3x Founder 🇩🇰🇮🇳🇵🇹 | 2x Author | Head of AI (13+yr) | Knowledge Graph Expert

Rahul Kumar  • Peerlist

Timeln is an innovative background research agent designed to enhance the way researchers, AI builders, and heavy readers manage their information. This tool acts as an autonomous memory, capturing everything you read across the web, documents, and social media platforms without any manual effort. Timeln organizes your knowledge into a command center that adapts based on what it learns from your reading habits, creating a seamless and intelligent research experience.<h3>Key Features</h3><ul><li>Autonomous Capture: Effortlessly captures and organizes content as you browse, eliminating the need for manual bookmarks or tags.</li><li>Intelligent Retrieval: Instantly retrieves relevant snippets and sources when you need them, ensuring you never lose track of important information.</li><li>AI-Powered Organization: Automatically summarizes, tags, and connects new insights to your existing knowledge base, building a second brain without any manual input.</li><li>Privacy-First Design: Ensures your personal information and research remain secure while providing a powerful tool for knowledge management.</li></ul>With Timeln, users can enjoy a zero-effort capture system that allows them to focus on deep work while the agent quietly organizes and enhances their research memory. The tool's AI capabilities discover connections you might have missed, providing a comprehensive view of your learning journey over time. Start your free trial today and experience the transformation from information chaos to organized clarity.

Timeln - Your AI Brain

Timeln is an innovative background research agent designed for PhDs, AI researchers, and heavy readers. It autonomously captures everything you read across the web, documents, and social media, providing effortless memory and retrieval. Key features include autonomous memory, intelligent retrieval, and AI-powered organization. Timeln acts as your personal knowledge command center, organizing content based on your interests without manual effort. With zero manual tagging and sorting, it captures high-value content automatically, allowing you to focus on deep work. The platform supports a wide range of content, including articles, PDFs, and social media posts, and offers seamless integration with tools like LinkedIn and Twitter. Timeln also provides learning analytics to track your knowledge growth and insights sharing capabilities. Start your free trial today and experience the transformation from information chaos to organized clarity.

Timeln – You AI Brain

Timeln

It is a reusable solution developed by Unique Identity team, to perform benchmarking of various vendor specific biometric matching SDKs/algorithms available for various biometric modalities such as facial images, fingerprints, Iris, etc against a common test database. The Accenture Testing Framework: o	has been designed based upon Accenture’s expertise accrued over ten years of delivering identity solutions for Clients around the globe; o	uses performance evaluation methodology outlined in ISO/IEC 19795 standard. o	offers flexible, scalable, and easy-to-integrate interfaces for various biometric algorithms to help  our Clients compare different SDKs across multiple modalities and vendors; o	leverages Accenture’s Identity Framework to accelerate integration activities; o	is modular and rapidly-adapted to Client requirements with minimal rework; o	accepts client provided test dataset to perform evaluation and benchmarking. o	expedites the benchmarking process by building customised reports as per the requirements.

Accenture Testing Framework (ATF)

This project was developed to authenticate and identify an individual using his fingerprint. We developed an enhancement algorithm with can increase the accuracy of the system to a greater extent.

Fingerprint Recognition

Worked on a Biometric system dealing with the FOOTPRINT details. Used Footprint traits for identification/verification and establishing correlation between height,weight and age of an individual.

Individual Recognition using footprint image 

MeetingSense is an advanced meeting analytics tool that transforms the way organizations gain valuable insights from their meetings. This innovative solution leverages cutting-edge technologies in emotion recognition, natural language processing (NLP), and automated summarization to empower teams and drive meaningful outcomes. Capture Emotions and Analyze NLP: "MeetingSense" utilizes computer vision algorithms to accurately capture and analyze emotions from meeting video recordings. It provides real-time insights into participant engagement, sentiment dynamics, and overall meeting atmosphere. Leveraging sophisticated NLP techniques, the tool extracts key information such as questions asked, talk times, long monologs, and dominant topics, delivering comprehensive meeting analytics. Automated Summarization and Action Item Generation: With automated meeting summarization, "MeetingSense" condenses discussions into concise summaries, ensuring essential points are captured. The tool intelligently identifies action items, transforming important discussions into tangible tasks and follow-ups. This streamlines responsibility assignment and progress tracking, enhancing productivity and preserving critical outcomes. Efficiency and Collaboration: "MeetingSense" eliminates manual note-taking and tedious transcriptions, fostering efficient collaboration. Its intuitive interface and comprehensive insights empower teams to make informed decisions, drive engaging discussions, and achieve productive outcomes. By harnessing data-driven meeting analysis, this tool optimizes meeting culture, enhances team effectiveness, and facilitates data-informed decision-making.

MeetingSense (Emotions+Engagemnts)

Developing a Biometric security System using MultiModal Biometric traits such as Footprint and Palmprint.

Multi Modular Biometrics

Human palm print is a wide spreading biometric trait that has been used to detect an individual’s identity Palm print images of 94 individuals have been acquired using Scanning techniques. Images acquired by this technique are used for the identification of an individual by extracting principle lines as a feature of palm print images using Matlab. In this study an algorithm is proposed to compute and filter the first three principle lines of palm print images using Matlab that have been acquired using scanning technique. These characteristic features are used to estimate the identity of an individual. Experiments show that the principle lines from the palm prints can be a good source for any biometric security system.

Palmprint Recognition System

This work proposes an algorithm for biometric palm recognition of an individual using geometrical and texture properties. Biometrics is physiological characteristics of human beings, unique for every individual that are usually time invariant & easy to acquire. Palmprint is one of the physiological biometrics due to its stable & unique characteristics. The physical dimensions of a human palm contains information that is capable of authenticating the identity of an individual. In this study algorithm is proposed to extract a finger length as geometrical feature and principal lines as texture feature. Simulation results shows false accept rate (FAR) is 25%, false reject rate (FRR) as 18.75%, genuine acceptance rate (GAR) 81.25%, half total error rate (HTER) 21.87% and accuracy 78.12% in case of geometrical features whereas accuracy is 92.3% in case of texture feature.

Palmprint recognition using geometrical and t

Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What You Will Learn -> Set up a deep learning development environment on Amazon Web Services (AWS) -> Apply GPU-powered instances as well as the deep learning AMI -> Implement seq-to-seq networks for modeling natural language processing (NLP) -> Develop an end-to-end speech recognition system -> Build a system for pixel-wise semantic labeling of an image -> Create a system that generates images and their regions

Python Deep Learning Projects

An iOS application for Singapore government to authenticate the NRIC card using document verification using barcode detection and realtime face matching. I was responsible for: 1. Developing the inapp realtime video calling module. 2. UI/UX 3. Face quality analysis module