An Ontological Framework for Information Extraction From Diverse Scientific Sources

Zaman, Gohar; Mahdin, Hairulnizam; Hussain, Khalid; Rahman, Atta; Abawajy, Jemal; Mostafa, Salama A.

doi:10.1109/access.2021.3063181

Cited by 34 publications

(28 citation statements)

References 39 publications

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“…Table 5 presents a summary of the studies that reviewed in this section. The paper in [ 56 ] surveyed various supervised machine learning techniques of text classification. The results revealed that the most effective method for classifying a text is to combine related information into the classification process, because it enhances the result quality of the classification.…”

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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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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“…15. The selected metrics are already described in several studies and are most widely used for the experiments and studies of similar nature [45][46][47]. Additionally, in terms of the extent of the improvements, the CC value increased by 1.03%, while the MAPE, RMSPE, MAE, and RMSE values all decreased by 35.895%, 32.696%, 15.58%, and 7.133%, respectively.…”

Section: Baggingmentioning

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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“…From comparison is it apparent that proposed scheme has two major advantages over the other schemes. First one is complete range of diseases not just one type [41][42][43][44][45][46]. Second advantage is that the data can be analyzed and visualized for many dimensions like gender, location, time, and age-group.…”

Section: Comparisonmentioning

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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An Ontological Framework for Information Extraction From Diverse Scientific Sources

Cited by 34 publications

References 39 publications

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

Machine Learning Empowered Electricity Consumption Prediction

Geo-Spatial Disease Clustering for Public Health Decision Making

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