Abdul Rehman Azam

Abdul Rehman Azam

About

Detail

AI services | Full stack AI/ML (Multiple AI domains) | AI SaaS development | Product Enhancement Available for consultations via mentoga.com/aazam
Punjab, Pakistan

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Résumé

Jobs verified_user 0% verified
  • A
    Community Lead
    AIML and Automation Experts
    May 2025 - Current (1 year 6 months)
  • Confidential
    AI/ML Consultant (Advisory, Strategy, Development, Architecture)
    Confidential
    Feb 2025 - Current (1 year 9 months)
  • Odea Integrations
    Lead GenAI Engineer
    Odea Integrations
    Feb 2025 - Apr 2025 (3 months)
  • topmateio
    Mentor and AI Expert
    topmateio
    Dec 2024 - Current (1 year 11 months)
    Reach me out from the following link.
    https://topmate.io/abdul_rehman_azam
  • A
    Lead ML Engineer
    Aircod Technologies
    Feb 2024 - Feb 2025 (1 year 1 month)
  • P
    Founder and CEO
    Prodigy Analytica
    Nov 2023 - Dec 2023 (2 months)
  • Donorbox
    Data Science and Machine Learning Engineer
    Donorbox
    Mar 2023 - Sep 2023 (7 months)
    Devised machine learning and deep learning solutions as a founding team member.
    Handled deployment, monitoring, and maintenance of AI models.
    Conducted data analysis for internal teams and provided insights to management.
  • Vroozi
    Senior Software Engineer- AI/ML
    Vroozi
    Sep 2022 - Mar 2023 (7 months)
    Devised machine learning and deep learning solutions as a founding team member.
    Managed deployment, monitoring, and maintenance of AI models.
    Automated the process of purchase requests, approvals, and invoicing.
  • Xavor Corporation
    Senior Software Engineer (Machine Learning,Data Science)
    Xavor Corporation
    Sep 2020 - Sep 2022 (2 years 1 month)
    Devised machine learning and deep learning solutions as part of the AI team.
    Handled deployment, monitoring, and maintenance of AI models.
    Provided AI modules for Xavor's smart products.
  • PNY Trainings
    AI Instructor
    PNY Trainings
    Jun 2020 - Sep 2020 (4 months)
  • Xavor Corporation
    Machine Learning Engineer
    Xavor Corporation
    Oct 2019 - Sep 2020 (1 year)
Education verified_user 0% verified
  • University of Engineering and Technology Lahore
    Master's degree, Computer Science
    University of Engineering and Technology Lahore
    Jan 2017 - Dec 2020 (4 years)
    My specialization is in the field of Machine Learning and Deep Learning
    I have studied following notable courses
    -Machine Learning
    -Knowledge Discovery in Databases (KDD)
    -Computer Vision
  • National University of Sciences and Technology NUST
    Bachelor of Engineering (B.E, Computer Engineering
    National University of Sciences and Technology NUST
    Jan 2011 - Dec 2015 (5 years)
  • G
    Faculty of Science, Pre-Engineering
    Garrison Degree College Lahore Cantt
    Jan 2010 - Dec 2011 (2 years)
Projects (professional or personal) verified_user 0% verified
  • S
    Security Surveillance System through Real Time Video Processing
    Dec 2014 - Mar 2015 (4 months)
    Algorithms having Intrusion detection, Alarm generation ,Objects Classification and intrusion level specification.
  • C
    Control of RC car Through Speech Recognition
    Feb 2014 - Current (12 years 9 months)
    RC car controlled by Speech Recognition ( used .NET Library) Windows Application. Communication between both modules by Bluetooth HC-05 module.
  • S
    Speaker Recognition and Tracking
    Dec 2013 - Current (12 years 11 months)
    Using MFCC (Mel Frequency Co-efficient Constraints) and KNN classifier Algorithm in MATLAB.
Publications verified_user 0% verified
  • T
    Scene Recognition by Joint Learning of DNN from Bag of Visual Words and Convolutional DCT Features
    Taylor Francis
    May 2021
    Scene recognition is used in many computer vision and related applications, including information retrieval, robotics, real-time monitoring, and event-classification. Due to the complex nature of the task of scene recognition, it has been greatly improved by deep learning architectures that can be trained by utilizing large and comprehensive datasets. This paper presents a scene classification method in which local and global features are used and are concatenated with the DCT-Convolutional features of AlexNet. The features are fed into AlexNet's fully connected layers for classification. The local and global features are made efficient by selecting the correct size of Bag of Visual Words (BOVW) and feature selection techniques, which are e
  • IEEE
    Scene Recognition of Surveillance Data using Deep Features and Supervised Classifiers
    IEEE
    Apr 2019
    Precise labeling of an image based on its semantic description is quite challenging task and has its significant applications in surveillance area. Majority of scene classification techniques during past few decades have targeted low level feature by handcraft engineering or unsupervised feature extraction techniques. In this paper, we aim to categorize scene classes for surveillance systems by exploiting deep convolutional features to manifold projection along with supervised classification algorithms. A topology is constructed to depict high dimensions of convolution heat-maps to 128D salient features. Parameters of pre-trained network are tuned to precisely fit with the output of our problem. Experimental results depict that our methodol
  • IEEE
    Modified Texture Features from Histogram and Gray Level Co-occurence Matrix of Facial Data for Ethnicity Detection
    IEEE
    Sep 2018
    Ethnicity detection from facial images is very important information and it has numerous applications in the area of visual imagery. There are many texture features used for this method such as Local Binary Pattern, Harlick features etc. Most of the manuscripts are utilizing combination of other features with texture features and then classification algorithms. We have modified the texture features with the inclusion of harlick features of gray level co-occurrence matrix for ethnicity detection. These features are extracted from the segmented skin image. Also for texture features, effect of Bagging along with Random trees is highlighted to carry out efficient classification. We have performed evaluation of past texture features and our prop