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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Rakhra, Manik Singh, Arun Singh, Dalwinder Kaur, Baljinder Shruti Lovely Professional University Department of Computer Science & Engineering Phagwara India

ISBN: (纸本)9798350350357

Cloud computing has completely changed how people and organizations access and manage data and services. However, the centralized nature of the cloud storage and processing introduces significant security concerns, especially in the context of data privacy and confidentiality. Traditional cryptographic methods are often used to secure data in the cloud, but they may not provide the desired level of security and efficiency when used in isolation. Hybrid cryptography, a fusion of symmetric and asymmetric cryptography techniques, has emerged as a promising solution to address these challenges in cloud computing. This abstract explores the concept of hybrid cryptography in the context of cloud computing, highlighting its role in enhancing security while maintaining computational efficiency. The hybrid cryptography approach leverages the strengths of symmetric and asymmetric materials encryption methods. Symmetric encryption is well-known for its computational efficiency, making it suitable for encrypting large volumes of data in cloud storage. Asymmetric encryption, on the other hand, excels in secure key exchange and authentication, vital components in cloud computing scenarios where multiple parties need access to encrypted data. © 2024 IEEE.

关键词： Cryptography

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Analyzing False Positives in Bankruptcy Prediction with Explainable AI

Analyzing False Positives in Bankruptcy Prediction with Expl...

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2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Mahajan, Akshat Shukla, Kaushal Kumar Department of Computer Science and Engineering Varanasi India

ISBN: (纸本)9781665456272

With the rise of powerful machine learning solutions, it has become easy to create highly accurate solutions for financial services, yet they fail to comply with financial regulations as they lack transparency and explainability. Bankruptcy prediction is one of the major issues in finance and in the bid to create a highly efficient model which minimizes false negatives where we correctly classify companies that are going to be bankrupt, we see a tradeoff with an increase in false positive cases where companies that are not going to be bankrupt are also flagged. In this paper, we have used a post hoc model explainability technique called SHAP to explain the ML-based bankruptcy prediction model on Taiwan's bankruptcy dataset and Polish company dataset by generating local as well as global explanations. We have also used the SHAP model to understand how different features contributed to false cases and compare feature attribution with overall model feature relevance to generate an in-depth study of false positive cases. © 2023 IEEE.

关键词： Finance

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A comprehensive analysis of threat vectors in software-defined networking

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Multimedia Tools and Applications 2024年第39期83卷 86791-86832页

作者： Dahiya, Seema Sehrawat, Harkesh Kharb, Seema Siwach, Vikas University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Rohtak India Department of Computer Science and Engineering SRM University Sonepat Sonipat India

In recent years, cyber-attacks have become more frequent and advanced, targeting critical infrastructure, businesses, homes, and government agencies. Detecting and preventing these attacks at the earliest stage possible is crucial to avoid serious harm, including data breaches. Researchers and experts in cybersecurity are looking to Software-Defined Networking (SDN) technologies as a solution to enhance real-time defense against cyber-attacks. SDN revolutionizes traditional networking by offering unprecedented flexibility and control over network resources, making it possible to adapt quickly to emerging threats. SDN provides logically centralized network control by separating the control plane from the data plane. This enables network programming and can block network activity when malicious movement is spotted. This paper presents a comprehensive analysis of threat vectors in SDN. It examines the various ways in which SDN networks are vulnerable to cyber-attacks, including network infrastructure, application layer, and SDN controller. The paper also evaluates the effectiveness of existing security measures and proposes future research directions to enhance SDN security. Overall, the paper highlights the potential of SDN as a powerful tool in the fight against cybercrime and emphasizes the importance of continued research and development to improve SDN security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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Analytical Results of Heart Attack Prediction Using Data Mining Techniques 1

Analytical Results of Heart Attack Prediction Using Data Min...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Abhijith, S. Mishra, Shaleen Prathap, Boppuru Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

In the modern era of living a fast lifestyle, people are not more conscious of their food eating and lifestyle. Due to these reasons, the chances of having a cardiac-related disease have risen drastically. This paper has studied the various supervised and unsupervised machine learning algorithms in comparative methods with best accuracy. Models like classification algorithms, regression algorithms, and clustering algorithms have been used for this paper. This research paper majorly focuses on patients with certain medical attributes that indicate a higher risk of heart disease. The model almost gives a good accuracy for all the regression and classification models when compared to the clustering models. Among all the algorithms, random forest and decision tree gives better accuracy © 2023 IEEE.

