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QTFN:A General End-to-End Time-Frequency Network to Reveal the Time-Varying Signatures of the Time Series

Big Data Mining and Analytics 2024年第3期7卷 905-919页

作者： Tao Chen Yang Jiao Lei Xie Hongye Su State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China Shenzhen Key Laboratory of Intelligent Bioinformatics and College of Computer Science and Control EngineeringShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonstationary time series are ubiquitous in almost all natural and engineering *** the time-varying signatures from nonstationary time series is still a challenging problem for data *** Time-Frequency Distribution(TFD)provides a powerful tool to analyze these ***,they suffer from Cross-Term(CT)issues that impair the readability of ***,to achieve high-resolution and CT-free TFDs,an end-to-end architecture termed Quadratic TF-Net(QTFN)is proposed in this *** by classic TFD theory,the design of this deep learning architecture is heuristic,which firstly generates various basis functions through ***,more comprehensive TF features can be extracted by these basis ***,to balance the results of various basis functions adaptively,the Efficient Channel Attention(ECA)block is also embedded into ***,a new structure called Muti-scale Residual Encoder-Decoder(MRED)is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder ***,although the model is only trained by synthetic signals,both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.

关键词： Time-Frequency Analysis(TFA) Multi-scale Residual Encoder-Decoder(MRED) quadratic Time-Frequency Distribution(TFD)

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Learning a Mixture of Conditional Gating Blocks for Visual Question Answering

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Journal of computer science & technology 2024年第4期39卷 912-928页

作者： Qiang Sun Yan-Wei Fu Xiang-Yang Xue School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China Academy for Engineering and Technology Fudan UniversityShanghai 200433China School of Data Science Fudan UniversityShanghai 200433China School of Computer Science Fudan UniversityShanghai 200433China

As a Turing test in multimedia,visual question answering(VQA)aims to answer the textual question with a given ***,the“dynamic”property of neural networks has been explored as one of the most promising ways of improving the adaptability,interpretability,and capacity of the neural network ***,despite the prevalence of dynamic convolutional neural networks,it is relatively less touched and very nontrivial to exploit dynamics in the transformers of the VQA tasks through all the stages in an end-to-end ***,due to the large computation cost of transformers,researchers are inclined to only apply transformers on the extracted high-level visual features for downstream vision and language *** this end,we introduce a question-guided dynamic layer to the transformer as it can effectively increase the model capacity and require fewer transformer layers for the VQA *** particular,we name the dynamics in the Transformer as Conditional Multi-Head Self-Attention block(cMHSA).Furthermore,our questionguided cMHSA is compatible with conditional ResNeXt block(cResNeXt).Thus a novel model mixture of conditional gating blocks(McG)is proposed for VQA,which keeps the best of the Transformer,convolutional neural network(CNN),and dynamic *** pure conditional gating CNN model and the conditional gating Transformer model can be viewed as special examples of *** quantitatively and qualitatively evaluate McG on the CLEVR and VQA-Abstract *** experiments show that McG has achieved the state-of-the-art performance on these benchmark datasets.

关键词： visual question answering Transformer dynamic network

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ACOVMD: Automatic COVID-19 misinformation detection in Twitter using self-trained semi-supervised hybrid deep learning model

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International Social science Journal 2024年第252期74卷 713-730页

作者： Birunda, S. Selva Devi, R. Kanniga Muthukannan, M. Babu, M. Mahesh Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Kelambakkam India Department of Civil Engineering KCG College of Technology Tamil Nadu Chennai India

During the COVID-19 pandemic, online social networks are extensively utilized, more than ever before by 8.4%, resulting in the propagation of false information related to COVID-19. Despite the existence of many fake news detection models;annotation inconsistency, memory consumption, accurate and self-trained efficient algorithms for detecting the emerging COVID-19 misinformation tweets are still challenging. Hence, the main aim of this work is to come up with a self-trained semi-supervised model that accurately and automatically detects the reliability of emerging COVID-19 tweets without delay. In this work, COVID-19 tweet dataset is created in English Language from the period January 2020 to January 2022 as a ground truth database. Then self-trained semi-supervised hybrid deep learning model is proposed to train both supervised and unsupervised components simultaneously using the created dataset. The proposed model is self-trained repeatedly and the model gets updated to predict the reliability of upcoming COVID-19 tweets that differ from training tweets. We performed experiments multiple times by limiting the percentage amount of labelled tweets shown to the model, namely 80%, 50%, 40%, 30%, 20% and 10% labelled tweets, respectively. Experimental results show that the proposed model achieves 80.92% accuracy and 98.15% accuracy in the 10% and 80% label-seen experiments, respectively. This shows a clear rising trend in the performance curve. Therefore, this technique will be useful for effectively classifying voluminous amounts of emerging tweets generated as part of the COVID-19 infodemic. The proposed model may efficiently use a huge amount of unlabelled tweets and enhance the model's generalization performance. © 2023 John Wiley & Sons Ltd.

