scholarly journals Application of Spatiotemporal Analysis and Knowledge Discovery for Databases in the Bureau of Fire Protection as Incident Report System: Tool for Improving Fire Services

2021 ◽  
pp. 519-533
Author(s):  
Francis Balahadia ◽  
◽  
Albert Vinluan ◽  
Dennis Gonzales ◽  
Melvin Ballera ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Fire Protection ◽  
Fire Risk ◽  
Weather Conditions ◽  
Developmental Model ◽  
Knowledge Discovery In Databases ◽  
Future Research ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Fire Patterns

Purpose–This study aims to contribute to the fire research by developing a fire report management system for the BFP that can analyze spatiotemporal attributes of fire and 520apply Knowledge Discovery in Databases (KDD) methods to identify patterns of fire incidents in the city of Manila.Method–The proponents applied the Knowledge Discovery in Databases (KDD) methods for the processing of identifying fire patterns as well as the application of SMOTE, One-Hot Encoding, and Agile Method as Software developmental model. Result–The records obtained from the BFP headquarters in Manila had a total of 3,506 cases during the six years from 2011 to 2016. The accuracy of the Decision Tree classifier model was 95.92%.Using KDD approach, it generated decision rules fire pattern in Manila. Most fire causes fall under the 'Under Investigation' category while Residential-Commercial types of establishments in Intramuros were affected. Lastly, the fire occurred in the mornings, during Sundays when most people are in their homes and the majority of which took place in the Pandacan district.Conclusion–The application of KDD in building a predictive model to be integrated into the system was the major part of this project. The outputs generated by the system can provide material for use in more accurate fire risk assessments, more efficient allocation of fire resources and personnel, and more targeted fire awareness and prevention programs.Recommendation–Future research in this area may include other factors contributing to a higher likelihood of fire incidences such as weather conditions and other geographical attributes of fire-prone locations. Analysis of these and other relevant factors may allow the BFP to gainfurther insights into the causes of fire incidents, which will enable the agency to make the necessary adjustments and changes in their current fire prevention and risk reduction programsPractical Implication–This study provides direct implication for the Bureau of Fire Protection and community through the given insights of the fire activities and the created model of the system that determine Manila's fire patterns that help identify appropriate information about fire activities and preventive measures of fire incidents.

scholarly journals Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree Classifier

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 1177-1188 ◽  
Author(s):  
Edson Farias de Oliveira ◽  
Maria Emilia de Lima Tostes ◽  
Carlos Alberto Oliveira de Freitas ◽  
Jandecy Cabral Leite
Keyword(s):  
Decision Tree ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Decision Tree Classifier ◽  
Tree Classifier

scholarly journals Classification of Level of Rice Pest Incidence Based on Weather Information

2021 ◽  
pp. 32-43
Author(s):  
V. Jinubala ◽  
P. Jeyakumar
Keyword(s):  
Gall Midge ◽  
Weather Conditions ◽  
Stem Borer ◽  
Vital Role ◽  
Classification Model ◽  
Decision Tree Classifier ◽  
Weather Information ◽  
Tree Classifier ◽  
Rice Pest

Aims: To classify the rice pest data based on the weather attributes using a machine learning approach, a decision tree classifier, and to validate the performance results with other existing techniques through comparison. Design: Rice pest classification using C5.0 algorithm Methodology: We collected rice pest data from the crop fields of various regions in the state of Maharashtra of India. The dataset contains the name of the region (Taluk), period (week), pest data, temperature, rainfall, and relative humidity. The data is collected from 39 taluks within four districts in different weeks of the year of 2013-2014. The weather information plays a vital role in this rice pest data analysis, because based on the weather, pest infestation varies in all the regions. The pests considered in this research are Yellow Stem borer, Gall midge, Leaf folder, and Planthopper. The collected dataset is given as input to the classifier, where 75% of data from the dataset is used for training, and 25% of data are used for testing the classifier. Results: The proposed C5.0 algorithm performed better in the classification of rice pest dataset based on weather attributes. The C5.0 algorithm achieved 88.99% accuracy, 78.81% sensitivity, and 89.11% specificity, which are higher in performance when compared with other techniques. Compared with the other different methods, the C5.0 algorithm achieved 1.3 to 8.5% improved accuracy, 2.4 to 9% improved sensitivity, and 0.8 to 7.8% improved specificity. Conclusion: Early detection of pest and pest based diseases is an essential process to avoid major crop losses. The proposed classification model is designed to classify the level of pest infestations based on weather attributes, as level of infestations caused by the rice pest varies based on weather conditions. The C5.0 algorithm classified the rice pest data based on the weather attributes in the dataset.

