2022 IEEE World AI IoT Congress (AIIoT)最新文献_第4页

Fake News Detection in Social Networks Using Data Mining Techniques 利用数据挖掘技术检测社交网络中的假新闻

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817287

Hebah Alquran, Shadi Banitaan

引用次数: 0

A Distributed Average Cost Reinforcement Learning approach for Power Control in Wireless 5G Networks 无线5G网络功率控制的分布式平均成本强化学习方法

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817168

A. Ornatelli, A. Giuseppi, A. Tortorelli

引用次数: 0

COVID-19 Prediction based on Infected Cases and Deaths of Bangladesh using Deep Transfer Learning 基于深度迁移学习的孟加拉国COVID-19感染病例和死亡预测

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817160

Khan Md Hasib, S. Sakib, J. Mahmud, Kamruzzaman Mithu, Md. Saifur Rahman, Mohammad Shafiul Alam

{"title":"COVID-19 Prediction based on Infected Cases and Deaths of Bangladesh using Deep Transfer Learning","authors":"Khan Md Hasib, S. Sakib, J. Mahmud, Kamruzzaman Mithu, Md. Saifur Rahman, Mohammad Shafiul Alam","doi":"10.1109/aiiot54504.2022.9817160","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817160","url":null,"abstract":"The severely infectious virus known as “COVID- 19” has wreaked havoc on the planet, trapping to keep the disease from spreading, while billions of people are staying inside. Every experts and professionals in many disciplines are working tirelessly to create a vaccine and preventative techniques to help the globe overcome this difficult crisis. In Bangladesh, the number of persons infected with Coronavirus is particularly alarming. A accurate prognosis of the epidemic, on the other hand, may aid in the management of this contagious illness until a remedy is discovered. This study aims to forecast impending COVID-19 exposed instances and fatalities using a time series dataset utilizing proposed deep transfer learning model where encoder-decoder CNN-LSTM along with deep CNN pretrained models such as: ResNet-50, DenseNet-201, MobileNet-V2, and Inception-ResNet-V2 performed. We also predict the regular exposed instances and fatalities throughout the following 180 days in data visualization segment using AIC and BIC selection criteria. The suggested paradigms are also used to anticipate Bangladesh's daily confirmed cases and daily which is evaluated by error based on three performance criteria. We discovered that ResNet-50 performs better among others for predicting infected case and deaths owing to COVID-19 in Bangladesh in terms of MAPE, MAE and RMSE evaluations.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"33 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":"129292040","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: 13

Comparing Pretrained Image-Net CNN with a Siamese Architecture for Few-Shot Learning Applications in Radar Systems 比较预训练的Image-Net CNN与Siamese架构在雷达系统中的少镜头学习应用

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817228

Cesar Martinez Melgoza, Kayla Lee, Tyler Groom, Nate Ruppert, K. George, Henry Lin

{"title":"Comparing Pretrained Image-Net CNN with a Siamese Architecture for Few-Shot Learning Applications in Radar Systems","authors":"Cesar Martinez Melgoza, Kayla Lee, Tyler Groom, Nate Ruppert, K. George, Henry Lin","doi":"10.1109/aiiot54504.2022.9817228","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817228","url":null,"abstract":"Over the years, the increase in electronic devices and innovation towards technological capabilities have resulted in an increase in traffic in the electromagnetic spectrum, thus making it harder for radar systems to distinguish multiple emitters with added interference. Traditional methods for classification, such as machine learning, prove to be a suitable solution for this problem, however these models require an enormous amount of data to train and evaluate. This experiment implements a Few-Shot learning framework and evaluates the performance of different Neural Network Architectures such as a standard Convolutional Neural Network, and a Siamese Network from a previous experiment. The experiment will utilize different kinds of hardware equipment. This includes the ZCU104 FPGA board, AD-FMCOMMS2-EBZ RF module, the Jetson TX2, and NVIDIA Titan RTX. The hardware equipment will be evaluated using performance metrics such as hardware acceleration, to find the best medium between computational power, acceleration speed, and evaluation accuracy.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"103 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":"116975154","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

Employing Edge Computing to Enhance Self-Defense Capabilities of IoT Devices 利用边缘计算增强物联网设备的自我防御能力

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817368

Jack Li, Yim-Fun Hu

引用次数: 0

Evaluation of Naïve Bayesian Algorithms for Cyber-Attacks Detection in Wireless Sensor Networks Naïve贝叶斯算法在无线传感器网络网络攻击检测中的应用

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817298

Shereen S. Ismail, H. Reza

{"title":"Evaluation of Naïve Bayesian Algorithms for Cyber-Attacks Detection in Wireless Sensor Networks","authors":"Shereen S. Ismail, H. Reza","doi":"10.1109/aiiot54504.2022.9817298","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817298","url":null,"abstract":"Wireless Sensor Network (WSN) is one of the Internet of Things (IoT) operating platforms, which has proliferated into a wide range of applications. These networks comprise many resource-restricted sensors in terms of sensing, communication, storage, and power. Security becomes a critical concern to protect the network of scarce resources from any malicious activities that target the network. Several solutions have been presented in the literature; however, machine learning has proven its appropriateness in designing energy-efficient detection measures for cyber-attacks targeting WSNs. This paper presents a WSN security performance evaluation of three Naïve Bayesian machine learning classification technique variants: Gaussian Naïve Bayes, Multinomial Naïve Bayes, and Bernoulli Naïve Bayes, compared to three well-known base algorithms: K-Nearest Neighbors, Support Vector Machine, and Multilayer Perceptron. We applied Spearman correlation as a univariate feature selection. The specialized dataset, WSN-DS, was used for training and testing purposes. The performance of the six classifiers was evaluated in terms of accuracy, probability of detection, positive prediction value, probability of false alarm, probability of misdetection, memory usage, processing time, prediction time, and complexity.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"35 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":"115463968","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: 9

