Big-data Management using Map Reduce on Cloud: Case study, EEG Images' Data

Sahar Mahdie Klim; Sahar Mahdie Klim

doi:10.22153/kej.2017.11.004

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Publication Date

Fri Mar 31 2017

Journal Name

Al-khwarizmi Engineering Journal

Volume

13

Issue Number

1

DOI

10.22153/kej.2017.11.004

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Big-data Management using Map Reduce on Cloud: Case study, EEG Images' Data

Big-data

Cloud Computing

Electroencephalogram

MapReduce

Hadoop

Sahar Mahdie Klim

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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 researchers and specialist with an easy and fast method of handling the EEG big data.

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Publication Date

Fri May 01 2026

Journal Name

2026 Xxix International Conference On Soft Computing And Measurements (scm)

Hierarchical Multi-Stage Intrusion Detection with Feature Inheritance and Prediction Verification

Autoencoder Feature Extraction

Hieratical Intrusion Detection System (HIDS)

Internet of Things (IoT) Security

Imbalanced Data Handling

Nada

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One of the challenges faced by traditional intrusion detection systems based on machine learning or deep learning is instability when dealing with unbalanced network traffic, leading to failure in detecting certain attacks (minority classifications). Additionally, they struggle with multi-stage attacks, resulting in an increase in false alarms. This paper presents a hierarchical intrusion detection system supported by a Prediction Verification Layer (PVL) and a Feature Inheritance Mechanism (FIM). Where PVL contributes to documenting the system’s final decision and increasing sensitivity to minority attacks, FIM also helps in inheriting features from previous layers and correcting errors as much as possible. Additionally, it allows for ad

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Publication Date

Sun Dec 04 2016

Journal Name

Baghdad Science Journal

Synthesis, Characterization, Antimicrobial and Theoretical Studies of V(IV),Fe(III),Co(II),Ni(II), Cu(II), and Zn(II)Complexes with Bidentate (NN) Donar Azo Dye Ligand

"Azo compounds

coordination compound of Azo

spectral data

antimicrobial activity

synthesis and crystal structure of Azo compounds"

Rehab A. M.

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The new 4-[(7-chloro-2,1,3-benzoxadiazole)azo]-4,5-diphenyl imidazole (L) have been synthesized and characterized by micro elemental and thermal analyses as well as 1H.NMR, FT-IR, and UV-Vis spectroscopic techniques. (L) acts as a ligand coordinating with some metal ionsV(IV), Fe(III), Co(II), Ni(II), Cu(II), and Zn(II). Structures of the new compounds were characterized by elemental and thermal analyses as well as FT-IR and UV-Vis Spectra. The magnetic properties and electrical conductivities of metal complexes were also determined. Study of the nature of the complexes formed in ethanol following the mole ratio method.. The work also include a theoretical treatment of the formed complexes in the gas phase, this was done using the (hyperch

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(2)

Publication Date

Mon Mar 31 2025

Journal Name

Iraqi Statisticians Journal

Hypothesis Testing for Non-Normal Multiple Compact Regression Model

Multiple compact regression model

Bayesian Approach

Bayes Factor

Generalized multivariate transmuted Bessel distribution

Kernel functions

Jaundice percentage in the blood component data.

Ons Edin

Noor Saleem

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Generalized multivariate transmuted Bessel distribution belongs to the family of probability distributions with a symmetric heavy tail. It is considered a mixed continuous probability distribution. It is the result of mixing the multivariate Gaussian mixture distribution with the generalized inverse normal distribution. On this basis, the paper will study a multiple compact regression model when the random error follows a generalized multivariate transmuted Bessel distribution. Assuming that the shape parameters are known, the parameters of the multiple compact regression model will be estimated using the maximum likelihood method and Bayesian approach depending on non-informative prior information. In addition, the Bayes factor was used

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Publication Date

Sat Oct 01 2016

Journal Name

Journal Of Theoretical And Applied Information Technology

Factors affecting global virtual teams’ performance in software projects

Today the trend is to perform software development work via distributed geographical area among team

individual or even as an organization. However

due to the global market and international presence of many companies

there is a need to implement a global virtual teams. The global virtual team members are gradually engaged in globalized business environments across space

time and organizational boundaries via information and communication technologies. A global virtual team relies on communication

collaboration

and information exchange are the most important criteria in global virtual teams’ operations. The purpose of this paper is to answer two research questions. The first research question is to identify the factors affecting global virtual teams’ performance. A systematic literature review was conducted to answer the first research question. The second research question is on what the rank of the factor affecting the global virtual teams’ performance according to their level of effect on global virtual teams’ performance. Online survey was conducted within 103 developers and IT managers from eight IT companies to answer the second research question. The Statistical Package for Social Science (SPSS 22) was used to analyze the collected data. In this study

we investigated factors that affect global virtual teams’ performance

factors considered include cultural differences

language problems

time-zone differences

team size

technical problems

lack of trust

lack of sufficient training

and ICT problems. Also the findings indicated that lack of sufficient training is the highest level of effect on global virtual teams’ performance. On the other hand

Ali Yahya Gheni

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