A proposed architecture of big educational data using hadoop at the University of Kufa

Nowadays, educational data have been increased rapidly because of the online services provided for both students and staff. University of Kufa (UoK) generates a massive amount of data annually due to the use of e-learning web-based systems, network servers, Windows applications, and Students Information System (SIS).  This data is wasted as traditional management software are not capable to analysis it. As a result, the Big Educational Data concept rises to help education sectors by providing new e-learning methods, allowing to meet individual demands and reach the learners' goals, and supporting the students and teacher’s interaction. This paper focuses on designing Big Data analysis architecture, based on the Hadoop in the UoK and the same case for other Iraqi universities. The impact of this work, help the students learn, emphasizing the need of academic researchers and data science specialist for learning and practicing Big Data analytics and support the analysis of the e-learning management system and set the first step toward developing data repository and data policy in UoK

Improving saddle stitching line using affordable embedded system

In most printing factories, the stitching machine is considered as a significant tool in accomplishing the printing process cycle, such as in the Printing House of the University of Kufa (PHUK), complete their jobs using a cheap manual machine, and thus this leads to an increase in the number of employees and work hours. That is because the automated stitching machine of production is very costly. A decent printing house design maximizes production with a minimum investment in new equipment parts. However, a decent PHUK layout alone cannot reach the intended aims unless firmly linked with a developed production line of an automated stitching machine for the purpose of reducing cost, time, and efforts. This article focused on designing and developing automatic saddle stitching machines for folded paper sheet products such as newspapers, magazines, catalogs, exam sheets, etc. using accommodate devices such as Arduino and infrared sensors. Furthermore, the proposed design is applied in PHUK successfully and it showed that the cost of the stitching machine and the manpower is reduced by 60 percent, also the time is reduced by 70 percent. Finally, one of the significant implications of this work is using IT in management of resources

American standard code for information interchange mapping technique for text hiding in the RGB and gray images

One of the significant techniques for hiding important information (such as text, image, and audio) is steganography. Steganography is used to keep this information as secret as possible, especially the sensitive ones after the massive expansion of data transmission through the Internet inside a conventional, non-secret, file, or message. This paper uses the American standard code for information interchange (ASCII) mapping technique (AMT) to hide the data in the color and grey image by converting it in a binary form, also convert the three levels of the red, green, and blue (RGB) image and grey image in the binary form, and then hide the data through hiding every two bits of the text in the two bits of one of the levels from the RGB image and grey image that means the text will be distributed throughout the images and allows hiding large amounts of data. That will send the information in a good securing way

Reaction to News of Oil and Gas Sector Companies in Kuwait

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Arrhythmia Detection Based on New Multi-Model Technique for ECG Inter-Patient Classification

This paper presents a novel model for arrhythmia detection based on a cascading technique that utilizes a combination of the One-Sided Selection (OSS) method, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms, this model denoted by (OWSK) model to classify four types of electrocardiogram (ECG) heartbeats following inter-patient scheme. The OWSK model consists of three stages. The first stage involves resampling using the One-Sided Selection (OSS) method to solve the imbalance problem and reduce data by removing noisy, borderline, and redundant samples. The second stage involves using Wavelet Transformation (WT) and Power Spectral Density (PSD) to extract the most relevant frequency domain features. The third stage involves a cascading process by constructing the classifier from SVM trained on the whole dataset to classify normal and abnormal beats. Then, KNN (K-Nearest Neighbors) is trained on only the three irregular minority classes to classify the three types of arrhythmias for the detection of ventricular ectopic beats, supraventricular ectopic beats, and fusion beats (V, S, and F). The performance of the proposed model is evaluated in terms of different metrics, including accuracy, recall, precision, and F1 score. The results show the superiority of the proposed model in medical diagnosis compared to the latest works, where it achieves 90%, 90%, 93%, and 91% for accuracy, recall, precision, and F1 score under the inter-patient paradigm and 98%, 98%, 98%, and 98% under the intra-patient paradigm