Machine Learning Algorithm to Identify the Fault Data Identification Using Multi-Class Support Vector Machine

An experiment was conducted to the raw web log files, in a controlled lab environment, by using KDD technique and M-SVM algorithm. Based on the experiment conducted, the M-SVM algorithm generates 98.68% for true positive rate and 1.32% for false positive rate which indicates the significant efficiency of the new web log file classification and data transformation technique used in this research work. MSVM model identified fault data identification in more accurate with less time when compared to existing SVM model

Bounds on Energy and Laplacian Energy of Graphs

Let G be simple graph with n vertices and m edges. The energy E(G) of G, denotedby E(G), is dened to be the sum of the absolute values of the eigenvalues of G. Inthis paper, we present two new upper bounds for energy of a graph, one in terms ofm,n and another in terms of largest absolute eigenvalue and the smallest absoluteeigenvalue. The paper also contains upper bounds for Laplacian energy of graph

Minimum Covering Seidel Energy of a Graph

In this paper we have computed minimum covering Seidel energies ofa star graph, complete graph, crown graph, complete bipartite graph and cocktailparty graphs. Upper and lower bounds for minimum covering Seidel energies of agraphs are also established.DOI : http://dx.doi.org/10.22342/jims.22.1.234.71-8

Minimum Dominating Distance Energy of a Graph

Recently we introduced the concept of minimum dominating energy[21]. Motivatedby this paper,we introduced the concept of minimum dominating distance energyEDd(G) of a graph G and computed minimum dominating distance energies of a Stargraph,Complete graph,Crown graph and Cocktail graphs. Upper and lower boundsfor EDd(G) are also established.DOI : http://dx.doi.org/10.22342/jims.20.1.133.19-2

Assessment of Radon in groundwater and associated human risk from Sankarabarani River Sub Basin, Southern India

Radon (222Rn) and associated human risk assessment in groundwater from quaternary shallow aquifers of Sankarabarani River sub basin, Southern India has been attempted by considering 41 groundwater samples and analysed for 222Rn using scintillation Radon monitoring system. The Radon ranges between 0.140±0.01 Bq l-1 to 7.869±0.33 Bq l-1 with an average of 1.797±0.12Bq l-1 and found to be within the maximum contamination level of Environmental Protection Agency (11.1 Bq l-1). The doses of ingestion and inhalation calculated for radon varies between 0.709 µSv y-1 to 39.933µSv y-1 with an average of 9.121µSv y-1which is within the prescribed dose limit of 100µSv y-1 by World Health Organisation. Uranium speciation attempted suggests saturated Haiweeite and Soddyite as sources for uranium/radon into the aquifer systems. The Eh-pH diagram suggests uraninite solubility within the pH ranges 6 to 8 within the groundwater environment

Machine Learning Algorithm for Development of Enhanced Support Vector Machine Technique to Predict Stress

Stress is a common risk factor for many diseases A correct and efficient prediction model is required to predict stress levels for targeted prevention and intervention in the personal healthcare domain Before preventing the event of stress-related diseases stress should be detected and managed early However surveys are used to evaluate an individual s stress condition with ease of measurement and requiring little time However anything that puts high demands on a person makes it stressful This includes positive events such as getting married buying a house going to college or receiving a promotion Of course not all stress is caused by external factors Stress can also be internal or self-generated when a person worries excessively about something that may or may not happen or have irrational pessimistic thoughts about life This article aims to develop a predictive model to find the interruption of stress using an efficient way One of the successive machine learning algorithm is SVM This paper proposed to enhance the parameters of SVM which is used to improve the efficiency for predicting stress This article proposed an Enhanced Support Vector Machine classifier to predict Stress The stress dataset is downloaded from the Kaggle repository with 951 instances and 21 attribute

Investigation of Submarine Groundwater Discharge using Thermal Satellite and Radon mapping along the East Coast of the Tamil Nadu and Pondicherry Region, India

Submarine groundwater discharge (SGD) demarcated as a significant component of hydrological cycle found to discharge greater volumes of terrestrial fresh and recirculated seawater to the ocean associated with chemical constituents (nutrients, metals, and organic compounds) aided by downward hydraulic gradient and sediment-water exchange. Delineating SGD is of primal significance due to the transport of nutrients and contaminants due to domestic, industrial, and agricultural practices that influence the coastal water quality, ecosystems, and geochemical cycles. An attempt has been made to demarcate the SGD using thermal infrared images and radon-222 (222Rn) isotope. Thermal infrared images processed from LANDSAT-8 data suggest prominent freshwater fluxes with higher temperature anomalies noted in Cuddalore and Nagapattinam districts, and lower temperature noted along northern and southern parts of the study area suggest saline/recirculated discharge. Groundwater samples were collected along the coastal regions to analyze Radon and Physico-chemical constituents. Radon in groundwater ranges between 127.39 Bq m-3 and 2643.41 Bq m-3 with an average of 767.80 Bq m-3. Calculated SGD fluxes range between -1.0 to 26.5 with an average of 10.32 m day-1. Comparison of the thermal infrared image with physio-chemical parameters and Radon suggest fresh, terrestrial SGD fluxes confined to the central parts of the study area and lower fluxes observed along with the northern and southern parts of the study area advocate impact due to seawater intrusion and recirculated seawater influence

Estimating groundwater inputs from Sankarabarani River Basin, South India to the Bay of Bengal evaluated by Radium (226Ra) and nutrient fluxes

Sankarabarani river basin gains significance due to presence of major industrial, agricultural, urban development and tourist related activities has influenced the water quality in the estuarine environment.  Investigations about river water quality has been attempted but not more studies focus about the evaluation of groundwater discharge a significant process that connects groundwater and the coastal seawater have been attempted.  For the present study, radium (226Ra) a naturally occurring isotope was measured at three locations and used as effective tracers for estimating the groundwater discharge along with nutrient inputs to the Bay. Groundwater samples representing north east monsoon (December, 2017) has been collected during tidal variation in three locations (Location A- away from the coast towards inland, Location B-intermediate between Location A and the coast and Location C-at the estuary). 226Ra mass balance calculated groundwater fluxes irrespective of tidal variations were 2.27×108 m3/d, 2.19×108 m3/d and 5.22×107m3/d for A, B and C locations respectively. The nutrients like Dissolved inorganic nitrogen (DIN), Dissolved inorganic Phosphate (DIP) and Dissolved Silica (DSi) were found to be influencing the coastal groundwater by contributing fluxes to the sea of about 679.33 T mol/day. The study suggests increasing radium and nutrient fluxes to the Bay altering the coastal ecosystems would result in surplus algal blooms creating hypoxia