Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Charge transport and structural morphology of HCl-doped polyaniline

The d.c. and a.c. (100 Hz–1 MHz) conductivities of HCl-doped polyaniline have been measured in the temperature range 77–300 K. At 77 K, the a.c. conductivity data, (), can be described by the relation ()=As, where the parameter s lies close to unity and decreases with increase in the doping level. The ratio of measured a.c. to d.c. conductivity shows dispersion at 77 K, which decreases with increase in the doping level. This decrease is found to be sharp around pH3.0. In the temperature range 77–150 K, the observed d.c. conductivity data can be described by Mott's three dimensional variable range hopping (VRH) model. Scanning electron microscopy studies reveal a sharp change in structural morphology of HCl-doped polyaniline at a pH3.0. A strikingly remarkable structural morphology has been observed in the formc of a channel at this pH value. This change is accompanied by a rapid increase in d.c. conductivity, dielectric constant, along with sharp changes in structural morphology, which indicates the existence of a doping-induced structural conductivity correlation in this system. © 1998 Chapman & Hall     

EPR study of Cu2+ in Na2O-NaF-B2O3-Bi2O3 glasses

Electron paramagnetic resonance (EPR) studies have been performed on the glass system (30–x) NaF-xNa₂O-50B₂O₃-20Bi₂O₃ doped with CuO as a paramagnetic probe. The calculated g values indicate that Cu²⁺ is in a tetragonally elongated octahedral co-ordination, and the Jahn-Teller character gives rise to anisotropic hyperfine structure. The spin-Hamiltonian parameters and -bonding parameter, ², are calculated. Varying the fluoride ion concentration and its effect on these parameters is also discussed.     

High order parameter-robust numerical method for time dependent singularly perturbed reaction–diffusion problems

We introduce a high order parameter-robust numerical method to solve a Dirichlet problem for one-dimensional time dependent singularly perturbed reaction-diffusion equation. A small parameter ε is multiplied with the second order spatial derivative in the equation. The parabolic boundary layers appear in the solution of the problem as the perturbation parameter ε tends to zero. To obtain the approximate solution of the problem we construct a numerical method by combining the Crank–Nicolson method on an uniform mesh in time direction, together with a hybrid scheme which is a suitable combination of a fourth order compact difference scheme and the standard central difference scheme on a generalized Shishkin mesh in spatial direction. We prove that the resulting method is parameter-robust or ε-uniform in the sense that its numerical solution converges to the exact solution uniformly well with respect to the singular perturbation parameter ε. More specifically, we prove that the numerical method is uniformly convergent of second order in time and almost fourth order in spatial variable, if the discretization parameters satisfy a non-restrictive relation. Numerical experiments are presented to validate the theoretical results and also indicate that the relation between the discretization parameters is not necessary in practice.     

A new approach for generating efficient sample from market basket data

Classical data mining algorithms require expensive passes over the entire database to generate frequent items and hence to generate association rules. With the increase in the size of database, it is becoming very difficult to handle large amount of data for computation. One of the solutions to this problem is to generate sample from the database that acts as representative of the entire database for finding association rules in such a way that the distance of the sample from the complete database is minimal. Choosing correct sample that could represent data is not an easy task. Many algorithms have been proposed in the past. Some of them are computationally fast while others give better accuracy. In this paper, we present an algorithm for generating a sample from the database that can replace the entire database for generating association rules and is aimed at keeping a balance between accuracy and speed. The algorithm that is proposed takes into account the average number of small, medium and large 1-itemset in the database and average weight of the transactions to define threshold condition for the transactions. Set of transactions that satisfy the threshold condition is chosen as the representative for the entire database. The effectiveness of the proposed algorithm has been tested over several runs of database generated by IBM synthetic data generator. A vivid comparative performance evaluation of the proposed technique with the existing sampling techniques for comparing the accuracy and speed has also been carried out.     

Identifying some important success factors in adopting agile software development practices

Agile software development (ASD) is an emerging approach in software engineering, initially advocated by a group of 17 software professionals who practice a set of “lightweight” methods, and share a common set of values of software development. In this paper, we advance the state-of-the-art of the research in this area by conducting a survey-based ex-post-facto study for identifying factors from the perspective of the ASD practitioners that will influence the success of projects that adopt ASD practices. In this paper, we describe a hypothetical success factors framework we developed to address our research question, the hypotheses we conjectured, the research methodology, the data analysis techniques we used to validate the hypotheses, and the results we obtained from data analysis. The study was conducted using an unprecedentedly large-scale survey-based methodology, consisting of respondents who practice ASD and who had experience practicing plan-driven software development in the past. The study indicates that nine of the 14 hypothesized factors have statistically significant relationship with “Success”. The important success factors that were found are: customer satisfaction, customer collaboration, customer commitment, decision time, corporate culture, control, personal characteristics, societal culture, and training and learning.     

As-if-serial exception handling semantics for Java futures

Exception handling enables programmers to specify the behavior of a program when an exceptional event occurs at runtime. Exception handling, thus, facilitates software fault tolerance and the production of reliable and robust software systems. With the recent emergence of multi-processor systems and parallel programming constructs, techniques are needed that provide exception handling support in these environments that are intuitive and easy to use. Unfortunately, extant semantics of exception handling for concurrent settings is significantly more complex to reason about than their serial counterparts.In this paper, we investigate a similarly intuitive semantics for exception handling for the future parallel programming construct in Java. Futures are used by programmers to identify potentially asynchronous computations and to introduce parallelism into sequential programs. The intent of futures is to provide some performance benefits through the use of method-level concurrency while maintaining as-if-serial semantics that novice programmers can easily understand — the semantics of a program with futures is the same as that for an equivalent serial version of the program. We extend this model to provide as-if-serial exception handling semantics. Using this model our runtime delivers exceptions to the same point it would deliver them if the program was executed sequentially. We present the design and implementation of our approach and evaluate its efficiency using an open source Java virtual machine.     

Speech enhancement using Teager energy operated ERB-like perceptual wavelet packet decomposition

In recent past, wavelet packet (WP) based speech enhancement techniques have been gaining popularity due to their inherent nature of noise minimization. WP based techniques appeared as more robust and efficient than short-time Fourier transform based methods. In the present work, a speech enhancement method using Teager energy operated equal rectangular bandwidth (ERB)-like WP decomposition has been proposed. Twenty four sub-band perceptual wavelet packet decomposition (PWPD) structure is implemented according to the auditory ERB scale. ERB scale based decomposition structure is used because the central frequency of the ERB scale distribution is similar to the frequency response of the human cochlea. Teager energy operator is applied to estimate the threshold value for the PWPD coefficients. Lastly, Wiener filtering is applied to remove the low frequency noise before final reconstruction stage. The proposed method has been applied to evaluate the Hindi sentences database, corrupted with six noise conditions. The proposed method’s performance is analysed with respect to several speech quality parameters and output signal to noise ratio levels. Performance indicates that the proposed technique outperforms some traditional speech enhancement algorithms at all SNR levels.     

Autonomous clustering using rough set theory

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.