§ 00 // Monograph 2026·

Available for AI Research & Engineering

Diya Chanda

B.Tech CSE (AI & ML)|The Neotia University (9.54 CGPA)|IEEE & Springer Author

“Bridging empirical deep learning research with production systems engineering—specializing in explainable AI, multi-task vision architectures, and grounded RAG pipelines.”

Diya Chanda is an AI researcher and machine learning engineer bridging empirical deep learning with production web systems. Specializing in explainable AI, computer vision, and RAG-augmented intelligent architectures, she is a published IEEE & Springer author dedicated to engineering robust, high-accuracy intelligent platforms.

Read Selected Research Inspect Applied Systems Curriculum Vitae

Scholarly Indices:ResearchGate·GitHub·LinkedIn

Diya Chanda — AI Researcher & Machine Learning Engineer

Plate 01 // Diya ChandaKolkata, IN

SeatThe Neotia Univ.

CGPA9.54 / 10.0

Papers4 (IEEE & Springer)

FocusXAI · Vision · RAG

Published Papers

IEEE ICRITO & Springer LNNS

9.54

Academic CGPA

The Neotia University

99%

Diagnostic Accuracy

Multi-Headed CNN + Grad-CAM

04+

Engineered Systems

Full-Stack RAG & Vision

§ 01 // Empirical Research·Peer-Reviewed Bibliography

Research & Publications

04 Peer-Reviewed ArticlesIEEE Xplore · Springer LNNS · Explainable Deep Learning (XAI)

[01]2025

IEEE · IEEE Xplore

18-19 September 2025

Noida NCR, India

DOI: 10.1109/ICRITO66076.2025.11241706

FruitQ-GradeX: Determining Fruit Quality and Grading with Explainable Deep Learning

Venue: 2025 12th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

Authors: Shibdas Dutta, Subhrendu Guha Neogi, Diya Chanda, Arpan Pramanik, Özgün Girgin, Enes Ladin Öncül

Key Results:98% fruit classification accuracy99% quality detection accuracy

IEEE Xplore Article

[02]2026

Springer · Springer LNNS

21 July 2026

Springer LNNS Book Series

DOI: 10.1007/978-3-032-21901-5_35

Hyperspectral Fruit and Vegetable Classification Using Convolutional Neural Networks with EfficientNetB3

Venue: Data Mining and Information Security (ICDMIS 2025) — Lecture Notes in Networks and Systems (LNNS, volume 1915), Springer (pp. 449–469)

Authors: Shibdas Dutta, Subhrendu Guha Neogi, Arpan Pramanik, Diya Chanda, Özgün Girgin, Enes Ladin Öncül

Key Results:99.29% training accuracy97.21% test accuracyBenchmarked against 20+ architectures

SpringerLink Chapter Springer Book Series

[03]2025

IEEE · IEEE Xplore

18-19 September 2025

Noida NCR, India

DOI: 10.1109/ICRITO66076.2025.11241535

CropSense: Explainable Deep Learning Framework for Accurate Quality Detection in Solanaceous Crops

Venue: 2025 12th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

Authors: Shibdas Dutta, Subhrendu Guha Neogi, Shiladitya Chowdhury, Vikrant Chole, Arpan Pramanik, Diya Chanda

Key Results:99.9% crop classification accuracy98.5% quality detection accuracy30% lower parameter overhead

IEEE Xplore Article

[04]2025

IEEE · IEEE Xplore

01-03 September 2025

Talegaon, India

DOI: 10.1109/COMPUTINGCON64838.2025.11376762

An Explainable Deep Learning Approach for Quality Assessment in Solanaceous Crops

Venue: 2025 International Conference on Computing and Communications (COMPUTINGCON)

Authors: Shibdas Dutta, Barshan Adhikari, Arpan Pramanik, Diya Chanda

Key Results:98.45% potato classification accuracy97.49% tomato classification accuracy98.5% quality assessment accuracy

IEEE Xplore Article

§ 02 // Applied Intelligence·Production Systems & Web Architectures

Featured Systems & Projects

04 System BlueprintsEnterprise compliance platforms, climate-smart agronomy engines, generative AI assistants, and explainable computer vision architectures.

