2022 IEEE World AI IoT Congress (AIIoT)最新文献_第3页

ComparativeAnalysisofARIMAandLSTMM achine Learning Algorithm for Stock PricePrediction arima&lstmm机器学习算法在股票价格预测中的比较分析

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817176

Mohammad Monirujjaman Khan, Md. Farabi Alam, Shoumik Mahabub Ridoy

引用次数: 0

Automated Determination of Mushroom Edibility Using an Augmented Dataset 使用增强数据集的蘑菇可食性自动测定

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817321

S. Chawathe

引用次数: 1

No-Clear for Nuclear 核不清除

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817180

Sanskar Raj Marahata, Eman Abdelfattah, Sandra Ibrahim, Audrina Dobrevic

引用次数: 0

An Evaluation of IoT DDoS Cryptojacking Malware and Mirai Botnet 物联网DDoS加密劫持恶意软件及Mirai僵尸网络评估

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817163

Adam Borys, A. Kamruzzaman, Hasnain Nizam Thakur, Joseph C. Brickley, M. Ali, Kutub Thakur

引用次数: 5

NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains NFDLM:用于物联网领域DDoS攻击检测的基于轻量级网络流的深度学习模型

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/AIIoT54504.2022.9817297

K. Saurabh, T. kumar, Uphar Singh, O. P. Vyas, R. Khondoker

引用次数: 3

Predicting Audio Training Learning Outcomes Using EEG Data and KNN Modeling 利用脑电数据和KNN模型预测音频训练学习结果

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817164

Abel Desoto, Ethan Santos, Francis Liri, K. Faller, Devin Heng, Joshua Dodd, K. George, Julia R. Drouin

{"title":"Predicting Audio Training Learning Outcomes Using EEG Data and KNN Modeling","authors":"Abel Desoto, Ethan Santos, Francis Liri, K. Faller, Devin Heng, Joshua Dodd, K. George, Julia R. Drouin","doi":"10.1109/aiiot54504.2022.9817164","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817164","url":null,"abstract":"People are constantly surrounded by some form of sound, which can occasionally interfere with their daily tasks such as conversation. When sound interferes with daily activities, it becomes noise that is undesired sound. Depending on the surroundings, one may be subjected to varying levels of noise, resulting in hearing challenges especially for those with hearing disabilities. Researchers have tested how the brain interprets information and shown that the brain can be ‘primed’ to quickly tune hearing and effectively learn to understand sounds. This concept is used to propose a software-based training solution that utilizes EEG signals to identify whether or not a person with a hearing disability is learning. This can be applied for the training of those with disabilities and eliminate the need of a doctor to administer and make the process faster and simpler. An overall framework for the proposed system and outline of the essential components are presented. The research is extended by refining the testing and experiment methods, resolving some of the weaknesses of the research and performing similar studies with a larger participant pool. Furthermore, a machine learning algorithm, K-Nearest Neighbor (KNN), is applied to evaluate EEG data and predict a subject's understanding of distorted audio.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121241636","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

Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM 基于深度特征与光tgbm融合的胸部x线图像COVID-19分类

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.48550/arXiv.2206.04548

H. Nasiri, Ghazal Kheyroddin, M. Dorrigiv, Mona Esmaeili, A. Nafchi, Mohsen Ghorbani, P. Zarkesh-Ha

{"title":"Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM","authors":"H. Nasiri, Ghazal Kheyroddin, M. Dorrigiv, Mona Esmaeili, A. Nafchi, Mohsen Ghorbani, P. Zarkesh-Ha","doi":"10.48550/arXiv.2206.04548","DOIUrl":"https://doi.org/10.48550/arXiv.2206.04548","url":null,"abstract":"The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for future analysis.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"257O 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122261295","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: 16

Graph Attention Neural Network Distributed Model Training 图注意力神经网络分布式模型训练

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817156

Armin Esmaeilzadeh, Mina Esmail Zadeh Nojoo Kambar, Maryam Heidari

引用次数: 0

Violence Detection Using Computer Vision Approaches 使用计算机视觉方法进行暴力检测

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817374

Khalid Raihan Talha, Koushik Bandapadya, Mohammad Monirujjaman Khan

{"title":"Violence Detection Using Computer Vision Approaches","authors":"Khalid Raihan Talha, Koushik Bandapadya, Mohammad Monirujjaman Khan","doi":"10.1109/aiiot54504.2022.9817374","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817374","url":null,"abstract":"Violent crime has always been a major social problem. The rise of violent behavior in public areas can be attributed to a variety of factors. Greed, frustration, and hostility among individuals, as well as social and economic anxieties, are the primary causes of increased violence. It is critical to protect our possessions, as well as our lives, from threats such as robbery or homicide. It is impossible to prevent crime and violent acts unless brain signals are studied and a certain pattern deduced from criminal ideas is detected in real-time. Due to its technological viability, it has yet to be realized. However, We can identify violent activity in public spaces by using the concepts of computer vision (a subfield of deep learning) technology. The goal of this project is to build a real-time violent activity monitoring system that will be capable of detecting violence very quickly and efficiently. The public of any city can benefit from it, as it will allow the people of the law enforcement department to take necessary actions to prevent violent activities. When the system is implemented, it will be able to detect the speed of the movements of people and their distances from other people walking in public places by using cameras. The system will mainly detect the speed of hand and leg movements of a person who will be very close to another person. If anyone is identified as a violent maker, the server-side of the system will notify the people who will be responsible for preventing violence in a very short time. The system was built using the concepts of computer vision and neural networks. The system has been developed and tested initially on the personal computing devices of the system developers. This system is very easy to design and develop, making it very easy to use for any kind of public area surveillance. At the same time, the system gives its desired output due to its high accuracy.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129204625","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

A Survey on Deep Learning Techniques for Joint Named Entities and Relation Extraction 联合命名实体及关系抽取的深度学习技术综述

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817231

Mina Esmail Zadeh Nojoo Kambar, Armin Esmaeilzadeh, Maryam Heidari

引用次数: 2