2022 IEEE World AI IoT Congress (AIIoT)最新文献

{"title":"A Deep Learning Approach for Automatic Scoliosis Cobb Angle Identification","authors":"R. R. Maaliw, Julie Ann B. Susa, A. Alon, A. Lagman, Shaneth C. Ambat, M. B. García, K. Piad, M. C. F. Raguro","doi":"10.1109/aiiot54504.2022.9817290","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817290","url":null,"abstract":"Efficient and reliable medical image analysis is indispensable in modern healthcare settings. The conventional approaches in diagnostics and evaluations from a mere picture are complex. It often leads to subjectivity due to experts' various experiences and expertise. Using convolutional neural networks, we proposed an end-to-end pipeline for automatic Cobb angle measurement to pinpoint scoliosis severity. Our results show that the Residual U-Net architecture provides vertebrae average segmentation accuracy of 92.95% based on Dice and Jaccard similarity coefficients. Furthermore, a comparative benchmark between physician's measurement and our machine-driven approach produces an acceptable mean deviation of 1.57 degrees and a T-test p-value of 0.9028, indicating no significant difference. This study has the potential to help doctors in prompt scoliosis magnitude assessments.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"3 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":"124553726","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}

{"title":"LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks","authors":"K. Saurabh, Saksham Sood, Prashant Kumar, Uphar Singh, Ranjana Vyas, O. P. Vyas, M. M. Khondoker","doi":"10.48550/arXiv.2207.00424","DOIUrl":"https://doi.org/10.48550/arXiv.2207.00424","url":null,"abstract":"In the recent years, we have witnessed a huge growth in the number of Internet of Things (loT) and edge devices being used in our everyday activities. This demands the security of these devices from cyber attacks to be improved to protect its users. For years, Machine Learning (ML) techniques have been used to develop Network Intrusion Detection Systems (NIDS) with the aim of increasing their reliability/robustness. Among the earlier ML techniques DT performed well. In the recent years, Deep Learning (DL) techniques have been used in an attempt to build more reliable systems. In this paper, a Deep Learning enabled Long Short Term Memory (LSTM) Autoencoder and a 13-feature Deep Neural Network (DNN) models were developed which performed a lot better in terms of accuracy on UNSW-NB15 and Bot-loT datsets. Hence we proposed LBDMIDS, where we developed NIDS models based on variants of LSTMs namely, stacked LSTM and bidirectional LSTM and validated their performance on the UNSW_NB15 and BoTloT datasets. This paper concludes that these variants in LBDMIDS outperform classic ML techniques and perform similarly to the DNN models that have been suggested in the past.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"28 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":"133412825","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}

{"title":"Ensuring Web Integrity through Content Delivery Networks","authors":"Zhao Xiang Lim, Xiu Qi Ho, D. Tan, Weihan Goh","doi":"10.1109/aiiot54504.2022.9817199","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817199","url":null,"abstract":"The use of Hypertext Transfer Protocol Secure (HTTPS) has been widely adopted to ensure confidentiality, integrity and authenticity of information exchanged between web users and servers. Content Delivery Networks (CDNs) are a popular option to efficiently deliver content to any user on the web with promises of reduced latency. However, users have no way to verify if the intended content is served by the CDN even with HTTPS as users can only verify the connection to the CDN. To address this gap, we developed HTTP Authenticated Response Content (HARC), a solution to ensure end-to-end authenticity of response contents regardless of the presence of intermediate nodes such as CDNs.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"12 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":"130191256","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}

{"title":"Feature Fusion Network Based on Hybrid Attention for Semantic Segmentation","authors":"Xinchen Xie, Chen Li, Lihua Tian","doi":"10.1109/aiiot54504.2022.9817347","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817347","url":null,"abstract":"In the deep learning based real-time image semantic segmentation task, there are high requirements for the inference speed of the network. Due to the small amounts of parameters of the lightweight backbones, the calculation speed is often faster, which meets the requirements of real-time tasks. However, the ability of the lightweight networks to extract features is relatively weak, resulting in much worse segmentation accuracy than the large model. Therefore, how to make full use of the lightweight networks to extract more image information to achieve better segmentation performance has become a key problem. Here, we propose an efficient feature fusion network based on attention mechanism. First, the widely used MobileNetV2 is selected as the lightweight backbone network, and then spatial attention and channel attention are calculated for both high-resolution low-level features and low-resolution high-level features, thus the final feature map got a global receptive field. Besides, through the multi-levels supervised learning for each stage of the backbone, the multi-stage auxiliary loss function enables the network to be trained more effectively. Finally, on the cityscapes dataset, the our proposed network reached 74.12% mIoU, and the inference speed remained at 110 fps.","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":"130915679","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}