CampusSphere — Enterprise Accreditation & Institutional Credit Banking System

Live Production Platform

SYS VIEWPORT 01Enterprise Full-Stack Platform

[SYS 01]·Enterprise Full-Stack Platform

CampusSphere

Enterprise Accreditation & Institutional Credit Banking System

Multi-tier enterprise compliance and credit banking platform for higher education institutions. Streamlines student co-curricular verification via a two-stage pipeline (Faculty Advisor Stage 1 & Admin Stage 2), dynamic Credit Policy Engine mapped to NAAC Criteria 1–7, cryptographic public credential verification (/verify/[id]), and 1-click NAAC/NIRF CSV compliance reports.

Architectural Telemetry & Capabilities

Two-Stage Verification Pipeline (Faculty & Admin)

Dynamic NAAC Criteria 1–7 Credit Engine

Cryptographic Public Credential Verification

OpenTelemetry Distributed Tracing & Upstash Redis

Stack:Next.js 15React 19Node.jsExpressPostgreSQLSupabaseUpstash RedisCloudinaryOpenTelemetryTailwind CSS

Launch Live System Inspect Source

JalDrishti (जलदृष्टि) — Climate-Smart Agronomy & Precision Irrigation Advisory

Research & Advisory System

SYS VIEWPORT 02Edge-AI & Precision Agronomy

[SYS 02]·Edge-AI & Precision Agronomy

JalDrishti (जलदृष्टि)

Climate-Smart Agronomy & Precision Irrigation Advisory

State-of-the-art precision irrigation and crop advisory platform for Indian agriculture. Combines FAO-56 Penman-Monteith Evapotranspiration modeling, SoilGrids satellite physics, Smart Rain Hold warnings (≥5.0mm override), weather-driven pathogen risk forecasting for 5 critical crops, and JalSathi AI—a multilingual voice-enabled RAG agronomy assistant.

Architectural Telemetry & Capabilities

FAO-56 Penman-Monteith Hydrological Engine

Smart Rain Hold Warning (≥5mm Precipitation)

Multilingual Voice RAG (Bengali, Hindi, English)

Weather-Driven Pathogen Risk Telemetry

Stack:FastAPIPython 3.11FlutterPostgreSQLChromaDBHuggingFaceGroq Llama-3-70BOpen-Meteo APISoilGrids APIRedis Cloud

Inspect SourceResearch & Advisory Engine

RecipeAI — Generative AI Sous-Chef & Culinary Intelligence Web App

Live Web Application

SYS VIEWPORT 03Generative AI & Web Platform

[SYS 03]·Generative AI & Web Platform

RecipeAI

Generative AI Sous-Chef & Culinary Intelligence Web App

Context-aware culinary AI web application that brings an expert AI Sous-Chef into the kitchen. Features Pantry Chef (ingredient-first zero-waste cooking), on-demand dish generation, real-time context-aware conversational AI assistant powered by Llama-3.3-70B, Clerk JWT authentication, and Supabase Postgres activity history.

Architectural Telemetry & Capabilities

Pantry Chef (Ingredient-First Cooking)

Context-Aware Conversational AI Sous-Chef

Clerk Authentication & Backend JWKS Verification

Supabase Postgres Activity History & Clear

Stack:React 18TypeScriptFastAPIPythonGroq Llama-3.3-70BClerk AuthSupabase PostgresTailwind CSSFramer Motion

Launch Live System Inspect Source

FruitQ-GradeX — Explainable Deep Learning Fruit Quality Grading

Live Streamlit App

SYS VIEWPORT 04Deep Learning & Vision Research

[SYS 04]·Deep Learning & Vision Research

FruitQ-GradeX

Explainable Deep Learning Fruit Quality Grading

Multi-task deep learning system that simultaneously classifies fruit species and grades commercial quality. Powered by a custom multi-headed CNN achieving 97.75% fruit classification accuracy and 98.21% quality accuracy across 9,146 images with Grad-CAM decision heatmaps and real-time webcam inference.

