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Nadeem Majeed

@nadeemajeedch

5.0
1

0.0

0.0

100%

Project Manager/Data Scientist/FullStack Developer

$30 USD / Hour

・

Flag of

Pakistan (9:41 AM)

・

Joined on August 8, 2008

$30 USD / Hour

・

Data Engineering and Full Stack Development Specialist As a seasoned professional in the realms of Data Engineering and Full Stack Development, I bring a robust skill set and extensive experience to the table. My expertise lies in seamlessly integrating data-driven solutions from the backend to the frontend, ensuring optimal performance and user satisfaction. Data Engineering: I have a proven track record in architecting and implementing robust data pipelines and ETL processes. My experience extends to designing and maintaining scalable databases, ensuring efficient data storage, retrieval, and manipulation. Proficient in utilizing technologies such as Apache Spark, Apache Kafka, and SQL, I excel in optimizing data workflows for maximum efficiency. Full Stack Development: On the front end, I have a mastery of modern JavaScript frameworks, including React and Angular, enabling the creation of intuitive and responsive user interfaces. Backed by a strong foundation in server-side development using Node.js, Python (Django/Flask), or Java, I am adept at crafting the server logic and APIs that power dynamic web applications. I have experience working with containerization technologies like Docker and orchestration tools such as Kubernetes to ensure seamless deployment and scalability. Technical Proficiency: Languages: Python, JavaScript (Node.js), Java, SQL, HTML, CSS Database Technologies: MySQL, PostgreSQL, MongoDB, Redis Frameworks and Libraries: React, Angular, Django, Flask, Express.js, Spring Boot Data Processing: Apache Spark, Apache Kafka Version Control: Git Containerization and Orchestration: Docker, Kubernetes Tools: Jira, Confluence Key Competencies: Data Modeling and Architecture: Designing efficient and scalable data models, implementing data warehouses, and optimizing database performance. ETL Processes: Developing and maintaining Extract, Transform, Load (ETL) processes to ensure seamless data flow across systems. Full Stack Development: Crafting responsive and user-friendly interfaces, implementing server-side logic, and creating RESTful APIs for seamless communication between the front and back end. DevOps Integration: Implementing CI/CD pipelines, utilizing containerization for deployment, and ensuring the scalability and reliability of applications. Collaboration and Communication: Effective teamwork, clear communication, and documentation to facilitate seamless collaboration across multidisciplinary teams. With 22 years of industry experience, I have PhD in Computer Engineering and have a number of international certifications. you can see my certifications on: [login to view URL]

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Portfolio

Portfolio

10155415
The use of mobile apps is increasing rapidly. These apps have thousands of reviews which are widely acknowledged as a valuable resource for the community involved in the development of mobile apps. In this study, we contend that these reviews can be used to generate software change request documents for improving mobile apps. A pre-requisite for generating such a document is the identification of Software Change Requests (SCR) from the user reviews. we have scrapped review of seven Mobile Apps and developed a dataset that can be used for training of machine learning techniques for the automatic identification of SCRs.We have documented the annotation guidelines that are used to distinguish between SCR and non-SCR sentences. These guidelines can be used to enhance the developed dataset, as well as to develop new datasets. As another contribution, we have evaluated the effectiveness of five supervised learning techniques for their ability to identify sentences from user reviews.

Extracting Software Change Requests from Mobile App Reviews

Extracting Software Change Requests from Mobile App Reviews

10155393
An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction: Cardiac disease treatments are often subjected to the acquisition and analysis of a vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability by using uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data.

Efficient SMOTE-Based DL Model for Heart Attack Prediction

10155374
Alzheimer's disease (AD) is the most common form of dementia, which results in memory-related issues in subjects. An accurate detection and classification of AD alongside its prodromal stage i.e., mild cognitive impairment (MCI) is of great clinical importance. In this paper, an Alzheimer detection and classification algorithm is presented. The bag of visual word approach is used to improve the effectiveness of texture based features, such as gray level co-occurrence matrix (GLCM), scale invariant feature transform, local binary pattern and histogram of gradient. The importance of clinical data provided alongside the imaging data is highlighted by incorporating clinical features with texture based features to generate a hybrid feature vector. The features are extracted from whole as well as segmented regions of magnetic resonance (MR) brain images representing grey matter, white matter and cerebrospinal fluid. https://www.sciencedirect.com/science/article/pii/S1746809418300508

Multi-class Alzheimer's disease classification

10155359
Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI Cerebral Microbleeds (CMBs) are considered an essential indicator in the diagnosis of critical cerebrovascular diseases such as ischemic stroke and dementia. The framework consists of three phases: brain extraction, extraction of initial candidates based on threshold and size-based filtering, and feature extraction and classification of CMBs from other healthy tissues in order to remove false positives using Support Vector Machine, Quadratic Discriminant Analysis, and ensemble classifiers.

Detection of cerebral microbleeds in brain MRI

10155341
Computer-assisted language learning (CALL) systems provide an automated framework to identify mispronunciation and give useful feedback. this research investigates the use of the deep convolutional neural network for mispronunciation detection of Arabic phonemes. This model uses convolutional neural network features (CNN_Features)-based technique and a transfer learning-based technique to detect mispronunciation detection.

