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Link Prediction of biomedical data using Hetrosim GCN-GAT

Link Prediction of biomedical data using Hetrosim GCN-GAT

2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Mythili, R. Parthiban, N. College of Engineering and Technology School of Computing SRM Institute of Science and Technology Department of DataScience and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350319200

With the emergence of large number of Electronic Medical Records (EMRs), the researchers turned their attention towards the prediction, recommendation systems with these available data. This paper deals with the input of the patient details and the drugs administered to them for the different health conditions. The Drug to Drug Interaction (DDI) prediction is essential in order to avoid the unintended adverse reactions and making the drugs less effective. It is represented in the form of graphs with nodes and edges/links. Link prediction is often used to detect the existence of link or edges present in between the specified nodes. Graph Neural Network based algorithm HetrosimGCN-GAT is proposed to address the underlying link prediction tasks. A heterogeneous network is initially built using the drug-drug, protein-protein similarity matrices and drug protein matrices. The embedded representation of the drug and protein is then obtained using the graph convolutional neural network (GCN). Graph attention mechanism provides a weighting factor for every connect in order to assess the significance or highlight the contribution of distinct neighbourhood nodes to the central node. The unknown DDI and Drug Protein Interaction is predicted by applying the inner product decoder. The performance of HetrosimGCN-GAT is evaluated against the baseline methods and its outperforms the baseline methods. © 2023 IEEE.

关键词： drug to drug interactions GNNs knowledge graph link prediction

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LDChain: A Lightweight and Scalable Blockchain System for Dynamic IoT Scenarios 20th

LDChain: A Lightweight and Scalable Blockchain System for ...

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20th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2024

作者： Wang, Jianrong Cao, Yuxuan Hu, Dengcheng Li, Qi Li, Sen Li, Xuewei Liu, Xiulong College of Intelligence and Computing Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China School of Future Technology Tianjin University Tianjin China

ISBN: (纸本)9789819628292

As a distributed ledger, blockchain inherently guarantees data consistency. However, when applied to the Internet of Things (IoT), existing solutions employing blockchain systems struggle to address the scalability demands posed by the dynamic mobility of heterogeneous devices. To this end, we propose Lightweight DAG Chain (LDChain), a lightweight and scalable blockchain system tailored for IoT scenarios. LDChain connects weak nodes and strong nodes through a hierarchical DAG consensus algorithm. For weak nodes, we introduce a transaction-based DAG consensus mechanism utilizing a snapshot synchronization strategy to cater to the dynamic nature of weak nodes and the scalability demands of the system. The integrated snapshot synchronization strategy, based on the second latest block transaction selection algorithm, reduces the storage and computational overhead for resource-restricted devices. For strong nodes, we devise a block-based DAG consensus mechanism and a repackaging strategy, which resolves transaction conflicts across regions by managing conflicting transactions through a priority queue. We implement a prototype of LDChain and conduct experiments in a network environment comprising 10 cloud servers, simulating several strong nodes and weak nodes. The experimental results show that our system achieves a throughput exceeding 7000 transactions per second (TPS) and a transaction confirmation latency of 1.3 s with 100 nodes. © IFIP International Federation for Information Processing 2025.

关键词： Scalability

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SGMFuzz: State Guided Mutation Protocol Fuzzing

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 71-75页

作者： Wen, Zhenyu Yu, Jianfeng Huang, Zening Wu, Yiming Hong, Zhen Ranjan, Rajiv Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou310023 China Newcastle University School of Computing Newcastle upon TyneNE4 5TG United Kingdom

Protocol implementations are fundamental components in network communication systems, and their security is crucial to the overall system. Fuzzing is one of the most popular techniques for detecting vulnerabilities and has been widely applied to the security evaluation of protocol implementations. However, due to the lack of machine-understandable prior knowledge and effective state-guided strategies, existing protocol fuzzing tools tailored for stateful network protocol implementations often suffer from shallow state coverage and generate numerous invalid test cases, thereby impacting the effectiveness of the testing process. In this letter, we introduce SGMFuzz, a grey-box fuzzing tool that combines a state-guided mutation mechanism to detect security vulnerabilities in protocol implementations. SGMFuzz uses the feedback collected during fuzzing to construct a finite-state machine, which aids in a deeper exploration of the program. Additionally, we design a message-aware module to enhance the tool's ability to generate valid test cases. Our evaluation demonstrates that, compared to the most advanced and widely used network protocol fuzzing tools, SGMFuzz increases the number of discovered execution paths by over 15% on average and improves state transition coverage by over 10%, providing a more comprehensive security assessment of protocol implementations. © 2019 IEEE.