{"title":"Enhanced Data-Driven LoRa LP-WAN Channel Model in Birmingham","authors":"A. ElSabaa, F. Guéniat, Wenyan Wu, Martin P. Ward","doi":"10.1109/aiiot54504.2022.9817253","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817253","url":null,"abstract":"Innovative solutions providing better coverage and minimized power consumption by end nodes such as Low Power Wide Area Networks (LP-WAN) have facilitated the advances towards improved IoT connectivity. Long Range Wide Area Net-work (LoRaWAN) technology stands out as one leading platform of LP-WANs receiving vast attention from both industry and academia. Performance evaluation of LoRaWAN is promising, in particular in the field of outdoor localization and object tracking. Limitations of node ranging and tracking without the need of energy-draining solutions like GPS, however, has not been tackled thoroughly. In this work, we explore the performance of the LoRa LP-WAN technology using real-life measurements in Birmingham, UK, using commercially available equipment. We present a channel attenuation model that can be utilized to estimate the path loss in 868 MHz ISM band in urban-similar areas. The proposed channel model is then compared to previously well-identified empirical path loss models and enhanced by detecting and eliminating outlier data from the obtained real measurements for an optimal fitted model. We, further, propose a novel RSSI distribution-based and k-means clustering to enhance the power-to-distance prediction accuracy that improves absolute errors by 4% and 18%.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"75 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":"132543683","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}

{"title":"Real-Time Dynamic Object Grasping with a Robotic Arm: A Design for Visually Impaired Persons","authors":"Francis Liri, Austin Luu, K. George, Axel Angulo, Johnathan Dittloff","doi":"10.1109/aiiot54504.2022.9817307","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817307","url":null,"abstract":"Robotic arms have increasingly been used in applications such as manufacturing and medical. Often, physically impaired individuals have difficulty completing tasks such as picking an object off a shelf or picking items from the refrigerator. They rely on caregivers and others to help them complete tasks. Therefore to address this issue, research is ongoing into how to improve the lives of such persons using robotic arms and other technologies. This work builds on existing research which utilizes object recognition and grasp detection components to identify a bottle and obtain its real world coordinates but did not fully integrate the solution with a robotic arm [1]. We fully integrate the object recognition and grasp detection components with a Dobot Magician robotic arm. Using an eye-to-hand translation approach, we determine the translation matrix using experimental results. We used an Intel RealSense D455 camera to generate images for object detection and grasp point detection. The grasp point coordinates are passed to the robotic arm which performs the translation before moving the arm to grasp the bottle. Our tests with the fully integrated robotic arm show that the solution is feasible and using the given translation and depth accuracy the robotic arm can pick a bottle placed randomly in a given area.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"4 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":"130852153","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}

{"title":"Deep Learning Autoencoder based Anomaly Detection Model on 4G Network Performance Data","authors":"Md Rakibul Ahasan, Mirza Sanita Haque, Mohammad Rubbyat Akram, Mohammed Fahim Momen, Md. Golam Rabiul Alam","doi":"10.1109/aiiot54504.2022.9817338","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817338","url":null,"abstract":"A 4G network stands for a fourth-generation mobile network that enables 4G capable mobile phones to connect with the internet faster than ever. It is possible because of faster authentication between mobile phone and network entity. The network entities are sophisticated and require constant monitoring in terms of fault management and performance management. However, the fault is very rare in that network nodes, but a deviation of performance is normal. This deviation is known as an anomaly, and machine learning is useful for detecting an anomaly. In this paper, deep neural network autoencoder-based anomaly detection is discussed over 4G network performance data. An autoencoder can mimic an output from its input and provide superior performance when the data properties are similar. Further elaboration in this paper is how different properties of autoencoder hidden layer count, variable threshold measurement etc influence the anomaly detection outcome of 4G network performance data. At last, an autoencoder configuration is recommended for anomaly detection of 4G network performance data.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"144 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":"116414925","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}

