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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Computational Methods for Analysing Biometric systems 3

Computational Methods for Analysing Biometric Systems

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Multani, Fahim Bijlwan, Sheela Upadhyay, Viyat Varun Goyal, Bhawna Dogra, Ayush Lepcha, Dawa Chyophel Ies College of Technology Bhopal Department of Computer Science Engineering Madhya Pradesh India Uttaranchal School of Computing Sciences Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Gla University Department of Mechanical Engineering Mathura Mathura India University Center for Research and Development Chandigarh University Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Department Electronics and Communication Engineering Punjab Mohali India

ISBN: (纸本)9798350342338

The use of biometric data, such as various anatomical and behavioural attributes in automatic identification and authentication systems for access control, surveillance, computer security, and law enforcement are increasing. Several nations are already or will soon begin to use biometric technology. In this paper, there is a in detail analysis of innovative biometric systems and also the research study of how the computational techniques used intelligent data system in order to analyse biometric system, The efficacy of biometric technologies will depend on how well they work. Biometric system evaluation examines how well biometric systems adapt to particular characteristics. Planning and carrying out biometric tests are frequently handled by an impartial third party. A measure of confidence in the validity of the claim might be one of the options the algorithm offers. Applications that validate a user's address or confirm their identity at the point of sale are examples of verification applications. © 2023 ieee.

关键词： Attributes Biometric data Computational techniques Innovative Research Technology

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The Hybrid Research Stand based on LabVIEW within IoT 11

The Hybrid Research Stand based on LabVIEW within IoT

引用

11th ieee International Conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Osolinskyi, Oleksandr Kochan, Volodymyr Kolodiichuk, Liubomyr Sapozhnyk, Grygoriy Molga, Agnieszka West Ukrainian Natl Univ Dept Informat Comp Syst & Control 3 Peremoga Sq UA-46020 Ternopol Ukraine SE NULES Ukraine Berezhany Agrotech Inst 20 Academichna St UA-47501 Berezhany Ternopil Region Ukraine Kazimierz Pulaski Univ Technol & Humanities Radom Ul Malczewskiego 29 PL-26600 Radom Poland

This paper describes a hybrid laboratory stand builded on the conception of IoT, its structure, main software modules and test results are described. Authors proposed a concept according to which the stand can simulta... 详细信息

ISBN: (纸本)9781665426053

关键词： IoT LabView Arduino Hybrid research stand lab work

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Wind Turbine Instrumentation System using Labview 3

Wind Turbine Instrumentation System using Labview

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3rd International Conference on intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Sudharsan, G. Srinivasa Vishnupriyan, J. Anand, K. Vijay Chidambarathanu, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Ece Chennai India Chennai Institute of Technology Center for Energy Research Chennai India R.M.K. Engineering College Department of Electronics and Instrumentation Engineering Chennai India R.M.K. Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

This work proposes an electronics circuit-based monitoring and control panel for wind turbines. In the wind business, wind turbine monitoring is a critical task. This work describes the process of building a monitoring and control panel system that uses LabVIEW to record and analyze various metrics such as wind speed, wind direction, tip speed ratio, energy generated, power and current power using LabVIEW and buzzer indication. The measuring equipment must function properly in order to produce the highly qualified data required for an accurate wind gust prediction. This work aims to create a promising measuring and monitoring system for wind parameter measurement. The graphical user interface makes even the most critical processes much easier to control and monitor. © 2022 ieee.

关键词： Wind turbines

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Principle of Ultra-high-speed Parallel acquisition and Demodulation of Fiber Bragg Grating Spectra

Principle of Ultra-high-speed Parallel Acquisition and Demod...

