iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations

Naseer, Sheraz; Fati, Suliman Mohamed; Muneer, Amgad; Ali, Rao Faizan

doi:10.1109/access.2022.3144226

Cited by 7 publications

(7 citation statements)

References 40 publications

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“…Accuracy can be defined as the proportion of correct predictions to the total number of predictions made by the system [15].…”

Section: 21accuracymentioning

confidence: 99%

Using Machine Learning Algorithms to Analyze Customer Churn with Commissions Rate for Stocks in Brokerage Firms and Banks

Kaya

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Stock commission rates of banks and brokerage firms are a critical factor for investors. These rates affect the cost of stock investments. In this article, we will discuss the importance of stock commission rates of brokerage firms and banks and how they are determined. To enhance a slightly different approach to customer churn management, data set derived from a banks and brokorage firm has been analyzed. The data set which contains 7816 entries and 14 columns features has been derived from a publicly open-access database and reflects transactions of the firm. Decision Tree, Random Forest, K-NN, Gaussion NB and XGBoost algorithms have been used as analyzing methods and performance of the analysis has been evaluated via three accuracy measures. Two approaches are included for model creation. According to the first analysis results, the Gaussion NB, for second approach the K-NN algorithms gave the best result.

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“…Accuracy can be defined as the proportion of correct predictions to the total number of predictions made by the system [15].…”

Section: 21accuracymentioning

confidence: 99%

Using Machine Learning Algorithms to Analyze Customer Churn with Commissions Rate for Stocks in Brokerage Firms and Banks

Kaya

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…CNNs are meant to address learning issues that need high-dimensional input data with complex spatial structures, and they have yielded excellent results in fields such as computer vision [12,13], medical imaging [14], amino acid sequencing [10,11], and illness prediction. CNNs attempt to construct hierarchical filters to turn massive quantities of input data into accurate class labels with a small number of trainable parameters.…”

Section: Modeling Prediction Using Cnnmentioning

confidence: 99%

“…The Area Under the Curve (AUC) indicates how a classifier can differentiate between classes and is utilized as a ROC curve summary. The greater the AUC, the better the model's efficiency in differentiating the positive and negative samples [10]. In other words, the ROC curve is also known as a recall of the false positive and true positive rates.…”

Section: Receiver Operating Characteristics Curve (Roc)mentioning

confidence: 99%

“…Deep learning (DL) is a practical research approach in the field of ML that involves training computers to carry out activities that are typically carried out by humans [9]. It is distinguished by a hierarchical architecture that spans multiple layers and features a progression of steps for data processing [10]. Recurrent Neural Networks (RNN) and Convolutional Neural Networks are the two categories that fall under the domain of deep learning [11].…”

Section: Introductionmentioning

confidence: 99%

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Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Alqushaibi¹,

Hasan²,

Abdulkadir³

et al. 2023

Computers, Materials &Amp; Continua

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Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world's diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals' lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.

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“…Accuracy is a ratio of the true detected cases to the total cases, and it has been utilized to evaluate models on a balanced dataset [24]. Accordingly, it can be calculated as (1):…”

Section: Accuracymentioning

confidence: 99%

Predicting customers churning in banking industry: A machine learning approach

Muneer

Ali

Alghamdi

et al. 2022

IJEECS

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<span lang="EN-US">In this era, machines can understand human activities and their meanings. We can utilize this ability of machines in various fields or applications. One specific field of interest is a prediction of churning customers in any industry. Prediction of churning customers is the state of art approach which predicts which customer is near to leave the services of the specific bank. We can use this approach in any big organization that is very conscious about their customers. However, this study aims to develop a model that offers a meaningful churn prediction for the banking industry. For this purpose, we develop a customer churn prediction approach with the three intelligent models Random Forest (RF), AdaBoost, and Support Vector Machine (SVM). This approach achieves the best result when the Synthetic Minority Oversampling Technique (SMOTE) is applied to overcome the unbalanced dataset and the combination of undersampling and oversampling. The method on SMOTED data has produced excellent results with a 91.90 F1 score and overall accuracy of 88.7% using RF. Furthermore, the experimental results show that RF yielded good results for the full feature-selected datasets.</span>

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iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations

Cited by 7 publications

References 40 publications

Using Machine Learning Algorithms to Analyze Customer Churn with Commissions Rate for Stocks in Brokerage Firms and Banks

Using Machine Learning Algorithms to Analyze Customer Churn with Commissions Rate for Stocks in Brokerage Firms and Banks

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Predicting customers churning in banking industry: A machine learning approach

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