2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)最新文献_第6页

Deep-learning based framework for sentiment analysis in Urdu language 基于深度学习的乌尔都语情感分析框架

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787451

Maria Masood, F. Azam, Muhammad Waseem Anwar, Jalees Ur Rahman

{"title":"Deep-learning based framework for sentiment analysis in Urdu language","authors":"Maria Masood, F. Azam, Muhammad Waseem Anwar, Jalees Ur Rahman","doi":"10.1109/ICoDT255437.2022.9787451","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787451","url":null,"abstract":"In recent times, Sentiment analysis has become a significant means for framing a successful business and can be very helpful in predicting customer trends to help organizations in their decision-making process. Though many software applications are available in the market for text analysis, one of the major limitations of such applications is that they are developed for rich languages like English, German, Spanish, Arabic, etc. and less popular languages like Urdu, Hindi, Roman Urdu are neglected due to lack of availability of resources. Therefore, this research project will provide an implementation of sentiment analysis in the Urdu language. Firstly, preprocessing is performed and a small-scale manual dictionary of 830 Urdu stem words is introduced for stemming. Then a deep learning-based framework of LSTM is used for Urdu sentiment analysis. Experimental results show high classification accuracy of 86.03% and 0.89 F1 Score with the use of LSTM that captures sequence information effectively to analyze sentiments than the conventional supervised machine learning approaches.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"252 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":"123591992","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

Multiclass Heartbeat Classification using ECG Signals and Convolutional Neural Networks 基于心电信号和卷积神经网络的多类心跳分类

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787419

Muhammad Deedahwar Mazhar Qureshi, Daniel Peralta Cámara, E. De Poorter, R. Mumtaz, A. Shahid, I. Moerman, Timo De Waele

{"title":"Multiclass Heartbeat Classification using ECG Signals and Convolutional Neural Networks","authors":"Muhammad Deedahwar Mazhar Qureshi, Daniel Peralta Cámara, E. De Poorter, R. Mumtaz, A. Shahid, I. Moerman, Timo De Waele","doi":"10.1109/ICoDT255437.2022.9787419","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787419","url":null,"abstract":"Given a large enough time series signal from an ECG signal, it is possible to identify and classify heartbeats not only into normal and abnormal classes but into multiple classes including but not limited to Normal beat, Paced beat, Atrial Premature beat and Ventricular flutter as originally suggested by benchmark electrocardiogram (ECG) datasets like the MIT-BIH Arrhythmia Dataset. There are multiple approaches that target ECG classifications using Machine and Deep Learning like One Class SVM, ELM, Anogan etc. These approaches require either very high computational resources, fail to classify classes apart from normal/abnormal classes or fail to classify all classes with an equivalent or near-equivalent accuracy. With these limitations in mind, this paper proposes a deep learning approach using Convolutional Neural Networks (CNNs) to classify multiple classes of heartbeats in an efficient, effective, and generalized manner. By using the MIT-BIH Arrhythmia dataset to filter and segment individual correctly structured heartbeats, we have designed a network which can be trained on different classes of heartbeats and present robust, accurate and efficient results. The class imbalance prevalent in the MIT-BIH dataset has been dealt with using Synthetic Minority Over-sampling Technique (SMOTE). The robustness of the model is increased by adding techniques of loss minimization such as dropout and early stop-ping. The approach gives an accuracy of approximately 96% and an extremely short time span for class prediction(classification), i.e., less than 1 second. The results are also illustrated over multiple (10) classes to exemplify the generality of the model. We have illustrated these results over multiple (10) classes to exemplify generality of the model.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"42 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":"132647072","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 Composite Dataset of Lumbar Spine Images with Mid-Sagittal View Annotations and Clinically Significant Spinal Measurements 具有中矢状位视图注释和临床意义的脊柱测量的腰椎图像的复合数据集

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787452

R. Masood, Taimur Hassan, H. Raja, Bilal Hassan, J. Dias, N. Werghi

{"title":"A Composite Dataset of Lumbar Spine Images with Mid-Sagittal View Annotations and Clinically Significant Spinal Measurements","authors":"R. Masood, Taimur Hassan, H. Raja, Bilal Hassan, J. Dias, N. Werghi","doi":"10.1109/ICoDT255437.2022.9787452","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787452","url":null,"abstract":"The modern computer-aided screening systems re-quire a large amount of well-annotated training data to produce robust and consistent diagnostic performance. Furthermore, the public datasets designed to evaluate automated spinal disorders screening frameworks lack quantitative labels, which are marked by expert radiologists and clinically validated by spinal surgeons. This paper presents a dataset containing high-resolution (and well-labeled) mid-sagittal views of lumbar spine magnetic resonance imaging (MRI) scans. These scans also contain vertebral body masks along with clinically significant spinal measurements, including lumbar height, intervertebral body distances, vertebral body sidewall dimensions, vertebral body superior and inferior end-plates dimensions, lumbar lordotic angles, and lumbosacral angles. The mid-sagittal view MRI scans within the proposed dataset were first procured, and then they were manually marked by the expert radiologists and validated by the expert spinal surgeons. Afterward, different spinal measurements were recorded, which serves as a benchmark to evaluate the autonomous frameworks for predicting spinal misalignments. In addition to this, the proposed dataset is, to the best of our knowledge, the first composite database that contains lumbar spine mid-sagittal images along with spinal attributes and detailed markings of radiologists duly verified by the spinal surgeons. The proposed dataset, unlike its competitors, also introduces a quantitative vote to the clinicians and researchers in the assessment process of lumbar spine disorders. Apart from this, the dataset is publicly available at https://data.mendeley.com/datasets/k3b363f3vz/2.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"317 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":"131869650","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

