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Planning and managing of requirement change management (RCM) process in global software development (GSD) are a complicated task, but the RCM plays a significant role in developing the quality software within time and budget. The key aim of this study is to prioritize the factors that could positively influence the RCM program in GSD context. To achieve the study objective, the questionnaire survey study was conducted to get the feedback of the practitioners concerning the success factors of RCM in GSD context. Moreover, the fuzzy analytical hierarchy process (FAHP) was applied. The application of FAHP is novel in this research domain as it has been effectively applied previously in various other research areas, for example, supplier selection, electronics and electrical, personnel selection, and agile software development. The results of this study will provide the prioritization‐based taxonomy of RCM success factors and also contribute by introducing the novel FAHP approach in the research domain of RCM in GSD. The FAHP approach assists the practitioners to reduce the uncertainty and vague opinions of RCM experts.

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Hearing-impaired people use sign language to express their thoughts and emotions and reinforce information delivered in daily conversations. Though they make a significant percentage of any population, the majority of people can't interact with them due to limited or no knowledge of sign languages. Sign language recognition aims to detect the significant motions of the human body, especially hands, analyze them and understand them. Such systems may become life-saving when hearing-challenged people are in desperate situations like heart attacks, accidents, etc. In the present study, deep learning-based hand gesture recognition models are developed to accurately predict the emergency signs of Indian Sign Language (ISL). The dataset used contains the videos for eight different emergency situations. Several frames were extracted from the videos and are fed to three different models. Two models are designed for classification, while one is an object detection model, applied after annotating the frames. The first model consists of a three-dimensional convolutional neural network (3D CNN), while the second comprises of a pre-trained VGG-16 and a recurrent neural network with a long short-term memory (RNN-LSTM) scheme. The last model is based on YOLO (You Only Look Once) v5, an advanced object detection algorithm. The prediction accuracies of the classification models were 82% and 98%, respectively. YOLO based model outperformed the rest and achieved an impressive mean average precision of 99.6%.

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