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Telecom Churn Prediction based on Deep Learning Approach
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      The transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than other prediction models and achieves a high accuracy rate of 91%. Furthermore, it was noticed that the deep learning model outperforms a small size Neural Network for the customer churn prediction.

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
3D scenes semantic segmentation using deep learning based Survey
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Abstract<p>Semantic segmentation realization and understanding is a stringent task not just for computer vision but also in the researches of the sciences of earth, semantic segmentation decompose compound architectures in one elements, the most mutual object in a civil outside or inside senses must classified then reinforced with information meaning of all object, it’s a method for labeling and clustering point cloud automatically. Three dimensions natural scenes classification need a point cloud dataset to representation data format as input, many challenge appeared with working of 3d data like: little number, resolution and accurate of three Dimensional dataset . Deep learning now is the po</p> ... Show More
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Publication Date
Mon Jan 09 2023
Journal Name
2023 15th International Conference On Developments In Esystems Engineering (dese)
Deep Learning-Based Speech Enhancement Algorithm Using Charlier Transform
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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Journal Of Science
Enhancement Digital Forensic Approach for Inter-Frame Video Forgery Detection Using a Deep Learning Technique
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    The digital world has been witnessing a fast progress in technology, which led to an enormous increase in using digital devices, such as cell phones, laptops, and digital cameras. Thus, photographs and videos function as the primary sources of legal proof in courtrooms concerning any incident or crime. It has become important to prove the trustworthiness of digital multimedia. Inter-frame video forgery one of common types of video manipulation performed in temporal domain. It deals with inter-frame video forgery detection that involves frame deletion, insertion, duplication, and shuffling. Deep Learning (DL) techniques have been proven effective in analysis and processing of visual media. Dealing with video data needs to handle th

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Publication Date
Thu Jun 06 2024
Journal Name
Journal Of Applied Engineering And Technological Science (jaets)
Deep Learning and Its Role in Diagnosing Heart Diseases Based on Electrocardiography (ECG)
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Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad

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Publication Date
Sun Feb 27 2022
Journal Name
Iraqi Journal Of Science
A New Method in Feature Selection based on Deep Reinforcement Learning in Domain Adaptation
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    In data mining and machine learning methods, it is traditionally assumed that training data, test data, and the data that will be processed in the future, should have the same feature space distribution. This is a condition that will not happen in the real world. In order to overcome this challenge, domain adaptation-based methods are used. One of the existing challenges in domain adaptation-based methods is to select the most efficient features so that they can also show the most efficiency in the destination database. In this paper, a new feature selection method based on deep reinforcement learning is proposed. In the proposed method, in order to select the best and most appropriate features, the essential policies

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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
Tue Dec 27 2022
Journal Name
2022 3rd Information Technology To Enhance E-learning And Other Application (it-ela)
Diabetes Prediction Using Machine Learning
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

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Publication Date
Tue Jan 30 2024
Journal Name
Iraqi Journal Of Science
Diabetes Diagnosis Using Deep Learning
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     Hyperglycemia is a complication of diabetes (high blood sugar). This condition causes biochemical alterations in the cells of the body, which may lead to structural and functional problems throughout the body, including the eye. Diabetes retinopathy (DR) is a type of retinal degeneration induced by long-term diabetes that may lead to blindness. propose our deep learning method for the early detection of retinopathy using an efficient net B1 model and using the APTOS 2019 dataset. we used the Gaussian filter as one of the most significant image-processing algorithms. It recognizes edges in the dataset and reduces superfluous noise. We will enlarge the retina picture to 224×224 (the Efficient Net B1 standard) and utilize data aug

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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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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