Template-Type: ReDIF-Article 1.0 Author-Name: Simone Maria Da Silva Lima Author-X-Name-First: Simone Maria Da Silva Author-X-Name-Last: Lima Author-Name: Caroline Maria De Miranda Mota Author-X-Name-First: Caroline Maria De Miranda Author-X-Name-Last: Mota Author-Name: Danielle Freitas Santos Marinho Author-X-Name-First: Danielle Freitas Santos Author-X-Name-Last: Marinho Title: Inequalities in the geographic distribution of chronic diseases in Brazil: an index methodology Abstract: The purpose of the present article is to compare the geographic distribution of nine chronic diseases in Brazil: arterial hypertension, arthritis/rheumatism, back/spine, bronchitis/asthma, cancer, chronic renal failure, depression, diabetes and heart disease. The data used is from the Brazilian National Health Survey (PNS) composed of 60,202 participants (≥ 18 years old). The morbidity rate of diseases was calculated for 27 units of Brazil. A geographic chronic disease index (gCDI) was formulated as a summary measure to group and compare the distribution of these illnesses based on factor analysis (FA). The observation of trends in health-related indexes and maps can be an advantage to analyse large databases. The final index indicated regional differences showing that the South of Brazil had more individuals with chronic diseases compared to the North of the country mainly for arterial hypertension, depression, diabetes, and heart disease. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 89-104 Issue: 2 Volume: 14 Year: 2022 Keywords: public health; Brazilian National Health Survey; PNS; Brazil; non-communicable diseases; NCDs; chronic disease index; factor analysis. File-URL: http://www.inderscience.com/link.php?id=124732 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:2:p:89-104 Template-Type: ReDIF-Article 1.0 Author-Name: Srilakshmi Inuganti Author-X-Name-First: Srilakshmi Author-X-Name-Last: Inuganti Author-Name: R. Rajeshwara Rao Author-X-Name-First: R. Rajeshwara Author-X-Name-Last: Rao Title: Recognition of online handwritten Telugu stroke by detected dominant points using curvature estimation Abstract: Online handwritten Telugu character is a mix of strokes, which are from pen-down to pen-up positions. The preliminary objective of feature extractions (FE) is to distinguish the stroke from other strokes. In this paper, we propose a FE method for Telugu strokes by utilising dominant points (DP). This is a non-parametric approach. The procedure initially defines the regions of support (ROS) for each coordinate as per the local properties. With this ROS, the curvature is estimated for every point on the curves and also is utilised to gauge DP. The points encompassing local maximum curvatures are stated as DP. The proposed feature also includes the direction between consecutive DPs of the stroke. The proposed mechanism is verified with HP-Lab data available in the UNIPEN format as it encompasses Telugu characters. It is perceived as of the outcomes that the proposed feature enhances recognition accuracy over the chosen dataset. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 140-158 Issue: 2 Volume: 14 Year: 2022 Keywords: online handwritten character recognition; OHCR; dominant points; curvature estimation; bending value; two-phase classifier; region of support; ROS. File-URL: http://www.inderscience.com/link.php?id=124754 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:2:p:140-158 Template-Type: ReDIF-Article 1.0 Author-Name: Rajesh Veluthan Author-X-Name-First: Rajesh Author-X-Name-Last: Veluthan Title: Long-term corporate social responsibility agenda considering climate change policy and conservation of forest - an exploratory analysis of Kerala-based companies Abstract: Nowadays, corporate social responsibility (CSR) is being viewed as a valuable approach for achieving stronger relations with an organisation's internal and external stakeholders. Also, environmental management initiatives are crucial in the present scenario owing to the growing environmental concerns. The business organisations also view environment-related CSR activities as a social obligation and a way of paying back to society. In light of such considerations, the present paper discusses the significance of CSR statutory policies related to CSR, climate change and conservation of forest, and the organisational motives behind taking CSR initiatives with particular reference to the Kerala-based organisations. The purpose is to recognise the challenges sourced by climate change along with shortage of forest conservation and the role that can be assumed by the organisation-based CSR initiatives in re-mediating such rising environmental concerns. Also, the benefits of undertaking such efforts for the organisations have been discussed. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 159-168 Issue: 2 Volume: 14 Year: 2022 Keywords: corporate social responsibility; CSR programs; statutory regulations; climate change; environmental responsibility; forest conservation; Kerala. File-URL: http://www.inderscience.com/link.php?id=124755 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:2:p:159-168 Template-Type: ReDIF-Article 1.0 Author-Name: Parita Shah Author-X-Name-First: Parita Author-X-Name-Last: Shah Author-Name: Priya Swaminarayan Author-X-Name-First: Priya Author-X-Name-Last: Swaminarayan Title: Machine learning-based sentiment analysis of Gujarati reviews Abstract: Opinion examination is the investigation of applied information in an articulation, like appraisals, assessments, sentiments, or perspectives toward a point, individual, or component. Positive, negative, and unbiased articulations are altogether conceivable. The authors of this exploration have built a dataset of Gujarati film audits and give the discoveries produced by the proposed calculation message in the wake of performing sentiment examination utilising a five different machine classifier. The authors fostered various datasets to test our calculation's capacities with different machine classifiers. This paper clarifies how information was gathered to shape a dataset, as well as Gujarati text pre-handling, include determination, and order techniques. According to the results of the investigation, all of the classifiers are performing brilliantly, generating overall precision greater than 75%, however KNN is unable to produce preferred precision above the others. