The death rate is caused by breast cancer in women is increasingly high and growing. A number of people are getting to lose this part of their body due to late diagnosis of this disease. This therefore requires the development of an efficient and accurate diagnosis approach that will aid providing the knowledge of the type of breast cancer type and severity in order to reduce the mortality rate through the disease. This need serves as the major motivation for this work. In this paper, we proposed a fuzzy expert system for diagnosis of and treatment recommendation of breast cancer problems which provide physicians and patients with information of the cancer type and treatment recommendation. The application was designed using JAVA programming language, MATLAB and SQLite database engine. This application permits update of new information as a means of knowledge. The evaluation showed that the inclusion of the fuzzy inference system improved the accuracy and precision of the system from 0.8 to 0.9. The system is user-friendly and has high level of acceptability from the validation conducted at the end of the research.

Fruit grading is a process that affect quality control and fruit-processing industries to meet the efficiency of its production and society. However, these industries have suffered from lack of standards in quality control, higher time of grading and low product output because of the use of manual methods. To meet the increasing demand of quality fruit products, fruit-processing industries must consider automating their fruit grading process. Several algorithms have been proposed over the years to achieve this purpose and their works were based on color, shape and inability to handle large dataset which resulted in slow recognition accuracy. To mitigate these flaws, we develop an automated system for grading and classification of apple using Convolutional Neural Network (CNN) used in image recognition and classification. Two models were developed from CNN using ResNet50 as its convolutional base, a process called transfer learning. The first model, the apple checker model (ACM) performs the recognition of the image with two output connections (apple and non-apple) while the apple grader model (AGM) does the classification of the image that has four output classes (spoiled, grade A, grade B & grade C) if the image is an apple. A comparison evaluation of both models were conducted and experimental results show that the ACM achieved a test accuracy of 100% while the AGM obtained recognition rate of 99.89%.The developed system may be employed in food processing industries and related life applications.

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