2022 International Conference on Frontiers of Information Technology (FIT)最新文献_第3页

A Machine Learning-Based Approach for Improving TCP Congestion Detection Mechanism in IoTs 基于机器学习的物联网中TCP拥塞检测机制改进方法

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00035

Madeha Arif, Usman Qamar, Amreen Riaz

引用次数: 0

Bankruptcy Prediction using Diverse Machine Learning Algorithms 利用不同的机器学习算法进行破产预测

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00029

Ahmad Hassan, Nazish Yousaf

{"title":"Bankruptcy Prediction using Diverse Machine Learning Algorithms","authors":"Ahmad Hassan, Nazish Yousaf","doi":"10.1109/FIT57066.2022.00029","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00029","url":null,"abstract":"Bankruptcy prediction is a large field of finance and accounting science. The importance of this field stems from determining the risk to a business’s stability. Financial instability prediction aims to develop a predictive model that incorporates multiple econometric parameters to forecast a firm’s financial situation in the future. This research documents our observations while exploring, constructing, and comparing some of the widely used classification models: extreme gradient boosting, decision trees, random forests, quadratic discriminant analysis, neural networks, adaptive boosting, gaussian naïve bayes, balanced bagging, and logistic regression, which apply to bankruptcy prediction. Our focus is on the bankruptcy dataset of Polish companies, where statistical features are curated collections created using synthetic features. We start by performing data preprocessing and exploratory analysis involving the imputation of missing values using popular imputation techniques such as mean, k-nearest neighbours (K-NN), expectation maximization (EM), and multivariate imputation by chained equations, also known as (MICE). To address the data imbalance issue, we oversample the minority class labels using the synthetic minority oversampling approach (SMOTE). Then the data modelling is done using k-fold cross validation on the aforementioned models and the imputed and resampled datasets. Finally, we get 36 different analyses for nine models on four different imputed datasets, then we assess and evaluate the models’ performance on the validation datasets and rank the models accordingly.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134624351","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

Unsupervised Vessel Segmentation Method in Retinal Images 视网膜图像中的无监督血管分割方法

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00022

Zanib Qaiser, Waqar Ahmad, Mir Yasir Umair, Z. Mahmood

引用次数: 1

Hybrid Deep Learning model for ECG-based Arrhythmia Detection 基于ecg的心律失常检测混合深度学习模型

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00058

Amina Ashfaq, N. Anjum, Salman Ahmed, Nayyer Masood

引用次数: 0

Template-based Automatic code generation for Web application and APIs Using Class Diagram 使用类图为Web应用程序和api自动生成基于模板的代码

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00067

Irfan Ullah, Irum Inayat

引用次数: 1

CoviFake: A Framework to Detect and Analyze Fake COVID19 Tweets CoviFake:一个检测和分析假covid推文的框架

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00060

Tooba Asif, Bilal Tahir, Yasir Saleem, M. Mehmood

{"title":"CoviFake: A Framework to Detect and Analyze Fake COVID19 Tweets","authors":"Tooba Asif, Bilal Tahir, Yasir Saleem, M. Mehmood","doi":"10.1109/FIT57066.2022.00060","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00060","url":null,"abstract":"Along with the unprecedented impact of the COVID-19 pandemic on human lives, a new crisis of fake and false information related to disease has also emerged. Primarily, social media platforms such as Twitter are used to disseminate fake information due to ease of access and their large audience. However, automatic detection and classification of fake tweets is challenging task due to the complexity and lack of contextual features of short text. This paper proposes a novel CoviFake framework to classify and analyze fake tweets related to COVID-19 using vocabulary and non-vocabulary features. For this purpose, first, we combine and enhance ‘CTF’ and ‘COVID19 Rumor’ datasets to build our COVID19-sham dataset containing 25,388 labelled tweets. Next, we extract the vocabulary and 12 non-vocabulary features to compare the performance of six state-of-the-art machine learning classifiers. Our results highlight that the Random Forest (RF) classifier achieves the highest accuracy of 94.53% with the combination of top 2,000 vocabulary and 12 non-vocabulary features. In addition, we developed a large-scale dataset of CoviTweets containing 7.88 million English tweets posted by 3.8 million users during two months (March-April, 2020). The analysis of CoviTweets leveraging our framework reveals that the dataset contains 1.64 million (20.87%) fake tweets. Furthermore, we perform an in-depth examination by assigning a ‘fakeness score’ to hashtags and users in CoviTweets.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132065054","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

Development of an Embedded Device for Stroke Prediction via Artificial Intelligence-Based Algorithms 基于人工智能算法的脑卒中预测嵌入式设备的开发

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00034

Muhammad Usama Baloch, Mudassar Ahmad, Rehan Ashraf, Muhammad Asif Habib

{"title":"Development of an Embedded Device for Stroke Prediction via Artificial Intelligence-Based Algorithms","authors":"Muhammad Usama Baloch, Mudassar Ahmad, Rehan Ashraf, Muhammad Asif Habib","doi":"10.1109/FIT57066.2022.00034","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00034","url":null,"abstract":"The most significant cause of disability among the aged and the old is stroke, which causes several social or financial challenges. A stroke may result in death if it is not addressed properly. Patients with such a stroke are often identified with odd bio-signals (i.e., EMG). Therefore, if patients are correctly examined in real-time while being watched and quantified in their physiological signals, they may get the right therapy immediately. However, many stroke screening and diagnostic systems involve pricey and challenging to use in live imaging technologies, i.e., CT or MRI. With the use of real-time Artificial Intelligent (AI) signals, the proposed research created a stroke prediction system that can identify strokes. The system uses Machine Learning (ML) algorithms (XgBoost, Random Forest, Decision Tree, and Voting Classifier). Real-time EMG (Electromyography) bio-signals from the arm and forearm were recorded, after which critical characteristics were released, and prediction models were based on routine activities. It is observed that using the proposed collected data set for four features, the classification accuracy of Random Forest is almost 95%, and it performs better than other classification algorithms such as XgBoost 91%, Decision Tree and Voting Classifier both have (85%).","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"67 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126260301","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

