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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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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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Graphical User Interface for intelligent automotive with vehicle to vehicle communication and adaptive light controls using image processing and machine learning 3

Graphical User Interface for intelligent automotive with veh...

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

作者： Jose, Robin Mathew, Jis Devadhas, G. Glan Mary Synthia Regis Prabha, D.M. Shinu, M.M. Dhanoj, M. Vimal Jyothi Engineering College Eie Department Kerala Kannur India Noorul Islam Center for Higher Education Eee Department India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Navigation systems are a vital part of the current traffic system. Advancements in driving technology have brought about radical changes in driving behaviour and reduced routing time. However, there are also risks to users in terms of distraction or inattention. Driving in night possess more risk than in day time driving due to abuse of high beams of the head lamb. Similar to the night driving, travelling in foggy day is also difficult for all road user. As far as the disruptive effects of navigation systems are concerned, the empirical conclusions are heterogeneous. The project is aimed to develop a low and effective Advance driver assistance system which includes vehicle to vehicle communication and intelligent headlight control. The project also aims to study and analyse different multi-disciplinary techniques which include supervised machine learning techniques o effectively classify road surface conditions using data collected from smartphones to ensure a safe and comfortable driving. A Graphical User Interface was developed which increases the usability of the system. In particular, visual distraction caused by navigation systems in relation to map navigation was reviewed. The project aims to analyse the data in such a way as to improve road safety when using a navigation system in unfamiliar areas. The results show that less glances of more than 2 seconds were found on the navigation system while map navigation leads to higher off-road times. © 2022 ieee.

关键词： Navigation systems

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A Comparison of Kubernetes and Kubernetes-Compatible Platforms 11

A Comparison of Kubernetes and Kubernetes-Compatible Platfor...

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

作者： Telenyk, Sergii Sopov, Oleksii Zharikov, Eduard Nowakowski, Grzegorz Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst 37 Prosp Peremohy Kiev Ukraine Cracow Univ Technol Ul Warszawska 24 Krakow Poland

ISBN: (纸本)9781665426053

Nowadays, Kubernetes is an advanced container orchestration tool due to its high reliability, scalability and fault tolerance. However, Kubernetes requires a significant number of resources for its work. Therefore, to ensure the operation of Kubernetes in conditions of limited resources, lightweight analogues such as MicroKubernetes and K3S were created. These platforms provide easier deployment and support. In this paper, the authors analyze performance metrics for orchestration actions such as adding/removing nodes and starting/stopping deployments in terms of resource utilization, cluster startup speed, and consumed time for lightweight platforms and original Kubernetes. The results show that the original Kubernetes outperforms MicroKubernetes and K3S in many tests, but K3S demonstrates better disk utilization. On the other hand, MicroKubernetes demonstrates worst results in the performed tests.

关键词： container virtualization performance MicroKubernetes K3S deployment

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Real-Time Anomaly Detection in 5G Networks Through Edge computing 3

Real-Time Anomaly Detection in 5G Networks Through Edge Comp...

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3rd ieee International Conference on intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Shaik, Riaz Raju, Dara Behera, Prakash Chandra Changala, Ravindra Mary, S. Suma Christal Balakumar, A. Kl University Department of Cse Andhra Pradesh Vaddeswaram India Vignana Bharathi Institute and Technology Dept. of Computer Science & Engineering Aushapur Ghatkesar India St. Claret College Department of Science Bangalore India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of Ece samayapuram Trichy India

