In the modern age and many prestigious applications use the recommendation method to play an important role. The system of recommendations collected apps, built a global village and provided enough information for development. This paper presents an overview of the approaches and techniques produced in the recommendation framework for collaborative filtering. Collaborative filtering, material and hybrid methods were the method of recommendation. In producing personalised recommendation the technique of collaborative filtering is particularly effective. There have been several algorithms over ten years of study, but no distinctions have been made between the various strategies. Indeed, there is not yet a widely agreed way to test a collaborative filtering algorithm. In this work we compare various literature techniques and review each one’s characteristics to emphasise their key strengths and weaknesses.

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.

Tweets based micro blogging is the most widely used social media to share the opinions in terms of short messages. Tweets facilitate business men to release the products based on the user interest which thereby produces more profits to their business. It also helps the government to monitor the public opinion which leads to better policies and standards. The large number of tweets on different topics are shared daily so, there is a need to identify trending topics. This paper proposes a method for automatic detection of hot topics discussed predominantly in social media by aggregating tweets of similar topics into manageable clusters. This produces hot topic detection irrespective of the current user location. A Modified Density Peak Clustering (MDPC) algorithm based hot topic detection is proposed. Local density of traditional Density Peak Clustering (DPC) is redefined by using the gaussian function in the calculation of dc (threshold distance). The traditional DPC considering some random value as dc (threshold distance) this gives a negative impact on the cluster formation thereby return inappropriate clusters. This can be solved by using the MDPC. The MDPC algorithm works by taking the cosine similarity between the tweets as the input and produces clusters of similar tweets. The cluster having a greater number of tweets is considered as hot topic which is frequently discussed by most of the users on twitter. Events 2012 dataset is collected with streaming API. This contains tweets from 2012 to 2016. The dataset consists of 149 target events and 30 million tweets. Experimental result shows that the proposed algorithm performs better than the traditional algorithms such as density peak clustering, K-means clustering, and Spectral clustering. It has produced the accuracy of 97%.

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