Multiclass Classification of Brain MRI through DWT and GLCM Feature Extraction with Various Machine Learning Algorithms

This study delves into the domain of medical diagnostics, focusing on the crucial task of accurately classifying brain tumors to facilitate informed clinical decisions and optimize patient outcomes. Employing a diverse ensemble of machine learning algorithms, the paper addresses the challenge of multiclass brain tumor classification. The investigation centers around the utilization of two distinct datasets: the Brats dataset, encompassing cases of High-Grade Glioma (HGG) and Low-Grade Glioma (LGG), and the Sartaj dataset, comprising instances of Glioma, Meningioma, and No Tumor. Through the strategic deployment of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) features, coupled with the implementation of Support Vector Machines (SVM), k-nearest Neighbors (KNN), Decision Trees (DT), Random Forest, and Gradient Boosting algorithms, the research endeavors to comprehensively explore avenues for achieving precise tumor classification. Preceding the classification process, the datasets undergo pre-processing and the extraction of salient features through DWT-derived frequency-domain characteristics and texture insights harnessed from GLCM. Subsequently, a detailed exposition of the selected algorithms is provided and elucidates the pertinent hyperparameters. The study's outcomes unveil noteworthy performance disparities across diverse algorithms and datasets. SVM and Random Forest algorithms exhibit commendable accuracy rates on the Brats dataset, while the Gradient Boosting algorithm demonstrates superior performance on the Sartaj dataset. The evaluation process encompasses precision, recall, and F1-score metrics, thereby providing a comprehensive assessment of the classification prowess of the employed algorithms

Recovery of acerbic anaerobic digester for biogas production from pomegranate shells using organic loading approach

Anaerobic digestion of pomegranate shells was conducted in 25 L bioreactor operating at 35±0.5°C. The digester showed a reasonable amount of biogas (0.71 m3/kg VS fed) and methane (55.7%) with stable pH and acid: alkali profiles when operated at organic loading rate (OLR) from 1.0 to 3.0 kg VS/day/m−3. The reactor exhibited stable performance with methane yield of 0.44 m3/kg VS fed and reduction of 38.5% volatile solids (VS) As organic loading rate increased to 3.5 kg VS/day/m−3, accumulation of volatile fatty acid (VFA; 2797 ppm), mainly propionic acid (1617 ppm) was noticeable. The digester turned sour (pH 4.32) with lower biogas (2.5 Ld−1) and methane (30.80%) production, reflecting the case of overloading. Reversal of organic loading rate from 3.5 to 3.0 kg VS/day/m−3 gradually restored the upset anaerobic digester to normal profile in 4 weeks as judged from a gradual increase in biogas (6.5 Ld−1) and methane (58.4%)

Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of Hybrid Diffusion Imaging based on BFOR Signal Basis

We propose a large deformation diffeomorphic metric mapping algorithm to align multiple b-value diffusion weighted imaging (mDWI) data, specifically acquired via hybrid diffusion imaging (HYDI), denoted as LDDMM-HYDI. We then propose a Bayesian model for estimating the white matter atlas from HYDIs. We adopt the work given in Hosseinbor et al. (2012) and represent the q-space diffusion signal with the Bessel Fourier orientation reconstruction (BFOR) signal basis. The BFOR framework provides the representation of mDWI in the q-space and thus reduces memory requirement. In addition, since the BFOR signal basis is orthonormal, the L2 norm that quantifies the differences in the q-space signals of any two mDWI datasets can be easily computed as the sum of the squared differences in the BFOR expansion coefficients. In this work, we show that the reorientation of the $q$-space signal due to spatial transformation can be easily defined on the BFOR signal basis. We incorporate the BFOR signal basis into the LDDMM framework and derive the gradient descent algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally, we extend the previous Bayesian atlas estimation framework for scalar-valued images to HYDIs and derive the expectation-maximization algorithm for solving the HYDI atlas estimation problem. Using real HYDI datasets, we show the Bayesian model generates the white matter atlas with anatomical details. Moreover, we show that it is important to consider the variation of mDWI reorientation due to a small change in diffeomorphic transformation in the LDDMM-HYDI optimization and to incorporate the full information of HYDI for aligning mDWI

A reliable micro-grid with seamless transition between grid connected and islanded mode for residential community with enhanced power quality

This paper presents a reliable micro-grid for residential community with modified control techniques to achieve enhanced operation during grid connected, islanded and resynchronization mode. The proposed micro-grid is a combination of solar photo-voltaic (PV), battery storage system and locally distributed DG systems with residential local loads. A modified power control technique is developed such that, local load reactive power demand, harmonic currents and load unbalance is compensated by respective residential local DG. However, active power demand of all local residential load is shared between the micro-grid and respective local DG. This control technique also achieves constant active power loading on the micro-grid by supporting additional active power local load demand of respective residential DG. Hence, proposed modified power control technique achieves transient free operation of the micro-grid during residential load disturbances. An additional modified control technique is also developed to achieve seamless transition of micro-grid between grid connected mode and islanded mode. The dynamic performance of this micro-grid during grid connected, islanded and re-synchronization mode under linear and non-linear load variations is verified using real time simulator (RTS)

