2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)最新文献_第3页

Emotion Recognition from Facial Images using Hybrid Deep Learning Models 使用混合深度学习模型从面部图像中识别情绪

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787474

Arfa Fatima Yaseen, A. Shaukat, Maria Alam

{"title":"Emotion Recognition from Facial Images using Hybrid Deep Learning Models","authors":"Arfa Fatima Yaseen, A. Shaukat, Maria Alam","doi":"10.1109/ICoDT255437.2022.9787474","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787474","url":null,"abstract":"With the advancement of robots and their assimilation in daily chores, it has become essential to maintain an effective mode of communication between them and humans, that in turn requires development of highly intelligent systems so that robot can sense and adapt the behavior accordingly. The recognition of actual human emotion and its exact interpretation by the machines poses a great challenge to computer vision community, and in quest to improve the adaptivity of machines, a variety of deep learning convolutional neural network (CNN) algorithms have been proposed to serve the purpose. But as Facial Expression Recognition’s (FERs) have found their applications in a number of society verticals like health, education, marketing, gaming, surveillance etc. The single algorithm to provide perfect recognition in all the scenarios has never been established so far; however, the research is still in progress to develop the substitutes or new models to improve the recognition process. In this paper, deep learning algorithms have been utilized for classifying the facial expression of the humans.Two benchmark datasets of facial expression images have been used. The proffered method investigated the effectiveness of DCNN with the help of multiple models. EfficientNetB0, DenseNet169 and a combined model of EfficientNetB0+VGG16 have been proposed to be used in our work.With the hybrid model, we have achieved recognition accuracy of 90.6% and 95.6% on FER2013 and JAFFE datasets respectively. The recognition rates achieved are competitive as compared to previous reported results in literature on the two datasets.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"7 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":"125365044","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: 4

DDRF: The Drone Data Regulatory Framework Based on Blockchain DDRF:基于区块链的无人机数据监管框架

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787458

Xuhan Liao, Yu Su, Yuhe Qiu, Shiping Huang, Yuyan Sun, Teng Hu

{"title":"DDRF: The Drone Data Regulatory Framework Based on Blockchain","authors":"Xuhan Liao, Yu Su, Yuhe Qiu, Shiping Huang, Yuyan Sun, Teng Hu","doi":"10.1109/ICoDT255437.2022.9787458","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787458","url":null,"abstract":"Civilian UAVs have developed rapidly in recent years. However, due to the lack of supervision, cumbersome flight plan application process, and other problems, many people use drones at will, and many accidents occur as a result. We proposed a framework for using blockchain to regulate drone behavior and implemented a demo. With this framework, users don’t have to go through a complicated approval process to apply for a flight plan. Besides, because of the nature of blockchain, the system implemented based on our framework is transparent and decentralized, which meets the requirement of supervision. Information is authentic and all on-chain information is available for viewing. If there is a drone accident, the person responsible can also be identified based on immutable information records. Moreover, we also process the data to make the overhead of appending repeating data smaller and make automated data checks. The framework includes three modules, a data processing module, a data registration module, and a data audit module. Our demo implements these modules based on Hyperledger Fabric. We carry out experiments on the model, and the results showed our framework is efficient.","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":"130963673","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

Physical Adversarial Attack Scheme on Object Detectors using 3D Adversarial Object 基于三维对抗对象的目标检测器物理对抗攻击方案

