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PIPELINE SAFETY EARLY WARNING METHOD FOR distributed SIGNAL USING BILINEAR CNN AND LIGHTGBM

PIPELINE SAFETY EARLY WARNING METHOD FOR DISTRIBUTED SIGNAL ...

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IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Yang, Yiyuan Li, Yi Zhang, Haifeng Tsinghua Univ Int Grad Sch Shenzhen Shenzhen Peoples R China Tsinghua Univ Res Inst Guangzhou Peoples R China

ISBN: (纸本)9781728176055

Oil and gas pipelines are known as the backbone of global energy, and securing their safety is crucial for energy supply. In this study, we utilized a novel machine learning method based on the spatiotemporal features of distributed optical fiber sensor signals to monitor the safety of oil and gas pipelines in real time. Encouraging empirical results on a large amount of data collected from real sites confirmed that our model could accurately locate and identify the damage events of a pipeline in real time under strong noise and various hardware conditions, and could effectively handle the signal drift problem. Furthermore, as a generalized tool, the proposed solution could be applied to other industrial inspection fields. Our codes and video demos are available at https://***/yyysjz1997/B-CNN_LGBM-PSEW.

关键词： energy pipeline safety early warning industrial distributed time-space signal processing action recognition bilinear convolutional neural network lightgbm

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ACOUSTIC REFLECTORS LOCALIZATION FROM STEREO RECORDINGS USING neural networkS

ACOUSTIC REFLECTORS LOCALIZATION FROM STEREO RECORDINGS USIN...

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IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Bologni, Giovanni Heusdens, Richard Martinez, Jorge Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781728176055

Acoustic room geometry estimation is often performed in ad hoc settings, i.e., using multiple microphones and sources distributed around the room, or assuming control over the excitation signals. We propose a fully convolutional network (FCN) that localizes reflective surfaces under the relaxed assumptions that (i) a compact array of only two microphones is available, (ii) emitter and receivers are not synchronized, and (iii) both the excitation signals and the impulse responses of the enclosures are unknown. Our FCN is trained in a supervised fashion to predict the likelihood of reflective surfaces at specific distances and directions-of-arrival (DOA). When a single reflective surface is present, up to 80% of real and virtual sources are detected, while this figure approaches 50% in rectangular rooms. Experiments on real-world recordings report similar accuracy as with artificially reverberated speech signals, validating the generalization capabilities of the framework.

关键词： Source localization acoustic reflectors walls image-source model convolutional neural network deep learning

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Enhancing Dietary Monitoring using Deep Learning: Food Recognition and Calorie Estimation 15

Enhancing Dietary Monitoring using Deep Learning: Food Recog...

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15th International conference on Computing Communication and networking Technologies, ICCCNT 2024

作者： Suneela, M. Sahithi, Mandadi Vamsi, Nuthalapati Naik, Nunsavathu Balaji Chowdary, Narra Sujitha Vignan's Lara Institute of Technology & Science College Dept. of Computer Science & Engineering Guntur India

ISBN: (纸本)9798350370249

The project 'Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation' introduces an innovative method aimed at improving dietary tracking and fostering healthier eating habits. By employing cutting-edge deep learning techniques, its core objective is to precisely identify various food items and rapidly estimate their caloric content, offering users advanced and personalized monitoring capabilities. Utilizing the Python programming language alongside the MobileNet architecture model, the project underwent rigorous training and evaluation using the expansive Food 101 dataset, comprising 37,046 food images distributed across 101 distinct classes. Notably, the model demonstrated exceptional performance, achieving a training accuracy of 97.02% and a validation accuracy of 98.17%, highlighting the efficacy of this approach in accurately categorizing a wide array of food items. This system provides users with several crucial functionalities. Furthermore, it furnishes users with essential insights by estimating the caloric content of recognized foods, facilitating effective monitoring of dietary intake. The intelligent diet monitoring capabilities enabled by Food Recognition and Calorie Estimation empower users to make informed choices regarding their dietary preferences. Through the continuous tracking and analysis of their daily food consumption, users can glean valuable insights into their nutritional habits, set personalized goals, and make necessary adjustments to achieve a well-balanced and healthy diet. Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation stands as a prominent illustration of successful implementation of deep learning techniques, particularly with the utilization of the MobileNet architecture, for food recognition and calorie estimation. With its remarkable accuracy, real-time processing capabilities, and intelligent monitoring features, this project has the potential to revolutioni

关键词： Convolutional neural network (CNN) algorithm Datasets MobileNet architecture Web application

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Globally Consistent Vertical Federated Graph Autoencoder for Privacy-Preserving Community Detection 17th

Globally Consistent Vertical Federated Graph Autoencoder for...

