Text Mining Approaches for Postmarket Food Safety Surveillance Using Online Media.

Food contamination and food poisoning pose enormous risks to consumers across the world. As discussions of consumer experiences have spread through online media, we propose the use of text mining to rapidly screen online media for mentions of food safety hazards. We compile a large data set of labeled consumer posts spanning two major websites. Utilizing text mining and supervised machine learning, we identify unique words and phrases in online posts that identify consumers' interactions with hazardous food products. We compare our methods to traditional sentiment-based text mining. We assess performance in a high-volume setting, utilizing a data set of over 4 million online reviews. Our methods were 77-90% accurate in top-ranking reviews, while sentiment analysis was just 11-26% accurate. Moreover, we aggregate review-level results to make product-level risk assessments. A panel of 21 food safety experts assessed our model's hazard-flagged products to exhibit substantially higher risk than baseline products. We suggest the use of these tools to profile food items and assess risk, building a postmarket decision support system to identify hazardous food products. Our research contributes to the literature and practice by providing practical and inexpensive means for rapidly monitoring food safety in real time.

Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply.

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches.

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model.

In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.

A review on the state-of-the-art privacy-preserving approaches in the e-health clouds.

Cloud computing is emerging as a new computing paradigm in the healthcare sector besides other business domains. Large numbers of health organizations have started shifting the electronic health information to the cloud environment. Introducing the cloud services in the health sector not only facilitates the exchange of electronic medical records among the hospitals and clinics, but also enables the cloud to act as a medical record storage center. Moreover, shifting to the cloud environment relieves the healthcare organizations of the tedious tasks of infrastructure management and also minimizes development and maintenance costs. Nonetheless, storing the patient health data in the third-party servers also entails serious threats to data privacy. Because of probable disclosure of medical records stored and exchanged in the cloud, the patients' privacy concerns should essentially be considered when designing the security and privacy mechanisms. Various approaches have been used to preserve the privacy of the health information in the cloud environment. This survey aims to encompass the state-of-the-art privacy-preserving approaches employed in the e-Health clouds. Moreover, the privacy-preserving approaches are classified into cryptographic and noncryptographic approaches and taxonomy of the approaches is also presented. Furthermore, the strengths and weaknesses of the presented approaches are reported and some open issues are highlighted.

A comparative analysis of parallel computing approaches for genome assembly.

Over the last two decades, we have witnessed a tremendous growth of sequenced genomic data. However, the algorithms and computational power required to expeditiously process, classify, and analyze genomic data has lagged considerably. In bioinformatics, one of the most challenging and computationally intensive processes, which may take up to weeks of compute time, is the assembly of large size genomes. Several computationally feasible sequential assemblers have been devised and implemented to assist in the process. A few algorithms also have been parallelized to speed up the assembly process. However, very little has been done to thoroughly analyze such parallel algorithms using the specific metrics of parallel computing paradigm. It is essential to investigate parallel assembly algorithms to ascertain their scalability and efficiency. The genomic data varies considerably in size that ranges from a few thousand units of data to several billions. Moreover, the degree of repetition in the data also exhibits high variance from one set to another. Therefore, we must establish an association between the nature, size, and degree of repetition in the genomic data and the best parallel assembly algorithm. The paper includes a comparative analysis of some of the most widely used approaches to assemble genomes using the parallel computing paradigm.