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IG-GRD: A Model Based on Disentangled Graph Representation Learning for Imaging Genetic data Fusion 20th

IG-GRD: A Model Based on Disentangled Graph Representation L...

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20th international conference on Intelligent computing (ICIC)

作者： Feng, Shuang Wang, Letian Li, Chang Wan, Xiaohua Zhang, Fa Hu, Bin Beijing Inst Technol Sch Med Technol Beijing Peoples R China

ISBN: (纸本)9789819755806;9789819755813

Integrating imaging and genetic data provides a comprehensive approach to analyze brain disorders from different perspectives, which has important implications for the early diagnosis of Alzheimer's Disease (AD) and the exploration of its underlying mechanisms. Current fusion methods focus primarily on the correlation between modalities or rely on decision-level fusion. However, due to the heterogeneity of imaging and genetic data, as well as the necessity to simultaneously consider their correlation and independence, current methods often face challenges in adequately integrating and fully learning from multimodal information. Therefore, in this paper, we propose a novel multimodal data fusion method, named IG-GRD, based on graph representation learning for imaging and genetic data. Firstly, we construct imaging graphs and genetic graphs based on the characteristics of fMRI and SNP data, mapping the data from these two modalities into a unified representation space. Subsequently, we use a disentangled representation learning method on multimodal graphs that considers structural information and complex relationships between nodes to capture common and private graph representations. Finally, the disentangled feature graphs are fused at the graph level to synthesize the collaborative and individual effects of imaging and genetic information on the disease. Experimental results demonstrate that IG-GRD excels not only in recognizing mild cognitive impairment (MCI), but also in identifying brain regions and genes closely associated with AD and cognition. This work offers a novel methodology for the fusion of imaging and genetic data and provides new directions for the early diagnosis of AD and the investigation of its pathogenesis.

关键词： Imaging Genetics Disentangled Graph Representation Learning data Fusion

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IPM: Information Lossless Pre-training Strategy for Molecular Property Prediction

IPM: Information Lossless Pre-training Strategy for Molecula...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Ruomei Li, Shangzhe Zhu, He Hou, Yue Peng, Xingyu Yuan, Haitao Wu, Junran Xu, Ke Beihang University State Key Laboratory of Complex & Critical Software Environment Beijing100191 China Central University of Finance and Economics School of Statistics and Mathematics Beijing100081 China Nanyang Technological University Singtel Cognitive and Artificial Intelligence Lab for Enterprises 639798 Singapore

ISBN: (纸本)9798350386226

Given the pivotal role of molecular property prediction in drug development and material science, graph self-supervised learning has been implemented in molecular representation learning to compensate for the shortage of labeled molecules. However, current proposed methods often focus on designing data augmentation schemes and leveraging domain knowledge to improve performance, which inevitably leads to molecular semantics loss and limited generalization capability. To the end, we propose IPM, an Information lossless Pretraining strategy for Molecular property prediction that leverages the information of both the original graph and line graph of molecules. Specifically, by contrasting the given graph with the corresponding line graph, the graph encoder can fully learn the generic molecular semantic representation without profound domain knowledge. We also design a new message-passing scheme that retains information consistency during message passing between two kinds of graphs. Additionally, we present two graph contrastive losses for performance fixing and over-smoothing prevention during the learning process. Experimental results on multiple regression tasks for molecular property prediction demonstrate the effectiveness of IPM against state-of-the-art (SOTA) methods. © 2024 ieee.

关键词： Self-supervised learning

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Construction of Hospital Network Security Intrusion Detection System Based on Big data Clustering Algorithm

Construction of Hospital Network Security Intrusion Detectio...

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2024 international conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Jiang, Peng Qi, Haiyan Wuhan University Hospital of Stomatology Hubei Wuhan430070 China

ISBN: (纸本)9798350369212

In today's digital medical environment, the network threats faced by hospital networks are increasing day by day, so an efficient and real-time Intrusion Detection System (IDS) is urgently needed to protect patients' privacy and the security of medical data. This paper is devoted to building a hospital network security IDS based on Big data (BD) clustering algorithm. Hospital network includes complex and diverse data flow, sensitivity of patient privacy and multi-level structure of medical information. In order to meet these challenges, this paper chooses BD clustering algorithm to effectively identify abnormal behaviors and improve the accuracy and real-time performance of intrusion detection. In the actual system construction, we choose the distributed computing framework Apache Spark and BD storage system MongoDB as the core technical basis. Through the improved K-means algorithm, we realized the clustering analysis of hospital network data, and combined with real-time data stream processing technology, we realized the timely response to abnormal behavior. In the experimental stage, this study uses a data set that simulates hospital network traffic, and verifies the excellent performance of the system in intrusion detection by comparing the accuracy of different clustering algorithms. The experimental results show that the improved K-means algorithm has high accuracy in the hospital network environment, and the system can effectively deal with network data of different scales and changes in real time. Hospital network security IDS based on BD clustering algorithm will play an important role in the field of medical information security and provide a feasible solution for building a safer and more efficient medical network environment. © 2024 ieee.

