Now a days, we are completely dependent on the systems and also on the internet. Most of our valuable information is stored in the system or in the cloud. So, it’s very necessary to protect our data and also our system from the outside intruders and also from the intruders already entered into our system. To protect our system, intrusion detection systems are constructed using many traditional techniques. Some of them are decision tree, KDD, data mining techniques, Artificial Neural Network. But there are many flaws in these systems. In this paper, we have used the improvised dragonfly optimization algorithm. Here the traditional dragonfly optimization algorithm is improved by adding the convergence and fitness function. This usage of improvised dragonfly optimization algorithm in detecting the intruders has given the best performance than the other methods. To justify the statement, we have shown the experimental study and also comparative study with other two optimization algorithm. Here first the detection rate is computed on the darknet 2020 dataset by using the traditional dragonfly algorithm, then the detection rate is again computed on the same dataset but with the improved the dragonfly optimization algorithm. Finally, in the comparative study it’s very clear that the improved dragonfly optimization algorithm produced the accurate result than the other optimization algorithm.

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In recent days many people are working on twitter data as the tweets are easily available and also provide reliable data. Collecting and processing these tweets produces promising and accurate results in solving many real world problems. Common problem faced by most of the people is traffic congestion. Traffic congestion results in traffic jams, mental and physical health disturbance. So to avoid this, our paper tried to show the methodology which can bring out promising results. In this paper for processing the tweet data we have used the common approach of Term Frequency-Inverse Document Frequency (TF-IDF) and discussed the application of brainstorming optimization algorithm (BSO) to avoid traffic congestion. We have also introduced the density peak clustering (DPC) to train the brain storming optimization technique. This paper has shown the modified BSO and DPC on the tweets to bring out the results which show traffic conditions at various places. We have justified our work by conducting the experiment.

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In data analysis applications for extraction of useful knowledge, clustering plays an important role. The major shortcoming of traditional clustering algorithms is exhibiting poor performance in solving complex data cluster problems. This research paper introduces a novel hybrid optimization technique based clustering approach. This paper is designed with two main objectives: designing efficient function optimization algorithm and developing advanced data clustering approach. In achieving the first objective, the standard TOA is first enhanced by hybridizing with Lévy flight trajectory and benchmarked on 23 functions. A new clustering approach is developed by conjoining k-means algorithm and Lévy flight TOA. Tested the numerical complexity of the proposed novel clustering approach on 10 UCI clustering datasets and 4 web document cluster problems. Conducted several simulation experiments and done an analysis of the results. The obtained graphical and statistical analysis reveals that the proposed novel clustering approach yields better quality clusters.