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0.408
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All Journal International Journal of Electrical and Computer Engineering International Journal of Robotics and Control Systems
Maghrabie, Hussein M.
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Hybrid metaheuristic algorithms: a recent comprehensive review with bibliometric analysis Nassef, Ahmed M.; Abdelkareem, Mohammad Ali; Maghrabie, Hussein M.; Baroutaji, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7022-7035

Abstract

Metaheuristic algorithms are widely used in various applications. Collaborating two or more algorithms in a hybrid form has shown great improvements in terms of the algorithm's performance. This paper highlights the recently published work during the last decade from a quantitative perspective. The biometric measures include the number of publications, citations, average citations per publication, h-index, and field-weighted citation impact (FWCI) based on the data extracted from the Scopus database. Statistical measures, co-occurrence and co-authorship maps, and illustrative graphs have been implemented using software tools. According to the collected data, about 3469 articles have been published during the last decade with an increasing rate of 44.1 papers per year. Most of these articles have been published as journal articles with a percentage of 68.3%, followed by conference articles occupied 29.5%. China, India and Iran contributed the largest number of articles at 1076, 965, and 239, respectively. Parouha, Verma, and Kamel, are the top-ranked authors with 14, 10, and 9 publications, respectively. The most areas of interest are computer science, engineering and mathematics with publication percentages of 27.69%, 25.55% and 13.91%, respectively. The data presented in this paper gives the researchers a clear image of this hot topic to start new research.
Automated Water Cooling and Solar Tracking for Efficiency Improvement of PV Systems: A Systematic Review Hamed, Ahmed Hassan; Sharkawy, Abdel-Nasser; Hamdan, I.; Maghrabie, Hussein M.
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1642

Abstract

This article presented previous efforts for overcoming low photovoltaic (PV) solar panel electrical efficiencies resulted from excess heat problem reached in hot climates. Utilizing water cooling, temperature-controlled water cooling and solar tracking solar systems are discussed in this paper. Water is a perfect medium can be used for absorbing excess heat due to its high thermal capacity, availability and low cost. In addition to, utilizing control systems for water cooling systems based on Arduino unit and microcontroller chip which can be interfaced with Bluetooth, WIFI, and Internet of Things (IOT) enhances saving time and effort in large PV solar plants and PV performance. Solar tracking systems, depend on light-dependent resistors (LDRs) which are resistors operated by incident light, or ultraviolet (UV) sensors which are detectors based on incident ultraviolet radiation sensing enhances PV performance. Solar tracking systems enhances PV electrical efficiency compared to fixed PV panels. PV efficiencies of latest studies were presented and compared. Utilizing water cooling systems enhances PV electrical efficiency up to 30%, using an ON-OFF temperature-controlled water-cooling systems increased overall efficiency up to 51.4% and can reduce consumption of water up to 29.28%. In addition to, using two solar tracking systems enhances PV solar panel efficiency up to 65%. The increase in PV installation faces challenges includes millions of solar waste tons that harms environment and human health. However, it can be eliminated utilizing recycling technologies. Artificial intelligence (AI), machine learning techniques would enhance PV performance analyzing and data collection.
Co-Authors Abdelkareem, Mohammad Ali Baroutaji, Ahmad Hamdan, I. Hamed, Ahmed Hassan Nassef, Ahmed M. Sharkawy, Abdel-Nasser