Informatics Publishing Limited

S. Prakasam , Dr. M. Venkatachalam , Dr. M.  Saroja

Abstract

Hardware/software codesign is the main approach to designing the embedded systems. One of the primary steps of the hardware/software codesign is the hardware/software partitioning. Most electronic systems, whether self-contained or embedded, have a predominant digital component consisting of a hardware platform which executes software application programs. Hardware/software co-design means meeting system-level objectives by exploiting the synergism of hardware and software through their concurrent design. Hardware/software partitioning is a crucial problem in embedded system design. Co-design problems have different flavors according to the application domain, implementation technology and design methodology. The optimization algorithm which is recently introduced is called Grey Wolf Optimizer (GWO) is introduced for better performance. Digital hardware design has increasingly more similarities to software design. Hardware circuits are often described using modeling or programming languages, and they are validated and implemented by executing software programs, which are sometimes conceived for the specific hardware design.

Keywords

Hardware/Software, Co-design partitioning, Genetic Algorithm (GA), Grey Wolf Optimizer (GWO)

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K. Chitra Lekha ¹, S. Prakasam ¹

Affiliations
1 Department of Computer Science and Applications, SCSVMV University, Enathur, IN

Abstract

In Data mining, Classification of data is very emblematic task. Given a vector of attributes, to predict the value of selected discrete class variable correctly is the goal of Classification. The main objective of this work is from the performance evaluation of different classifiers of data mining techniques to find the best classifier. A study has been conducted during December 2016-January 2017 with different category respondents of 1023. The questionnaire was designed to collect the factors about common cyber crime threats among the various sectors respondents in and around Chennai. For the purpose of concluding best classifier among different classifiers, the WEKA tool is used. In this study, J48, BayesNet, RandomForest, Logistic classifiers were used for the purpose of analyzing the best classifier for the cyber crime dataset. The Classification technique that has latent to extensively advance the common or predictable methods will be recommended for use in many sectors.

Keywords

BayesNet, Classification, J48, Kappa Statistic, Logistic, RandomForest, Simulation error, WEKA.

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S. Lavanya ¹, S. Prakasam ¹

Affiliations
1 Department of CSA, SCSVMV University, Kanchipuram - 631561, Tamil Nadu, IN

Abstract

Objective: Reliability and Energy conservation are two main factors to be considered in wireless sensor networks. Wireless Sensor Networks (WSN) is widely used in time-critical applications. WSN are resource constrained, so developing energy efficient protocols is a major concern in sensor networks. Multilayer clusters are preferred for sensors used in Remote Monitoring applications. When the position of sensor changes then it becomes very difficult to conserve energy as the cluster head is mainly responsible for delivery of the data. Protocols like EADUC, TLPER follows Multilayer clustering approach. The main aim of this paper is to develop dynamic clusters with less energy consumption. Methods/Statistical Analysis: The proposed method D-MCDA is efficient when the location of nodes varies dynamically and the clusters are formed based on their locations which proves efficient when compared to other protocols. First all the nodes are involved in the formation of the cluster and then the Cluster head is selected. The actual data packet is sent from the source to destination and the consumption of energy is calculated for various protocols and their results are compared. Findings: As energy consumption was the main motivation, use of multilayer and additional cluster heads have reduced the energy consumption by 0.019. The Proposed D-MCDA algorithms have performed better in terms of average energy consumption per node and in the process of designing a cluster. The proposed algorithm is compared with the existing algorithm like TLPER and MCDA. Applications/Improvements: Energy Consumption is very essential in applications like Remote monitoring, process control and automations. Reliability is another issue to be considered as it is another major reason for energy depletion. Reliability on the basis of congestion avoidance scheme, acknowledgement mechanisms can be considered which can reduce the energy consumption to a major level.

Keywords

Cluster Analysis, Cluster Head, Energy Consumption, Sensor Networks.

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K. Nithyanandan ¹, S. Prakasam ¹

Affiliations
1 Department of Computer Science and Applications, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, IN

Abstract

The authors of this study have built an outstanding data mining model for the classification of respiratory issues in children and adults, which they have applied in their research. Deep learning ensembles are built by utilising support vector regression (SVR), long short-term memory neural networks (LSTMs), and a metaheuristic optimization (MHO) strategy that incorporates nonlinear learning in the DL ensemble. A collection of LSTMs with variable hidden layers and neurons is used to detect and exploit the underlying relationships in order to overcome the limitations of a single deep learning approach limited generalisation skills and robustness when faced with diverse input. The LSTM classification is then combined with a nonlinear-learning SVR and MHO to optimise the top-layer parameters. Nonlinear-learning meta-layer and LSTM classification. Finally, the final classification of the ensemble is provided by the fine-tuning meta-layer. Using data from six benchmark studies as well as energy consumption data sets, the proposed EDL is put to the test in two classification scenarios: ten-ahead and one-ahead classification.

Keywords

Data Mining, Ensemble Deep Learning, Support Vector Regression, Metaheuristic Optimization Algorithm

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D. Archana ¹, S. Prakasam ¹

Affiliations
1 Department of Computer Science and Application, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University, IN

Abstract

As the use of swarm intelligence algorithms grows, so does the interest in placing nodes in a Wireless Sensor network. For this reason, the RSSI range model positioning algorithm has been replaced by a more accurate one. With the help of this paper, you can solve complex structural optimization problems with the Grasshopper Optimization Algorithm (GOA). Optimization problems can be solved using this algorithm, which was inspired by the behaviour of grasshopper colonies. CEC2005 is used to test the GOA algorithm quality and quantitative performance. Trusses with a total of 53 and 3 cantilever beams are used to demonstrate the design practicality. It appears that the proposed algorithm outperforms well-known and recently developed algorithms in this area. GOA ability to solve real-world problems with unknown search spaces is demonstrated by its use in the real world.

Keywords

WSN (Wireless Sensor Network), GOA (Grasshopper Optimization Algorithm), RSSI, Whale Optimization Algorithm

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