ISBN: (纸本)9798350361186

Strong anomaly detection techniques are becoming more and more necessary as 5G networks develop in order to maintain network performance, security, and dependability. This study leverages the capabilities of Mobile Edge computing (MEC) to present a novel method for anomaly detection in 5G networks. By processing data closer to the network edge, the integration of MEC offers an efficient and decentralized architecture that lowers latency and improves real-time detection capabilities. The distributed module takes advantage of its close proximity to network devices by using sophisticated algorithms for anomaly detection, which are implemented at the mobile edge. The system can quickly detect abnormalities from typical network activity by utilizing capabilities including Flow Collection, Anomaly Symptom Detection, and Network Anomaly Detection. The distributed module provides anomalous information to the centralized decision-making module for thorough examination. It takes into account variables like resource use and network traffic and integrates this data with metrics gathered from monitoring modules. Because of its adaptive characteristics, the system may expand anomaly detection components, enhance detection functions, and modify virtualized resources in response to shifting network circumstances. The evaluation findings reveal that the suggested anomaly detection method performs well in 5G networks, with decreased false positives, increased responsiveness, and better flexibility to changing network conditions. Using MEC not only makes anomaly detection more effective, but it also fits in with the 5G design, which makes it a viable option for protecting the upcoming generation of communication networks. It obtains 95% accuracy in classification. The suggested approach has proven to be resilient in handling security issues by producing outcomes that are either comparable to or better than those attained by other techniques that have been previously presented in the s

关键词： 5G mobile communication systems

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3D Adaptive SMCM Filtering of CT Images 11

3D Adaptive SMCM Filtering of CT Images

引用

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

作者： Draganov, Ivo Mironov, Rumen Tech Univ Sofia 8 Kliment Ohridski Blvd Sofia 1756 Bulgaria

ISBN: (纸本)9781665426053

In this paper a new implementation of the Saint-Marc-Chen-Medioni (SMCM) filter is proposed in a 3D variant. It uses adaptive selection of the threshold k, found from a 2D projection of the input image. The algorithm has been tested over Computed Tomography (CT) images with Additive White Gaussian Noise (AWGN) at variance levels 0.01 and 0.001. It leads to better quality of filtered images in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), compared to 3D Gaussian and 3D Averaging filters. The proposed 3D SMCM filter is considered applicable for filtering of other types of 3D images, such as Magnetic Resonance Images (MRI).

关键词： AWGN SMCM filter Gaussian filter CT image 3D

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Enabling Identity-Based Integrity Auditing and data Sharing with Sensitive Information Hiding for Secure Cloud Storage 3

Enabling Identity-Based Integrity Auditing and Data Sharing ...

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

作者： Asha, S. Punitha, K. Joshi, Tanmay School of Computer Science and Engineering Centre for Cyber Physical Systems India Vellore Institute of Technology Chennai India School of Computer Science and Engineering India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

With the increase in the data growth, where data storage becomes difficult if the user wants to store it on a local drive, which is why many organizations and people prefer storing their data on a cloud storage. With accessibility of cloud storage, people can remotely use cloud storage and avail data storage. Remote data integrity has been suggested as a way to ensure the integrity of data stored in clouds. The health care system is one example of cloud storage, and the cloud file there may include sensitive data. This sensitive information should not be shared with others. One solution would be to encrypt the whole file to avoid sensitive data sharing, but then this way the file would be unusable to others. The method for implementing data sharing with sensitive information disguised in remote data integrity audits is one topic that hasn't been looked into yet. This technique does this by creating a sanitizer that will clean up data blocks that correspond to the sensitive information in the file and change those signatures into valid ones for the cleansed file. These signatures are utilised during the integrity auditing procedure to confirm the cleaned-up file. As a result, this scheme allows files to be shared with others, while also maintaining data security by sanitizing blocks of sensitive data, with the data integrity auditing still able to be executed efficiently without any problems. Identity-based cryptography, which the proposed solution is built on, makes complex certificate administration simple. The proposed method is efficient and secure, according to the performance analysis and security analysis. © 2022 ieee.

关键词： Cloud storage

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Visual data acquisition for Measuring Devices using Deep Learning 3

Visual Data Acquisition for Measuring Devices using Deep Lea...

