Age group classification is a complex task that is used to classify facial images or videos into predetermined age categories. It is an important task due to its numerous applications such as health, security, authentication system, recruitment, and also in intelligent social robots. Convolutional Neural Network (CNN) has recently shown excellent performance in analysing human face images and videos. This paper proposed an age group classification task using CNN that trained and tested with an All-Age Face (AAF) dataset. FaceNet deep learning model that uses CNN was applied in this study to compute a 128-d embedding that quantifies the face of the age group. The experiment included two age groups: Adolescence and Mature Adulthood. The proposed age group classification model achieved 84.90% accuracy for the training images and 85.12% accuracy for the test images. The experimental results showed that CNN is capable of achieving competitive classification accuracy throughout two age groups in the AAF dataset with unbalanced data distribution.

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Manufacturing scheduling is a combinatorial problem, particularly in dynamic environment. This paper presents a rescheduling method in solving manufacturing scheduling which uses two main components namely as ontology to model scheduling and PSM. Two main problems are used to solve, firstly is static manufacturing problem and secondly is dynamic manufacturing problem which focus on rescheduling when new job arrive. Computational results show that schedule can be devised efficiently and correct. The schedule efficiency and schedule stability of new schedule is good and can sustain mostly of operations from initial schedule.

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