Abdallah Bari | ICARDA - Academia.edu (original) (raw)
Books by Abdallah Bari
Machine Learning (ML), which is a subset of Artificial intelligence (AI), enhances the ability of... more Machine Learning (ML), which is a subset of Artificial intelligence (AI), enhances the ability of a computer to learn, from data, without being explicitly programmed end-to-end. As ML and AI learn they acquire the ability to carry out cognitive functions, such as perceiving, learning, reasoning and automatically digging deeper to identify important insights or leading to new discovery. With the advance in machine learning, in particular its Deep Learning (DL) subset, ML is rapidly spreading across sectors and will continue to do so at an even higher rate with the ever increasing growth of Big Data. Gartner predicts that companies will combine Big Data and Machine Learning to carry out some or most of their service processes by 40% in 2022, up from 5% in 2017.
ML is used to accelerate data-driven discovery in research and development. Recently, it has enabled scientists to discover largely unknown diversity of viruses, amounting to thousands of previously unknown viruses. The book refers to previous as well recent research work, with colleagues, where ML was used to capture subtle variation and to discover rare items, such as rare genes which researchers have so long sought for in vain. Such processes to identify genes or medicine can be daunting, as it may take years and can be expensive and the outcome can be uncertain. ML is used today to shorten the time and even help to identify medicine that can be more effective for people with a particular gene, which will help in turn in personalized medicine.
ML is a critical ingredient for intelligent applications and provides the opportunity to further accelerate discovery processes as well as enhancing decision making processes. These trends promise that every sector will be data-driven and will be using machine learning in the cloud to incorporate artificial intelligence applications and to ultimately supplement existing analytical and decision making tools.
The book introduces ML and its potential along with some ML applications using Spark and R platforms combined. While Spark has the possibility to scale and speed up analytics, it harness R language‘s machine learning capabilities beyond what is available on Spark or any other Big Data system. R and Spark can share codes and different types of data and carry out powerful large scale machine learning capabilities. Machine learning with Spark and R language combined can not only speed up but also light up Big Data Discovery.
Working with Big Data to scale Big Data’ s Discovery refers to the different techniques and tools... more Working with Big Data to scale Big Data’ s Discovery refers to the different techniques and tools used to address the ambiguity and the uncertainty as well as the scaling challenges with the arrival of Big Data, spanning data integration, data preparation and data analytics. The tools presented in this book lend themselves also to scale as the technology and the needs evolve.
Papers by Abdallah Bari
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
A chapter highlighting the importance of gene flow in the book "Applied Mathematics and Omic... more A chapter highlighting the importance of gene flow in the book "Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits"
A key breeding objective for bread wheat grown in the dry regions of Western Asia and North Afric... more A key breeding objective for bread wheat grown in the dry regions of Western Asia and North Africa is to enhance its adaptation to drought and its related salinity. Two newly-developed genotypes of synthetic hexaploid wheat, ‘SW-3’ and ‘SW-4’, their parental durum wheat variety ‘Jennah Khetifa’ and a dry-land bread wheat variety ‘Cham 6’, were compared for plant growth in saline hydroponic culture. They were also compared for root water-uptake and growth in soil culture in pots under combined water deficit and salinity stresses. Under saline hydroponic culture for five weeks, ‘SW-3’ developed a larger leaf area than the other genotypes. In saline soils for the period up to maturity, ‘SW-4’ and ‘Cham 6’ had higher root water uptake than the others. Only ‘SW-4’ developed normal grains and was clearly tolerant of soil salinity. ‘Cham 6’ developed normal spikes but ceased to fill the grains after heading. It may be assumed that salinity stress depressed root water-uptake at the early st...
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
Complexus Mundi - Emergent Patterns in Nature, 2006
Thinking in Patterns, 2004
Machine Learning (ML), which is a subset of Artificial intelligence (AI), enhances the ability of... more Machine Learning (ML), which is a subset of Artificial intelligence (AI), enhances the ability of a computer to learn, from data, without being explicitly programmed end-to-end. As ML and AI learn they acquire the ability to carry out cognitive functions, such as perceiving, learning, reasoning and automatically digging deeper to identify important insights or leading to new discovery. With the advance in machine learning, in particular its Deep Learning (DL) subset, ML is rapidly spreading across sectors and will continue to do so at an even higher rate with the ever increasing growth of Big Data. Gartner predicts that companies will combine Big Data and Machine Learning to carry out some or most of their service processes by 40% in 2022, up from 5% in 2017.
