2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)最新文献

Feature Attention Network for Simultaneous Nuclei Instance Segmentation and Classification in Histology Images 基于特征关注网络的组织图像同步核实例分割与分类

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441474

G. M. Dogar, M. Fraz, S. Javed

引用次数: 1

Attention Based Residual Network for Effective Detection of COVID-19 and Viral Pneumonia 基于关注的残差网络有效检测COVID-19和病毒性肺炎

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441485

M. A. Nawshad, Usama Aleem Shami, Sana Sajid, M. Fraz

{"title":"Attention Based Residual Network for Effective Detection of COVID-19 and Viral Pneumonia","authors":"M. A. Nawshad, Usama Aleem Shami, Sana Sajid, M. Fraz","doi":"10.1109/ICoDT252288.2021.9441485","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441485","url":null,"abstract":"The current coronavirus (COVID-19) pandemic has led us to the healthcare, global poverty and socioeconomic crisis. One of the most significant task in this pandemic is to accurately and efficiently diagnose the COVID-19 patients and to monitor them to make prompt decisions and take appropriate actions for their monitoring, management and treatment. The early diagnosis of COVID-19 was a very troublesome and difficult challenge that CAD (Computer-Aided Diagnosis) methods successfully tackled. The CXR (chest X-ray) method proved to be a very low-cost and effective alternative to Computed Tomography (CT) scan and Real Time Polymerase Chain Reaction (RT-PCR) test, which were previously the most commonly used methods for COVID-19 diagnosis. Till now, very few CAD based techniques have been proposed to effectively detect COVID-19, but their efficiency is limited due to a number of factors. In this study, we have proposed a deep learning model using the Convolutional Block Attention Module with ResNet32. For training the model, Kaggle’s dataset containing CXR images has been used. The dataset contains a total of 3886 images. Moreover, 64% of data has been used for training, 20% for testing and 16% for validation. We have experimented with different CNN architectures with different approaches like Transfer Learning, Data Augmentation and attention module. With 97.69% accuracy, the ResNet32 with attention module outperformed other architectures and approaches, improving the baseline network efficiency. This promising and efficient classification accomplishment of our proposed methodology demonstrates that it is well suited for CXR image classification in COVID-19 diagnosis in terms of both accuracy and cost.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127491348","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

Speech Quality Assessment using Mel Frequency Spectrograms of Speech Signals 语音信号Mel频谱图的语音质量评估

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441536

Shakeel Zafar, I. Nizami, Muhammad Majid

引用次数: 2

Predicting Most Influential Paper Award Using Citation Count 利用引用数预测最具影响力论文奖

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441487

Fatima Sadaf, M. Shahid, Muhammad Arshad Islam

{"title":"Predicting Most Influential Paper Award Using Citation Count","authors":"Fatima Sadaf, M. Shahid, Muhammad Arshad Islam","doi":"10.1109/ICoDT252288.2021.9441487","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441487","url":null,"abstract":"The early identification of the influential papers is of great significance for assessing the scientific achievements of researchers and institutions as it can help in addressing the processes in an academic and scientific field, such as promotions, recruitment decisions, and funding allocation. This work evaluates features for predicting the most influential paper award that is given by several renowned conferences, ten years subsequent to their publication. The data of five renowned conferences, i.e., ICSE, ICFP, POPL, PLDI, and OOPSLA is used to predict the long-term citations to identify the most influential paper of the respective conference. GD boost model is considered to be better performing among the five different machine learning algorithms. The results show that a three to five years of the time window is good enough to evaluate the most influential paper award. Additionally, the assessment of time window and the citation trajectory of awarded and non awarded papers shows that the citation trajectory of the awarded paper vary from the Citation gain patterns of non-awarded paper.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129285841","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 Novel Fog-based Framework for Preventing Cloud Lock-in while Enabling Searchable Encryption 一种新的基于雾的防止云锁定的框架，同时启用可搜索的加密

