2022 International Conference on Artificial Intelligence of Things (ICAIoT)最新文献

Breast Cancer Detection Using Various Classification Models Combined with Transfer Learning 结合迁移学习的多种分类模型的乳腺癌检测

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121840

Nesil Bor, Talya Tümer Sivri, Nergis Pervan Akman, A. Berkol, Yahya Eki̇ci̇

{"title":"Breast Cancer Detection Using Various Classification Models Combined with Transfer Learning","authors":"Nesil Bor, Talya Tümer Sivri, Nergis Pervan Akman, A. Berkol, Yahya Eki̇ci̇","doi":"10.1109/ICAIoT57170.2022.10121840","DOIUrl":"https://doi.org/10.1109/ICAIoT57170.2022.10121840","url":null,"abstract":"When mortality rates are considered, breast cancer is among the most common forms of cancer worldwide. As with every cancer, early diagnosis and treatment are the most effective method of preventing breast cancer. Artificial intelligence’s development in the health sector has decreased the margin of error compared to the old mammographic and manual methods. It has now begun to be obtained much earlier and with a low margin of error. Several researchers have worked on the segmentation and categorization of breast cancer using various imaging modalities. One of the imaging methods with the highest sensitivity for diagnosis is the ultrasonic imaging modality. For this purpose, ultrasound images are used in this study, and there are three categories of images in the dataset: normal, benign, and malignant images. This study aims to develop a technique for spotting and diagnosing breast cancers using ultrasound images. Deep learning techniques are key alternatives to feature-based approaches for overcoming their numerous drawbacks. Convolutional neural network models that have been previously trained and machine learning classifiers are used together in this study. This research compares six distinct pre-trained models: MobileNetV1, MobileNetV2, DenseNet121, DenseNet169, ResNet50, and ResNet101, and various classifiers such as Support Vector Machine, Adaptive Boosting, K-Nearest Neighbors, Random Forest, Bootstrap Aggregating and Extreme Gradient Boosting. As a result of the experiments, it was seen that the highest accuracy scores are achieved by using the MobileNetV2 pre-trained model when looking at overall accuracy percentages with nine different classifiers. In addition, when these nine different classifier algorithms are examined among themselves in particular MobileNetV2, Support Vector Machine, K-Nearest Neighbor and Long Short-Term Memory gave the best accuracy results.","PeriodicalId":297735,"journal":{"name":"2022 International Conference on Artificial Intelligence of Things (ICAIoT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125418122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Hilbert Envelope Based Respiration Rate Measurement from PPG Signal for Wearable Vital Signs Monitoring Devices 基于自动希尔伯特包络的呼吸速率测量可穿戴生命体征监测设备的PPG信号

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121855

G. L. K. Reddy, M. Manikandan, R. B. Pachori

{"title":"Automated Hilbert Envelope Based Respiration Rate Measurement from PPG Signal for Wearable Vital Signs Monitoring Devices","authors":"G. L. K. Reddy, M. Manikandan, R. B. Pachori","doi":"10.1109/ICAIoT57170.2022.10121855","DOIUrl":"https://doi.org/10.1109/ICAIoT57170.2022.10121855","url":null,"abstract":"Respiratory rate (RR) is one of the most vital signs to predict symptoms of serious illnesses and also used as a vital indicator or significant physiological parameter for early disease warning (early detection of patient deterioration) and to monitor person’s physical and emotional stress. In this paper, we propose an automated Hilbert envelope based respiration rate estimation method using the photoplethysmogram (PPG) signal. The proposed Hilbert transform RR (HT-RR) method is tested by using the signals taken from BIDMC and CapnoBase databases. On the benchmark performance metrics, the proposed method had an mean absolute error (MAE) in terms of median (25th–75th percentile) of 3.7(1.8–5.5) breaths per minute (brpm) and 2.6 (0.8–5.5) brpm for 30 and 60 second PPG signals respectively. Evaluation results further showed that the processing time of 4.81 ± 0.80 milliseconds are required to compute RR value from 30 seconds duration PPG signal. The method has great potential in improving the accuracy and reliability of wearable and portable diagnosis system. It is observed that the proposed method outperforms the recent RR estimation methods.","PeriodicalId":297735,"journal":{"name":"2022 International Conference on Artificial Intelligence of Things (ICAIoT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125547962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time Series Nowcasting of India’s GDP with Machine Learning 机器学习对印度GDP的时间序列临近预测

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121883

Nimisha Malik, Bhavik Agarwal

引用次数: 0

Delay Root Cause Analysis and 3D Modeling of LTE Control Communication Using Machine Learning 基于机器学习的LTE控制通信延迟根本原因分析和三维建模

