Classification of EEG Signal by Using Optimized Quantum Neural Network

In recent years the algorithms of machine learning was used for brain signals identifing which is a useful technique for diagnosing diseases like Alzheimer's and epilepsy. In this paper, the Electroencephalogram (EEG) signals are classified using an optimized Quantum neural network (QNN) after normalizing these signals, wavelet transform (WT) and the independent component analysis (ICA), were utilized for feature extraction.  These algorithms used to reduces the dimensions of the data, which is an input to the optimized QNN for the purpose of performing the classification process after the feature extraction process. This research uses an optimized QNN, a form of feedforward neural network (FFNN), to recognize (EEG) signals. The Particle swarm optimization (PSO) algorithm was used to optimize the quantum neural network, which improved the training process of the system's performance. The optimized (QNN) provided us with somewhat faster and more realistic results. According to simulation results, the total classification for (ICA) is 82.4 percent, while the total classification for (WT) is 78.43 percent; from these results, using the ICA for feature extraction is better than using WT

Electrocardiograph signal recognition using wavelet transform based on optimized neural network

Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased

Understanding the Mechanism of Abrasive-Based Finishing Processes Using Mathematical Modeling and Numerical Simulation

Recent advances in technology and refinement of available computational resources paved the way for the extensive use of computers to model and simulate complex real-world problems difficult to solve analytically. The appeal of simulations lies in the ability to predict the significance of a change to the system under study. The simulated results can be of great benefit in predicting various behaviors, such as the wind pattern in a particular region, the ability of a material to withstand a dynamic load, or even the behavior of a workpiece under a particular type of machining. This paper deals with the mathematical modeling and simulation techniques used in abrasive-based machining processes such as abrasive flow machining (AFM), magnetic-based finishing processes, i.e., magnetic abrasive finishing (MAF) process, magnetorheological finishing (MRF) process, and ball-end type magnetorheological finishing process (BEMRF). The paper also aims to highlight the advances and obstacles associated with these techniques and their applications in flow machining. This study contributes the better understanding by examining the available modeling and simulation techniques such as Molecular Dynamic Simulation (MDS), Computational Fluid Dynamics (CFD), Finite Element Method (FEM), Discrete Element Method (DEM), Multivariable Regression Analysis (MVRA), Artificial Neural Network (ANN), Response Surface Analysis (RSA), Stochastic Modeling and Simulation by Data Dependent System (DDS). Among these methods, CFD and FEM can be performed with the available commercial software, while DEM and MDS performed using the computer programming-based platform, i.e., "LAMMPS Molecular Dynamics Simulator," or C, C++, or Python programming, and these methods seem more promising techniques for modeling and simulation of loose abrasive-based machining processes. The other four methods (MVRA, ANN, RSA, and DDS) are experimental and based on statistical approaches that can be used for mathematical modeling of loose abrasive-based machining processes. Additionally, it suggests areas for further investigation and offers a priceless bibliography of earlier studies on the modeling and simulation techniques for abrasive-based machining processes. Researchers studying mathematical modeling of various micro- and nanofinishing techniques for different applications may find this review article to be of great help

Dynamics of MHD Convection of Walters B Viscoelastic Fluid through an Accelerating Permeable Surface Using the Soret–Dufour Mechanism

The MHD convective Walters-B memory liquid flow past a permeable accelerating surface with the mechanism of Soret-Dufour is considered. The flow equation constitutes a set of partial differential equations (PDEs) to elucidate the real flow of a non-Newtonian liquid. The radiation thermo-physical parameters were employed based on the use of Roseland approximation. This implies the fluid employed in this exploration is optically thick. Utilizing suitable similarity terms, the flow equation PDEs were simplified to become total differential equations. The spectral homotopy analysis method (SHAM) was utilized to provide outcomes to the model. The SHAM involves the addition of the Chebyshev pseudospectral approach (CPM) alongside the homotopy analysis approach (HAM). The outcomes were depicted utilizing graphs and tables for the quantities of engineering concern. The mechanisms of Soret and Dufour were separately examined. The imposed magnetism was found to lessen the velocity plot while the thermal radiation term elevates the temperature plot because of the warm particles of the fluid.This research was funded by a grant of the Romanian Ministry of Research, Innovation and Digitalization, project number PFE 26/30.12.2021, PERFORM-CDI@UPT100—The increasing of the performance of the Polytechnic University of Timis, oara by strengthening the research, development and technological transfer capacity in the field of “Energy, Environment and Climate Change” at the beginning of the second century of its existence, within Program 1—Development of the national system of Research and Development, Subprogram 1.2—Institutional Performance—Institutional Development Projects—Excellence Funding Projects in RDI, PNCDI III.info:eu-repo/semantics/publishedVersio

Characterization of Microstructure and Properties of Additively Manufactured Materials under Room and Elevated Temperatures

The utilisation of additive manufacturing (AM) has brought about a significant transformation in the manufacturing process of materials and components, since it allows for the creation of complex geometries and customised designs. The primary objective of this study is to conduct a thorough analysis of the microstructure and characteristics of materials produced by additive manufacturing techniques, including the effects of varying temperatures ranging from ambient temperature to increased levels. Microstructural analysis encompasses several methods, including optical microscopy, scanning electron microscopy (SEM), and X-ray diffraction (XRD), which are employed to investigate the grain structure, porosity, and phase composition. Standardised testing procedures are employed to assess mechanical qualities, such as tensile strength, hardness, and fracture toughness. temperature analysis methods, such as differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), are utilised in order to examine the temperature stability and phase transitions. This study investigates the impact of various printing factors, including layer thickness, printing speed, and build orientation, on the resultant microstructure and characteristics. This study aims to address the disparity between theoretical understanding and actual implementation, therefore facilitating the wider use of additively made materials in businesses that need exceptional performance in many environments

