ABSTRACT
The presence of dyes in water stream is a major environmental problem that affects aquatic and human life negatively. Therefore, it is essential to remove dye from wastewater before its discharge into the water bodies. In this study, Banyan (Ficus benghalensis, F. benghalensis) tree leaves, a low-cost biosorbent, were used to remove brilliant green (BG), a cationic dye, from an aqueous solution. Batch model experiments were carried out by varying operational parameters, such as initial concentration of dye solution, contact time, adsorbent dose, and pH of the solution, to obtain optimum conditions for removing BG dye. Under optimum conditions, maximum percent removal of 97.3% and adsorption capacity (Qe) value of 19.5 mg/g were achieved (at pH 8, adsorbent dose 0.05 g, dye concentration 50 ppm, and 60 min contact time). The Langmuir and Freundlich adsorption isotherms were applied to the experimental data. The linear fit value, R2 of Freundlich adsorption isotherm, was 0.93, indicating its best fit to our experimental data. A kinetic study was also carried out by implementing the pseudo-first-order and pseudo-second-order kinetic models. The adsorption of BG on the selected biosorbent follows pseudo-second-order kinetics (R2 = 0.99), indicating that transfer of internal and external mass co-occurs. This study surfaces the excellent adsorption capacity of Banyan tree leaves to remove cationic BG dye from aqueous solutions, including tap water, river water, and filtered river water. Therefore, the selected biosorbent is a cost-effective and easily accessible approach for removing toxic dyes from industrial effluents and wastewater.
ABSTRACT
The anatomy of red blood cells (RBCs) in blood smear images plays an important role in the detection of several diseases. The automated image-based technique is fast and accurate for the analysis of blood cells morphology that can save time of both pathologists as well as that of patients. In this paper, we propose a novel method which segment and identify varied RBCs in a given blood smear images. In the proposed method, the central pallor and whole cell information are used, after using color processing followed by double thresholding of blood smear images. The shape and size variances of cells are calculated for the identification of abnormalities in peripheral blood smear images. We used cross-validation accuracy weighted probabilistic ensemble (CAWPE). It is a heterogeneous ensembling technique of nearly equivalent classifiers produced on averagely significant better classifiers (regarding errors and probability estimates) as compared to a wide range of potential parent classifiers. The proposed method is tested on 3 sets of images. The sets of images were prepared in a local government hospital by expert pathologists. Each image set has varied photographic conditions. The method was found accurate in term of results, closer to the ground truth. The average accuracy of the proposed method is 97% for the segmentation of single cells and 96% for overlapped cells. The variance (σ2) of accuracy is 3.5 and the deviation (σ) is 1.87.
Subject(s)
Image Processing, Computer-Assisted , Pallor , Erythrocytes , Humans , MicroscopyABSTRACT
The advancement of computer- and internet-based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient-doctor diagnostics is extended to a more robust advanced concept of E-health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud-based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape-based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross-validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.
