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

Deep Learning: Convolutional Neural Networks for Medical Image Analysis - A Quick Review 深度学习:用于医学图像分析的卷积神经网络-快速回顾

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787469

H. Sharif, Faisal Rehman, Amina Rida

引用次数: 7

Surface EMG Signal Analysis using Hand-Crafted Features for Detection and Classification of GTC seizures 表面肌电信号分析使用手工特征检测和分类GTC癫痫发作

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787476

Maryam Naveed, Sajid Gul Khawaja, M. Usman Akram

引用次数: 0

Enhancement of Depth Map through Weighted Combination of Guided Image Filters in Shape-From-Focus 聚焦形状引导图像滤波器加权组合增强深度图

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787464

Zubair Ahmed, Ahsan Shahzad, Usman Ali

引用次数: 2

Deep Framework for Predicting COVID-19 and Related Lung diseases using CXR Images 基于CXR图像预测COVID-19及相关肺部疾病的深度框架

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787453

Wajeha Fareed, Anum Abdul Salam, Usman M. Akram, M. Alam

{"title":"Deep Framework for Predicting COVID-19 and Related Lung diseases using CXR Images","authors":"Wajeha Fareed, Anum Abdul Salam, Usman M. Akram, M. Alam","doi":"10.1109/ICoDT255437.2022.9787453","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787453","url":null,"abstract":"Along with a health crisis, COVID-19 has also led the world towards an economical barrier. So far the virus has effected approximately 400 Millions causing 5 Million deaths and is expanding everyday. There is an urge to stop the exponential growth of the contagious disease, only possible through an early diagnosis of the disease. Currently, several testing techniques are being used to diagnose COVID-19, among them Polymerase Chain Reaction (PCR) is a gold standard globally. However, due to it’s processing time, cost and less sensitivity towards COVID-19, physicians suggest to correlate the results with radiological tests preferably Chest X-Ray (CXR) imaging since it consumes less time and is more sensitive towards COVID-19. To overcome the pandemic many research groups have been working on the solution. Several Computer Aided Diagnostic (CAD) systems have been proposed by the researchers however, they lack robustness and stability towards blind datasets. Moreover, majority of the CAD systems provide binary classification between healthy and COVID-19, various lung abnormalities resembles COVID-19 in terms of their structural appearance and can be falsely classified as COVID-19. In this paper, we have proposed a deep model using EfficinetNet-B0 as a baseline model. Our proposed model has been trained on the largest available CXR dataset of COVID-19 comprising CXR images of normal, Viral Pneumonia, Lung Opacity and COVID-19 effected lungs and yielded an accuracy of 99.46%. Proposed model has been blind tested on four publicly available datasets achieving highest accuracy of 99.96%. Furthermore, the model is transfer learned and fine tuned on another publicly available CXR dataset and evaluated to be 85.26% accurate for 20 epochs.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"77 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":"126243103","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

Incremental Instance Segmentation for the Gleason Tissues Driven Prostate Cancer Prognosis Gleason组织驱动前列腺癌预后的增量实例分割

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787434

Taimur Hassan, A. Ahmed, Bilal Hassan, Muhammad Shafay, Ayman Elbaz, J. Dias, N. Werghi

{"title":"Incremental Instance Segmentation for the Gleason Tissues Driven Prostate Cancer Prognosis","authors":"Taimur Hassan, A. Ahmed, Bilal Hassan, Muhammad Shafay, Ayman Elbaz, J. Dias, N. Werghi","doi":"10.1109/ICoDT255437.2022.9787434","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787434","url":null,"abstract":"Prostate cancer (PCa) is the second most commonly diagnosed cancer in men and the fifth-highest cause of death globally. Early-stage prostate cancer is frequently asymptomatic and has an indolent course, requiring active observation. Early detection and recognition of Gleason tissue can help handle the PCa spread. Therefore, many deep learning-based systems have been proposed by researchers in order to screen the PCa. Moreover, acquiring such large-scale, well-annotated data can improve the performance of screening and detecting PCa. However, this process is typically challenging and impractical. This paper addresses this issue by proposing a novel knowledge distillation-driven instance segmentation framework. This approach is fused with incremental few-shot training and allows the traditional semantic segmentation models to grade the PCa utilizing instance-aware segmentation, along with the extraction of correlated samples of the Gleason tissue patterns. Furthermore, the proposed approach has been validated on a dataset that contains around 71.7M whole slide image patches. Our approach has outperformed the state-of-the-art models by 2.01% in terms of mean IoU and 9.69% in terms of F1 score for the extraction of Gleason tissue instances and grading PCa, respectively.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"13 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":"126056755","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: 2