Knowledge Creation in Inquiring Organizations using KDD

2011 ◽  
pp. 133-153
Author(s):  
John D. Murray ◽  
Thomas L. Case ◽  
Adrian B. Gardiner
Keyword(s):  
Data Mining ◽  
Knowledge Acquisition ◽  
Knowledge Discovery ◽  
Knowledge Creation ◽  
Knowledge Discovery In Databases ◽  
Future Research ◽  
Information Foraging ◽  
Inquiring Systems ◽  
Concept Of Information

Churchman (1971) emphasized the continual learning nature of organizations as part of their ontological fabric. Accordingly, he proffered the view of organizations as inquiring systems whose actions result in the creation of knowledge. To this end, many modern organizations have attempted to create knowledge by using technologies, such as Data Mining and Knowledge Discovery in Databases (KDD). Although quite powerful, these technologies depend heavily upon the skill and insights of the analyst. We propose that the role of the analyst in the application of these technologies is poorly understood. To advance our understanding in this regard, we dedicate the first part of this chapter to describing the KDD process and relate it to the five philosophical perspectives of organizational knowledge acquisition, as originally discussed by Churchman (1971). In the second part of the chapter, we draw parallels between the process of knowledge acquisition via KDD with the concept of information foraging (Pirolli & Card, 1999). Information foraging theory is offered as a research lens through which we can investigate the role of human judgment in KDD. These insights lead us to propose a number of areas for possible future research. Based on our insights into information foraging and knowledge creation, the chapter concludes by introducing a new organizational metaphor into corporate epistemology: inquiring organizations as knowledge foragers.

Potential Impacts of Extreme Heat and Bushfires on Dementia

2021 ◽  
Vol 79 (3) ◽  
pp. 969-978
Author(s):  
Taya L. Farugia ◽  
Carla Cuni-Lopez ◽  
Anthony R. White
Keyword(s):  
Rural Communities ◽  
Natural Disasters ◽  
Low Socioeconomic Status ◽  
Forest Fires ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Extreme Heat ◽  
Indigenous Populations ◽  
Future Research ◽  
Diagnosis Of Dementia

Australia often experiences natural disasters and extreme weather conditions such as: flooding, sandstorms, heatwaves, and bushfires (also known as wildfires or forest fires). The proportion of the Australian population aged 65 years and over is increasing, alongside the severity and frequency of extreme weather conditions and natural disasters. Extreme heat can affect the entire population but particularly at the extremes of life, and patients with morbidities. Frequently identified as a vulnerable demographic in natural disasters, there is limited research on older adults and their capacity to deal with extreme heat and bushfires. There is a considerable amount of literature that suggests a significant association between mental disorders such as dementia, and increased vulnerability to extreme heat. The prevalence rate for dementia is estimated at 30%by age 85 years, but there has been limited research on the effects extreme heat and bushfires have on individuals living with dementia. This review explores the differential diagnosis of dementia, the Australian climate, and the potential impact Australia’s extreme heat and bushfires have on individuals from vulnerable communities including low socioeconomic status Indigenous and Non-Indigenous populations living with dementia, in both metropolitan and rural communities. Furthermore, we investigate possible prevention strategies and provide suggestions for future research on the topic of Australian bushfires and heatwaves and their impact on people living with dementia. This paper includes recommendations to ensure rural communities have access to appropriate support services, medical treatment, awareness, and information surrounding dementia.

scholarly journals Application of the Socio-Ecological System Framework to Forest Fire Risk Management: A Systematic Literature Review

2021 ◽  
Vol 13 (4) ◽  
pp. 2121 ◽  
Author(s):  
Ingrid Vigna ◽  
Angelo Besana ◽  
Elena Comino ◽  
Alessandro Pezzoli
Keyword(s):  
Risk Management ◽  
Community Involvement ◽  
Forest Ecosystems ◽  
Local Community ◽  
Fire Risk ◽  
Future Research ◽  
Specific Reference ◽  
Clear Understanding ◽  
Wildfire Risk ◽  
Starting Point

Although increasing concern about climate change has raised awareness of the fundamental role of forest ecosystems, forests are threatened by human-induced impacts worldwide. Among them, wildfire risk is clearly the result of the interaction between human activities, ecological domains, and climate. However, a clear understanding of these interactions is still needed both at the global and local levels. Numerous studies have proven the validity of the socioecological system (SES) approach in addressing this kind of interdisciplinary issue. Therefore, a systematic review of the existing literature on the application of SES frameworks to forest ecosystems is carried out, with a specific focus on wildfire risk management. The results demonstrate the existence of different methodological approaches that can be grouped into seven main categories, which range from qualitative analysis to quantitative spatially explicit investigations. The strengths and limitations of the approaches are discussed, with a specific reference to the geographical setting of the works. The research suggests the importance of local community involvement and local knowledge consideration in wildfire risk management. This review provides a starting point for future research on forest SES and a supporting tool for the development of a sustainable wildfire risk adaptation and mitigation strategy.

scholarly journals A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 169
Author(s):  
Sergi Gómez-Quintana ◽  
Christoph E. Schwarz ◽  
Ihor Shelevytsky ◽  
Victoriya Shelevytska ◽  
Oksana Semenova ◽  
...  
Keyword(s):  
Patent Ductus Arteriosus ◽  
Heart Defects ◽  
Ductus Arteriosus ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Heart Sounds ◽  
Decision Tree Classifier ◽  
Support Tool ◽  
Tree Classifier ◽  
Patent Ductus

The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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