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In this era, the interaction between Human and Computers has always been a fascinating field. With the rapid development in the field of Computer Vision, gesture based recognition systems have always been an interesting and diverse topic. Though recognizing human gestures in the form of sign language is a very complex and challenging task. Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task. This paper proposes a RGB and RGB-D static gesture recognition method by using a fine-tuned VGG19 model. The fine-tuned VGG19 model uses a feature concatenate layer of RGB and RGB-D images for increasing the accuracy of the neural network. Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model. The authors achieved 94.8% recognition rate and compared the model with other CNN and traditional algorithms on the same dataset.

As the rates of business and technological changes accelerate, misalignments between business and IT architectures are inevitable. Existing alignment models, while important for raising awareness of alignment issues, have provided little in the way of guidance for actually correcting misalignment and thus achieving alignment. This paper introduces the BITAM (Business IT Alignment Method) which is a process that describes a set of twelve steps for managing, detecting and correcting misalignment. The methodology is an integration of two hitherto distinct analysis areas: business analysis and architecture analysis. The BITAM is illustrated via a case study conducted with a Fortune 100 company.

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