2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)最新文献

Machine Learning based Theoretical Framework for Failure Prediction, Detection, and Correction of Mission-Critical Flight Software 基于机器学习的关键任务飞行软件故障预测、检测和修正理论框架

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787424

Muhammad Waqas Ahmad, M. Akram, K. Saghar, Wasi Haider Butt, Rashid Ahmad, Ali Hassan

{"title":"Machine Learning based Theoretical Framework for Failure Prediction, Detection, and Correction of Mission-Critical Flight Software","authors":"Muhammad Waqas Ahmad, M. Akram, K. Saghar, Wasi Haider Butt, Rashid Ahmad, Ali Hassan","doi":"10.1109/ICoDT255437.2022.9787424","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787424","url":null,"abstract":"Mission-critical flight software acts as the control mechanism for autonomous flights and lies at the heart of next-generation developments in the aviation industry. Most state-of-the-art technological evolution is realized through the use of contemporary software which implements the essentially required, novel, innovative, and featuring value additions. Real-time physical exposure and the data-driven flying nature of aerial vehicles make them vulnerable to an ever-evolving new threat spectrum of cyber security. Nation or state-sponsored cyber attacks through sensors’ data corruption, hardware Trojans, or counterfeit wireless signals may exploit dormant and residual software vulnerabilities. It may lead to severe and catastrophic consequences including but not limited to serious injury or death of the crew, extreme damage or loss to equipment and environment. We have proposed a machine learning based theoretical framework for real-time monitoring and failure analysis of autonomous flight software. It has been introduced to protect the mission-critical flight software from run-time data-driven semantic bugs and exploitation that may be caused by missing, jammed, or spoofed data values, due to malicious online cyber activities. The effectiveness of the proposed framework has been demonstrated by the evaluation of a real-world incident of grounding an aerial vehicle by the actors in their vicinity without the intent of the original equipment manufacturer (OEM). The results show that the reported undesired but successful cyber attack may has been avoided by the effective utilization of our proposed cyber defense approach, which is targeted at software failure prediction, detection, and correction for autonomous aerial vehicles.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"74 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120867856","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

ICoDT2 2022 Additional Reviewers ICoDT2 2022新增审稿人

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787445

引用次数: 0

An Ensemble Model for Software Defect Prediction 软件缺陷预测的集成模型

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787439

A. Ali, Attique ur Rehman, Ali Nawaz, Tahir Ali, M. Abbas

{"title":"An Ensemble Model for Software Defect Prediction","authors":"A. Ali, Attique ur Rehman, Ali Nawaz, Tahir Ali, M. Abbas","doi":"10.1109/ICoDT255437.2022.9787439","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787439","url":null,"abstract":"Software testing is one of the important ways to ensure the quality of software. It is found that testing cost more than 50% of overall project cost. Effective and efficient software testing utilizes the minimum resources of software. Therefore, it is important to construct the procedure which is not only able to perform the efficient testing but also minimizes the utilization of project resources. The goal of software testing is to find maximum defects in the software system. As world is continuously moving toward data driven approach for making important decision. Therefore, in this research paper we performed the machine learning analysis on the publicly available datasets and tried to achieve the maximum accuracy. The major focus of the paper is to apply different machine learning techniques on the datasets and find out which technique produce efficient result. Particularly, we proposed an ensemble learning models and perform comparative analysis among KNN, Decision tree, SVM and Naïve Bayes on different datasets and it is demonstrated that performance of Ensemble method is more than other methods in term of accuracy, precision, recall and F1-score. The classification accuracy of ensemble model trained on CM1 is 98.56%, classification accuracy of ensemble model trained on KM2 is 98.18% similarly, the classification accuracy of ensemble learning model trained on PC1 is 99.27%. This reveals that ensemble learning is more efficient method for making the defect prediction as compared other techniques.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116732065","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

Osteo-Doc: KL-Grading of Osteoarthritis Using Deep-Learning 骨-医生:骨关节炎的深度学习分级

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787470

Haider Masood, Eisha Hassan, A. A. Salam, Muwahida Liaquat

引用次数: 0

A Composite Retinal Fundus and OCT Dataset to Grade Macular and Glaucomatous Disorders 复合视网膜眼底和OCT数据集分级黄斑和青光眼疾病

