With the provision of rapid advancement in smart devices used in various technological fields exponentially increases the heterogeneity and energy consumption in internet of things (IoT). The technological leap in information and communication technology instigates the heterogeneity in smart devices, frameworks, architectures, communication technologies, and various industrial and nonindustrial applications. Therefore, a detailed taxonomy of IoT is proposed covering the diverse aspect of IoT lacking interoperability and energy efficiency. Existing research lacks the root causes of heterogeneity and energy consumption at the industrial and technological level. Keeping this in view, our research identified industrial integration and technological challenges. Moreover, we explore the effect on IoT devices when different types of energy harvesters are connected with IoT devices. Our comprehensive research addresses the various issues such as resource management, fog data analytics, energy consumption, heterogeneity, scalability, and the role of quality of service, data science, machine learning to accomplish interoperability and energy efficiency in IoT.

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Background: Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory. Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected. Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients. Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.

Introduction: Parkinson's disease (PD) is a neurological disorder, which is diagnosed on the basis of clinical history and examination alone as there are no diagnostic tests available. However, the current diagnosis highly depends on the knowledge and experience of clinicians and hence subjective in nature. Thus, the focus of this study is to develop a computer-aided diagnosis (CAD) method using T1-weighted magnetic resonance imaging (MRI) to differentiate PD from controls. Method: The proposed method utilizes graph-theory-based spectral feature selection method to select a set of discriminating features from whole brain volume. A decision model is built using support vector machine as a classifier with leave-one-out cross-validation scheme. The performance measures, namely, sensitivity, specificity, and classification accuracy, are utilized to evaluate the performance of the decision model. The efficacy of the proposed method is checked on volumetric 3D T1-weighted (1 mm iso-voxel) MRI dataset of 30 PD patients and 30 age and gender matched controls acquired with 1.5T MRI scanner. Results: Experimental results demonstrate that the proposed method is able to differentiate PD from controls with an accuracy of 86.67%, which encourages the use of CAD. The performance of the proposed method outperforms the existing methods except one. In addition, it is observed that the maximum number of selected features belong to caudate region followed by cuneus region. Thus, these regions may be considered as potential biomarkers in diagnosis of PD. Conclusion: The proposed method may be utilized by the clinicians for diagnosis of PD.

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