Intrusion Detection System (IDS) is a tool, or software application, that monitors network or system activity and detects malicious activity occurring. The protected evolution of the network must incorporate new threats and related approaches to avoid these threats. The key role of the IDS is to secure resources against the attacks. Several approaches, methods and algorithms of the intrusion detection help to detect a plethora of attacks. The main objective of this paper is to provide a complete system to detect intruding attacks using the Machine Learning technique which identifies the unknown attacks using the past information gained from the known attacks. The paper explains preprocessing techniques, model comparisons for training as well as testing, and evaluation technique.

The automated categorization (classification) of texts into predefined categories is one of the widely explored fields of research in text mining. Now-a-days, availability of digital data is very high, and to manage them in predefined categories has become a challenging task. Machine learning technique is an approach by which we can train automated classifier to classify the documents with minimum human assistance. This paper discusses the Naïve Bayes, Rocchio, k-Nearest Neighborhood and Support Vector Machine methods within machine learning paradigm for automated text categorization of given documents in predefined categories.

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k-Nearest Neighbor is a simple and effective classification method. The primary idea of this method is to calculate the distance from a query point to all of classified data points and make choice of a class which occurs maximum time in k closest neighbors. The Euclidean distance and cosine similarity the common choice for similarity metric among all the similarity measures. Apart from Euclidean and Cosine there are various similarity measures available and being used to calculate similarity in n-dimension vector space model for classification. Similarity calculation is complex operation and computationally need high time if vector dimension increases. Hence this paper explores the usefulness of nine different similarity measures in kNN and presents their experimental results on agriculture dataset. We also compared the time required to finish the classification task and concluded that Idivergence is taking minimum time compared to these algorithms.

AI applications have significantly evolved over the past few years and have found its applications in almost every business sector. AI is making a huge impact in all domains of the industry. Every industry looking to automate certain jobs through the use of intelligent machinery. This paper reviews the work of numerous researchers to get a brief overview about the current implementation of automation in agriculture. The aim of this paper is to identify gaps within the agricultural literature, and gaps in AI guidelines, that may need to be addressed. Moreover, Artificial Intelligence is now a reality in the Higher Education sector as we have started experimenting with the technology and reaping the benefits from the same. But this reality is marginal as there is still a long way to go for AI in the context of development and application in the Education sector. Healthcare organizations in different specialties are also getting more interested in how artificial intelligence can make accurate readings and results to several biological reports and thus gain a better diagnosis of the disease. The aim of this review is to keep track of new scientific accomplishments, to understand the availability of technologies, to appreciate the tremendous potential of AI in biomedicine, and to provide researchers in related fields with inspiration. New progress and breakthroughs will continue to push the frontier and widen the scope of AI applications, and fast developments are envisioned in the foreseeable future.