OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.
RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.
CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.
OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.
RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.
CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.
KEY FINDINGS: Research philosophy has been introduced to offer an alternative way to think about problem-driven research that is normally conducted. To clarify the research philosophy, four research paradigms, i.e. positivism (or empiricism), postpositivism (or realism), interpretivism (or constructivism) and pragmatism, are investigated according to philosophical realms, i.e. ontology, epistemology, axiology and logic of inquiry. With the application of research philosophy, some examples of quantitative and qualitative research were elaborated along with the conventional research approach. Understanding research philosophy is crucial for pharmacy researchers and pharmacists, as it underpins the choice of methodology and data collection.
CONCLUSIONS: The review provides the overview of research philosophy and its application in pharmacy practice research. Further discussion on this vital issue is warranted to help generate quality evidence for pharmacy practice.