Software quality is the fundamental requirement for a user, academia person, software developing organizations and researchers. In this paper a model for object-oriented Software Bug Prediction System (SBPS) has been developed. This model is capable of predicting the existence of bugs in a class if found, during software validation using metrics. The designed model forecasts the occurrences of bugs in a class when any new system is tested on it. For this experiment some open source similar types of defect datasets (projects) have been collected from Promise Software Engineering Repository. Some of these datasets have been selected for prediction of bugs, of which a few are not involved in model construction. First of all, we have formulated some hypotheses corresponding to each and every metric, and from metrics validation based on hypothesis basis finally 14 best suitable metrics have been selected for model creation. The Logistic Regression Classifier provides good accuracy among all classifiers. The proposed model is trained and tested on each of the validated dataset, including validated Combined Dataset separately too. The performance measure (accuracy) is computed in each case and finally it is found that the model provides overall averaged accuracy of 76.27%.

COVID-19 epidemic has affected our daily life disturbing the world trade and transport. Wearing a face mask has become a new necessity for safety. In the near future, many institutions will ask the customers to wear masks to avail of their services. Therefore, face mask detection has become a necessity to help society. This paper presents a simplified approach to achieve this purpose using some packages like TensorFlow, Keras, OpenCV and Scikit-Learn. This method detects the face from the image in frame and then identifies if it has worn a mask or not. As in a surveillance task, it can also detect a face along with a mask in movement through image processing. The method attains accuracy up to 93% and 91.2% respectively on two datasets. We explore optimized values of parameters using the Sequential CNN (Convolutional Neural Network) model to detect the presence of masks correctly. Keywords: Face Mask Detection, Convolutional Neural Network, TensorFlow, Keras, Image Processing

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Every day we see many people, who are facing illness like deaf, dumb etc. There are not as many technologies which help them to interact with each other. They face difficulty in interacting with others. Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Computer recognition of sign language deals from sign gesture acquisition and continues till text/speech generation. Sign gestures can be classified as static and dynamic. However static gesture recognition is simpler than dynamic gesture recognition but both recognition systems are important to the human community. The ASL American sign language recognition steps are described in this survey. There are not as many technologies which help them to interact with each other. They face difficulty in interacting with others. Image classification and machine learning can be used to help computers recognize sign language, which could then be interpreted by other people. Earlier we have Glove-based method in which the person has to wear a hardware glove, while the hand movements are getting captured. It seems a bit uncomfortable for practical use. Here we use visual based method. Convolutional neural networks and mobile ssd model have been employed in this paper to recognize sign language gestures. Preprocessing was performed on the images, which then served as the cleaned input. Tensor flow is used for training of images. A system will be developed which serves as a tool for sign language detection. Tensor flow is used for training of images. Keywords: ASL recognition system, convolutional neural network (CNNs), classification, real time, tensor flow

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