Human-robot interaction (HRI) occupies an essential role in the flourishing market for intelligent robots for a wide range of asymmetric personal and entertainment applications, ranging from assisting older people and the severely disabled to the entertainment robots at amusement parks. Improving the way humans and machines interact can help democratize robotics. With machine and deep learning techniques, robots will more easily adapt to new tasks, conditions, and environments. In this paper, we develop, implement, and evaluate the performance of the machine-learning-based HRI model in a collaborative environment. Specifically, we examine five supervised machine learning models viz. the ensemble of bagging trees (EBT) model, the k-nearest neighbor (kNN) model, the logistic regression kernel (LRK), the fine decision trees (FDT), and the subspace discriminator (SDC). The proposed models have been evaluated on an ample and modern contact detection dataset (CDD 2021). CDD 2021 is gathered from a real-world robot arm, Franka Emika Panda, when it was executing repetitive asymmetric movements. Typical performance assessment factors are applied to assess the model effectiveness in terms of detection accuracy, sensitivity, specificity, speed, and error ratios. Our experiential evaluation shows that the ensemble technique provides higher performance with a lower error ratio compared with other developed supervised models. Therefore, this paper proposes an ensemble-based bagged trees (EBT) detection model for classifying physical human–robot contact into three asymmetric types of contacts, including noncontact, incidental, and intentional. Our experimental results exhibit outstanding contact detection performance metrics scoring 97.1%, 96.9%, and 97.1% for detection accuracy, precision, and sensitivity, respectively. Besides, a low prediction overhead has been observed for the contact detection model, requiring a 102 µS to provide the correct detection state. Hence, the developed scheme can be efficiently adopted through the application requiring physical human–robot contact to give fast accurate detection to the contacts between the human arm and the robot arm.

U-net convolutional neural network (CNN) is a famous architecture developed to deal with medical images. Fine-tuning CNNs is a common technique used to enhance their performance by selecting the building blocks which can provide the ultimate results. This paper introduces a method for tuning U-net architecture to improve its performance in medical image segmentation. The experiment is conducted using an x-ray image segmentation approach. The performance of U-net CNN in lung x-ray image segmentation is studied with different activation functions, optimizers, and pooling-bottleneck-layers. The analysis focuses on creating a method that can be applied for tuning U-net, like CNNs. It also provides the best activation function, optimizer, and pooling layer to enhance U-net CNN’s performance on x-ray image segmentation. The findings of this research showed that a U-net architecture worked supremely when we used the LeakyReLU activation function and average pooling layer as well as RMSProb optimizer. The U-net model accuracy is raised from 89.59 to 93.81% when trained and tested with lung x-ray images and uses the LeakyReLU activation function, average pooling layer, and RMSProb optimizer. The fine-tuned model also enhanced accuracy results with three other datasets.

A novel approach proposes in this research to improve the performance of the query over encrypted database. This approach based on using hash map function to generate a unique hash value for each and every sensitive data. In this a proposed approach, there are no relationship between the hashed value and encrypted value. This method can reduce the cost of the encryption and decryption operations and improve the query performance. Results of the set of experiments show that the query performance over encrypted data reduces the response time to 25s, 51s, 34s, 61s and 31s for queries 1, 2, 3,4 and 5; respectively. The comparison with other methods are carried out when database size is ranging from 50,000 to 100,000 rows.

One of the challenges in Attribute-Based Access Control (ABAC) implementation is acquiring sufficient metadata against entities and attributes. Intelligent mining and extracting ABAC policies and attributes make ABAC implementation more feasible and cost-effective. This research paper focuses on attribute extraction from an existing enterprise relational database management system – RDBMS. The proposed approach tends to first classify entities according to some aspects of RDBMS systems. By reverse engineering, some metadata elements and ranking values are calculated for each part. Then entities and attributes are assigned a final rank that helps to decide what attribute subset is a candidate to be an optimal input for ABAC implementation. The proposed approach has been tested and implemented against an existing enterprise RDBMS, and the results are then evaluated. The approach enables the choice to trade-off between accuracy and overhead. The results score an accuracy of up to 80% with no overhead or 88% of accuracy with 65% overhead.