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The paper puts forward an intelligent approach that deals with the computation of an optimal path with collision avoidance for the stick-carrying twin moving from a pre-assumed start position to a predefined goal position. It has been solved through the efficient implementation of modified cuckoo search, sine cosine algorithm, and particle swarm optimization to design a hybrid algorithm aimed at using the communal advantages of the search and position update ability of these algorithms. The benefits are realized by integrating the egg-laying behavior of the cuckoo species to achieve an efficient global search strategy with modified parameters, local search strategy of particle swarm optimization, and greedy approach of sine cosine algorithm. The proposed algorithm is validated using 10 standard benchmark functions, computer simulation using C language, and real robot platform using Epuck robot to illustrate minimal time, shortest distance, collision avoidance, path smoothness, synchronized action, and reduced energy usage in terms of the path traveled, execution time, the number of steps, and the number of turns in the static as well as the dynamic environment. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a degenerative disease that leads to brain disorder and nonfunctioning of different body parts. Deep learning tools like artificial neural network (ANN), convolution neural network (CNN), regression Analysis (RA), and so on, has been considered to a great extent in recent days. Several data sets based on the motor and nonmotor symptoms are applied to different classifier for correct identification of Parkinson's patient from healthy people. In this paper, hybridization of two deep learning tools such as, RA and ANN are done for effective diagnosis of the disease by probability estimation. The communal merits of individual approaches of the existing approaches are realized in this context for accurate probability estimation. Data preprocessing and probability estimation of preprocessed data is done in RA. The second existing approach is used to identify the PD patient by comparing with a predefined threshold value of a neuron. The estimation is performed on the data set of speech recognition, iron content, and pulse rate among a group of people. The proposed approach is compared with the existing approaches like, SVM and k‐NN classifier. The computed result reveals the superiority of the proposed algorithm with 93.46% accuracy. [ABSTRACT FROM AUTHOR]