Muhammad Ali M. Profile

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5.0

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$17 USD / 小时

islamabad,

pakistan

$17 USD / 小时

目前这里是10:17 下午

五月 9, 2016已加入

0 推荐

Muhammad Ali M.

@malimasood

5.0

(3条评论)

5.8

100%

$17 USD / 小时

islamabad,

pakistan

$17 USD / 小时

75%

工作已完成

100%

在预算内

100%

按时

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Data Scientist | Machine Learning Expert

I have Ph.D degree in big data and machine learning domain. I am also a founder of a software company (Zaytrics PVT LTD) , our company specializes in blockchain technology, blockchain based mobile or web applications, data mining, natural language processing, big data, machine learning, VOIP application, Andriod/IOS app development, web development, and python based application. We love to solve challenging problem, as it can be seen in our completed projects listed in the section below. We have developed many projects, our recent projects include Intelligent query based trading analytics application for cryptocurrency, Chatbot for shopping application, and Call center application for marketing. Our team specializes in Usability Engineering, therefore, our products are easy to use for layman. Apart from this, out motto is to make products maintainable, so the changes at later stage don't cost too much.

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文件夹

10082224

StonAI helps manage your project documents and tasks faster and efficient with high accuracy. It uses advanced AI models to
assist in managing projects efficiently.

The problem we targeted is that "Project information and documents are large that make it impossible for any advanced archiving and for staff to remember and utilize them efficiently when needed throughout the project life cycle, thus staff often spends 1/3 of their time looking for
information and documents".

The project manages many types of documents (like contract, BOQ, Tender Addendum, Submittals, and many others). StonAI uses advance OCR and NLP technology to convert the unstructured document into structured format thus allowing user to search and manage risks. The models are designed in a way that it increases the productivity by 40%.

https://app.stonai.com

StonAI | A Smart Construction Management Solution

10073352

True Crime Crypto NFT Marketplace on Cardano

The system is developed by the name of chappie.ai. The aim of the system is to assist agent in calling on different marketing campaigns. Chappie has an custom developed VOIP solution in which we developed everything from scrap. We have also developed state-of-the-art solution that allows the owner to replace the HUMAN agent with the AI based BOT. The Bot will be handling the marketing conversation without letting the user on the phone know about it. The system has following features:
1. Outbound calling
2. AI based calling
3. Automatic calling
4. Privacy setting
5. Cybersecurity measure
6. AI based call feedback scoring
7. Preview dialer
8. Progressive dialer
9. Campaign creating
10. User rights management
11. Agents Activity monitoring
12. AI based chatting
13. AI based suggestive model for helping the agent answer queries
14. Billing
15. Reporting system
16. Different type of user support
17. Admin Panel
18. Many other features

Call Center solution for marketing

This project uses the cryptocurrency data to guide the user in making trades. This project mainly has two phases: the first phase is to use cryptocurrency API to fetch all the data in real-time. From this API, the system will provide query-based interfaces, where a proper syntax will be used to extract the required information. Apart from this, an analysis toolkit will be given so that the user can perform different forms of analysis. Finally, we will be providing an admin panel to control the client-side application. The aim is to develop a generic, plug & play system that allows us to add more components without increasing manpower.
Key Features:
1. Data Gathering from multiple sources
2. 100+ actions & indicators
3. Natural language query system
4. Intelligent parser
5. AI base analysis
6. Charts to guide the user
7. Payment
8. Buying & selling of coins
9. Watch list and trade list
10. Admin Panel
11. Many others

A Trading query based system for expert traders

8431483

LoadEX: A smart and low cost delivery Solution

LinkedIn in one of the strict platform that disallows crawling or spider activities. The crawlers we build is robust, flexible, and efficient, thus allow it to execute in a stealthy way. The following are the features of the crawler:

1. Allow multiple accounts to run in parallel
2. Filter results
3. Allow multiple proxies to run in parallel
4. Dynamic crawler activities
5. Stealthy mode
6. Data analytics
7. User logs

Intelligent LinkedIN Spider

10082224

StonAI | A Smart Construction Management Solution

10073352

True Crime Crypto NFT Marketplace on Cardano

Call Center solution for marketing

A Trading query based system for expert traders

8431483

LoadEX: A smart and low cost delivery Solution

Intelligent LinkedIN Spider

变更已保存

展示3中的1-3条评论

筛选评论规则：

5.0

$5,845.00 USD

Machine Learning: Crawl specific websites, extract relevant articles, fetch relevant data

Ali is a machine learning expert who took a thoughtful approach to our project. It was a difficult task (interpreting unstructured text) and he came up with a good solution. He was very easy to work with. A nice, patient man. I recommend you consider him for your project.

Web Scraping

Machine Learning (ML)

Data Mining

Data Science

Closed User

@pyhk

•

4 年前

5.0

$2,480.00 USD

Python (SCRAPY) for LinkedIn

Muhammad was a pleasure to work with. Very responsive and willing to work through some challenges to complete the project.

Python

Web Scraping

Software Architecture

MySQL

Data Mining

Syed R.

@danho

•

6 年前

5.0

$230.00 USD

Machine Learning (k-means core based) on spark with (python or scala)

Good Person

Python

Machine Learning (ML)

Scala

Spark

Closed User

@Wissem1993

•

6 年前

经验

Center for Advance Studies in Engineering

8月 2013 - 现在

I have to teach various courses.

Zaytrics PVT LTD

11月 2016 - 现在

These days many provide different software solutions to different projects over the globe. We are a team of professionals from various areas (like AI, Social Network Analysis, Data Mining, Semantic Web, Usability Engineering, Web Development, Graphic Designers, Application Development, Gaming and many more). This make a perfect blend in the application that other companies can't provide. We also provide the Cheapest solution with effectiveness and efficiency.

Nanyang Technological University

9月 2018 - 12月 2019 (1 年, 3 个月)

I worked as a research associate and team lead for a $10 million project; the main goal was to develop solutions to help solve crime in an efficient and effective way.

教育

PhD

Quaid-e-Azam University, Islamabad,

Pakistan

2014 - 2020

(6 年)

Mphil (Computer Science)

Quaid-e-Azam University, Islamabad,

Pakistan

2010 - 2013

(3 年)

BS (Software Engineering)

Riphah International University,

Pakistan

2005 - 2009

(4 年)

出版物

Using Graph Embedding and Machine Learning to Identify Rebels on Twitter

Journal of Informetrics

During the last two decades, the number of incidents from extremists have increased, so as the use of social media. Therefore, we propose a Supervised Rebel Identification (SRI) framework to identify rebels on Twitter. The framework consists of a novel mechanism to structure the users' tweets into a directed user graph. We convert the user graph into graph embedding to use these semantics within the machine learning algorithms.

Context-Aware Sliding Window for Sentiment Classification

IEEE Access

Sentiment classification is an active area of research with applications in many domains. Many researchers in the past have proposed techniques to identify sentiments with reasonable accuracy. However, the focus is more on the syntactic and semantic features of the documents. These features are effective but they ignore the user's past sentiments. In this paper, we propose seven variations of Context-aware Sliding Window (CSW) features on different machine learning and deep learning algorithms.

MFS-LDA: a multi-feature space tag recommendation model for cold start problem

Emerald

MFS-LDA is a novel latent Dirichlet allocation (LDA)-based model which exploits multiple feature spaces (title, contents, and tags) for recommending tags. Evaluation of a publicly available data set consisting of around 20,000 Wikipedia articles that are tagged on a social bookmarking website shows a significant improvement over existing LDA-based tag recommendation methods. The originality of MFS-LDA lies in segregation of features for removing bias toward dominant features.