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Efficient real-time clustering is a relevant topic in big data streams. Data stream clustering needs necessarily a short time execution frame with bounded memory utilizing a one-scan process. Because of the massive volumes and dynamics of data streams, parallel clustering solutions are urgent. This paper presents a new approach for this trend, with advantages to…

In 1980, an application was developed to track, manage and store documents in electronic format. Scan technology has enabled organizations to digitize papers for easier document storage and tracking. Document management tools have since developed by introducing new functionalities, related to security, users services, workflow and audit. Our research is part of the context of…

With the addition of thing of internet applications to 5G smartphones, mobile standby time and ultra-low-power sensing systems have become increasingly important. Nowadays, such sensing systems typically reduce power consumption to microwatts. This paper presents segmented and non-binary weighted capacitance DACs, low power, high resolution, an asynchronous clock, a spark-detect, vcm-based switching and direct switching,…

Recurrent Neural Networks has been widely used by researchers in the domain of weather prediction. Weather Prediction is forecasting the atmosphere for the future. In this proposed paper, Deep LSTM networks has been implemented which is the variant of RNNs having additional memory block and gates making them capable of remembering long term dependencies. Fifteen…

Big Data Analytics (BDA) is important to utilize data from hospital systems to reduce healthcare costs. BDA enable queries of large volumes of patient data in an interactively dynamic way for healthcare. The study objective was high performance establishment of interactive BDA platform of hospital system. A Hadoop/MapReduce framework was established at University of Victoria…