Abdallah Bari - Profile on 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.
Papers by Abdallah Bari
Increase in olive cultivation: implications for both water use and genetic resources use in the southern countries of the Mediterranean region (WANA)
Applied Mathematics (Unlocking the Potential of Mathematical Conceptual Frameworks)
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
Applied Mathematics in Genetic Resources: Toward a Synergistic Approach Combining Innovations with Theoretical Aspects
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"
Searching for traits of resistance to pests in plant genetic resources in the context of adaptation to climate change
NEWSLETTER NO 14 - Plant Genetic Resources in West Asia and North Africa - 1997
Assessment of plant genetic diversity for Water Use Efficiency - Workshop
Sub-setting Genetic Resources - Math based
Climate-Change Implications for Drylands and Farming Communities
Applied Omics Technologies
Root Water-Uptake and Plant Growth in Two Synthetic Hexaploid Wheat Genotypes Grown in Saline Soil Under Controlled Water-Deficit Stress
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...
Variétés Élites Tunisiennes de Blé Dur à Cultiver sous Conditions de Stress Salin = Durum Wheat Tunisian Elite Varieties to Cope with Salinity Stress
Annales de l'Inrat, 2016
Traits for Testing, Screening, and Improving Salt Tolerance of Durum Wheat Genetic Resources
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
Analysis of Geographical Distribution Patterns in Plants Using Fractals
Complexus Mundi - Emergent Patterns in Nature, 2006
Fractals and Plant Water Use Efficiency
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.
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.
Increase in olive cultivation: implications for both water use and genetic resources use in the southern countries of the Mediterranean region (WANA)
Applied Mathematics (Unlocking the Potential of Mathematical Conceptual Frameworks)
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
Applied Mathematics in Genetic Resources: Toward a Synergistic Approach Combining Innovations with Theoretical Aspects
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"
Searching for traits of resistance to pests in plant genetic resources in the context of adaptation to climate change
NEWSLETTER NO 14 - Plant Genetic Resources in West Asia and North Africa - 1997
Assessment of plant genetic diversity for Water Use Efficiency - Workshop
Sub-setting Genetic Resources - Math based
Climate-Change Implications for Drylands and Farming Communities
Applied Omics Technologies
Root Water-Uptake and Plant Growth in Two Synthetic Hexaploid Wheat Genotypes Grown in Saline Soil Under Controlled Water-Deficit Stress
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...
Variétés Élites Tunisiennes de Blé Dur à Cultiver sous Conditions de Stress Salin = Durum Wheat Tunisian Elite Varieties to Cope with Salinity Stress
Annales de l'Inrat, 2016
Traits for Testing, Screening, and Improving Salt Tolerance of Durum Wheat Genetic Resources
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
Analysis of Geographical Distribution Patterns in Plants Using Fractals
Complexus Mundi - Emergent Patterns in Nature, 2006
Fractals and Plant Water Use Efficiency
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.
Use of fractals and moments to describe olive cultivars
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...
PLoS ONE, 2013
Efficient methods to explore plant agro-biodiversity for climate change adaptive traits are urgen... more Efficient methods to explore plant agro-biodiversity for climate change adaptive traits are urgently required. The focused identification of germplasm strategy (FIGS) is one such approach. FIGS works on the premise that germplasm is likely to reflect the selection pressures of the environment in which it developed. Environmental parameters describing plant germplasm collection sites are used as selection criteria to improve the probability of uncovering useful variation. This study was designed to test the effectiveness of FIGS to search a large faba bean (Vicia faba L.) collection for traits related to drought adaptation. Two sets of faba bean accessions were created, one from moisture-limited environments, and the other from wetter sites. The two sets were grown under well watered conditions and leaf morpho-physiological traits related to plant water use were measured. Machine-learning algorithms split the accessions into two groups based on the evaluation data and the groups created by this process were compared to the original climate-based FIGS sets. The sets defined by trait data were in almost perfect agreement to the FIGS sets, demonstrating that ecotypic differentiation driven by moisture availability has occurred within the faba bean genepool. Leaflet and canopy temperature as well as relative water content contributed more than other traits to the discrimination between sets, indicating that their utility as drought-tolerance selection criteria for faba bean germplasm. This study supports the assertion that FIGS could be an effective tool to enhance the discovery of new genes for abiotic stress adaptation.
Field Crops Research, 2010