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jih-553
Minimizing total Completion Time and Maximum late Work Simultaneously
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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,…,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
Mon Jul 10 2023
Journal Name
Journal Of Engineering
Experimental Study of Pre-Cast Reinforced Concrete Deep Beams (Hallow Core section) Retrofitting with Carbon Fiber Reinforced Polymer (CFRP)
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Experimental programs based test results has been used as a means to find out the response of individual elements of structure. In the present study involves investigated behavior of five reinforced concrete deep beams of dimension (length 1200 x height 300 x width150mm) under two points concentrated load with shear span to depth ratio of (1.52), four of these beams with hallow core and
retrofit with carbon fiber reinforced polymer CFRP (with single or double or sides Strips). Two shapes of hallow are investigated (circle and square section) to evaluated the response of beams in case experimental behavior. Test on simply supported beam was performed in the laboratory & loaddeflection, strain of concrete data and crack pattern of

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Publication Date
Sun Sep 06 2015
Journal Name
Baghdad Science Journal
Single-input Multiple-output Signals Third-order Active-R Filter for different Circuit Merit Factor (Q)
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Single-input Multiple-output Signals Third-order Active-R Filter for different Circuit Merit Factor Q Configuration is proposed. This paper discusses a new configuration to realize third-order low pass, band pass and high pass. The presented circuit uses Single-input Multiple-output signals, OP-AMP and passive components. This filter is useful for high frequency operation, monolithic IC implementation and it is easy to design .This circuit gives three filter functions low-pass, high-pass and band-pass. This filter circuit can be used for different merit factor (Q) with high pass band gain. This gives better stop-band attenuation and sharper cut-off at the edge of the pass-band. Thus the response shows wider pass-band. The Ideal value of thi

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Publication Date
Fri Jan 13 2023
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Detection of Chronic Obstructive Pulmonary Disease among Smokers Aged ≥40 years Attending Primary Health Care Centers in Baghdad-AL-Karkh and AL-Resafa: الكشف عن الالكشف عن مرض الانسداد الرئوي المزمن بين المدخنين الذين تزيد أعمارهم عن 40 عامًا يراجعون مراكز الرعاية الصحية الأولية في بغداد - الكرخ و الرصافة
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Background: Chronic obstructive pulmonary disease causes permanent morbidity, premature mortality and great burden to the healthcare system. Smoking is it's most common risk factor and Spirometry is for diagnosing COPD and monitoring its progression.

Objectives: Early detection of chronic obstructive pulmonary disease in symptomatic smokers’ ≥ 40years by spirometry.

Methods: A cross sectional study on all symptomatic smokers aged ≥ 40 years attending ten PHCCs in Baghdad Alkarkh and Alrisafa. Those whose FEV1/FVC was <70% on spirometry; after giving bronchodilator, were considered COPD +ve.

Results:  Overall, airway obstruction was seen in

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Publication Date
Tue Jan 30 2024
Journal Name
Iraqi Journal Of Science
Machine Learning Based Crop Yield Prediction Model in Rajasthan Region of India
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     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

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Publication Date
Sat Sep 30 2023
Journal Name
Iraqi Journal Of Science
A New Efficient Hybrid Approach for Machine Learning-Based Firefly Optimization
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     Optimization is the task of minimizing or maximizing an objective function f(x) parameterized by x. A series of effective numerical optimization methods have become popular for improving the performance and efficiency of other methods characterized by high-quality solutions and high convergence speed. In recent years, there are a lot of interest in hybrid metaheuristics, where more than one method is ideally combined into one new method that has the ability to solve many problems rapidly and efficiently. The basic concept of the proposed method is based on the addition of the acceleration part of the Gravity Search Algorithm (GSA) model in the Firefly Algorithm (FA) model and creating new individuals. Some stan

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Publication Date
Wed Aug 31 2022
Journal Name
Iraqi Journal Of Science
Data Mining Methods for Extracting Rumors Using Social Analysis Tools
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       Rumors are typically described as remarks whose true value is unknown. A rumor on social media has the potential to spread erroneous information to a large group of individuals. Those false facts will influence decision-making in a variety of societies. In online social media, where enormous amounts of information are simply distributed over a large network of sources with unverified authority, detecting rumors is critical. This research proposes that rumor detection be done using Natural Language Processing (NLP) tools as well as six distinct Machine Learning (ML) methods (Nave Bayes (NB), random forest (RF), K-nearest neighbor (KNN), Logistic Regression (LR), Stochastic Gradient Descent (SGD) and Decision Tree (

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Publication Date
Wed Dec 29 2021
Journal Name
Anatolian Congresses 8th International Applied Sciences Congress
The Effect of Twin-Row Silage Maize Harvesting on Single Row Silage Machine Performance
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In this study, the harvest of maize silage with the cross double row sowing method were tested with a single row disc silage machine in two different PTO applications (540 and 540E min-1) and at two different working speeds v1, v2 (1.8 and 2.5 km h-1). The possibilities of harvesting with a single row machine were revealed, and performance characteristics such as hourly fuel consumption, field-product fuel consumption and PTO power consumption were determined in the trials. The best results in terms of hourly fuel consumption and PTO power consumption were determined in the 540E PTO application and V1 working speed. When the fuel consumption of the field-product is evaluated, it is obtained with V2 working speed and 540E PTO application. As

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Publication Date
Sun Apr 29 2018
Journal Name
Iraqi Journal Of Science
Intelligent Age Estimation From Facial Images Using Machine Learning Techniques
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     Lately, a growing interest has been emerging in age estimation from face images because of the wide range of potential implementations in law enforcement, security control, and human computer interactions. Nevertheless, in spite of the advances in age estimation, it is still a challenging issue. This is due to the fact that face aging process is not only set by distinct elements, such as genetic factors, but by extrinsic factors, such as lifestyle, expressions, and environment as well. This paper applied machine learning technique to intelligent age estimation from facial images using J48 classifier on FG_NET dataset. The proposed work consists of three phases; the first phase is image preprocessing which include

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Publication Date
Tue Jun 20 2023
Journal Name
Baghdad Science Journal
Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

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Publication Date
Sat Apr 15 2023
Journal Name
Journal Of Robotics
A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

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