Detection of COVID-19 in High Resolution Computed Tomography Using Vision Transformer 基于视觉变压器的高分辨率计算机断层扫描检测COVID-19

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00025

Aroosa Yaqoob, Abdul Basit, Abdul Rahman, Abdul Hannan, Kaleem Ullah

{"title":"Detection of COVID-19 in High Resolution Computed Tomography Using Vision Transformer","authors":"Aroosa Yaqoob, Abdul Basit, Abdul Rahman, Abdul Hannan, Kaleem Ullah","doi":"10.1109/FIT57066.2022.00025","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00025","url":null,"abstract":"In the current pandemic, precise and early diagnose of COVID-19 patient remained a crucial task for control of the spread of the COVID-19 virus in the healthcare sector. Due to the unexpected spike in COVID-19 cases, the majority of countries have experienced scarcity and poor testing rate. Chest X-rays and CT scans have been discussed in the literature as a viable source of testing for COVID-19 disease in patients. However, manually reviewing the CT and x-ray images is time-consuming and prone to error. Taking account into these constraints and the improvements in data science, this research proposed a Vision Transformer-based deep learning pipeline for COVID-19 diagnose from CT-based imaging. Due to the scarcity of large data sets, three open-source datasets of CT scans are pooled to generate 27370 images of covid and non- covid individuals. The proposed vision transformer-based model accurately diagnoses COVID-19 from normal chest CT images with an accuracy of 98 percent. This research would assist the practitioner, radiologist and doctors in early and accurate diagnose of COVID-19.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124805673","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

Impact of Demographics on Automated Criminal Tendency Detection from Facial Images 人口统计学对面部图像自动犯罪倾向检测的影响

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00026

Negarish Mushtaq, Khizer Ali, Momina Moetesum, I. Siddiqi

{"title":"Impact of Demographics on Automated Criminal Tendency Detection from Facial Images","authors":"Negarish Mushtaq, Khizer Ali, Momina Moetesum, I. Siddiqi","doi":"10.1109/FIT57066.2022.00026","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00026","url":null,"abstract":"An individual’s face can provide important insight about his personal traits like age, psychology, health, ethnicity, emotions, kinship, and much more. The biometric potentials of facial images make them an ideal tool for various forensic inferences. One such interesting area of research is the detection of criminal tendencies in people from their facial images. Several studies have proposed machine and deep learning-based solutions for this purpose. However, to the best of our knowledge, none have explicitly analyzed the impact of demographic attributes on the performance of such systems. In this paper, we provide an in-depth analysis to measure the impact of three important demographic properties i.e. age, gender, and ethnicity on facial image-based criminality detection systems. For this purpose, a balanced dataset is prepared as there was no such dataset available with age, race, and gender splits. The performance of various convolutional neural network architectures (VGG-16, VGG-19, and FaceNet) is evaluated to assess their potential in perceiving criminal tendencies. Based on the outstanding performance of FaceNet, it is selected to measure the impact of different demographic groups in detecting criminal tendencies from facial images. The analysis presented in this study can prove vital for the development of robust and unbiased systems that can provide reliable proactive solutions for the security of all communities.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121547811","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

Contextual Word Embedding based Clustering for Extractive Summarization 基于上下文词嵌入的聚类提取摘要

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00039

Shah Faisal, Atif Khan, S. Yousaf, Muhammad Umair

{"title":"Contextual Word Embedding based Clustering for Extractive Summarization","authors":"Shah Faisal, Atif Khan, S. Yousaf, Muhammad Umair","doi":"10.1109/FIT57066.2022.00039","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00039","url":null,"abstract":"Currently, the amount of content on the internet is expanding tremendously. One reason for the abundance of information is that numerous online resources cover similar themes, posing challenges and opportunities for natural language processing (NLP). People find it challenging to summarize thousands of documents on the same topic manually. Consequently, it is desirable to have multiple documents automatically summed up. This work proposed a contextual word embedding-based clustering technique for extractive summarization. At first, documents are split into sentences, and then each word in all sentences is given an embedding based on its context using the FastText embedding method. The averaged word embeddings are then used to create sentence embeddings/vectors. The Fuzzy C-Mean clustering algorithm is then applied to the collection of sentence embeddings to form clusters of semantically similar sentences. Based on the text features, the sentences inside each cluster are ranked. The final extracted summary comprises representative sentences taken from the highest-ranked sentences within each cluster. The effectiveness of the suggested methodology is tested in the context of the ROUGE evaluation metric and Document Understanding Conference (DUC) 2002 data set. Experimental results demonstrated that the presented technique outperformed the benchmark summarization techniques in terms of ROUGE measures.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126339014","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