Applications of statistical techniques and artificial neural networks: A review

Jat, Dharm Singh; Dhaka, Poonam; Limbo, Anton

doi:10.1080/09720510.2018.1475073

Cited by 8 publications

(8 citation statements)

References 4 publications

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“…Firstly the Statistical Package for the Social Sciences (SPSS), the software package is used to analyse the infected cases by COVID-19 and to find out the association between gender, age and citizen [18].…”

Section: Statistical Modelsmentioning

confidence: 99%

Artificial Intelligence and Psychosocial Support During the COVID-19 Outbreak

Dhaka

2020

Studies in Big Data

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World Health Organization (WHO) declared new coronavirus disease, COVID-19 as a pandemic in January 2020 and stated that support is needed for mental health and psychosocial wellbeing during this pandemic. Machine learning is the subset of artificial intelligence which can provide resources to overcome the current mental health crisis during COVID-19. It provides the opportunity to solve challenges with the ability to learn from experiences automatically. This chapter gives useful insight to assess the feasibility and efficacy of artificial intelligence and psychosocial support during the COVID-19 outbreak. The demographic data of Namibia during the infestation period of COVID-19 spread is presented and analysed by statistical methods about age, citizen and gender. Further in this study, a regression model is developed, which gives the relationship between the independent variable which is the date of infestation and a dependent variable which is the number of patients, and the available data is being used to predict the future value of infected COVID-19 outbreak in a similar environment.

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Section: Statistical Modelsmentioning

confidence: 99%

Artificial Intelligence and Psychosocial Support During the COVID-19 Outbreak

Dhaka

2020

Studies in Big Data

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“…The neural network architecture mimics the natural functioning of the brain, in which billions of interconnected neurons transmit signals to each other to generate activity or action for the various functions of the body (Shapshak, 2018). In order to apply this natural phenomenon to computing, artificial neural networks are designed to simulate the neuron structure and processes of the brain, with a view to creating cognitively intelligent computer systems that can be deployed to solve complex problems ( Jat, Dhaka & Limbo, 2018). Neural networks go through training processes to enable them to learn and develop the required capability and mastery to solve various problems.…”

Section: Neural Network Ensemblementioning

confidence: 99%

Intelligent Malware Detection Using a Neural Network Ensemble Based on a Hybrid Search Mechanism

Akandwanaho¹,

Kooblal²

2019

Afr. j. inf. commun. (Online)

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Malware threats have become increasingly dynamic and complex, and, accordingly, artificial intelligence techniques have become the focal point for cybersecurity, as they are viewed as being more suited to tackling modern malware incidents. Specifically, neural networks, with their strong generalisation performance capability, are able to address a wide range of cyber threats. This article outlines the development and testing of a neural network ensemble approach to malware detection, based on a hybrid search mechanism. In this mechanism, the optimising of individual networks is done by an adaptive memetic algorithm with tabu search, which is also used to improve hidden neurons and weights of neural networks. The adaptive memetic algorithm combines global and local search optimisation techniques in order to overcome premature convergence and obtain an optimal search outcome. The results from the testing prove that the proposed method is strongly adaptive and efficient in its detection of a range of malware threats, and that it generates better results than other existing methods.

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“… 11 MLP consists of neurons arranged in an input layer, one or more hidden layers and an output layer. 12 MLP containing more than one hidden layer is called deep MLP (DMLP). The input layer includes multiple attributes and input variables, which the model uses to classify the data into different categories.…”

Section: Introductionmentioning

confidence: 99%

“…Each intermediate neuron performs a weighted summation of its inputs and passes the sum to an activation function to produce a value that represents the neuron’s firing intensity. 12 Each layer of neurons activates the sequential layer, eventually generating the output variables in the output layer. 12 The output variables are digital series representing the defined categories that the model aims to classify.…”

Section: Introductionmentioning

confidence: 99%

“… 12 Each layer of neurons activates the sequential layer, eventually generating the output variables in the output layer. 12 The output variables are digital series representing the defined categories that the model aims to classify. DMLP has no restriction on the type or number of input variables; it considers every possible interaction between input variables, enhancing complexity and classification capability.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study

Mac

Tian

et al. 2022

BMJ Open Resp Res

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RationaleSpirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretation of spirometry alone.MethodsWe built a multilayer perceptron model using full PFTs from 748 patients, interpreted according to international guidelines. Inputs included spirometry (forced vital capacity, forced expiratory volume in 1 s, forced mid-expiratory flow25–75), plethysmography (total lung capacity, residual volume) and biometrics (sex, age, height). The model was developed with 2582 PFTs from 477 patients, randomly divided into training (80%), validation (10%) and test (10%) sets, and refined using 1245 previously unseen PFTs from 271 patients, split 50/50 as validation (136 patients) and test (135 patients) sets. Only one test per patient was used for each of 10 experiments conducted for each input combination. The final model was compared with interpretation of 82 spirometry tests by 6 trained pulmonologists and a decision tree.ResultsAccuracies from the first 477 patients were similar when inputs included biometrics+spirometry+plethysmography (95%±3%) vs biometrics+spirometry (90%±2%). Model refinement with the next 271 patients improved accuracies with biometrics+pirometry (95%±2%) but no change for biometrics+spirometry+plethysmography (95%±2%). The final model significantly outperformed (94.67%±2.63%, p<0.01 for both) interpretation of 82 spirometry tests by the decision tree (75.61%±0.00%) and pulmonologists (66.67%±14.63%).ConclusionsDeep learning improves the diagnostic acumen of spirometry and classifies lung physiology better than pulmonologists with accuracies comparable to full PFTs.

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Applications of statistical techniques and artificial neural networks: A review

Cited by 8 publications

References 4 publications

Artificial Intelligence and Psychosocial Support During the COVID-19 Outbreak

Artificial Intelligence and Psychosocial Support During the COVID-19 Outbreak

Intelligent Malware Detection Using a Neural Network Ensemble Based on a Hybrid Search Mechanism

Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study

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