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17th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Fang, Yutong Huang, Qingqing Ye, Enjie Guo, Wenzhong Guo, Kun Chen, Xiaoqi Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Community detection is a trendy area in research on complex network analysis and has a wide range of real-world applications, like advertising. As people become increasingly concerned about privacy, protecting participants’ privacy in distributed community detection has become a new challenge. When applied to not identically and independently distributed (Non-IID) data, most federated graph algorithms suffer from the weight divergence problem caused by diversified training of local and global models, resulting in accuracy degradation. Furthermore, the privacy protection approaches based on anonymization, such as differential privacy (DP), and cryptography, such as homomorphic encryption (HE), incur accuracy loss and high time consumption, respectively. In this paper, we propose a globally consistent vertical federated graph autoencoder (GCVFGAE) algorithm, which builds a globally consistent model among the coordinator and all participants to solve the Non-IID graph data problem. As well, an attribute blinding strategy based on security aggregation is developed to protect the network privacy of each participant without losing accuracy. Both real-world and artificial networks’ experiments show that our algorithm reaches higher accuracy than the existing vertical federated graph neural networks (GNNs) and the simple distributed graph autoencoder without federated learning and detects communities identical to those found by the standard graph autoencoder (GAE). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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Secure distributed processing of NG with Updatable Decomposition Data and Parameters

Secure Distributed Processing of NG with Updatable Decomposi...

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2022 International conference on networking and network Applications, NaNA 2022

作者： Miyajima, Hirofumi Shigei, Noritaka Miyajima, Hiromi Shiratori, Norio Nagasaki University Nagasaki Japan Kagoshima University Kagoshima Japan Chuo University Tokyo Japan

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Machine learning using distributed data, such as federative learning (FL) and secure multiparty computation (SMC), is demanded to achieve both utility and confidentiality when using confidential data. There is a trade-off between utility and confidentiality, and in general, SMC can offer better confidentiality than FL and better utility than homomorphic encryption. In machine learning with SMC, confidentiality is improved by decomposing individual data and parameters into multiple pieces, storing each piece on each server, and learning without restoring the data or parameters themselves. However, once the conventional methods randomly decompose data and parameters, the decomposition remains permanently fixed. The fixed decomposition is considered undesirable because it gives malicious attackers more opportunities to attack the data and model. In this paper, we propose a secure distributed processing of neural gas (NG), which is one of unsupervised machine learning. In addition to the decomposition of data, the proposed method can update the decomposition of parameters during learning. Each server can independently update the decomposition of both data and parameters in the proposed method, and the data and parameters are never restored during learning. Our simulation result shows that it can achieve the same level of learning accuracy as the conventional methods. © 2022 IEEE.

关键词： Cryptography

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Recurrent Convolution neural network with Group-Wise Enhancement for Student English Classroom Learning Behavior Recognition

Recurrent Convolution Neural Network with Group-Wise Enhance...

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distributed Systems, Computer networks and Cybersecurity (ICDSCNC), International conference on

作者： Shanshan Lang Xin Sui School of Foreign Language and Literature Changchun Humanities and Sciences College Changchun China Public Computer Department Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Due to the increase in the number of students joining colleges across the world, current college classrooms are larger, and this has a stressed university lecturer teaching those large classes and conducting lectures efficiently. Therefore, the Recurrent Convolution neural network with Group-Wise Enhancement (RCNNGWE) is proposed in this research for English classroom learning behavior recognition. The bilateral filter is used for preprocessing to reduce noise when reserving edges and enhancing the visual feature clarity. Then, the feature extraction is performed by Scale-Invariant Feature Transform (SIFT) which extracts visual features to scale and orientation. The RCNNGWE works well because it is able to simulate environment within the classroom as well as the different behaviors of students in the class. To further propagate key behavioral patterns the proposed model, need to localize groupwise operations which helps to improve recognition accuracy. The GWE continues to enrich the functionality of the proposed model by grouping inputs and processing them in a separate aggregate, which enables identification of behaviors of students. The RCNNGWE obtained 93.75% accuracy, 87.27% f1-score, 86.41% precision, 87.26% mean Average Precision (mAP) and 91.64% recall for Student Learning Behavior (SLB) dataset.

关键词： Visualization Accuracy Convolution Aggregates neural networks Noise Education Transforms Feature extraction Pattern recognition

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Data Mining Technology in the International Student Evaluation Using Artificial neural network with Latent Dirichlet Allocation

Data Mining Technology in the International Student Evaluati...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Hao Dong Tingxing Yan Xi'an Shiyou University Xi'an China

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Recently, Educational Data Mining (EDM) has increased remarkable consideration. The significant aim of educational institutions is to offer efficient education for students to improve academic performance. When data mining approaches are used in the context of education, they can discover hidden knowledge and patterns that can aid in decision-making processes focused at improving an educational system. Forecasting the student's achievements is a significant problem for higher education administrators. To address this challenge, this research proposes the Machine Learning (ML) method of Artificial neural network (ANN) with the Latent Dirichlet Allocation (LDA) approach for the prediction of the performance of the international student's academics. The presented approach considered the last semester for the international student's performance as well as their personal features were examined. The pre-processing techniques like cleaning, integration, transformation, feature scaling as well as reduction were utilized for the prevention of anomalies as well as high dimensionality reduction in the collected data and to acquire the most correlated features. The experimental results shows that the proposed ANN-LDA approach achieves the accuracy of 95.7% when compared to the previous works like K-Nearest Neighbor (KNN) and Support Vector Machine (SVM).

