South Asian parents' experiences of adjustment following a diagnosis of learning disability and/or an autism spectrum disorder for their child: a grounded theory approach

BACKGROUND: There is a great deal of literature pertaining to White parents' experiences of having a child with a learning disability. Some of this literature focuses on parents' experiences of the disclosure of diagnosis of learning disability and/or an autism spectrum disorder, as well as how they come to accept and adjust to a diagnosis. However, very little research has investigated the experiences of South Asian families.MATERIALS AND METHODS: This qualitative study used a grounded theory methodology to explore the experiences of seven South Asian parents in relation to the disclosure of diagnoses as well as issues relating to adjustment post-diagnosis. Semi-structured interviews were used to gather data.RESULTS: The results outline variable experiences in relation to the process of adjustment following a diagnosis. Four core categories were derived from the data to represent stages in a hypothesised model of adjustment. These were: 'obtaining a diagnosis'; 'constructing meanings'; 'finding possibilities for action'; and 'reconstructing roles and identities'. These core categories were embedded within a number of important contextual influences.CONCLUSIONS: The theoretical and clinical implications of the hypothesised model of adjustment are discussed. A methodological critique is provided before outlining reflections on the findings generated

Arabic (Indian) Handwritten‏ ‏Digits Recognition Using Multi feature and KNN Classifier

تقدم هذه الورقة نظام التعرف على أرقام مكتوبة بخط اليد العربية على أساس الجمع بين أساليب الاستخراج متعددة المزايا، مثل   الملف الجانبي العلوي، ورأسية _ الإسقاط الأفقي وتحويل جيب التمام منفصلة مع  الانحراف المعياري.   يتم استخراج هذه الميزات من الصورة بعد تقسيمها الى عدة كتل.   المصنف KNN يستخدم لغرض التصنيف. يتم اختبار هذا العمل مع قاعدة بيانات ADBase القياسية (الأرقام العربية)، والتي تتكون من  70,000 أرقام  تم كتابتها من قبل 700 شخص مختلف.  في النظام المقترح يستخدم 60000  صورة رقم  لمرحلة التدريب و 10000 صورة رقم في مرحلة الاختبار. حقق هذا العمل دقة تعرف على  الارقام مقدارها  97.32٪.This paper presents an Arabic (Indian)  handwritten digit recognition system based on combining  multi feature  extraction methods, such a upper_lower  profile, Vertical _ Horizontal projection and Discrete Cosine Transform (DCT) with Standard Deviation σi called (DCT_SD)  methods. These  features are extracted from the image  after dividing it by several blocks. KNN classifier used  for classification purpose. This work is tested with the ADBase standard database (Arabic numerals),  which consist of 70,000 digits were 700 different writers write  it. In proposing system used 60000 digits, images for training phase and 10000 digits, images in testing phase. This work  achieved  97.32%  recognition  Accurac

Research Project on Biometrics

As technology evolves, and our personal details become more and more susceptible to hacking, one of the most important concerns we now have is being sure our phones, computers, networks are secure. We have begun to move on from simple password and motion patterns onto using facial recognition, and other forms of biometric recognition technology to protect our private information. The iPhone 5 for example, now presents features to identify uniquely the owner of the device, in the form of fingerprint detection. So we decided to focus on the topic biometrics for this research paper, which is basically how to identify an individual based on his characteristics

Isolation and Characterization of Diesel – Degrading Bacteria from the Antarctica

