There has been a steady rise in the number of patients suffering from Alzheimer's disease (AD) all over the world. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. The patient's records are collected from National Institute on Aging, USA. The Sample consisted of initial visits of 496 subjects seen either as control or as patients. Patients were concerned about their memory at the National Institute on Aging. It also consisted of patients and caregiver interviews. This research work presents different models for the classification of different stages of Alzheimer's disease using various machine learning methods such as Neural Networks, Multilayer Perceptron, Bagging, Decision tree, CANFIS and Genetic algorithms. The classification accuracy for CANFIS was found to be 99.55% which was found to be better when compared to other classification methods. Based on the outcome of classification accuracies, various management and treatment strategies such as pharmacotherapeutic and non pharmacotherapeutic interventions for mild, moderate and severe AD were elucidated, which can be of enormous use for the medical professionals in diagnosis and treatment of AD.

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The Internet of Things (IoT) paradigm envisions to support the creation of several applications that aids in the betterment of the society from various sectors such as environment, finance, industry etc. These applications are to be user-centric for their larger acceptance by the society. With the increase in the number of sensors that should are getting connected to the IoT infrastructure, there is an augmented increase in the amount of data generated by these sensors. Therefore it becomes a fundamental requirement to search for the sensors that produce the most applicable data required by the application. In this regard, context parameters of the sensors and the application users can be utilized to effectively filter out sensors from a large group. This paper proposes a sensor search scheme based on semantic-weights and fuzzy clustering. We have modified the traditional fuzzy c-means clustering algorithm by incorporating the semantic and context attributes of the sensors to obtain fuzzy clusters. During the query resolution phase, the query is directed to the most appropriate cluster. These clusters are formed through the use of linguistic variables rather than quantitative attributes and thus aid in effective user-centric search results. Experimental results indicate that the proposed scheme achieves better performance when compared to the existing techniques.

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