GRBF-NN based ambient aware realtime adaptive communication in DVB-S2

Rahman, Atta

doi:10.1007/s12652-020-02174-w

Cited by 20 publications

(16 citation statements)

References 18 publications

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“…The dataset contains 547 samples that are classified through the SVM K-fold cross-validation method. The model was evaluated using various statistical parameters containing accuracy, miss rate, precision, hit rate, true positive rate (TPR), recall, true negative rate (TNR), selectivity, false omission rate (FOR), false discovery rate (FDR), F-score, and F2-score [31][32][33][34]. These parameters can be expressed as:…”

Section: Resultsmentioning

confidence: 99%

Supervised Machine Learning-Based Prediction of COVID-19

Rahman

Naseer²,

Majeed

et al. 2021

Computers, Materials &Amp; Continua

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COVID-19 turned out to be an infectious and life-threatening viral disease, and its swift and overwhelming spread has become one of the greatest challenges for the world. As yet, no satisfactory vaccine or medication has been developed that could guarantee its mitigation, though several efforts and trials are underway. Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment. In this regard, healthcare experts, researchers and scientists have delved into the investigation of existing as well as new technologies. The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease. The state-of-the-art research in Artificial intelligence (AI), Machine learning (ML) and cloud computing have encouraged healthcare experts to find effective detection schemes. This study aims to provide a comprehensive review of the role of AI & ML in investigating prediction techniques for the COVID-19. A mathematical model has been formulated to analyze and detect its potential threat. The proposed model is a cloud-based smart detection algorithm using support vector machine (CSDC-SVM) with cross-fold validation testing. The experimental results have achieved an accuracy of 98.4% with 15-fold cross-validation strategy. The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.

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Section: Resultsmentioning

confidence: 99%

Supervised Machine Learning-Based Prediction of COVID-19

Rahman

Naseer²,

Majeed

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The disadvantage of a neural network is that it is difficult to observe or grasp what happens within the network structure after applying the model on a set of inputs [27]. There are several applications of ANN in various areas of study like classification [28], security [29][30][31], prediction [32], estimation [33], engineering [34][35][36], healthcare [37][38][39] and many more [40].…”

Section: The Proposed Techniques 31 Artificial Neural Networkmentioning

confidence: 99%

Machine Learning Empowered Electricity Consumption Prediction

Metrik¹,

Musleh²

2022

Computers, Materials &Amp; Continua

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Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes a medium-term prediction model that can predict electricity consumption for a given location in Saudi Arabia. Hence, this study implemented a standalone Artificial Neural Network (ANN) model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi Arabia. The dataset used in this research is gathered exclusively from the Saudi Electric Company. The pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine facets. The number of neurons and learning rate of the standalone model were optimized using hyperparameter tuning. Finally, the standalone model was tested against the bagging ensemble using the optimized ANN. The bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN model. The results for the proposed model produced 0.9116 Correlation Coefficient (CC), 0.2836 Mean Absolute Percentage Error (MAPE), 0.4578, Root Mean Squared Percentage Error (RMSPE), 0.0298 MAE, and 0.069 Root Mean Squared Error (RMSE), respectively.

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“…USA government prefers to its doctors to use electronic way for fast processing. Figure 12 shows that in Utah (UT) state, the top Dx code for the month of July was R41.844 that corresponds to Frontal lobe and executive function deficit disease [31][32][33][34][35][36][37][38][39][40].…”

Section: Monthly Analysismentioning

confidence: 99%

Geo-Spatial Disease Clustering for Public Health Decision Making

Rahman

2022

IJCAI

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An explosion of interest has been observed in disease mapping with the developments in advanced spatial statistics, data visualization and geographic information system (GIS) technologies. This technique is known as "Geo-Spatial Disease Clustering," mainly used for visualization and future disease expansion prediction. Its importance has been overwhelmingly observed since the COVID-19 pandemic outbreak. Government, Medical Institutes, and other medical practices gather large amounts of data from surveys and other sources. This data is in the form of notes, databases, spread sheets and text data files. Mostly this information is in the form of feedback from different groups like age group, gender, provider (doctors), region, etc. Incorporating such heterogeneous nature of data is quite challenging task. In this regard, variety of techniques and algorithms have been proposed in the literature, but their effectiveness varies due to data types, volume, format and structure of data and disease of interest. Mostly, the techniques are confined to a specific data type. To overcome this issue, in this research, a data visualization technique combined with data warehousing and GIS for disease mapping is proposed. This includes data cleansing, data fusion, data dimensioning, analysis, visualization, and prediction. Motivation behind this research is to create awareness about the disease for the guidance of patients, healthcare providers and government bodies. By this, we can extract information that describes the association of disease with respect to age, gender, and location. Moreover, the temporal analysis helps earlier prediction and identification of disease, to be care of and necessary avoiding arrangements can be taken. Povzetek: Analizirana je vloga vizualizacije podatkov in rešitev za naloge prostorskega gručenja bolezni, tj. ugotavljanje in napovedovanje širjenja bolezni.Informatica 46 (2022) 21-31 A.u. Rahman et al.results and discussions, while section 5 concludes the paper.

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GRBF-NN based ambient aware realtime adaptive communication in DVB-S2

Cited by 20 publications

References 18 publications

Supervised Machine Learning-Based Prediction of COVID-19

Supervised Machine Learning-Based Prediction of COVID-19

Machine Learning Empowered Electricity Consumption Prediction

Geo-Spatial Disease Clustering for Public Health Decision Making

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