“Karim has sound knowledge in Machine learning technologies. He likes to accept new challenges. He is a good team player, pro-active in nature. Karim is devoted and is an asset for any team and Organization.”
About
I’m a AI Architect and Senior Data Scientist with over 13 years of industry experience…
Experience & Education
Licenses & Certifications
Publications
-
Ethical Considerations and Responsible Governance of Generative AI: A Systematic Review
Premierscience
See publicationGenerative artificial intelligence (AI), a transformative technology capable of generating or creating text, images, and other content, has revolutionized industries while raising critical ethical and governance challenges. This review systematically examines key ethical considerations, such as intellectual property rights, bias, fairness, misinformation, data privacy, environmental impact, and the need for human oversight. These challenges highlight complexities in governing generative AI…
Generative artificial intelligence (AI), a transformative technology capable of generating or creating text, images, and other content, has revolutionized industries while raising critical ethical and governance challenges. This review systematically examines key ethical considerations, such as intellectual property rights, bias, fairness, misinformation, data privacy, environmental impact, and the need for human oversight. These challenges highlight complexities in governing generative AI, requiring robust international guidelines and best practices. By analyzing existing frameworks and case studies, our review identifies significant gaps in current research and policy. Key findings emphasize the importance of multi-stakeholder collaboration among policymakers, industry leaders, and researchers in developing an adaptive governance framework that prioritizes transparency, accountability, and inclusivity to mitigate risks and promote responsible AI. The review highlights the importance of sustainable AI in addressing environmental concerns and advocates for policies that ensure equitable access while addressing societal impacts such as the spread of misinformation and the potential for exacerbating existing inequalities. By synthesizing insights from diverse sources, this study provides actionable recommendations to guide the ethical and responsible governance of generative AI technologies that align with evolving technological advancements and societal needs.
-
Investigating the Efficacy of Multimodal Large Language Models in Cross-Domain Knowledge Transfer
Premierscience
See publicationMultimodal large language models (MLLMs) have emerged as powerful tools for a diverse range of applications, particularly in enabling effective cross-domain knowledge transfer. By leveraging multimodal embeddings and transfer learning, MLLMs process and understand information from text, images, videos, and audio, enabling their capacity to generalize across various domains’ content without requiring domain-specific training. This research investigates the efficacy of MLLMs in transferring…
Multimodal large language models (MLLMs) have emerged as powerful tools for a diverse range of applications, particularly in enabling effective cross-domain knowledge transfer. By leveraging multimodal embeddings and transfer learning, MLLMs process and understand information from text, images, videos, and audio, enabling their capacity to generalize across various domains’ content without requiring domain-specific training. This research investigates the efficacy of MLLMs in transferring knowledge across different domains, focusing on their underlying mechanisms that facilitate generalization, including the capture of semantic relationships and patterns. We examine factors influencing the effectiveness of cross-domain knowledge transfer, such as the similarity between source and target domains, the quality and quantity of training data, and the architecture of the MLLM. Through empirical studies and case analyses, we demonstrate the potential of MLLMs to revolutionize various fields, including healthcare, education, and engineering. Experimental results highlight the capacity of MLLMs to improve context comprehension and reduce computational overhead, suggesting a scalable and adaptable future for AI systems poised to drive innovation and transformation across diverse industries.
-
Sentiment Analysis and Big Data processing
Nirma University
See publicationThe explosion of Web 2.0 has led to increased activity in Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has
been an eruption of interest in people to mine these vast resources of data to see it’s
sentiment. Sentiment Analysis or Opinion Mining is the computational treatment of
opinions, sentiments and subjectivity of text. Now a days most of the website contains
discussion section below their article, where user provides review…The explosion of Web 2.0 has led to increased activity in Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has
been an eruption of interest in people to mine these vast resources of data to see it’s
sentiment. Sentiment Analysis or Opinion Mining is the computational treatment of
opinions, sentiments and subjectivity of text. Now a days most of the website contains
discussion section below their article, where user provides review and opinion regarding
article.
Everyday so many articles about business, sports, politics, news are being posted.
There are plenty of platform where people read and give their opinion about article. One
thing that does not exist and can be provided on article page is Sentiment analysis result
so that user can see the polarity/sentiment of the content in page. Trending articles,
URL in various domain always excite users. We are collecting such trending articles
from social media, calculate the sentiment result and representing it to user as per his
social media interest and likes. User can also manually enter his favourite URL or text
content to get the sentiment for the same. Fastest pattern matching algorithm, social
media interest based recommendation, locale based article recommendation using shortest
distance algorithm , KNN algorithm for relative sentiment result; all of them combined
into single application to avail something new in front of web users.
