Tagline:Co-founder Faculty.Bio | Tech Product Manager | Business Developer| Entrepreneur | Speaker
Canada
I have worked for public, private and non-profit companies as Web developer, AI researcher, Data Engineer, Machine learning engineer and Program Manager.
I'm also a Sessional Instructor, teaching "Machine Learning," "Data Science," and "Big Data Platforms" courses.
Recently, working as a Business Developer and Technical Product Manager.
Board member of MWAF, Orsi Software Ltd and Manitoba Women Agriculture and Food.
Co-founder Faculty.Bio
Organization:Faculty.BioLocation:Canada
**Impact: Built an online academic platform that’s growing organically with more than 400 hundred users globally
Organization:Protein Industries CanadaLocation:Canada
Organization:University of WinnipegLocation:Manitoba, CA
Organization:Enterprise Machine Intelligence & Learning Initiative(EMILI)Location:Canada
Organization:National Research Council Canada / Conseil national de recherches Canada(NRC)Location:Montreal, Canada
Organization:SIR Montimer B. Davis Jewish General Hospital(JGH)Location:Montreal, Canada
Organization:Freelance.comLocation:Iran
Organization:Envato MarketLocation:Iran
Organization:Sooren Software Structures CoLocation:Iran
Deep Convolutional Neural Networks (CNN) can be a solution to perform this computer vision task. In this thesis, seven different CNN models are deployed to classify 1 million images - from the TerraByte dataset - of eleven very similar plant species [13]. This robust approach divides the problem into two main steps: the first step, called the generalist, identifies similar plants and separates them into different groups that contain indistinguishable plant species. The second step, called specialist, is used to classify plants within the groups of indistinguishable plants, including five weed and seven crop species, with high accuracy. The generalist-specialist CNN network shows that the hierarchical network outperforms simple CNN models in terms of accuracy and classifying similar plant images. The contributions of this thesis are the exploration of different CNN models and the improved performance of those models by designing and implementing the generalist-specialist CNN models for classifying similar plant images.
Organization:Mitacs, Terrabyte, University of Winnipeg
Automatically distinguishing different types of plant images is a challenging problem relevant to both Botany and Computer Science disciplines. Plant identification at the species level is a computer vision task called fine-grained categorization, which focuses on differentiating between hard-to-distinguish object classes. This classification problem is complicated and challenging because of the lack of annotated data, inter-species similarity, the large-scale features in appearance, and a large number of plant species. A plant classification system capable of addressing the complexity of this computer vision problem has important implications for society at large, not only in public computer science education but also in numerous agricultural activities such as the automatic detection of cash crops and non-crop plants (called weeds).
Organization:NRC
Worked with NRC, JRH hospital and the health community to combat COVID-19 in Canada by using my background knowledge in web designing and developing and utilizing Data science and explainable approaches to chest X-ray images.
A practical COVID-19 prediction and interpretation have been developed to assist the clinician in predicting the severity of the COVID-19 virus for each patient.
The goal of this project was to visual and crops and weeds images to choose the right images and delete the wrong ones. The impact of project increased the accuracy on the test data by 26%.
After data visualization and clearing process, we split the data into training, validation and test dataset.
Image feature extraction CNN Architectures on VGG, Resnet, InceptionNet, and XceptionNet. A Gold mine dataset for computer vision is the ImageNet dataset. It consists of about 14 M hand-labelled annotated images which contain over 22,000 day-to-day categories. Every year ImageNet competition is hosted in which the smaller version of this dataset (with 1000 categories) is used with an aim to accurately classify the images. Many winning solutions of the ImageNet Challenge have used state of the art convolutional neural network architectures to beat the best possible accuracy thresholds. In this kernel, I have discussed popular architectures such as VGG16, 19, ResNet, AlexNet etc. In the end, I have explained how to generate image features using pre-trained models and use them in machine learning models.
Organization:Univerrsity of Winnipeg- TerraByte Team
Worked on a million number of labelled plant images. The following steps have been applied:
Organization:Univerrsity of Winnipeg
In a hierarchical CNN classifier, the upper nodes classify the input images into subgroups
[109]. Then, deeper levels classify deeper discrimination, for example, for fruit or human face
classification problem. The upper nodes classify fruit or human face images into subgrouping
like yellow-coloured objects together or human faces together [109]. Then, deeper nodes
classify bigger differences, such as "lemon" v/s "orange" fruits or "old man," v/s "kid boy,"
human faces. Study [109] proves that hierarchical CNN models perform at par or even
better than standard CNNs. In tree-based or hierarchical CNN structures, initial layers of
a CNN from the top of structure learn very general features [87] that have been exploited
for transfer learning [110, 86]. [108] used the hierarchical CNN’s structure to deal with
distinguishing the similar classes. They designed multi-layer hierarchical CNNs where an
abstract higher-level network initially determines which subnetwork a sample should be
directed to. Lower level networks are designed to find discriminating features amongst
similar classes in the subnetwork. Each sub-network is called a class assignment classifier.
To formulate a definition of the concept of digital agriculture, we consider the stages of
agricultural development. Agriculture 1.0 is the first level of agriculture that was based on the
use of manual labour (early 20th century). Agriculture 2.0 is called the "Green Revolution"
(the late 1950s), when fertilizers, pesticides began to be actively used [55]. Fertilizer is a
natural or artificial substance containing the chemical elements that improve the growth and
productiveness of plants [66]. Moreover, a pesticide is any substance used to kill, repel,
or control certain forms of plant or animal life that are considered to be pests. Pesticides include herbicides for destroying weeds and other unwanted vegetation, insecticides for
controlling a wide variety of insects, fungicides used to prevent the growth of moulds and
mildew, disinfectants for preventing the spread of bacteria, and compounds used to control
mice and rats [65].
