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A Cloud-Based Smart IoT Platform for Personalized Healthcare data Gathering and Monitoring System 3

A Cloud-Based Smart IoT Platform for Personalized Healthcare...

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3rd ieee Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Vellela, Sai Srinivas Reddy, Vuyyuru Lakshma Roja, D. Rao, G Ramachandra Khader Basha, S.K. Kumar, K Kiran Chalapathi Institute of Technology Dept. of CSE-Data Science AP Guntur India KoneruLakshmaiah Education Foundation Dept. of Cse AP Vaddeswaram India Chalapathi Institute of Technology Dept. of Cse AP Guntur India

ISBN: (纸本)9798350302288

Several technological and informational technologies are currently developing quickly and profoundly altering our contemporary way of life. The Internet of Things (IoT) has become one of the most well-known and frequently discussed technologies out of all those now in use. Researchers, doctors, and patients are facing a wide range of new opportunities and difficulties as a result of the development of numerous new technologies. Yet, it is becoming increasingly challenging to manage and process the massive amounts of data they produce on local systems. Cloud computing's scalability offers a practical solution to this issue. The goal of this study is to present a cloud-based, intelligent IoT platform for a customized healthcare data gathering and monitoring system. This platform intends to advance healthcare services and improve remote patient monitoring. The system may collect data from various sources, combine it with a flexible semantic web, store it in the cloud for future analysis, and show it in an intuitive manner. It is crucial to prioritize privacy issues while developing a healthcare monitoring system that manages Electronic Medical Records (EMRs). The recommended remedy effectively protects privacy while utilizing standard IoT characteristics. © 2023 ieee.

关键词： Internet of things

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Enhanced Clustering Framework for Unveiling Hidden Patterns in IoT-Based Structured and Unstructured data 3

Enhanced Clustering Framework for Unveiling Hidden Patterns ...

引用

3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Das, Prabhat Kovuri, Karthik Saha, Sajal The Assam Kaziranga University Department of IT India Adamas University Department of CSE India The Assam Kaziranga University Department of CSE India

ISBN: (纸本)9798331523893

Clustering algorithms play a pivotal role in uncovering hidden patterns and relationships within diverse datasets. This study discusses about an enhanced real-time clustering approach based on the Birch methodology, optimized for analyzing both structured and unstructured data. Structured data, consisting of IoT sensor readings, and unstructured image data, captured using Raspberry Pi devices deployed in various locations, were analyzed to identify meaningful patterns. For structured data, the algorithm effectively clustered environmental parameters, revealing correlations and anomalies. For unstructured data, high-dimensional image features extracted using a pre-trained VGG16 model were clustered, uncovering patterns in texture, color, and structure. Experimental results demonstrate the algorithm's scalability and efficiency, achieving high-quality clusters with average Silhouette Scores of 0.87 and 0.81 for structured and unstructured datasets, respectively. These findings highlight the algorithm's adaptability and its potential for real-time analytics in diverse applications such as environmental monitoring and image-based decision-making systems. © 2025 ieee.

关键词： Clustering algorithms

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A survey on divergent classification of social media networking 3

A survey on divergent classification of social media network...

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3rd ieee International Conference on computing, Communication, and intelligent systems, ICCCIS 2022

作者： Geetha, V. Sujatha, N. Madurai Kamaraj University Lady Doak College Department of Computer Science Tamilnadu Madurai India P.G and Research Department of Computer Science Tamilnadu Madurai India

ISBN: (纸本)9781665462006

Social media networking encircles a group of websites and applications. The main usage of such a networking for the people is to communicate, interact, share and collaborate with others. There are different social media networks such as Twitter, WhatsApp, Facebook, LinkedIn, Instagram, Snapchat, YouTube, Reddit, Quora, Digg of Discussion Forums, Pinterest, Yelp, Zomato, tripAdvisor of Consumer Review Networks, WordPress, Tumblr of Blogging and Publishing Networks. The objective of this research paper is to find the origin, data and features of the social media networks. In recent years, people from various sectors like individuals, students, doctors, lawyers, engineers, and business are using social media widely. This social media network is easy to convey the messages in the form of text, image, audio, and video both locally and internationally. The content of the conversation may be structured or unstructured. But it can be processed and analyzed by the data scientist to predict the interest of social media users by applying machine or deep learning algorithms. © 2022 ieee.