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 105-121 Issue: 2 Volume: 14 Year: 2022 Keywords: N-gram; feature selection; sentiment evaluation; Gujarati language; film analysis; machine classifier. File-URL: http://www.inderscience.com/link.php?id=124763 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:2:p:105-121 Template-Type: ReDIF-Article 1.0 Author-Name: Abu Bakkar Siddique Author-X-Name-First: Abu Bakkar Author-X-Name-Last: Siddique Author-Name: Mahfuzulhoq Chowdhury Author-X-Name-First: Mahfuzulhoq Author-X-Name-Last: Chowdhury Title: A machine learning-based approach to predict university students' depression pattern and mental healthcare assistance scheme using Android application Abstract: Depression is particularly common among university students in developing countries like Bangladesh. University students may face challenges with their studies, relationships, drugs, and family issues, all of which are major or minor contributors to depression. This research study focuses on gaining useful insights into why university students in Bangladesh suffer from depression and predicting depression in university undergraduates for the purpose of referral to a psychiatric facility. A Google survey form was used to gather data for this study. After training and testing the dataset with five algorithms, the best methods for predicting depression among Bangladeshi undergraduate students were discovered. A comparison of various prediction algorithms such as logistic regression, KNN, SVM, random forest, decision tree, including accuracy, precision, recall, error rate, f-measure, mean absolute percentage error for analysis was done. We also designed and developed an Android mental healthcare mobile application to provide mental support to university students. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 122-139 Issue: 2 Volume: 14 Year: 2022 Keywords: depression; machine learning; prediction; evaluation; mental healthcare; Android application. File-URL: http://www.inderscience.com/link.php?id=124766 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:2:p:122-139 Template-Type: ReDIF-Article 1.0 Author-Name: Hetal A. Joshiara Author-X-Name-First: Hetal A. Author-X-Name-Last: Joshiara Author-Name: Chirag S. Thaker Author-X-Name-First: Chirag S. Author-X-Name-Last: Thaker Author-Name: Sanjay M. Shah Author-X-Name-First: Sanjay M. Author-X-Name-Last: Shah Author-Name: Darshan B. Choksi Author-X-Name-First: Darshan B. Author-X-Name-Last: Choksi Title: Detection of stragglers and optimal rescheduling of slow running tasks in big data environment using LFCSO-LVQ classifier and enhanced PSO algorithm Abstract: This paper plans to implement intelligent techniques in finding straggler tasks along with speculating their way of execution. Here, the LFCSO-LVQ is proposed to effectively identify the slow running tasks as of a bunch of user tasks, and the enhanced PSO is proposed for performing optimal rescheduling of the identified SR tasks. Initially, the collected data are preprocessed by means of identifying homogenous and heterogeneous tasks. After that, the Apache Spark split the preprocessed tasks into several sub-tasks. The features are extracted as of these subtasks for SR task prediction. An information gain-based linear discriminant analysis is proposed for feature selection approach that reduces the classifier's training time. Subsequent to FS, the selected ones are inputted to LFCSO-LVQ, which envisages the SR tasks of the dataset centred on the chosen features. After that, EPSO reschedules these predicted tasks to the other fastest nodes of virtual machine. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 1-21 Issue: 1 Volume: 14 Year: 2022 Keywords: big data; straggler detection; rescheduling of tasks; speculative execution; slow running tasks identification; learning vector quantisation; LVQ; optimal resource scheduling; particle swarm optimisation; PSO. File-URL: http://www.inderscience.com/link.php?id=121505 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:1:p:1-21 Template-Type: ReDIF-Article 1.0 Author-Name: Rayees Farooq Author-X-Name-First: Rayees Author-X-Name-Last: Farooq Title: Heywood cases: possible causes and solutions Abstract: The purpose of the study is to identify the causes and recommend possible solutions to the Heywood cases. The study reviews the literature from 1960-2021 using the keyword search, 'Heywood cases,' 'Improper solutions,' and 'Negative variance'. The studies were explored from selected databases viz. Google Scholar, Scopus and Web of Science. The study has found that fixing the negative variance to zero is the most widely used solution to the Heywood cases. The study also found that multivariate normality, small sample size with a large number of indicators, factor loadings of less than 0.5, and model misspecification are the possible causes of Heywood cases. The study suggests novel solutions to overcome the possibility of the Heywood cases, including fixing the negative variance to zero, maintaining the large sample size, and increasing the number of indicators in the construct. The study can be beneficial to the researchers who validate the model using CB-SEM. The study offers a basic understanding of the possible causes and novel solutions to the Heywood cases to help the researchers better develop the constructs/scales. The present research guides the researchers through the various effects of Heywood cases on the study's findings. