2023 3rd International Conference on Artificial Intelligence (ICAI)最新文献_第3页

A Hybrid Learning Approach for Automatic Data Labelling and Anomaly Detection in IoT Networks 物联网网络中自动数据标记和异常检测的混合学习方法

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136687

Rimsha Kanwal, Rimsha Kanwal, Umara Noor, Zahid Rashid

{"title":"A Hybrid Learning Approach for Automatic Data Labelling and Anomaly Detection in IoT Networks","authors":"Rimsha Kanwal, Rimsha Kanwal, Umara Noor, Zahid Rashid","doi":"10.1109/ICAI58407.2023.10136687","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136687","url":null,"abstract":"Internet of Things (IoT) is an environment in which digital equipment is augmented with sensors to share and receive data through network. When devices share data it can be effected by anomalies or any attack due to corrupted data or by any other uncertainty and ambiguity in data. The data can also be corrupted through a damage in device. These attacks or anomalies damage the working of the IoT networks. The anomalous data can be detected through detection techniques however most anomaly detection techniques depend upon labelled data but for IoT datasets, usually class labels are not available. Labeling is performed by a manual process which is time consuming and also costly. As data in IoT increases day by day so there is a need to label and classify data for future unseen data. In this paper a hybrid algorithm is proposed in which both clustering and classification techniques are applied for automatic labeling and classifying on IoT dataset. The model contains two function. In the first phase k-means clustering is employed for labelling dataset instances as normal or anomalous. In the second phase labelled dataset is used to train Random Forest model to detect anomalies in IoT networks. The results show that the proposed model is detecting anomalies in IoT networks with an accuracy 98%, precision 98 %, recall 98%, and F-meausre 0.98%.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130125003","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 Study & Proposed Architecture for Large Scale IoT Networks in the space of Public Transportation 公共交通领域大规模物联网网络体系结构研究与建议

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136684

Afaq Ahmad Khan, M. U. Farooq, A. Hassan

{"title":"A Study & Proposed Architecture for Large Scale IoT Networks in the space of Public Transportation","authors":"Afaq Ahmad Khan, M. U. Farooq, A. Hassan","doi":"10.1109/ICAI58407.2023.10136684","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136684","url":null,"abstract":"In today's modern society, Internet of Things has proved a substantial role in our lives. From medical applications, industries, smart homes to surveillance base systems, IoT has found itself a place in almost every pace of life. Transportation is one such area where IoTs play an important role. In the concept of smart cities, various IoT based Transportation projects have been proposed and developed across the globe. These projects have been theorized to cover major portions of an efficient and intelligent smart city. Although a major leap, these projects still have various gaps with respect to how various systems are designed and handled throughout the entire Traffic management system. Thus, the main aim of this paper is to propose and discuss the various prospects of a comprehensive Transportation system. A top-bottom system is presented, in which different views are presented IoTs can be implemented in public transportation in order to properly collect, manage and process data with the help of Software Defined Networks (SDN) and Network Function Virtualization (NFV). The proposed architecture is divided into three main parts; where the function, individual and collective working of each part is discussed, keeping in view the gaps of previous projects. Furthermore, several latest works in literature have also been incorporated to fully explain the functionality of the Transportation architecture.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"11 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116314908","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

Bearing Degradation Process Prediction based on Feedforward Neural Network 基于前馈神经网络的轴承退化过程预测

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136654

Syed Muhammad Haris, Muhammad Hunain Syed, S. Ahsan, Salman Ahmed Khan

{"title":"Bearing Degradation Process Prediction based on Feedforward Neural Network","authors":"Syed Muhammad Haris, Muhammad Hunain Syed, S. Ahsan, Salman Ahmed Khan","doi":"10.1109/ICAI58407.2023.10136654","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136654","url":null,"abstract":"As one of the most significant component of the rotary machinery, bearings play a vital role in smooth and reliable operation of the machinery. Estimating the remaining useful life (RUL) of bearings is essential for reducing the cost of maintenance and improving reliability. In this paper, a prognostics methodology based on artificial neural network (ANN) is proposed to improve the accuracy of RUL estimation for bearing. This is achieved by using features obtained from frequency, time and time-frequency domains. Popular techniques of Wavelet Packet Decomposition (WPD) and Fast Fourier Transform (FFT) are applied for feature extraction in time-frequency and frequency domains respectively. For effective prognostics, monotonicity and correlation-based feature selection criteria is used to discard redundant and unnecessary features. These features are then processed to be used as input into the ANN model. The model uses Feedforward Neural Network (FFNN) with the popular learning algorithm, Levenberg-Marquardt, for predicting the RUL. The results depict that this model is very effective for predicting the RUL of bearings. FFNN results are also compared with Gaussian Process Regression (GPR) algorithm results, showing the better performance of FFNN as compared to GPR.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123773319","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