关键词： Big data

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Performance Analysis of Hybrid MPI and OpenMP on Smith-Waterman Algorithm 3

Performance Analysis of Hybrid MPI and OpenMP on Smith-Water...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Ninama, Keyur Patel, Jainamkumar Girish, K.K. Reddy, Manideep Raya V S R S Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

In the rapidly advancing field of bioinformatics, sequence alignment is a pivotal task for elucidating genetic statistics and evolutionary relationships. As the volume and complexity of biological data continue to grow, it becomes imperative to employ effective computational techniques to manage this expansion. The Smith-Waterman algorithm is a key tool for sequence alignment;however, its performance can be constrained by the substantial size of contemporary datasets. To overcome this limitation, this paper explores a hybrid parallelization strategy that integrates message passing interface (MPI) with open multi-processing (OpenMP). This approach aims to significantly enhance the algorithm's efficiency by leveraging the strengths of both parallelization models. By optimizing the scalability and execution speed of the Smith-Waterman algorithm on advanced high-performance computing (HPC) systems, the hybrid technique not only improves performance but also enables more rapid and accurate biological data analysis. © 2025 ieee.

关键词： Open data

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Using CoordConv for Tabular data Detection and Structure Recognition 3

Using CoordConv for Tabular Data Detection and Structure Rec...

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3rd ieee International Conference on computing, Communication, and intelligent systems, ICCCIS 2022

作者： Ambulgekar, Apoorva Lad, Naman Doshi, Krunal Bari, Pranit Dwarkadas J. Sanghvi College of Engineering Department of Computer Engineering Mumbai India St. Francis Institute of Technology Department of Information Technology Mumbai India

ISBN: (纸本)9781665462006

This paper explores the usage of CoordConv, the novel upgrade to general convolutional layers in the problem of Tabular data Detection and Cell-Based Structure Recognition. CoordConv has been shown to provide considerably better results in the domain of Object Detection than its counterpart. The authors integrate it within the established Anchor optimization approach which leverages guided anchors to accomplish the task of recognizing rows and columns present in tabular data. In contrast to the majority of techniques implemented for Table Structure Recognition, the authors attempt to recognize the cells present in the tabular images instead of the rows and columns. They evaluate this method on the coveted ICDAR-19 dataset (International Conference on Document Analysis and Recognition - 2019) which comprises of scanned document images containing tabular regions and achieve results surpassing those of many popular techniques. They also apply this approach for the task of Table Detection and achieve results comparable to other established techniques. © 2022 ieee.

关键词： Object detection

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Integrating Deep Learning Based Anomaly Detection with Extended Reality: A Case Study on Extensive Railways Monitoring 3

Integrating Deep Learning Based Anomaly Detection with Exten...

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3rd ieee International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Di Summa, Maria Cardellicchio, Angelo Mosca, Nicola Ricci, Marina Renò, Vito Vadukkal, Udith Krishnan Vadakkum Stella, Ettore Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing National Research Council of Italy Via Amendola 122 D-O Bari70126 Italy

ISBN: (纸本)9798350378009

The highly demanding safety standards adopted in the railway context imply that cutting-edge technologies must be used to limit accidents and ensure their complete avoidance. As such, developing integrated monitoring systems, which also exploit eXtended Reality technologies along with deep-learning-based anomaly detection techniques, becomes crucial to support the awareness of a planning operator throughout the maintenance operations required to comply with high-quality standards. This work addresses the abovementioned problem by proposing a framework composed of three different steps: data collection and preparation, anomaly detection via deep neural networks, and presentation of the achieved results. Specifically, the final step involves displaying the anomaly detector results in a virtual environment, reproducing the railway line under analysis. This environment will provide the planning operator with a complete platform to explore, use to plan maintenance interventions, and gather detailed reports to improve the overall safety of the railway line effectively. © 2024 ieee.

关键词： Railroad accidents

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Learning Spiking Neural Network from Easy to Hard Task 3

Learning Spiking Neural Network from Easy to Hard Task

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3rd International Conference on Digital Society and intelligent systems, DSInS 2023

作者： Tang, Lingling Hu, Jiangtao Yu, Hua Liu, Surui Chu, Jielei School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611730 China Chengdu Customs District Technological Center Chengdu610041 China China E-port Data Center Chengdu Branch Chengdu610041 China

ISBN: (纸本)9798350331387

para>Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL. © 2023 ieee.

