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A Novel Graph-Based Structural Dissimilarity Measure for Video Summarization 3

A Novel Graph-Based Structural Dissimilarity Measure for Vid...

引用

3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Ma, Caixia Lyu, Lei Lyu, Chen Shandong Normal University School of Information Science and Engineering Shandong Jinan China

ISBN: (纸本)9781665459112

Effective detection of shot boundaries is important for video summarization methods based on shot boundary detection. However, various gradual shot boundaries (such as, fade in, fade out, dissolve) pose a great challenge to shot boundary detection. Previous work constructs graph models based on feature histograms from images and analyzes the structural changes of graphs, thus improving the detection of gradual shots. In this paper, we develop a new quantization method to calculate the structural change of the graph so as to more accurately locate the gradual shot boundaries. Statistical analysis methods are performed to analyze the data to be detected with past data to achieve real-time shot boundary detection. Experimental results on the VSUMM dataset show that our method outperforms some state-of-the-art methods on the F-Score. © 2022 IEEE.

关键词： Graphic methods

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Design of online intelligent patrol system for substation based on deep learning 3

Design of online intelligent patrol system for substation ba...

引用

3rd international conference on Electronics, Electrical and Information engineering, ICEEIE 2023

作者： Li, Jingya Fang, Mengran Wang, Zheng Cai, Li State Grid Information and Communication Industry Group Co. Ltd. Beijing Branch Beijing China

ISBN: (纸本)9781510671423

In order to ensure the stable, safe and efficient operation of the power system, it is need to continuously strengthen the inspection of transmission lines in the power system. The traditional manual patrol has a large workload and low efficiency, and it is difficult to realize manual patrol when geological disasters occur. On-line detection system of transmission lines can judge a certain fault abnormality of transmission lines in real time and accurately. In this article, an intelligent pre-alarm model for online inspection system of transmission lines is proposed, which uses image recognition technology as the main judgment method to judge the fault abnormality of transmission lines in real time, accurately and reliably. This model applies convolutional neural network (CNN) to the state detection and fault identification of transmission lines, thus providing technical support for the construction of on-line intelligent patrol system of substations. The simulation results show that compared with other methods, the proposed method has higher detection accuracy, faster detection speed and lower detection error, and can intuitively and accurately grasp the equipment status of the substation. The application of the intelligent pre-alarm system can effectively realize the intelligent identification of abnormal faults of transmission lines and their equipment. © 2023 SPIE.

关键词： Electric lines

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Neural Precision: Advanced Techniques for Brain Tumor Classification and Detection 3

Neural Precision: Advanced Techniques for Brain Tumor Classi...

引用

3rd international conference on Machine learning and data engineering, ICMLDE 2024

作者： Kumar, Aditya Jain, Sourabh Sonepat131023 India

In the quest for advancement in Deep learning techniques, many new principle directions emerged into the light, but this paper is dedicated to usage of computer vision in healthcare, majorly brain tumors. This paper concentrates on enhancing the detection and Classification of neural diseases, with a particular focus on brain tumors. This paper incorporates state-of-the-art techniques such as Gray Level Co-occurrence Matrix (GLCM) algorithms, the concept of skip connections, and the VGG19 model to perform logical and regressive analysis. Additionally, Softmax functions are employed to carry out the Classification task. Certain aspects of upsampling and downsampling data, along with dimensional extension, are also utilized to make the algorithm compatible with the Convolutional Neural Networks (CNN) model. To train the machine to fully understand the complex relationship between the images and classes, the dataset contains 7, 022 images, with 20% of the data used for validation. This paper is distinct in that, unlike many other studies that show considerable variation between testing and validation results, the findings here demonstrate consistent and stable performance. The model achieved an accuracy of 93.4% and reduced the loss to 27.5%. © 2025 The Author(s).

关键词： Accuracy CNN Deep learning Downsampling GLCM Loss Skip Connections Softmax State-of-the-art Upsampling VGG19

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The Text Classification Active learning Using Transfer learning Methods 3

The Text Classification Active Learning Using Transfer Learn...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Katyal, Ashu Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

In recent years, convolutional neural networks, or CNN have made it feasible to do text categorization with great accuracy. Zhang et al. used character-level decomposition and a CNN with reasonably deep layers to achieve high accuracy in text categorization. Real-world text-classification issues might be difficult to solve without a large enough labelled sample set. One solution is transfer studying, which use a network trained on any job as the seed network for another. While it is well-established that transfer learning works well for image identification, it is less clear what kinds of data it works best with and how successful it is for tasks in the processing of natural languages, such as document categorization. In this study, we first present a character-level convolutional neural network (CNN) that utilises a residual network's architecture to build a network with more advanced layers specifically for Japanese text categorization. We then show that by applying transfer learning across two specific datasets, we can enhance classification accuracy. We also present a method for measuring the success of transfer instruction and apply it to the assessment of our model. © 2023 IEEE.

