In these modern times where internet has become widely popular and used by almost everyone, anyone can share or upload articles without any credibility. False news refers to articles that are published with the intent of deliberately misleading readers. In the recent times false news on internet has become more and it has become a major problem as it is difficult to differentiate between the real and the false news. False news and false posts have become more prevalent on social media sites such as Face book and Twitter. From these platforms the news will be spread like wild fire without any authenticity. It can be used to sway election outcomes against certain candidates, can be used for click baiting purposes, and can be used to earn revenue by misleading the users. In this paper we will use natural language processing techniques like bag of words and TD-IDF and machine learning concepts of classification algorithms like SVM and passive aggressive classifier to train our machine to differentiate false news from real news and we will compare the accuracy of methods used to find accurate model.

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To improve the performance of the wireless network, different types of topologies were introduced like start, ring, bus, mesh and dynamic topology. However, providing the security is the very difficult because of the unique environment. But, affording the security is most required task to avoid less data transmission and high data loss. Hence, a novel Chimp based Associativity routing (CbAR) was proposed for predicting and neglecting the malicious events in the mesh network. The dataset that has utilized for the performance testing process is CICIDS database. Besides, the robustness of the proposed model is validated by launching the Denial of Service (DoS) attack in the mesh topology. Moreover, the planned model is tested in the python environment. Finally, the communication and attack prediction parameters like tthroughput, accuracy, delay, transmission time, packet drop and data transfer rate have been validated and compared with other existing models. In that, the presented model has gained high accuracy, throughput and data transfer rate. Also, it has minimized delay and data flow rate.

Data anonymization should support the analysts who intend to use the anonymized data. Releasing datasets that contain personal information requires anonymization that balances privacy concerns while preserving the utility of the data. This work shows how choosing anonymization techniques with the data analyst requirements in mind improves effectiveness quantitatively, by minimizing the discrepancy between querying the original data versus the anonymized result, and qualitatively, by simplifying the workflow for querying the data.

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