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 79-88 Issue: 1 Volume: 14 Year: 2022 Keywords: heywood cases; improper solutions; misspecification; item-per construct rule; mahalanobis D2; multivariate normality. File-URL: http://www.inderscience.com/link.php?id=121506 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:1:p:79-88 Template-Type: ReDIF-Article 1.0 Author-Name: Apsara Karki Nepal Author-X-Name-First: Apsara Karki Author-X-Name-Last: Nepal Author-Name: Ghulam Muhammad Shah Author-X-Name-First: Ghulam Muhammad Author-X-Name-Last: Shah Author-Name: Farid Ahmad Author-X-Name-First: Farid Author-X-Name-Last: Ahmad Title: Is matching in different situations equally applicable for impact evaluation studies when using observational data? Abstract: Under certain circumstances, randomisation of interventions may not be applicable in development activities. Quasi-experimental research design such as matching often helps creating counterfactuals for impact evaluations. Although different types of statistical matching methods are available, their relative performance is generally unknown to practitioners. Using five sets of household survey data collected from samples of treatment and comparison groups from four countries in the Hindu Kush Himalaya region, we examine the extent of covariate imbalances before and after matching using four different matching methods. For small samples with enough imbalances in the covariates, the nearest neighbour matching does not perform well, but matching with stratification works better. The performance of radius and kernel matching falls in between nearest neighbour and matching with stratification. We find that the matching is useful when covariates imbalance is high before matching but less useful for sample with relatively balanced covariates before matching. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 55-78 Issue: 1 Volume: 14 Year: 2022 Keywords: matching; variable imbalance; impact evaluation; propensity scores; comparison group; observational data. File-URL: http://www.inderscience.com/link.php?id=121512 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:1:p:55-78 Template-Type: ReDIF-Article 1.0 Author-Name: Meenal Jain Author-X-Name-First: Meenal Author-X-Name-Last: Jain Author-Name: Vikas Saxena Author-X-Name-First: Vikas Author-X-Name-Last: Saxena Title: An ECOSVS-based support vector machine for network anomaly detection Abstract: In this paper, the support vector machine (SVM) classification technique to classify normal and attack traffic in the Spark distributed environment has been introduced and evaluated. In terms of classification speed, SVM suffers from the important shortcomings of high time and memory training complexities, which depend on the training set size. The authors have proposed an effective correlation-based support vector selection (ECOSVS) algorithm for SVM speed optimisation. ECOSVS-based SVM performed better when compared with the other three supervised classifiers, namely, logistic regression (LR), decision tree (DT), and random forest (RF) in terms of accuracy and training time. Apache Spark's RDD structure has been used for the detection of network-based anomalies. The analysis of the said algorithm was performed on two publicly available network datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset and Coburg Intrusion Detection Datasets (CIDDS-2017). The results showed that our proposed algorithm reduced the training set size of NSL-KDD and CIDDS-2017 datasets to 99.3% and 85%, respectively. Accuracies of 80% and 87% for the ECOSVS-based SVM classifier were achieved. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 32-54 Issue: 1 Volume: 14 Year: 2022 Keywords: ECOSVS; support vector machine; SVM; anomaly detection; Apache Spark. File-URL: http://www.inderscience.com/link.php?id=121513 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:1:p:32-54 Template-Type: ReDIF-Article 1.0 Author-Name: Sharaf AlKheder Author-X-Name-First: Sharaf Author-X-Name-Last: AlKheder Author-Name: Fahad AlRukaibi Author-X-Name-First: Fahad Author-X-Name-Last: AlRukaibi Author-Name: Ahmad Aiash Author-X-Name-First: Ahmad Author-X-Name-Last: Aiash Title: Location and time factors' effect on types of traffic accident in Kuwait Abstract: Location and time-period can be highly correlated with traffic accidents types. In this study, 287,983 accidents which happened in 2013, 2014, 2016, and 2017 were collected from the General Traffic Department of Kuwait. Four governorates including Kuwait-City, Hawally, Al Farwaniya, and Al Ahmadi were selected as they had the highest rates of accidents. The types of covered traffic accidents were crashes, run-over, and rollover accidents. Afterward, the location and the year where and when the accident occurred were chosen to be the independent variable and the dependent variable was the type of accident. A multinomial logit regression model was chosen to identify the significant variables and determine the correlation between predictors and the dependent variable. The results showed that both location and time were significant variables that influence the occurrence of certain types of accidents. According to the model results, rollover accidents had higher odds of happening in Al Ahmadi governorate. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 22-31 Issue: 1 Volume: 14 Year: 2022 Keywords: traffic accidents; multinomial logit model; location; time-period; Kuwait. File-URL: http://www.inderscience.com/link.php?id=121517 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:14:y:2022:i:1:p:22-31