It is crucial to confirm the legitimacy of educational degree credentials, particularly when hiring new employees, since falsified paperwork can seriously disrupt operations and reduce productivity. Conventional verification techniques are susceptible to delays, mistakes, and data manipulation because they mostly rely on human procedures and centralized databases. A single, safe platform for smooth communication between issuers, holders, and verifiers is absent from these systems. This study suggests a decentralized, blockchain-based issuer validation and certificate verification solution to overcome these drawbacks. The approach ensures data immutability and tamper resistance by storing certificate hashes on the blockchain using Ethereum. Every participant in the network is represented as a peer node, including the issuer, holder, validator, and verifier. Even in cases when the certificate cannot be located, a hash-based search method drastically cuts down on certificate lookup time. According to experimental evaluation, the system provides quick, dependable verification and is economical in terms of gas use. The maintenance and validation of certificates is made safe, transparent, and effective by this integrated method.
Madiha Sadaf, Afshan Fatima, Ruqiya Fatima· American Journal of AI Cyber...· 0 citations
PriChain gives data owners the authority to manage who may access and alter their on-chain data, guaranteeing that redaction can only be carried out by authorized users while maintaining data confidentiality.
S. Kulsum, Lalitha Saroja Ch, Ruqiya Fatima· American Journal of AI Cyber...· 0 citations
The installation of a real-time visual tracking system with an active pan-tilt camera for indoor human motion detection is presented, which shows that the inclusion of YOLOv10 significantly improves detection precision and temporal consistency.
Ayman Javid Hussain, Lalitha Saroja Ch, Ruqiya Fatima· International Journal of AI...· 0 citations
A deep learning-based system that uses chest X-ray pictures to automatically detect tuberculosis, using transfer learning using MobileNetV2 and DenseNet architectures to classify chest Xrays as either TB-positive or Healthy, reaching notable accuracy.
Zoya Nasreen, Afshan Fatima, Ruqiya Fatima· International Journal of AI...· 0 citations
This study proposes an Advanced Surveillance Framework that makes use of YOLOv10, a next-generation real-time object detection algorithm that greatly outperforms conventional single-sensor approaches in precision, recall, and real-time responsiveness.
Sadiya Begum, Lubna Nausheen, Ruqiya Fatima· International Journal of Eng...· 0 citations
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