As s widely use of exchanging private information in various communication applications, the issue to secure it became top urgent. In this research, a new approach to encrypt text message based on genetic algorithm operators has been proposed. The proposed approach follows a new algorithm of generating 8 bit chromosome to encrypt plain text after selecting randomly crossover point. The resulted child code is flipped by one bit using mutation operation. Two simulations are conducted to evaluate the performance of the proposed approach including execution time of encryption/decryption and throughput computations. Simulations results prove the robustness of the proposed approach to produce better performance for all evaluation metrics with res

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

The aim of this investigation was to study the impact of various reaction parameters on wastewater taken from Al-Wathba water treatment plant on Tigris River in south of Baghdad, Iraq with sodium hypochlorite solution. The parameters studied were sodium hypochlorite dose, contact time, initial fecal coliform bacteria concentration, temperature, and pH. In a batch reactor, different concentrations of sodium hypochlorite solution were used to disinfect 1L of water. The amount of hypochlorite ions in disinfected water was measured using an Iodimetry test for different reaction times, whereas the Most Probable Number (MPN) test was used to determine the concentration of coliform bacteria. Total Plate Count (TPC) was utilized in this study to

The ascorbic acid content of juices of some fruits and pharmaceutical tablets of Vitamin C was determined by a homemade apparatus of DIE technique using a thermocouple as heat sensor. The method is simple, speed, low cost and the different types of turbid, colored samples can be analyzed without any problem. The results were of a valuable accuracy and precision, and the recovery of results was with acceptable values

     Arab translators have always paid great attention to the translation of the Persian literary genres, in particular, contemporary Iranian novels. They have always translated for the most prominent Iranian novelists such as Jalal Al Ahmad, Sadiq Hidayat, Mahmoud Dowlatabadi, Bozorg Alavi, Ismail Fasih‎, Houshang Golshiri, Gholam-Hossein Saedi, Simin Daneshvar, Sadiq Chubak, Samad Behrangi and others that have succeeded in perfectly picturing the Iranian society.

      Within the perspectives of Arab translators and by using the descriptive - analytical approach, the present study provides an analytical study of the translation into Arabic some of the modern Persian novels. Moreove

   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared erro

Secure data communication across networks is always threatened with intrusion and abuse. Network Intrusion Detection System (IDS) is a valuable tool for in-depth defense of computer networks. Most research and applications in the field of intrusion detection systems was built based on analysing the several datasets that contain the attacks types using the classification of batch learning machine. The present study presents the intrusion detection system based on Data Stream Classification. Several data stream algorithms were applied on CICIDS2017 datasets which contain several new types of attacks. The results were evaluated to choose the best algorithm that satisfies high accuracy and low computation time.

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system