MapReduce has become a popular programming model for processing and running large-scale data sets with a parallel, distributed paradigm on a cluster. Hadoop MapReduce is needed especially for large ...scale data like big data processing. In this paper, we work to modify the Hadoop MapReduce Algorithm and implement it to reduce processing time.
The COVID-19 pandemic wreaks havoc on healthcare systems all across the world. In pandemic scenarios like COVID-19, the applicability of diagnostic modalities is crucial in medical diagnosis, where ...non-invasive ultrasound imaging has the potential to be a useful biomarker. This research develops a computer-assisted intelligent methodology for ultrasound lung image classification by utilizing a fuzzy pooling-based convolutional neural network FP-CNN with underlying evidence of particular decisions. The fuzzy-pooling method finds better representative features for ultrasound image classification. The FPCNN model categorizes ultrasound images into one of three classes: covid, disease-free (normal), and pneumonia. Explanations of diagnostic decisions are crucial to ensure the fairness of an intelligent system. This research has used Shapley Additive Explanation (SHAP) to explain the prediction of the FP-CNN models. The prediction of the black-box model is illustrated using the SHAP explanation of the intermediate layers of the black-box model. To determine the most effective model, we have tested different state-of-the-art convolutional neural network architectures with various training strategies, including fine-tuned models, single-layer fuzzy pooling models, and fuzzy pooling at all pooling layers. Among different architectures, the Xception model with all pooling layers having fuzzy pooling achieves the best classification results of 97.2% accuracy. We hope our proposed method will be helpful for the clinical diagnosis of covid-19 from lung ultrasound (LUS) images.
•This research develops an intelligent methodology for ultrasound image classification.•The designed methodology utilizes a fuzzy pooling-based CNN.•This research has the capacity to explain the prediction of the models.
Lung cancer is one of the main leading causes of cancer death in all over the world. Accurate prediction of lung cancer survivability can enable physicians to make more reliable decisions about a ...patient's treatment. The objective of this research is to design robust machine learning model with supervised regression model to predict survivability of the lung cancer patients. This work includes Multiple Linear Regression, Support Vector Regression with Radial Function, Random Forest, Extreme Gradient Boosting Tree regression algorithms to build an ensemble model using stacking technology with meta-learner Gradient Boosting Machine. This experiment is performed on large SEER 2011-2017 dataset. The novel model achieved a high root mean squared error (RMSE) value of 8.58459 on the test dataset which outperforms the base models. The experimentation results show that the proposed system attains better result compared to the existing models.
This study describes an automated detection of polyp type as it is very important to determine the existence of dysplasia—a stage leading to the development of gastrointestinal cancer. The polyp-type ...classification is performed by a multiclass support vector machine from feature-fusion of bi-dimensional empirical mode decomposition (BEMD) and convolutional neural network (CNN). An extensive experiment is performed using standard datasets by extracted features from the individual technique as well as a fusion of features from BEMD and CNN. The fusion technique confirms satisfactory performance compared to other techniques with an accuracy of 98.94%. Moreover, it shows potentiality in precisely classifying some challenging polyps even though these are somehow confusing for human experts.
The abnormal growth of tissues that disarray the typical organization of cells is popularly known as polyps. The polyp on the gastrointestinal is a primary sign of gastrointestinal cancer. False ...diagnosis is extremely high using traditional diagnosis procedures that make the polyp diagnosis is a crucial task in real-time colonoscopy. We have developed a polyp detection methodology using a combination of hand-crafted and automated feature extraction techniques. In this study, we have experimented with different convolutional neural network (CNN) architectures and hand-crafted feature extractors to select the best combination. The combined approach of the fine-tuned Xception model with non-subsampled contourlet transform (NSCT) performed significantly well. Besides, we have applied the multi-criteria frame selection technique for selecting the best images from colonoscopy videos. Afterward, the feature extractors have worked on enhanced patch images of selected frames. This study has also experimented with dimensionality reduction techniques to remove irrelevant features from the combined feature vector. We designed an algorithm to localize the polyp regions using the outcomes of patch images. The method did significantly well on several available public datasets. This work might be helpful for the endoscopist during real-time endoscopy to detect polyps.
Seasonal vegetables play a crucial role in both nutrition and commerce in Bangladesh. Recognizing this significance, our research introduces the 'SeasVeg' dataset, comprising images of ten varieties ...of seasonal vegetables sourced from Dhaka and Pabna regions. These include Carica papaya, Momordica dioica, Abelmoschus esculentus, Lablab purpureus, Trichosanthes cucumerina, Trichosanthes dioica, Solanum lycopersicum, Brassica oleracea, Momordica charantia, and Raphanus sativus. Our dataset encompasses 4500 images, 1500 original and 3000 augmented, meticulously captured under natural light conditions to ensure authenticity. While our primary focus lies in leveraging machine learning and deep learning techniques for advancements in agriculture science, particularly in aiding healthcare aspects with seasonal vegetables and nutrition's, we acknowledge the versatile utility of our dataset. Beyond healthcare, it serves as a valuable educational resource, facilitating children's and toddlers' learning to identify these vital vegetables. This dual functionality broadens the dataset's appeal and underscores its societal impact beyond the realm of healthcare. Besides, the research culminates in the implementation of machine learning models, achieving noteworthy accuracy. We get the highest 99 % accuracy with the ResNet50 pre-trained CNN model and a good 94 % accuracy with the InceptionV3 pre-trained CNN model when it comes to the computer-aided vegetable classification. However, the 'SeasVeg' dataset represents not only a significant stride in healthcare innovation but also a promising tool for educational endeavors, catering to diverse stakeholders and fostering interdisciplinary collaboration.
A new protocol has been developed for cell culture and in vitro regeneration of Abrus precatorius that holds enormous potentiality for preparation of medicines. In vitro grown calli were cultured in ...Murashige and Skoog (MS) liquid media in agitated condition fortified with 0.5 mg/l 6-Benzylaminopurine. Growth curve of cells revealed that the cells continued to grow until 12 days of culture and got the highest peak from day 6-8. Isolated cell was found to produce highest 8.2% calli when suspended on MS medium supplemented with 0.5 mg/l 6-Benzylaminopurine and 0.1 mg/l 1-Naphthaleneacetic acid. Callus derived from single cell produced highest number of embryo (25-28%) cultured on MS medium fortified with 2.0 mg/l 6-Benzylaminopurine and 0.2 mg/l 1-Naphthaleneacetic acid. The bipolar embryos were selected and optimum shoot formation was recorded on MS medium supplemented with 2.0 mg/l 6-Benzylaminopurine and 0.1 mg/l 1-Naphthaleneacetic acid. The optimum root induction was noticed in MS medium supplemented with 1.0 mg/l 3-Indolebutyric acid. Rooted plantlets were successfully transferred to potting soil and acclimatized to outdoor conditions.
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