Communication in wireless network is possible with an air medium. Due to the high security threats in this system, the network may face various difficulties. One of the major threat is jamming attack which comes under Denial Of Service (DOS) attack. Jamming attack is common among many exploits that compromises the wireless environment. The work of authorized users is by denying service to as legitimate traffic is jammed by the overwhelming frequencies of illegitimate traffic.In order to mitigate the impact of jamming, three techniques are being used: (a) how to thwart jamming in control channel (b) An Anti-jamming broadcast communication using Uncoordinated Spread Spectrum (USS) technique (c) Mitigating jamming impact in timing channel. We analyse these methods for reducing jamming inside a network.

Mobile Ad hoc networks (MANET) are characterized by wireless connectivity, continuous changing topology, distributed operation and ease of deployment. The data is being transmitted from source node to destination through multiple intermediate nodes ie., in a multi-hop fashion. Each node has a particular range in which the transmission takes places. When a packet is being transmitted they move from one range to the other range in the network where this may lead to packet loss due to link failure and dynamic changing nature. There are many traditional routing protocols which may prevent from this data loss, but they all are susceptible to the node mobility. Here the traditional protocols are being compared with the geographic routing protocols in terms of packet delivery ratio and transmission delay.

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Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always results in death. Chemotherapy, surgery, radiation therapy, and immunotherapy are all potential treatments for multiple myeloma; however, the majority of patients are identified with the disease at an advanced stage, at which time it is resistant to these therapies. After obtaining a diagnosis of advanced multiple myeloma, the average length of time that a person lives is one year after hearing this news. There is a substantial link between asbestos exposure and mesothelioma (MM). Using an approach that enables feature selection and machine learning, this article proposes a classification and detection method for mesothelioma cancer. The CFS correlation-based feature selection approach is first used in the feature selection process. It acts as a filter, selecting just the traits that are relevant to the categorization. The accuracy of the categorization model is improved as a direct consequence of this. After that, classification is carried out with the help of naive Bayes, fuzzy SVM, and the ID3 algorithm. Various metrics have been utilized during the process of measuring the effectiveness of machine learning strategies. It has been discovered that the choice of features has a substantial influence on the accuracy of the categorization.

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