2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)最新文献_第4页

Multimodal Emotion Recognition using Deep Learning Architectures 使用深度学习架构的多模态情感识别

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787437

Iram Hina, A. Shaukat, M. Akram

引用次数: 3

Diagnosis of Leukemia Disease through Deep Learning using Microscopic Images 利用显微图像的深度学习诊断白血病

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787449

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

{"title":"Diagnosis of Leukemia Disease through Deep Learning using Microscopic Images","authors":"Qurat ul Ain, Shahzad Akbar, Syed Ale Hassan, Zunaira Naaqvi","doi":"10.1109/ICoDT255437.2022.9787449","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787449","url":null,"abstract":"The abnormal production of WBC (white blood cell) in the bone marrow is known as leukemia. Leukemia is one of the most affecting diseases around the globe. Several types of ML (machine learning) and DL (deep learning) classification models have been presented in the literature to detect this disease, but they still possess some drawbacks. This study proposes a framework for detecting five classes of leukemia: ALL, AML, CLL, CML, and normal cell. In this research, a pre-trained DCNN (deep convolutional neural network) has been proposed for the detection of leukemia through microscopic images. Pre-processing of microscopic images improves the contrast and removes the noise by enhancing and filtering images. Segmentation of microscopic images is used to highlight the area of the disease. Alex-Net and ResNet-34 architecture are used for classification purposes. After comparing these two models through statistical parameters, ResNet-34 attained the most accurate result than Alex-Net using the publicly available ALL-IDB dataset, the evaluation through the statistical parameters revealed that ResNet-34 attained a classification accuracy of 98.4% on ALL, 98.4% on AML, 98.13% over CLL, 98.14 over CML. AlexNet attained 96.1% classification accuracy on ALL, 95.5% on AML, 95.7% on CLL, and 96.8% on CML. The proposed framework significantly outperforms existing technologies and can be used in clinical applications.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"140 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":"116502261","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: 8

Evaluation of the Electromagnetic Forces in the Zigzag Transformer using a Computational Method 用计算方法评估之字形变压器电磁力

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787448

Kamran Dawood, Güven Kömürgöz, Fatih Isik

{"title":"Evaluation of the Electromagnetic Forces in the Zigzag Transformer using a Computational Method","authors":"Kamran Dawood, Güven Kömürgöz, Fatih Isik","doi":"10.1109/ICoDT255437.2022.9787448","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787448","url":null,"abstract":"The use of power electronics is increasing day by day due to which power system stability and quality of power supply are big issues for the electric suppliers. Harmonic distortions are one of the main factors affecting the overall power quality and stability of the electric power network. The harmonic effect can be decreased using a zigzag transformer. This type of transformer has three windings. The applied current is generally very high during the zero-sequence impedance measurement of the zigzag transformer and during the high current levels, electromagnetic forces are one of the main causes of transformer failures. Digital technologies and computational methods have advanced more rapidly than any other innovations. In this work, electromagnetic forces acting on the zigzag transformer during the normal condition and during the zero-sequence impedance measurement have been investigated for the first time. For the calculation of the electromagnetic forces, the two-dimensional finite element computational technique has been used. The results of both conditions are also compared with the analytical method. This study will not only help the zigzag transformer designers but also would be useful for the researchers to evaluate and differentiate the effect of both conditions in the zigzag transformers.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"64 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":"116124709","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 Big Data Analytics and Edge Computing in Modern IoT Applications: A Systematic Literature Review 大数据分析和边缘计算在现代物联网应用中的作用:系统文献综述

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787416

Ali E. M. Saeed, M. K. Khattak, S. Rashid

引用次数: 0

NLP based Model for Classification of Complaints: Autonomous and Intelligent System 基于NLP的投诉分类模型:自主智能系统

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787456

Qurat-ul-Ain, A. Shaukat, Usman Saif

{"title":"NLP based Model for Classification of Complaints: Autonomous and Intelligent System","authors":"Qurat-ul-Ain, A. Shaukat, Usman Saif","doi":"10.1109/ICoDT255437.2022.9787456","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787456","url":null,"abstract":"Artificial intelligence nowadays is playing a vital role in our society. It is just minimizing human labor and effort in every field. Industrial sector is feeding their large amount of structured and unstructured data to find out useful information for scientific research. The main alarming thing is how to operate the huge feedback data, which is in the form of complaints i.e., in text format. Here, we have proposed a model which automatically classifies the complaints by analyzing the text with the help of machine learning and NLP (Natural Language Processing) methods. We have initially collected a dataset from a portal containing complaints of citizens. For validation, we have also used another dataset of complaints from the Consumer Complaint Database. After tokenizing, stemming and lemmatization, different feature extraction techniques like count vectorizer and TF-IDF are used to convert all the textual data into numerical data. Then different machine learning algorithms are used to classify the complaints into their categories. In our gathered dataset, 10 different divisions for complaints are used and an accuracy of more than 70% is achieved with all classifiers. Similarly on the Consumer Complaint dataset, 86% accuracy has been achieved. The proposed model is helpful in saving a lot of time, as there is no need to go through each complaint and categorizing manually.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"14 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":"126886565","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

