Industrial ergonomics systems are designed to improve productivity and the work environment. Different regulations are considered to protect the health, safety and to improve the conditions of workers. In the field of clothing, the working conditions are still painful and cause occupational diseases especially the musculoskeletal disorders. The main objective of this study is to analyze the ergonomic of garment manufacturing to applying the standard "ISO 11228-3". The risk of musculoskeletal disorders is determined then the risk index is deduced. According to a Nordic survey evaluated on the study population, the most frequent MSDs were at the back (78%), hand and wrist (76%), neck (52%), shoulders (48%). The OCRA index found varies from 8.75 to 26.41 and the proposed ergonomic index varies between 1.11 and 1.21 depending on the machine used.

Social network is an important part of people’s daily life, which essentially can be considered as a black box, providing great insight into the current trends of people.  It has been proved that data obtained from Social Network platforms, including Facebook, Twitter, Instagram, Baidu etc., can be employed in a variety of different fields. Technically, social networks can be shown as nodes and edges, inspired from graph theory, this theory play an important role to express the structure and relations of the network. Big data mining, natural language processing (NLP), text mining and machine learning techniques are mainly utilized for processing and analysing the data. The analysis of social web can reveal the real trends of the people on any subjects, can detect terror activities before attacks, can help to understand political tendencies, cultural or global believes etc. This study is a review of social web mining that aims to examine the literature of social web mining and help to understand the techniques. However this study also proposes some novel approaches by taking into accounts data of the images on the web which will enrich the handled information while mining the social web. The study also points to the semantic web for reaching more clear inferences from social networks than at present.