Farah Shahid

1.7k citations

12 papers · 1.2k indexed · 2 hit papers · h-index 8

Modeling and Simulation top 2%
- COVID-19 epidemiological studies 1
Energy Engineering and Power Technology top 5%
Artificial Intelligence top 5%
- Solar Radiation and Photovoltaics 5
- Anomaly Detection Techniques and Applications 2
Electrical and Electronic Engineering top 10%
- Energy Load and Power Forecasting 8
- Electric Power System Optimization 5
- Integrated Energy Systems Optimization 1
Management Science and Operations Research top 5%
Radiology, Nuclear Medicine and Imaging
- COVID-19 diagnosis using AI 4
Renewable Energy, Sustainability and the Environment
- Photovoltaic System Optimization Techniques 1

Co-authors: Aneela Zameer Muhammad Muneeb Muhammad Asif Zahoor Raja Ammara Mehmood Steffen Eger Kamran Safdar Junaid Arshad Asifullah Khan
Cited by: Modeling and Simulation Energy Engineering and Power Technology Artificial Intelligence
Partner nations: Pakistan United Arab Emirates China

In The Last Decade

Farah Shahid

11 papers receiving 1.2k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

2021 A novel genetic LSTM model for wind power forecast
2020 Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

Peers

Countries citing papers authored by Farah Shahid

Since Specialization

Citations

This map shows the geographic impact of Farah Shahid's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Farah Shahid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Farah Shahid more than expected).

Fields of papers citing papers by Farah Shahid

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Farah Shahid. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Farah Shahid. The network helps show where Farah Shahid may publish in the future.

Co-authorship network

The 15 scholars most cited alongside Farah Shahid, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Farah Shahid Line = papers co-authored together Farah Shahid links everyone, so they are left out of the graph.

All Works

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12 of 12 papers shown

#	Work
1	Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU Aneela Zameer,Fatima Jaffar,Farah Shahid,Muhammad Muneeb,Rizwan Khan,Rubina Nasir	2023	25
2	Smart Health Care Management System for Diagnosis of Lungs Cancer Ahed Abugabah,Farah Shahid,Amjad Alafeef,Rizwan Khan	2023	0
3	Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19 Ahed Abugabah,Farah Shahid	2023	1
4	1D Convolutional LSTM-based wind power prediction integrated with PkNN data imputation technique Farah Shahid,Atif Mehmood,Rizwan Khan,Ahmad Al Smadi,Muhammad Yaqub,Mutasem K. Alsmadi,Zhonglong Zheng	2023	15
5	Information Systems in Medical Settings: A Covid-19 Detection System Using X-Ray Scans Ahmad Al Smadi,Ahed Abugabah,Shadrack Fred Mahenge,Farah Shahid	2022	2
6	Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning Farah Shahid,Wood David A.,Nisar Humaira,Aneela Zameer,Steffen Eger	2022	101
7	A comparative study of machine learning models for malware detection Earum Mushtaq,Farah Shahid,Aneela Zameer	2022	2
8	A novel genetic LSTM model for wind power forecastbreakdown → Farah Shahid,Aneela Zameer,Muhammad Muneeb	2021	368
9	Intelligent forecast engine for short-term wind speed prediction based on stacked long short-term memory Farah Shahid,Aneela Zameer,Muhammad Javaid Iqbal	2021	16
10	Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTMbreakdown → Farah Shahid,Aneela Zameer,Muhammad Muneeb	2020	484
11	A novel wavenets long short term memory paradigm for wind power prediction Farah Shahid,Aneela Zameer,Ammara Mehmood,Muhammad Asif Zahoor Raja	2020	156
12	Wind power prediction using a three stage genetic ensemble and auxiliary predictor Farah Shahid,Asifullah Khan,Aneela Zameer,Junaid Arshad,Kamran Safdar	2020	44

About Farah Shahid

Farah Shahid is a scholar working on Health Informatics, Energy Engineering and Power Technology and Artificial Intelligence, having authored 12 papers that have together received 1.2k indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (8 papers), Solar Radiation and Photovoltaics (5 papers), Electric Power System Optimization (5 papers), COVID-19 diagnosis using AI (4 papers), Anomaly Detection Techniques and Applications (2 papers), COVID-19 epidemiological studies (1 paper), Integrated Energy Systems Optimization (1 paper) and Photovoltaic System Optimization Techniques (1 paper). The work is most often cited by research in Modeling and Simulation (149 citations), Energy Engineering and Power Technology (65 citations) and Artificial Intelligence (470 citations). Farah Shahid has collaborated with scholars based in Pakistan, United Arab Emirates and China. Frequent co-authors include Aneela Zameer, Muhammad Muneeb, Muhammad Asif Zahoor Raja, Ammara Mehmood, Steffen Eger, Kamran Safdar, Junaid Arshad, Asifullah Khan, Rizwan Khan and Ahmad Al Smadi.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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