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Dumpy: A Waste Management App

Dumpy: A Waste Management App

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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Chetan, S.R. Karanth, Adithya B Dheeraj, D. Singh R, Rohan Arun, Jaideep Global Academy of Technology Information Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350302820

A developing kind of trash management called smart waste management uses technology to streamline the operations of waste collection, transportation, and disposal. These kind of systems can increase operational effectiveness, lower costs, and promote environmental sustainability by utilizing sensors, analytics, and automation. Additionally, these systems can be used for route optimization, real-time monitoring of garbage levels, and preventative maintenance of waste collection equipment. An overview of the idea is indicated, along with a few of its possible advantages. Dumpy is a waste management app that helps users to dispose of and compost waste generated by a household. We collect, compost, and reward people for generating less waste. © 2023 IEEE.

关键词： Real time systems

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Patient Risk Classification based on Vital Signs using Machine Learning Algorithm 4

Patient Risk Classification based on Vital Signs using Machi...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Prabhu, Savithri Bairy, G. Muralidhar Sampathila, Niranjana Chadaga, Krishnaraj Manipal Institute of Technology Manipal Academy of Higher Education Department of Biomedical Engineering Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350349900

Infectious disease is always a serious issue in the world due to the impact of the spread of the disease and mortality. Now a days heart disease is also the most common disease. Heart disease, chronic disease and COVID19 reflected by changes in vital signs. The major vital signs are blood pressure, pulse rate and body temperature. The evolution in Machine Learning plays an important role to improve healthcare facilities and save human lives. The effective diagnosis of heart disease in a shorter time is very important to save human lives. At the same time COVD-19 is also affecting many people worldwide. Machine learning techniques are used for diagnosis of disease. It uses datasets of earlier patient reports. This paper discusses machine algorithms to diagnose and differentiate infectious diseases, chronic disease, and heart disease. As a result, patients facing high risk can be shifted to the ICU and other patients to normal wards. These algorithms are effective in disease screening. © 2024 IEEE.

关键词： Adversarial machine learning

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A Dynamic Bayesian-Based Comprehensive Trust Evaluation Model for Dispersed Computing Environment

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China Communications 2023年第2期20卷 278-288页

作者： Hongwen Hui Zhengxia Gong Jianwei An Jianzhong Qi School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Graduate School of University of Science and Technology Beijing Guangdong 528399China China Academy of Information and Communications Technology(CAICT) Beijing 100191China Department of Information Engineering North China University of TechnologyBeijing 100144China

Dispersed computing is a new resourcecentric computing *** to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its *** trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing *** this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing ***,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the *** the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding ***,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary *** experiments are introduced to verify the performance of the *** with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.

关键词： dispersed computing trust evaluation model malicious node interaction success rate detection rate

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I $^{2}$ RNN: An Incremental and Interpretable Recurrent Neural Network for Encrypted Traffic Classification

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IEEE Transactions on Dependable and Secure Computing 2023年 1-14页

作者： Song, Zhuoxue Zhao, Ziming Zhang, Fan Xiong, Gang Cheng, Guang Zhao, Xinjie Guo, Shize Chen, Binbin College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Southeast University Nanjing China Information Systems Technology and Design Singapore University of Technology and Design Singapore