关键词： Network protocols

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A Lumped Elements Loaded Over-Hundred-Octave Super-Wideband Antenna 7

A Lumped Elements Loaded Over-Hundred-Octave Super-Wideband ...

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7th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2024

作者： Xie, Cuiqin Chang, Jing Xia, Yinfeng Zhang, Huakun Anhui Vocational and Technical College School of Computing and Information Technology Hefei230011 China Anhui University School of Electronic and Information Engineering Hefei230039 China Harbin Engineering University College of Information and Communication Engineering Harbin150001 China

ISBN: (纸本)9798350384437

In this paper, an over-hundred-octave super-wideband antenna is proposed. The proposed antenna is composed of the ground plane, coaxial probe, the hollowed-out conical radiator, four short circuit posts and several loaded lumped elements. By properly optimizing the model of the proposed antenna and adjusting the parameters of the lumped elements, the performance of the antenna in the low frequency band has been greatly improved. By numerically optimizing, the simulated results show that the proposed antenna can realize 537:1 -10-dB bandwidth from 0.01GHz-5.37GHz, which is expected to meet the application requirements of integrated and wide spectrum. © 2024 IEEE.

关键词： Antenna grounds

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The Impact of Domain Name Server(DNS)over Hypertext Transfer Protocol Secure(HTTPS)on Cyber Security:Limitations,Challenges,and Detection Techniques

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Computers, Materials & Continua 2024年第9期80卷 4513-4542页

作者： Muhammad Dawood Shanshan Tu Chuangbai Xiao Muhammad Haris Hisham Alasmary Muhammad Waqas Sadaqat Ur Rehman Faculty of Information Technology Beijing University of TechnologyBeijing100124China National Center of Robotics and Automation University of Engineering and TechnologyPeshawar25000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Engineering Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUnited Kingdom School of Engineering Edith Cowan UniversityPerthWA 6027Australia School of Computer Science and Engineering University of SalfordManchesterM54BRUnited Kingdom

The DNS over HTTPS(Hypertext Transfer Protocol Secure)(DoH)is a new technology that encrypts DNS traffic,enhancing the privacy and security of ***,the adoption of DoH is still facing several research challenges,such as ensuring security,compatibility,standardization,performance,privacy,and increasing user *** significantly impacts network security,including better end-user privacy and security,challenges for network security professionals,increasing usage of encrypted malware communication,and difficulty adapting DNS-based security ***,it is important to understand the impact of DoH on network security and develop newprivacy-preserving techniques to allowthe analysis of DoH traffic without compromising user *** paper provides an in-depth analysis of the effects of DoH on *** discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security ***,this paper highlights the need for continued research and development to ensure the effectiveness of DoH as a tool for improving privacy and security.

关键词： DNS DNS over HTTPS cybersecurity machine learning

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4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Srishwal, Dhruvi Shibu, P.S. Vardan, Harsha Sajjad Ibrahim, A. Gouthaman, P. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department Of Networking And Communications Kattankulathur603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology and Technology Department Of Networking And Communications Kattankulathur603203 India

ISBN: (纸本)9798331529635

Heart arrhythmias, if not detected early, can lead to severe conditions like stroke or heart failure, making timely and accurate classification critical. Traditional methods of analyzing Electrocardiogram (ECG) data are manual, time-consuming, and prone to errors due to the complexity of ECG signals. This paper presents an automated approach for arrhythmia classification using vector databases and similarity search algorithms. By converting raw ECG time-series data into vectorized representations, the system enables efficient pattern recognition and real-time analysis. Machine learning models applied to the vectorized data enhance classification accuracy, while the use of a vector database allows for quick similarity searches, facilitating real-time comparison of patient data with known arrhythmia patterns. The proposed method significantly reduces analysis time and increases precision, offering a robust framework for real-time monitoring and early detection, thereby improving patient outcomes and enabling timely medical interventions. © 2024 IEEE.

关键词： Dimensionality reduction

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Optimization Research of Particle Swarm Optimization Algorithm in BP Neural Network Model 2

Optimization Research of Particle Swarm Optimization Algorit...