关键词： K-means clustering

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Reliable Network Performance for Edge Networks with QoS-Aware Adaptive Routing 8

Reliable Network Performance for Edge Networks with QoS-Awar...

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8th IEEE International Conference on Edge Computing and Communications, EDGE 2024

作者： Hamdan, Osama Abu Arslan, Engin University of Texas at Arlington Department of Computer Science and Engineering United States

ISBN: (纸本)9798350368499

Edge networks are commonly used to collect data from remote areas with limited accessibility such as deserts and mountaintops. They rely on wireless communication to stream the collected data to data centers, which makes them susceptible to bandwidth fluctuations due to extreme weather conditions such as snowstorms. Although these networks are designed to have alternate routes to recover from link failures, existing rigid network management solutions (i.e., OSPF) make it difficult to utilize alternate routes when the bandwidth of wireless links degrades. This paper proposes an adaptive routing algorithm, RENET, to quickly detect bandwidth fluctuations and re-adjust flow paths to increase the utilization of available links. RENET introduces a novel delay-based measurement technique to quickly detect bandwidth fluctuations in wireless links. Experimental results collected in real-world and emulated networks show that RENET offers a robust solution to bandwidth fluctuations and outperforms the state-of-the-art solutions by more than 30% in terms of QoS satisfaction rate. Moreover, RENET increases the quality of adaptive video streams by up to 54%. © 2024 IEEE.

关键词： Network routing

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Yoga Pose Estimation using PoseNet Model 1

Yoga Pose Estimation using PoseNet Model

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Sharan, Karanam Sai Sumathi, R. Anvitha, Karnatakam Sai Kishan, Kolaparthi Venkata Satya Naga Bathineedi, Dhathri Department of Computer Science and Engineering Kalasalingam Academy of Research and Education India

ISBN: (纸本)9798331533557

Yoga has become an integral part of exercise practice in the holistic health regimen, greatly impacting on the physical, mental, and emotional well-being. It enhances flexibility, strength, and balance while promoting relaxation and stress reduction. To reap these benefits without causing harm, it is essential to perform yoga poses correctly. A reliable estimator of the pose is needed to maintain proper alignment and gain the maximum from each session. The proposed model integrated with CNN, helps users achieve correct postures by pausing the session timer until the user successfully maintains the correct alignment. This ensures that the user remains focused on achieving proper posture before moving to the next asana. The proposed approach supports users in improving their yoga practicing techniques by ensuring that progression can be done when optimal posture is maintained, encouraging a safe and effective yoga practice that aligns with personal health goals. © 2024 IEEE.

关键词： CNN Fitness objectives Health benefits Performance measures Posenet Posture correction Yoga learn

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Revolutionizing Supply Chain Management: The Role of Blockchain and Explainable AI 5th

Revolutionizing Supply Chain Management: The Role of Blockch...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Sharma, Sonika Sharma, Ajay Kumar Patel, Mayank Rathore, Narendra Singh Department of Computer Science Engineering GITS Udaipur India GITS Udaipur India

ISBN: (纸本)9789819761050

Security of food is necessary as a nation should have no scarcity of food and its people should be able to obtain, access, and consume an adequate amount of food. Ensuring and maintaining high-quality, safe products is a critical aspect of food security, and the supply chain process. Food producers are therefore obliged to provide wholesome and nourishing goods. The study presents a model that is user friendly and has a simple web-based design of front end. The model is XAI based and works on the Faster RCNN model. With the help of the XAI technique, the user will comprehend ML and AI techniques on a deeper level. To assist in digitizing food product information, quickly retrieving the data, and identifying any data that is hidden in the QR code that might have affected the food's security and texture, a Faster RCNN model is suggested based on EAI. The experiments’ findings showed that, when payload and user count increase, the suggested method needs less response time than other current approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Food ingredients