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787422

Abeer Toheed, M. Yousaf, Rabnawaz, A. Javed

{"title":"Physical Adversarial Attack Scheme on Object Detectors using 3D Adversarial Object","authors":"Abeer Toheed, M. Yousaf, Rabnawaz, A. Javed","doi":"10.1109/ICoDT255437.2022.9787422","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787422","url":null,"abstract":"Adversarial attacks are being frequently used these days to exploit different machine learning models including the deep neural networks (DNN) either during the training or testing stage. DNN under such attacks make the false predictions. Digital adversarial attacks are not applicable in physical world. Adversarial attack on object detection is more difficult as compared to the adversarial attack on image classification. This paper presents a physical adversarial attack on object detection using 3D adversarial objects. The proposed methodology overcome the constraint of 2D adversarial patches as they only work for certain viewpoints only. We have mapped an adversarial texture onto a mesh to create the 3D adversarial object. These objects are of various shapes and sizes. Unlike adversarial patch attacks, these adversarial objects are movable from one place to another. Moreover, application of 2D patch is limited to confined viewpoints. Experimentation results show that our 3D adversarial objects are free from such constraints and perform a successful attack on object detection. We used the ShapeNet dataset for different vehicle models. 3D objects are created using Blender 2.93 [1]. Different HDR images are incorporated to create the virtual physical environment. Moreover, we targeted the FasterRCNN and YOLO pre-trained models on the COCO dataset as our target DNN. Experimental results demonstrate that our proposed model successfully fooled these object detectors.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"95 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":"116404617","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

Paving the way to cardiovascular health monitoring using Internet of Medical Things and Edge-AI 利用医疗物联网和边缘人工智能为心血管健康监测铺平道路

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787432

M. Talha, R. Mumtaz, Abdur Rafay

{"title":"Paving the way to cardiovascular health monitoring using Internet of Medical Things and Edge-AI","authors":"M. Talha, R. Mumtaz, Abdur Rafay","doi":"10.1109/ICoDT255437.2022.9787432","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787432","url":null,"abstract":"The Internet of Medical Things (IoMT) has revolutionized the healthcare domain, with the introduction of remote real-time monitoring. This emerging technology has not only relieved the burden of hospital resources, but also paved the way for efficient monitoring and management of patients. According to World Health Organization (WHO), in Pakistan, cardiovascular diseases (CVD) are leading cause of deaths that amount to nearly 200,000 deaths annually. This results in high mortality rates all over Pakistan. To uplift the current architecture of healthcare in Pakistan, it is vital to develop a sustainable solution for continuous health monitoring and arrest anomalous physiological behavior before they become life-threatening. In the same pretext, this study proposes a working paper which aims to disseminate interim results of smart cardiac health monitoring using an amalgam of IoMT and Machine Learning (ML) techniques. The primary objective of the proposed research is to integrate state-of-the-art ML classification algorithms to detect, in near real-time, abnormal human vitals like electrocardiogram(ECG), heart-rate(HR), blood pressure(BP), etc. As network latency is critical to this application, therefore, to improve overall Quality of Service (QoS) of the system, we propose to fuse Edge Intelligence interfaced with multiple IoMT enabled bio-sensors. These sensors will form a body area sensor network(BSN) that records cardiac-related human vitals. In our preliminary research, that is presented in this paper, we trained several machine learning algorithms on the MIMIC-III clinical data-set and reviewed their performance. Among the 7 tested supervised classification algorithms, Random-Forest achieved the highest accuracy of 95% on the test set. Finally, to offer a remote patient management and monitoring panel, we developed an authenticated web-portal to inculcate data privacy and security in the proposed system.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"22 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":"128474834","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

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787438

A. Shahid, M. Akram, Anum Abdul Salam, Jahan Zeb

{"title":"Image Popularity Prediction Over Time For the Span Of 30 Days Using Machine learning Techniques","authors":"A. Shahid, M. Akram, Anum Abdul Salam, Jahan Zeb","doi":"10.1109/ICoDT255437.2022.9787438","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787438","url":null,"abstract":"The popularity of social media content define its destiny: some of the uploaded content get famous among people within minutes while others just get completely unnoticed. But why is this so? This work addresses this question, discusses all the features related to social content that is responsible for its popularity or negligence, and proposes a system to predict the popularity of the content for the span of 30 days before actually uploading the content on any social media platform. There are some common features in the social content that gets fame, in this research work we have evaluated the effect of different features on popularity. The proposed model predicts the popularity score in the form of the number of views for the next 30 days after uploading the content. The content popularity score can be used by companies to improve their marketing strategies, targeting the right audience sagaciously, managing the resources efficiently, and making the strategical decisions. In the paper, the detailed methodology is discussed to design a model that can perform the task of Image Popularity Prediction (IPP) efficiently. A critical analysis is also performed on the results obtained from single features, combinational features, and features obtained by applying different techniques. This research work manifests that the features related to the image context i.e user features and photo features outperform other features related to the content.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"59 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":"122580107","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 Theoretical CNN Compression Framework for Resource-Restricted Environments 资源受限环境下的CNN压缩理论框架