关键词： Curricula

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Design and Implementation of Indoor Visual Autonomous Navigation System for Quadrotor UAV Based on Binocular Camera and IMU 3

Design and Implementation of Indoor Visual Autonomous Naviga...

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3rd International Conference on Computer Science, Electronic Information Engineering and intelligent Control Technology, CEI 2023

作者： Tang, Jiaze Guo, Tianxing Liudan Li, Junbao Sun, Jinwei Huang, Wenbin Wang, Qisong Electrical Measurement Technology and Intelligent Control Institute School of Instrumention Science and Engineering Harbin Institute of Technology Harbin150080 China Schools of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9798350372052

Addressing the challenge of unmanned aerial vehicles (UAVs) navigating indoors without satellite positioning, this study introduces a novel method combining binocular camera and inertial measurement unit (IMU) data. This approach overcomes the structural differences between visual and inertial information, integrating them effectively for enhanced indoor autonomous navigation. The research presents a gradient-free path planning framework, pivotal for enhancing the UAV's ability to execute efficient and stable flight in intricate indoor settings. The cornerstone of this system is an advanced path planning algorithm, adept at swiftly identifying optimal routes and adapting to environmental shifts. The system's efficacy was rigorously evaluated through simulations and real-world tests, confirming its precision in positioning and path planning without reliance on satellite data. The findings demonstrate the system's capacity for precise localization and effective route determination, utilizing an integration of visual and inertial inputs. This research significantly contributes to the autonomous flight capabilities of UAVs, offering a valuable reference for future UAV autonomous navigation systems. By delving into visual-inertial fusion navigation technology, the study paves the way for expanded indoor UAV applications. © 2023 ieee.

关键词： Antennas

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Optimizing data Movement in Heterogeneous computing: A LASSA-based Approach for Efficient Nucleation List Precomputation 3

Optimizing Data Movement in Heterogeneous Computing: A LASSA...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Bhowmik, Biswajit Girish, K.K. Pandey, Himanshu Prabhanjans, Piyus National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

In the rapidly evolving landscape of heterogeneous computing, the efficiency of data movement between CPUs and GPUs can make or break system performance. Despite advancements in parallel processing, existing methods for managing data transfers - particularly in GPU offloading scenarios - suffer from significant inefficiencies. These inefficiencies are particularly evident in nucleation list precomputation for non-equilibrium solidification models, where redundant data movements and complex dynamic work-sharing in OpenMP lead to significant performance overhead. To tackle this issue, this paper proposes a novel solution that integrates the Location-Aware Heap Static Single Assignment (LASSA) algorithm into the compilation process. This approach identifies and eliminates redundant memory copy operations, optimizing data transfers and reducing overhead. The findings reveal a dramatic performance boost, with up to a 9.6-fold increase in efficiency. By addressing the specific challenges of nucleation list precomputation, this work provides valuable insights into optimizing data movement in heterogeneous computing environments, paving the way for enhanced performance in parallel programming models. © 2025 ieee.

关键词： Cellular Automata Models Heterogeneous computing Nucleation List Precomputation OpenMP Performance Issues System Efficiency

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Towards Adaptive, Real-time Monitoring of Food Quality using Smart Sensors 3

Towards Adaptive, Real-time Monitoring of Food Quality using...

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3rd ieee International Conference on Autonomic computing and Self-Organizing systems (ACSOS)

作者： Henrichs, Elia Krupitzer, Christian Univ Hohenheim Dept Food Informat Stuttgart Germany Univ Hohenheim Sci Computat Lab Stuttgart Germany

ISBN: (数字)9781665451420

ISBN: (纸本)9781665451420

Adaptive software systems can reduce food waste and improve food safety. Such systems include smart sensors to monitor the food's condition and machine learning-based data analysis to predict the food's quality and shelf life. In particular, monitoring is challenging for several reasons, e.g., the energy supply/demand and the reliability of the sensors. Therefore, this work sketches how the Multi-Level Observer/Controller architecture from Organic computing might be applied for the adaptive monitoring of packaged foods.

关键词： food supply chain adaptive monitoring Multi-Level Observer/Controller architecture intelligent packaging

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