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2nd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2023

作者： Cai, Yongheng Guo, Jiadong Zhong, Junhao Zhuhai College of Science and Technology School of Computing Zhuhai China Zhuhai College of Science and Technology Electronic Information Engineering College Zhuhai China Zhuhai College of Science and Technology School of Pharmacy and Food Science Zhuhai China

ISBN: (纸本)9798350371451

In today's big data era, the application of artificial neural network (ANN) in the field of machine learning has attracted increasing attention. But there are still some problems in the research of BP neural network, such as slow convergence speed, over-fitting and so on. As an optimization algorithm, particle swarm optimization algorithm can overcome some problems existing in BP neural network at present. Based on this background, PSO optimization algorithm is used to optimize BP neural network in order to improve the performance of neural network. Our results show that the average absolute error (MAE) of PSO-BP model is 0.005 and 0.023 lower than that of GA-BP model and single BP model. In terms of root mean square error (RMSE), PSO-BP model shows a smaller RMSE (0.105), while GA-BP model and single BP model have RMSE of 0.128 and 0.208. In addition, PSO-BP model achieved a higher R2 value (0.96), while GA-BP model and single BP model achieved a higher R2 value of 0.92 and 0.84. The most striking thing is that in the classification task, the accuracy of PSO-BP neural network reaches 91.7526%, which is 2.3253% and 12.0174% higher than GA-BP model and pure BP model. These results show that the BP neural network optimized by particle swarm optimization algorithm has higher accuracy and better performance than the unoptimized BP model and GA-BP model. To sum up, this study has made some contributions in the field of machine learning, and provided an important method for improving prediction performance and optimizing neural network models. © 2023 IEEE.

关键词： Machine learning

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua Science and technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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FlexFlow- The Nutrients and Recommendation Application Using Machine Learning 4

FlexFlow- The Nutrients and Recommendation Application Using...

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4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Elavarasi, D. Kavitha, R. Jayabarathan, Jayson K. Aanjankumar, S. Mount Zion College of Engineering and Technology Department of Computer Science and Engineering Pudukkottai India Velammal College of Engineering Madurai India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331529635

Flexflow is an advanced web application designed to provide personalized dietary recommendations tailored to individual needs and health goals. By considering factors such as age, sex, height, weight, activity level, and dietary preferences, Flexflow generates customized meal plans and offers valuable insights into nutritional composition, relevant recipes, and exercise recommendations. This comprehensive approach empowers users to make informed dietary choices and achieve their health and wellness objectives. By utilizing data-driven insights and personalized recommendations, Flexflow has the potential to significantly improve dietary habits and promote healthier lifestyles across diverse populations. © 2024 IEEE.

关键词： Food ingredients

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Hybrid Multiple Cryptography for Data Encryption 8

Hybrid Multiple Cryptography for Data Encryption

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Anuradha, M. Loganathan, Sathyapriya Suseela, G. Selvan, Mercy Paul Nalini, M. Chitra Devi, D. S.A.Engineering College Dept of Cse India Faculty of Engineering and Technology Srm Institute of Science and Technology Computing Technologies India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Dept of Networking and Communications India Sathyabama Institute of Science & Technology Cse Department India

ISBN: (纸本)9798350396638

Since the information technology revolution, cryptography has been essential for protecting sensitive information and communication. A plethora of algorithms have been developed to safeguard data and protect data integrity, with the goal of improving data security and protection. Modern cryptography is based on a variety of encryption techniques, namely Hybrid cryptography and multiple cryptography. Hybrid cryptography combines asymmetric key and symmetric key encryption to combine the strengths of both. Multiple cryptography is the process of encrypting a previously encrypted message one or more times, each time using the same or a different symmetric or asymmetric algorithm. The study proposes an approach that seeks to incorporate both hybrid and multiple cryptography as a way to combine all of their benefits while also minimizing the drawbacks that each technique has on its own to provide an additional layer of security. Although these techniques have all contributed to data protection, not all of them have been successful in providing a high level of security as they have shown a propensity for being targeted and exploited over time. This research project implements a cryptosystem based on Hybrid Multiple Cryptography (HMC) which is a novel scheme for data encryption that offers a greater level of security for sending data in an Android messaging application, in order to showcase the system's security capabilities in real time. © 2023 IEEE.

关键词： Cryptography

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