In the present study, BiVO4/CuCr2O4 nanocomposites synthesized via a chemical route are applied as a photocatalyst for the degradation of methylene blue (MB) dye. The photocatalytic activity results indicated a substantial degradation of MB dye by ~90% over the surface of nanocomposite catalyst under visible light illumination. The nanocomposite showed a photocatalytic activity for MB dye degradation which is three times higher compared to that of BiVO4. This has been attributed to photogenerated electron-hole pair charge separation. The prepared photocatalysts were characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), UV-Vis absorption and photoluminescence spectroscopy. Furthermore, an oxidizing reagent such as H2O2 was added to the photocatalytic system, which may act as an alternative electron scavenger and resulting in a notably enhanced rate of pollutant destruction. In addition, the effect of polyaniline has also been studied by synthesizing an organic/inorganic hybrid material (BiVO4/CuCr2O4/PANI). It has been observed that 95% photodegradation of organic dye takes place on the nanocomposite surface with visible light. A possible mechanism explaining the origin of enhanced performance of nanocomposite and nanohybrid is proposed.

The evolution of intelligent and data-driven systems has pushed for the tectonic transition from ancient medication to human-centric Healthcare 4.0. The rise of Internet of Things, Internet of Systems, and wireless body area networks has endowed the health care ecosystem with a new digital transformation supported by sophisticated machine learning and artificial intelligence algorithms. Under this umbrella, health care recommendation systems have emerged as a driver for providing patient-centric personalized health care services. Recommendation systems are automatic systems that derive the decisions on the basis of some valid input parameters and vital health information collected through wearable devices, implantable equipments, and various sensor. Therefore, to understand the state-of-the-art developments in the health care ecosystem, this paper provides a comprehensive survey on health care recommendation systems and the associated paradigms. This survey starts from the ancient health care era and move toward the Healthcare 4.0 in a phased manner. The road map from Healthcare 1.0 to Healthcare 4.0 is analyzed to highlight different technology verticals supporting the digital transformation. This study also provides the systematic review of the health care systems, the types of health care systems, and the recommender systems. Moreover, a deep analysis of health care recommender systems and its types is also presented. Finally, the open issues and challenges associated with the adaption and implementation of human-centric Healthcare 4.0 ecosystem are discussed. This is provided to find out the possible research questions and gaps so that the corresponding solutions could be developed in the near future. KEYWORDScollaborative filtering-based recommender systems, content-based recommender systems, context-aware recommender system, health care recommender system, Healthcare 4.0, human-centric systems INTRODUCTIONThe increased demand of the health statistics and the variations in looking for information tactics can be pragmatic around the globe. 1 As per the latest study, 81% of the adults in the United States utilize the Internet services, and out of them, 59%

No abstract

Internet of Things (IoT) and Data science have revolutionized the entire technological landscape across the globe. Because of it, the health care ecosystems are adopting the cutting‐edge technologies to provide assistive and personalized care to the patients. But, this vision is incomplete without the adoption of data‐focused mechanisms (like machine learning, big data analytics) that can act as enablers to provide early detection and treatment of patients even without admission in the hospitals. Recently, there has been an increasing trend of providing assistive recommendation and timely alerts regarding the severity of the disease to the patients. Even, remote monitoring of the present day health situation of the patient is possible these days though the analysis of the data generated using IoT devices by doctors. Motivated from these facts, we design a health care recommendation system that provides a multilevel decision‐making related to the risk and severity of the patient diseases. The proposed systems use an all‐disease classification mechanism based on convolutional neural networks to segregate different diseases on the basis of the vital parameters of a patient. After classification, a fuzzy inference system is used to compute the risk levels for the patients. In the last step, based on the information provided by the risk analysis, the patients are provided with the potential recommendation about the severity staging of the associated diseases for timely and suitable treatment. The proposed work has been evaluated using different datasets related to the diseases and the outcomes seem to be promising.

No abstract