MusCare+: Muscle Monitoring for Anomalies MusCare+:肌肉异常监测

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817161

Nicholas Foley, Chen-Hsiang Yu

{"title":"MusCare+: Muscle Monitoring for Anomalies","authors":"Nicholas Foley, Chen-Hsiang Yu","doi":"10.1109/aiiot54504.2022.9817161","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817161","url":null,"abstract":"- Muscles are an essential part of everyday life and any damage or illness that affects them can cause massive problems. Patients who are diagnosed with muscle injuries and illnesses largely remain unmonitored, even though a few appointments they have with doctors annually. Moving from unmonitored to constant monitoring can not only paint a better picture of how a muscle condition is progressing, but it also can inform medical professionals if their treatment regimen is actually working. In this paper, we propose a new system that can monitor muscle health of a patient and predict the muscle conditions. This system mainly focuses on the shoulder but could be expanded to other areas of the body. By utilizing the strength of machine learning and the Android platform, we created a platform that can monitor muscle health quickly and easily. The current prototype system is not only able to display live data gathered from an EMG sensor, but it can also predict whether the muscle is currently flexed or relaxed. Although there is a limitation in current prototype system, a more robust machine learning algorithm could be trained to give a wide array of muscle health predictions.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"56 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":"122002038","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: 1

Energy Efficient Double Critic Deep Deterministic Policy Gradient Framework for Fog Computing 面向雾计算的节能双批评家深度确定性策略梯度框架

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817157

Bhargavi Krishnamurthy, S. Shiva

{"title":"Energy Efficient Double Critic Deep Deterministic Policy Gradient Framework for Fog Computing","authors":"Bhargavi Krishnamurthy, S. Shiva","doi":"10.1109/aiiot54504.2022.9817157","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817157","url":null,"abstract":"-Nowadays the data is growing at a faster pace and the big data applications are required to be more agile and flexible. There is a need for a decentralized model to carry out the required substantial amount of computation across edge devices as they has led to the innovation of fog computing. Energy consumption among the edge devices is one of the potential threatening issues in fog computing. Their high energy demand also contributes to higher computation cost. In this paper Double Critic (DC) approach is enforced over the Deep Deterministic Policy Gradient (DDPG) technique to design the DC-DDPG framework which formulates high quality energy efficiency policies for fog computing. The performance of the proposed framework is outstanding compared to existing works based on the metrics like energy consumption, response time, total cost, and throughput. They are measured under two different fog computing scenarios i.e., fog layer with multiple entities in a region and fog layer with multiple entities in multiple regions. Mathematical modeling reveals that the energy efficiency policies formulated are of high quality as they satisfy the quality assurance metrics, such as empirical correctness, robustness, model relevance, and data privacy.","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":"126705267","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

Heart failure survival prediction using machine learning algorithm: am I safe from heart failure? 使用机器学习算法预测心力衰竭生存:我是否安全?

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817303

M. Mamun, Afia Farjana, Miraz Al Mamun, Md Salim Ahammed, Md Minhazur Rahman

{"title":"Heart failure survival prediction using machine learning algorithm: am I safe from heart failure?","authors":"M. Mamun, Afia Farjana, Miraz Al Mamun, Md Salim Ahammed, Md Minhazur Rahman","doi":"10.1109/aiiot54504.2022.9817303","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817303","url":null,"abstract":"Heart Failure (HF) is a prevalent ailment worldwide, and despite significant medical advancements in the past few decades, cardiovascular disease is still the leading cause of death. Although HF itself is a critical risk for patient survival, other co-existing pathophysiological conditions can present a significant threat to patient survival. Because so many elements contribute to a patient's survival in heart failure, predicting the chances of survival without using a computational technique can be difficult for cardiac doctors, eventually preventing the patient from receiving correct care. Fortunately, categorization and prediction models exist, which can assist cardiologists in designing proper treatment schemes using relevant medical data. This study aims to develop prediction models for patient survival in HF conditions. In this paper, we analyzed the UCI heart failure dataset containing relevant medical information of 299 HF patients. We applied several machine learning classifiers to predict the patient survival from HF-related pathophysiological parameters and analyzed the features corresponding to the most crucial risk factors using the correlation matrix. Our prediction models used the following machine learning techniques- Logistic Regression, Decision Tree, Support Vector Machine, XGBoost, LightGBM, Random Forest, KNN, and Bagging and were able to find a better result. Also, this paper presents a comparative study by analyzing the performance of different machine learning algorithms. Our analysis indicates that LightGBM achieved the highest Accuracy of 85% and AUC of 93% in predicting patient survival of HF patients compared to other machine learning algorithms.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"29 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":"125228930","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: 23

MVE-based Reinforcement Learning Framework with Explainability for improving Quality of Experience of Application Placement in Fog Computing 基于mve的可解释性强化学习框架提高雾计算中应用放置体验质量

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817331

Bhargavi Krishnamurthy, S. Shiva, Saikat Das, Ph.D.

引用次数: 1