ML is used to accelerate data-driven discovery in research and development. Recently, it has enabled scientists to discover largely unknown diversity of viruses, amounting to thousands of previously unknown viruses. The book refers to previous as well recent research work, with colleagues, where ML was used to capture subtle variation and to discover rare items, such as rare genes which researchers have so long sought for in vain. Such processes to identify genes or medicine can be daunting, as it may take years and can be expensive and the outcome can be uncertain. ML is used today to shorten the time and even help to identify medicine that can be more effective for people with a particular gene, which will help in turn in personalized medicine.
ML is a critical ingredient for intelligent applications and provides the opportunity to further accelerate discovery processes as well as enhancing decision making processes. These trends promise that every sector will be data-driven and will be using machine learning in the cloud to incorporate artificial intelligence applications and to ultimately supplement existing analytical and decision making tools.
The book introduces ML and its potential along with some ML applications using Spark and R platforms combined. While Spark has the possibility to scale and speed up analytics, it harness R language‘s machine learning capabilities beyond what is available on Spark or any other Big Data system. R and Spark can share codes and different types of data and carry out powerful large scale machine learning capabilities. Machine learning with Spark and R language combined can not only speed up but also light up Big Data Discovery.
Working with Big Data to scale Big Data’ s Discovery refers to the different techniques and tools... more Working with Big Data to scale Big Data’ s Discovery refers to the different techniques and tools used to address the ambiguity and the uncertainty as well as the scaling challenges with the arrival of Big Data, spanning data integration, data preparation and data analytics. The tools presented in this book lend themselves also to scale as the technology and the needs evolve.
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
A chapter highlighting the importance of gene flow in the book "Applied Mathematics and Omic... more A chapter highlighting the importance of gene flow in the book "Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits"
A key breeding objective for bread wheat grown in the dry regions of Western Asia and North Afric... more A key breeding objective for bread wheat grown in the dry regions of Western Asia and North Africa is to enhance its adaptation to drought and its related salinity. Two newly-developed genotypes of synthetic hexaploid wheat, ‘SW-3’ and ‘SW-4’, their parental durum wheat variety ‘Jennah Khetifa’ and a dry-land bread wheat variety ‘Cham 6’, were compared for plant growth in saline hydroponic culture. They were also compared for root water-uptake and growth in soil culture in pots under combined water deficit and salinity stresses. Under saline hydroponic culture for five weeks, ‘SW-3’ developed a larger leaf area than the other genotypes. In saline soils for the period up to maturity, ‘SW-4’ and ‘Cham 6’ had higher root water uptake than the others. Only ‘SW-4’ developed normal grains and was clearly tolerant of soil salinity. ‘Cham 6’ developed normal spikes but ceased to fill the grains after heading. It may be assumed that salinity stress depressed root water-uptake at the early st...
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
Complexus Mundi - Emergent Patterns in Nature, 2006
Thinking in Patterns, 2004
In this paper, we report the results obtained by using fractals in combination with moments and s... more In this paper, we report the results obtained by using fractals in combination with moments and statistical classifiers for the characterization and identification of olive genotypes. Twenty-four olive stone features (e.g. aspect ratio, perimeter, roundness, eccentricity, invariant moments, surface features, etc.) were analyzed to characterize and identify nine genotypes with 90% overall classification accuracy.
The Journal of Agricultural Science, 2003
Morphological description based on features of the olive stone, such as its surface and shape, ca... more Morphological description based on features of the olive stone, such as its surface and shape, can help to determine an olive cultivar's identity. The description, however, is based on visual examination and is thus affected by the examiner's expertise. Although the eye has the capacity to discern texture and shape, the values that are assigned to score different levels or descriptor states, such as a highly scabrous to smooth surface or a circular to elliptic shape, are categorical values. Studies on scoring methodology have shown that the assignment to categories or classes is problematic. The purpose of the present work was to classify olive cultivars by computer-image analysis of olive stone characteristics using mathematical tools, such as fractal geometry and moments. Fractals were used to extract texture information, and moments were used to extract shape information. The results revealed an overall classification accuracy of more than 90% using a Mahalanobis distance...
Field Crops Research, 2010