Moreover, pesticide contamination moves away from the target plants, resulting in
environmental pollution. Such chemical residues impact human health through environmental
and food contamination [104]. It is generally accepted that pesticides play an important role
in agricultural development because they can reduce the losses of farm products and improve
the affordable yield and quality of food [3, 96]. Fertilizers and pesticides are a necessary evil
for industrial agriculture [76]. They have adverse effects on soil, plants, environment, and
human health [11]. Nevertheless, high efficiency in the agricultural sector is not currently
possible without herbicides and pesticides [14]. Also, there is a high demand to produce
more agricultural foods and products to meet the growing population. It is vital to have
precise agriculture with less waste and sustainable outcomes [45].
After agriculture 2.0, using fertilizers and pesticides, came agriculture 3.0 called precision
agriculture (the 1990s and 2000s), and agriculture 4.0 called digital agriculture (early 2010s)
[55]. They utilize digital technologies and technical means in agricultural production making
it possible to bring exact measurements to a new level when information on all agricultural
processes and operations exist in digital form, and, at the same time, the transfer, processing
and analysis of data are automated.
The development of technological advances has been growing year by year [88]. Using
digital agriculture to classify plants is one of the trends [50]. Crop and weeds can largely
vary within the same field. Identifying weeds and removing them from the field contributes
significantly to the final yield. Digital agriculture allows scanning large areas of a plant and
distinguishes between weeds and crops. In recent years, research into digital agriculture
using machine learning methods has become an active area of study. [47] believed weeding
is an effective way to increase crop yields. Their focus is on improving weed and crop
recognition accuracy on weed dataset [59]. They found CNN [33] are favourable for multiclass
crops and weeds recognition with limited labelled data in agricultural recognition tasks.
Read more on my Article on the Link below.
Using a public dataset of 4,234 plant images from the Aarhus University Signal Processing group in collaboration with the University of Southern Denmark, that consists of descriptions under a controlled condition concerning camera radiance and stabilization. The project aims to solve the problem of plant seedling classification problems utilizing the transfer leaning technique by exploring three famous Convolutional Neural Network models, namely AlexNet, VGG16, and Xception CNN architectures to pick the right model and provide solutions to increase its performance and accuracy. Thisproject is compared with the ISI paper worked on the same dataset but with different approaches, published in 2019. The trained model achieved 97% accuracy during testing. The tuned CNN candidate model can significantly deal with overfitting problems to improve the performance of the model.
I don’t just speak tech, I speak business too!
Organization:FacultyBio
Organization:Protein Industries Canada
Issuer:The Forum
Issuer:NorthForge
I had my initial experience successfully deviating a pitch fro Faculty.Bio, and I’m so honored that I’ve accepted the position of Program Founder at NorthForge. North Forge is a vibrant incubator accelerator and entrepreneur community that empowers innovative science-based, technology-enabled, and advanced manufacturing startups.
Issuer:Mitacs
I am honored to have been selected as a recipient of the MITACS Accelerate Fellowship, a prestigious recognition that speaks to my commitment to advanced research and collaboration within the academic and industrial realms. The MITACS Accelerate Fellowship is a testament to the merit of the collaborative initiatives undertaken during my academic pursuits.
As a MITACS Accelerate Fellow, I have embarked on a journey of impactful collaboration, engaging in cutting-edge research projects that not only contribute to the academic discourse but also address industry-specific issues. The fellowship has provided me with a unique opportunity to work alongside leading experts in both academic and industrial settings, promoting knowledge exchange, innovation, and the application of research outcomes to solve real-world problems.
Being awarded the MITACS Accelerate Fellowship is a recognition of my dedication to collaborative, applied research that transcends traditional academic boundaries. I am grateful for the support and opportunities provided by MITACS, and I look forward to further contributing to the advancement of knowledge and innovation through this prestigious fellowship.
I took 2 important courses, named Pattern Recognition and Computer Vision at the University of Winnipeg. I got A+ for both courses. Among 15 students I got selected as a top student for the winter semester 2019.
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Field of study:Applied Computer ScienceSchool:The University of WinnipegLocation:Manitoba, Canada
Exploring Deep Neural Networks for Plant Image Classification
Field of study:Computer Software EngineeringSchool:Islamic Azad University,Science And Research Branch
Modeling RFID Systems and Security and Privacy Implications
From: 2021, Until: 2023
Organization:University Of Winnipeg, PACEField:Computer Science
Developed and delivered comprehensive courses on Big Data Platforms, with a strong focus on Google Cloud Platform (GCP) and Amazon Web Services (AWS).
Conducted engaging and interactive classroom sessions, offering in-depth instruction on Big Data Platform principles, GCP, and AWS. Utilized hands-on labs, real-world examples, and case studies to enhance student comprehension.
Designed and implemented practical exercises and labs to reinforce theoretical knowledge. Fostered a hands-on learning environment that encouraged experimentation and problem-solving.
From: 2021, Until: 2023
Organization:Univerrsity of WinnipegField:Computer science
Lectured the course Foundation of Data Science
Designed course outline and course material (including lectures, labs and activities)
Conducted engaging and interactive classroom sessions, offering in-depth concepts on Statistical Machine Learning models and Python Libraries. Utilized hands-on labs, real-world examples, and case studies to enhance student comprehension.