Evolutionary Intelligence

We present new data analytics-based predictions results that can help governments to plan their future actions and also help medical services to be better prepared for the future. Our system can predict new corona cases with 99.82% accuracy using susceptible infected recovered (SIR) model. We have predicted the results of new COVID cases per day for dense and highly populated country i.e. India. We found that traditional statistical methods will not work efficiently as they do not consider the limited population in a particular country. Using the data analytics-based curve we predicted four most likely possibilities for the number of new cases in India. Hence, we expect that the results mentioned in the manuscript help people to better understand the progress of this disease.

In this work, we introduce a template-based computational method to recognize chords through an audio recording of a musical instrument. The algorithm is based on a temporal frequency analysis using Gabor’s filter banks. These filters are centered over adjusted frequencies of musical notes in different octaves and the adjustment is accomplished in terms of the detunings on the recording. Using the results in the filtering stage, a geometric mean of each chord is calculated. It is important to mention that these statistics are calculated from the combination of notes that form each chord and are automatically grouped as templates. The presence of chords is determined from these metrics. Several experiments are carried out for major, minor, augmented, diminished and suspended chords played on acoustic guitar, classic guitar, electric guitar, piano and ukulele. A comparative study against machine-learning classifiers is presented. The results show a superior performance of the present approach. In addition, the proposed method presents the advantage that it does not require a training stage, in contrast with the methods based on machine-learning algorithms. This reduces significatively the storage and time requiered for processing.

In 1948 Turing presented a general representation scheme by which to achieve artificial intelligence—his unorganised machines. Significantly, these were a form of discrete dynamical system and yet dynamical representations remain almost unexplored within evolutionary computation. Further, at the same time as also suggesting that natural evolution may provide inspiration for search mechanisms to design machines, he noted that mechanisms inspired by the social aspects of learning may prove useful. This paper presents results from an investigation into using Turing’s dynamical representation designed by Evolutionary Programming and a new imitation-based, i.e., cultural, approach. Moreover, the original synchronous and an asynchronous form of unorganised machines are considered.

Metaheuristic optimization algorithms are widely used in motion planning of redundant robotic manipulators. Existing methods may converge to a local minimum. In this paper, a new multi-group particle swarm optimization algorithm (PSOEL) is proposed to solve the motion planning of manipulators. PSOEL consists of one elite group and several child groups. The population is initialized with a pre-selection mechanism in which the members of the elite group are initialized with the best-performing particles of the child groups. In the process of iteration, the elite group and the child groups evolve separately. When the elite group falls into a local optimum or is inferior to child groups for a certain time, an interaction mechanism is triggered. In the interaction mechanism, some of the best particles selected from the child groups will replace the bad particles of the elite group. With these mechanism of pre-selection and interaction, PSOEL can jump out of the local optimum and reach the global optimum or global suboptimum. Simulation results show that the proposed algorithm PSOEL is superior to the compared algorithms and converges toward the optimum.

Distillery industries in several regions all over the world pose a serious risk, as it generates unpleasant compounds. Under such circumstances, it seeks an effective spent wash treatment, to eliminate the contaminants. Accordingly, this paper provides a relevant review regarding the distillery spent wash treatments, associated with the proper treatments and coagulants. At first, it reviews 67 recent research papers, from which 24 papers belong to distillery spent wash treatment and remaining belongs to other treatments. Further, it extends the valuable chronological review on distillery spent wash treatment. In addition, it describes the several processes such as anaerobic treatment, aerobic treatment, nanofiltration, reverse osmosis, adsorption and electrochemical treatments, adopted to treat distillery spent wash as reported in the literature. In the same way, it analyses the usage of different types of coagulants such as natural, electro and chemical coagulants used in distillery spent wash treatment. To the next of the coagulant analysis, it checks out the performance review of entire contributions on distillery spent wash treatment. The conventional process for distillery spent wash treatment having limitations such as limited removal efficiency, high operating cost and maintenance also it needs a high detention time that eventually increases the on the whole treatment process time. The aforesaid limitation can be overcome by adopting soft computing techniques. Soft computing has been widely studied and applied in the past three decades for engineering and scientific research computing. In environmental engineering, engineers and researchers have effectively used various techniques of soft computing like fuzzy logic, artificial neural networks, adaptive neuro-fuzzy inference systems, and support vector machines which can be useful for the researchers to achieve further research on distillery spent wash treatment.

Vehicle detection and classification has been an area of application of image processing and machine learning which is being researched extensively in accordance with its importance due to increasing number of vehicles, traffic rule defaulters and accidents. This paper aims to review various methodologies used and how it has evolved to give better results in the past years, closely moving towards usage of machine learning. This has resulted in advancing the problem statement towards helmet detection followed by number plate detection of defaulters. Object detection and Text recognition that are available in various frameworks offer built-in models which are easy to use or offer easy methods to build and train customized models.

The purpose of this paper is to investigate a new method for EEG signals classification. A powerful method for detecting these signals can greatly contribute to areas such as making robotic arms for disabled people, mind reading and lie detection tools. To this end, this study makes two interesting contributions. As a major contribution, a new classifier based on a radial basis function neural network (RBFNN) is presented. As the center determination method of a RBFNN classifier has a high impact on the final classification results, we have adopted Jellyfish search (JS) algorithm for choosing the centers of the Gaussian functions in the hidden layer of the RBFNN classifier. Additionally, Locally Linear Embedding (LLE) technique is investigated for reducing the dimensionality of EEG signals. Two series of various experiments are designed to validate our proposals. In the first set of the experiments, the proposed RBFNN classifier is compared with other state-of-the-art RBFNN classifiers. In the second set of the experiments, the performances of the proposed EEG signals classifications are evaluated on a challenging dataset for EEG signals classification. The experimental results demonstrate the superiority of our proposed method even compared to the methods based on the convolutional neural networks.