关键词： CNN residual network text classification transfer learning

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Risk Prediction of Freezing of Gaits Episodes in Parkinson's Patients Using Gated Recurrent Unit 3

Risk Prediction of Freezing of Gaits Episodes in Parkinson's...

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Ayus, Ishan Azad, Chandrashekhar National Institute of Technology Jamshedpur Department of Computer Science and Engineering Jharkhand Jamshedpur India Iter Siksha 'o' Anusandhan University Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

Freezing of gait (FOG) presents a temporary impact on the movement of Parkinson's disease patients. This is associated with sudden falls leading to injuries. Preventive measures can be useful in tackling such unforeseen situations. The early prediction and detection of FOG episodes are essential for improving the quality of life of the patients. This study presents two innovative hybrid deep learning models named Fuzzy Activation Function Bidirectional Gated Recurrent Unit (FAF-Bi-GRU) and Fuzzy Activation Function Self-Attention Bidirectional Gated Recurrent Unit (FAF-Self-Attention-Bi-GRU). The hybrid FAF-Bi-GRU has incorporated the fuzzy logic principles with the Bidirectional Gated Recurrent unit (Bi-GRU). Similarly, the proposed FAF-Self-Attention-Bi-GRU Network integrates both attention modules with fuzzy logic. This study also evaluates the performance of Bi-GRU with proposed hybrid models for the identification of better-performing recurrent neural networks. In the accelerometer dataset, the hybrid FAF-Self-Attention-Bi-GRU outperforms with an accuracy of 98.142%, precision of 98.409%, recall of 97.891%, f1 score of 98.149%, specificity of 98.399%, and G-mean of 98.144%. While in the gyroscope dataset, the hybrid FAF- Bi-LSTM outperforms with an accuracy of 99.195%, precision of 99.275%, recall of 99.119%, f1 score of 99.196%, specificity of 99.276%, and G-mean of 99.197%. This study presents the potential of the proposed hybrid models for the prediction of FOG using wearable sensor data. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Instructional framework for using ChatGPT to aid interdisciplinary courses 3

Instructional framework for using ChatGPT to aid interdiscip...

引用

3rd international Symposium on Computer Applications and Information Systems, ISCAIS 2024

作者： Chen, Lihua Zeng, Desheng Pan, Zhihong Information Engeering Institute Guangdong Innovative Technical College Dongguan523960 China School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou510520 China

ISBN: (纸本)9781510681316

This paper explores using the AIGC tool ChatGPT to enhance interdisciplinary teaching in colleges and universities. Interdisciplinary courses, crucial for nurturing well-rounded talents, face challenges such as teachers' limited interdisciplinary knowledge, outdated teaching resources, and students' difficulties grasping complex concepts. ChatGPT offers extensive knowledge, instant response, and personalized learning to address these issues, supporting teachers with interdisciplinary teaching resources, instructional design, and assessment tools while providing students with customized learning materials. The proposed Interdisciplinary Practice Teaching Solution, comprising four core modules, facilitates the organic integration of interdisciplinary learning. A practical project, "Portfolio Analysis of Stock Funds," is used as a case study to demonstrate ChatGPT-aided interdisciplinary teaching. It showcases its effectiveness in cultivating students' abilities to deconstruct projects, clarify concepts, provide targeted hints, and critically validate and integrate projects. Experimental data confirm the framework's excellent performance across various indicators. © 2024 SPIE.

关键词： Students

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Short-term Traffic Flow Prediction based on GMO-DELM 3

Short-term Traffic Flow Prediction based on GMO-DELM

引用

3rd international conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

作者： Tian, Zhiyan He, Di Wuhan University of Technology School of Transportation and Logistics Engineering Wuhan China Wuhan University of Technology School of Naval Architecture Ocean and Energy Power Engineering Wuhan China

ISBN: (数字)9781665485951

ISBN: (纸本)9781665485951

Short-term traffic flow prediction is the premise of making full use of intelligent transportation system and plays an important role in urban traffic planning. In this paper, a deep extreme learning machine model based on grey wolf optimizer (GWO-DELM) is proposed. The grey wolf optimizer is used to optimize the input weights of hidden layers of deep extreme learning machine to improve the prediction accuracy of the model. In order to verify the effectiveness of the model, seven data sets of M25 High-speed Heathrow Airport were used to predict the traffic flow. Compared with the existing long short-term memory network model (LSTM), BP neural network model and unoptimized deep extreme learning machine model (DELM), the results show that: The traffic flow predicted by this optimization model fits the actual traffic flow best, and the prediction error is effectively reduced. © 2022 IEEE.

