Výsledky vyhledávání

Akademický článek

Time Series Reconstruction With Feature-Driven Imputation: A Comparison of Base Learning Algorithms

Autor: Nadeem Bashir, Adil Aslam Mir, Ali Daud, Muhammad Rafique, Amal Bukhari

Publikováno v: IEEE Access, Vol 12, Pp 85511-85530 (2024)

Addressing the challenge of missing values is a critical step when preparing and analyzing data. This process, known as imputation, helps ensure the dataset is complete, accurate, and reliable. As a result, the possibility of bias and errors in subse

Externí odkaz: https://doaj.org/article/e701caae716c40cd825fa39121790b5a

Zobrazit plný text záznamu

Akademický článek

An Improved Imputation Method for Accurate Prediction of Imputed Dataset Based Radon Time Series

Autor: Adil Aslam Mir, Fatih Vehbi Celebi, Muhammad Rafique, Lal Hussain, Ahmed S. Almasoud, Masoud Alajmi, Fahd N. Al-Wesabi, Anwer Mustafa Hilal

Publikováno v: IEEE Access, Vol 10, Pp 20590-20601 (2022)

This article primarily focuses on the performance evaluation of a new methodology, imputation by feature importance (IBFI), to serve its imputed dataset in further regression scenarios when dealing with soil radon gas concentration (SRGC) time-series

Externí odkaz: https://doaj.org/article/d09adc7d0b5d4494a68e36aa1a9bd140

Zobrazit plný text záznamu

Akademický článek

Anomalies Prediction in Radon Time Series for Earthquake Likelihood Using Machine Learning-Based Ensemble Model

Autor: Adil Aslam Mir, Fatih Vehbi Celebi, Hadeel Alsolai, Shahzad Ahmad Qureshi, Muhammad Rafique, Jaber S. Alzahrani, Hany Mahgoub, Manar Ahmed Hamza

Publikováno v: IEEE Access, Vol 10, Pp 37984-37999 (2022)

The ability to predict the radioactive soil radon gas concentration is important for human beings because it serves as a precursor to earthquakes. Several studies have been conducted across the globe to confirm the correlation of radon emission dynam

Externí odkaz: https://doaj.org/article/ef8b2d12cff0409e83f3b399f6036634

Zobrazit plný text záznamu

Akademický článek

Analysis of Proposed and Traditional Boosting Algorithm with Standalone Classification Methods for Classifying Gene Expresssion Microarray Data Using a Reject Option

Autor: Adil Aslam Mir, Lal Hussain, Muhammad Hammad Waseem, Amjad Aldweesh, Saim Rasheed, El Sayed Yousef, Malik Sajjad Ahmed Nadeem, Elsayed Tag Eldin

Publikováno v: Applied Artificial Intelligence, Vol 36, Iss 1 (2022)

In medical field, accurate decisions are very important as they risk human lives. decision support system (DSS) plays important role in making accurate decisions and used for classification/prediction. In gene expression analysis, genes are not only

Externí odkaz: https://doaj.org/article/36ef1196f98d4a878d8766e04abedbd0

Zobrazit plný text záznamu

Akademický článek

K-Means and K-Medoids: Cluster Analysis on Birth Data Collected in City Muzaffarabad, Kashmir

Autor: Syed Ali Abbas, Adil Aslam, Aqeel Ur Rehman, Wajid Arshad Abbasi, Saeed Arif, Syed Zaki Hassan Kazmi

Publikováno v: IEEE Access, Vol 8, Pp 151847-151855 (2020)

In the field of medical, each and every analysis is decisive as the study links to life of the subject under observation. One of the most vital area in the field of medical is the healthcare of expecting women in low income countries. High mortality

Externí odkaz: https://doaj.org/article/ecca43c151c345fe9a17b543dc3f9fa9

Zobrazit plný text záznamu

Akademický článek

Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography

Autor: Shahzad Ahmad Qureshi, Aziz Ul Rehman, Adil Aslam Mir, Muhammad Rafique, Wazir Muhammad

Publikováno v: Frontiers in Physiology, Vol 12 (2022)

The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime im

Externí odkaz: https://doaj.org/article/633cd19ab31c4c1ba6878e0b7d4c5270

Zobrazit plný text záznamu

Akademický článek

Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data.

Autor: Adil Aslam Mir, Kimberlee Jane Kearfott, Fatih Vehbi Çelebi, Muhammad Rafique

Publikováno v: PLoS ONE, Vol 17, Iss 1, p e0262131 (2022)

A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing

Externí odkaz: https://doaj.org/article/feb4b0bbeedf4d3a9d625b68ed58dda4

Zobrazit plný text záznamu

Plný text ve formátu HTML

Akademický článek

A Novel Hybrid Learning System Using Modified Breaking Ties Algorithm and Multinomial Logistic Regression for Classification and Segmentation of Hyperspectral Images

Autor: Syed Taimoor Hussain Shah, Shahzad Ahmad Qureshi, Aziz ul Rehman, Syed Adil Hussain Shah, Arslan Amjad, Adil Aslam Mir, Amal Alqahtani, David A. Bradley, Mayeen Uddin Khandaker, Mohammad Rashed Iqbal Faruque, Muhammad Rafique

Publikováno v: Applied Sciences, Vol 11, Iss 16, p 7614 (2021)

A new methodology, the hybrid learning system (HLS), based upon semi-supervised learning is proposed. HLS categorizes hyperspectral images into segmented regions with discriminative features using reduced training size. The technique utilizes the mod

Externí odkaz: https://doaj.org/article/97718759435f403db084ea12169641c1

Zobrazit plný text záznamu

Akademický článek

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Vyhledávací nástroje:

Upřesnit hledání