Ahmed Mahir Sultan Rumi

Rabib Jahin Ibn Momin, Ahmed Mahir Sultan Rumi, Rezwana Reaz

Academic examination systems worldwide continue to rely on centralised, opaque record-keeping that is often vulnerable to credential forgery, result tampering, examiner bias, and the absence of transparent re-evaluation pathways. Existing blockchain-based approaches in education focus predominantly on post-hoc certificate storage or online-only examination portals, leaving the complete onsite examination lifecycle, from conducting exams through scrutiny, largely unaddressed. This paper proposes ParikkhaChain, a blockchain-based framework that covers the entire examination lifecycle of an onsite examination system with three distinguishing contributions: (i) anonymous script evaluation through cryptographic hashing of answer scripts before examiner access, thereby eliminating identity-based bias; (ii) a transparent evaluation and scrutiny workflow backed by an immutable on-chain audit trail that records every mark submission and grade revision; and (iii) inclusion of privacy-preserving verification using zero-knowledge proofs and off-chain storage mechanisms. The system is architected around four Solidity smart contracts deployed on the Ethereum blockchain. The proposed architecture is the first initiative to our knowledge to support physical examination process, anonymous marking, and re-evaluation transparency. We successfully simulate full exam cycles of an onsite exam to grade-sheet generation using a working prototype on a large scale of 100 courses and hundreds of teachers and students. The experimental results show that the system can manage online examinations of hundreds of courses, students and faculties efficiently with great throughput, low storage, and transaction cost. Our codebase is available in open source form at https://github.com/AhmedRumi/CSE6608-ParikkhaChain

Shahriar Kabir, Istiak Ahmmed Rifti, H. M. Shadman Tabib et al.

The proliferation of drones in civilian airspace has raised urgent security concerns, necessitating robust real-time surveillance systems. In response to the 2025 VIP Cup challenge tasks - drone detection, tracking, and payload identification - we propose a dual-stream drone monitoring framework. Our approach deploys independent You Only Look Once v11-nano (YOLOv11n) object detectors on parallel infrared (thermal) and visible (RGB) data streams, deliberately avoiding early fusion. This separation allows each model to be specifically optimized for the distinct characteristics of its input modality, addressing the unique challenges posed by small aerial objects in diverse environmental conditions. We customize data preprocessing and augmentation strategies per domain - such as limiting color jitter for IR imagery - and fine-tune training hyperparameters to enhance detection performance under conditions of heavy noise, low light, and motion blur. The resulting lightweight YOLOv11n models demonstrate high accuracy in distinguishing drones from birds and in classifying payload types, all while maintaining real-time performance. This report details the rationale for a dual-modality design, the specialized training pipelines, and the architectural optimizations that collectively enable efficient and accurate drone surveillance across RGB and IR channels.