Several isolates of bacteria with diesel-degrading capability have been isolated and characterized from Antarctica. Three strains; isolates J2(p), J7(p) and G(k) were isolated and selected for further investigations. Microbiological analysis such as gram staining and molecular phylogenetics were used to identify the bacteria. Bacterial growth optimization was studied based on carbon source, nitrogen source, pH and temperature. Biodegradation of diesel oil was monitored by quantitative gas chromatography (GC) analysis. The log phase for isolate J2(p) and G(k) were shown in between day four and day six while the log phase for isolate J7(p) was found in between day six until day eighth. Isolate J2(p), J7(p) and G(k) were identified as Pseudomonas stutzeri, Pseudomonas fluorescens and Rhodococcus sp., respectively using substrate utilization patterns (Biolog). Isolate J2(p) and G(k) showed optimum growth at 3% diesel concentration whilst isolate J7(p) was optimum at 2.5% diesel. Isolate J2(p) exhibits an optimum concentration of ammonium sulphate at 2% whilst isolate J7(p) and G(k) exhibit an optimum concentration of ammonium sulphate at 1%. The optimum pH for growth of all isolates J2(p), J7(p) and G(k) were pH 7.13, 7.15 and 7.23, respectively. All the isolates showed that 10°C was the optimum temperature for bacterial growth. The biodegradation of diesel oil by isolate J2(p) increased in efficiency from the second to the sixth day of incubation, increasing from 1.4 to 18.8% and remain constant until the eighth day. The biodegradation efficiency decreased from 18.8 to 6.3% after the eighth day. The Biodegradation efficiency (BE) of isolate J2(p) was negligible until day 2 where a linear increase in BE to 19% on the sixth day occurs. For isolate J7(p), the biodegradation efficiency was between the second and eighth day of incubation, which increasing from 0.8 to 18.7%. Then it decreased from 18.7 to 9.4% between the eighth day and tenth day of incubation. The biodegradation efficiency of isolate G(k) increase from 0.9 to 17.4% from the second to the sixth day of incubation before decreasing to day ten from 17.4 to 9.9%. Isolate J2(p) was chosen for the screening of enzyme assays. Activity was detected in isolate J2(p). The enzymes activity was distributed in cell-free extracts, soluble fraction and particulate fraction with respective activities for each fraction for n - alkane oxidizing enzyme at 0.04, 0.19 and 0.23 μmol/min-1, DCPIP-dependent dehydrogenase at 0.002, 0.006 and 0.02 μmol/min-1 and aldehyde reductase activity at 0.02, 0.09 and 0.24 μmol/min-1

Sense-Based Arabic Information Retrieval Using Harmony Search Algorithm

Information Retrieval (IR) is a field of computer science that deals with storing, searching, and retrievingdocuments that satisfy the user need. The modern standard Arabic language is rich in multiple meanings (senses) for manywords and this is substantially due to lack of diacritical marks. The task for finding appropriate meanings is a key demand inmost of the Arabic IR applications. Actually, the successful system should not be interested only in the retrieval quality andoblivious to the system efficiency. Thus, this paper contributes to improve the system effectiveness by finding appropriatestemming methodology, word sense disambiguation, and query expansion for addressing the retrieval quality of AIR. Also, itcontributes to improve the system efficiency through using a powerful metaheuristic search called Harmony Search (HS)algorithm inspired from the musical improvisation processes. The performance of the proposed system outperforms the one inthe traditional system in a rate of 19.5% while reduces the latency in an approximate rate of 0.077 second for each query

Mobile Robot Path Planning Method Using Firefly Algorithm for 3D Sphere Dynamic & Partially Known Environment

اذا البحث يقترح طريقة لحل مشكلة تخطيط مسار الروبوت المتحرك في ضمن بيئة شبه معروفة ثلاثية الابعاد كروية الشكل باستخدام نسخة معدلة من خوارزمية الحشرات المضيئة Firefly Algorithm والتي تمكنت بنجاح من ايجاد طريق شبه مثالي خالي من التصادم مع العوائق بسرعة وسهولة وملاحة آمنة على طول الطريق حتى الوصول للهدف. In this paper, a new method is proposed to solve the problem of path planning for a mobile robot in a dynamic-partially knew three-dimensional sphere environment by using a modified version of the Firefly Algorithm that successfully finds near optimal and collision-free path while maintaining quick, easy and completely safe navigation throughout the path to the goal

A Survey on Emotion Recognition for Human Robot Interaction

With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy

Modern drowsiness detection techniques: a review

According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness

A survey on bio-signal analysis for human-robot interaction

The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems