T. Vignesh ¹, K. K. Thyagharajan ², D. Murugan ³, M. Sakthivel ⁴, S. Pushparaj ⁴

Affiliations
1 Department of Computer Science and Engineering, S. A. Engineering College, Chennai - 600077, Tamil Nadu, IN
2 R. M. D Engineering College, Kavaraipettai - 601206, IN
3 Department of Computer Science and Engineering, M. S. University, Tirunelveli - 627012,Tamil Nadu, IN
4 Department of Geography, University of Madras, Chennai - 600005, Tamil Nadu, IN

Abstract

Objectives: To classify the satellite images into different land use/land cover classes such as water, building, cropland, forest, etc, to monitor the environmental impacts. Method: In this paper, images are grouped into various clusters using a novel SVD trace function clustering algorithm. The clustered samples are used as a training set in a novel unsupervised Ensemble Minimization Learning algorithm (EML) for classification. The main aim of using EML is to classify the forest, vegetative land patterns, build up area in rural and urban areas with the use of best accuracy rate. Finding: Our proposed methods provides 90.56% classification rate with low error rate. This EML applies multinomial probit model and ensembles simulated data set and improves the learning of nonlinear relationships between the classified attributes. Multinomial probit model is used to bring all the related possible segmented values to fall into one single category, thus increasing the classification accuracy. Our proposed methods experimented with three different real data sets. The experimental results indicate that our proposed unsupervised model outperforms than the previous techniques. Application: It could be using for land use/land cover change detection, under water object identification, coastal area monitoring, etc. Improvement: In future it could be apply in video data and could be improve the classification accuracy also.

Keywords

Ensemble Minimization Learning algorithm, Land use/Land Cover Classification, Multinominal Probit Model, SVD Trace Function, Unsupervised Algorithm.

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S. Arunkumar ¹, V. Vijay Anand ¹, N. Kumarappan ², V. Suveetha ², B. Toziba ², T. Vignesh ²

Affiliations
1 SOM, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
2 School of Civil Engineering, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN

Abstract

Objectives: To study the demographic profile of the construction laborers working in rural and urban areas of Tanjore district, to find out the significant factors which influences the safety and health practices in the construction sites as per the perception of laborers (Construction site workers) and to provide suggestive strategies to raise the level of awareness of the significance of safety and health practices at the construction sites. Methods/Analysis: The population of study deals with the construction workers who work in selected construction sites located in the rural areas of Tanjore district.The study adopted the non-probability convenient sampling. The schedule was self- administered to 132 respondents with the help of the researcher.The data were subject to reliability analysis to check the internal consistency and validity test such as content validity & face validity were applied. The data were analyzed using percentage analysis to depict the demographic profile and specific statistical tools used for the analysis was multiple regression analysis for determining the overall satisfaction level of construction workers. SPSS version 16 was used for data anlaysis. Findings: The research study reveals that the significant factors responsible for the safety and health practices are lack of first aid training program, the firm pressurizes workers to complete the work beyond their limits, the workers feel nervous and mental phobia when working under high altitude, safety net not provided for scaffolding, lack of temporary railings for staircases, caution and reflector are not provided to denote the sign of danger. Applications/Improvement: The study provides some suggestive strategies to enhance the safety performance level and the study need to add more items related to safety measures and test the instrument with more samples both in rural and urban areas.

Keywords

Awareness, Construction Sites, Laborers, Rural Areas, Safety, Training.

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