引用

3rd Novel intelligent and Leading Emerging Sciences Conference, NILES 2021

作者： Abdelrahman, Youssef El-Salamony, Mostafa Khalifa, Mohamed Institute of Aviation Engineering and Technology Giza Egypt Peking University Beijing China

ISBN: (纸本)9781665421577

Most of the measurement devices in the university labs are not computerized. Hence, unsteady measurements are difficult to capture. In order to retrieve the measured data to computers, expensive data acquisition systems are needed to link these devices to computers. To overcome this issue a cost-efficient solution is proposed. This article proposes a methodology to convert the readings of LCDs of the various measuring devices into a digital form using computer vision. The procedure is successfully implemented and the results are presented. © 2021 ieee.

关键词： Optical character recognition

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An Aircraft Identification System Using Convolution Neural Networks

An Aircraft Identification System Using Convolution Neural N...

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

作者： Viktor Turchenko Iryna Turchenko Nadiia Vasylkiv Department for Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine Department of Computing and Software McMaster University Hamilton Canada Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

The task of aircraft identification using their registration number is important as visual identification remains the only method of their identification in case of air traffic control systems or radar failure. In this paper, an analysis of known solutions was carried out and an aircraft identification system was developed using a pre-trained YOLO (You Only Look Once) deep convolutional neural network and Optical Character Recognition (OCR) technology. The system consists of three modules: the M1 module is designed for the detection of region of interest-tail, the M2 module is designed for the detection of region of interest-registration number, the M3 module is designed for the classification of the registration number. 2309 aircraft images were used for training, 426 for validation and 408 for testing from the Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft) dataset. Our results show that the mean average precision for the M1 and M2 YOLOv7 models on the test set were 0.92 and 0.85 respectively, and for the module M3 OCR technology the classification error was 0.2.

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Search of the Deviation from the Natural Process Using Stepanets Approach for Classification of Functions 11

Search of the Deviation from the Natural Process Using Stepa...

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

作者： Chertov, Oleg Zhuk, Ivan Serdyuk, Anatolii Igor Sikorsky Kyiv Polytech Inst 37 Prospekt Peremohy UA-03056 Kiev Ukraine NASU Inst Math 3 Tereschenkivska St UA-01024 Kiev Ukraine

ISBN: (纸本)9781665426053

Outlier detection is a classic task having many different ways to solve. However, in a number of tasks, it is important to find less pronounced deviations from the natural flow of a certain process. This article proposes to search for such deviations using Stepanets approach for classification of functions describing the corresponding process. It is shown that in some cases this approach allows to reveal better the deviation from standard behavior than a direct comparison of it with current behavior. The proposed approach is illustrated by the example of the analysis of data on the results of football matches in the 2013/14 season in the French Ligue 2.

关键词： template matching classification of functions Stepanets approach (psi,beta)-derivatives

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AI Based Chatbot for Hospital Management System 3

AI Based Chatbot for Hospital Management System

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3rd International Conference on computing, Analytics and Networks, ICAN 2022

作者： Dammavalam, Srinivasa Rao Chandana, N. Rao, T Rajeshwar Lahari, A. Aparna, B. VNRVJIET Department of It Hyderabad India

ISBN: (纸本)9781665499446

The healthcare sector represents one of the most significant segments of the economy. A reliable healthcare system ensures a strong economy by increasing life expectancy, contributing to national growth, and reducing the burden of families. The purpose of this project is to implement a proper healthcare management system integrating all the basic functionalities powered by an Artificial intelligent chatbot that is capable of having a very organic conversation with the user and solving their queries using knowledge base. The knowledge base is a real-time data collected in a JSON format which is pre-processed to make it ready for further processing using bag of words. The information is received and delivered in both speech and text formats. The chatbot can provide navigation links according to the requests of a user. Furthermore, it is capable of predicting the problem by performing symptom diagnosis and recommending a doctor to be consulted or any immediate measures to be taken. In addition, it also provides information regarding diagnostics beforehand. © 2022 ieee.

关键词： Knowledge based systems

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