SparkFlow: Towards High-Performance Data Analytics for Spark-based Genome Analysis

Filgueira, Rosa; Awaysheh, Feras M.; Carter, Adam; White, D. J.; Rana, Omer

doi:10.1109/ccgrid54584.2022.00123

Cited by 5 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early [6] work by scientists like Peter Shor and Lov Grover set the stage for quantum algorithms that could make problems like factorization and search much faster. Also, the creation of quantum error correction codes, especially the surface code, has been very helpful in fixing the fact that quantum information is naturally fragile.Companies like IBM, Google, and Rigetti Computing have [7] been working hard over the past few years to build and improve quantum computers. When Google's Sycamore processor reached quantum dominance, it was a major turning point in the field because it showed that a quantum computer could do some jobs better than traditional supercomputers.…”

Section: Review Of Literaturementioning

confidence: 99%

Frontiers of Computing - Evolutionary Trends and Cutting-Edge Technologies in Computer Science and Next Generation Application

Samir N. Ajani,

2024

jes

View full text Add to dashboard Cite

The cutting edge of computing is always changing because people are always looking for new ways to do things and combining cutting-edge tools. There are a number of natural trends that will affect the next generation of apps. Quantum computing is one of the most important trends. It is a new way of doing things that uses the rules of quantum physics to do calculations at speeds that are impossible with regular computers. Quantum computing could help solve hard issues in security, optimization, and modeling, which would lead to new science discoveries and technological progress.Neuromorphic computing, which is based on the structure of the human brain, is another new realm. The goal of this revolutionary way of computing is to create artificial neural networks that can learn, change, and process information in ways that are similar to how biological systems do it. There is a lot of promise for neuromorphic computing to improve machine learning and make AI apps more efficient and complex.The world of computing is also changing because artificial intelligence (AI) and edge computing are coming together. Edge computing brings computing power closer to the data source, which lowers delay and improves real-time processing. This is why it is so important for the growth of technologies like self-driving cars, smart cities, and the Internet of Things (IoT).Adding blockchain technology is another step forward that will make sure deals in open systems are safe and clear. Blockchain is used for more than just cryptocurrency. It has an impact on supply chain management, healthcare, and banking, among other areas. The quantum computing, neuromorphic computing, the combination of AI and edge computing, and the use of blockchain technology are all on the cutting edge of computing. Together, these trends bring computer science into a new era and lay the groundwork for groundbreaking uses that will shape the future of technology.

show abstract

Section: Review Of Literaturementioning

confidence: 99%

Frontiers of Computing - Evolutionary Trends and Cutting-Edge Technologies in Computer Science and Next Generation Application

Samir N. Ajani,

2024

jes

View full text Add to dashboard Cite

show abstract

From Data to Discoveries: Bioinformatics Strategies for Analyzing Metabolomics, Proteomics, and Gene Editing Data

Rana,

Kaur,

Walia

et al. 2024

Metabolomics, Proteomics and Gene Editing Approaches in Biofertilizer Industry

View full text Add to dashboard Cite

A cognitive deep learning approach for medical image processing

Fakhouri,

Alawadi,

Awaysheh

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

show abstract

SparkFlow: Towards High-Performance Data Analytics for Spark-based Genome Analysis

Cited by 5 publications

References 39 publications

Frontiers of Computing - Evolutionary Trends and Cutting-Edge Technologies in Computer Science and Next Generation Application

Frontiers of Computing - Evolutionary Trends and Cutting-Edge Technologies in Computer Science and Next Generation Application

From Data to Discoveries: Bioinformatics Strategies for Analyzing Metabolomics, Proteomics, and Gene Editing Data

A cognitive deep learning approach for medical image processing

Contact Info

Product

Resources

About