A Review On Agile Decision Making In Crisis Management

Agility in decision making has a potential to resolve crisis management; therefore a specific agile decision making technique should be implemented in crisis management. Crisis management requires the agility in decision making in order to resolve the crisis. The decision made has to be flexible enough so that the solution can be delivered on time. In having the decision, there are people that will contribute some suggestion, opinion, experience or knowledge. The virtual knowledge sharing is the vital part on delivering the agile decision making. This paper reviews such methods on agile decision making towards crisis management and the relation with virtual knowledge sharing

Analysis of two adjacent articulation Quranic letters based on MFCC and DTW

—Reciting al-Quran in the correct way is an obligatory duty for Muslims, and therefore learning al-Quran is a continuous education until the correct recitation is achieved. It is important to learn Tajweed rules to master the recitation of Quranic verses. Moreover, mastering the pronunciation of Arabic sounds is the first and key step to achieve accurate recitation of al-Quran. The rules were guided by the Islamic Scholars in fields related to al-Quran from their knowledge and experiences. Very limited researches were found in the perspective of sciences and engineering. In this paper two Quranic letters (ذ and ز) that are articulated from adjacent points of articulation were analyzed using Mel- frequency coefficient analysis. MFCCs matrices were calculated then compared using the dynamic time warping DTW technique to calculate the similarity matrices and find the similarity distance. Results show that letters from the same point of articulation have less similarity distance compared to the letters from different point of articulation

Features identification and classification of alphabet (ro) in leaning (Al-Inhiraf) and repetition (Al-Takrir) characteristics

—It is important for Muslim to recite the Quran properly with the correct Tajweed. which includes the use of correct characteristics (sifaat) and point of articulations (makhraj). To this date, there are limited researches done focusing on classifying the Quranic letters according to the characteristics. In this study, the focus is given to the classification of the characteristics of the Quranic letters for the purpose of developing an automated self-learning system for supporting the conventional method of Quranic teaching and learning. The characteristics of Quranic letters, which are the focus in this paper are Leaning and Repeating, where both consists of ر) ro) alphabet. Several methods of feature extractions and analysis were implemented such as Formant Analysis, Power Spectral Density (PSD), and Mel Frequency Cepstral Coefficient (MFCC) to come out with the suitable features that best represent the correct characteristics of the alphabet. Once the features had been identified, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) were used as the classifier. The results show that QDA with all 19 features trained achieved the highest percentage accuracy for both Leaning (اإلنحراف – Al-Inhiraf) and ّكرير) Repetition الت– Al-Takrir) characteristics with of 82.1% and 95.8% of accuracy respectivel

Analysis of Formant Frequencies of the Correct Pronunciation of Quranic Alphabets Between Kids and Adults

It is an obligation for a Muslim to become skilled and proficient in reciting Al-Quran considering that Al-Quran is the fundamental source of revelation from Allah SWT. In Al-Quran, there are 28 alphabets where each of them has their own unique sound. The Quranic alphabets produce sound that are characterized from their point of articulation (Makhraj) and their characteristics (Sifaat). Knowing the correct way of pronunciation through engineering perspective may help Muslim in learning Al-Quran, in the sense that the signal of the experts can be used in Quranic teaching and learning as a reference model. Since both adults and children possess different vocal tract, therefore there will be different outcomes of the pronunciation between both experts. The features identification of the pronunciation of both experts is needed to represent the actual and correct pronunciation that will be used as a reference for Quranic teaching and learning at later. In this paper, the focus was on the identification and analysis of the correct pronunciation of the Quranic alphabets on the data obtained from adults and children experts. The first and second formant frequencies (F1 and F2) were used as the features where they were used to represent the pronunciation of each alphabet for both adults and children category. The speech analysis software PRAAT was used to accomplish the pre-processing of the data using Spectral Subtraction technique and also used to measure the F1 and F2 values. Linear Discriminant Analysis (LDA) was used for classification of the signals and results shows that some of the alphabets can be identified uniquely using F1 and F2 features of the two categories

Classification of the correct Quranic letters pronunciation of male and female reciters

Recitation of the Holy Quran with the correct Tajweed is essential for every Muslim. Islam has encouraged Quranic education since early age as the recitation of the Quran correctly will represent the correct meaning of the words of Allah. It is important to recite the Quranic verses according to its characteristics (sifaat) and from its point of articulations (makhraj). This paper presents the identification and classification analysis of Quranic letters pronunciation for both male and female reciters, to obtain the unique representation of each letter by male as compared to female expert reciters. Linear Discriminant Analysis (LDA) was used as the classifier to classify the data with Formants and Power Spectral Density (PSD) as the acoustic features. The result shows that linear classifier of PSD with band 1 and band 2 power spectral combinations gives a high percentage of classification accuracy for most of the Quranic letters. It is also shown that the pronunciation by male reciters gives better result in the classification of the Quranic letters