Background: Neuroimaging is an important tool in early detection of Alzheimer’s disease (AD) which is a serious neurodegenerative brain disease among the elderly subjects. Independent component analysis (ICA) is arguably one of the most widely used algorithm for the analysis of brain imaging data, which can be used to extract intrinsic networks of brain from functional magnetic resonance imaging (fMRI). Method: Witnessed by recent studies, a more flexible model known as restricted Boltzmann machine (RBM) can also be used to extract spatial maps and time courses of intrinsic networks from resting state fMRI, moreover, RBM shows superior temporal features than ICA. Here, we seek to employ RBM to improve performance of classifying individuals. Experiments are performed on healthy controls and subjects at early stage of AD, i.e., cognitive normal (CN) and early mild cognitive impairment participants (EMCI), and two types of data, i.e., structural magnetic resonance imaging (sMRI) and fMRI data. Results: (1) By separately employing ICA for sMRI and fMRI, the features extracted from fMRI improve classification accuracy by 7.5% for CN and EMCI; (2) instead of applying ICA to fMRI, using RBM further improves classification accuracy by 7.75% for CN and EMCI; (3) the lesions at early stage of AD are more likely to occur in the regions around slices 4, 6, 10, 14, 19, 51 and 59 of the whole brain in the longitudinal direction. Conclusion: By using fMRI instead of sMRI and RBM instead of ICA, we can classify CN and EMCI more efficient.

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: Breast cancer is essential to be detected in primitive localized stage for enhancing the possibility of survival since it is considered as the major malediction to the women society around the globe. Most of the intelligent approaches devised for breast cancer necessitates expertise that results in reliable identification of patterns that conclude the presence of oncology cells and determine the possible treatment to the breast cancer patients in order to enhance their survival feasibility. Moreover, the majority of the existing scheme of the literature incurs intensive labor and time, which induces predominant impact over the diagnosis time utilized for detecting breast cancer cells. An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization (IABC-ABFO) scheme is proposed for facilitating better rate of local and global searching ability in selecting the optimal features subsets and optimal parameters of ANN considered for breast cancer diagnosis. In the proposed IABC-ABFO approach, the traditional ABC algorithm used for cancer detection is improved by integrating an adaptive bacterial foraging process in the onlooker bee and the employee bee phase that results in an optimal exploitation and exploration. The results investigation of the proposed IABC-ABFO approach facilitated using Wisconsin breast cancer data set confirmed an enhanced mean classification accuracy of 99.52% on par with the existing baseline cancer detection schemes.