Investigation of Microstructure and Mechanical Properties of High-Temperature Super Alloy under Room and Elevated Temperature

This research investigates the microstructural characteristics and mechanical properties of a high-temperature superalloy under different temperature settings. The objective of this study is to analyse the alloy’s reaction to thermal stress, with a specific focus on both room and increased temperatures. By employing sophisticated microscopy techniques, researchers are able to closely examine the development of microstructural characteristics, which provides valuable understanding of phase changes and the dynamics of grains. Simultaneously, evaluations of mechanical properties, including tensile strength, hardness, and resilience, offer a holistic comprehension of the alloy’s operational characteristics. This research enhances the overall understanding of the alloy’s appropriateness for high-temperature applications by considering a wide range of temperatures. The results not only contribute to our fundamental understanding of materials science but also have ramifications for the development of alloys that can endure severe heat conditions

Sustainable Materials for Water Treatment: A Comprehensive Review

The increasing apprehension regarding water shortage and environmental contamination has heightened the pursuit of sustainable remedies in the field of water treatment. This detailed research examines the use of sustainable materials in water treatment systems. This study aims to examine the pressing demand for environmentally friendly and highly effective methods of water treatment. It comprehensively explores a diverse range of sustainable materials, encompassing both natural biomaterials and sophisticated nanomaterials. The evaluation of key features such as adsorption capacity, selectivity, and regeneration potential is conducted for each material, hence offering valuable insights into their suitability for the purpose of pollutant removal and water purification. The present study provides a critical evaluation of the appropriateness of these sustainable materials by an examination of key criteria like adsorption capacity, selectivity, and regeneration capabilities. The aforementioned attributes, which are crucial for the elimination of pollutants and unwanted substances, highlight the significant contribution of these materials towards the progression of water purification methodologies. In addition to their practical attributes, the analysis explores the ecological consequences and enduring viability of these substances, emphasising the need of mitigating detrimental impacts on natural systems and their associated services. The evaluation further evaluates the environmental consequences and long-term viability of these materials, placing emphasis on their contribution to addressing water-related difficulties. By integrating the most recent research discoveries and technical progress, this literature review not only provides a thorough examination of sustainable materials used in water treatment, but also emphasises potential directions for further investigation and improvement in this crucial field

Evaluation treatment planning system for oropharyngeal cancer patient using machine learning

Oropharyngeal cancer (OPC) comprises a group of various malignant tumours that grow in the throat, larynx, mouth, sinuses, and nose. The research aims: to investigate the performance of the OPC VMAT model by comparison to clinical plans in terms of dosimetric parameters and normal tissue complication probabilities. Purpose: Tune the model which at least matches the performance of clinical created photon treatment plans and analyse and find the most appropriate strategic plan scheme for OPC. Methods and materials: The machine learning (ML) plans are compared to the reference plans (clinical plans) based on dose constraints and target coverage. VMAT oropharynx ML model of Raystation development 11B version (non-clinical) was used. A model was trained by using different modalities. A different strategy of machine learning and clinical plans was performed for five patients. The dose Prescribed for OPC is 70 Gy, 2 Gy per fraction (2Gy/Fx). The PTV was derived for the primary tumour and secondary tumour, PTV+7000 cGy and PTV_5425 cGy volumetric modulated arc therapy (VMAT) were used with beams performing a full 360° rotation around the single isocenter. Results: Organs at risk were observed that the volume of L-Eye in clinical plan (AF) for the case1 treatment planning could be successfully used ensuring efficiency and lower than MLVMAT and MLVMAT-org plans were 372 cGy, 697 cGy and 667 cGy respectively, while showed case2, case3, case4 and case5 are better to protect the critical organs in ML plan compare with a clinical plan. DHI for the PTV-7000 and PTV-5425 is between 1 and 1.34, While DCI for PTV-7000 and PTV-5425 is between 0.98 and 1

Determination of Optimum Machining Parameters for Face Milling Process of Ti6A14V Metal Matrix Composite

This paper shows the novel approach of Taguchi-Based Grey Relational Analysis of Ti6Al4V Machining parameter. Ti6Al4V metal matrix composite has been fabricated using the powder metallurgy route. Here, all the components of TI6Al4V machining forces, including longitudinal force (Fx), radial force (Fy), tangential force (Fz), surface roughness and material removal rate (MRR) are measured during the facing operation. The effect of three process parameters, cutting speed, tool feed and cutting depth, is being studied on the matching responses. Orthogonal design of experiment (Taguchi L9) has been adopted to execute the process parameters in each level. To validate the process output parameters, the Grey Relational Analysis (GRA) optimization approach was applied. The percentage contribution of machining parameters to the parameter of response performance was interpreted through variance analysis (ANOVA). Through the GRA process, the emphasis was on the fact that for TI6Al4V metal matrix composite among all machining parameters, tool feed serves as the highest contribution to the output responses accompanied by the cutting depth with the cutting speed in addition. From optimal testing, it is found that for minimization of machining forces, maximization of MRR and minimization of Ra, the best combinations of input parameters are the 2nd stage of cutting speed (175 m/min), the 3rd stage of feed (0.25 mm/edge) as well as the 2nd stage of cutting depth (1.2 mm). It is also found that hardness of Ti6Al4V MMC is 59.4 HRA and composition of that material remain the same after milling operation