Nadeem Majeed Profile

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$30 USD / 小时

lahore,

pakistan

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Nadeem Majeed

@nadeemajeedch

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Project Manager/Data Scientist/FullStack Developer

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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文件夹

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

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

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

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

Mobile Application Development Center Logo

10155415

Extracting Software Change Requests from Mobile App Reviews

10155393

Efficient SMOTE-Based DL Model for Heart Attack Prediction

Multi-class Alzheimer's disease classification

10155359

Detection of cerebral microbleeds in brain MRI

10155341

Mispronunciation detection using Deep Learning

Mobile Application Development Center Logo

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5.0

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Do you own a Motorola Atrix with Finger scanner ?

Very helpful.

PHP

Closed User

@bioxsl

•

12 年前

经验

Data Science Department, University of the Punjab, Lahore

9月 2019 - 现在

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.

教育

PhD Computer Engineering

University of Engineering and Technology, Taxila,

Pakistan

2010 - 2015

(5 年)

资质

Project Management Institute

2020

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.

Global Institute

2021

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

AXELOS

2021

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

出版物

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.

Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI

https://www.sciencedirect.com/science/article/pii/S0045790617332767

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.

Mispronunciation detection using deep convolutional neural network and transfer learning-based model

https://ieeexplore.ieee.org/iel7/6287639/8600701/08695703.pdf

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.

An adaptive doctor-recommender system

https://www.tandfonline.com/doi/pdf/10.1080/0144929X.2019.1625441

A hybrid doctor-recommender system is proposed, by combining different recommendation approaches; content base, collaborative and demographic filtering to effectively tackle the issue of doctor recommendation. The proposed system addresses the issue of personalization by analyzing a patient's interest in selecting a doctor. It uses a novel adaptive algorithm to construct a doctor's ranking function.

Multi-class Alzheimer's disease classification using image and clinical features

https://www.sciencedirect.com/science/article/pii/S1746809418300508

Cardiac disease treatments are often subjected to the acquisition and analysis of a vast quantity of digital cardiac data. These data’s utilization becomes more important when dealing with critical diseases like a heart attack where patient life is often at stake. The research work presents a cost-effective solution to predict heart attacks with high accuracy and reliability by predicting heart attack via various machine learning algorithms without the involvement of any feature engineering.