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787431

Zahra Waheed Awan, S. Khalid, Sajid Gul

{"title":"A Theoretical CNN Compression Framework for Resource-Restricted Environments","authors":"Zahra Waheed Awan, S. Khalid, Sajid Gul","doi":"10.1109/ICoDT255437.2022.9787431","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787431","url":null,"abstract":"Convolutional Neural Network (CNN) is considered as one of the most significant algorithms of deep learning that has made impressive achievements in many areas of computer vision and natural language processing. In the current times of big data, input data dimensions keep on increasing which leads to the development of complex CNN models for processing such big data. This has made CNN computationally intensive and limits its practical application to some extent. To address the aforementioned issue, this paper presents a detailed review of various network compression methods existing in literature. Two most commonly deployed network compression methods have been discussed including pruning and quantization which can be coupled with CNN to increase its applicability. The main goal of presenting this comprehensive review of the state-of-the-art pruning and quantization-based network compression schemes is to significantly improve trade-off between CNN architectural size and computational cost versus its performance in resource restricted environments. Additionally, this paper also exploits the challenges posed by these techniques when implemented for large-scale CNNs. In this context, paper also presents a novel framework to perform network compression of CNN to meet the requirements of resource-restricted devices.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"44 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":"114497156","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

Automatic Detection and classification of Scoliosis from Spine X-rays using Transfer Learning 利用迁移学习从脊柱x射线中自动检测和分类脊柱侧凸

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787480

Arslan Amin, Moneeb Abbas, A. A. Salam

{"title":"Automatic Detection and classification of Scoliosis from Spine X-rays using Transfer Learning","authors":"Arslan Amin, Moneeb Abbas, A. A. Salam","doi":"10.1109/ICoDT255437.2022.9787480","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787480","url":null,"abstract":"Scoliosis is a typical spinal disease that causes the spine to curve. Early treatment during the formation of the spine can greatly reduce the chances of health issues. Diagnosis of scoliosis relies on X-ray imaging, using X-ray images to diagnose lumbar, cervical, and thoracic spinal structures have traditionally proven difficult and time-consuming. Many clinical applications of spinal imaging require the accurate and robust identification of vertebrae from medical images. This paper presents an automated approach using deep learning to detect the spine’s curvature using its spinal column. Models of deep learning could be used to assist with the increasing volume of medical imaging data as well as provide initial interpretation of images gathered in primary care. Deep learning algorithms are a quicker and more efficient alternative to manual X-ray investigation for scoliosis detection. X-ray images of spine curvature are used to detect and classify scoliosis using a pre-trained EfficientNet model. In the first stage, the model was evaluated without augmentation, in which we achieved an accuracy of 78 %. In the second step, we augment the training data by using machine learning techniques, and after that, we achieved an accuracy of 86 %. Our findings show that the proposed automatic scoliosis identification method can accurately detect and classify spine curvature in X-ray images.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"43 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":"114559751","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 Decision Making Approach for Street Lockdown to Cope with Covid-19 Cases by Using Shortest Path Selection Mechanism for Unplanned Colonies 基于非计划群体最短路径选择机制的街道封锁应对Covid-19病例决策方法