关键词： Long short-term memory

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Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual learning 1

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23rd international conference on intelligent data engineering and automated learning (IDEAL)

作者： Kim, Tae-Heon Moon, Hyung-Jun Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea Yonsei Univ Dept Artificial Intelligence Seoul 03722 South Korea

ISBN: (数字)9783031217531

ISBN: (纸本)9783031217524;9783031217531

Continual learning is a novel learning setup for an environment where data are introduced sequentially, and a model continually learns new tasks. However, the model forgets the learned knowledge as it learns new classes. There is an approach that keeps a few previous data, but this causes other problems such as overfitting and class imbalance. In this paper, we propose a method that retrains a network with generated representations from an estimated multivariate Gaussian distribution. The representations are the vectors coming from CNN that is trained using a gradient regularization to prevent a distribution shift, allowing the stored means and covariances to create realistic representations. The generated vectors contain every class seen so far, which helps preventing the forgetting. Our 6-fold cross-validation experiment shows that the proposed method outperforms the existing continual learning methods by 1.14%p and 4.60%p in CIFAR10 and CIFAR100, respectively. Moreover, we visualize the generated vectors using t-SNE to confirm the validity of multivariate Gaussian mixture to estimate the distribution of the data representations.

关键词： Continual learning Memory replay Sample generation Multivariate gaussian distribution Expectation-maximization

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Wearable IMUs: Advancing Human Motion Analysis with Deep learning 3rd

Wearable IMUs: Advancing Human Motion Analysis with Deep Le...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Das, Satyesh Das, Divyesh Parashar, Ashana Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais India Electronics and Computer Engineering Thapar Institute of Engineering and Technology Patiala India Mobile Automation Vodafone Intelligent Solutions Nashik India

ISBN: (纸本)9789819726707

In recent times, there has been a notable surge in the exploration of studying human body movements through the utilization of inertial measurement units that can be worn. This trend stems from its substantial impact on academic and industrial circles, encompassing applications ranging from portable healthcare solutions to sports and interfacing with computers through human interaction. Developing human activity recognition (HAR) systems that achieve remarkable accuracy in identification while relying on just one sensor remains an ongoing technological hurdle. In this paper, we explore both supervised and partially supervised approaches using convolutional neural networks and denoising autoencoders approaches. The goal of our study revolves around increasing the classification precision pertaining to previous related works and decreasing reliance on human-engineered features. Since the raw IMU data is composed of time series, it is split using a running window approach and segments of roughly one second of data are fed to the neural networks. The above approaches are tested with two different variations of the original dataset, obtained with data augmentation approaches, to balance uneven class distribution. The dataset contains measurements of one single IMU sensor positioned in the belt of different users performing seven actions: running, jumping, walking, falling, sitting, standing, and lying. The score of best 8-layer CNN-based system was 0.972. The worst represented class, falling, with just 2 min of recorded data, has an F1-score of 0.919. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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CNN-BiLSTM-ATTENTION: A Novel Neural Network with Attention Mechanism for NLOS Identification of UWB Signal 3

CNN-BiLSTM-ATTENTION: A Novel Neural Network with Attention ...

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3rd international conference on intelligent Communications and Computing, ICC 2023

作者： Yang, Yang Ke, Han Gan, Wang Deng, Zhongliang School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350308327

Ultra-wideband signals have been widely used in high-precision indoor positioning because of their large bandwidth, low delay and strong penetration. However, the complex indoor environment will hinder the propagation of ultra-wideband signals, resulting in unexpected non-line-of-sight ranging errors and positioning errors. The channel impulse response (CIR) of a signal contains rich feature information, which can be used to classify line-of-sight signals and non-line-of-sight signals. In this paper, we propose a network model combining the ATTENTION mechanism with the bidirectional long short-term memory network and convolutional neural network, which can learn many feature information in signal CIR through the model, so as to realize the model's high accuracy classification. The length of the learned CIR can be adjusted as needed to balance the accuracy and computation of the network. In this paper, the optimal structural design is selected by comparing CNN-BiLSTM-ATTENTION of different structures. In this paper, the DW1000 chip dataset of Decawave company is used for training and testing, and the classification accuracy is as high as 87.91%. At the same time, compared with the existing mainstream deep learning methods, the results show that the proposed model has higher classification accuracy than LSTM, CNN-LSTM and CNN-BiLSTM. © 2023 IEEE.

关键词： Impulse response

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