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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Sivakumar, Soubraylu Vinay, B. Sai Arulmurugan, A. Prakash, U.M. Kumar, T. Rajesh Sridevi, S. Srm Institute of Science and Technology Computer Technologies School of Computing Chennai Kattankulathur India Saveetha Institute of Medical and Technical Sciences Computer Science and Engineering Saveetha School of Engineering Chennai India Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India

ISBN: (纸本)9798350344387

In the rapidly advancing realm of healthcare, ensuring the well-being of critical care patients stands as a paramount mission. Various cutting-edge sensors are available in the market, proficient in monitoring vital health. A substantial number of healthcare facilities still rely on manual processes, a practice that is not only repetitive and labor-intensive but also fraught with the potential for human errors. This pressing need for immediate, accurate responses in the context of critical care has driven the development of a comprehensive solution. Our proposed system brings together security, data normalization, and efficient data exchange, underpinned by a unified approach. Security, a cornerstone of healthcare, finds its realization in the implementation of JSON Web Tokens (JWT). This technology ensures the privacy and integrity of communication between healthcare entities. Crucial patient data, including personal particulars, lifeline parameters, and historical records, is securely housed in a scalable and non-redundant Postgres SQL database. In emergencies, time is of the essence. To expedite responses, an innovative tagged QR code technique is introduced. The unified approach doesn't simply prioritize security;it underscores the significance of data standardization and efficient data exchange. A robust security framework has been meticulously designed to protect sensitive patient information, minimize the risk of data breaches, and prevent unauthorized access. Furthermore, data normalization ensures that patient data is consistently structured and formatted, facilitating compatibility with diverse healthcare platforms and devices. The normalization additionally enhances interoperability, enabling seamless information exchange between different systems and healthcare providers. In summary, our unified approach, marked by state-of-the-art security, data normalization, and efficient data tagging, sets a new standard for patient care in critical settings, ensurin

关键词： Critical Care Emergency Health Care JSON Web Token Patient monitoring Postgres SQL QR Code

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YOLOv8 for Pedestrian Detection: A Comparative Study for Pedestrian Detection 3

YOLOv8 for Pedestrian Detection: A Comparative Study for Ped...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sivaraman, G. Sophiya, E. Diviya, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

This research analyzes the usefulness of YOLOv8 for robust pedestrian recognition in demanding real world situations. A custom-trained YOLOv8 model is evaluated against SSD models, displaying better accuracy in detecting and localizing human classes within images characterized by occlusions and variable scales. We analyze the model's performance across confidence thresholds, demonstrating a positive balance between precision and recall. While noting computational considerations, our findings demonstrate YOLOv8's potential for advancing pedestrian detection systems, opening the way for enhanced safety and efficiency in applications such as autonomous driving, surveillance, and smart city efforts. © 2024 IEEE.

关键词： Deep learning

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Towards LLM-driven Automated Verification of Best Practices in Digital Public Infrastructures

Towards LLM-driven Automated Verification of Best Practices ...

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2024 SICSA Workshop on Reasoning, Evaluation and Application of Large Language Models, SICSA-REALLM 2024

作者： Markovic, Milan Sripada, Somayajulu Chakrabarti, Sujit Kumar Diddigi, Raghuram Bharadwaj Department of Computing Science University of Aberdeen United Kingdom Department of Computer Science International Institute of Information Technology Bangalore India Department of Data Science and Artificial Intelligence International Institute of Information Technology Bangalore India

In this position paper, we discuss three distinct approaches for assessing risks associated with Digital Public Infrastructures (DPI) and how Large Language Models could provide automated knowledge extraction to support such analyses at scale. We further outline future research directions inspired by the domain of collective intelligence and formal verification methods. © 2024 Copyright for this paper by its authors.

关键词： Digital Public Infrastructures Formal Verification Infrastructure Analysis Large Language Model

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An Extended Agglomerative Hierarchical Clustering Techniques 2

An Extended Agglomerative Hierarchical Clustering Techniques

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Maravarman, M. Babu, S. Pitchai, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattangulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur India

ISBN: (纸本)9798350315905

Clustering is a significant method of data analytics in real world environments since human labelling of the data is often costly. Clustering was developed as an alternative to manual tagging. In the field of data analytics, hierarchical clustering is of critical significance, particularly in light of the exponential rise of data derived from the normal world. You may derive a variety of hierarchical agglomerative clustering algorithms from this architecture by providing an inter-cluster semantic similarity, an expression patterns of the -similarity graph, and a cover procedure. These three pieces of information are required. According to the findings of our experiments, our approaches are not only more efficient than conventional hierarchical algorithms, but they also produce smaller agglomerative hierarchical clustering while maintaining the same level of clustering effectiveness. It is generally agreed that topology management is an effective strategy for addressing these challenges. This method groups nodes together for the purpose of managing them and/or carrying out a variety of duties in a dispersed way, such as resource management. There are many quality-driven goals that may be accomplished by clustering, despite the fact that approaches for clustering are mostly renowned for their ability to reduce energy usage. The purpose of this study is to provide a comprehensive explanation on various enhanced agglomerative hierarchical clustering techniques. In addition to this, the authors have provided certain criteria, on the basis of which one may also assess which of these previously described algorithms is the most effective. © 2023 IEEE.

