Allwin Kenneth P

CEO

Coimbatore, Tamil Nadu, India5 yrs 5 mos experience
Highly Stable

Key Highlights

  • Strong foundation in deep learning and machine learning.
  • Leadership experience in student associations.
  • Proven ability to work on complex technical projects.
Stackforce AI infers this person is a skilled candidate in the SaaS industry with a focus on machine learning and deep learning technologies.

Contact

Skills

Core Skills

Deep LearningMachine Learning

Other Skills

Analytical SkillsC (Programming Language)C++CAD/CAMComputer Numerical Control (CNC)Computer-Aided Design (CAD)Computer-Aided Engineering (CAE)Continuous ImprovementContinuous Process ImprovementCritical ThinkingEnglishEvent ManagementEvent OrganizationEvent PlanningGradskey

About

A college student. And always interested in working and coming up with new ideas. Also a person interested in hardworking, meanwhile always rely on smart work.

Experience

5 yrs 5 mos
Total Experience
2 yrs 4 mos
Average Tenure
1 yr 8 mos
Current Experience

Kct business school

3 roles

Vice President - Students Association

Promoted

Sep 2025Present · 9 mos

Batch Officer - Students Association

Feb 2025Aug 2025 · 6 mos

Business Student

Sep 2024Aug 2025 · 11 mos

Department of computer science, kct

President

Sep 2023Sep 2024 · 1 yr · Coimbatore, Tamil Nadu, India

  • To cultivate a community of passionate individuals prepared to make significant contributions in the field of computer science and engineering, preparing them for successful careers in the digital era. And to create responsible citizens and good human beings.

Samsung india

Samsung Prism

Mar 2023Feb 2024 · 11 mos · Coimbatore, Tamil Nadu, India · Remote

  • In my recent internship we worked on our project aimed at detecting meme videos using advanced deep learning techniques. This project involved creating a robust pipeline to analyze a combination of visual, textual, and audio features for comprehensive meme video detection.
  • Key technologies and methodologies included:
  • > Recurrent Neural Networks (RNNs): We utilized RNNs to analyze video sequences, allowing the model to capture and understand temporal patterns essential for video-based data processing.
  • > Optical Character Recognition (OCR): To extract and interpret text from video visuals, We implemented OCR techniques, enabling the identification of potential memes through captions, text overlays, and other textual cues.
  • > Spectrogram Analysis: We integrated audio analysis through spectrograms, which facilitated the detection of specific audio cues or music that are characteristic of meme videos.
  • By combining these approaches, the project delivered a sophisticated solution for meme video detection and identifying meme content across diverse platforms. This hybrid model allowed for a comprehensive analysis of visual, textual, and audio elements.
Deep LearningRecurrent Neural Networks (RNNs)Optical Character Recognition (OCR)Spectrogram AnalysisMachine Learning

Rochester institute of technology

Research Intern

Nov 2022Jan 2024 · 1 yr 2 mos · Rochester, New York, United States · Remote

  • Numerous Internet Service Providers (ISP) provide Internet services to organizations all over the world. Internet communications is impacted by the huge routing tables and the complex operations of the routers. This is attributed to the complexity in routing protocols. To overcome these issues Expedited Internet Bypass Protocol (EIBP) is being developed.
  • To address this performance challenges in EIBP we have a clean slate approach which is the Expedited Internet By- pass protocol (EIBP). EIBP works in parallel with IP and has no dependency on layer 3 protocols. Networks are designed around a modular architecture which consists of Core Routers, Distribution Routers, and Access Routers in three different tiers. This architecture is scalable and easy to trouble shoot. EIBP exploits the structure in network architectures to auto-assign addresses to routers in the network.
  • EIBP captures the relative position of a router in the network structure into a routable address and uses this information to route packets. EIBP thus introduces a new auto-addressing scheme that does not require route discovery. Routers running EIBP acquire multiple routable addresses to provide immediate fallback paths in the event of a path failure. EIBP does not use IP addresses. For backward compatibility with IP, it operates at layer 2.5 in parallel with IP at layer 3. EIBP forwards traffic between end IP systems and networks by encapsulating them in EIBP headers, which use the EIBP, assigned routable addresses.

Kumaraguru college of technology

Student

Dec 2020Sep 2024 · 3 yrs 9 mos · Coimbatore, Tamil Nadu, India

Education

Kumaraguru College of Technology

Master of Business Administration - MBA

Sep 2024Jul 2026

Kumaraguru College of Technology

Bachelor of Engineering - BE — Computer Science

Dec 2020Apr 2024

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