance. Experimental results indicate that KGC-N achieves a Mean Ranking of 196 and a Mean Reciprocal Ranking of 0.6127, outperforming baseline methods. © 2024 IEEE.

关键词： Knowledge graph

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Calibration Bottleneck: Over-compressed Representations are Less Calibratable 41

Calibration Bottleneck: Over-compressed Representations are ...

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41st international conference on machine learning, ICML 2024

作者： Wang, Deng-Bao Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Key Lab. of Computer Network and Information Integration Southeast University MOE China

Although deep neural networks have achieved remarkable success, they often exhibit a significant deficiency in reliable uncertainty calibration. This paper focus on model calibratability, which assesses how amenable a model is to be well recalibrated post-hoc. We find that the widely used weight decay regularizer detrimentally affects model calibratability, subsequently leading to a decline in final calibration performance after post-hoc calibration. To identify the underlying causes leading to poor calibratability, we delve into the calibratability of intermediate features across the hidden layers. We observe a U-shaped trend in the calibratability of intermediate features from the bottom to the top layers, which indicates that over-compression of the top representation layers significantly hinders model calibratability. Based on the observations, this paper introduces a weak classifier hypothesis, i.e., given a weak classification head that has not been over-trained, the representation module can be better learned to produce more calibratable features. Consequently, we propose a progressively layer-peeled training (PLP) method to exploit this hypothesis, thereby enhancing model calibratability. Our comparative experiments show the effectiveness of our method, which improves model calibration and also yields competitive predictive performance. Copyright 2024 by the author(s)

关键词： Deep neural networks

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Duet: Efficient and Scalable Hybrid Neural Relation Understanding 40

Duet: Efficient and Scalable Hybrid Neural Relation Understa...

引用

40th IEEE international conference on data Engineering, ICDE 2024

作者： Zhang, Kaixin Wang, Hongzhi Lu, Yabin Li, Ziqi Shu, Chang Yan, Yu Yang, Donghua Harbin Institute of Technology Massive Data Computing Lab China

ISBN: (纸本)9798350317152

Learned cardinality estimation methods have achieved high precision compared to traditional methods. Among learned methods, query-driven approaches have faced the work-load drift problem for a long time. Although both data-driven and hybrid methods are proposed to avoid this problem, most of them suffer from high training and estimation costs, limited scalability, instability, and long-tail distribution problems on high-dimensional tables, which seriously affects the practical application of learned cardinality estimators. In this paper, we prove that most of these problems are directly caused by the widely used progressive sampling. We solve this problem by introducing predicate information into the autoregressive model and propose Duet, a stable, efficient, and scalable hybrid method to estimate cardinality directly without sampling or any non-differentiable process, which can not only reduce the inference complexity from O(n) to O(1) compared to Naru and UAE but also achieve higher accuracy on high cardinality and high-dimensional tables. Experimental results show that Duet can achieve all the design goals above and be much more practical. Besides, Duet even has a lower inference cost on CPU than that of most learned methods on GPU. © 2024 IEEE.

关键词： machine learning

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Deep Transfer learning Based Multi-Classification of Dental Diseases X-Ray Images 4

Deep Transfer Learning Based Multi-Classification of Dental ...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Narmadha, R.P. Karthik, B. Mithiloshini, B. Saranya, R. KIT-KalaignarKarunanidhi Institute of Technology Dept. Artificial intelligence and data science Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

This research work proposes a deep transfer learning model for multi-classification purposes within dental diseases classed by their X-ray images. The paper elaborates typical challenges posed by dentistry, which faces many diagnosis issues;the decisive process of proper classification into types of diseases requires being fine and efficient. Most state-of-the-art methodologies developed through CNNs and DNNs have been found to yield fair results, but they both suffer from overfitting and are time-consuming processes. The new model uses the ResNet50 to get over those limitations. Dental X-ray pictorials are to be grouped into four distinct categories using the proposed architecture: cavities, fillings, impacted teeth, and implants. The system proposed should classify these four categories in X-ray images with an accuracy greater than 92%. Therefore, the proposed system will automatically identify areas requiring clinical treatment. Expected outcome is a strong and dependable tool for enhanced dental disease diagnosis by better accuracy, efficiency, and performance in the evvaluation of X-ray pictures. © 2024 IEEE.

关键词： Convolutional neural networks

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E-learning Paradigm in Cloud computing and Pertinent Challenges in Models Used for Cloud Deployment 3rd

E-Learning Paradigm in Cloud Computing and Pertinent Challen...

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3rd international conference on Computer Vision, High Performance computing, Smart Devices, and Networks, CHSN 2022

作者： Patel, Dhaval Chaudhary, Sanjay Madhav University Rajasthan Pindwara307026 India Janardan Rai nagar Rajasthan Vidyapeeth University Rajasthan Udaipur India

ISBN: (纸本)9789819966899

The pedagogical science has improved immensely in each section of learning and education due to adoption of recent developments in Information and Communication Technologies (ICT) domain. Adopting web-based tools has aided to new thrust in imparting education through remote means. The deployment of cloud network is quickly aided to all disciplines of learning and at all levels. A user can access, from any remote location, a data stored over cloud platform using their computers, tablets or smartphones, and can also make perturbations in data on need basis. The method of E-learning becomes seamless when cloud-based techniques are employed. This also helps in cost cutting and cumbersomeness of data processing as the major chunk of services is operated by third party infrastructures. Both cloudbased and conventional E-learning when combined offers a greater advantage to teachers, students and institutes albeit with a clutch that the security becomes a major issue of concern. The present review is an analysis of perks of deploying cloud-based learning and the security concerns that come with it, as observed through various studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing

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Deep learning Based Detection of Toxic Mushrooms in Karnataka 2

Deep Learning Based Detection of Toxic Mushrooms in Karnatak...

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2nd international conference on machine learning and data Engineering, ICMLDE 2023

作者： Subramani, Sivakannan Imran, F.A. Ttm, Abhishek Karthik, Sanjay M. Yaswanth, J. Department of Advanced Computing St Joseph's University Bangalore560027 India

Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, distinguishing toxic mushrooms from non-toxic ones is challenging, as their visual characteristics can be misleading. Deep learning, a subset of artificial intelligence, offers a promising approach to detecting and classifying mushrooms based on their toxicity. By training deep neural networks on datasets of labelled mushroom images, it becomes possible to create models capable of accurately differentiating between toxic and non-toxic mushrooms. Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, manually distinguishing toxic mushrooms from non-toxic ones is a challenging and time-consuming task. This research work is to help the local farmers and exporters to identify the difference between the toxic and the edible mushroom which will eventually boost production and will directly have an impact on the economic growth in India. This paper compared different deep learning algorithms such as Support Vector machine (SVM), Resnet50, YOLO V5 and Alexnet, which have been found to be the most useful in classifying the mushrooms effectively as toxic and non-toxic respectively. The patterns in the images of the mushrooms have been predicted through the above classifiers. The validation techniques show SVM to be the most effective with an accuracy of 83%. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image classification

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