The Effects Of Green Colour On Patients Under Recovering According To Noble Alquran

This study focuses on the effect of green color specifically in acceleration recovering of patients under treatment in treatment centers and its impact on the psychological aspect of the treatment, in particular, which is one of the stages of complementary and important in treatment as in cases of post-surgical and other cases which have been based on this study, the green color had ever been in the noble ALQURAN many times and places and focus on how the color calls for calm and balance, growth and peace of mind as well as based on scientific studies took the green color as a main subject to study and its effects on the psychological aspect of human. This study is simple but it comes as part of what’s called now days the science of Quranic miracle which to utilize what is stated in the noble ALQURAN in all aspects of life, including scientific, legal, legislative, and life sciences, mathematics, and many other issues .This study is an approach according to our perception humble as stated in the noble ALQURAN, because the reality is out of our thinking Border and what we want to reach is access to its virtual image simple signals colorimetric contained in the noble ALQURAN, which could use them to find the determinants or guide or standard of design according to researchers, architects and specialists in find a comfortable designs and commensurate with the requirements of users

Temporal salience based human action recognition

This paper proposes a new approach for human action recognition exploring the temporal salience. We exploit features over the temporal saliency maps for learning the action representation using a local dense descriptor. This approach automatically guides the descriptor towards the most interesting contents, i.e. the salience region, and obtains the action representation using solely the saliency information. Outperforming results on Weizmann, DHA and KTH datasets confirm the efficiency of the proposed approach as compared to the state-of-the-art methods, in terms of accuracy and robustness to the variations inside the action and similarities among actions. The proposed method outperforms by 2.7% with DHA, 1% with KTH and it is comparable in the case of Weizmann

A New Approach of Rough Set Theory for ‎Feature Selection and Bayes Net Classifier ‎Applied on Heart Disease Dataset

درسنا في هذا البحث اختيار الصفات بالاعتماد على نهج جديد من  خوارزمية مجموعة التقريب حيث تعتمد هذه الطريقة على اختيار الصفات الأكثر تاثيرا. لجئنا الى انتقاء الصفات اختصارا للوقت , وجود الصفة تؤثر على دقة النتائج او قد تكون الصفة غير متوفرة . تم تطبيق الخوارزمية على بيانات امراض القلب لاختيار افضل الصفات المؤثرة. ان المشكلة الرئيسية هو كيفية تشخيص الإصابة فيما لو كان مصاب بمرض القلب من عدمه.هذه المشكلة تمثل تحدي لان لا نسطيع اتخاذ القرار بصورة مباشرة. تعتمد الطريقة المقترحة على ترميز البيانات الاصلية .ان الناتج من هذه الخوارزميه هي الصفات الأكثر أهمية حيث تهمل الصفات السيئة والغير ضرورية.وتم تطبيق النتائج على خوارزمية شكبة بيزينت كخوارزمية للتنبؤ بالمرض وقد حصلنا على النتائج 82.17 , 83.49 , 74.58 عند استخدام جميع الصفات ,12 , 7 طول الصفات على التوالي.وتم تطبيق نتائج خوارزمية مجموعة التقريب الاصلية على خوارزمية البيزين وحصلنا على النتائج 58.41 ,81.51  عند استخدام 2 , 12 طول الصفات على التواليIn this paper a new approach of rough set features selection has been proposed. Feature selection has been used for several reasons a) decrease time of prediction b) feature possibly is not found c) present of feature case bad prediction. Rough set has been used to select most significant features. The proposed rough set has been applied on heart diseases data sets. The main problem is how to predict patient has heart disease or not depend on given features. The problem is challenge, because it cannot determine decision directly .Rough set has been modified to get attributes for prediction by ignored unnecessary and bad features. Bayes net has been used for classified method. 10-fold cross validation is used for evaluation. The Correct Classified Instances were 82.17, 83.49, and 74.58 when use full, 12, 7 length of attributes respectively. Traditional rough set has been applied, the minimum Correct Classified Instances were 58.41 and 81.51 when use 2 length of attributes respectivel

The Role of Toxocaral Larvae in the Transmission of Microbiological Infection

The purpose of this research was to study the relationship between the Toxocara canis larvae and the transmission of bacteria in mice. Special attention was given to the attempt to understand the mechanisms by which the larvae could play a role in the dissemination of infection. Radioactive isotopes were used to label the Escherichia coli bacteria. Three isotopes were used, one β emitter (32p) and the two others were 59Fe and 51Cr as ɣ emitters. Exposure of the larvae to labelled bacteria, both in vitro and in vivo resulted in obtaining evidence that they could carry the bacteria. When the experiment s were done in vivo, the results strongly suggested that the T. cants larvae were able to disseminate the bacteria from the intestine to all the organs of the animals tested. The attempt to check whether the larvae exposed to the bacteria in a test tube could disseminate bacteria to the organs of mice, showed inconclusive results . Confirmatory experiments were carried out by using bacteriological and serological techniques. Evidence was obtained that the larvae were able to carry and might be able to disseminate the E. coli to the organs of the mouse. The result s will be discussed in relation to the possible dangers of these worms in the dissemination of disease-causing micro-organisms from the intestine to other parts of the body