关键词： Support vector machines Performance evaluation Education Artificial neural networks Learning (artificial intelligence) Cleaning Data mining

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ResNet110 and Mask Recurrent Convolutional neural network Based Detection and Classification of Colorectal Cancer

ResNet110 and Mask Recurrent Convolutional Neural Network Ba...

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distributed Computing and Optimization Techniques (ICDCOT), International conference on

作者： Laith H Alzubaidi Priyanka K Suhas S K B Kiran Kumar V Malathy The Islamic university Najaf Iraq Dept of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering S.E.A College of Engineering and Technology Bangalore India Department of Information Technology CVR College of Engineering Hyderabad Telangana India Department of ECE SR University Warangal India

Globally, Colorectal Cancer (CRC) is the one most significant cancer types as well as it grows in a region of colon of large intestine. An early CRC detection is supportive to handle the impression of accumulating cancer cells. However, the detection process of utilizing the colon images is expensive as well as time-consuming. Recently, Deep Learning (DL)-based approaches are developed for the CRC detection. Therefore, this research proposes the efficient pre-trained architecture for the detection and classification of CRC. Initially, the benchmark dataset of Warwick-QU is collected for the estimation of the effectiveness of the proposed method. Then, the pre-processing is done by the utilization of Noise removal as well as Contrast Enhancement. Then, pre-processed data is to extracted by the utilization the pre-trained architecture. Finally, the outcome of extracted features is classified by the utilization of improved Mask Recurrent-Convolutional neural network (Mask R-CNN). The effectiveness of the proposed method is validated by various performance metrices and it achieves the accuracy of 99.67%, specificity of 99.56%, sensitivity of 99.57%, precision of 97.36% and F1-score of 98.34% when compared to the existing methods such as ResNet-50, Stacked Gated Recurrent Unit (SGRU) and Mobile Net.

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Sentiment Analysis through Text using Squeeze and Excitation Attention Mechanism based Recurrent neural network

Sentiment Analysis through Text using Squeeze and Excitation...

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distributed Systems, Computer networks and Cybersecurity (ICDSCNC), International conference on

作者： Sheela S V Radhika K R Kartikeya Mishra Shashi Ranjan Singh Snehil Gupta Information Science and Engineering BMS College of Engineering Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Sentiment analysis is the field of research to identify helpful data by utilizing sentiments of the people shared through social media like Twitter and Facebook. However, some sentiment analysis approaches attain minimum accuracy due to inconsistencies between the tweet text and the assigned sentiment label. Hence, this research proposes the Squeeze and Excitation Attention mechanism based Recurrent neural network (SEA-RNN) approach for the sentimental analysis in the text. Initially, the Twitter United States (US) Airline dataset is performed to implement the proposed method. Then, pre-processing techniques like removing the stop words, stemming and lemmatization are employed to enhance the quality of the dataset. Then, Bidirectional Encoder Representations from Transformers (BERT) is employed in the feature extraction process to identify and extract relevant features. Finally, the SEA-RNN approach is proposed for classifying sentiments into three classes such as positive, neutral and negative. The experiment results show that the proposed SEA-RNN attains a better accuracy of 99.84% as compared to the existing methods like Universal Language Model Fine-Tuning" (ULMFiT) with Support Vector Machine (SVM).

关键词： Support vector machines Sentiment analysis Recurrent neural networks Accuracy Attention mechanisms Social networking (online) Blogs Noise Feature extraction Transformers

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Cloud-Enabled neural networks for Intelligent Vehicle Emissions Tracking and Analysis

Cloud-Enabled Neural Networks for Intelligent Vehicle Emissi...

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Automation and Computation (AUTOCOM), International conference on

作者： N Anusha J Gnana Jeslin V. Srividhya Nukala Sujata Gupta R. Meenakshi C. Srinivasan Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology Gandipet Hyderabad India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

A system for smart vehicle emissions monitoring and analysis using cloud computing and neural networks is presented in this paper. As global environmental concerns develop, monitoring and reducing vehicle emissions is crucial. Scalability and real-time data processing are issues for traditional approaches. Thus, cloud-enabled neural network architecture for scalable and efficient vehicle emissions monitoring using distributed computing is developed. A neural network model trained on various vehicle emission datasets, allowing for precise pattern analysis and prediction of emissions. The scalability of the system is guaranteed by using cloud resources, which can accommodate the rising amount of data created by an increasing number of vehicles. By incorporating cloud-based data processing seamlessly, real-time monitoring is accomplished, enabling immediate analysis and prompt emission alerts. The suggested emissions tracking platform is robust and scalable to intelligent transportation systems by revealing vehicle emissions’ environmental effect. Future smart city and sustainable urban transportation developments are built on cloud computing and neural networks. The proposed system shows how cloud-enabled neural networks may solve complicated vehicle emissions problems.

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