引用

intelligent Manufacturing, advanced Sensing and Big data (IMASBD), International Conference on

作者： Yiqi Chen Yanfang Wang Yi Liu Han Song Wuhan University of Technology Wuhan China AECC Sichuan Gas Turbine Research Institute Mianyang China Shaoxing Institute for Advanced Research Wuhan University of Technology Wuhan China

ISBN: (数字)9798331524456

ISBN: (纸本)9798331524463

In order to improve the demodulation speed of the fiber Bragg grating demodulation system, this article puts forward the principle of ultra-high-speed parallel acquisition and demodulation of fiber Bragg grating spectra. The principle prototype built based on Field Programmable Gate Array can achieve a spectral acquisition and demodulation speed of 50 MHz. The result indicates that the prototype based on this principle can achieve accurate and high-speed demodulation of fiber Bragg gratings.

关键词： Bragg gratings Prototypes Bandwidth Logic gates Sensor systems Manufacturing Transient analysis Monitoring intelligent sensors Demodulation

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data Driven Modelling and Prediction Of Rainfall 3

Data Driven Modelling and Prediction Of Rainfall

引用

3rd International Conference on intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Anusree, P.S. Devadhas, G.Glan Shinu, M.M. Mary Synthia Regis Prabha, D.M. Dhanoj, M. Vimal Jyothi Engineering College Electronics & Instrumentation Kerela Kannur India Vimal Jyothi Engineering College Eie Department Kerela Kannur India Noorul Islam Center for Higher Education Eee Department Kumaracoil India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The prediction of weather and is difficult because these phenomena are highly non-linear and complicated phenomena. Technology based on artificial intelligence enables knowledge processing and is utilised in predicting. Synthetic neural network (ANN) has emerged as an alluring substitute for conventional statistical techniques for anticipating the behaviour of nonlinear systems The purpose of this paper is to prevent tools to model and predict rainfall behavior form past observations based on past observation. There are two fundamentally different approaches that are used in the paper to develop a model, both based on statistical methods based on ANNs. The prediction efficiency was evaluated based on 115years of mean annual rainfall between 1901and 2015. © 2022 ieee.

关键词： Neural networks

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A Financial Decision Support Optimization System Based on Artificial Intelligence and data Mining Algorithms

A Financial Decision Support Optimization System Based on Ar...

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Electrical Engineering, Big data and Algorithms (EEBDA), ieee International Conference on

作者： Natalie Cao National University of Singapore Singapore

Effective financial decision-making is crucial for organizational growth, yet it is often impeded by limitations in information availability and subjective biases. While traditional computer-based financial decision support systems (FDSS) have marginally enhanced decision quality, they still need further improvement with data inaccuracies, limited analytical capabilities, and slow response times. This paper examines the potential of advanced Artificial Intelligence (AI) and data mining algorithms to revolutionize FDSS. We critically analyze the shortcomings of current FDSS and introduce an innovative approach incorporating AI-driven data mining into electronic financial decision support frameworks. This integration aims to create more sophisticated, agile, and intelligent tools, addressing the evolving demands of financial decision-makers. Our study highlights the transformative impact of AI and data mining on FDSS and sets the stage for future advancements in intelligent financial decision-making.

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Principal Component Analysis Visualization and State Discovery with Soil data

Principal Component Analysis Visualization and State Discove...

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ieee International Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Miki Sirola Markku Koskinen Tatu Polvinen Mari Pihlatie University of Helsinki Helsinki Finland

data exploration helps to gain understanding of the dataset and the system itself. There are methodologies to handle large number of sensors as well. In this paper operational states are defined to interpret physical behaviour in a soil ecosystem. Dimensionality reduction is achieved with Principal Component Analysis (PCA) method giving another view to the soil dataset from spring term. K-means algorithm groups data densities by clustering the data. This grouping is the basis for defining operational states in the system. Soil data as a part of an ecosystem involves specific features. In the applied approach dynamic visualization including animations constitute an important exploration view. All experiments are realized in Jupyter programming environment with Python 3 programming language. Related literature about data visualization is reviewed. Combining methods and tools with this data as a result soil ecosystem features are recognized.

