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Remote health monitoring applications with the advent of Internet of Things (IoT) technologies have changed traditional healthcare services. Additionally, in terms of personalized healthcare and disease prevention services, these depend primarily on the strategy used to derive knowledge from the analysis of lifestyle factors and activities. Through the use of intelligent data retrieval and classification models, it is possible to study disease, or even predict any abnormal health conditions. To predict such abnormality, the Convolutional neural network (CNN) model is used, which can detect the knowledge related to disease prediction accurately from unstructured medical health records. However, CNN uses a large amount of memory if it uses a fully connected network structure. Moreover, the increase in the number of layers can lead to an increase in the complexity analysis of the model. Therefore, to overcome these limitations of the CNN-model, we propose a CNN-regular target detection and recognition model based on the Pearson Correlation Coefficient and regular pattern behavior, where the term ''regular'' denotes objects that generally appear in similar contexts and have structures with low variability. In this framework, we develop a CNN-regular pattern discovery model for data classification. First, the most important health-related factors are selected in the first hidden layer, then in the second layer, a correlation coefficient analysis is conducted to classify the positively and negatively correlated health factors. Moreover, regular patterns' behaviors are discovered through mining the regular pattern occurrence among the classified health factors. The output of the model is subdivided into regular-correlated parameters related to obesity, high blood pressure, and diabetes. Two distinct datasets are adopted to mitigate the effects of the CNN-regular knowledge discovery model. The experimental results show that the proposed model has better accuracy, and low computational load, compared with three different machine learning techniques methods.

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Wireless communication technology is the future of communication, but rapid growth in wireless technology has led to a scarcity in the spectrum. Thus, the world has moved away from fixed spectrum allocation to dynamic spectrum allocation. Cognitive radio technology is a rapidly growing technique that allows spectrum to be shared between licensed or primary users (PUs) and unlicensed or secondary users (SUs). The SUs are allowed to use the licensed channels in the absence of the PUs. Upon the arrival of a PU on the channel, the SU has to leave the channel and resume its transmission on another channel. This process is known as spectrum mobility, and the shift in the channel is known as spectrum handoff. Typical transmission of data using this technology requires numerous spectrum handoffs, leading to fluctuations in the spectrum allotted to SUs. Reducing the number of handoffs and providing SUs with a better transmission environment require choosing an efficient handoff strategy. The current handoff strategies face various drawbacks that reduce the efficiency of the network. This paper presents a probabilitybased centralized device for increasing the efficiency of spectrum handoffs in cognitive radio networks. The handoff strategy presented in this paper improves the accuracy in sensing the right channel for handoff, reduces the energy consumed in the process, reduces the handoff time, and speeds up the transmission of data. This paper presents a complete model of the system, along with the detailed study of its parameters that proves the effectiveness of the technique.

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