关键词： Network intrusion

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Biogas Fermentation Monitoring Research Based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning 13

Biogas Fermentation Monitoring Research Based on Borderline-...

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13th international conference of Information and Communication Technology, ICTech 2024

作者： Xiu, Pengfei Fu, Lijun Sun, Yukun Wang, Hongliang Li, Xuerao Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350376258

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model. ©2024 ieee.

关键词： Decision making

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EMoNet: An environment causal learning for molecule OOD generalization

EMoNet: An environment causal learning for molecule OOD gene...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Limin Yang, Kuo Du, Wenjie Yi, Zhongchao Zhou, Zhengyang Wang, Yang Univ. of Science and Technology of China School of Software Engineering Hefei China Univ. of Science and Technology of China School of Data Science Hefei China

ISBN: (纸本)9798350386226

data-driven molecular computing has become an increasingly popular topic in AI for molecular and bioinformatic science. Current molecular modeling exploits the Graph Neural Networks (GNNs) to achieve the representation but they mostly fail to generalize to out-of-distribution (OOD) samples. Even though recent advances on OOD-oriented graph learning discovered the invariant rationale on graphs, they still ignore two important issues, i.e., 1) the increasing number and types of molecules expand patterns of environments on graphs, resulting in failures of invariant rationale based models, 2) the associations between discovered molecular subgraphs and corresponding properties are complex where causal substructures cannot fully interpret the labels. To this end, we propose an environment causal learning framework, EMoNet, to tackle the unresolved OOD challenge in molecular science. Specifically, we model the graph environments via bypassing invariant subgraphs. We first incorporate chemistry principle into our graph growth generator to imitate environment growth, and then devise an environment-GIB to squash out environment and finally introduce a cross-attention causal aggregation, allowing dynamic interactions between environments and invariances. We perform experiments on seven datasets and extensive experiments demonstrate strong generalization ability of EMoNet. © 2024 ieee.

关键词： Molecular docking

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Fault Classification based on Deep Learning for Photovoltaic Systems 2

Fault Classification based on Deep Learning for Photovoltaic...

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2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Kelin, A. R. Darshika Sudhakar, K. Surekha, Pothireddy Palanivel, R. Al-Jawahry, Hassan M. Karunya Institute of Technology and Sciences Data Science and Cyber Security Karunya Nagar Coimbatore India Department Electronics and Communication Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Telangana Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

In general, high power electronic converters such as Multilevel Inverters (MLI), that find their use in many applications, create difficulties of augmented switch count, capacitor flow current and reverse current flowing from inductive loads. above problems, in turn, result in faults, without which MLI reliability is unattainable. Therefore, fault classification and tolerance strategies are of paramount contribute to keeping reliability of inverters. Herein we present a deep learning (DL)-based fault classification approach for 15-level CHB Multi-level Inverter. proposed system includes pre-processing based on Adaptive Hilbert-Huang filtering, segmentation by a Partial Differential Equation (PDE), feature extraction utilizing Scale-Invariant Feature Transform (SIFT), and fault signal classification being performed by a Convolutional Neural Network (CNN) trained with a Honey Badger Optimization Algorithm (HBOA). MATLAB was applied to our method of identifying a set of four complex fault types for purpose of measurement. results of HBOA-optimized CNN have demonstrated highest accuracy rate of 99.91%. © 2024 ieee.

关键词： Electric inverters

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Research on University Sports Information Management System Based on Big data and Cloud computing 5

Research on University Sports Information Management System ...

