Recognition of isolated handwritten Arabic characters

The challenges that face the handwritten Arabic recognition are overwhelming such as different varieties of handwriting and few public databases available. Also, teaching the non-Arabic speaker at the young age is very difficult due to the unfamiliarity of the words and meanings. So, this project is focused on building a model of a deep learning architecture with convolutional neural network (CNN) and multilayer perceptron (MLP) neural network by using python programming language. This project analyzes the performance of a public database which is Arabic Handwritten Characters Dataset (AHCD). However, training this database with CNN model has achieved a test accuracy of 95.27% while training it with MLP model achieved 72.08%. Therefore, the CNN model is suitable to be used in the application device

Stress in parents of children with autism: A Malaysian experience

This study examines differences in parental stress between parents of Autism Spectrum Disorder (ASD) children (n=21) and Typically Developed (TD) children (n=41) in Malaysia. This study also compares the ages of parents of ASD children with parents of TD children with stress as a variable in these parents. Parents completed the Parental Stress Index (brief Malay version) and a socio-demographic questionnaire. Parents with ASD children were found to be significantly more stressed compared to parents of TD children (p<0.001). Significant scores were also found in the Parent-Child Dysfunctional Interaction (P-CDI) sub-scale (p<0.001) as well as Difficult Child (DC) and Parental Distress (PD) sub-scales with lower significance (p<0.05). Results also indicate that the 30-35-year-old age group among ASD parents was significantly found to be more stressed compared with parents of TD children of the same ages. Implications of the findings regarding support and intervention for families with ASD are also discussed

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

Improving emotional well-being through nature

Abstract— Nature may be used as a treatment method for patients with mental disorders which has yet to be implemented despite numerous evidence showing that being in touch with nature links to increased happiness, positive affect, positive social interactions, and a sense of meaning and purpose in life, as well as reduced mental distress. This project focuses on demonstrating the benefits of nature towards improving emotional well-being of people based on the detection of Electrodermal Activity (EDA). EDA signals will be collected from the controlled and experimental participants for the identification of nature’s positive impact towards participants’ mental well-being. Raw signals will require pre-processing and feature extraction which will use an open-source Python toolkit called PyEDA. The project will also incorporate machine learning for EDA signal classification which also utilizes python open-source software. The results will be observed and studied based on its accuracy in classifying the EDA signals between the controlled and experimental conditions. This project will be heading towards improving mental health treatment by proving that incorporating the world’s most abundant resources, nature together with the incorporation of biosensors for mental health detection will help close the gap between mental health patients and clinicians

Around view monitoring system with motion estimation in ADAS application

Around View Monitoring (AVM) system uses multiple cameras as the sensor that is mounted on several positions on the vehicle to produce a display of top view image from the surrounding environment of the vehicle that is not readily visible to the driver because of the limited field of view of the driver. The risk of parking accident could be reduced by developing the system that can monitor the surrounding area, detecting parking slot lane and obstacle. A few seconds of early warning would significantly decrease the chances of accidents. This system can assist in the parking area and navigating through a narrow space area. Current AVM available usually needed another sensor to ensure a good performance output. But this is cost consuming besides increasing the computational time and resource. Here, proposed an AVM system that will integrate with the motion estimation algorithm to produce a good result. The AVM image sequence is from a camera input mounted on the vehicle. The algorithm to be tested is Gunnar Farneback. Movement in sequential frames is detected and converted to the real-world position change. This paper will compare the algorithms in various condition. The accuracy of the result was measured

AUGMENTATIVE AND ALTERNATIVE COMMUNICATION METHOD BASED ON TONGUE CLICKING FOR MUTE DISABILITIES

This paper presents a pilot study for a novel application of converting tongue clicking sound to words for people with the inability to speak. 15 features of speech that are related to speech timing patterns, amplitude modulation, zero crossing and peak detection were extracted. The experiments were conducted with three different patterns using binary Support Vector Machine (SVM) classification with 10 recordings as training data and 10 recordings as development data. Peak size outperformed all features with 85% classification rate for pattern P1-P3 whereas multiple features produced 100% classification rate for P1-P2 and P2-P3. A GUI based system was developed to validate the trained classifier. Multiclass SVM were constructed based on the best features obtained from binary SVM classification outcome, namely peak size and skewness amplitude modulation, and then tested on 15 recordings. The GUI based multiclass SVM obtained a satisfying performance of 67% correct classification of the test data set. ABSTRAK: Kertas ini membentangkan panduan kajian kepada aplikasi terkini dalam menukar bunyi klik pada lidah kepada perkataan untuk orang yang mempunyai kehilangan upaya dalam bertutur. 15 ciri khas berkaitan pertuturan adalah pola masa, modulasi nilai tertinggi, tiada titik persilangan dan nilai terpilih yang dikesan. Eksperimen telah dijalankan dengan tiga corak berlainan menggunakan perduaan Mesin Vektor Sokongan  (SVM) klasifikasi dengan 10 rakaman sebagai data terlatih dan 10 rakaman sebagai data yang dibina. Saiz tertinggi yang melebihi semua ciri-ciri pada 85% kadar klasifikasi dilihat pada corak P1-P3, sedangkan ciri-ciri pelbagai telah terhasil pada 100% kadar klasifikasi P1-P2 dan P2-P3. Sistem berdasarkan GUI telah dibina bagi menilai ciri terlatih. Kelas pelbagai SVM telah dibina berdasarkan ciri-ciri terbaik dan dihasilkan daripada klasifikasi perduaan SVM, iaitu saiz tertinggi dan modulasi saiz tertinggi tidak linear, dan telah diuji dengan 15 rakaman. Kelas pelbagai SVM yang didapati melalui GUI ini adalah memberangsangkan iaitu 67% klasifikasi adalah tepat pada set data yang diuji

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

Microphone-independent speech features for automatic depression detection using recurrent neural network

Depression is a common mental disorder that has a negative impact on individuals, society, and the economy. Traditional clinical diagnosis methods are subjective and necessitate extensive expert participation. Because it is fast, convenient, and non-invasive, automatic depression detection using speech signals is a promising depression objective biomarker. Acoustic feature extraction is one of the most challenging techniques for speech analysis applications in mobile phones. The values of the extracted acoustic features are significantly influenced by adverse environmental noises, a wide range of microphone specifications, and various types of recording software. This study identified microphone-independent acoustic features and utilized them in developing an end-to-end recurrent neural network model to classify depression from Bahasa Malaysia speech. The dataset includes 110 female participants. Patient Health Questionnaire 9, Malay Beck Depression Inventory-II, and subjects’ declaration of Major Depressive Disorder diagnosis by a trained clinician were used to determine depression status. Multiple combinations of speech types were compared and discussed. Robust acoustic features derived from female spontaneous speech achieved an accuracy of 85%