Case-Based Reasoning (CBR) is a branch of AI that is employed to solving problems which emphasizes the use of previous solutions in solving similar new problems. This work presents TAMDS, a Temperament and Mood Detection system which employs Case-Based Reasoning technique. The proposed system is adapted to the field of psychology to help psychologists solve part of the problems in their complex domain. We have designed TAMDS to detect temperament and moods of individuals. A major aim of our system is to help individuals who are out of reach of a professional psychologist to manage their personality and moods because as humans, moods affect our perceptions, personal health, the way we view the world around us and the way we react to it.

Continuous miniaturization of mobile devices has greatly increased its adoption and use by people in various facets of our lives. This has also increased the popularity of face recognition and image processing. Face recognition is now being employed for security purpose opening up the need for further research in recent time. Image compression becomes useful in cases when images need to be transmitted across networks in a less costly way by increasing data volume while reducing transmission time. This work discusses our findings on image compression and its effect on face recognition systems. We studied and implemented three well known face recognition algorithms and observed their recognition accuracy when gallery / probe images were compressed and/or uncompressed as one would naturally expect. For compression purposes, we adopted the JPEG and JPEG2000 coding standard. The face recognition algorithms studied are PCA, ICA and LDA. As a form of an extensive research, experiments conducted include both in compressed and uncompressed domains where the three algorithms have been exhaustively analyzed. We statistically present the results obtained which showed no significant depreciation in the recognition accuracies.