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787482

Taimur Hassan, H. Raja, Bilal Hassan, M. Akram, J. Dias, N. Werghi

{"title":"A Composite Retinal Fundus and OCT Dataset to Grade Macular and Glaucomatous Disorders","authors":"Taimur Hassan, H. Raja, Bilal Hassan, M. Akram, J. Dias, N. Werghi","doi":"10.1109/ICoDT255437.2022.9787482","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787482","url":null,"abstract":"Retinopathy represents a group of retinal diseases that causes severe visual impairments and even blindness. Many researchers have publicly released datasets containing fundus or optical coherence tomography (OCT) scans to screen retinal diseases like macular edema (ME) and age-related macular degeneration (AMD). These datasets also contain clinical markings to analyze the retinal layers and retinal lesions within normal and abnormal pathologies. However, to the best of our knowledge, no dataset provides the clinically graded fundus and OCT images reflecting the geographic AMD, neovascular AMD, acute central serous retinopathy (CSR), chronic CSR, centrally involved DME (ci-DME), and glaucomatous pathologies. Furthermore, the majority of the publicly available OCT datasets are acquired through Spectralis Machines, which limits the thorough evaluation of autonomous frameworks to screen retinal pathologies irrespective of the scanner specifications. To overcome these challenges, we present a novel dataset containing composite fundus and OCT scans of each patient, along with detailed annotations for extracting the retinal layers and retinal lesions. Also, contrary to its competitors, the proposed dataset is acquired through Topcon 3D OCT 2000 machine that can be utilized for training (or evaluating) any autonomous frameworks to give the lesion-aware screening and severity grading of the above-mentioned retinal diseases as per the clinical standards. Moreover, in this paper, we are also releasing the retinal annotation software alongside the proposed dataset. This software can help clinicians in quickly marking both fundus and OCT scans, which can be saved later on in any image format. Overall, the proposed dataset contains 9,268 OCT scans and 180 fundus scans from 105 subjects depicting healthy, ci-DME, geographic AMD, neovascular AMD, acute CSR, chronic CSR, and glaucomic pathologies.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130633026","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

A Review of Autonomous Glaucomatous Grading via OCT Imagery 基于OCT图像的青光眼自主分级研究进展

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787418

H. Raja, Taimur Hassan, Bilal Hassan, L. Seneviratne, J. Dias, N. Werghi

{"title":"A Review of Autonomous Glaucomatous Grading via OCT Imagery","authors":"H. Raja, Taimur Hassan, Bilal Hassan, L. Seneviratne, J. Dias, N. Werghi","doi":"10.1109/ICoDT255437.2022.9787418","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787418","url":null,"abstract":"Glaucoma is a progressive and degenerative optic neuropathy that damages the optic nerve due to increased intraocular pressure and yields various visual field abnormalities. Any damage to the optic nerve (due to glaucoma) is irreversible because of the incapacity of the retinal nerve fibers to regenerate. However, the glaucomatous progression can be properly cured if it is diagnosed in the early stages. Furthermore, health authorities worldwide have prioritized the early detection and prevention of glaucomatous progression, and in this regard, presenting a comprehensive insight on the recent literature serves as a critical benchmark to both researchers and clinicians. Towards this end, this paper presents a detailed overview of the state-of-the-art clinical and technical methods related to glaucomatous detection, grading, and prognosis. The clinical work focuses on glaucoma diagnosis and progression tracking in different retinal regions such as the retinal layers, optic never head, and lamina cribrosa, while the technical works focus on producing lesion-aware screening and grading of glaucomatous pathologies via optical coherence tomography.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121702993","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

Detection of Liver Cancer through Computed Tomography Images using Deep Convolutional Neural Networks 基于深度卷积神经网络的计算机断层图像肝癌检测

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787429

Zunaira Naaqvi, Shahzad Akbar, Syed Ale Hassan, Qurat ul Ain

{"title":"Detection of Liver Cancer through Computed Tomography Images using Deep Convolutional Neural Networks","authors":"Zunaira Naaqvi, Shahzad Akbar, Syed Ale Hassan, Qurat ul Ain","doi":"10.1109/ICoDT255437.2022.9787429","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787429","url":null,"abstract":"Liver cancer is the fifth most common type of tumor in men and the ninth most common type of tumor in women. After taking a sample of liver tissue, imaging tests like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), can be used to diagnose the liver tumor. In recent studies, accurate detection of liver cancer with minimum computational time and computational complexity is a major issue remained the challenge. This research proposes a framework to segment the cancerous area through CT scan images using entropy thresholding technique. Additionally, it uses two CNN models, U-Net and Google-Net, for the classification of liver cancer. The proposed method employs the 3D-IRCADb01 dataset, which consists of CT slices of liver tumor patients. The U-Net performed better than other networks with 98.5% accuracy, 0.83 DSC, 99.5% recall, and 98.75% F1. Biotechnology uses this method for an early and accurate diagnosis of liver tumors that is likely to save many lives. Proposed method outperformed than existing state-of-art methods and is suitable for clinical applications to assist doctors in diagnosing liver cancer.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126266713","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: 7

