Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Arslan Amjad"'
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
Autor:
Iqra Yousaf, Bushra Tabassum, Basit Jabbar, Muhammad Arslan Amjad, Uzma Qaisar, Anwar Khan, Rida Khalid, Olawale Samuel Adeyinka, Idrees Ahmad Nasir
Publikováno v:
Journal of Plant Protection Research, Vol vol. 64, Iss No 3, Pp 242-252 (2024)
The cotton aphid Aphis gossypii is a major agricultural pest of cotton that causes substantial damage to the crop not only by sucking sap but also through virus transmission. Globally adopted traditional and contemporary approaches to control aphid i
Externí odkaz:
https://doaj.org/article/916f7cebf4434b069a8f485d1adad500
Autor:
Muhammad Arslan Amjad, Jamshaid Akbar, Rida Asghar, Nageen Zeeshan, Abu Bakar Munir, Ahmed Umer Sohaib, Sharjeel Ahmad
Publikováno v:
RADS Journal of Pharmacy and Pharmaceutical Sciences. 7:206-214
Objective: To determine the current knowledge and practices regarding voluntary blood donation among pharmacy (Pharm-D) students of Lahore, Pakistan. Methods: A cross-sectional quantitative survey was performed among Pharm-D students of two private a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd04a2c7b7c6bf013bbb5180af1bdd6e
https://doi.org/10.37962/jpps.v7i4.337
https://doi.org/10.37962/jpps.v7i4.337
Autor:
Muhammad Arslan Amjad, Jamshaid Akbar, Hafiz Obaid, Muhammad Qamar-uz Zaman, Abu Bakar Munir, Ahmed Umer Sohaib, Sharjeel Ahmad
Publikováno v:
RADS Journal of Pharmacy and Pharmaceutical Sciences. 7:181-185
Objective: To evaluate the prescription writing pattern and errors in prescriptions at different health care units of Central Punjab, Pakistan. Methods: A descriptive cross-sectional study design was adopted to conduct the present study. Data collect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::18f218654414aa9869f98b1ffffd178e
https://doi.org/10.37962/jpps.v7i4.327
https://doi.org/10.37962/jpps.v7i4.327
Publikováno v:
ISC2
The COVID-19 pandemic has signified the interconnected nature of our world demonstrating that no one is safe until everyone is safe. The social and economic turmoil caused by the pandemic is devastating and revealing a dramatic loss of human life wor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5125236856101b9ac72f7e294d33779
https://doi.org/10.1109/isc253183.2021.9562904
https://doi.org/10.1109/isc253183.2021.9562904
Publikováno v:
Vibrational Spectroscopy. 99:124-129
The development of a classification system based on the Raman spectra of milk samples is proposed in present study. Such development could be useful for nutritionists in suggesting healthy food to infants for their proper growth. Previously, molecula
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7593b0a9e41310593fdb77087fa5711
https://doi.org/10.1016/j.vibspec.2018.09.003
https://doi.org/10.1016/j.vibspec.2018.09.003
Autor:
Mayeen Uddin Khandaker, Arslan Amjad, Syed Adil Hussain Shah, Syed Taimoor Hussain Shah, Amal Alqahtani, Aziz ul Rehman, Shahzad Ahmad Qureshi, Adil Aslam Mir, Muhammad Rafique, D.A. Bradley, Mohammad Rashed Iqbal Faruque
Publikováno v:
Applied Sciences
Volume 11
Issue 16
Applied Sciences, Vol 11, Iss 7614, p 7614 (2021)
Volume 11
Issue 16
Applied Sciences, Vol 11, Iss 7614, 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://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e5120d2b2407fe5301cd88fed0a000d
https://doi.org/10.3390/app11167614
https://doi.org/10.3390/app11167614