Skip to content

Author

Afshan Fatima

3 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

A BLOCKCHAIN-BASED DECENTRALIZED FRAMEWORK FOR CERTIFICATE AUTHENTICATION AND ISSUER TRUST

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 · 0 citations
Open access Aug 2026

AUTOMATED HEART DISEASE DETECTION FROM ECHOCARDIOGRAPHIC IMAGE VIA DEEP NEURAL NETWORK

A deep learning-based method for automatically classifying heart conditions from echocardiography data using the EfficientNetB0 architecture, which has the potential to improve cardiovascular disease prognosis and early detection, thereby increasing the scalability of sophisticated diagnostic capabilities in a variety of healthcare settings.

Taha Tahseen, Afshan Fatima · 0 citations
Review Open access Aug 2026

AUTOMATED DETECTION OF TUBERCULOSIS FROM CHEST X-RAY IMAGES USING DEEP LEARNING

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 · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.