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Image Authentication Using Watermarking Approach 3

Image Authentication Using Watermarking Approach

引用

3rd International Conference on intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Naik, Shreeya Sherlekar, Sampada Singh, Heena Pulgam, Namita Ramrao Adik Institute of Technology Deemed to Be D y Patil University Department of Computer Engineering Nerul India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

In healthcare, maintaining the confidentiality of patient records is critical to preserving trust between healthcare providers and patients. Now a days, consultation sessions are provided online which requires the healthcare professional to be aware of the patient's medical history for better understanding of their purpose of visit. This creates a need for an online data transmission system that helps healthcare professionals to send the patient records to and forth. While this said system does exist, it is observed that often, sensitive data transmitted via this system over public networks has led to data losses or modifications by an unauthorized third party which may lead to the misdiagnosis of a patient's issue. Therefore, the need for a system that ensures the data transmission takes place in a protected channel and is authenticated at the receiving healthcare professional's end, making sure that the sensitive data received by them has not been tampered with, is born. The proposed system's functions are to watermark the image with the logo of the healthcare institution that is sending the data. The watermarked image further undergoes double encryption processes for additional security before being sent to the receiver where it would be authenticated and decrypted using a randomly generated key sent by the sender. systems performance is evaluated by calculating different evaluation parameters like SSIM and PSNR. The BER and NCC values for the extracted watermark are calculated once the image sent is authenticated and decrypted using the key. Thus, the proposed system is dynamic, robust and has achieved the objective of creating a secure environment for transmission of sensitive data abiding confidentiality. © 2022 ieee.

关键词： Sensitive data

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Optimizing Production and Inventory Management for Defective Items in Manufacturing systems 3

Optimizing Production and Inventory Management for Defective...

引用

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

作者： Ahmed, Khandakar Rabbi Hossain, Arafat Asif, MD Amaddus Bepary Mohammad, Mahathir Rahaman, Mustafizur Dewan, MD Arafat Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Central Michigan University Dept. of Business Data Analytics Mt Pleasant United States Southern California State University Dept. of Business Administration and Management Los AngelesCA United States Southern California State University Dept. of Business Administration and Management United States Westcliff University Dept. of Cloud Engineering IrvineCA United States Los Angeles City College Dept. of Computer Science Los Angeles United States

ISBN: (纸本)9798331523893

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems. © 2025 ieee.

关键词： Industry 4.0

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Alzheimer's Disease Classification Approach Using MRI Imaging 3

Alzheimer's Disease Classification Approach Using MRI Imagin...

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3rd International Mobile, intelligent, and Ubiquitous computing Conference, MIUCC 2023

作者： Khaled, Eman Amr, Farah Essam, Farah Nouh, Maryam Sherif, Youssef Yakan, Zyad Gayar, Ziad El Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Scinece Cairo Egypt

ISBN: (纸本)9798350306231

Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Magnetic Resonance Imaging (MRI) data. For an early diagnosis of the disease, various medical and diagnostic approaches are being investigated. Even while MRI is a useful tool for locating AD-related brain symptoms, the acquisition process is time-consuming, largely because workflow bottlenecks must be manually evaluated. In order to find the best effective method for detecting the disease, this research examines the basic technique for analyzing MRI images. To carry out our study to slow progression of the disease by the use of Alzheimer's disease (AD) prognosis, a dataset from The Alzheimer's Disease Neuroimaging Initiative (ADNI) will be imported and fitted. The outcomes highlight the tremendous potential of integrating imaging data for automated categorization of Alzheimer's disease (AD) using multidisciplinary AI techniques. With a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being applied to manage the genetic sequence input, the suggested solution merges machine learning, bioinformatics, and other image processing techniques. After various experiments by checking the results accuracy, it is stated that the CNN model is never enough to provide us with the desired accuracy either by training on both skull stripped data or the GM tissue segmented data. Although, it is relatively better at the skull stripped dataset training, but the results accuracy and predicted classes show that inferring some classifiers after extracting the features from the CNN would increase the accuracy and results. After applying Support Vector Machine SVM-RBF, SVM-POLY, and XGBoost, it is concluded that the training of the Skull Stripped dataset with features extracted from the CNN model we provided an

关键词： Magnetic resonance imaging

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