A Non-catalytic Deep Desulphurization Process using Hydrodynamic Cavitation

A novel approach is developed for desulphurization of fuels or organics without use of catalyst. In this process, organic and aqueous phases are mixed in a predefined manner under ambient conditions and passed through a cavitating device. Vapor cavities formed in the cavitating device are then collapsed which generate (in-situ) oxidizing species which react with the sulphur moiety resulting in the removal of sulphur from the organic phase. In this work, vortex diode was used as a cavitating device. Three organic solvents (n-octane, toluene and n-octanol) containing known amount of a model sulphur compound (thiophene) up to initial concentrations of 500 ppm were used to verify the proposed method. A very high removal of sulphur content to the extent of 100% was demonstrated. The nature of organic phase and the ratio of aqueous to organic phase were found to be the most important process parameters. The results were also verified and substantiated using commercial diesel as a solvent. The developed process has great potential for deep of various organics, in general, and for transportation fuels, in particular

An Improved, Highly Efficient Method for the Synthesis of Bisphenols

An efficient synthesis of bisphenols is described by condensation of substituted phenols with corresponding cyclic ketones in presence of cetyltrimethylammonium chloride and 3-mercaptopropionic acid as a catalyst in extremely high purity and yields

Colorimetric method for simultaneous estimation of amlodipine besylate from plasma

Aim: The present work was to develop the method of analysis which can estimate drug in combined form without prior separation. Materials and method: By using UV spectroscopy colorimetric method was used for determination of Amlodipine besylate (AML) from plasma. Result and conclusion: This method is based on the formation of green colour in reaction between AML and 0.4 % Ferric chloride (FC) and 0.2 % Potassium ferricyanide (PF).The absorbance was measured at 775 nm. Result of tablet analysis showed % S.D. values in the range of 098.22 to 100,63%. Standard deviation value for tablet analysis by using methanol ranging from 98.01 to 101,13 % which proves the ability of the method to remain unaffected by small but deliberate change in reaction conditions and this method is used for estimation of AML from biological samples.Objetivo. El objetivo del presente estudio era desarrollar un método de análisis que permitiera estimar la cantidad de fármaco en forma combinada sin separación previa. Material y Método. Se utilizó espectroscopía colorimétrica UV para la determinación de Amlodipino Besilato (AML) plasmático Resultados. El presente método está basado en la formación de color verde en la reacción entre Amlodipino Besilato (AML) y cloruro férrico 0,4% y ferrocianuro potásico 0,2%. La medida de la absorbancia se realizó a 775nm. El resultado del análisis de los comprimidos mostró unos valores de DE comprendidos entre 098,22 y 100,63%. El valor de la DE utilizando metanol oscilan entre 98,01 y 101,13% lo que demuestra la capacidad del método de permanecer inalterado por pequeños pero intencionados cambios en las condiciones de la reacción, este método es usado para la estimación de Amlodipino Besilato (AML) en muestras biológicas

FAST DISPERSING TABLETS REVIEW

ABSTRACT In this investigation fast dissolving tablets were prepared using different Superdisintegrants like crospovidone, croscarmellose sodium and sodium starch glycolate by direct compression method. FDTs were evaluated for physicochemical properties like thickness, uniformity of weight, content uniformity ,hadness, friability, wetting time ,dispersion time in vitro disintegration time and in vitro dissolution. Wetting time of formulations containing Croscarmellose sodium was least and tablets showed fastest disintegration. The drug release from FDTs increased with increasing concentration of superdisintegrants and was found to be highest with formulations containing Croscarmellose sodium. The tablet disintegrated within 16 to 45 seconds. Almost 96% of drug was released from the forumaltion within 16 min. The release of drug from FDTs was found to follow non-Fickian diffusion kinetics.Stability studies of the tablets shows non significant change

Recent Decline in Antarctic Sea Ice Cover From 2016 to 2022: Insights From Satellite Observations, Argo Floats, and Model Reanalysis

Ever since the abrupt drop in Antarctic sea ice extent (SIE) began in spring of 2016, as opposed to its consistent growth (1.95% decade–1 from 1979 to 2015), the SIE in the satellite era has reached record lows in 2017 and 2022. From spring 2016, the satellite-based SIE remained consistently lower than the long-term mean, with the trend dropping to 0.11% decade–1 from 1979 to 2022. The top record lowest SIE years were observed from 2016 to 2022, corresponding to the warmest years dating back to 1979. With this background, the rare features of Antarctic polynyas reoccurred frequently and the west Antarctic Peninsula remained ice-free throughout 2022. Recently, the SIE dropped to a record low in June 2022, July 2022, August 2022, January 2023, and February 2023, which were 13.67%, 9.91%, 6.79%, 39.29%, 39.56% below the long-term mean value, respectively for months described above. We find that the observed decline in SIE during 2016–2022 occurred due to the combined influences from the intensification of atmospheric zonal waves with enhanced poleward transport of warm-moist air and anomalous warming in the Southern Ocean mixed layer (>1°C). Although the sudden sea ice decline in spring of 2016 occurred corresponding to the transitional climate shift from IPO– (Interdecadal Pacific Oscillation, 2000–2014) to IPO+ (2014–2016), the recent decline after 2016 occurred in a dominant IPO– and Southern Annular Mode (SAM+). CMIP6 models showed a consistent decrease in ensemble-mean SIE from 1979 to 2022. The model trend exhibits similarities to the recent declining trend in SIE from satellite observations since 2016, suggesting a possible shift towards a warmer climatic regime