Malaria is a curative disease, with therapeutics available for patients, such as drugs that can prevent future malaria infections in countries vulnerable to malaria. Though, there is no effective malaria vaccine until now, although it is an interesting research area in medicine. Local descriptors of blood smear image are exploited in this paper to solve parasitized malaria infection detection problem. Swarm intelligence is used to separate the red blood cells from the background of the blood slide image in adaptive manner. After that, the effective corner points are detected and localized using Harris corner detection method. Two types of local descriptors are generated from the local regions of the effective corners which are Gabor based f

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential f

This article dealt with the evolutionary interpretation in three parts: First, it focused on the conceptual framework of evolutionary interpretation of International Treaties, its philosophical and legal foundation and its determinants. As for the second topic, it dealt with the position of the International Court of Justice from the evolutionary interpretation, studying and analyzing its precedents in this aspect and the resulting proposed and adopted criteria. The third topic dealt with the position of the judiciary of human rights through analyzing the rulings of the European Court of Human Rights and the Inter - American Court of Human Rights based on the criteria that were produced by the judicial practices, which varied according t

Background: Factor V Leiden is considered the most common inherited risk factor for venous thrombosis in Caucasian populations, including those in the Eastern Mediterranean region. While several studies have addressed Factor V Leiden prevalence in patients with venous thrombosis in the Eastern Mediterranean countries, none have been reported from Iraq.
Objective: To study the prevalence of Factor V Leiden in an unselected group of Iraqi patients with Deep Venous thrombosis.
Materials and Methods: A total of 50 unselected patients with deep venous thrombosis referred to the Medical City Teaching Hospital in Baghdad, Iraq, as well as 40 age and sex matched controls, were enrolled. The evaluation included in addition to detailed histo

order to increase the level of security, as this system encrypts the secret image before sending it through the internet to the recipient (by the Blowfish method). As The Blowfish method is known for its efficient security; nevertheless, the encrypting time is long. In this research we try to apply the smoothing filter on the secret image which decreases its size and consequently the encrypting and decrypting time are decreased. The secret image is hidden after encrypting it into another image called the cover image, by the use of one of these two methods" Two-LSB" or" Hiding most bits in blue pixels". Eventually we compare the results of the two methods to determine which one is better to be used according to the PSNR measurs

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog