Based on a problem raised by Gao et. al. (Bull. Malays. Math. Sci. Soc., 41 (2018) 443–453.), we construct  a family of cubic graphs which are double-edge blow-up of ladder graphs. We determine the full friendly index sets of these cubic graphs by embedding labeling graph method. At the same time, the corresponding labeling graphs are  provided. 

Many different methods are applied and used in an attempt to solve higher order nonlinear boundary value problems (BVPs). Galerkin weighted residual method (GWRM) are widely used to solve BVPs. The main aim of this paper is to find the approximate solutions of fifth, seventh and ninth order nonlinear boundary value problems using GWRM. A trial function namely, Bezier Polynomials is assumed which is made to satisfy the given essential boundary conditions. Investigate the effectiveness of the current method; some numerical examples were considered. The results are depicted both graphically and numerically. The numerical solutions are in good agreement with the exact result and get a higher accuracy in the solutions. The present method is quit efficient and yields better results when compared with the existing methods. All problems are performed using the software MATLAB R2017a.

One of the most common problem in the design of robotic technology is the path planning. The challenge is choosing the robotics’ path from source to destination with minimum cost. Meta-heuristic algorithms are popular tools used in a search process to get optimal solution. In this paper, we used Crow Swarm Optimization (CSO) to overcome the problem of choosing the optimal path without collision. The results of CSO compared with two meta-heuristic algorithms: PSO and ACO in addition to a hybrid method between these algorithms. The comparison process illustrates that the CSO better than PSO and ACO in path planning, but compared to hybrid method CSO was better whenever the smallest population. Consequently, the importance of research lies in finding a new method to use a new meta-humanistic algorithm to solve the problem of robotic path planning.

Predicting human emotion from speech is now important research topic. One’s mental state can be understood by emotion. The proposed research work is emotion recognition from human speech. Proposed system plays significant role in recognizing emotion while someone is talking. It has a great use for smart home environment. One can understand the emotion of other who is in home or may be in other place. University, service center or hospital can get a valuable decision support system with this emotion prediction system. Features like-MFCC (Mel-Frequency Cepstral Coefficients) and LPC are extracted from audio sample signal. Audios are collected by recording speeches. A test also applied by combining self-collected dataset and popular Ravdees dataset. Self-collected dataset is named as ABEG. MFCC and LPC features are used in this study to train and test for predicting emotion. This study is made on angry, happy and neutral emotion classes. Different machine learning algorithms are applied here and result is compared with each other. Logistic regression performs well as compared to other ML algorithm.