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441477

M. Raj, Shahzaib Tahir, Fawad Khan, Hasan Tahir, Zeeshan Zulkifl

{"title":"A Novel Fog-based Framework for Preventing Cloud Lock-in while Enabling Searchable Encryption","authors":"M. Raj, Shahzaib Tahir, Fawad Khan, Hasan Tahir, Zeeshan Zulkifl","doi":"10.1109/ICoDT252288.2021.9441477","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441477","url":null,"abstract":"Cloud computing has helped in managing big data and providing resources remotely and ubiquitously, but it has some latency and security concerns. Fog has provided tremendous advantages over cloud computing which include low latency rate, improved real-time interactions, reduced network traffic overcrowding, and improved reliability, however, security concerns need to be addressed separately. Another major issue in the cloud is Cloud Lock-in/Vendor Lock-in. Through this research, an effort has been made to extend fog computing and Searchable Encryption technologies. The proposed system can reduce the issue of cloud lock-in faced in traditional cloud computing. The SE schemes used in this paper are Symmetric Searchable Encryption (SSE) and Multi-keyword Ranked Searchable Encryption (MRSE) to achieve confidentiality, privacy, fine-grained access control, and efficient keyword search. This can help to achieve better access control and keyword search simultaneously. An important use of this technique is it helps to prevent the issue of cloud/vendor lock-in. This can shift some computation and storage of index tables over fog nodes that will reduce the dependency on Cloud Service Providers (CSPs).","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116327864","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

Plant Disease Identification Using Transfer Learning 利用迁移学习进行植物病害鉴定

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441512

Muhammad Sufyan Arshad, Usman Rehman, M. Fraz

引用次数: 14

Application of Deep Learning for Weapons Detection in Surveillance Videos 深度学习在监控视频武器检测中的应用

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441523

Tufail Sajjad Shah Hashmi, Nazeef Ul Haq, M. Fraz, M. Shahzad

{"title":"Application of Deep Learning for Weapons Detection in Surveillance Videos","authors":"Tufail Sajjad Shah Hashmi, Nazeef Ul Haq, M. Fraz, M. Shahzad","doi":"10.1109/ICoDT252288.2021.9441523","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441523","url":null,"abstract":"Weapon detection is a very serious and intense issue as far as the security and safety of the public in general, no doubt it’s a hard and difficult task furthermore, its troublesome when you need to do it automatically or with some of the AI model. Different object detection models are available but in case of weapons detection it is difficult to detect the weapons of distinctive size and shapes along with the different colors of the background. Currently, a great deal of Convolutional Neural Network (CNN) based deep learning approaches are proposed for the recognition and classification in real-time. In this paper, we have done the comparative analysis of the two versions which is a state of the art model called YOLOV3 and YOLOV4 for weapons detection. For training purpose, we create weapons dataset and the images are collected from google images along with a portion of different assets. We annotate the images one by one manually in different formats in light of fact that YOLO needs annotation file in text format and some other models need annotation file in XML format. We trained both the versions on a large data set of weapons and afterward tested their results for comparative analysis. We explained in the paper that YOLOV4 performs obviously superior to the YOLOV3 in terms of processing time and sensitivity yet we can compare these two in precision metric. The implementation details and trained models are made public at this link:https://cutt.ly/5kBEPhM.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130065234","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: 14

Deep Learning Based Land Cover and Crop Type Classification: A Comparative Study 基于深度学习的土地覆盖与作物类型分类的比较研究

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441483

Asim Khan, M. Fraz, M. Shahzad

引用次数: 1

Blockchain Based Formal Modelling of Patient Management in Hospital Emergency System 基于区块链的医院急救系统患者管理形式化建模

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441528

Tayyaba Naseer Khan, N. Zafar

{"title":"Blockchain Based Formal Modelling of Patient Management in Hospital Emergency System","authors":"Tayyaba Naseer Khan, N. Zafar","doi":"10.1109/ICoDT252288.2021.9441528","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441528","url":null,"abstract":"an emergency department is a fundamental part of hospital systems providing medical treatment services and critical care that specializes in emergency care of patients. In the emergency department, patients are treated without an appointment. Most of the traditional hospital emergency management systems utilize inefficient manual record management procedures that may lead to serious problems. Specifically, a failed emergency services system may result in loss of lives which need modern technologies for developing smart health emergency systems. There exists a lot of work in this area but still, it needs many improvements making use of the state-of-the-art technologies. Therefore, in the present paper blockchain-based formal model of patient management in a hospital emergency system is proposed by integrating the Internet of Things (IoT), Unified Modelling Language (UML), and formal methods. Emergency patient management system functionality includes patient registration, payments, and patient discharge modules. In software development, UML is a standardized modelling language that is used to portray the model of a software system requirement comprehensively. IoT healthcare devices are utilized to track a patient’s health condition and actors are employed to take decisions as specified. The blockchain-based model offered system security and privacy mechanism additionally preventing alteration of the record. Formal methods using mathematical models will be applied for validation and model analysis.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126709513","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 Survey of Deep Neural Network in Acoustic Direction Finding 深度神经网络在声波测向中的研究进展

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441527

Mohiz Ahmad, Muhammad Muaz, M. Adeel

引用次数: 10