In this paper we proposed a new method for selecting a smoothing parameter in kernel estimator to estimate a nonparametric regression function in the presence of missing values. The proposed method is based on work on the golden ratio and Surah AL-E-Imran in the Qur'an. Simulation experiments were conducted to study a small sample behavior. The results proved the superiority the proposed on the competition method for selecting smoothing parameter.

The study evaluated the recovery performance of any home herbs group as first aid in some emergency cases. The study involved 10 items of herbs (chamomile, black pepper, cloves, cinnamon, and anise) which used in acute cases such as cold, colic, inflammation of the intestine and tonsillitis, and (hibiscus, catnip, dried lime, cress seed, and ginger) were used for chronic cases such as hypertension, cardiovascular disease, and arthritis. The results showed the herbs had healing power and efficiency in the primary remedy for the above-mentioned diseases. The side effects, the contradictions, and the overdoses of these substances were discussed. It was concluded that home herbs could be used for treatment in emergency cases until moder

Colonialism invades the Third World countries, physically and psychologically. This article exposes but sample of the physical and psychological consequences of colonialism. The Beekeeper of Aleppo (2019) by the British novelist, Christy Lefteri is a typical novel to diagnose the harsh circumstances of individuals within and after the disaster. Since it depicts characters from Asian countries, it would be a best representative for all Asian people who suffer colonialism. Migration toward anonymity is the mere option for the colonized people. Aftermath, they experience displacement, trauma, and the loss of identity.

          The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed