The Socio-Economic Implications of Decline in Artisanal Fishing and its Effect on Women: A Case Study of Shams Pir Island, Karachi

Artisanal fishing is carried out in the coastal villages of Karachi. Through centuries, these fishing villages have maintained their traditional practices of fishing. Shams Pir Island is in vicinity of Karachi coast. The main occupation of islanders is artisanal fishing which is extremely threatened by various factors. Historically, in coastal communities of Karachi, women were part of fishing and its related activities. With the decline in traditional fishing methods, females have been withdrawn from fishing work. Implications of this process have been drastic for fishing households such as increased poverty, unemployment, environmental degradation etc. The research is conducted to highlight these problems especially from gender perspective. It is exclusively based on females of Shams Pir Island. Female’s socio-economic conditions, family status, perceptions on different contemporary issues pertaining to the endangered livelihood of their family and its relation with environmental changes is investigated in particular. In total, sixty women representing fishing households, few community leaders and representatives of fisher folk’s NGO were interviewed. The results emphasized the livelihood issues of females and their perceptions on environment and climate change. The vulnerabilities of females in fishing communities have been discussed in detail. ______

Online Urdu Handwritten Text Recognition for Mobile Devices Using Intelligent Techniques

Recognition of online cursive handwriting is difficult and challenging task. This become more cumbersome when it comes to mobile devices where we have small writing area, different writing styles and stroke sequence variation. This research introduces different approaches for recognition of Urdu handwritten characters and word for mobile devices. Comparing all points is resource hungry approach especially for mobile phones. Looking at challenges of mobile phones, Urdu characters are recognized using vectors. It results in fast processing and efficient recognition of Urdu characters consuming lesser resources of the devices. Nevertheless, for complex inputs i.e. online handwritten words proposed technique did not provide good results. Urdu words are compounds of different characters connected with each other. Correct segmentation of online cursive handwriting is difficult to achieve on small writing area. Segmentation free approach is used to recognize Urdu words written on mobile screen. In segmentation free recognition of cursive handwriting, writing rules plays very important role in good and accurate recognition. Looking at shape and location of each character within word, writing rules are designed for Urdu language. A new algorithm is proposed to preprocess the complex input and preserve shape of the actual input. Fuzzy association rules are used to link secondary stroke with their respective primary strokes. Different classifiers such as hidden Markov model (HMM), fuzzy logic, K nearest neighbor (KNN), hybrid HMM fuzzy, hybrid KNN fuzzy and convolutional neural network (CNN) are used for the classification. Statistical tests are applied to find the significance of classifiers results. Experimental results show that by applying proposed preprocessing algorithms and writing rules, recognition rates are impressively enhanced. Vector quantization method provides better results for recognition of online Urdu handwritten characters and hybrid KNN and fuzzy classifier for recognition of ligatures and words in mobile phones.