Projects
-
Machine Translation (Hindi < - > English Translation)
- Present
See projectMachine translation, sometimes referred to by the abbreviation MT is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
We are trying to build our own MT tool for Asia's biggest Stock Exchange to monitor regional news which influence the stock price.
We are using Neural machine translation (NMT), which is an approach to machine translation that uses a large neural network. It departs from…Machine translation, sometimes referred to by the abbreviation MT is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
We are trying to build our own MT tool for Asia's biggest Stock Exchange to monitor regional news which influence the stock price.
We are using Neural machine translation (NMT), which is an approach to machine translation that uses a large neural network. It departs from phrase-based statistical translation approaches that use separately engineered subcomponents. Google and Microsoft translation services now use NMT. Google uses Google Neural Machine Translation (GNMT) in preference to its previous statistical methods. -
Data analytics-based, Artificial Intelligence mechanism to detect rumours in Stock market
See projectThis is Data analytics-based, artificial intelligence mechanism that tracks the performance of listed companies in relation to news about those companies on 30+ digital media platform and social media platforms, like Twitter and Facebook
The primary objective of the verification is that the mechanism will detect and mitigate potential risks of market manipulation, rumour, and reduce information asymmetry arising from digital media platforms, including from social media.
We built a…This is Data analytics-based, artificial intelligence mechanism that tracks the performance of listed companies in relation to news about those companies on 30+ digital media platform and social media platforms, like Twitter and Facebook
The primary objective of the verification is that the mechanism will detect and mitigate potential risks of market manipulation, rumour, and reduce information asymmetry arising from digital media platforms, including from social media.
We built a statistical model through training of historical web articles, manually tagged or untagged by data scientists and business users. The model uses NLP based artificial intelligence to identify rumors. It is enriched with 5K+ company names using the n-gram technique, filtration of 80 suspected and 20 rejection keywords, 25+ websites and 300+ web source integrations. This is a continuous process, and involves adding the company list, keywords and web links slowly and steadily to ensure maximum accuracy. -
Auto answering system
Aim is to process large dataset and creating auto answering which should provide exact expected answer in instant.
Main tasks -
Data classification, Domain detection, Query understanding,
Word sense disambiguation, Co-reference resolution, NER+Focus+Answer type detection, Answer processing
Key tools and technology -
- Natural language processing (NLP), Machine learning
- Python , pylinkgrammar, swi-prolog
- Hadoop, Apache lucene, solr and other helping apache…Aim is to process large dataset and creating auto answering which should provide exact expected answer in instant.
Main tasks -
Data classification, Domain detection, Query understanding,
Word sense disambiguation, Co-reference resolution, NER+Focus+Answer type detection, Answer processing
Key tools and technology -
- Natural language processing (NLP), Machine learning
- Python , pylinkgrammar, swi-prolog
- Hadoop, Apache lucene, solr and other helping apache packages
- NLTK, scikit learn, Stanford nlp package (pos, ner, co-reference resolution, sentence parsing etc)
-
User input classification for NLP based Auto answering system
Worked in team for developing domain classification system to identify domain of input query. This classification narrow down the space of answering user's question.
System uses machine learning classifier with large dbpedia datasets along with other curated datasets. -
Software Defined Radio for FM signals
-
Creating the Software Defined Radio(SDR) which listen the live signal, process it and give the output in form of audio, video or text format.
Other creators -
Bewaulf Cluster development
-
Developing cluster using which we can share high loaded task among multiple system to improve the speedup and performance.
Other creators -
Character recognization using Neural networks
-
Developing browser based Character recolonization system.
Other creators -
Open Volunteer Grid(OVG) system
-
This project is aimed to bring commercialization in the work of Volunteer Grid system. Currently available Volunteer systems are purely research and academia based, like B.O.I.N.C.
Our system's goal is to attract volunteer CPU power contributor from all over the world and they get paid as per the idle CPU power they have contributed.Other creators -
Packet sniffer and analyzer
-
This software is based on sniffing different types of network packets like TCP, UDP, ICMP, ARP from network. Extracting all the filed of these packets and getting network information. This project was implemented using core JAVA.
-
AI based Auto answering system for Texas Education Consultative Service
-
Developing AI based system which can handle FAQs and similar question related to Educational service in Texas which is providing fully-integrated solutions that enhance educational outcomes by providing reliable information, improved processes and superior compliance with local, state, and federal guidelines.
Using Machine learning, NLP concepts, FuzzyWuzzy string matching algorithm, Artificial Intelligence Markup Language (AIML) for processing the question and getting answer.Other creators
Languages
-
English
-
Recommendations received
-
LinkedIn User
3 people have recommended Mohammed Karimkhan
Join now to viewOther similar profiles
Explore more posts
Explore collaborative articles
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
Explore More