Handcrafted and Deep features based Classification of Scoliosis 基于手工和深度特征的脊柱侧凸分类

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787459

Joddat Fatima, Mashood Mohsan, Muhammad Umair Qaisar, M. Hamza, Muhammad Zeeshan Tahir, G. Zaman

引用次数: 0

Framework for Live Migration of FPGA based ECB-mode AES-128 accelerator 基于FPGA的esb模式AES-128加速器实时迁移框架

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787468

Yame Asfia, S. G. Khawaja, Muhammad Asad, Alina Mirza

引用次数: 0

TM-BERT: A Twitter Modified BERT for Sentiment Analysis on Covid-19 Vaccination Tweets TM-BERT:用于对Covid-19疫苗接种推文进行情绪分析的Twitter改进BERT

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787395

Muhammad Talha Riaz, M. Shah Jahan, S. G. Khawaja, A. Shaukat, Jahan Zeb

引用次数: 3

A Novel Approach for Periodically Updating Rough Approximations Upon Multi-Dimension Variation 一种基于多维变化的周期更新粗糙逼近的新方法

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787436

Faryal Nosheen, Usman Qamar, S. Raza

{"title":"A Novel Approach for Periodically Updating Rough Approximations Upon Multi-Dimension Variation","authors":"Faryal Nosheen, Usman Qamar, S. Raza","doi":"10.1109/ICoDT255437.2022.9787436","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787436","url":null,"abstract":"In present era, transformation of almost all fields of life toward digitalization, poses various challenges. One of them is effective data analysis of large datasets and its complexity multiplies when dataset evolves with time. Dominance based rough set theory is a mathematical based tool, which efficiently probes hidden patterns from preference ordered based datasets. But in case of large datasets, computation of DRSA approximations becomes crucial step. In conventional DRSA algorithm, approximation sets have to be re-calculated when some change occurs in data over time. Therefore, repetitive calculations further increase the computational cost of approximations in real-time domain. In this paper, we researched the execution cost of approximations and designed a periodic approach to efficiently update DRSA approximations when variations occur in an object set and value set of decision attribute. We tested and compared the proposed dynamic approach with conventional approach and another dynamic approach, using UCI datasets. The results have shown that the proposed approach marked 98% reduction in computational time in comparison with conventional approach and 25% reduction in comparison with dynamic approach while updating DRSA approximations upon multi-dimensional variations.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"184 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":"115594309","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

IoT and MQTT based web monitoring of a solar living laboratory 基于物联网和MQTT的太阳能生活实验室网络监控

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787471

A. Rachid, Abdelghani Djedjig

引用次数: 1

Reconfigurable Architecture for Real-time Decoding of Canonical Huffman Codes 规范霍夫曼码实时解码的可重构结构

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787442

Rimsha Tariq, S. G. Khawaja, M. Akram, Farhan Hussain

引用次数: 0

A Reliable Learning Based Task Offloading Framework for Vehicular Edge Computing 一种可靠的基于学习的车辆边缘计算任务卸载框架

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2022-05-24 DOI: 10.1109/ICoDT255437.2022.9787462

Balawal Shabir, A. Malik, A. U. Rahman, M. A. Khan, Z. Anwar

{"title":"A Reliable Learning Based Task Offloading Framework for Vehicular Edge Computing","authors":"Balawal Shabir, A. Malik, A. U. Rahman, M. A. Khan, Z. Anwar","doi":"10.1109/ICoDT255437.2022.9787462","DOIUrl":"https://doi.org/10.1109/ICoDT255437.2022.9787462","url":null,"abstract":"Vehicular fog computing is an evolving solution for the delay sensitive computations at the vehicular edge. Due to the rapidly changing environment, effective resource utilisation becomes quite challenging. Centralised solution are proposed to improve the resource utilisation efficiency but with the added cost of central management and lower efficiency of the resource sharing environment. Distributed task offloading solutions are presented to address the issue; however, it results in an uneven workload distribution without considering the reliability of the communication between the nodes. In this work, we propose a fully distributed task offloading framework that minimises the residence time of the system under the task failure constraints. This overall improves the straggler effect by guaranteeing the task offloading delay at the vehicular edge by replicating the tasks at different vehicular destinations. The proposed work only keeps the tasks with the fastest response time and tasks with the slower response times are removed from the execution queues improving the task resource utilisation efficiency of the resource sharing environment.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"218 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":"132816099","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