Mispronunciation detection using Deep Learning

Mispronunciation detection using Deep Learning

1116719
Mobile Application Development Center Logo

Mobile Application Development Center Logo

10155415
The use of mobile apps is increasing rapidly. These apps have thousands of reviews which are widely acknowledged as a valuable resource for the community involved in the development of mobile apps. In this study, we contend that these reviews can be used to generate software change request documents for improving mobile apps. A pre-requisite for generating such a document is the identification of Software Change Requests (SCR) from the user reviews. we have scrapped review of seven Mobile Apps and developed a dataset that can be used for training of machine learning techniques for the automatic identification of SCRs.We have documented the annotation guidelines that are used to distinguish between SCR and non-SCR sentences. These guidelines can be used to enhance the developed dataset, as well as to develop new datasets. As another contribution, we have evaluated the effectiveness of five supervised learning techniques for their ability to identify sentences from user reviews.

Extracting Software Change Requests from Mobile App Reviews

Extracting Software Change Requests from Mobile App Reviews

10155393
An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction: Cardiac disease treatments are often subjected to the acquisition and analysis of a vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability by using uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data.

Efficient SMOTE-Based DL Model for Heart Attack Prediction

10155374
Alzheimer's disease (AD) is the most common form of dementia, which results in memory-related issues in subjects. An accurate detection and classification of AD alongside its prodromal stage i.e., mild cognitive impairment (MCI) is of great clinical importance. In this paper, an Alzheimer detection and classification algorithm is presented. The bag of visual word approach is used to improve the effectiveness of texture based features, such as gray level co-occurrence matrix (GLCM), scale invariant feature transform, local binary pattern and histogram of gradient. The importance of clinical data provided alongside the imaging data is highlighted by incorporating clinical features with texture based features to generate a hybrid feature vector. The features are extracted from whole as well as segmented regions of magnetic resonance (MR) brain images representing grey matter, white matter and cerebrospinal fluid. https://www.sciencedirect.com/science/article/pii/S1746809418300508

Multi-class Alzheimer's disease classification

10155359
Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI Cerebral Microbleeds (CMBs) are considered an essential indicator in the diagnosis of critical cerebrovascular diseases such as ischemic stroke and dementia. The framework consists of three phases: brain extraction, extraction of initial candidates based on threshold and size-based filtering, and feature extraction and classification of CMBs from other healthy tissues in order to remove false positives using Support Vector Machine, Quadratic Discriminant Analysis, and ensemble classifiers.

Detection of cerebral microbleeds in brain MRI

10155341
Computer-assisted language learning (CALL) systems provide an automated framework to identify mispronunciation and give useful feedback. this research investigates the use of the deep convolutional neural network for mispronunciation detection of Arabic phonemes. This model uses convolutional neural network features (CNN_Features)-based technique and a transfer learning-based technique to detect mispronunciation detection.

Mispronunciation detection using Deep Learning

Mispronunciation detection using Deep Learning

1116719
Mobile Application Development Center Logo

Mobile Application Development Center Logo

5.0 · 1 Review
Reviews
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Experience

Associate Professor / Data Scientist

Sep, 2019 - Present

•

6 years, 2 months

Data Science Department, University of the Punjab, Lahore

Sep, 2019 - Present

•

6 years, 2 months

Research / Teaching Post Graduate Students, working on multiple projects of Data Science. Consultancy as Data Scientist to multiple companies. Managing multiple projects as Project Manager.

Sep, 2019 - Present

•

6 years, 2 months

Education

University of Engineering and Technology, Taxila

2010 - 2015

•

5 years

PhD Computer Engineering

Flag of

Pakistan

2010 - 2015

•

5 years

Qualifications

Project Management Professional (PMP)®

2020

Project Management Institute

The PMP is the gold standard of project management certification. Recognized and demanded by organizations worldwide, the PMP validates your competence to perform in the role of a project manager, leading and directing projects and teams.

2020

Lean Six Sigma Green Belt

2021

Global Institute

Lean Six Sigma Green Belt is a professional who is well versed in the core to advanced elements of Lean Six Sigma Methodology, who leads improvement projects and serves as a part of more complex improvement projects

2021

PRINCE2® Agile

2021

AXELOS

PRINCE2 Agile Practitioner takes the knowledge acquired at the Foundation level and applies it to the workplace, using real-world management examples.

2021

Publications

An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

https://www.hindawi.com/journals/sp/2021/6621622/

Cardiac disease treatments are often subjected to the acquisition and analysis of a vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability by using uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data.

Extracting Software Change Requests from Mobile App Reviews

https://ieeexplore.ieee.org/iel7/9680270/9679822/09680294.pdf

The mobile apps have thousands of reviews which are widely acknowledged as a valuable resource for the community involved in the development of mobile apps. We contend that these reviews can be used to generate software change request documents for improving mobile apps. A pre-requisite for generating such a document is the identification of Software Change Requests (SCR) from the user reviews. Manual processing of this large number of reviews to identify SCRs is a resource-intensive task....

Blind Image Deblurring Using Laplacian of Gaussian (LoG) Based Image Prior

International Journal of Innovations in Science & Technology

It is possible to deconvolve a blurred image into its original form without any knowledge of the actual image or the process that leads it to be blurred, known as a point spread function. Two phases are involved in producing a blurred image: convolution and deconvolution of the PSF from the blurred image. Video conferencing, diagnostic imaging, and celestial imaging all require this blind deconvolution, but it is difficult to calculate the PSF before the operation.

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