A Comprehensive Analysis on EEG Signal Classification Using Advanced Computational Analysis

Electroencephalogram (EEG) has been used in a wide array of applications to study mental disorders. Due to its non-invasive and low-cost features, EEG has become a viable instrument in Brain-Computer Interfaces (BCI). These BCI systems integrate user\u27s neural features with robotic machines to perform tasks. However, due to EEG signals being highly dynamic in nature, BCI systems are still unstable and prone to unanticipated noise interference. An important application of this technology is to help facilitate the lives of the tetraplegic through assimilating human brain impulses and converting them into mechanical motion. However, BCI systems are remarkably challenging to implement as recorded brain signals can be unreliable and vary in pattern throughout time. In the initial work, a novel classifier structure is proposed to classify different types of imaginary motions (left hand, right hand, and imagination of words starting with the same letter) across multiple sessions using an optimized set of electrodes for each user. The proposed technique uses raw brain signals obtained utilizing 32 electrodes and classifies the imaginary motions using Artificial Neural Networks (ANN). To enhance the classification rate and optimize the set of electrodes of each subject, a majority voting system combining a set of simple ANNs is used. This electrode optimization technique achieved classification accuracies of 69.83%, 94.04% and 84.56% respectively for the three subjects considered in this work. In the second work, the signal variations are studied in detail for a large EEG dataset. Using the Independent Component Analysis (ICA) with a dynamic threshold model, noise features were filtered. The data was classified to a high precision of more than 94% using artificial neural networks. A decreased variance in classification validated both, the effectiveness of the proposed dynamic threshold systems and the presence of higher concentrations of noise in data for specific subjects. Using this variance and classification accuracy, subjects were separated into two groups. The lower accuracy group was found to have an increased variance in classification. To confirm these results, a Kaiser windowing technique was used to compute the signal-to-noise ratio (SNR) for all subjects and a low SNR was obtained for all EEG signals pertaining to the group with the poor data classification. This work not only establishes a direct relationship between high signal variance, low SNR, and poor signal classification but also presents classification results that are significantly higher than the accuracies reported by prior studies for the same EEG user dataset

FT-IR PROFILE SCREENING OF BIOACTIVE CHEMICAL COMPONENTS IN AQUEOUS EXTRACT OF ABRUS PRECATORIUS LINN PLANT LEAF

Objective: The FT-IR profile screening is aimed to focus on the bioactive chemical components analysis of aqueous extract of Abrus precatorius Linn plant leafs. Methods: The profile screening for the bioactive chemical components analysis was performed with standard methods by using FT-IR spectral technique. Results: The aqueous extract of the leaf were screened for the various bioactive functional chemical components. The spectrum of FT-IR showed the presence different functional groups of chemical constituents such as alcohols, phenols, carboxylic acids, amide, aldehydes, ketones, alkanes, alkenes, aromatics, esters, ethers, aliphatic amines, aromatic amines, peptides, nitro compounds, sulphone, phosphonate, phosphoramide, phosphonic acid, phosphine, silane, amine oxides, aromatic substituted compounds, nitroso, sulphate ester and alkyl halides compounds, which showed 27 major characteristic bands of bioactive chemical components. Conclusion: The results confirm the fact that leaf of Abrus precatorius Linn plant possesses different bioactive functional chemical components and generated the FT-IR spectrum profile for the medicinally important plant

Photovoltaic inverters experimentally validate power quality mitigation in electrical systems

Power quality is improved by utilizing solar inverters in electrical grids and this study probes it. A combination of the solar power system with wind energy management using the multi-objective particle swarm optimization (CMOPSO) algorithm is employed in this system. Control calculations are based on Clark and reverse Clark transformations and facilitated by a phase-locked loop (PLL) circuit. STATCOM helps maintain voltage levels and mitigate power quality issues. Power quality (PQ) monitoring tracks voltage variations and noise. Conversely, the study addresses challenges in integrating renewables using the multi-objective multi-verse optimization (MOMVO) algorithm. MATLAB is used for control, monitoring, and analysis. Results show voltage distortion, but the proposed method achieves 92% higher efficiency, demonstrating its effectiveness. This validates the importance of photovoltaic (PV) technology for integrating renewable energy sources

Phytochemicals and Antioxidant Activity Investigation of Butea monosperma Lam. Leaves Ethanol Extract

The aim of this study was to investigate phytochemicals and antioxidant activity of plant Butea monosperma Lam. leaves ethanol extract. The different extracts of this plant were reported the rich source contents of bioactive phytochemicals in the leaves and afford for various pharmacological activities. The ethanol extract of leaves was subjected to investigate phytochemicals and antioxidant activity by using DPPH in vitro system. The provided evidence of results concluded that the ethanol extract of Butea monosperma Lam. leaves are potential sources of natural bioactive phytochemicals and showed potent in vitro antioxidant activity with their IC50 value of 44.16 μg/ml. Therefore phytochemical investigation of plant leaves ethanol extract was noted various bioactive phytochemicals, which may serve as a potent source of natural antioxidants

A Neutrosophic Solution of Heat Equation by Neutrosophic Laplace Transform

This article aims to study the one-dimensional neutrosophic heat equation. In this study, we discuss the one-dimensional heat equation using neutrosophic numbers and provide numerical example to demonstrate the effectiveness of the neutrosophic Laplace transform method

c-Continuity, c-Compact and c-Separation Axioms via Soft Sets

This paper focuses on the concept of S c-open sets as a generalization of classical c-open sets in topology. The reason behind introducing S c-open sets is to overcome the limitations of traditional open sets in handling uncertainty and vagueness prevalent in decision-making processes. Moreover, the paper makes significant contributions to the discussion of the concepts of soft topological spaces (STS ) by utilizing S c-open sets that investigate the theoretical foundations and mathematical properties of S c-open sets, exploring their relationships with other soft open sets and soft topological concepts. Overall, the paper aims to provide a comprehensive understanding of STS and their properties and theorems utilizing the concept of S c-open set and explores the theoretical foundations, mathematical properties, and relationships of these sets while extending their application to domains such as S c-continuity, S c-separation axiom and S c compactness

A Python Framework for Neutrosophic Sets and Mappings

In this paper we present an open source framework developed in Python and consisting of three distinct classes designed to manipulate in a simple and intuitive way both symbolic representations of neu trosophic sets over universes of various types as well as mappings between them. The capabilities offered by this framework extend and generalize previous attempts to provide software solutions to the manipulation of neutrosophic sets such as those proposed by Salama et al. [21], Saranya et al. [23], El-Ghareeb [7], Topal et al. [29] and Sleem [26]. The code is described in detail and many examples and use cases are also provide

A Python Framework for Neutrosophic Sets and Mappings

In this paper we present an open source framework developed in Python and consisting of three distinct classes designed to manipulate in a simple and intuitive way both symbolic representations of neutrosophic sets over universes of various types as well as mappings between them. The capabilities offered by this framework extend and generalize previous attempts to provide software solutions to the manipulation of neutrosophic sets such as those proposed by Salama et al. [21], Saranya et al. [23], El-Ghareeb [7], Topal et al. [29] and Sleem [26]. The code is described in detail and many examples and use cases are also provided