2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)最新文献_第4页

High-Resolution Remote Sensing Image Classification through Deep Neural Network 基于深度神经网络的高分辨率遥感图像分类

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441541

Shafaq Rasheed, Fawad, Muhammad Adeel Asghar, Saqlain Razzaq, Mehwish Anwar

{"title":"High-Resolution Remote Sensing Image Classification through Deep Neural Network","authors":"Shafaq Rasheed, Fawad, Muhammad Adeel Asghar, Saqlain Razzaq, Mehwish Anwar","doi":"10.1109/ICoDT252288.2021.9441541","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441541","url":null,"abstract":"Remote sensing in image processing is popular in urban monitoring, forest detection, and disaster Monitoring. The high-resolution satellite images are classified into their respective classes through their distinctive features. Innovation in image acquisition has played a critical role in the process of recognition. However, the geometric and photometric variations require the extraction of invariant features. This paper presents a robust strategy that can classify such high-resolution images, also in case of changes in geometry and photometry. The employed dataset consists of images located in the Headwater Region of China. The images of the database include variations in illumination, viewpoint, and scale. Robust and distinctive features collected from the fully connected layer of the DNN model are classified through a multi-class support vector machine. The Gaussian kernel type parameter of SVM is used for the classification in our experiments. The results show our proposed approach provides 93.8% classification accuracy, which is better than many recently reported works.","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":"130516498","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

Formal Modeling and Automation of E-Payment Smart Parking System 电子支付智能停车系统的形式化建模与自动化

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441482

Fatima Jameel, N. Zafar

{"title":"Formal Modeling and Automation of E-Payment Smart Parking System","authors":"Fatima Jameel, N. Zafar","doi":"10.1109/ICoDT252288.2021.9441482","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441482","url":null,"abstract":"In recent years, the concept of smart city gained a lot of popularity which includes various smart components such as smart homes, smart offices, smart parking, smart sewerage system, smart transportation, smart buildings, and smart hospital. Smart parking system is the main component of a smart city because searching for parking places, managing parking system and payment for available parking is a major issue for drivers which cause various parking problems such as air pollution, fuel consumption, traffic congestion, and waste of time. Many models of smart parking system have been developed but still, there is a need for improvement because of the introduction of various state-of-the-art technologies in the modeling of electronic payment of smart parking systems. In this paper Internet of Things (IoT) based different payment methods of smart parking system will be presented using UML, Automata, and VDM-SL. Unified Modeling Language (UML) will be used to realize the requirements and design the system’s model. Automata theory will be used to represent the behavior of the electronic payment system. The functionality of the system includes the different parking payment methods for indoor and outdoor parking areas. The model will be developed using Vienna Development Method-Specification Language (VDM-SL). The model is analyzed using the VDM-SL toolbox.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"45 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":"121488321","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

Rotation Aware Object Detection Model with Applications to Weapons Spotting in Surveillance Videos 旋转感知目标检测模型及其在监控视频武器定位中的应用

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441538

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

{"title":"Rotation Aware Object Detection Model with Applications to Weapons Spotting in Surveillance Videos","authors":"Nazeef Ul Haq, Tufail Sajjad Shah Hashmi, M. Fraz, M. Shahzad","doi":"10.1109/ICoDT252288.2021.9441538","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441538","url":null,"abstract":"Detection of weapons automatically is very important for improving the security and prosperity of people, in any case, it is a troublesome undertaking due to huge assortment of size, shape and presence of weapons. View point varieties what’s more, impediment likewise are the reasons which makes this errand more troublesome. Further, the present detection algorithms of objects process rectangular areas, anyway a thin and long rifle may truly cover simply a little part of zone and the rest may contain unessential subtleties. To beat this issue, we propose a deep learning based model for detection of weapons with orientation, which not only gives rotation aware bound box but also improves the detection performance. The proposed model provides orientation with the help of angle classification by dividing angle into eight different classes. To train our model for weapon recognition another new dataset containing of around 6400 pictures is assembled from the web and afterward manually annotated that. We also provide three standard horizontal annotation format of our dataset as ground truth along with oriented ground truth for further exploration in future. The proposed model is assessed on this dataset, also, the near investigation with off-the rack object indicators yields predominant execution of proposed model, estimated with the standard assessment procedures. The dataset and the model implementation are made publicly available at this link: https://bit.ly/2TyZICF.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"23 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":"125384462","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 Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification 一种基于蜉蝣算法的人工神经网络(ANN)

