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Plugging and Playing with Variety of Data using Multi-Model Database and Polyglot Persistence 3

Plugging and Playing with Variety of Data using Multi-Model ...

引用

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

作者： Sachdeva, Shelly Vasava, Jignisa National Institute of Technology Delhi Computer Science and Engineering Delhi India

ISBN: (纸本)9798350369083

This paper explores handling variety of data (structured, semi-structured, and unstructured). It discusses various data models and compares them based on speed, storage, and use case scenarios. To leverage the management of diverse types of data, the current research focuses on two main approaches: multi-model database and polyglot persistence. Utilizing diverse data optimizes storage by employing a multi-model database that accommodates various data models within a single database. On the other hand, polyglot persistence stores data across multiple databases, which a mediator handles. Interoperability among healthcare entities is particularly challenging due to the diverse nature of healthcare. The study stands out by assessing scalability and performance analysis using a multi-model database and polyglot persistence on a healthcare dataset. This paper stores the healthcare dataset in a multi-model database (ArangoDB) and performs various queries to demonstrate its functionality. It has been observed during implementation that polyglot persistence manages diverse types of data by leveraging SQLlite and MongoDB within a single web application backend, facilitating flexible data management tailored to specific data models. Plugging and playing with a variety of data offers a flexible solution to manage variety of data effectively in modern healthcare settings. © 2024 IEEE.

关键词： Structured Query Language

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Ultra-Narrowband Circular Dichroism Induced by Chiral Quasi-Bound States in All-Silicon Dielectrics in the Terahertz Range 12

Ultra-Narrowband Circular Dichroism Induced by Chiral Quasi-...

引用

12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Sun, Hongrui Yang, Yuhang Yang, Hongyan He, Quanlin Yu, Fangzhou Zhi, Wenlong Li, Jianqing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350355802

Circular dichroism has extensive applications in fields such as ultra-sensitive biosensing and wavefront manipulation within chiral structures. However, traditional terahertz chiral structures face challenges in effectively addressing narrowband issues due to linewidth limitations. Recently, the introduction of the concept of quasi-bound states in the continuum (quasi-BIC) in optical resonant systems has demonstrated extremely high quality factors and very low radiation losses, providing a theoretical foundation for achieving ultra-narrow linewidth wave control. In this study, we innovatively construct bound states in the continuum (BIC) in a planar all-silicon terahertz metasurface with in-plane C2 symmetry and broken mirror symmetry. This system not only exhibits symmetry-protected BICs but also reveals parameter-tuned BICs. Through methods such as band structure calculations and far-field polarization analysis, we achieve an ultra-narrow linewidth with an extremely high quality factor at the quasi-BIC frequency, successfully realizing ultra-narrowband terahertz chirality. Our findings provide a novel solution for terahertz narrowband chirality and spin-selective asymmetric transport, potentially driving technological advancements in related fields. © 2024 IEEE.

关键词： Dichroism

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Congestion Based Adaptive Association Mechanism for IEEE 802.11ah Based Large IoT Network 5th

Congestion Based Adaptive Association Mechanism for IEEE 80...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： R, Nandhini R, Radha School of Computer Science and Engineering Vellore Institute of Technology–Chennai Chennai India

ISBN: (纸本)9783031770807

The IEEE 802.11 ah standard is the prominent protocol for medium range communication in the domain of Internet of Things (IoT), facilitating interaction between wireless sensor nodes and access points. Supporting up to approximately 8192 nodes connected to a single access point through single hop strategies, IEEE 802.11 ah employs CSMA/CA for the authentication and association process of sensor nodes with access point. However, the standard encounters challenges such as significant collisions and prolonged network setup times when attempting to associate an excessive number of wireless sensor nodes with a single access point, primarily due to authentication thresholds set at intervals. In this research, we propose a dynamic adjustment of the authentication threshold based on network congestion status. Learning parameters including the number of requests served successfully by the access point in the past, present and a congestion threshold are leveraged to adapt to current network congestion levels. Through evaluation using the LILD method in the ns-3 simulator, our approach demonstrates considerable enhancements in terms of collision reduction and network setup efficiency. This research endeavours to promote responsible network management practices for improving the performance and reliability of IEEE 802.11 ah based IoT deployments. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Internet of things

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A New Score Function of Interval-Valued Intuitionistic Fuzzy Values 23

A New Score Function of Interval-Valued Intuitionistic Fuzzy...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Pichit, Sitthikun Chen, Shyi-Ming National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798331528041

This paper proposes a new score function (SF) of interval-valued intuitionistic fuzzy values (IVIFVs) in order to overcome the shortcomings of the existing SFs of IVIFVs, which are not be able to distinguish the ranking orders (ROs) of alternatives in some situations. We design the proposed SF of IVIFVs based on the properties that the larger the membership degrees of IVIFVs results in the larger the score values (SCVs) of IVIFVs;the smaller the non-membership degrees of IVIFVs results in the larger the SCVs of IVIFVs. The experimental result shows that the proposed SF of IVIFVs outperforms the existing SFs of IVIFVs. © 2024 IEEE.

