Evaluation of enhanced chemical coagulation method for a case study on colloidal liquid particle in wastewater treatment: Statistical optimization analysis and implementation of machine learning

Coal mines are one of the largest sources of energy supply and generate significant volumes of wastewater. Chemical coagulation is one of the most effective methods for wastewater treatment. In this research, ferric and aluminum-based coagulants, along with polyacrylamide flocculants with positive, negative, and neutral charges, were utilized in chemical coagulation. After applying the Plackett-Burman screening method, it was found that ferric chloride coagulant, neutral flocculant, and slow mixing duration had the greatest impact. The chemical coagulation process was modeled and optimized by examining these factors using the Box-Behnken statistical design as input parameters and sedimentation velocity as the output. Under optimal conditions, the values for ferric chloride coagulant, neutral flocculant, mixing time in slow mode, and sedimentation velocity were determined to be 106.3 mg/L, 3.98 mg/L, 29.6 min, and 1.10 cm/min, respectively. Under optimal conditions, the removal percentages of pollutants, including TSS, turbidity, TDS, COD, and BOD, were obtained at 100%, 100%, 87%, 93%, and 81%, respectively. The experimental data were fitted using the BBD and ANN methods. Both models demonstrated very high agreement, but the ANN method performed better with an AAD% of 0.66, an MSE of 0.0001, and an R2 value of 0.99. All results were calculated with a confidence level above 98%, indicating that both models had very high reliability in modeling and prediction.

Model selection criteria for image restoration

In this brief, the image restoration problem is approached as a learning system problem, in which a model is to be selected and parameters are estimated. Although the parameters which correspond to the restored image can easily be obtained, their quality depend heavily on a proper choice of the regularization parameter that controls the tradeoff between fidelity to the blurred noisy observed image and the smoothness of the restored image. By analogy between the model selection philosophy that constitutes a fundamental task in systems learning and the choice of the regularization parameter, two criteria are proposed in this brief for selecting the regularization parameter. These criteria are based on Bayesian arguments and the Kullback-Leibler divergence and they can be considered as extensions of the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) for the image restoration problem.

Novel genetic-neuro-fuzzy filter for speckle reduction from sonography images

Edge-preserving speckle noise reduction is essential to computer-aided ultrasound image processing and understanding. A new class of genetic-neuro-fuzzy filter is proposed to optimize the trade-off between speckle noise removal and edge preservation. The proposed approach combines the advantages of the fuzzy, neural, and genetic paradigms. Neuro-fuzzy approaches are very promising for nonlinear filtering of noisy images. Fuzzy reasoning embedded into the network structure aims at reducing errors while fine details are being processed. The learning method based on the real-time genetic algorithms (GAs) performs an effective training of the network from a collection of training data and yields satisfactory results after a few generations. The performance of the proposed filter has been compared with that of the commonly used median and Wiener filters in reducing speckle noises on ultrasound images. We evaluate this filter by passing the filter's output to the edge detection algorithm and observing its ability to detect edge pixels.Experimental results show that the proposed genetic-neuro-fuzzy technique is very effective in speckle noise reduction as well as detail preserving even in the presence of highly noise corrupted data, and it works significantly better than other well-known conventional methods in the literature.

Artificial neural networks for document analysis and recognition

Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.