Qari abdulbasit abdulsamad

Qari Waheed Zafar is most popular and well-known Sana-Khawan e Mustufa (P.B.U.H). Qari Waheed Zafar has won many national and international awards. He has released many albums. His Famous Albums are Saba E Madina, Panjh Surah, Saba-e-Madina, Bahar e madina and Madinay Ke Gali. Below are selected compilation of his famous Naats. Biography Qari Waheed Zafar Qasmi was born in 1962 in Pakistan. He has started reciting Quran-e-Majeed at the age of 7. He became Hafiz-e-Quran when he was just of 9 years old. He had learnt Quran-Majeed by heart. He has also learned Qirat and starting Naat Sharif as well. National and International Awards Qari Waheed Qasmi has won many national and international Awards. By the Grace of Allah Qari Sahab has won first competition in 1969 in Malaysia for Pakistan and thereafter in Libya in 1979. in 1980, he won a competition of Tilawat-e-Quran in Saudi Arabia. Again he won a competition on Syria. The honor for Durood-o-Salam inside the Roza-e-Rasool (PBUH) in 1987 is the most cherished event of his life Career Qari Sahab started his career in 1970 from Radio programs. His first program was "Sar Chashma-e-Hidayat". After that program he did many program in televisionas well as on radio. His first Naat which got very fame all over the world was "Allah Hu Allah Hu" on television. Later on he has recited many popular naats. Some of his famous naats are Qaseeda Burda, Faslon ko Takaluf, Zahe Muqadar and so on. We have unique and

A Qari’ means in Arabic language ” the one who recites Qur’an with proper Tajweed rules and nice voice”. Its plural in Arabic is (“qurraa” قراء). To be a Qari’, you do not need to memorize the Quran but, it is preferred. The quadrumvirate of Abdul-Basit, El Minshawy, Mustafa Ismail, and Al-Hussary are considered according to most of Islamic world the most famous reciters of modern times as they had a great influence on the Islamic world. Here is a list of the pioneering reciters of the Quran. Afghanistan Muhammad ibn Tayfour Sajawandi (died at 560 Ah). He is known for his contributions to the Islamic science of Recitation and Tajweed, creating a set of rules and markers used to indicate the pronunciation and pauses of Quranic recital, known as Sajawandi stop signs or Rumuz al-Awqaf as-Sajāwandīy Egypt In Egypt, the senior reader of Quran is known as Shaykh al-Maqari which means the head of reciting schools. -you can listen to a sample of recitation of every Qari’ by clicking on their names. Muhammad Rifat (1882-1950).Mohamed Salamah ( 1899-1982).Abdul Basit Abus-Samad (1927-1988).Mustafa Ismail (1905-1978).Mahmoud Khalil Al-Hussary (1917-1980). Mohamed Siddiq El-Minshawi (1920-1969). Indonesia *click on every Qari’ name to listen to his recitation Maria UlfahMu’min Ainul MubarakMuammar Z.A. Saudi reciters *click on evey Qari name to listen to his recitation Abdurrahman As-sodays, the head Imam of Al-masjid Al-haramSaad Al-GhamidiSaud Ash-shuriamAli GaberFares Abbadyaser Al-doosriAli Al-HuzaifiMashari Affasi , kuwait reciter Pakistan Waheed Zafar QasmiZahir Qasmi Qari Muhammad FarooqHassan Ali Kasi This is all the names of Qurraa we managed to collect in this list. undoubtedly, there are a lot of other great reciters. If you would like to recite Quran beautifully and smoothly with proper Tajweed rules like these reciters, join our Tajweed course through this link

Shows similar performance in the form of accuracy values. Figure 2 shows the accuracy of the models. We select the accuracy parameter because the data is almost balanced with reference to classes. In Figure 2 and Table 1, it can be noted that Random Forest and the Naïve Bayes have the highest accuracy. We represent the accuracy as the percentage. This means that the Random forest accuracy of 88% is linked to the recognition of the Qari. As such, Random Forest can recognize the Qari with 88% accuracy. The total error the Random forest will make in identifying the Qari will be only 12%.Similarly, the accuracy of Naïve Bayes is also 88%. This is coinciding with the Random Forest. Therefore, the Naïve Bayes classifiers learn the Qari with a good recognition model like that of the Random Forest. In Figure 2, and Table 1, the lower performance is exhibited by the J48. The performance of the J48 is 78%. This means that the J48 classifier model has a 22% chance of not recognizing the Qari correctly. This is higher compared to the Random forest and the Naïve Bayes, which is only 12%. This low performance could be due to the sensitivity of the decision trees (J48) to the noise in the data.3.2. Performance analysis based on image featuresFor the conversion of the audio recitation to the image representation, the Fourier transform approach is used. After Fourier transformation, the frequency response coefficients are combined as a time-domain structure. This time-domain structure

Is represented as an image signal. The image-based feature extraction of the Auto-Correlogram approach [17] for feature extraction is used. These features have shown very good performance in state-of-the-art [17].Figure 3. Accuracy of the spectrogram-based recognition modelsTable 2: Performance analysis of the Spectrogram featuresClassifierAccuracyNaïve Bayes81%J4878%Random Forest78%Figure 3 and Table 2 show the performance analysis of the image-based representation of the Quran recitation audio.In Figure 3 and Table 2, the Naïve Bayesian algorithm has the highest accuracy. The performance parameter is Accuracy, which is used for balanced classes. Similar to Figure 2, the accuracy is represented as the percentage. The Naïve Bayes classifier’s accuracy of 81% means that Naïve Bayes is capable of recognizing the Qari with an 81% accurate model. The chance of the error being made by the Naïve Bayes in identifying the Qari is 19%. This error can be explained as such that out of 100 recitations performed by different Qari, only 81 recitations are correctly identified and mapped to the corresponding Qari of the recitation. The accuracy of the Random Forest is 78%. This means that 22 samples out of 100 samples of recitations will be wrongly identified and mapped to a different Qari. Interestingly, the accuracy of J48 is also 78%. The general trend, however, is that Random Forest normally has a higher classification accuracy than J48 in state-of-the-art.The same detection performance for both the Random Forest and the J48 is an exciting result. Since both of the algorithms work on the same principle of decision