Preferred Language
Articles
/
YhbaFooBVTCNdQwCuZAN
Hybrid Deep Learning Model for Singing Voice Separation
...Show More Authors

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achieves (4.81) dB GNSDR gain, (7.28) dB GSIR gain, and (3.39) dB GSAR gain in comparison to current approaches

Scopus Crossref
View Publication
Publication Date
Thu Jan 01 2026
Journal Name
Sustainable Civil Infrastructures
The Impact of the Jesko Strategy on Learning the Skill of Smashing in Volleyball
...Show More Authors

View Publication
Scopus Crossref
Publication Date
Wed Jan 28 2026
Journal Name
F1000research
Enhancing Solar Power Forecasting Accuracy Using HMPCS and Machine Learning Techniques: An Applied Study
...Show More Authors

Background Solar irradiance is a nonlinear and intermittent function, which makes accurate forecasting of solar power generation a challenge. The high variability of meteorological conditions is not well represented by conventional atmospheric models, thus hampering forecasting skill and model robustness. In this work, an advanced hybridization of multi-population cuckoo search (HMPCS) algorithm with machine learning (ML) methods is developed to enhance the prediction performance of photovoltaic (PV) power forecasting with more reliability. Methods In this study, a hybrid modeling framework is proposed, called HMPCS–ML framework which captures the global search capacity of HMPCS and predictive power of sophisti

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sun Jan 01 2017
Journal Name
National Journal Of Physiology, Pharmacy And Pharmacology
The attitudes of final year medical and pharmacy students to interprofessional learning in Iraq
...Show More Authors

View Publication
Crossref (1)
Crossref
Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
Post COVID-19 Effect on Medical Staff and Doctors' Productivity Analysed by Machine Learning
...Show More Authors

The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T

... Show More
View Publication Preview PDF
Scopus (8)
Crossref (13)
Scopus Crossref
Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Engineering
Effect Of Technology Based Learning As A Supplement To Traditional Technology On Student's Achievement
...Show More Authors

This paper describes a practical study on the impact of learning's partners, Bluetooth Broadcasting system, interactive board, Real – time response system, notepad, free internet access, computer based examination, and interaction classroom, etc, had on undergraduate student performance, achievement and involving with lectures. The goal of this study is to test the hypothesis that the use of such learning techniques, tools, and strategies to improve student learning especially among the poorest performing students. Also, it gives some kind of practical comparison between the traditional way and interactive way of learning in terms of lectures time, number of tests, types of tests, student's scores, and student's involving with lectures

... Show More
View Publication Preview PDF
Crossref
Publication Date
Tue Oct 20 2026
Journal Name
Journal Of Studies And Researches Of Sport Education
The impact of the V-shape strategy on learning basic skills in a breaststroke
...Show More Authors

View Publication
Publication Date
Wed Jun 30 2021
Journal Name
Journal Of Economics And Administrative Sciences
The Impact of the Strategic Leadership in Strategic Learning Exploratory Research at Anbar University
...Show More Authors

        This study aims to know the extent of the impact of Strategic Leadership as an independent variable in Strategic Learning as a dependent variable to help the senior leadership in Anbar University to take the right decisions to develop Strategic Learning programs in light of the circumstances of the Covid-19 and the sudden decisions adopted by the university to switch to E-learning and to blend. The survey was conducted by distributing a questionnaire that was adopted as a primary tool in data collection from the study sample represented by the university's senior leaders, An intentional random sample of (105) was selected from our community of (127), the data were analyzed by (SPSS) Depe

... Show More
View Publication Preview PDF
Crossref (2)
Crossref
Publication Date
Wed Jan 01 2025
Journal Name
Journal Of Engineering And Sustainable Development
Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning Techniques
...Show More Authors

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

... Show More
View Publication
Scopus (5)
Crossref (3)
Scopus Crossref
Publication Date
Mon May 06 2024
Journal Name
Journal Of Ecological Engineering
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
...Show More Authors

The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying

... Show More
Preview PDF
Scopus (14)
Crossref (14)
Scopus Clarivate Crossref
Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Intelligent Systems
A study on predicting crime rates through machine learning and data mining using text
...Show More Authors
Abstract<p>Crime is a threat to any nation’s security administration and jurisdiction. Therefore, crime analysis becomes increasingly important because it assigns the time and place based on the collected spatial and temporal data. However, old techniques, such as paperwork, investigative judges, and statistical analysis, are not efficient enough to predict the accurate time and location where the crime had taken place. But when machine learning and data mining methods were deployed in crime analysis, crime analysis and predication accuracy increased dramatically. In this study, various types of criminal analysis and prediction using several machine learning and data mining techniques, based o</p> ... Show More
View Publication
Scopus (18)
Crossref (10)
Scopus Clarivate Crossref