Treatment of persistent organic pollutants in wastewater using hydrodynamic cavitation in synergy with advanced oxidation process

Persistent organic pollutants (POPs) are very tenacious wastewater contaminants. The consequences of their existence have been acknowledged for negatively affecting the ecosystem with specific impact upon endocrine disruption and hormonal diseases in humans. Their recalcitrance and circumvention of nearly all the known wastewater treatment procedures are also well documented. The reported successes of POPs treatment using various advanced technologies are not without setbacks such as low degradation efficiency, generation of toxic intermediates, massive sludge production, and high energy expenditure and operational cost. However, advanced oxidation processes (AOPs) have recently recorded successes in the treatment of POPs in wastewater. AOPs are technologies which involve the generation of OH radicals for the purpose of oxidising recalcitrant organic contaminants to their inert end products. This review provides information on the existence of POPs and their effects on humans. Besides, the merits and demerits of various advanced treatment technologies as well as the synergistic efficiency of combined AOPs in the treatment of wastewater containing POPs was reported. A concise review of recently published studies on successful treatment of POPs in wastewater using hydrodynamic cavitation technology in combination with other advanced oxidation processes is presented with the highlight of direction for future research focus

Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation

Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clusteringmethods is presented

Emotion recognition using multilayer perceptron and generalized feed forward neural network

367-371This paper explores performance analysis of multilayer perceptron neural network (MLPNN) and generalized feed forward neural network (GFFNN) for detection of 7 human emotions (neutral, anger, boredom, disgust, fear, happiness, sadness) using speech signals. Overall accuracy was found as follows: MLPNN, 93%; and GFFNN, 99%.

BRAIN Journal - Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation

<i>Abstract</i><div><br></div><div><div>Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clustering</div><div>methods is presented.</div></div><div><br></div><div><b>Find more here:</b></div><div><b>https://www.edusoft.ro/brain/index.php/brain/article/view/420</b><br></div

BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 1. Diagnosis Rate in different Countrie

<p>In MRI images, the amount of data is too much for manual segmentation. The procedure is<br> tedious, time, labor consuming, subjective and requires expertise. This gave way to methods that are<br> computer-aided with user interaction at varying levels. These methods are automatic and objective<br> and the results are highly reproducible. We designed software tool for locating brain tumor, based<br> on unsupervised clustering methods and analyzed its performance</p

BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor

<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p