Architectural Telemetry & Capabilities

Dual Simultaneous Prediction Heads

98.21% Quality Assessment Accuracy

Grad-CAM Decision Heatmap Interpretability

Real-Time Streamlit & Webcam Inference

Stack:TensorFlowKerasMulti-Headed CNNGrad-CAMOpenCVStreamlitPython

Launch Live System Inspect Source

§ 03 // Career Trajectory·Research Appointments & Tenures

Professional Experience

04 Verified AppointmentsRigorous summer research appointments, industry software engineering internships, and specialized cybersecurity tenures developing machine learning models, production RESTful APIs, and secure networks.

[01]June 2026

4 Weeks

Summer Research Internship

Durgapur, West Bengal

Domain: AI & Deep Learning

AI Research Intern

National Institute of Technology (NIT) Durgapur

Conducted research on lightweight deep learning architectures by optimizing EfficientNet variants for image classification tasks with a focus on model efficiency and inference speed.

Key Deliverables & Research Focus

Evaluated architectural variants across the EfficientNet family
Benchmarked performance on open-source image datasets
Analyzed trade-offs between accuracy, parameter efficiency, and FLOPs
Optimized deep learning models for efficient execution

Applied Tooling:PythonPyTorchTensorFlowEfficientNetDeep LearningComputer VisionModel Optimization

[02]July 2025

4 Weeks

Summer Training & Internship

Guwahati, Assam

Domain: Backend Engineering

Backend Development Intern

Xetalabs

Built production-grade RESTful APIs and full-stack web applications using Flask. Designed database schemas, implemented JWT-based authentication, and deployed services on cloud infrastructure.

Key Deliverables & Research Focus

Developed 5+ RESTful API endpoints with proper error handling & validation
Implemented JWT authentication and role-based access control
Built admin dashboard with Flask-Admin and Jinja2 templates
Deployed applications on Railway with CI/CD pipeline

Applied Tooling:PythonFlaskREST APIsSQLAlchemyPostgreSQLJWT AuthJinja2RailwayGitPostman

[03]June 2025

6 Weeks

Summer Training & Internship

West Bengal, India

Domain: Cybersecurity

Cyber Security Intern

Webel PVT. LTD & The Neotia University

Intensive hands-on training program covering offensive and defensive cybersecurity. Gained practical experience in penetration testing, vulnerability assessment, and incident response using industry-standard tools.

Key Deliverables & Research Focus

Performed penetration testing on simulated enterprise networks
Conducted vulnerability scanning using Nmap, Burp Suite & Wireshark
Studied OWASP Top 10 vulnerabilities with live exploitation labs
Implemented firewall rules and IDS/IPS configurations

Applied Tooling:Kali LinuxNmapBurp SuiteWiresharkMetasploitOWASPNetwork SecurityEthical HackingFirewallsIDS/IPS

[04]July 2024

4 Weeks

Summer Training & Internship

West Bengal, India

Domain: Cybersecurity

Cyber Security Intern

DataSpace Academy & The Neotia University

Comprehensive foundations program in cybersecurity covering cryptography, threat modeling, digital forensics, and secure coding practices. Completed a capstone project on network threat analysis.

Key Deliverables & Research Focus

Learned cryptographic protocols — AES, RSA, SHA hashing
Performed threat modeling using STRIDE framework
Analyzed network traffic for anomalies using Wireshark
Completed capstone: Automated threat detection script in Python

Applied Tooling:PythonCryptographyWiresharkThreat ModelingDigital ForensicsSTRIDELinux CLIBash Scripting

§ 04 // Laboratory Matrix·Technical Competency & Tooling

Technical Stack & Capabilities

27 Benchmarked TechnologiesA curated directory of frameworks, libraries, runtime environments, and infrastructure tools utilized across deep learning research and full-stack systems engineering.