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441473

Syed Kumayl Raza Moosavi, M. Zafar, Malik Naveed Akhter, Shahzaib Farooq Hadi, Noman Mujeeb Khan, Filippo Sanfilippo

{"title":"A Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification","authors":"Syed Kumayl Raza Moosavi, M. Zafar, Malik Naveed Akhter, Shahzaib Farooq Hadi, Noman Mujeeb Khan, Filippo Sanfilippo","doi":"10.1109/ICoDT252288.2021.9441473","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441473","url":null,"abstract":"Training of Artificial Neural Networks (ANNs) have been improved over the years using meta heuristic algorithms that introduce randomness into the training method but they might be prone to falling into a local minima in a high-dimensional space and have low convergence rate with the iterative process. To cater for the inefficiencies of training such an ANN, a novel neural network is presented in this paper using the bio-inspired algorithm of the movement and mating of the mayflies. The proposed Mayfly algorithm is explored as a means to update weights and biases of the neural network. As compared to previous meta heuristic algorithms, the proposed approach finds the global minima cost at far less number of iterations and with higher accuracy. The network proposed, which is named Mayfly Algorithm based Neural Network (MFANN) consists of an input layer, a single hidden layer of 10 neurons and an output layer. Two University of California Irvine (UCI) database sample datasets have been used as benchmark for this study, namely the Banknote Authentication (BA) and the Cryotherapy, for which the training accuracy achieved is 99.2350% and 96.6102%, whereas the Testing accuracy is 99.1247% and 90% respectively. Comparative study with grey wolf optimization neural network (GWONN) and particle swarm optimization neural network (PSONN) reveal that the proposed MFANN achieves 1–2% better accuracy with training dataset and 2% better accuracy with testing dataset.","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":"125842677","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: 5

Group Activity Recognition in Visual Data: A Retrospective Analysis of Recent Advancements 视觉数据中的群体活动识别:近期进展的回顾性分析

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441478

Shoaib Sattar, Yahya Sattar, M. Shahzad, M. Fraz

引用次数: 1

Diagnose a Disease: A Fog Assisted Disease Diagnosis Framework with Bidirectional LSTM 诊断疾病:基于双向LSTM的雾辅助疾病诊断框架

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441475

Hamza Javaid, Summra Saleem, B. Wajid, Usman Ghani Khan

{"title":"Diagnose a Disease: A Fog Assisted Disease Diagnosis Framework with Bidirectional LSTM","authors":"Hamza Javaid, Summra Saleem, B. Wajid, Usman Ghani Khan","doi":"10.1109/ICoDT252288.2021.9441475","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441475","url":null,"abstract":"The Coronavirus (COVID-19) pandemic has created a huge havoc on a global scale including Pakistan and its surrounding regions in South Asia. The underdeveloped medical infrastructure and inadequate healthcare staff have become a dilemma during this pandemic fostering the need for digital health system. In this paper we propose Diagnose A Disease (DAD), a novel telehealth solution in Pakistan for remote patient monitoring and disease diagnosis. The three layered hybrid architecture of DAD comprises of data collection layer, analytics engine layer and cloud storage layer. In the first module, vital physiological signs of patients are measured and recorded through a set of wearable sensors. The next module makes use of fog enabled cloud framework for resource management of worker nodes. The analytics engine module also includes a trained Bidirectional Long Short Term Memory neural network model for heart disease, blood pressure and diabetes classification. Finally, the last module makes use of the cloud service for data storage, analysis and distributed secured health data sharing among medical authorities. The telehealth solution comes with emergency notifications, standard clinical guidelines and many advanced features with fog service to reduce latency and delays that becomes crucial in healthcare applications. PureEdgeSim, a simulation toolkit for fog environments is used to evaluate the proposed DAD model in terms of latency, bandwidth usage, power consumption, execution period and accuracy. Results depict that the proposed architecture performed well in handling real time requests, resource utilization and response time for healthcare decision making which further enhances its utility in real life situations.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"14 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":"131047636","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