Privacy protected recognition of activities of daily living in video

This paper proposes a new method to protect the privacy while retaining the ability to accurately recognise the activities of daily living for video-based monitoring in ambient assisted living applications. The proposed method obfuscates the human appearance by modelling the temporal saliency in the monitoring video sequences. It mimics the functionality of neuromorphic cameras and explores the temporal saliency to generate a mask to anonymise the human appearance. Since the anonymising masks encapsulate the temporal saliency with respect to motion in the sequence, they provide a good basis for further utilisation in activity recognition, which is achieved by representing the HOG features on privacy masks. The proposed method has resulted in excellent anonymising performances compared using the cross correlation measures. In terms of activity recognition, the proposed method has resulted in 5.6% and 5.4% improvements of accuracies over other anonymisation methods for Weizmann and DHA datasets, respectively

Theoretical Measuring for Negative Chromatic Dispersion Curves of Photonic Crystal Fiber by Gaussian Function

Negative dispersion curves in a typical type of high negative chromatic dispersion photonic crystal fiber(PCF) have been investigated in this paper. The depended class of (PCF) has double-core structure (core- region: which has inner core and outer core) with a honeycomb photonic lattice in the cladding region. Negative dispersion curves deviated from core-region of this type of fibers will be investigated. The investigation has depended an estimation process using an approximation function to create a mathematical model that enables us to measure negative dispersion curves. The influence of inner-core parameters (dcore d1 and d2) on dispersion curves has been investigated by varying the values of these parameters.  Negative dispersion curves that were introduced by a previous study using finite-difference frequency-domain (FDFD)method for this class of(PCFs) are directly included in this work in order  to measure matching ratio with our results.   Gaussian approximation function has been considered to estimate our mathematical model. Keywords: Photonic crystal fiber, Theoretical model, Negative chromatic dispersion, Gaussian function

On-spot quantification of modafinil in generic medicines purchased from the Internet using handheld Fourier transform-infrared, near-infrared and Raman spectroscopy

Poor quality medicines represent an expanding global public health threat facilitated by the Internet. A recent survey showed that one in five students have used modafinil to enhance learning ability mainly purchased from Internet sources. The aim of this work was to develop on-the-spot and simple methods for the quantification of modafinil in generic medicines using Fourier transform-infrared (FTIR), near-infrared (NIR) and Raman spectroscopy along with partial least square regression (PLSR). Modafinil tablets were measured in intact form using NIR and Raman and in powdered form using FTIR, NIR and Raman. Additionally, powder mixtures of crushed modafinil tablets and excipient(s) were prepared either by diluting the crushed tablets with excipient(s), or sequentially adding excipient(s) to the crushed tablets. Three PLSR models were constructed in MATLAB 2014a from powder mixtures and two from intact and powdered tablets. For FTIR and Raman spectroscopy, PLSR models based on tablets gave linear calibration curve with correlation coefficient (r2) values above 0.94 and a root mean square error of calibration (RMSEC) below 0.96% m/m. Conversely, the PLSR model based on powder sequential addition gave the highest accuracy using the NIR spectra (r2 = 0.99, RMSEC = 1.15% m/m). The latter model showed accuracy in predicting the concentration of the active pharmaceutical ingredient in modafinil generic medicines proving their authenticity. The overall results showed that the combination of the three spectroscopic methods with PLSR offered a rapid technique for authenticating generic modafinil medicines

Making sense of neuromorphic event data for human action recognition

Neuromorphic vision sensors provide low power sensing and capture salient spatial-temporal events. The majority of the existing neuromorphic sensing work focus on object detection. However, since they only record the events, they provide an efficient signal domain for privacy aware surveillance tasks. This paper explores how the neuromorphic vision sensor data streams can be analysed for human action recognition, which is a challenging application. The proposed method is based on handcrafted features. It consists of a pre-processing step for removing the noisy events followed by the extraction of handcrafted local and global feature vectors corresponding to the underlying human action. The local features are extracted considering a set of high-order descriptive statistics from the spatio-temporal events in a time window slice, while the global features are extracted by considering the frequencies of occurrences of the temporal event sequences. Then, low complexity classifiers, such as, support vector machines (SVM) and K-Nearest Neighbours (KNNs), are trained using these feature vectors. The proposed method evaluation uses three groups of datasets: Emulator-based, re-recording-based and native NVS-based. The proposed method has outperformed the existing methods in terms of human action recognition accuracy rates by 0.54%, 19.3%, and 25.61% for E-KTH, E-UCF11 and E-HMDB51 datasets, respectively. This paper also reports results for three further datasets: E-UCF50, R-UCF50, and N-Actions, which are reported for the first time for human action recognition on neuromorphic vision sensor domain