(MRS.) A.R. USIOBAIFO

Implementing blockchain-based certification systems is pivotal for enhancing the security, transparency, and efficiency of credential verification in academic and professional sectors. This study explores the design and deployment of an NFT-based certificate verification system, leveraging Ethereum blockchain, smart contracts, and decentralized storage (IPFS) to issue, verify, and manage digital credentials. By applying blockchain technology, smart contract logic, and decentralized file systems, this research addresses challenges such as certificate forgery, inefficient manual verification, and limited global accessibility. Key methodologies include the use of Solidity smart contracts for minting NFTs, Web3.js for blockchain interaction, and React.js for user interface development, alongside IPFS for secure off-chain certificate storage. The system is designed to reflect real-world use cases involving students, educational institutions, and verifiers, supporting operations such as certificate issuance, revocation, and verification. Outcomes include instant, tamper-proof verification processes, reduced operational costs, and improved user trust in credential authenticity. By integrating emerging technologies like Web3 and decentralized identity (DID), this research establishes a foundation for scalable, secure, and adaptable certification systems capable of meeting the evolving needs of education and professional validation

Electronic mail (email) remains a primary communication channel globally, making it a prime target for malicious actors. The continuous rise in spam emails poses severe threats, including network congestion, financial losses, and security vulnerabilities like phishing and malware distribution. Traditional rule-based spam filters struggle to keep pace with the evolving tactics of pammers, creating a critical need for more adaptive and robust solutions. This project presents the Design and Implementation of a Spam Email etection System Using Artificial Intelligence.The system leverages Natural anguage Processing (NLP) and Machine Learning techniques to accurately classify mails as "Spam" or "Ham" (legitimate). The methodology involves data collection using a standardized public dataset, followed by intensive text preprocessing including tokenization, stop-word removal, and stemming. Text data is converted into numerical vectors using Term Frequency-Inverse Document requency (TF-IDF). Multiple AI algorithms, including Naïve Bayes, Support Vector Machines (SVM), and Random Forest, are implemented and evaluated to determine the most effective classifier.The system was developed using Python, utilizing libraries such as Scikit-Learn and NLTK, and deployed with a user-friendly graphical interface. Experimental results demonstrate that the AI-powered system achieves high performance, with the top-performing model attaining an accuracy rate exceeding [Insert Accuracy, e.g., 98.2%] and a low false-positive rate. This research concludes that integrating artificial intelligence significantly enhances email security by providing dynamic, real-time protection against malicious messages, thereby ensuring a safer digital communication ecosystem.