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121830

S. Mohammed, Muhammad Ilyas

{"title":"Delay Root Cause Analysis and 3D Modeling of LTE Control Communication Using Machine Learning","authors":"S. Mohammed, Muhammad Ilyas","doi":"10.1109/ICAIoT57170.2022.10121830","DOIUrl":"https://doi.org/10.1109/ICAIoT57170.2022.10121830","url":null,"abstract":"This study investigates and evaluates delay root cause analysis and 3D modeling of LTE control communication utilizing sophisticated machine learning for network testing. The research studied LTE protocols for 5th-generation mobile telephony and provided guidelines for controlling LTE frequency for background knowledge, although it used an independent technique that did not employ LTE standards. 512 elements of input-output MIMO were employed for 100-GHz and 128 elements for mid-band sub-6-GHz. LOS is always 0.5. This paper is about LTE, not 3D modeling of LTE control path loss type communication using machine learning. This work’s route loss depends on cross-pol beam LTE polarization (±45o). The receiver (Rx) operations and transmitter (Tx) activities in the estimated distance of 0.5 km at an approximate altitude of 15.25 m. Distance, handover authentication, rain, atmosphere, and sub-6GHz vs 100GHz weather conditions affect path loss. The methodology has enhanced the spatial variety by boosting transmitting power and transmitting efficiency. Authorizing and sanctioning ANN-based LTE frequency for both mid-band sub-6-GHz and 100-GHz is possible due to its planning and development using open-source material and strategy with high transmission power and rate under doubtful handover confirmation using MIMO input/yield receiving wires. This theory examines LTE innovation dimensioning as unbiased for various handover verification and allows input boundary alterations for various organization arrangement setups for LTE recurrent data transmission from 6 GHz to 100 GHz for three climate sorts. This cycle should be seen as an undeniable level way to examine LTE networks under various air conditions. Using signal handling tool compartment and explicit AI-based ANN calculation from AI toolkit in MATLAB R2019a, it is possible to create a result answer for three climate types in a dataset with an LTE communication level of exactness of downpour assimilation and abundance foliage miss fort.","PeriodicalId":297735,"journal":{"name":"2022 International Conference on Artificial Intelligence of Things (ICAIoT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128445639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BERT-based Extractive Text Summarization of Scholarly Articles: A Novel Architecture 基于bert的学术文章提取摘要:一种新颖的架构

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121826

Sheher Bano, Shah Khalid

引用次数: 3

Big Data and Cybersecurity: A Review of Key Privacy and Security Challenges 大数据与网络安全:关键隐私与安全挑战综述

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121822

Abdulnaser Fashakh, Hasan Abddulkader

引用次数: 0

Performance Enhancement of MANET Against Wormhole Attacks Using Modified Ad-hoc On-demand Distance Vector Protocol 利用改进的自组织按需距离矢量协议增强MANET抗虫洞攻击的性能

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121744

Hussein Ali H. Jawdat Hussein Jawdat, M. Ilyas

{"title":"Performance Enhancement of MANET Against Wormhole Attacks Using Modified Ad-hoc On-demand Distance Vector Protocol","authors":"Hussein Ali H. Jawdat Hussein Jawdat, M. Ilyas","doi":"10.1109/ICAIoT57170.2022.10121744","DOIUrl":"https://doi.org/10.1109/ICAIoT57170.2022.10121744","url":null,"abstract":"Mobile Ad-hoc network (MANET) has improved to be essential components of our daily lives. Due to its compatibility with multimedia data interchange in a mobile context, MANETs are employed in a variety of applications today, including those for crisis management and the battlefield, The popularity of infrastructure-less networks has grown along with the popularity of ad hoc networks in recent years as a result of the rise in wireless devices and technological developments MANETs have brought about a new type of technologies that allow them to operate without a fixed infrastructure. The dynamic nature of the MANET network makes it susceptible to numerous attacks. One of these is the wormhole, which spreads data from one site to another and can damage the network. If the source node chooses this fictitious route, the attacker has a backup plan to deliver or drop packets. In this paper, we proposed a technique by modifying the Ad-hoc On-demand Distance vector protocol (AODV) in the stage of RREQ and RREP with the sequence number transaction and the detection timer(DT). The proposed method when reached to 100 nodes, achieved the throughput of 95.5kbps, energy consumption of 55.9joule, end to end delay of 0.973sec and Packet Delivery Ratio (PDR) of 96.5%.","PeriodicalId":297735,"journal":{"name":"2022 International Conference on Artificial Intelligence of Things (ICAIoT)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128067626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of Microstrip Patch Antenna for C-band Applications c波段微带贴片天线的设计

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121831

Maher Muayad Hefdhi, R. H. Thaher

引用次数: 0

A Modified Wavelet Threshold Approach for Reducing Various Noise with Statistical Results 基于统计结果的改进小波阈值降噪方法

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121838

Saad Hussein Abed Hamed, Omar Khalid Salih Alhafidh, Y. S. Younis, Warif B. Yahia, Saleem Meften, Ali Hasan Ali

引用次数: 0

Context-Based Visual Sentiment Analysis for Social Media Data 基于上下文的社交媒体数据视觉情感分析

2022 International Conference on Artificial Intelligence of Things (ICAIoT) Pub Date : 2022-12-29 DOI: 10.1109/ICAIoT57170.2022.10121853

D. J. Mohammed, Hiba J. Aleqabie

引用次数: 0