Rainfall Prediction System Using Machine Learning Fusion for Smart Cities

Rahman, Atta; Abbas, Sagheer; Gollapalli, Mohammed; Ahmed, Rashad; Aftab, Shabib; Ahmad, Munir; Khan, Muhammad Adnan; Mosavi, Amir

doi:10.3390/s22093504

Cited by 80 publications

(35 citation statements)

References 31 publications

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“…The proposed model split the dataset of oral cancer prediction into 70% for training and 30% for testing. After the testing phase, the proposed model used numerous performance statistical parameters [ 30 , 42 , 43 , 44 , 45 , 46 , 47 ] like Classification Accuracy (CA), Classification Miss Rate (CMR), Specificity, Sensitivity, F1-Score, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Positive Ratio (LPR), Likelihood Negative Ratio (LNR) and Fowlkes-Mallows Index to evaluate the results of oral cancer prediction. The proposed model represents Ç for predicted true positive values, ø for predicted true negative values, ∂ for predicted false positive values, and µ for predicted false negative values.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Rahman

Alqahtani

Aldhafferi

et al. 2022

Sensors

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Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

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Section: Simulation and Resultsmentioning

confidence: 99%

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Rahman

Alqahtani

Aldhafferi

et al. 2022

Sensors

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“…So, we applied both AGDPM and AlexNet models on training and testing data, and choose the bestperformed model to predict the single-gene inheritance disorder, mitochondrial inheritance disorder, and multifactorial inheritance disorder. Before the selection of the bestpredicted model, we applied several statistical performance parameters [26]- [31] like Miss classification rate (MCR), sensitivity, specificity, Positive predicted value (PPV), Classification accuracy (CA), Negative predicted value (NPV), False-negative ratio (FNR), f1-score, Likelihood positive ratio (LPR), False positive ratio (FPR), Likelihood negative ratio (LNR) and Fowlkes-Mallows index (FMI) on predicted results. Statistical performance parameters are described below in the form of mathematical equations with respect to the confusion matrix and in these equations ∂, Ø, µ & represents the true-positive, false-positive, true-negative results and false-negative results respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

Advance Genome Disorder Prediction Model Empowered With Deep Learning

et al. 2022

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A major and essential issue in biomedical research is to predict genome disorder. Genome disorders cause multivariate diseases like cancer, dementia, diabetes, cystic fibrosis, leigh syndrome, etc. which are causes of high mortality rates around the world. In past, theoretical and explanatory-based approaches were introduced to predict genome disorder. With the development of technology, genetic data were improved to cover almost genome and protein then machine and deep learning-based approaches were introduced to predict genome disorder. Parallel machine and deep learning approaches were introduced. In past, many types of research were conducted on genome disorder prediction using supervised, unsupervised, and semi-supervised learning techniques, most of the approaches using binary problem prediction using genetic sequence data. The prediction results of these approaches were uncertain because of their lower accuracy rate and binary class prediction techniques using genome sequence data but not genome disorder patients' data with his/her history. Most of the techniques used Ribonucleic acid (RNA) gene sequence and were not often capable of handling bid data effectively. Consequently, in this study, the AlexNet as an effective convolutional neural network architecture proposed to develop an advance genome disorder prediction model (AGDPM) for predicting genome multi classes disorder using a large amount of data. AGDPM tested and compare with the pre-trained AlexNet neural network model and AGDPM gives the best results with 89.89% & 81.25% accuracy of training and testing respectively. So, the advance genome disorder prediction model shows the ability to efficiently predict genome disorder and can process a large amount of patients' genome disorder data with a multi-class prediction method. AGDPM has proved that it is capable to predict single gene inheritance disorder, mitochondrial gene inheritance disorder, and multifactorial gene inheritance disorder with respect to various statistical performance parameters. So, with the help of AGDPM biomedical research will be improved in terms to predict genetic disorders and put control on high mortality rates.

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“…[ 75 ] Govt. Hospital data Supervised Machine Learning 98.4% Zagrouba et al [ 76 ] Hospital data Supervised Machine Learning 96.79% Ahmed et al [ 77 ], 77 Hospital data Fuzzy Rule Based System 88.78% Sujata et al2019 [ 79 ] PD data from UCI repository Kernel based chaotic Firefly model 90% Dash et al, 2017 [ 80 ] PD data from UCI repository Enhanced chaos-based Firefly model 97.20% Khan et al, 2020 [ 92 ] Hospital data SVM for heart disease prediction 93.33% Rehman et al, 2020 [ 93 ] Hospital data Deep extreme learning machine for Diabetes Type II 92.8% Ghazal et al, 2022 [ 95 ] 1920 images Transfer learning 87.1% Alqudaihi et al, 2021 [ 96 ] Voice data COVID-19 detection by Cough sound using ML – Alsunaidi et al, 2021 [ 97 ] Big data Big data analytics for COVID-19 detection – Alhaidari et al, 2021 [ 98 ] E-triage data E-triage of COVID-19 patients using e-triage – …”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

Ahmed,

Alotaibi,

Atta-ur-Rahman

et al. 2022

SN COMPUT. SCI.

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Epilepsy is the second most common neurological disease after Alzheimer. It is a disorder of the brain which results in recurrent seizures. Though the epilepsy in general is considered as a serious disorder, its effects in children are rather dangerous. It is mainly because it reasons a slower rate of development and a failure to improve certain skills among such children. Seizures are the most common symptom of epilepsy. As a regular medical procedure, the specialists record brain activity using an electroencephalogram (EEG) to observe epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate and depend on the specialist’s experience; therefore, automated detection of epileptic pediatric seizures might be an optimal solution. In this regard, several techniques have been investigated in the literature. This research aims to review the approaches to pediatric epilepsy seizures’ identification especially those based on machine learning, in addition to the techniques applied on the CHB-MIT scalp EEG database of epileptic pediatric signals.

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Rainfall Prediction System Using Machine Learning Fusion for Smart Cities

Cited by 80 publications

References 31 publications

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Advance Genome Disorder Prediction Model Empowered With Deep Learning

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

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