AI/ML & Deep Learning

Research Core

05 tools

Multi-headed CNNs, Vision Transformers, Grad-CAM interpretability, and scientific computing.

PyTorchTensorFlowKerasscikit-learnOpenCV

Domain Index 01Verified Stack

Generative AI & NLP

Grounded Systems

04 tools

Context-grounded RAG pipelines, semantic embeddings, vector indexing, and LLM orchestration.

LangChainRAGLLMsVector Search

Domain Index 02Verified Stack

Programming Languages

Algorithmic Foundation

04 tools

Object-oriented architectures, low-level data structures, declarative queries, and backend scripting.

PythonJavaSQLC

Domain Index 03Verified Stack

Web & Backend Architecture

Production APIs

05 tools

High-throughput async REST microservices, client-side rendering, and responsive state management.

FastAPINode.jsExpress.jsReact.jsREST APIs

Domain Index 04Verified Stack

Databases & Cloud Storage

Data Infrastructure

05 tools

ACID relational databases, NoSQL document stores, in-memory caching, and managed cloud backend.

PostgreSQLMongoDBSupabaseAWSRedis

Domain Index 05Verified Stack

Tools & CI/CD Deployment

DevOps & Pipeline

04 tools

Containerization, automated continuous integration, version control workflows, and cloud releases.

DockerGitGitHubGitHub Actions

Domain Index 06Verified Stack

Core Engineering Synthesis

01 // Empirical Research

PyTorch, TensorFlow, multi-headed CNNs, Grad-CAM interpretability, and cyclic learning rate optimization.

02 // Applied AI & RAG

FastAPI, Groq Llama-3.3, LangChain, ChromaDB vector indexing, and soil satellite physics APIs.

03 // Production Platforms

Next.js 15, PostgreSQL, Supabase, Upstash Redis caching, Docker containerization, and OpenTelemetry tracing.

§ 05 // Research Monograph·Academic Tenets & Foundations

About Me

The Neotia University · Kolkata, IndiaB.Tech CSE (AI & ML) · Cumulative GPA: 9.54 / 10.0

I am a B.Tech Computer Science & Engineering undergraduate specializing in Artificial Intelligence and Machine Learning at The Neotia University, maintaining a 9.54 CGPA. My work bridges empirical deep learning research with production-grade engineering, focusing on explainable computer vision, multi-modal neural architectures, and intelligent systems.

Academic Profile & Registry

InstitutionThe Neotia University

DegreeB.Tech Computer Science & Engineering

SpecializationArtificial Intelligence & Machine Learning

Cumulative GPA

9.54 / 10.0Top Decile

Tenure2023 – 2027

LocationWest Bengal, India

Engineering Mission

“To engineer interpretable, high-accuracy intelligent systems that seamlessly transition from academic research into robust real-world production environments.”

Interests & Explorations

Deep Learning ResearchGrounded AI & RAGComputer Vision & Grad-CAMAlgorithmic Problem SolvingPrecision Agriculture AIFull-Stack Web SystemsBadmintonEmerging Tech Exploration

Core Research & Engineering Pillars

Generative Systems

Grounded AI & RAG Architectures

Architecting reliable, context-aware retrieval-augmented generation pipelines that ground model responses in verified domain knowledge while mitigating hallucinations.

Explainable AI (XAI)

Deep Learning & Vision Research

Designing multi-headed CNNs, Vision Transformers (ViTs), and Grad-CAM interpretability layers that provide transparent, visual decision rationales for mission-critical diagnostics.

Model Optimization

Precision Edge-AI Systems

Engineering lightweight neural networks with over 30% parameter reduction, optimized for low-latency inference on agricultural and healthcare edge sensors.

