55 research outputs found
Hydraulic simulations to evaluate and predict design and operation of the Chashma Right Bank Canal
No full textIrrigation systems / Irrigation canals / Flow control / Velocity / Canal regulation techniques / Hydraulics / Simulation models / Design / Operations / Crop-based irrigation / Distributary canals / Water delivery / Policy / Protective irrigation / Water allocation / Water requirements / Sedimentation / Water distribution / Equity / Water conveyance / Pakistan / Chashma Right Bank Canal
The Reflection of Islamic Culture and Beliefs in the Stories of Dr. Akram Osman
No full textThe sacred religion of Islam encompasses its own distinct principles, laws, and worldview, as presented through the Holy Quran, a divine guide for humanity. This guidance shapes the beliefs, actions, and morals of Muslims according to an Islamic perspective. Islam is a comprehensive and complete religion that addresses all aspects of individual, familial, social, economic, political, and cultural life. It provides everything necessary for human guidance and prosperity, as conveyed to humanity by Prophet Muhammad (PBUH), the savior of mankind, who is the final prophet, and Islam is the ultimate and final religion. In Islamic societies, the responsibility of promoting and conveying Islamic teachings was not limited to religious scholars alone. Muslim poets and writers also undertake this significant mission, using their literary talents in both poetry and prose to inspire individuals and societies with the values and teachings of this sacred religion. The late Dr. Mohammad Akram Osman, a distinguished author from Afghanistan, made remarkable contributions to storytelling that not only enriched the world of literature but also served as a guide for humanity in learning and understanding Islamic culture and beliefs. This article aims to explore the reflection of Islamic culture and beliefs in the stories of Dr. Mohammad Akram Osman, highlighting his ability to intertwine Islamic teachings with literary expression
بیسویں صدی کا پنجابی زبان و ادب اور ڈاکٹر فقیر محمد فقیر
No full textDr. Faqir Muhammad Faqir is a 20th century renowned poet, author, researcher, critic, historian and above all, a lover of Punjabi language. He rendered valuable services to Punjabi language by compiling classical Punjabi literature along producing new prose and verse. He was given the title of "Father of Punjabi Language" and "The Omer Khayyam of the Punjab" by the literary circles. This article covers his literary contributions to Punjabi language and literature besides determining his status in 20th century Punjabi literati.
A reappraisal of attitudes to the 'People of the Book' in the Qur'an and hadith, with particular reference to Muslim fiscal policy and the covenant of 'Umar
No full textEThOS - Electronic Theses Online ServiceGBUnited Kingdo
Author Correction: Mortality outcomes with hydroxychloroquine and chloroquine in COVID-19 from an international collaborative meta-analysis of randomized trials.
No full textThe original version of this Article contained an error in the spelling of the author Muhammad Shahzad, which was incorrectly given as Muhammad Shehzad. This has now been corrected in both the PDF and HTML versions of the Article
Collected Papers (on Neutrosophic Theory and Its Applications in Algebra), Volume IX
No full textThis ninth volume of Collected Papers includes 87 papers comprising 982 pages on Neutrosophic Theory and its applications in Algebra, written between 2014-2022 by the author alone or in collaboration with the following 81 co-authors (alphabetically ordered) from 19 countries: E.O. Adeleke, A.A.A. Agboola, Ahmed B. Al-Nafee, Ahmed Mostafa Khalil, Akbar Rezaei, S.A. Akinleye, Ali Hassan, Mumtaz Ali, Rajab Ali Borzooei , Assia Bakali, Cenap Özel, Victor Christianto, Chunxin Bo, Rakhal Das, Bijan Davvaz, R. Dhavaseelan, B. Elavarasan, Fahad Alsharari, T. Gharibah, Hina Gulzar, Hashem Bordbar, Le Hoang Son, Emmanuel Ilojide, Tèmítópé Gbóláhàn Jaíyéolá, M. Karthika, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Huma