Traffic classification occupies a significant role in cybersecurity and network management. The widespread of encryption transmission protocols such as SSL/TLS has led to the dominance of deep learning based approaches. In cybersecurity, strong adversaries often complicate their strategies by constantly developing emerging attacks. Meanwhile, security practitioners desire to grasp the reasons for inference results. However, existing deep learning approaches lack efficient adaptation for incremental traffic types and often have less interpretability. In this paper, we propose I $^{2}$ RNN, an Incremental and Interpretable Recurrent Neural Network for encrypted traffic classification. The I $^{2}$ RNN proposes a novel propagation process to extract the sequence fingerprints from sessions with local robustness. Meanwhile, this proposal provides interpretability including time-series feature attribution and inter-class similarity portrait. Moreover, we design I $^{2}$ RNN in an incremental manner to adapt to emerging traffic types. The I $^{2}$ RNN only needs to train an additional set of parameters for the newly added traffic type rather than retraining the whole model with the entire dataset. Extensive experimental results show that our I $^{2}$ RNN can achieve remarkable performance in traffic classification, incremental learning, and model interpretability. Compared with other local interpretability methods, our I $^{2}$ RNN exhibits excellent stability, robustness, and effectiveness in the interpretation of network traffic data. IEEE

关键词： Cryptography

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Securing Comparative Study of Leaf Disease Identification with Different Deep Learning Models Using Hyperledger Fabric 4

Securing Comparative Study of Leaf Disease Identification wi...

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4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Das, Subhashis Banerjee, Soumendu Banerjee, Projat Das, Nabanita Computer Science and Engineering Academy of Technology West Bengal India Master of Computer Applications Academy of Technology West Bengal India Blockchain Researcher and Developer Ibm India Private Limited Greater Noida India

ISBN: (纸本)9798350389128

Plant Disease is a serious problem that needs our utmost attention. There are different techniques to detect plant diseases. Our focus is the analysis of various models to detect leaf diseases from images. Such analysis will help in choosing an appropriate model for such tasks which can be incorporated in handheld devices through an app which can be a frugal way to detect leaf diseases and appropriate measures can be taken. © 2024 IEEE.

关键词： Contrastive Learning

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E-Health Records Stored Over the Cloud with Automated Medication Reminders for Enhanced Patient Care 4

E-Health Records Stored Over the Cloud with Automated Medica...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Indhuja, E. Angelina, J.Jane Rubel Subhashini, S.J. Ajay Kumar, B. Amulya, L. Gopi, G. Kalasalingam Academy of Research & Education Department of Computer Science and Engineering Krishnankovil India Srm Madurai College for Engineering and Technology Department of Computer Science and Engineering Sivagangai India

ISBN: (纸本)9798350300093

This hospital management system aims to develop a user-friendly and efficient system using PHP as the front-end interface and MySQL as the database. The system enables the management of patient information, doctor information, prescription details, and appointment details. The system provides a centralized platform for the management of these aspects, enabling healthcare providers to access and manage the data in real-time from anywhere. The system allows authorized users to add or remove doctor details, manage patient appointments and claims securely. The system has been designed to ensure the protection of personal data to speed up data processing. The system provides various features such as appointment scheduling, patient record management, doctor record management, prescription management, and billing management. Overall, the hospital management system is a reliable and efficient solution that streamlines the management of healthcare facilities. © 2023 IEEE.

关键词： Information management

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Evaluation and Enhancement of Standard Classifier Performance by Resolving Class Imbalance Issue Using Smote-Variants Over Multiple Medical Datasets

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SN computer science 2025年第3期6卷 1-30页

作者： Kumar, Vinod Kumar, Ravi Kant Singh, Sunil Kumar School of Computer Applications and Information Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering SRM University AP Andhra Pradesh Guntur India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India karnataka Bengaluru India

In the era of machine learning we are solving the classification problems by training the labeled classes. But sometimes due to insufficient data in some of the training classes, the system training is inadequate for these minority classes. In this case the output for the classes obtained from the less amount of trained data are miserably inappropriate and biased towards the classes having more data. This problem is known as a class imbalance problem. In such cases, standard classifiers tend to be overpowered by the expansive classes and disregard the little ones. As a result, the performance of machine learning and the deep learning algorithms are also reducing and sometimes highly unacceptable too, mainly if it is related to crucial data like medical and health related. Though various researchers provided some methods to solve this problem but mostly they are problem specific and suitable with the specific classifier only. To find a generalized and effective solution to this problem, we have applied various smote variants for solving the imbalanced factors in dataset and finally improved the performance of the various machine learning and deep learning algorithms. We have experimented and analyzed the effects of SMOTE variants on various machine learning techniques over six standard medical datasets. We have found that SMOTE variants are very effective, and they improve the standard performance measures (Accuracy, Precision, Recall and F1-Score). Additionally, based on our research, it is feasible to determine which smote variation works best with machine learning methods and datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalanced Classifiers Machine learning Medical datasets Smote variants

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