HARResNext: An efficient ResNext inspired network for human activity recognition with inertial sensors HARResNext:一个高效的受ResNext启发的网络，用于惯性传感器的人体活动识别

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787447

H. Imran, Kiran Hamza, Zubair Mehmood

{"title":"HARResNext: An efficient ResNext inspired network for human activity recognition with inertial sensors","authors":"H. Imran, Kiran Hamza, Zubair Mehmood","doi":"10.1109/ICoDT255437.2022.9787447","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787447","url":null,"abstract":"Human activity recognition (HAR) based on wearable sensors has developed as a new study topic in the domains of artificial intelligence and pattern recognition. HAR has a wide range of applications, including sports activity detection, smart homes, and health assistance, to name a few. Mobile device sensors such as accelerometers, gyroscopes, and magnetometers can generate time-series data for HAR. Computer Vision (CV) methods were previously utilised for HAR, which has a number of drawbacks, including mobility, ambient conditions, occlusion, higher cost, and, most importantly, privacy. Using sensor data instead of typical computer vision techniques has various advantages. Their work is believed to have overcome virtually all of the limitations of computer vision techniques. The use of Machine Learning (ML) and Deep Neural Networks (DNN) to recognise human activity from inertial sensor data is widely established in the literature. In this paper, we introduce HARResNeXT, a novel convolutional neural network inspired by ResNeXT. It classifies Human Activities based on inertial sensors data of smartphone. The presented model has been evaluated on a dataset by WISDM (Wireless Sensor Data Mining) Lab. We have achieved 97% Precision, Recall and F1-score. Moreover, the average accuracy achieved is 96.62%. Comparison with previous studies showed the presented model out-performed state-of-the-art.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134258179","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: 3

Deep retinanet for melanoma lesion detection 深视网膜用于黑色素瘤病变检测

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787454

Hafeez Ur Rehman, Syed Adnan Shah, W. Ahmad, S. Anwar, Nudrat Nida

{"title":"Deep retinanet for melanoma lesion detection","authors":"Hafeez Ur Rehman, Syed Adnan Shah, W. Ahmad, S. Anwar, Nudrat Nida","doi":"10.1109/ICoDT255437.2022.9787454","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787454","url":null,"abstract":"Ever since the automation of melanoma detection, there is a huge challenge pertaining to irregularity in shape, size, location and color of dermoscopy images. Moreover, melanoma treatment seems a complicated task owing to inadequate details for diagnosis and limited visual inspection. Therefore, an auto-mated process of detection is required in dermoscopic images for efficient and timely detection and diagnosis of melanoma lesion. Consequently, we have localized melanoma using one stage object detector named RetinaNet. The proposed model is evaluated by conducting experiments on PH2 dataset. RetinaNet serves a single step object detector that efficiently and precisely detects melanoma region. Moreover, focal loss is also evaluated to avoid class imbalance between normal skin pixels and melanoma foreground segmentation. The proposed system showed a significant performance gain up-to 97% i.e. the average precision using PH2 sample images. Our system can be effectively utilized in automation of clinical decision support systems for practical diagnosis and prognosis of melanoma.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124108640","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: 3

Automatic Detection and classification of Correct placement of tubes on chest X-rays using deep learning with EfficientNet 利用effentnet的深度学习自动检测和分类胸部x射线管的正确放置

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787435

M. Abbas, Anum Abdul Salam, Jahan Zeb

{"title":"Automatic Detection and classification of Correct placement of tubes on chest X-rays using deep learning with EfficientNet","authors":"M. Abbas, Anum Abdul Salam, Jahan Zeb","doi":"10.1109/ICoDT255437.2022.9787435","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787435","url":null,"abstract":"In recent years, the rapid growth of data in healthcare has prompted a lot of interest in artificial intelligence (AI). Powerful AI algorithms are essential for extracting information from medical data and assisting clinicians in establishing quick and accurate diagnoses of a variety of ailments. In the current COVID-19 outbreak, critically ill patients were intubated and various medical tubes, including an endotracheal tube (ETT), were implanted to protect the airways. The Nasogastric tube (NGT) is used for feeding, whereas the Central Venous Catheter (CVC) is utilized for a variety of medical operations. The adoption of medical protocols by doctors to ensure proper tube installation is a major issue. Manual examination of CXR pictures takes time and frequently leads to misinterpretation. This research aims to create an Automated Medical Tube Detection System that can detect misplaced tubes from chest x-rays (CXR) using deep learning. As a result, using chest x-rays to detect poorly positioned tubes can save lives. On CXR the proposed CNN-based EfficientNet architecture efficiently detects and classifies incorrectly positioned tubes. After detailed experimentation, we were able to achieve 0.95 average area under the ROC curve (AUC).","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123733665","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