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COVID19 Outbreak:A Hierarchical Framework for User Sentiment Analysis

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computers, Materials & Continua 2022年第2期70卷 2507-2524页

作者： Ahmed F.Ibrahim M.Hassaballah Abdelmgeid A.Ali Yunyoung Nam Ibrahim A.Ibrahim Department of Computer Science Faculty of Computer ScienceNahda UniversityBanisuefEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt Faculty of Computers and Information Minia UniversityAlMiniaEgypt Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Social networking sites in the most modernized world are flooded with large data *** the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they *** Coronavirus pandemic has invaded the world and been given a mention in the social media on a large *** a very short period of time,tweets indicate unpredicted increase of *** reflect people’s opinions and thoughts with regard to coronavirus and its impact on *** research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and *** this paper,a hierarchical twitter sentiment model(HTSM)is proposed to show people’s opinions in short *** proposed HTSM has two main features as follows:constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width,as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner(VADER)sentiment *** tweets for each extracted important aspect can be categorized as follows:strongly positive,positive,neutral,strongly negative,or *** quality of the proposed model is validated by applying it to a popular product and a widespread *** results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.

关键词： COVID19 COVID data sentiment analysis hierarchical clustering sentiment tree

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Near Optimal Locality Sensitive Orderings in Euclidean Space 40

Near Optimal Locality Sensitive Orderings in Euclidean Space

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40th International Symposium on Computational Geometry, SoCG 2024

作者： Gao, Zhimeng Har-Peled, Sariel Department of Computer Science and Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Department of Computer Science University of Illinois UrbanaIL United States

ISBN: (纸本)9783959773164

For a parameter Ε ∈ (0, 1), a set of Ε-locality-sensitive orderings (LSOs) has the property that for any two points, p, q ∈ [0, 1)d, there exist an order in the set such that all the points between p and q (in the order) are Ε-close to either p or q. Since the original construction of LSOs can not be (significantly) improved, we present a construction of modified LSOs, that yields a smaller set, while preserving their usefulness. Specifically, the resulting set of LSOs has size M = O(Ed−1 log E), where E = 1/Ε. This improves over previous work by a factor of E, and is optimal up to a factor of log E. As a consequence we get a flotilla of improved dynamic geometric algorithms, such as maintaining bichromatic closest pair, and spanners, among others. In particular, for geometric dynamic spanners the new result matches (up to the aforementioned log E factor) the lower bound, Specifically, this is a near-optimal simple dynamic data-structure for maintaining spanners under insertions and deletions. © Zhimeng Gao and Sariel Har-Peled.

关键词： Structural optimization

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Classifying Enzymatic Functions Using Graph Neural Networks: Insights from the ENZYMES Dataset 22

Classifying Enzymatic Functions Using Graph Neural Networks:...

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22nd International Learning and Technology Conference, L and T 2025

作者： Attiah, Ameera Alagha, Mawaddah Sharab, Leen Effat University College of Engineering Computer Science Department Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

This study explores the use of Graph Neural Networks (GNNs) to classify enzyme functions using the ENZYMES dataset, which represents proteins and their interactions as graphs. The literature review highlights the evolution and efficacy of Graph Neural Networks (GNNs) in capturing complex relational data in graph structures, revolutionizing fields such as bioinformatics by facilitating advanced enzyme classification and potential drug discovery through enhanced analysis of biological interactions. The methodology uses the ENZYMES dataset from the TUDataset, which encodes protein structures into graphs for enzyme function classification. A structured Graph Neural Network was utilized with layers designed to capture complex graph hierarchies, employing preprocessing steps, advanced graph convolution techniques, and rigorous training procedures to ensure the model's effectiveness and reproducibility in predicting enzyme functions. The study demonstrates that the Graph Neural Network model effectively learned and generalized enzyme function classification, as evidenced by consistent improvements in training performance and strong testing results. The model's robustness was further validated through detailed analyses such as confusion matrices, ROC curves, and precision-recall metrics, which also identified specific areas for future improvement in feature engineering and training approaches to enhance accuracy and model interpretability. © 2025 IEEE.

关键词： Graph neural networks

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