Motor Imagery EEG Classification Using Fine-Tuned Deep Convolutional EfficientNetB0 Model 基于精细深度卷积effentnetb0模型的运动意象脑电分类

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136681

Muhammad Shahroze Ali, A. Hassan, Aqsa Rahim, Muhammad Hashir Ashraf, Amna Rahim, Shayaan Saghir

{"title":"Motor Imagery EEG Classification Using Fine-Tuned Deep Convolutional EfficientNetB0 Model","authors":"Muhammad Shahroze Ali, A. Hassan, Aqsa Rahim, Muhammad Hashir Ashraf, Amna Rahim, Shayaan Saghir","doi":"10.1109/ICAI58407.2023.10136681","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136681","url":null,"abstract":"This work proposes a new way to apply the deep convolutional EfficientN etB0 model for the classification to learn various electroencephalogram (EEG) signal properties on BCI competition IV dataset 2b. Several deep convolutional neural networks (DCNN)-based techniques have been applied to enhance the accuracy of motor imagery-based brain-computer interfaces (BCIs). The time-varying nature of various frequency bands makes extracting informative features from EEG signals difficult, causing the loss of DCNN classification accuracy. The EfficientNetB0 baseline model's feature extraction capability is used to overcome these limitations by first transforming EEG 1D signals into 2D images using the feature extraction technique of the Short-Time Fourier Transform (STFT) algorithm to train and evaluate the EfficientNetB0 model. The transfer learning approach is used to expand the initial feature sets for efficient model training in a short period. After learning from the dataset, the entire model is retrained and fine-tuned to work with the proposed layers. Our evaluated results demonstrated that the highest average Accuracy, Precision, Recall, F1-Score and MCC with the STFT method is 86.46%,88.2%, 91.2%,89.78% and 0.815 respectively over 10 epochs. According to the results, the proposed methodology outperforms other state-of-the-art DCNN models for feature extraction and classification of two-class motor imagery, namely right-hand and left-hand movements for the given dataset.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128485783","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

Real-time object detection and 3D scene perception in self-driving cars 自动驾驶汽车的实时目标检测和3D场景感知

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136623

Abdul Basit, Muhammad Usama Ejaz, Qirat Ayaz, F. Malik

引用次数: 0

Automatic Tree Counting from Satellite Imagery Using YOLO V5, SSD and UNET Models: A case study of a campus in Islamabad, Pakistan 利用YOLO V5、SSD和UNET模型从卫星图像中自动计数树木:以巴基斯坦伊斯兰堡的一个校园为例

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136679

Um e Hani, Sadia Munir, Shahzad Younis, Tariq Saeed, Hamad Younis

{"title":"Automatic Tree Counting from Satellite Imagery Using YOLO V5, SSD and UNET Models: A case study of a campus in Islamabad, Pakistan","authors":"Um e Hani, Sadia Munir, Shahzad Younis, Tariq Saeed, Hamad Younis","doi":"10.1109/ICAI58407.2023.10136679","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136679","url":null,"abstract":"Since the last 3 years, Pakistan has been focusing considerably on the increase in tree plantation in several areas throughout the country. With this increase in plantation, the need for up-to-date record keeping for upkeep of these trees across the country arises. The extensive research in object detection and image segmentation models have led to a much faster method of satellite image based tree counting to replace conventional counting methods. This paper focuses on tree detection and counting using satellite images, spanning a total of 8 years, of a university campus located in the capital of Pakistan. It effectively makes use of data augmentation techniques to improve the accuracy of the implemented models which include YOLOV5, UNET and SSD. The satellite images taken over the years are used to generate a new data set and then the produced dataset is augmented using the techniques of rotating, flipping, and patching. The augmented data set is fed into the object detection and image segmentation models for training. The models are then compared on the basis of loss and accuracy to see which model was better suited to carry future work. The concluding results gave accuracy of 32%, 81%, and 24% for the YOLO, UNET and SSD models respectively. Future improvements include the use of high-resolution images and a larger data set to enhance the accuracy of the resulting models.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129254494","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

Segmentation of oropharynx cancer in head and neck and detection of the organ at risk by using CT- PET images 头颈部口咽癌的CT- PET分割及危险器官的检测

