Human Activity Recognition (HAR) has become an active field of research in the computer vision community. Recognizing the basic activities of human beings with the help of computers and mobile sensors can be beneficial for numerous real-life applications. The main objective of this paper is to recognize six basic human activities, viz., jogging, sitting, standing, walking and whether a person is going upstairs or downstairs. This paper focuses on predicting the activities using a deep learning technique called Convolutional Neural Network (CNN) and the accelerometer present in smartphones. Furthermore, the methodology proposed in this paper focuses on grouping the data in the form of nodes and dividing the nodes into three major layers of the CNN after which the outcome is predicted in the output layer. This work also supports the evaluation of testing and training of the two-dimensional CNN model. Finally, it was observed that the model was able to give a good prediction of the activities with an average accuracy of 89.67%. Considering that the dataset used in this research work was built with the aid of smartphones, coming up with an efficient model for such datasets and some futuristic ideas pose open challenges in the research community.

In security authentication systems, face recognition has become an emerging new trend. Modern FR processes can detect whether the individual is real (live) or not through face recognition, thus preventing the systems from being compromised by displaying an actual person’s image. These new systems of face recognition are the result of recent advances in the field of computer vision and efficient algorithms for machine learning. This thesis describes in depth how the identification of a real time face can be order to create a safety alert framework for the workplace, the machine learning algorithm Haarcascade classifier was used to build four distinct classes for identification of security equipment and eventually identify the faces in both images and video using python open CV. Initially, face detection technique is performed to scan the face identity marks through.

The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

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Processing of data can include various operations depending on usage like culling, tagging, highlighting, indexing, searching, faceting, etc