Streaming Feature Grouping and Selection (Sfgs) For Big Data Classification

Real-time data has always been an essential element for organizations when the quickness of data delivery is critical to their businesses. Today, organizations understand the importance of real-time data analysis to maintain benefits from their generated data. Real-time data analysis is also known as real-time analytics, streaming analytics, real-time streaming analytics, and event processing. Stream processing is the key to getting results in real-time. It allows us to process the data stream in real-time as it arrives. The concept of streaming data means the data are generated dynamically, and the full stream is unknown or even infinite. This data becomes massive and diverse and forms what is known as a big data challenge. In machine learning, streaming feature selection has always been a preferred method in the preprocessing of streaming data. Recently, feature grouping, which can measure the hidden information between selected features, has begun gaining attention. This dissertation’s main contribution is in solving the issue of the extremely high dimensionality of streaming big data by delivering a streaming feature grouping and selection algorithm. Also, the literature review presents a comprehensive review of the current streaming feature selection approaches and highlights the state-of-the-art algorithms trending in this area. The proposed algorithm is designed with the idea of grouping together similar features to reduce redundancy and handle the stream of features in an online fashion. This algorithm has been implemented and evaluated using benchmark datasets against state-of-the-art streaming feature selection algorithms and feature grouping techniques. The results showed better performance regarding prediction accuracy than with state-of-the-art algorithms

A six-month retrospective study of resources burden by trauma victims in the surgical intensive care unit of a university hospital in Pakistan

Introduction: Trauma is the fourth leading cause of death globally and constitutes a huge burden on limited critical care resources. Aim This study aimed to identify the trauma patient burden in terms of resources used in the surgical intensive care unit (SICU) of Aga Khan University Hospital in Pakistan which also included characteristics and outcomes of trauma and non-trauma patients.Methods: We retrospectively reviewed all patient data for adult patients (\u3e16 years old) admitted to the SICU from July through December 2014.Results: Of 141 SICU cases included in our study period, 32 (22.7%) trauma patients were identified. On further stratification of trauma patients, road traffic injuries (43.8%), gunshot injuries (43.8%), and blast injuries (6.3%) were the most common, and about 73% of all trauma patients underwent emergency surgical interventions, comprising a huge burden on all resources. The average age of the trauma patients was significantly lower than non-trauma patients (36 years ± 13 vs. 49 years ± 19; p \u3c 0.01). The male-to-female ratio was 7:1 in trauma cases and 2:1 in non-trauma cases (p = 0.019). There was no statistically significant difference in mortality (31.3% vs. 42.2% p \u3e 0.05) and median length of stay [Median (interquartile range), 5(8) vs. 4(7); p \u3e 0.05] between trauma and non-trauma patients.Conclusions: Trauma constitutes a significant burden in terms of resources used for the SICU of the Aga Khan University, Pakistan. Trauma victims are predominantly young men in whom gunshot injuries are as common as road traffic injuries. Emergency surgical interventions comprise the largest draw on resources, followed by use of blood products, radiological, and laboratory investigations

Achieving Efficiency: Lessons From Four Top-Performing Hospitals

Synthesizes lessons from case studies of how four hospitals achieved greater efficiency, including pursuing quality and access, customizing technology, emphasizing communications, standardizing processes, and integrating care, systems, and providers

DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints

Emergency Management Benchmarking Study: Lessons for Increasing Supply Chain Resilience

The challenge to manage a business today is bigger than ever, because we cannot think only about our organization, but the intricate network of organizations that form our supply chain. Some organizations cope far better than others with both the prospect and the manifestation of unquantifiable risk -- they share a critical trait: resilience. This study researches emergency management organizations which are required to maintain a state of readiness for immediate reaction, and evaluates best practices in preparedness, detection, response and recovery. Extracting insights from multiple interviews, this research verified that most of the current emergency management best practices do indeed increase resilience without increasing redundancy; consequently, performance is improved in a cost-effectively way. Applications to supply chain management are made to recommended enhancements to overall resilience