Welcome to International Journal of Emerging Technologies in Engineering Research (IJETER)

Volume 9, Issue 12, December (2021)

S.No	Title & Authors	Full Text
1	Selection of the Best Petrol Car on the Basis of Qualitative and Quantitative Attributes Using Hybrid MCDM Methods Sameer Kumar Anand, Soupayan Mitra Abstract - Decision problems, especially, multi-criteria decision problems are very tough and complex in nature. Car selection is one of the complex and tough multi attribute decision problems. Due to advancement in technologies and competitiveness in the market, there are varieties of petrol car in the price range of 8-10 lakh available in the market. The choice of selection of the petrol car varies from person to person based on various qualitative and quantitative attributes. In this research paper, six important qualitative and six important quantitative attributes have been considered as per the group of experts of automobile engineering field for selection of the best petrol car from five shortlisted car. To solve this kind of complex multi criteria decision problem, hybrid MCDM (Fuzzy AHP and TOPSIS) method has been used. The weight of all the qualitative attributes have been determined by huge customer survey and group of experts while equal weights have been considered for all the quantitative attributes. The results obtained from MCDM hybrid (Fuzzy AHP and TOPSIS) method have been compared with the results obtained from another MCDM hybrid method (Fuzzy AHP and MOORA) method in order compare the rank of all the five shortlisted car.	Download
2	Optimal Feature Selection and Classification Algorithms for Intrusion Detection System Senthilnayaki B, Nivetha G, Venkatalakshmi K Abstract - The frequency of cyber-attacks has risen drastically as the Internet has grown in popularity, and intrusion detection systems (IDS) have become a critical component of information security. An intrusion detection system (IDS) is a piece of software that helps computer systems identify and respond to intrusions. This anomaly detection system builds a database of normal behaviour and deviations from it, which it uses to detect intrusions when they occur. Individual packets travelling over the network are monitored in network-based IDS, whereas host-based IDS investigates behaviour on a single computer or host. The feature selection contributes to the categorization time reduction. To successfully identify attacks, it was recommended and applied in this work. For this objective, optimal feature selection algorithms such as information gain ratio and genetic algorithm feature selection are applied in this proposed work. Using all these feature selection approaches, the KDD Cup dataset is utilised to find the best number of features. In addition, comparative analyses were done using different classification algorithms that were used to properly classify the data set. However, two classification algorithms are Support Vector Machine and Rule-Based Classification. This strategy is extremely good at detecting DoS attacks and reducing false alarms. The IDS can better identify assaults using the provided feature selection and classification approaches.	Download