Production Systems

Full-Stack ML Engineering

Transforming algorithmic research into production-grade, interactive web applications using Next.js, FastAPI, Streamlit, and cloud microservices.

Academic & Research Milestones

2023 – Present

B.Tech CSE (AI & ML) — The Neotia University

Commenced undergraduate studies focusing on core computer science foundations, mathematics for machine learning, data structures, and neural network architectures (Current CGPA: 9.54).

2024 – Present

Empirical AI Research & Conference Publications

IEEE & Springer Co-Author

Authored and published 4 peer-reviewed papers across IEEE ICRITO, IEEE COMPUTINGCON, and Springer LNNS, developing multi-task crop and fruit grading models.

2025 – Present

Production-Grade AI Platforms & Hackathons

Smart India Hackathon & Industry Internships

Selected for SIH 2024 (1st round), completed engineering internships at Xeta Labs and DataSpace Academy, deploying scalable web applications and AI APIs.

Guiding Principles

Empirical Research RigorGrounded AI & ReliabilityInterpretability & XAIClean Software ArchitectureContinuous Intellectual Curiosity

§ Academic Honors·Verified Accreditations & Awards

Certifications & Credentials

07 Verified AccreditationsPeer-reviewed presentation accreditations, hackathon awards, cloud infrastructure certifications, and industry engineering credentials.

Research & PresentationSep 2025

IEEE Conference Presentation Certificate

D Y Patil University, Ambi, Pune (IEEE Bombay Section)

“An Explainable Deep Learning Approach for Quality Assessment in Solanaceous Crops”

Presented original research on explainable multi-task hybrid CNN-ViT models for simultaneous crop disease detection and commercial quality grading before an international peer panel.

Explainable AIComputer VisionConference PresentationResearch Publication

Hackathons & CompetitionsSep 2024

Smart India Hackathon — Internal Selection Winner

The Neotia University

Selected as a winning team in the university-wide internal hackathon round for designing and presenting an innovative AI-powered solution for nationwide challenge problem statements.

Rapid PrototypingProblem SolvingSystem ArchitectureHackathon Defense

Academic Honors2025

Certificate of Merit — Journal Publication

Anuranan (The Neotia University Interdisciplinary Journal)

“AI-Driven Smart Waste Management System”

Awarded academic merit certificate for authoring an interdisciplinary paper on computer vision and IoT sensor fusion for sustainable urban waste classification.

Academic WritingIoT & VisionWaste ClassificationEnvironmental AI

Advanced TrainingMar 2025

IEEE SPS Short Term Training Program (STTP)

Dream Institute of Technology & IEEE Signal Processing Society

Completed an intensive week-long professional training program on deep learning signal processing, biosignal analysis (ECG/sound), and neural feature extraction.

Signal ProcessingDeep LearningBiosignals (ECG)Feature Extraction

Cloud & InfrastructureNov 2024

AWS Cloud Practitioner & Infrastructure

DataSpace Academy

Completed hands-on cloud architecture training covering Amazon EC2 compute instances, S3 object storage, IAM access policies, and serverless cloud deployment pipelines.

Amazon Web Services (AWS)EC2 & S3Cloud SecurityIAM Policies

Industry InternshipsJul 2024

Cybersecurity & Vulnerability Assessment Intern

DataSpace Academy

Executed penetration testing drills, network vulnerability scanning using Tenable Nessus, security auditing, and Linux system security hardening workflows.

Tenable NessusKali LinuxVulnerability AssessmentNetwork Auditing

Industry Internships2025

AI & Machine Learning Engineering Intern

Xeta Labs, Guwahati

Completed a 3-month engineering internship developing predictive machine learning pipelines, optimizing computer vision models, and building API inference backends.

Model OptimizationComputer VisionInference PipelinesPython & FastAPI

§ 06 // Direct Correspondence·Scholarly & Professional Inquiries