A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

Ibrar, Muhammad; Hassan, Muhammad; Shaukat, Kamran; Alam, Talha Mahboob; Khurshid, Khaldoon Syed; Hameed, Ibrahim A.; Aljuaid, Hanan; Luo, Suhuai

doi:10.1155/2022/2697303

Cited by 26 publications

(9 citation statements)

References 49 publications

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“…We evaluate our model by comparing it with other machine-learning models. The results are compared in terms of accuracy, precision, recall, and F1-Score, which can be calculated using parameters which are defined as [ 59 , 60 , 61 , 62 ]: True Positive (TP): It identifies how much the data instances are identified as recovery cases. True Negative (TN): It identifies how many data instances are categorised as death cases.…”

Section: Resultsmentioning

confidence: 99%

“…The most prevalent sign of advanced hepatic illness is ascites. The ascitic subtype of advanced Schistosomiasis is the most dangerous, accounting for 65–90 percent of cases [ 60 ]. Granulomatous inflammation can be caused by venous blockage and portal hypertension and leads to continuous fibrosis and a drop in plasma colloid osmotic pressure [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

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A Proposed Framework for Early Prediction of Schistosomiasis

et al. 2022

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Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Proposed Framework for Early Prediction of Schistosomiasis

et al. 2022

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“…The information may be used to build models for predicting academic success, locating at-risk students, and spotting problematic behavior. It is designed for use in research on student behavior and performance [38][39][40][41][42][43][44][45][46][47][48][49].…”

Section: Discussionmentioning

confidence: 99%

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Alsabhan

2023

Sensors

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Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic dishonesty during final exams since teachers are not directly monitoring students. We suggest a novel method in this study for identifying possible exam-cheating incidents using Machine Learning (ML) approaches. The 7WiseUp behavior dataset compiles data from surveys, sensor data, and institutional records to improve student well-being and academic performance. It offers information on academic achievement, student attendance, and behavior in general. In order to build models for predicting academic accomplishment, identifying at-risk students, and detecting problematic behavior, the dataset is designed for use in research on student behavior and performance. Our model approach surpassed all prior three-reference efforts with an accuracy of 90% and used a long short-term memory (LSTM) technique with a dropout layer, dense layers, and an optimizer called Adam. Implementing a more intricate and optimized architecture and hyperparameters is credited with increased accuracy. In addition, the increased accuracy could have been caused by how we cleaned and prepared our data. More investigation and analysis are required to determine the precise elements that led to our model’s superior performance.

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“…The study found the key areas of crucial local characteristics work in conjunction with the attention mechanism to exploit each expert’s job from many aspects fully. Seventy-five percent accuracy was achieved [ 20 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Human–Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention

Alsabhan

2023

Sensors

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Emotions have a crucial function in the mental existence of humans. They are vital for identifying a person’s behaviour and mental condition. Speech Emotion Recognition (SER) is extracting a speaker’s emotional state from their speech signal. SER is a growing discipline in human–computer interaction, and it has recently attracted more significant interest. This is because there are not so many universal emotions; therefore, any intelligent system with enough computational capacity can educate itself to recognise them. However, the issue is that human speech is immensely diverse, making it difficult to create a single, standardised recipe for detecting hidden emotions. This work attempted to solve this research difficulty by combining a multilingual emotional dataset with building a more generalised and effective model for recognising human emotions. A two-step process was used to develop the model. The first stage involved the extraction of features, and the second stage involved the classification of the features that were extracted. ZCR, RMSE, and the renowned MFC coefficients were retrieved as features. Two proposed models, 1D CNN combined with LSTM and attention and a proprietary 2D CNN architecture, were used for classification. The outcomes demonstrated that the suggested 1D CNN with LSTM and attention performed better than the 2D CNN. For the EMO-DB, SAVEE, ANAD, and BAVED datasets, the model’s accuracy was 96.72%, 97.13%, 96.72%, and 88.39%, respectively. The model beat several earlier efforts on the same datasets, demonstrating the generality and efficacy of recognising multiple emotions from various languages.

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A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

Cited by 26 publications

References 49 publications

A Proposed Framework for Early Prediction of Schistosomiasis

A Proposed Framework for Early Prediction of Schistosomiasis

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Human–Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention

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