A Deep Convolutional Neural Network Based Framework for Pneumonia Detection 基于深度卷积神经网络的肺炎检测框架

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441539

Sonain Jamil, Muhammad Sohail Abbas, Fawad, Muhammad Faisal Zia, Muhibur Rahman

引用次数: 3

Classification of Deepfake Videos Using Pre-trained Convolutional Neural Networks 使用预训练卷积神经网络对Deepfake视频进行分类

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441519

Momina Masood, Marriam Nawaz, A. Javed, Tahira Nazir, Awais Mehmood, Rabbia Mahum

{"title":"Classification of Deepfake Videos Using Pre-trained Convolutional Neural Networks","authors":"Momina Masood, Marriam Nawaz, A. Javed, Tahira Nazir, Awais Mehmood, Rabbia Mahum","doi":"10.1109/ICoDT252288.2021.9441519","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441519","url":null,"abstract":"The advancement of Artificial Intelligence (AI) has brought a revolution in the field of information technology. Furthermore, AI has empowered the new applications to run with minimum resources and computational cost. One of such applications is Deepfakes, which produces extensively altered and modified multimedia content. However, such manipulated visual data imposed a severe threat to the security and privacy of people and can cause massive sect, religious, political, and communal stress around the globe. Now, the face-swapped base visual content is difficult to recognizable by humans through their naked eyes due to the advancement of Generative adversarial networks (GANs). Therefore, identifying such forgeries is a challenging task for the research community. In this paper, we have introduced a pipeline for identifying and detecting person faces from input visual samples. In the second step, several deep learning (DL) based approaches are employed to compute the deep features from extracted faces. Lastly, a classifier namely SVM is trained over these features to classify the data as real or manipulated. We have performed the performance comparison of various feature extractors and confirmed from reported results that DenseNet-169 along with SVM classifier outperforms the rest of the methods.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"21 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":"125742648","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

Classy AA-NECTAR: Personalized Ubiquitous E-Learning Recommender System with Ontology and Data Science Techniques 经典AA-NECTAR:基于本体和数据科学技术的个性化泛在电子学习推荐系统

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441481

A. Tahir, Ahsan Ijaz, F. Javed

引用次数: 2

Multiscale Unified Network for Simultaneous Segmentation of Nerves and Micro-vessels in Histology Images 组织图像中神经和微血管同时分割的多尺度统一网络

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441509

Afia Rasool, M. Fraz, S. Javed

{"title":"Multiscale Unified Network for Simultaneous Segmentation of Nerves and Micro-vessels in Histology Images","authors":"Afia Rasool, M. Fraz, S. Javed","doi":"10.1109/ICoDT252288.2021.9441509","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441509","url":null,"abstract":"Among the analytic factors to study tumor aggressiveness and disease recurrence, density of micro-vessels (MVD), Lymphovascular Invasion (LVI) and Perineural Invasion (PNI) are considered key prognostic factors. The manual identification of micro-vessels and nerves is time consuming, laborious and highly prone to human error. Computational pathology is an emerging field striving to improve patient care by incorporating modern algorithms to the traditional analysis procedures of microscopic slides. To overcome the challenges of multi-scale, multi-shape and slight intensity variant histopathology structures, we have proposed a deep neural network based hybrid semantic segmentation architecture. The framework is specifically designed to improve the accuracy by focusing mega to minor object details. The encoder uses Multi-scale feature extraction block formed of ResNeXt Blocks. This organization is effective to encode coarse to fine grained features from all specifications and dimensions while limiting the number of learnable parameters. The decoder is a combination of feature fusion and feature erudition while step by step mapping them back to the pixel map. The proposed architecture is trained and tested on generated data set comprising 17,300 samples, prepared from 18 histopathological WSIs of oral cell carcinoma tissues. The trained architecture outperformed the existing segmentation networks like FCN, Unet, SegNet, Deeplabv3+ and a significant rise in accuracy regarding certain scenarios is observed.","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":"129493581","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: 5