Asim Abbas

Wasim Abbas, Salaki Reynaldo Joshua, Asim Abbas et al.

Encryption is crucial for securing sensitive data during transmission over networks. Various encryption techniques exist, such as AES, DES, and RC4, with AES being the most renowned algorithm. We proposed methodology that enables users to encrypt text messages for secure transmission over cellular networks. This approach utilizes the AES algorithm following the proposed protocols for encryption and decryption, ensuring fast and reliable data protection. This approach ensures secure text encryption and enables users to enter messages that are encrypted using a key at the sender's end and decrypted at the recipient's end, which is compatible with any Android device. SMS are encrypted with the AES algorithm, making them resistant to brute-force attempts. As SMS has become a popular form of communication, protecting personal data, email alerts, banking details, and transactions information. It addresses security concerns by encrypting messages using AES and cryptographic techniques, providing an effective solution for protecting sensitive data during SMS exchanges.

Mutahira Khalid, Raihana Rahman, Asim Abbas et al.

Knowledge graphs (KGs) serve as powerful tools for organizing and representing structured knowledge. While their utility is widely recognized, challenges persist in their automation and completeness. Despite efforts in automation and the utilization of expert-created ontologies, gaps in connectivity remain prevalent within KGs. In response to these challenges, we propose an innovative approach termed ``Medical Knowledge Graph Automation (M-KGA)". M-KGA leverages user-provided medical concepts and enriches them semantically using BioPortal ontologies, thereby enhancing the completeness of knowledge graphs through the integration of pre-trained embeddings. Our approach introduces two distinct methodologies for uncovering hidden connections within the knowledge graph: a cluster-based approach and a node-based approach. Through rigorous testing involving 100 frequently occurring medical concepts in Electronic Health Records (EHRs), our M-KGA framework demonstrates promising results, indicating its potential to address the limitations of existing knowledge graph automation techniques.