关键词： Fuzzy models

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Overlapping Community Detection based on Facets of Social Network: An Empirical Analysis 4

Overlapping Community Detection based on Facets of Social Ne...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Das, Soumita Biswas, Anupam National Institute of Technology Silchar Department of Computer Science and Engineering India

ISBN: (纸本)9798350343670

Detection of overlapping communities is a challenging problem that has drawn a lot of research interest. This is motivated by the fact that in real-world networks, individuals frequently join multiple groups subsequently, resulting in overlapping communities. In this paper, we presented a comprehensive analysis of numerous state-of-the-art overlapping community detection algorithms in order to understand the relative efficiency of the corresponding algorithms in handling specific issues. We consider issues like the facets of the social networks that are used for overlapping community detection, time complexity, accuracy, and quality. However, the accuracy and quality metrics are not sufficient to evaluate the comparative performance of community detection algorithms because these measures use an indirect approach for comparing the algorithms. Therefore, we have additionally used a direct evaluation metric namely, topological variance for performance analysis of the community detection algorithms. Experiments are conducted on several widely used real world networks. This study allows us to identify the algorithms that work well in different scenarios. As a result, we arrive at findings that direct our algorithm selection procedure in accordance with predetermined goals. © 2024 IEEE.

关键词： Direct comparative analysis Evaluation metrics Facets of social network Overlapping community

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Securing Patient Health Records Using Blockchain

Securing Patient Health Records Using Blockchain

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Geethan, Daniel Anandha Arjun, P. Sanjay, K.S. Kanniga Devi, R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

Traditional Electronic Health Records (EHRs) raise red flags about data privacy, security, and patient control. This research tackles these issues by proposing a new system where patients call the shots on their medical information. Our blockchain and InterPlanetary File System (IPFS) system puts patients in the driver's seat, granting them complete ownership and oversight of their health data. Patients securely submit their formatted medical history through a dedicated website, where it's stored on IPFS using a decentralized platform like Pinata. A unique code (Content Identifier or CID) acts like a key, linking the data to the patient's blockchain address via a smart contract (coded rules) for guaranteed accuracy and tracking. Our design prioritizes patient-controlled access. Healthcare providers can't access the data without the patient's explicit go-ahead. Access requests are sent directly to the patient, who has the final say on approval, ensuring their data privacy is always protected. Approved requests include the CID, allowing authorized physicians to retrieve the patient's data directly from IPFS storage. But patient control goes beyond just reacting to access requests. Our system empowers patients to be proactive. Using their unique login credentials, they can grab their CID directly from the blockchain. This retrieval process can be integrated into a secure patient portal or mobile app. With their CID in hand, patients can directly share it with chosen physicians, fostering collaboration in care without relying on intermediary platforms. This streamlined data exchange empowers patients and fosters a more collaborative healthcare environment where they actively manage their health information. © 2024 IEEE.

关键词： Electronic health record

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Development of a Naive Bayes-based Framework for Optimizing Crop Recommendation System and Enhancing Agricultural Yield Prediction 4

Development of a Naive Bayes-based Framework for Optimizing ...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swetha, P. Senthilkumar, J. Kalaignar Karunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

This research study proposes a comprehensive Crop Recommendation System that uses advanced machine learning to boost agricultural productivity. By analyzing key environmental and soil factors, including soil composition, pH, temperature, humidity, and rainfall, the system offers customized, data-driven crop suggestions to optimize yield and promote sustainable farming. The system evaluates several algorithms, including Naive Bayes, Decision Tree, Logistic Regression, and Random Forest, and identifies Naive Bayes as the top performer for accurately predicting crop suitability. This multi-algorithm approach represents a significant advancement in precision agriculture, empowering farmers and policymakers with actionable insights to drive improvements in the agricultural sector. Leveraging historical data, the Crop Recommendation System supports farmers, consultants, and policymakers in making more informed and strategic crop selection decisions. The system's precision and adaptability make it a valuable resource for stakeholders focused on improving resource efficiency and fostering sustainable agriculture, ultimately enhancing food security and minimizing environmental impact. © 2025 IEEE.