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5th international conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Lei, Ming Wang, Yong YiBin Vocational and Technical College Sichuan Yibin China

ISBN: (纸本)9798331527662

This paper proposes a sports information management system based on Hadoop platform, which integrates convolutional neural network (CNN) technology to achieve efficient data management and intelligent decision support. Through this architecture system can effectively process and analyze the massive data from university sports activities, so as to improve the efficiency and accuracy of data processing. Secondly, Convolutional Neural Network (CNN) technology is introduced to realize face recognition function. The application of this technology not only enhances the security of the system, but also improves the speed and accuracy of athlete identity verification. The automatic feature extraction capability of CNN enables the system to quickly identify key information in complex image data, which provides a strong technical support for the intelligence of sports management. In addition, the system provides rich data API interfaces, which can seamlessly connect various sports resources in colleges and universities, and realize data sharing and exchange. By building a unified sports big data environment, the system promotes the optimal allocation and efficient use of resources, and provides comprehensive data support for sports teaching, training and competition. This system builds a powerful sports information management platform by integrating Hadoop platform and CNN technology. This not only improves the ability of data processing and storage, but also greatly promotes the modernization process of college sports management through intelligent decision support. © 2024 ieee.

关键词： Convolutional neural networks

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Leveraging Graph Neural Networks for complex Network Traffic Signal Processing 2

Leveraging Graph Neural Networks for Complex Network Traffic...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Kumar, S Vinod Shrivastava, Anurag Aravinda, K. Kansal, Lavish Kalra, Ravi Sabah, Hawraa Ali Institute of Aeronautical Engineering Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

With the development of deep learning algorithms, automated interpretation has advanced significantly, but simultaneous interpretation of speech and sign language still presents a major obstacle. In order to close the communication gap between the hearing and the deaf populations, this study offers novel multimodal deep learning techniques for the simultaneous interpretation of speech and sign language. To analyze and interpret visual and aural input concurrently, the suggested approach combines cutting-edge neural network topologies with sophisticated signal processing methods. This study is centered on the innovative combination of recurrent neural networks (RNNs), namely Long Short-Term Memory (LSTM) networks for auditory speech processing, and convolutional neural networks (CNNs) for visual sign language interpretation. The extraction and integration of temporal and spatial data from both modalities is made easier by this combination. To improve the model's emphasis on relevant signals in a dynamic conversational setting, attention methods are also included. The model was trained and assessed using a large dataset that included a variety of spoken and sign languages. When compared to the current approaches, the findings show a considerable improvement in accuracy and efficiency. This work sheds light on the intricate structure of multimodal communication and its computer modelling, in addition to contributing to the area of automated *** creating new opportunities for inclusive communication technology, our study improves the effectiveness and accessibility of real-time, cross-modal interpretation. © 2024 ieee.

关键词： Convolutional neural networks

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Deep Learning Techniques for Advanced Large-Scale data Processing 15

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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

作者： Sunil, M.P. Shekhawat, Jyoti Vanaja, T. Mohammed, Vazeer Ali Thaware, Shailesh J. Singh, Vaishali Karnataka Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer & Information System Lewis University Illinois United States Department of Computer Engineering Vishwakarma Institute of Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350370249

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information. ©2024 ieee.

关键词： Large datasets

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Image Processing: Unlocking the Secrets of Visual Information 2

Image Processing: Unlocking the Secrets of Visual Informatio...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Lavanya, D. Bhushan, Kavi Kant, Gur Sharan Sountharraj, A.C. Srivastav, Akanksha Begum, M. Sharmila Sri Venkateswara College of Engineering Computer Science and Engineering Department Andhra Pradesh Tirupati India Ccs University Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science U.P Meerut India Dr. Ngp Art's and Science College Department of Computer Science with Data Analytics Coimbatore-48 India Tula's Institute Department of Computer Science and Engineering Dehradun India Maharaja Institute of Technology Department of Computer Science Mysore India

ISBN: (纸本)9798350384277

In the age of image processing, the hidden ability can be encoded with visual information that is not only observed but also painstakingly carried out;in this way, the complex secrets that are encoded in each pixel have been made clear. Image processing is the magic that has increased the amount of clarity, significance, and utility while transforming stationary arrays of images into dynamic narratives. The image's quality is typically influenced by several variables, such as temperature, light, and noise. Due to its persistent presence in various digital photographs, noise plays a crucial function in the image. Throughout the image coding, transmission, and development procedure, noise consequences could arise. The field of noise removal in image processing has grown to be quite interesting and vibrant. Recently, there has been a notable advancement regarding the filtering of various noise types for image processing in digital format. This research paper aims to discuss image processing and denoising various noises in images to improve overall quality. In terms of methodology systematic qualitative research has been conducted that helps the research gather information from a wide range of sources. Moreover, this paper finds that image processing has significant potential in this digital age. By denoising images, this phenomenon can be applied in different applications in every sector. © 2024 ieee.

关键词： Image denoising

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