Khan, Madad Khan, Mohsin Khan, Hee Sik Kim, Seon Jeong Kim, Valeri Kromov, R. M. Latif, Madeleine Al-Tahan, Mehmat Ali Ozturk, Minghao Hu, S. Mirvakili, Mohammad Abobala, Mohammad Hamidi, Mohammed Abdel-Sattar, Mohammed A. Al Shumrani, Mohamed Talea, Muhammad Akram, Muhammad Aslam, Muhammad Aslam Malik, Muhammad Gulistan, Muhammad Shabir, G. Muhiuddin, Memudu Olaposi Olatinwo, Osman Anis, Choonkil Park, M. Parimala, Ping Li, K. Porselvi, D. Preethi, S. Rajareega, N. Rajesh, Udhayakumar Ramalingam, Riad K. Al-Hamido, Yaser Saber, Arsham Borumand Saeid, Saeid Jafari, Said Broumi, A.A. Salama, Ganeshsree Selvachandran, Songtao Shao, Seok-Zun Song, Tahsin Oner, M. Mohseni Takallo, Binod Chandra Tripathy, Tugce Katican, J. Vimala, Xiaohong Zhang, Xiaoyan Mao, Xiaoying Wu, Xingliang Liang, Xin Zhou, Yingcang Ma, Young Bae Jun, Juanjuan Zhang
Machine learning-based electricity theft detection using support vector machines
No full textElectricity theft is a serious issue that many nations face, especially in developing areas where non-technical losses can make up a significant percentage of the overall losses sustained by utilities. Electricity theft detection (ETD) is a very challenging task because it frequently introduces irregularities in customer electricity consumption patterns. In recent times, machine learning (ML) techniques have been investigated as a potential solution for ETD. In this research, author propose electricity theft detection based on four kernel functions of support vector machines (SVM). The proposed method analyzes the electricity consumption patterns and then predicts the category of the user. The kernel functions utilized includes polynomial, sigmoid, radial basis function (RBF) and linear kernel function. For experimentation and model training, a dataset of Pakistani utility company is used, which contains the electricity consumption information. The results highlight SVM method works well for accurate ETD. The detection accuracy of the various kernel functions of SVM is 83%, 79%, 80%, and 76% for RBF, polynomial, sigmoid, and linear kernel functions, respectively, demonstrating the effectiveness of the proposed SVM-based method for theft detection. By leveraging these ML-based methods, utility companies can strengthen their ability to detect and prevent electricity theft, leading to improved revenue management and dependability of services
Research Community Mining via Generalized Topic Modeling
No full textMining research community on the basis of hidden relationships present between its entities is important
from academic recommendation point of view. Previous approaches discovered research community by
using network connectivity based distance measures (no text semantics) or by using poorer text semantics
and relationships of documents DL (Document Level) by ignoring richer text semantics and relationships
of VL (Venue Level). In this paper, we address this problem by considering richer text semantics and
relationships. We propose a VAT (Venue Author Topic Approach) based on Author-Topic model to discover
inherent community structures in a more realistic way by modeling from VL. We show how topics and
authors can be inferred for new venues and how author-to-author and venue-to-venue correlations can be
discovered. The positive relationship of topic denseness with ranking performance of proposed approach
is explained. Experimental results on research collaborative network \"DBLP\" demonstrate that proposed
approach significantly outperformed the baseline approach in discovering community structures and
relationships in large-scale network
Collected Papers (on various scientific topics), Volume XII
