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P. Subbulakshmi,S. Vimal,Y. Harold Robinson,Amit Verma,Janmenjoy Nayak 대한공간정보학회 2024 Spatial Information Research Vol.32 No.2
The Publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher, in consultation with the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Author P. Subbulakshmi has stated that the authors disagree with this retraction
Comparative Evaluation of Attribute-Enabled Supervised Classification in Predicting the Air Quality
P. Subbulakshmi,S. Vimal,Y. Harold Robinson,Amit Verma,Janmenjoy Nayak 대한공간정보학회 2023 Spatial Information Research Vol.31 No.4
Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.
Enhancement of biogas production from microalgal biomass through cellulolytic bacterial pretreatment
Kavitha, S.,Subbulakshmi, P.,Rajesh Banu, J.,Gobi, Muthukaruppan,Tae Yeom, Ick Elsevier 2017 Bioresource technology Vol.233 No.-
<P><B>Abstract</B></P> <P>Generation of bioenergy from microalgal biomass has been a focus of interest in recent years. The recalcitrant nature of microalgal biomass owing to its high cellulose content limits methane generation. Thus, the present study investigates the effect of bacterial-based biological pretreatment on liquefaction of the microalga <I>Chlorella vulgaris</I> prior to anaerobic biodegradation to gain insights into energy efficient biomethanation. Liquefaction of microalgae resulted in a higher biomass stress index of about 18% in the experimental (pretreated with cellulose-secreting bacteria) vs. 11.8% in the control (non-pretreated) group. Mathematical modelling of the biomethanation studies implied that bacterial pretreatment had a greater influence on sustainable methane recovery, with a methane yield of about 0.08 (g Chemical Oxygen Demand/g Chemical Oxygen Demand), than did control pretreatment, with a yield of 0.04 (g Chemical Oxygen Demand/g Chemical Oxygen Demand). Energetic analysis of the proposed method of pretreatment showed a positive energy ratio of 1.04.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Microalgal biomass pretreatment by bacteria enhances liquefaction of about 18%. </LI> <LI> Bacterial pretreatment increases the macromolecular release considerably. </LI> <LI> Experimental microalgae improves the methane to 0.08gCOD/gCOD comparing to control. </LI> <LI> Methane production rate increased with hydrolysis constant of about 0.24day<SUP>−1</SUP>. </LI> <LI> A positive energy ratio of about 1.04 was achieved in experimental microalgae. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
4-TOTAL MEAN CORDIAL LABELING OF ARROW GRAPHS AND SHELL GRAPHS
R. PONRAJ,S. SUBBULAKSHMI The Korean Society for Computational and Applied M 2023 Journal of applied and pure mathematics Vol.5 No.5
In this paper we investigate the 4-total mean cordial labeling behavior of arrow graphs, shell-Butterfly graph and graphs obtained by joining two copies of shell graphs by a path.
ON 4-TOTAL MEAN CORDIAL GRAPHS
PONRAJ, R.,SUBBULAKSHMI, S.,SOMASUNDARAM, S. The Korean Society for Computational and Applied M 2021 Journal of applied mathematics & informatics Vol.39 No.3
Let G be a graph. Let f : V (G) → {0, 1, …, k - 1} be a function where k ∈ ℕ and k > 1. For each edge uv, assign the label $f(uv)={\lceil}{\frac{f(u)+f(v)}{2}}{\rceil}$. f is called k-total mean cordial labeling of G if ${\mid}t_{mf}(i)-t_{mf}(j){\mid}{\leq}1$, for all i, j ∈ {0, 1, …, k - 1}, where t<sub>mf</sub> (x) denotes the total number of vertices and edges labelled with x, x ∈ {0, 1, …, k-1}. A graph with admit a k-total mean cordial labeling is called k-total mean cordial graph.
4-total mean cordial labeling of arrow graphs and shell graphs
R. Ponraj,S. Subbulakshmi 한국전산응용수학회 2023 Journal of Applied and Pure Mathematics Vol.5 No.5
In this paper we investigate the $4$-total mean cordial labeling behavior of arrow graphs, shell-Butterfly graph and graphs obtained by joining two copies of shell graphs by a path.