Wildfire detection in aerial images using deep learning 使用深度学习的航空图像野火检测

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787417

Hoor Ul Ain Tahir, Abdullah Waqar, S. Khalid, Syed Muhammad Usman

{"title":"Wildfire detection in aerial images using deep learning","authors":"Hoor Ul Ain Tahir, Abdullah Waqar, S. Khalid, Syed Muhammad Usman","doi":"10.1109/ICoDT255437.2022.9787417","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787417","url":null,"abstract":"Wildfires are one of the most expensive and lethal natural disasters on the planet, destroying millions of hectares of forest resources and endangering the lives of people and animals. Such accidents are time-sensitive and can result in significant loss of life and property if not dealt with timely. Detection of fire at an early stage using aerial videos can reduce personal and property losses. This research focuses on the detection of fire locations monitored by UAV drones. Predicting fire behavior can assist firefighters in improved fire management and forecasting for future events, as well as lowering the firefighters' risk to life. Recent advancements in aerial imagery suggest that these images are valuable in the detection of wildfire. Drones and Unmanned Aerial Vehicles (UAVs) are among the different methods and technology for aerial imagery that is being used to obtain information about the fire. We present a YOLOv5 based deep learning model for fire detection. The proposed method detects fire in a real-time environment with high accuracy by evaluating a video frame-by-frame to detect such anomalies in real-time and sends a warning to the relevant authorities. In terms of detection performance, our technique outperforms existing fire detection systems. On the FireNet and FLAME aerial picture datasets, we evaluated the proposed method's performance and achieved the F1-score of 94.44%.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"289 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":"122404504","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: 9

A Framework for Human Error, Weaknesses, Threats & Mitigation Measures in an Airgapped Network 气隙网络中人为错误、弱点、威胁和缓解措施的框架

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787441

Rizwan Ahmed Shaikh, Muhammad Sohaib Khan, Imran Rashid, H. Abbas, Farrukh I. Naeem, Muhammad Haroon Siddiqi

{"title":"A Framework for Human Error, Weaknesses, Threats & Mitigation Measures in an Airgapped Network","authors":"Rizwan Ahmed Shaikh, Muhammad Sohaib Khan, Imran Rashid, H. Abbas, Farrukh I. Naeem, Muhammad Haroon Siddiqi","doi":"10.1109/ICoDT255437.2022.9787441","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787441","url":null,"abstract":"Many organizations process and store classified data within their computer networks. Owing to the value of data that they hold; such organizations are more vulnerable to targets from adversaries. Accordingly, the sensitive organizations resort to an ‘air-gap’ approach on their networks, to ensure better protection. However, despite the physical and logical isolation, the attackers have successfully manifested their capabilities by compromising such networks; examples of Stuxnet and Agent.btz in view. Such attacks were possible due to the successful manipulation of human beings. It has been observed that to build up such attacks, persistent reconnaissance of the employees, and their data collection often forms the first step. With the rapid integration of social media into our daily lives, the prospects for data-seekers through that platform are higher. The inherent risks and vulnerabilities of social networking sites/apps have cultivated a rich environment for foreign adversaries to cherry-pick personal information and carry out successful profiling of employees assigned with sensitive appointments. With further targeted social engineering techniques against the identified employees and their families, attackers extract more and more relevant data to make an intelligent picture. Finally, all the information is fused to design their further sophisticated attacks against the air-gapped facility for data pilferage. In this regard, the success of the adversaries in harvesting the personal information of the victims largely depends upon the common errors committed by legitimate users while on duty, in transit, and after their retreat. Such errors would keep on repeating unless these are aligned with their underlying human behaviors and weaknesses, and the requisite mitigation framework is worked out.","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":"124411504","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

Utilization of DCT Coefficients for the Classification of Standard Datasets in Cloud/Edge Computing Environment 云/边缘计算环境下DCT系数在标准数据集分类中的应用

