Aarti Yadav ¹, Pooja Mittal ¹

Affiliations
1 Department of Computer Science and Applications, Maharshi Dayanand University (Campus), Rohtak - 124001, Haryana, IN

Abstract

In this research we have made a novel design for home automation by integrating solar based system with Internet of Things. The main motive of research is to simulate domestic device with integration of Solar based energy system. Here we have simulated the power consumption of home appliances. Energy source is solar system in this research. Solar operated system works with battery. The power is consumed as the devices are switch on. IOT based online simulator that will keep track of status of all device in remote database server.

Keywords

Home Automation, IoT, Smart Grid, Solar System, SOA (System Oriented Architecture), Web Services.

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Nasib Singh Gill ¹, Pooja Mittal ¹

Affiliations
1 Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak - 124001, Haryana, IN

Abstract

Objectives: Data mining approaches are used for developing the decision making systems. The current study proposes a novel hybrid model for diabetic prediction by using data mining techniques. The main objective of this study is to improve the accuracy rate by significantly reducing the size of the data under analysis at every stage. Methods/Statistical Analysis: To achieve the objectives, the PIMA Female Diabetic dataset, extracted from UCI repository, is used. The 10-fold cross validation method is used for extracting the testing and the training samples. Three rank based selection techniques are used for the attribute selection. The association between different attributes is identified and then clustering is performed under criticality using HMM and Fuzzy improved Neural Network. Findings: The data size reduces significantly when appropriate selection methods are applied in the respective sequence. For categorical data, the gain ratio attribute selection method out performs. Clustering is more effective when performed after identifying the exact associations among attributes. The proposed hybrid model achieved 92% of overall accuracy. The blend of supervised and un-supervised techniques achieved better results than the techniques when applied individually on the same data, as figured by the comparative analysis. The earlier prediction models worked either on classification or clustering. But in this present study, the classifiers and the clustering are performed. The Fuzzy improved Neural Networks are used for predicting the diabetes disease over the data. The result analysis proved that the prediction accuracy is poor (Naïve Bayes: 76.30%, Neural Networks: 75.13, Support Vector Machine: 77.47, K-Nearest neighbor: 69.79, Decision Tree (J48): 74.21), when the classifiers are implemented separately but when these are amalgamated with each other, produces better results. Application/ Improvements: The proposed hybrid model can be used as an expert system application, under the guidance of diabetic expert to assist the physicians for taking the decisions regarding the early diagnosis of the disease. In future, the proposed model can be applied on gender independent dataset. Further, the accuracy rate of the model can be improved by replacing the missing values of the dataset with the most appropriate value.

Keywords

Associative Clustering, Diabetes, Fuzzy Improved NN, Hidden Markov Model, Information Gain.

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Sindhu Grover ¹, Pooja Mittal ¹

Affiliations
1 Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak - 124001, Haryana, IN

Abstract

Objectives: To identify the Distributed Denial Of service (DDOS) attack in the network and generate a preventive path while performing the V2V communication. The objective of work is to reduce the communication delay and communication loss. Methods/Statistical Analysis: In this work a group controlled method is provided for vehicular network to observe the attack within the group. The parameter driven analysis is applied within the group and identified the attacker node. Once the attacker is identified, the preventive path is generated. The paper has presented the algorithmic model for preventive route formulation. Findings: In Vehicular Ad Hoc Network (VANET), the large node traffic exist which results heavy network communication. DDOS in this attack degrades overall network reliability. In this paper, a group controlled analysis model is proposed to identify the DDOS attacked node and to generate the preventive communication path. At the early stage, the Road Side Unit (RSU) region is processed under mobility parameters to generate the node groups. Each group is also defined with an established controller. Finally, the group communication is observed under communication parameters to identify the attacker node. The paper has explored the model and the associated work stages in detail. Application/Improvements: The work is defined specifically for vehicular network and to identify the DDOS attack. The group specific restricted region analysis is provided to recognize the attacker node and to generate the preventive path.

Keywords

DDOS, Group Based, Infrastructure Driven, VANET.

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