Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r

Administrative procedures in various organizations produce numerous crucial records and data. These
records and data are also used in other processes like customer relationship management and accounting
operations.It is incredibly challenging to use and extract valuable and meaningful information from these data
and records because they are frequently enormous and continuously growing in size and complexity.Data
mining is the act of sorting through large data sets to find patterns and relationships that might aid in the data
analysis process of resolving business issues. Using data mining techniques, enterprises can forecast future
trends and make better business decisions.The Apriori algorithm has bee

Background: Fetal macrosomia is usually distressing to obstetricians and neonatologists. In the current study, involved mothers had poor social and medical circumstances, as they were migrated forcefully within the country borders due to war, from their original homeland to safer camps which had miserable situations. Objectives: To study rate, risk factors, and complications of macrosomia in people with low socio-economic living conditions and missed medical follow up. Methods: All internally displaced pregnant women who gave birth to neonates weighed ≥4000 g were involved in the study. All required history, examination, care, and investigations were practiced by the attending obstetrician and neonatologist. Cases of normal birth weight n

     This paper presents an efficient system using a deep learning algorithm that recognizes daily activities and investigates the worst falling cases to save elders during daily life. This system is a physical activity recognition system based on the Internet of Medical Things (IoMT) and uses convolutional neural networks (CNNets) that learn features and classifiers automatically. The test data include the elderly who live alone. The performance of CNNets is compared against that of state-of-the-art methods, such as activity windowing, fixed sample windowing, time-weighted windowing, mutual information windowing, dynamic windowing, fixed time windowing, sequence prediction algorithm, and conditional random fields. Th

‎ This research aims to estimate stock returns, according to the ‎Rough Set Theory ‎approach, ‎test ‎its effectiveness and accuracy in predicting stock returns and their potential in the ‎field of ‎financial ‎markets, and rationalize investor decisions. The research sample is totaling (10) ‎companies traded at Iraq Stock Exchange. The results showed a remarkable ‎ ‎Rough Set Theory application in data reduction, contributing to the rationalization of ‎investment ‎decisions. The most prominent conclusions are the capability of rough set theory ‎in ‎dealing with financial data and applying it for forecasting stock ‎returns.‎The ‎research provides those interested in investing stocks in financial

     The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mea

This study aims to Statement of the relationship between Total Quality Management philosophy and Organizational performance from the point of view of the internal customer. A comparison has been made between two companies, one of which applies the requirements of TQM well and the other does not apply these requirements as the (General Company for Electrical Industries/ Diyala) and (General Company for Electrical Industries/ Baghdad) to conduct the search, During the questionnaire prepared for this purpose and distributed to a sample of 30 employees in the General Company for Electric Industries/ Diyala and (20) employees of the General Company for Electrical Industries/ Baghdad. Their answers were analyzed using a simple correlation coef

Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

Petroleum is one of the most important substances consumed by man at present times, a major energy source in this century, petroleum oils can cause environmental pollution during various stages of production, transportation, refining and use, petroleum hydrocarbons pollutions ranging from soil, ground water to marine environment, become an inevitable problem in the modern life, current study focused on bioremediation process of hydrocarbons contaminants that remaining in the bottom of gas cylinders and discharged to the soil. Twenty-four bacterial isolates were isolated from contaminated soils all of them gram negative bacteria, bacterial isolates screening to investigate the ability of biodegradation of hydrocarbons, these isolates