Heralded as a major scientific breakthrough of 2013, organic/inorganic lead halide perovskite solar cells have ushered in a new era of renewed efforts at increasing the efficiency and lowering the cost of solar energy. As a potential game changer in the mix of technologies for alternate energy, it has emerged from a modest beginning in 2012 to efficiencies being claimed at 20.1% in a span of just two years. This remarkable progress, encouraging at one end, also points to the possibility that the potential may still be far from being fully realized. With greater insight into the photophysics involved and optimization of materials and methods, this technology stands to match or even exceed the efficiencies for single crystal silicon solar cells. With thin film solution processability, applicability to flexible substrates, and being free of liquid electrolyte, this technology combines the benefits of Dye Sensitized Solar Cells (DSSCs), Organic Photovoltaics (OPVs), and thin film solar cells. In this review we present a brief historic perspective to this development, take a cognizance of the current state of the art, and highlight challenges and the opportunities.

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Medical Expert Systems is an active research area where data analysts and medical experts are continuously collaborating to make these systems more accurate and therefore, more useful in real life. Recent surveys by World Health Organization indicated a great increase in number of diabetic patients and the deaths that are attributed to diabetes each year. Therefore, early diagnosis of diabetes is a major concern among researchers and practitioners. The paper presents an application of automatic multilayer perceptron (AutoMLP) which is combined with an outlier detection method Enhanced Class Outlier Detection using distance based algorithm to create a novel prediction framework. AutoMLP is auto-tunable and performs parameter optimization automatically on the run during training process, which otherwise requires human intervention. Our framework performs outlier detection during pre-processing of data. A series of experiments are performed publicly available dataset: UCI (Prima Indian) and system achieved an accuracy of 88.7% which bests the highest reported results.

Urban centers across the globe are under immense environmental distress due to an increase in air pollution, industrialization, and elevated living standards. The unmanageable and mushroom growth of industries and an exponential soar in population has made the ascent of air pollution intractable. To this end, the solutions that are based on the latest technologies, such as the Internet of things (IoT) and Artificial Intelligence (AI) are becoming increasingly popular and they have capabilities to monitor the extent and scale of air contaminants and would be subsequently useful for containing them. With centralized cloud-based IoT platforms, the ubiquitous and continuous monitoring of air quality and data processing can be facilitated for the identification of air pollution hot spots. However, owing to the inherent characteristics of cloud, such as large end-to-end delay and bandwidth constraint, handling the high velocity and large volume of data that are generated by distributed IoT sensors would not be feasible in the longer run. To address these issues, fog computing is a powerful paradigm, where the data are processed and filtered near the end of the IoT nodes and it is useful for improving the quality of service (QoS) of IoT network. To further improve the QoS, a conceptual model of distributed fog computing and a machine learning based data processing and analysis model is proposed for the optimal utilization of cloud resources. The proposed model provides a classification accuracy of 99% while using a Support Vector Machines (SVM) classifier. This model is also simulated in iFogSim toolkit. It affords many advantages, such as reduced load on the central server by locally processing the data and reporting the quality of air. Additionally, it would offer the scalability of the system by integrating more air quality monitoring nodes in the IoT network.