Classification of Acute Lymphocytic Leukemic Blood Cell Images using Hybrid CNN-Enhanced Ensemble SVM Models and Machine Learning Classifiers

Acute Lymphocytic Leukemia is a dangerous kind of malignant cancer caused due to the overproduction of white blood cells. The white blood cells in our body are responsible for fighting against infections, if the WBC increases the immunity will decrease and it would lead to serious health conditions. Malignant cancers such as ALL is life threatening if the disease is not diagnosed at an early stage. If a person is suffering from ALL the disease needs to be diagnosed at an early stage before it starts spreading, if it starts spreading the person’s chances of survival would also reduce. Here comes the need of an accurate automated system which would assist the oncologists to diagnose the disease as early as possible. In this paper some of the algorithms that are enhanced to detect and classify ALL are incorporated. In order to classify the Acute Lymphocytic Leukemia a hybrid model has been deployed to improve the accuracy of the diagnosis and it is termed as Hybrid CNN Enhanced Ensemble SVM for the classification of malignancy. Machine Learning classifiers are also used to design the system and it is then compared with enhanced CNN based on the performance metrics

Formulation and evaluation of herbal lipstick from pigment of Nyctanthes arbor-tristis

The Herbal lipsticks are composed of natural colourants and compounds to protect the lips. The market place for organic products in various fields extends throughout the world on account of increased awareness among consumers of side effects related to protracted use of some synthetic colouring compounds, and therefore the current trend towards healthful biomaterials in products. This work aimed to formulate a natural lipstick from coloured pigments of Nyctanthes arbor-tristis (Night flowering jasmine) flowers. The use of natural colouring pigments in the product would minimize the side effects. This study focused on the extraction of colourant from N. arbor-tristis flowers and optimizing the formula for the preparation of lipstick and evaluating it. The results indicate that the prepared formulation was good and had minimal or no side effects on the lips

Classification of acute lymphoblastic leukaemia using machine learning algorithms

Acute Lymphoblastic Leukemia is a serious disease which may cause death if it is not detected at an early stage. It is common in children as well as adults. The detection of ALL is being done manually by examining the blood smear samples under a microscope. Manual blood testing has got several shortcomings such as it is slow and accuracy is also less. Generally, the inspection is done by an experienced pathologists and if there is any malformations the presence of lymphoblasts will be there. The accuracy of the diagnosis will be dependent on the experience of the operator.  The Proposed research work aims in improving the diagnosis of ALL using Machine Learning Classifiers.   Few classifiers haven been applied and compared on the segmented dataset images. The automated system can provide several advantages like it will minimize human intervention and it would provide more accurate results. In this research work EDES-SVM and EDSC-SVM have been used for classification.  Experimental results obtained are then compared with the results of other machine learning classifiers such as SVM, ESVM, DSC-SVM, DES-SVM. From the experimental results it is analysed that the proposed method outperforms the conventional methods.&nbsp

Formulation and evaluation of herbal lipstick from pigment of Nyctanthes arbor-tristis

574-578The Herbal lipsticks are composed of natural colourants and compounds to protect the lips. The market place for organic products in various fields extends throughout the world on account of increased awareness among consumers of side effects related to protracted use of some synthetic colouring compounds, and therefore the current trend towards healthful biomaterials in products. This work aimed to formulate a natural lipstick from coloured pigments of Nyctanthes arbor-tristis (Night flowering jasmine) flowers. The use of natural colouring pigments in the product would minimize the side effects. This study focused on the extraction of colourant from N. arbor-tristis flowers and optimizing the formula for the preparation of lipstick and evaluating it. The results indicate that the prepared formulation was good and had minimal or no side effects on the lips

Beneficial cyanobacteria and eubacteria synergistically enhance bioavailability of soil nutrients and yield of okra

Microorganisms in the rhizosphere mediate the cycling of nutrients, their enhanced mobilisation and facilitate their uptake, leading to increased root growth, biomass and yield of plants. We examined the promise of beneficial cyanobacteria and eubacteria as microbial inoculants, applied singly or in combination as consortia or biofilms, to improve growth and yields of okra. Interrelationships among the microbial activities and the micro/macro nutrient dynamics in soils and okra yield characteristics were assessed along with the changes in the soil microbiome. A significant effect of microbial inoculation on alkaline phosphatase activity was recorded both at the mid-crop and harvest stages. Microbial biomass carbon values were highest due to the Anabaena sp. - Providencia sp. (CR1 + PR3) application. The yield of okra ranged from 444.6–478.4 g−1 plant and a positive correlation (0.69) recorded between yield and root weight. The application of Azotobacter led to the highest root weight and yield. The concentration of Zn at mid-crop stage was 60–70% higher in the Azotobacter sp. and Calothrix sp. inoculated soils, as compared to uninoculated control. Iron concentration in soil was more than 2–3 folds higher than control at the mid-crop stage, especially due to the application of Anabaena-Azotobacter biofilm and Azotobacter sp. Both at the mid-crop and harvest stages, the PCR-DGGE profiles of eubacterial communities were similar among the uninoculated control, the Anabaena sp. - Providencia sp. (CW1 + PW5) and the Anabaena-Azotobacter biofilm treatments. Although the profiles of the Azotobacter, Calothrix and CR1 + PR3 treatments were identical at these stages of growth, the profile of CR1 + PR3 was clearly distinguishable. The performance of the inoculants, particularly Calothrix (T6) and consortium of Anabaena and Providencia (CR1 + PR3; T5), in terms of microbiological and nutrient data, along with generation of distinct PCR-DGGE profiles suggested their superiority and emphasized the utility of combining microbiological and molecular tools in the selection of effective microbial inoculants