Details

Publication Date

Thu May 04 2017

Journal Name

Ibn Al-haitham Journal For Pure And Applied Sciences

Volume

25

Issue Number

3

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76

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110

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Minimizing total Completion Time and Maximum late Work Simultaneously

schedule

multicriteria

single machine

bicriteria.

T. S.

F. Sh.

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In this paper, the problem of scheduling jobs on one machine for a variety multicriteria
are considered to minimize total completion time and maximum late work. A set of n
independent jobs has to be scheduled on a single machine that is continuously available from
time zero onwards and that can handle no more than one job at a time. Job i,(i=1,â€¦,n)
requires processing during a given positive uninterrupted time pi, and its due date d
i.
For the bicriteria problems, some algorithms are proposed to find efficient (Pareto)
solutions for simultaneous case. Also for the multicriteria problem we proposed general
algorithms which gives efficient solutions within the efficient range

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Publication Date

Sat Sep 30 2023

Journal Name

Iraqi Journal Of Science

Hybrid CNN-SMOTE-BGMM Deep Learning Framework for Network Intrusion Detection using Unbalanced Dataset

Bayesian Gaussian Mixture Model

Convolutional Neural Network

Deep Learning

Extreme Gradient Boosting

Machine Learning

Recursive Feature Elimination

Synthetic Minority Oversampling Technique

waad

Imad

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This paper proposes a new methodology for improving network security by introducing an optimised hybrid intrusion detection system (IDS) framework solution as a middle layer between the end devices. It considers the difficulty of updating databases to uncover new threats that plague firewalls and detection systems, in addition to big data challenges. The proposed framework introduces a supervised network IDS based on a deep learning technique of convolutional neural networks (CNN) using the UNSW-NB15 dataset. It implements recursive feature elimination (RFE) with extreme gradient boosting (XGB) to reduce resource and time consumption. Additionally, it reduces bias toward

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(3)

Publication Date

Sun Jan 10 2016

Journal Name

British Journal Of Applied Science & Technology

The Effect of Classification Methods on Facial Emotion Recognition ‎Accuracy

Facial emotions

feature selection

data clustering

modified K-Means clustering algorithm

LDA algorithm

Statistical classifier

Neural Network

Support Vector Machine (SVM)

Suhaila N.

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The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

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(2)

Publication Date

Tue Oct 20 2020

Journal Name

Ibn Al-haitham Journal For Pure And Applied Sciences

Comparison Among Three Estimation Methods to Estimate Cascade Reliability Model (2+1) Based On Inverted Exponential Distribution

inverted exponential distribution

Maximum Likelihood method

Uniformly Minimum Variance Unbiased method

Single Stage Shrinkage Estimator

Mean Squared Error and Cascade Reliability Model (2 1)

Sairan

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In this paper, we are mainly concerned with estimating cascade reliability model (2+1) based on inverted exponential distribution and comparing among the estimation methods that are used . The maximum likelihood estimator and uniformly minimum variance unbiased estimators are used to get of the strengths and the stress ;k=1,2,3 respectively then, by using the unbiased estimators, we propose Preliminary test single stage shrinkage (PTSSS) estimator when a prior knowledge is available for the scale parameter as initial value due past experiences . The Mean Squared Error [MSE] for the proposed estimator is derived to compare among the methods. Numerical results about conduct of the considered

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Publication Date

Sat Jan 01 2022

Journal Name

Turkish Journal Of Physiotherapy And Rehabilitation

classification coco dataset using machine learning algorithms

learning Machine

classification

MCOCO dataset

K Nearest Neighbor's

Stochastic Gradient Descent learning (SGD)

Logistic Regression Algorithm(LR)

and Multi-Layer Perceptron (MLP).

Rasool

Bushra

Raaid

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In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

Publication Date

Mon Apr 24 2017

Journal Name

Spe Kingdom Of Saudi Arabia Annual Technical Symposium And Exhibition

Optimization of Infill Drilling in Whicher Range Field in Australia

machine learning

reservoir simulation

asset and portfolio management

cumulative production

transient drainage area

production rate

reservoir model

infill well

tight gas reservoir

Modeling & Simulation

Omar

Aswin R.

Md Mofazzal

Akim H.

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Abstract<p>Now that most of the conventional reservoirs are being depleted at a rapid pace, the focus is on unconventional reservoirs like tight gas reservoirs. Due to the heterogeneous nature and low permeability of unconventional reservoirs, they require a huge number of wells to hit all the isolated hydrocarbon zones. Infill drilling is one of the most common and effective methods of increasing the recovery, by reducing the well spacing and increasing the sweep efficiency. However, the problem with drilling such a large number of wells is the determination of the optimum location for each well that ensures minimum interference between wells, and accelerates the recovery from the field. Detail</p> ... Show More

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