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787466

L. Chaudhary, Farhan Hussain, Umair Gillani

{"title":"Utilization of DCT Coefficients for the Classification of Standard Datasets in Cloud/Edge Computing Environment","authors":"L. Chaudhary, Farhan Hussain, Umair Gillani","doi":"10.1109/ICoDT255437.2022.9787466","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787466","url":null,"abstract":"In this work we propose efficient deep neural networks for classification that are well suited to the edge computing and cloud computing environment. These environments inherently have to deal with bandwidth limitations and bounded computational resources. Our proposed methods tend to reduce the bandwidth requirements and reduce the computational costs for running these deep learning algorithms. We extensively utilized the Discrete Cosine Transforms (DCT) to exploit the redundancy in the image datasets. In this work we present a deep neural network that predicts the most significant DCT coefficients for an image and then employs these important DCT coefficients for classification purpose. This makes the deep neural network to achieve classification by processing much less input information. Broadly two approaches were used for classification purpose. In the first approach classification was done by employing the most significant DCT coefficients and in the second approach low resolution images, constructed from a limited number of DCT coefficients were utilized for classification purpose. The experiments were performed on well-known greyscale and RGB image datasets like FASHION MNIST, CIFAR-10 and CIFAR-100. VGG-16 architecture is mainly used for classification. The experiments showed promising results and the classification accuracies achieved were almost the same as that achieved by full resolution images.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"20 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":"125987477","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

YAAD: Young Adult’s Affective Data Using Wearable ECG and GSR sensors YAAD:使用可穿戴ECG和GSR传感器的年轻人情感数据

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787465

Muhammad Najam Dar, Amna Rahim, M. Akram, Sajid Gul Khawaja, Aqsa Rahim

{"title":"YAAD: Young Adult’s Affective Data Using Wearable ECG and GSR sensors","authors":"Muhammad Najam Dar, Amna Rahim, M. Akram, Sajid Gul Khawaja, Aqsa Rahim","doi":"10.1109/ICoDT255437.2022.9787465","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787465","url":null,"abstract":"Emotions play a significant role in human-computer interaction and entertainment consumption behavior, which young adults commonly use. The main challenge is the lack of a publicly available dataset for young adults with emotion labeling of physiological signals. This article presents a multi-modal data set of Electrocardiograms (ECG) and Galvanic Skin Response (GSR) signals for the emotion classification of young adults. Signal acquisition was performed through Shimmer3 ECG and Shimmer3 GSR units wearable to the chest and palm of the participants. The ECG signals were acquired from 25 participants, while GSR signals were acquired from 12 participants while watching 21 emotional stimulus videos divided into three sessions. The data was self-annotated for seven emotions: happy, sad, fear, surprise, anger, disgust, and neutral. These emotional states were further self-annotated with five very low, low, moderate, high, and very high-intensity levels of felt emotion. The participant also annotated valence, arousal, and dominance scores through Google form against each provided stimulus. The base experimental results for classifying four classes of high valence high arousal (HVHA), high valence low arousal (HVLA), low valence high arousal (LVHA), and low valence low arousal for ECG data is reported with an accuracy of 69.66%. Our baseline method for the proposed dataset achieved 66.64% accuracy for the eight-class classification of categorical emotions. The significance of data lies in the more emotional classes and less intrusive sensors to mimic real-world applications. Young adult’s affective data (YAAD) is made publicly available, and it is valuable for researchers to develop behavioral assessments based on physiological signals.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"145 8 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":"131981196","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

Segmentation of Images Using Deep Learning: A Survey 使用深度学习的图像分割:综述

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787440

Hana Sharif, Faisal Rehman, Amina Rida, Aman Sharif

{"title":"Segmentation of Images Using Deep Learning: A Survey","authors":"Hana Sharif, Faisal Rehman, Amina Rida, Aman Sharif","doi":"10.1109/ICoDT255437.2022.9787440","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787440","url":null,"abstract":"In this present era of technology, segmentation of image is a fundamental and significant component of computer vision and image processing applications. These applications include medical image analysis, compression of images, surveillance, security footage, and many others. Researchers have proposed and developed various models, theories, and algorithms in this advancing field of image segmentation. Due to the success of a wide range of computer vision applications, work whose goal was to use deep learning algorithms for the development and usability of image segmentation was completed not long ago. In this study, a detailed review of the literature on image segmentation is being written, which covers a vast and extensive amount of work done by researchers at the time of writing this review. This review investigates the similarity and differences between the proposed models by researchers in the past. Along with similarities, we have also discussed the strength and challenges of the model under the umbrella of deep learning algorithms. In the end, future work has been discussed along with keeping in view the past work of researchers.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"13 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114030520","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