Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Lee, Ernesto; Rustam, Furqan; Aljedaani, Wajdi; Ishaq, Abid; Rupapara, Vaibhav; Ashraf, Imran

doi:10.1155/2021/4916494

Cited by 11 publications

(3 citation statements)

References 47 publications

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“…Ernesto Lee, et al [24] used various supervised ML algorithms, for detecting true pulsar candidates. For implementation, HTRU2 dataset was used.…”

Section: G Prediction Of Pulsars 1) Predicting Pulsars With Hybrid Re...mentioning

confidence: 99%

Emerging Trends In Pulsar Star Studies: A Synthesis Of Machine Learning Techniques In Pulsar Star Research

Thanu,

Subha

2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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The pulsar is an extremely magnetized gyrating neutron star having a radius of 10 -15 km. Pulsars provide the indirect evidence of the gravitational wave's existence. So, to study the gravitational waves identification of pulsars is mandatory. Pulsars are considered as the Universe's gift. Pulsars provide scientists and researchers with information of the physics of neutron stars, which are thought to be the densest materials in the universe. The reason why astronomers give importance to the pulsars, because they are the leading edge of the research, based on the gravity. All pulsars produce marginally distinct emission pattern and it varies to some extent with every rotation. Hence, a promising signal detection is termed as a candidate, which is averaged based on every rotation of the pulsars. Any absence of the additional information, implies that each candidate is a real pulsar. The valid signals are extremely hard to detect due to noise and radio frequency interference (RFI). To clear up with this issue, Machine Learning (ML) algorithms were used for automatically classifying, identifying and many other process of pulsar candidates. This survey paper talks about different techniques used by different researchers for the pulsar star classification, identification and still more, using ML techniques.

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“…Ernesto Lee, et al [24] used various supervised ML algorithms, for detecting true pulsar candidates. For implementation, HTRU2 dataset was used.…”

Section: G Prediction Of Pulsars 1) Predicting Pulsars With Hybrid Re...mentioning

confidence: 99%

Emerging Trends In Pulsar Star Studies: A Synthesis Of Machine Learning Techniques In Pulsar Star Research

Thanu,

Subha

2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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“…Rustum et al . propose a hybrid resampling approach and combine the extra tree classifier to predict Pulsars [ 31 ]. Rupapara et al .…”

Section: Introductionmentioning

confidence: 99%

Research on imbalance machine learning methods for MR$$T_1$$WI soft tissue sarcoma data

et al. 2022

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Background Soft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Pathological grading of the soft tissue sarcoma is a key factor in patient prognosis and treatment planning while the clinical data of soft tissue sarcoma are imbalanced. In this paper, we propose an effective solution to find the optimal imbalance machine learning model for predicting the classification of soft tissue sarcoma data. Methods In this paper, a large number of features are first obtained based on $$T_1$$ T 1 WI images using the radiomics methods.Then, we explore the methods of feature selection, sampling and classification, get 17 imbalance machine learning models based on the above features and performed extensive experiments to classify imbalanced soft tissue sarcoma data. Meanwhile, we used another dataset splitting method as well, which could improve the classification performance and verify the validity of the models. Results The experimental results show that the combination of extremely randomized trees (ERT) classification algorithm using SMOTETomek and the recursive feature elimination technique (RFE) performs best compared to other methods. The accuracy of RFE+STT+ERT is 81.57% , which is close to the accuracy of biopsy, and the accuracy is 95.69% when using another dataset splitting method. Conclusion Preoperative predicting pathological grade of soft tissue sarcoma in an accurate and noninvasive manner is essential. Our proposed machine learning method (RFE+STT+ERT) can make a positive contribution to solving the imbalanced data classification problem, which can favorably support the development of personalized treatment plans for soft tissue sarcoma patients.

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“…Many studies classify app reviews using different taxonomies [91,105,148,203,241,245,24,26,37,25,173], for various purposes: detection of potential feature requests, bug reports, complaints, and praises, etc. Even though many of them identify reviews related to app usability, there is no explicit mention of accessibility-related issues [112].…”

Section: Classification Of Text Documentsmentioning

confidence: 99%

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

Aljedaani

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The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students find that learning is extremely stressful during the pandemic.

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Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Cited by 11 publications

References 47 publications

Emerging Trends In Pulsar Star Studies: A Synthesis Of Machine Learning Techniques In Pulsar Star Research

Emerging Trends In Pulsar Star Studies: A Synthesis Of Machine Learning Techniques In Pulsar Star Research

Research on imbalance machine learning methods for MR$$T_1$$WI soft tissue sarcoma data

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

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