关键词： Data Analytics

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A Systematic Review of Data Augmentation Techniques in Natural Language Processing for Sentiment Analysis using Machine learning Techniques 4

A Systematic Review of Data Augmentation Techniques in Natur...

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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Patil, Uday Dhanaji Sudhakaran, Pradeep SRM Institute of Science and Technology Department of Computer Science & Engineering Tamil Nadu Kattankulathur603203 India Kolhapur India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331515720

Due to the enormous growth of mobile technology, Social media has created a crucial platform to express the feelings and opinions of people. Based on these opinion considerations, it is very beneficial for making strategic decisions in organizations, businesses, trading, political entities, and so on. To achieve effective improvement, sentiment analysis is taken into account, and the public opinion polarity for further reference. In this perspective, the main contribution of the study is to explore the benefits and limitations of various learning techniques under the evaluation of sentiment analysis. Generally, Sentiment analysis is evaluated by Natural language processing tasks with various learning techniques, to achieve effective generalization ability and efficiency, data augmentation techniques are integrated, which reduces the overfitting problem effectively. To explore the limitations of the traditional methods, 25 literary papers were taken into account to analyze various approaches, and methods under the utilization of distinct datasets. Together with this, the achieved evaluation metrics along with the achievements are also discussed briefly in the study. Additionally, the study provides the shortcomings and future scope of the conventional methods with a detailed description. The main objective of the survey includes knowledgeable categorization, which aims to explore a new model with the illustration of recent trends and achieve effective data augmentation in future analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

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Recent Advances in computer science and Communications 2024年第6期17卷 25-38页

作者： Pradhan, Devasis Behera, Majusha Gheisari, Mehdi Department of Electronics and Communication Engineering Acharya Institute of Technology Karnataka Bangalore560107 India School of Electronics Engineering Campus 12 KIIT deemed to be University Odisha Bhubaneswar India Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included. © 2024, Bentham science Publishers. All rights reserved.

关键词： Electronic health record

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Applications of Clustering in Cognitive Radio Networks 15

Applications of Clustering in Cognitive Radio Networks

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Joykutty, Anu Maria Baranidharan, B. Rajagiri School of Engineering & Technology Department of Computer Science & Engineering Kochi India SRM Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331300579

With the use of cognitive radio (CR) technology, wireless devices can utilize licensed spectrum when it is unused by the licensed users. CR was developed as a response to the wireless spectrum shortage issue caused by the static spectrum allocation. Using CR technology, the unlicensed users can opportunistically access the spectrum during the absence of usage by the licensed users. Clustering is a widely used technique in Cognitive Radio Networks (CRNs) to efficiently manage network resources, optimize spectrum utilization, and enhance overall network performance. This study focuses on the applications of clustering techniques in the area of CRNs like spectrum management, QoS Support, topology management, energy efficiency, routing etc. © Grenze Scientific Society, 2024.

关键词： Cognitive radio

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VIEPred: Predicting Viral Immune Evasion with Multimodal Representation Learning

VIEPred: Predicting Viral Immune Evasion with Multimodal Rep...

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2024 International Conference on Computational Intelligence and Communication System, CCICS 2024

作者： Liang, Banqing Wang, Na Huang, Hao Hou, Yijie Qiang, Xiaoli Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China

Predicting coronaviral immune evasion is crucial for identifying and responding to new COVID-19 variants in advance, thereby optimizing vaccine development and public health strategies to prevent further outbreaks. Hence, we present VIEPred, a model designed to predict mutations in every amino acid of the RBD. However, protein representation learning is a challenge task due to the hierarchical structure of proteins. Our method uses a sequence encoder based on dilated convolutions and attention mechanisms to generate sequence embeddings and a structure encoder employing geometric vector perceptrons (GVP) and graph neural networks (GNN) to produce protein structure representations. These features are combined using a cross-attention fusion module and subsequently processed through a fully connected layer to output the probability of viral immune evasion. Extensive ablation studies led to an optimal model with ACC of 88.4% and AUC of 86.8%. Compared to existing prediction methods, VIEPred demonstrates superior predictive performance across most evaluation metrics for both SARS-CoV-2 and influenza viruses. © Published under licence by IOP Publishing Ltd.

关键词： Graph neural networks

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TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA SHARPNESS 12

TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zou, Yingtian Kawaguchi, Kenji Liu, Yingnan Liu, Jiashuo Lee, Mong-Li Hsu, Wynne School of Computing National University of Singapore Institute of Data Science National University of Singapore Singapore Department of Computer Science & Technology Tsinghua University China

Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different distance measurements between source and target domains but fail to consider the optimization property of the learned model. As empirically shown in recent work, the sharpness of learned minima influences OOD generalization. To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization. In this paper, we give a rigorous connection between sharpness and robustness, which gives better OOD guarantees for robust algorithms. It also provides a theoretical backing for "flat minima leads to better OOD generalization". Overall, we propose a sharpness-based OOD generalization bound by taking robustness into consideration, resulting in a tighter bound than non-robust guarantees. Our findings are supported by the experiments on a ridge regression model, as well as the experiments on deep learning classification tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Regression analysis

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Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN) 6

Multi-Controller Architecture Design and Features in Softwar...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Prabhu, V. Balamurugan, P. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798331522667

Networking paradigm known as "Software-Defined Networking" (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can’t be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done. © 2025 IEEE.

关键词： Scalability

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