No full textThis twelfth volume of Collected Papers includes 86 papers comprising 976 pages on Neutrosophics Theory and Applications, published between 2013-2021 in the international journal and book series “Neutrosophic Sets and Systems” by the author alone or in collaboration with the following 112 co-authors (alphabetically ordered) from 21 countries: Abdel Nasser H. Zaied, Muhammad Akram, Bobin Albert, S. A. Alblowi, S. Anitha, Guennoun Asmae, Assia Bakali, Ayman M. Manie, Abdul Sami Awan, Azeddine Elhassouny, Erick González-Caballero, D. Dafik, Mithun Datta, Arindam Dey, Mamouni Dhar, Christopher Dyer, Nur Ain Ebas, Mohamed Eisa, Ahmed K. Essa, Faruk Karaaslan, João Alcione Sganderla Figueiredo, Jorge Fernando Goyes García, N. Ramila Gandhi, Sudipta Gayen, Gustavo Alvarez Gómez, Sharon Dinarza Álvarez Gómez, Haitham A. El-Ghareeb, Hamiden Abd El-Wahed Khalifa, Masooma Raza Hashmi, Ibrahim M. Hezam, German Acurio Hidalgo, Le Hoang Son, R. Jahir Hussain, S. Satham Hussain, Ali Hussein Mahmood Al-Obaidi, Hays Hatem Imran, Nabeela Ishfaq, Saeid Jafari, R. Jansi, V. Jeyanthi, M. Jeyaraman, Sripati Jha, Jun Ye, W.B. Vasantha Kandasamy, Abdullah Kargın, J. Kavikumar, Kawther Fawzi Hamza Alhasan, Huda E. Khalid, Neha Andalleb Khalid, Mohsin Khalid, Madad Khan, D. Koley, Valeri Kroumov, Manoranjan Kumar Singh, Pavan Kumar, Prem Kumar Singh, Ranjan Kumar, Malayalan Lathamaheswari, A.N. Mangayarkkarasi, Carlos Rosero Martínez, Marvelio Alfaro Matos, Mai Mohamed, Nivetha Martin, Mohamed Abdel-Basset, Mohamed Talea, K. Mohana, Muhammad Irfan Ahamad, Rana Muhammad Zulqarnain, Muhammad Riaz, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Muhammad Zeeshan, Anjan Mukherjee, Mumtaz Ali, Deivanayagampillai Nagarajan, Iqra Nawaz, Munazza Naz, Roan Thi Ngan, Necati Olgun, Rodolfo González Ortega, P. Pandiammal, I. Pradeepa, R. Princy, Marcos David Oviedo Rodríguez, Jesús Estupiñán Ricardo, A. Rohini, Sabu Sebastian, Abhijit Saha, Mehmet Șahin, Said Broumi, Saima Anis, A.A. Salama, Ganeshsree Selvachandran, Seyed Ahmad Edalatpanah, Sajana Shaik, Soufiane Idbrahim, S. Sowndrarajan, Mohamed Talea, Ruipu Tan, Chalapathi Tekuri, Selçuk Topal, S. P. Tiwari, Vakkas Uluçay, Maikel Leyva Vázquez, Chinnadurai Veerappan, M. Venkatachalam, Luige Vlădăreanu, Ştefan Vlăduţescu, Young Bae Jun, Wadei F. Al-Omeri, Xiao Long Xin.
Hybrid ANFIS-PI-Based Robust Control of Wind Turbine Power Generation System
No full textThis paper introduces a novel hybrid controller designed for a wind turbine power generation system (WTPGS) that utilizes a permanent magnet synchronous generator (PMSG). This hybrid controller combines the adaptability of an adaptive neuro-fuzzy inference system (ANFIS) with the simplicity of a proportional-integral (PI) controller. The PI controllers are traditionally used for stability and noise handling. ANFIS adds adaptability, making it more suitable to cope with the variable nature of wind energy. The primary objective of this hybrid strategy is to augment the overall control performance and reliability of PMSG-based WTPGS when encountered with continuous variable wind conditions. However, implementing the PI controller alone with the WTPGS often suffers from high overshoot and sluggish response in nonlinear systems like WTPGS. In contrast, ANFIS controllers offer superior performance to PI and other artificial intelligence controllers but are still susceptible to noise issues. In this paper, the proposed WTPGS system is designed in MATLAB/Simulink software where a hybrid controller (ANFIS-PI) is implemented in the machine-side converter (MSC) and grid-side converter (GSC) of a variable speed PMSG-based wind turbine to enhance its performance subjected to wind variations. The hybrid controller is implemented in such a way that the ANFIS controller is implemented in the outer layers while the PI controller is applied in the inner layers of both MSC and GSC. The simulation results for this hybrid controller in the MSC outperform those of the conventional PI controller. They demonstrate minimal overshooting and settling time, maintaining consistent stability even when subjected to various test signals at different intervals. Similarly, the GSC also surpasses conventional PI controllers, achieving a significant 6.4% reduction in maximum overshoot and a decrease of 4.36 seconds in settling time. This highlights its strong suitability for wind turbine applications
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