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136671

Maria Khan, Syed Fahad Tahir

{"title":"Segmentation of oropharynx cancer in head and neck and detection of the organ at risk by using CT- PET images","authors":"Maria Khan, Syed Fahad Tahir","doi":"10.1109/ICAI58407.2023.10136671","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136671","url":null,"abstract":"The detection of oropharynx cancer is very important. There are various applications of image segmentation in the medical field, such as locating the tumour, study of different anatomical structures, segmenting the object of interest etc. The segmentation of cancer is time consuming, and it requires a lot of human effort. The automated segmentation of cancer solves this problem. The goal of the research is to provide a deep learning method of segmenting an oropharynx cancer in 3D CT-PET images and find the organs at risk. The main challenge in the segmentation is that the organs are very dense or may overlap each other because, most of the organs share same intensity levels with the other surrounding tissues. We use the combination of CT -PET images to solve this problem because, the images provide the information both anatomical and metabolic about tumors. We used U-Net as our base model for the segmentation of tumour. The 3D Inception module is used at the encoder side and the 3D Res-Net module is used at the decoder side. The 3D squeeze and excitation (SE) module is also inserted in each encoder block of the model. We used a depth wise convolutional layer in both 3D Res-Net module and 3D Inception module. We used the precision, recall and Dice Similarity Coefficient (DSC) as our performance metrics and achieved precision 0.84849, recall 0.6225 and Dice Similarity Coefficient (DSC) 0.7183 and compared the results with the state of art. Our main contribution is finding the distance from the centre of the organs (nasal cavity, oral cavity, nasopharynx, brain stem, spinal cord, hypopharynx, larynx) to the oropharynx tumour. On the base of the minimum distance among all organs, we assume that organ will be at risk.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130797556","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

DeepFinancial Model for Exchange Rate Impacts Prediction of Political and Financial Statements 汇率对政治和财务报表影响预测的深度金融模型

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136658

Muhammad Asad Arshed, Shahzad Mumtaz, Mehmood Hussain, Rabbia Alamdar, Malik Tahir Hassan, Muhammad Tanveer

{"title":"DeepFinancial Model for Exchange Rate Impacts Prediction of Political and Financial Statements","authors":"Muhammad Asad Arshed, Shahzad Mumtaz, Mehmood Hussain, Rabbia Alamdar, Malik Tahir Hassan, Muhammad Tanveer","doi":"10.1109/ICAI58407.2023.10136658","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136658","url":null,"abstract":"The extensive use of social media led people to share emotions and opinions on social sites. Currently, the prediction of the exchange rate with the content of social sites, specifically Twitter, is an active research and challenge. In this study, we have proposed a deep learning model for the prediction of the exchange rate fluctuation with political and financial statements sentiments. In this study, we have considered USD dollar rates in terms of PKR currency rates for experiments as well as collective sentiment technique (positive, negative, and neutral for each day) considered after data preprocessing with natural language processing techniques. The Adaptive Synthetic (ADASYN) technique is used in this study for data balancing to avoid the overfitting of the machine and deep learning models. Deep learning based proposed model named “Deep Financial” is effective with the highest accuracy of 87.54% as compared to Support Vector Machine, K-Nearest Neighbor and Logistic Regression, for the prediction of exchange rate fluctuation with political and financial statements sentiments.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130949499","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

The next generation of cloud security through hypervisor-based virtual machine introspection 下一代云安全通过基于管理程序的虚拟机自省实现

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136655

Fazalur Rehman, Z. Muhammad, S. Asif, Hameedur Rahman

{"title":"The next generation of cloud security through hypervisor-based virtual machine introspection","authors":"Fazalur Rehman, Z. Muhammad, S. Asif, Hameedur Rahman","doi":"10.1109/ICAI58407.2023.10136655","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136655","url":null,"abstract":"Cloud computing has become increasingly prevalent in recent years, providing organizations with on-demand re-sources. While cloud infrastructure has matured with security en-hancements, attackers' strategies for launching attacks on cloud networks are also becoming more sophisticated, posing a risk to the system's confidentiality, integrity, and availability. Virtualization is a key aspect of cloud computing, which allows physical computers to share their resources and computing power. To secure cloud infrastructure, multiple defensive measures are used such as virtual level segregation, intrusion detection prevention systems (IDS/IPS), cloud access and security brokers (CASB), and endpoint detection & response. These safeguards are often run on the virtual machine shared across a common network, making them vulnerable to deceivability, insider threat, and network-level attacks. Previous research has primarily relied on the traditional approaches discussed, with limited compliance with hypervisor-based introspection. In this paper, we propose a novel hypervisor-based virtual machine introspection (HVMI) tool to detect and perform runtime forensic analysis of attacks on the cloud platform. The proposed solution consists of a client application that runs on a host of the cloud provider. In case of any security breach, the HVMI notifies the cloud provider and starts forensic analysis to detect and minimize the impact of the breach. Additionally, HVMI uses structured threat information expression (STIX) to generate standard threat details that are easy to understand and widely adopted by cyber professionals. STIX patterns may also be made publicly available, allowing security organizations to deduce defensive strategies against certain types of cyberattacks that occur in the cloud.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124167357","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

Humor Detection in English-Urdu Code-Mixed Language 英语-乌尔都语语码混合语言中的幽默检测

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136656

S. Bukhari, Anusha Zubair, Muhammad Umair Arshad

引用次数: 0