{"title":"Implementation of Machine Learning in BCI Based Lie Detection","authors":"M. Khalil, Maria Ramirez, Johnny Can, K. George","doi":"10.1109/aiiot54504.2022.9817162","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817162","url":null,"abstract":"In this study, EEG, fNIRS, and HRV signals, recorded from a group of subjects when they were answering a series of true or false questions, were used to see if there is a correlation between BCI results and lying. The EEG and fNIRS signals were collected with g.Nautilus fNIRS-8 headset, while HRV was measured using the Wellue Smart Pulse Oximeter for Adults and Infant connected to iPhone 8 via the ViHealth app. After all the subjects' BCI signals were collected, the raw data was processed in MATLAB and then put in a CSV file. The CSV file was put in MATLAB's Classification Learner KNN and SVM to determine the accuracy of the results. The accuracy of KNN and SVM functions had a range of 75% to 79.4%. The learner was able to predict 81.5% of the truths and 73.7% of the lies.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"7 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":"128660592","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}

{"title":"Predicting Cryptocurrency Price Change Direction from Supply-Side Factors via Machine Learning Methods","authors":"David W. Mayo, H. Elgazzar","doi":"10.1109/aiiot54504.2022.9817249","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817249","url":null,"abstract":"Cryptocurrency prices are highly variable. Predicting changes in cryptocurrency price is a hugely important topic to investors and researchers, with much existing research on demand-side factors. The goal of this research project is to design and implement machine learning models to predict future cryptocurrency price change direction based primarily on supply-side factors. Different unsupervised machine learning techniques are used to build the predictive models. These techniques include K Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayesian Classifier, and Random Forest Classifier. A dataset of 10 daily supply-side metrics for three prominent cryptocurrencies (Bitcoin, Ethereum, and Litecoin) at four different time horizons (ranging from one day to 30 days) are used to build and test the machine learning models. The outputs of these models indicate the predicted direction of the price movement over the time horizon (i.e., whether the price would go up or down), not the magnitude of the movement. Experimental results show that predictions were very unreliable for the shorter time spans but very reliable for the longest time spans. The Artificial Neural Network and Random Forest classifiers consistently outperformed the other techniques and achieved a prediction accuracy of over 90% in most models and over 95% in the best models. Experimental results show also that there is no significant difference in predictability between the three prominent cryptocurrencies.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"73 6 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":"131012837","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}

{"title":"Disrupting the Cooperative Nature of Intelligent Transportation Systems","authors":"S. Almalki, A. Abdel-Rahim, Frederick T. Sheldon","doi":"10.1109/aiiot54504.2022.9817238","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817238","url":null,"abstract":"The emergence of Cooperative Intelligent Trans-portation Systems (cITS) simplifies the exchange of traffic sit-uational information among vehicles within “close” proximity, which facilitates smooth traffic flow, reduces the congestion and saves energy. However, with such advantages come challenges represented by attackers who would compromise the vehicle system components, spoof false telemetry and/or control signals causing serious problems such as congestion and/or accidents. There is need for security mechanism that can identify and detect such misbehavior in cITSs more dependably. Several studies have proposed Intrusion Detection Systems for cITS depending on the contextual data exchanged between neighboring nodes. Those solutions rely on classifiers trained and readjusted online to reflect the dynamic nature of the cITS environment. These models are usually trained with a set of features selected based on insufficient data. This makes the feature significance estimation inaccurate due to data insufficiency collected from the online systems immediately after the model was updated. In this paper we address this issue by introducing a Proportional Conditional Redundancy Coefficient (PCRC) technique. The technique is used in the Enhanced Joint Mutual Information (EJMI) feature selection for better feature significance estimation. At each iteration, the PCRC increases the redundancy of the candidate feature proportional to the number of already-selected features while taking into consideration the class label. Such conditional redundancy is estimated for the individual features, which gives the feature selection technique the ability to perceive the attack characteristics regardless of the common characteristics of the attack. Unlike existing works, the proposed technique increases the weight of the redundancy term proportional to the size of the selected set. Consequently, the likelihood that a feature is redundant, given the class label, increases when more features are added to the selected set. By applying the proposed EJMI to select the features from the Next Generation Simulation (NGSIM) dataset of cITS, more accurate IDS has been trained as shown by the evaluation results. This helps to better protect the nodes in cITS against the cyberattacks.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"668 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":"122620091","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}