关键词： Agriculture Crop Recommendation System Decision Tree Logistic Regression Machine Learning Naive Bayes Random Forest

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Blockchain-Driven Key Management for Secure IoT 3

Blockchain-Driven Key Management for Secure IoT

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Brunda Sri Lakshmi, K.N. Keshavamurthy, Bettahally N. National Institute of Technology Dept of Computer Science and Engineering Goa India

ISBN: (纸本)9798350354348

Rapid growth of Internet of Things(IoT) technology has resulted an extraordinary surge in number of interconnected devices, raising significant concerns regarding security and privacy. Group key management plays a crucial role in securing communication among IoT devices within a network. In this paper, a novel approach to group key management is proposed leveraging blockchain technology to address the challenges of scalability, decentralization, and trust in IoT environments. Our solution employs blockchain as a distributed ledger to securely store and manage group keys, ensuring transparency, integrity, and resilience against attacks. We present a detailed architecture and protocol for group key generation, distribution, and revocation using smart contracts on the blockchain. Furthermore, we evaluate performance and security of our approach through simulation and analysis, demonstrating its effectiveness in providing secure and efficient key management for IoT devices. This research advances IoT security, providing a foundation for creating IoT ecosystems that are resilient and trustworthy. © 2024 IEEE.

关键词： Block-chain decentralization Internet of Things(IoT) privacy and security

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Hybrid Transfer Learning based Pomegranate Fruit Disease Identification

Hybrid Transfer Learning based Pomegranate Fruit Disease Ide...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Banerjee, Sumit Kumar Chakraborty, Priyanka Nath, Sayan Computer Science and Engineering Guru Nanak Institute of Technology Kolkata India

ISBN: (纸本)9798350378047

It is critical to identify and then classify diseases in pomegranate fruits to ensure a high-quality yield and reduce agricultural losses. Regretfully, large-scale monitoring is difficult since traditional methods of identifying illnesses are time-consuming and often require specialist knowledge. Advanced methods for automated disease classification have been made possible in recent years by machine learning (ML) and deep learning (DL). Transfer learning (TL), one of these approaches, has proven to be successful in addressing particular agricultural issues with sparse data by utilizing pre-trained models on big datasets. Here a method for classifying pomegranate fruit problems called Hybrid Transfer Learning (HTL) is proposed. This approach integrates domain-specific feature extraction with the benefits of TL and fine-tuning approaches. Images of both healthy and sick pomegranate fruit are fed into pre-trained convolutional neural networks (CNNs), such as ResNet, Inception, and VGG, to extract generalized visual information. Following this, a process of fine-tuning combines these features with a specially designed disease categorization module that is tailored to the distinct qualities of pomegranate disorders. Affected crops have caused the agricultural field to suffer and also affects the economy. However, the reason behind the infected crops may be crop diseases. To resolve this issue, Pomegranate fruit disease classification based on hybrid transfer learning is chosen. Different machine learning algorithms such as Random Forest Classifier, K-Nearest Neighbors algorithm with custom deep learning algorithms are used. Our run-time training precision is 96% and run-time validation precision is 93% respectively. © 2024 IEEE.

关键词： Federated learning

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Point Cloud Classification Fall Detection Method Based on Millimeter Wave Radar 24

Point Cloud Classification Fall Detection Method Based on Mi...

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4th International Joint Conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Zhou, Manyue Gong, Pengcheng Wu, Yuntao Liu, Jianyu School of Computer Science and Engineering Wuhan Institute of Technology Hubei Wuhan China

ISBN: (纸本)9798400710100

Falling at home is the most common cause of injury among the elderly. Wearable devices are inconvenient and costly, while camera systems pose risks to privacy. A fall detection method employing millimeter-wave radar coupled with a classification network is proposed to address these issues. This approach leverages the benefits of millimeter-wave radar and deep learning. The raw data processed by the radar yields the required information, which is then rendered into a point cloud. Subsequent voxelization enhances feature learning, enabling the model to function even with sparse data. The information is then fed into our custom-designed network for time series extraction to classify behaviors. Finally, the time series extraction network utilized in this study is compared with several popular networks, demonstrating that our system offers an effective technological avenue for target detection and posture analysis, and boasts high algorithmic precision in millimeter-wave radar fall detection. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Fall detection

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