Automatically Categorizing Software Technologies 自动分类软件技术

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787457

S. Khan, Wasi Haider Butt

引用次数: 0

Convolutional Neural Network Based Thermal Image Classification 基于卷积神经网络的热图像分类

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787443

Qirat Ashfaq, M. Usman Akram

{"title":"Convolutional Neural Network Based Thermal Image Classification","authors":"Qirat Ashfaq, M. Usman Akram","doi":"10.1109/ICoDT255437.2022.9787443","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787443","url":null,"abstract":"Classification of Thermal Images has been extensively used for its significant applications in many fields. There are many problems with the visible spectrum like object shadows, clothes or the body of a human being matches the background and different lighting conditions. These limitations are overcome by using thermal imaging. Each and every object emits heat (Infrared energy) according to its temperature. Normally the hotter object emits more radiation than the colder one. As all objects have a mostly different temperature so thermal camera detects them and these objects get appear as distinct objects. In the start, thermal imaging was used by the military for detection, recognition, and identification of enemy personnel and equipment. Nowadays it is extensively used in the detection of face, self-driving car, detection of pedestrians and it also has application in the field of environmental work that is monitoring for energy conservation and pollution control. This research presents a novel study for the classification of thermal images using convolutional neural networks (CNN). The research focused on developing a framework that detects multiple thermal objects using CNN. Developed a framework based on deep learning Inception v3 model; work with thermal images that are captured by Seek Thermal and FLIR. For training and testing of the model two datasets are used that include three classes’ cat, car, and man. For the FLIR dataset the highest accuracy achieved is 98.91% and for Seek thermal dataset highest accuracy achieved is 100%. A comparison of the proposed framework with some other CNN models (DenseNet, MobileNet, and YOLOv4), with a customized CNN model and with a conventional model is also presented. The results of the proposed framework and comparison with other models prove that the proposed framework is effective for the classification of thermal images.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"84 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":"121373130","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: 2

Machine Learning Algorithm Analysis for Detecting and Classification Faults in Power Transmission System 输电系统故障检测与分类的机器学习算法分析

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787450

J. Hassan, Imran Fareed Nizami

{"title":"Machine Learning Algorithm Analysis for Detecting and Classification Faults in Power Transmission System","authors":"J. Hassan, Imran Fareed Nizami","doi":"10.1109/ICoDT255437.2022.9787450","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787450","url":null,"abstract":"The importance of Power Transmission System PTS fault detection and classification is increasing day by day as because consumption of electricity is increasing. Short circuit fault in Power Transmission Line Network PTLN can cause severe damage to the power transmission system as well as economic loss. Power Transmission System requires new methods to detect and classify fault behaviour to prevent it from heavy damage. Machine Learning ML algorithms can be very effective to classify and detect various types of faults within the PTLN. There are variety of ML algorithms to recognise and classify the faults but as complexity of PTS is increasing day by day, reliability of these algorithms is decreasing. This study uses various types of ML algorithms to generate predictive models to evaluate what kind of algorithm is more appropriate to recognise and classify faults within the PTLN. Faults investigated in this research work include (L-L) double line fault, (L-L-L) three phase fault, (L-G) line to ground fault, (L-L-G) double line to ground fault, and (L-L-L-G) three phase fault with the involvement of the ground. The data was evaluated using six (06) ML algorithms that are Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (Knn), Random Forest, XGBoost (XGB) and Naive Bayes (NB) for recognise of fault and classification within the PTLN. The performance of ML algorithms obtained by comparing the results and determine which algorithm is fast and more accurate. These results can be used to create more effective ML algorithms for PTS. The results indicate that the application of ML algorithms for PTS task could improve the PTLN yield and save time for technical teams.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"137 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":"133878395","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

Semantic Keywords Extraction from Paper Abstract in the Domain of Educational Big Data to support Topic Clustering 教育大数据领域论文摘要语义关键词提取支持主题聚类

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787427

Ali Arshad, Wanghu Chen, Yang Liu, Nauman Ali Khan

{"title":"Semantic Keywords Extraction from Paper Abstract in the Domain of Educational Big Data to support Topic Clustering","authors":"Ali Arshad, Wanghu Chen, Yang Liu, Nauman Ali Khan","doi":"10.1109/ICoDT255437.2022.9787427","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787427","url":null,"abstract":"Keywords are the list of valuable words present in a paragraph, that help in quickly understanding the context of the paragraph. These keywords hold the generic and overall meaning of the paragraph. Extraction of valid and meaningful keywords from scientific documents became one of the hot topics for researchers. Such research not only facilitates better comprehension of articles but also explores the scientific manner of understanding big repositories of scientific documents. In this study, we propose Semantic keyword extraction by adding a new feature that includes domain-specific grammar rules and deduction of adjectives. Our algorithm incorporates frequencies of keywords that are appearing repeatedly. The proposed frame-work extracts the keywords from the scientific paper abstract to support topic clustering. Such topic clustering benefits the new researchers to easily and quickly find their research topic in the concerned field of educational big data. We have selected the educational big dataset that includes 1028 published research papers regarding education learning, education management, students’ information system, etc. For evaluating the results and performance of a Semantic Keyword Extractor, we have used a general dataset. The proposed keyword extractor gives a precision of 76.8% which outperforms other keywords extractors. In our research, our proposed framework classified scientific papers into 3 meaningful groups by using an unsupervised machine learning clustering technique called k-means.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"115 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":"114112152","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

Develop an Ontology for E-Commerce based on a Web Application to Assist Color-blind people 开发一个基于Web应用程序的电子商务本体，帮助色盲人士

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787475

M. U. Farooq, Usman Qamar

引用次数: 0