Address Management in IPv6 Network

Dr. Subbulakshmi T,Ankit Jain 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.2

In this we tend to use a plan of Subnetting for sophistication 'C' address to scale back the address area. We tend to purposed “Aggregate Variable Length Subnet Masking exploitation IP4” with the assistance of fastened Length Subnet Masking, Variable Length Subnet Masking and combination fastened length Subnet Masking. scientific discipline Addresses are at a premium, therefore we tend to minimize the whole scientific discipline usages. Here, we tend to ar operating with the Cisco Packet Tracer and introduced the management of address area and key management in IPv6 network. In unicast and multicast IPv6 networks key management is one among the key security problems. To realize secure communications in such networks reliable and competent key management theme ought to be obligatory and enforced, Whenever a brand new node is accepted to affix or leave the network, a brand new key ought to be generated and distributed to each nodes within the multicast group. Inadequately, this approach will increase the amount of keys transmitted (communication cost) of the key management, whereas variety of algorithms has been projected to handle this issue, most of them have severely affected the computation price (i.e., range of key coding, decryption, and derivation) of the key management. By concentrating on communication and computation prices, we tend to provide prominence to the chance of addressing the each prices while not having to sacrifice one for the sake of the opposite. during this paper, we tend to propose a light-weight key management theme for IPv6 networks, that is capable of reducing each communication and computation prices. The performance analysis demonstrates the potency of our projected methodology as compared with the present ones in reducing such prices, whereas at identical time maintaining each forward and backward securities.

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>

Analysis and Review the Data Using Big Data Hadoop

Ankit Jain,Subbulakshmi T. 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5

Big information is pool of huge and complicated information sets so it becomes tough to method information exploitation management tools. The term ‘Big Data’ illustrates innovative method and knowledge to capture, store, distribute, handle and evaluate petabyte or larger-sized datasets with high-speed and totally different structures. Huge knowledge may be structured, unstructured or semi-structured, leading to incapability of standard knowledge management ways. With the quick evolution of information, information storage and networking assortment capability, massive information area unit quickly growing altogether science and engineering domains. Knowledge is generated from numerous totally different sources and might arrive within the system at numerous rates. So as to method these giant amounts of information in a cheap and economical approach, similarity are employed. Huge knowledge may be knowledge whose scale, diversity, and quality need new design, techniques, algorithms, and analytics to manage it and extract price and hidden information from it. The analysis of huge information typically tough because it often involves assortment of mixed information supported completely different patterns or rules. The challenges embrace capture, storage, search, sharing, analysis, and visualization. The trend to massive information sets is owing to the additional info drawn from analysis of one large set of connected information, compared to separate smaller sets with constant total quantity of information. Massive data processing is that the ability of extracting helpful info from streams of information or datasets, that owing to its rate, variability and volume. This paper argues applications of huge processing model and conjointly massive data processing. Hadoop is that the core platform for structuring huge knowledge, and solves the matter of constructing it helpful for analytics functions. Hadoop is Associate in nursing open supply software system project that permits the distributed process of huge knowledge sets across clusters of goods servers. It’s designed to rescale from one server to thousands of machines, with a awfully high degree of fault tolerance.

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_mf (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 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.

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.

Q Learning MDP Approach to Mitigate Jamming Attack Using Stochastic Game Theory Modelling With WQLA in Cognitive Radio Networks

S.Vimal,Y. Harold Robinson,M.Kaliappan,Subbulakshmi Pasupathi,A.Suresh ICT플랫폼학회 2021 JOURNAL OF PLATFORM TECHNOLOGY Vol.9 No.1