The population of the country of Kenya is drastically increasing thus causing the number of possible blood donors to rise. Despite this, the blood collected and stored in most blood banks is not enough to cater for the huge demand. The demand has been due to increase of number of accidents experienced in the country and the advancement in medical procedures which calls for organ transplant and blood transfusion. Even though systems have been developed which can connect the donors and recipients and location tracking, most people are dying because they don`t get this vital commodity in good time. The process of donating blood has not been enticing. There is nothing that prompts a person to donate blood. This call for developing a gamified blood donor management system based on intelligent agents so as to increase the number of donors and keep the system performance at optimal level. The project adopts Goal-Oriented Methodology in the system development process. Two agents are developed: donors’ agent and the blood admin agent. The intelligent agents help in profiles personalization thus improving the system performance. Gamification technique is implemented in the system so as to increase the traffic of blood donors interacting with the system and participating in the donation exercise. This increase the number of blood donors hence enough blood is collected to cater for the huge demand.

Text summarization plays an important role in the area of natural language processing. The need for information all over the world to solve specific problems keeps on increasing daily. This poses a greater challenge as data stored on the internet has gradually increased exponentially over time. Finding out the relevant data and manually summarizing it in a short time is a challenging and tedious task for a human being. Text Summarization aims to compress the source text into a more concise form while preserving its overall meaning. Two major categories of text summarization methods exist namely: extractive and abstractive. The extractive technique concentrates on determining key themes using frequency analysis of sentences in the corpus of the text. Abstractive methods write a new summary with newly generated texts which do not appear in the corpus itself. This paper presents a hybrid model for text summarization using both extractive and abstractive techniques. Term Frequency (TF) – Inverse Document Frequency (IDF) was used for extractive and T5 Transformers for abstractive summarization. Iterative Incremental Methodology was adopted in the study. The hybrid model emerged as not the best choice compared to the extractive and abstractive as it had been hypothesized in the study when the accuracy and execution time of the summary generated was considered.