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787478

Tahira Sadaf, S. A. Khan, Usman Qamar

引用次数: 0

Program Committee 项目委员会

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ISM.2005.92

R. Abidi, M. Babik

{"title":"Program Committee","authors":"R. Abidi, M. Babik","doi":"10.1109/ISM.2005.92","DOIUrl":"https://doi.org/10.1109/ISM.2005.92","url":null,"abstract":"Raza Abidi, Dalhousie University, Canada Hussain Al-Aqrabi, University of Derby, United Kingdom Yuan An, Drexel University, USA Marian Babik, University of South Florida, USA Nik Bessis, University of Derby, United Kingdom Julien Bourgeois, University of Franche-Comté, France Periklis Chatzimisios, Alexander Technological Educational Institute of Thessaloniki, Greece Xue Chen, Shanghai University, China Jingde Cheng, Saitama University, Japan Alfredo Cuzzocrea, University of Calabria, Italy David Day, Sheffield Hallam University, United Kingdom Ewa Deelman, USC Information Sciences Institute, USA Weichang Du, University of New Brunswick, Canada Enver Ever, Middlesex University, United Kingdom Yaokai Feng, Kyushu University, Japan Zhiguo Gong, University of Macau Stephane Grumbach, Stevens Institute of Technology, USA Zhixing Huang, Southwest University, China Arabshian Knarig, Bell Labs, Alcatel-Lucent, USA KP Lam, Keele University, United Kingdom Maozhen Li, Brunel University, United Kingdom Jin Liu, Wuhan University, China Xiangfeng Luo, Shanghai University, China Xudong Luo, Sun Yat-sen University, China Jianhua Ma, Hosei University, Japan Maciej Malawski, University of Notre Dame, USA Osama Masfary, B2net, United Kingdom Ludek Matyska, Masaryk University, Czech Republic Ariel Oleksiak, Poznan Supercomputing and Networking Center, Poland Duncan Russell, DRTS Ltd, United Kingdom Yunchuan Sun, Beijing Normal University, China David Taniar, Monash University, Australia Ian Taylor, Cardiff University, United Kingdom Yicheng Tu, Institute of Informatics, SAS, Slovakia Gaaloul Walid, Institute Telecom & Management SudParis, France Hai Wang, Aston University, United Kingdom Ruili Wang, Massey University, New Zealand Alexander Woehrer, St. Pölten University of Applied Sciences, Austria Guandong Xu, Victoria University, Australia Junsheng Zhang, Institute of Scientific and Technical Information of China, China Yongjun Zheng, Middlesex University, United Kingdom Yongluan Zhou, University of Southern Denmark, Denmark","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"2016 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":"127268460","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

Role of Non-functional Requirements in projects’ success 非功能性需求在项目成功中的作用

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787463

Arsalan Ali, I. Khalil, Israr Ahmad, Iqra Parveen, Uzair Khaleeq uz Zaman

{"title":"Role of Non-functional Requirements in projects’ success","authors":"Arsalan Ali, I. Khalil, Israr Ahmad, Iqra Parveen, Uzair Khaleeq uz Zaman","doi":"10.1109/ICoDT255437.2022.9787463","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787463","url":null,"abstract":"Non-functional Requirements (NFRs) are the vital part of software development. NFRs define the quality attributes of the software product. According to the researchers’ nonfunctional requirements are of equal importance as of functional requirements. But among practitioners it is debatable, as most of them do not give significance value to NFRs because of the rapid development nature of the projects. In the literature it is encouraged to provide proper attention to non-functional requirements during the development of the project because it could lead to new functional requirements and hence would tend to change the development approach. It is also believed that the absence of non-functional requirement could cause extra cost or customer’s dissatisfaction. Hence, this paper aims to conduct an industrial survey to gather the data about how software practitioners think about the importance of nonfunctional requirements in the project success. Furthermore, we will be applying statistical testing on our data to analyze the results to check the significance role of NFRs in the success of the software project.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"89 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":"124396342","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