A Novel Approach to Data Extraction on Hyperlinked Webpages

Shaukat, Kamran; Masood, Nayyer; Khushi, Matloob

doi:10.3390/app9235102

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…Table 5 shows the result of the random undersampling technique with multiple feature selection techniques. The GB model performed poorly, with an accuracy of 68.1% in the case of ReliefF and 68.6% with the OneR feature selections technique [ 63 , 64 , 65 ]. The XGBOOST model performed very well for correlation and information gain feature selection techniques with an accuracy of 82.8%.…”

Section: Resultsmentioning

confidence: 99%

A Proposed Framework for Early Prediction of Schistosomiasis

et al. 2022

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Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.

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Section: Resultsmentioning

confidence: 99%

A Proposed Framework for Early Prediction of Schistosomiasis

et al. 2022

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“…The information may be used to build models for predicting academic success, locating at-risk students, and spotting problematic behavior. It is designed for use in research on student behavior and performance [38][39][40][41][42][43][44][45][46][47][48][49].…”

Section: Discussionmentioning

confidence: 99%

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Alsabhan

2023

Sensors

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Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic dishonesty during final exams since teachers are not directly monitoring students. We suggest a novel method in this study for identifying possible exam-cheating incidents using Machine Learning (ML) approaches. The 7WiseUp behavior dataset compiles data from surveys, sensor data, and institutional records to improve student well-being and academic performance. It offers information on academic achievement, student attendance, and behavior in general. In order to build models for predicting academic accomplishment, identifying at-risk students, and detecting problematic behavior, the dataset is designed for use in research on student behavior and performance. Our model approach surpassed all prior three-reference efforts with an accuracy of 90% and used a long short-term memory (LSTM) technique with a dropout layer, dense layers, and an optimizer called Adam. Implementing a more intricate and optimized architecture and hyperparameters is credited with increased accuracy. In addition, the increased accuracy could have been caused by how we cleaned and prepared our data. More investigation and analysis are required to determine the precise elements that led to our model’s superior performance.

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“…The proposed algorithm [26] is to reduce the stop words that have no semantic implication. This article [27] presents a corpus of 15000 web pages and applies a novel algorithm. The corpus is made by using a web crawler.…”

Section: Literature Reviewmentioning

confidence: 99%

Ontology-Based News Linking for Semantic Temporal Queries

Satti¹,

Ahmad²,

Muslim³

et al. 2023

Computers, Materials &Amp; Continua

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Daily newspapers publish a tremendous amount of information disseminated through the Internet. Freely available and easily accessible large online repositories are not indexed and are in an un-processable format. The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed. There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora, especially for South Asian languages. The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language. Lack of indexing, primarily semantic indexing of the daily news items, makes news items impracticable for any querying. Moreover, the most straightforward search facility does not support these unindexed news resources. Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages, i.e., Urdu. The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler. The corpus is then semantically linked and annotated with daily news items. Two techniques are proposed for image annotation, free annotation and fixed cross examination annotation. The second technique got higher accuracy. Build news ontology in protégé using Ontology Web Language (OWL) language and indexed the annotations under it. The application is also built and linked with protégé so that the readers and journalists have an interface to query the news items directly. Similarly, news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.

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A Novel Approach to Data Extraction on Hyperlinked Webpages

Cited by 11 publications

References 26 publications

A Proposed Framework for Early Prediction of Schistosomiasis

A Proposed Framework for Early Prediction of Schistosomiasis

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Ontology-Based News Linking for Semantic Temporal Queries

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