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Muhammad Aslam

Muhammad AslamMuhammad Aslam is a full professor of statistics in the Department of Statistics, King Abdulaziz University, Jeddah, in Saudi Arabia. He has published over 500 research papers in national and international well-reputed journals such as IEEE Access, Jurnal of Applied Statistics, European Journal of Operation Research, Information Sciences, International Journal of Fuzzy Systems, International Journal of Advanced Manufacturer Technology, and has authored three books published by VDM, Germany, Springer, and Wiley. Professor Aslam is recipient of the Meritorious Service Award in research from NCBAE, as well as the Research Productivity Award from the Pakistan Council for Science and Technology and King Abdulaziz University Excellence Awards in scientific research. He introduced the concept of Neutrosophic Statistical Quality Control (NSQC). He is the founder of Neutrosophic Inferential Statistics (NIS) and NSQC. His areas of interest include industrial statistics, neutrosophic inferential statistics, neutrosophic statistics, neutrosophic quality control, neutrosophic applied statistics, and classical applied statistics. Read More Read Less

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Drought Stress in Maize (Zea mays L.)
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$70
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28 Nov 2015
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Wireless Sensor Networks for Developing Countries
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Strategischer Managementprozess und nachhaltiges Wachstum pakistanischer Unternehmen
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Processus de gestion stratégique et croissance durable des entreprises pakistanaises
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Proces zarządzania strategicznego i zrównoważony rozwój pakistańskich przedsiębiorstw
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Processo di gestione strategica e crescita sostenibile delle aziende pakistane
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Processo de gestão estratégica e crescimento sustentável das empresas paquistanesas
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Deep Learning for Multimedia Processing Applications
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Deep Learning for Multimedia Processing Applications
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Divination Revealed
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26 Aug 2024
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Sustainable Pharmaceutical Product Development and Optimization Processes
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Crop Biofortification
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24 Apr 2025
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Deep Learning for Earth Observation and Climate Monitoring
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Practicing R for Statistical Computing
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Deep Learning for Multimedia Processing Applications
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Deep Learning for Multimedia Processing Applications
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