The aim of the research is to clarify the measurement of the impact regarding financial value of human resources on investors' decisions by considering that the human element is one of the assets of the company. Therefore, a set of criteria must be available to determine the applicability of these standards in the human resource because it has an effective role in the success for the company. Is to measure the value of human resources in a financial format according to the first two methods depends on the value and the second depends on the cost.

In order to achieve the objectives of the study, a questionnaire was designed to survey the views of a number of employees of the General Company for Leather Industries in order to arriv

ABSTRACT University college libraries are one of the most important information institutions for all researchers during their research and study life, they seeks to provide information sources such as; books, periodicals, theses, databases, Inquiry service and answering questions services in various disciplines to achieve its goals. In 2020, college libraries of all types stepped up to meet the needs of their users' as they responded to the impacts of COVID-19, also extended necessary lifelines to community members facing job losses, healthcare crises, and remote work and learning during an unprecedented and uncertain time. The research aim to identifying the services provided to the postgraduate students users at University of Baghdad coll

Health insurance and its benefits are of great importance and impact on the employees who represent the human capital of each organization because they are related to the health reality. The study took into account the most important and the last of his writing of the concepts and literary reviews and enriched the theoretical part of the practical side has addressed the financial data and analysis for the period from 2013 to 2017 to know the impact and the relationship between the variables that They were reviewed on the theoretical side. The study came out with a number of results, on the basis of which practical conclusions were drawn and reflected what was observed on the basis of which the recommendations were formulated

Radon concentrations are measured for water samples collected from twenty wells which were drilled in Hashimiya area in addition to twelve samples of surface water using Alpha Gaurd. 140 samples, 7 for each well, were collected represent wet season in continuous pumping and 20 samples, one for each well, were collected represent dry season. Concentration of radon in groundwater is many times of its concentration in surface water. The minimum concentration in groundwater is about (7) Bq/L and (5) Bq/L while the maximum concentration is about (31) Bq/L and (19) Bq/L in wet season and dry season respectively. The range of radon concentrations in river water is between (1.06) Bq/L and (1.21) Bq/L. This study has indicated that there is a flo

Sansevieriatrifasciata was studied as a potential biosorbent for chromium, copper and nickel removal in batch process from electroplating and tannery effluents. Different parameters influencing the biosorption process such as pH, contact time, and amount of biosorbent were optimized while using the 80 mm sized particles of the biosorbent. As high as 91.3 % Ni and 92.7 % Cu were removed at pH of 6 and 4.5 respectively, while optimum Cr removal of 91.34 % from electroplating and 94.6 % from tannery effluents was found at pH 6.0 and 4.0 respectively. Pseudo second order model was found to best fit the kinetic data for all the metals as evidenced by their greater R2 values. FTIR characterization of biosorbent revealed the presence of carboxyl a

    Semantic segmentation is effective in numerous object classification tasks such as autonomous vehicles and scene understanding. With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation. Most of the algorithms gain the required accuracy while compromising on their storage and computational requirements. The work showcases the implementation of Convolutional Neural Network (CNN) using Discrete Cosine Transform (DCT), where DCT exhibit exceptional energy compaction properties. The proposed Adaptive Weight Wiener Filter (AWWF) rearranges the DCT coefficients by truncating the high frequency coefficients. AWWF-DCT model reinstate the convolutional l

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

This paper deals with the thirteenth order differential equations linear and nonlinear in boundary value problems by using the Modified Adomian Decomposition Method (MADM), the analytical results of the equations have been obtained in terms of convergent series with easily computable components. Two numerical examples results show that this method is a promising and powerful tool for solving this problems.

This  article  co;nsiders a shrunken  estimator  Â·Of  Al-Hermyari·   and

AI Gobuii (.1) to estimate  the mean (8) of a normal clistributicm N (8 cr4)  with  known variance  (cr+),  when  <:I    guess value (So) av11il ble about the mean (B) as· an initial estrmate. This estimator is shown to be

more efficient tl1an the class-ical estimators  especially when 8 is close to 8•. General expressions .for bias and MSE -of considered  estitnator are gi 'en, witeh  some examples.  Nut.nerical cresdlts, comparisons  and

conclusions ate reported.

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an