Big Data Classification of Ultrasound Doppler Scan Images Using a Decision Tree Classifier Based on Maximally Stable Region Feature Points

The classification of ultrasound scan images is important in monitoring the development of prenatal and maternal structures. This paper proposes a big data classification system for ultrasound Doppler scan images that combines the residual of maximally stable extreme regions and speeded up robust features (SURF) with a decision tree classifier. The algorithm first preprocesses the ultrasound scan images before detecting the maximally stable extremal regions (MSER). A few essential regions are chosen from the MSER regions, along with the residual region that provides the best Region of Interest (ROI). SURF features points that best represent the region are detected using the gradient of the estimated cumulative region of interest. To extract the feature from the pixels that surround the SURF feature points, the Triangular Vertex Transform (TVT) transform is used. A decision tree classifier is used to train the extracted TVT features. The proposed ultrasound scan image classification system is validated using performance parameters such as accuracy, specificity, precision, sensitivity, and F1 score. For validation, a large dataset of 12,400 scan images collected from 1792 patients is used. The proposed method has an F1score of 94.12%, sensitivity, specificity, precision, and accuracy of 93.57%, 93.57%, and 97.96%, respectively. The evaluation results show that the proposed algorithm for classifying Doppler scan images is better than other algorithms that have been used in the past.&nbsp

Study of fetal doppler velocimetry versus non stress test as predictors of adverse perinatal outcome in high risk pregnancies

Background: Pregnancy is a unique, physiologically normal event in a women’s life. Objective of this study was to compare the efficacy of the doppler velocimetry versus non stress test in relation to perinatal outcome in high risk pregnancies.Methods: This is a prospective study conducted in the department of obstetrics and gynaecology, Narayana Medical College and Hospital. 100 women with high risk pregnancy were recruited. All were examined systematically, and Doppler velocimetry and non-stress test were done.Results: All cases were divided into four groups based on NST and doppler velocimetry of umbilical artery and middle cerebral artery. 10% of women had abnormal doppler. Middle cerebral artery doppler abnormality was noted in 3% and CPR abnormality in 3% of women in the study group. 15% had abnormal NST. In Group A, out of 88 patients 9 had fetal compromise. In Group B, out of 5 patients all had fetal compromise. In Group C, out of 4 patients none had fetal compromise. In Group D, all 3 patients had fetal compromise. In Group D, all 3 had neonatal deaths. Average birth weights in Group A was 2.7 kg, in Group B was 2 kg, in Group C was 2.5 kg, in Group D was 1.4 kg. Two (2.2%) newborn in Group A, 4 (80%) newborns in Group B, 3 (100%) in Group had Apgar < 7 at 5 minutes. 4 (4.5%) babies in Group A, 5 (100%) babies in Group B, 3 (100%) babies in Group D were admitted in NICU. Umbilical artery doppler was found to have sensitivity 46.6%, specificity - 94%, PPV - 93%, NPV - 54%. Middle cerebral artery doppler was found to have sensitivity 73.3%, specificity - 90%, PPV - 91.6%, NPV- 69.3%.Conclusions: In present study, highest percentage of perinatal complications and perinatal deaths were seen in groups with abnormal tests of NST and velocimetry. Group D had the worst perinatal outcome

Potential screening strategies for early prediction of pre-eclampsia

Background: This study was conducted to evaluate the efficacy of different biochemical and biophysical markers in the early weeks of gestation as screening tools for early prediction of preeclampsia.Methods: This hospital-based prospective observational study conducted on 52 pregnant women, at less than 13 weeks of gestation were recruited. Maternal serum inhibin A and USG uterine artery PI levels were analyzed among the pregnant women who subsequently developed PE and compare with those who did not develop PE. Methods used for the detection of markers were: chemiluminescence immunoassay (CLIA) for serum inhibin A levels, and uterine artery Doppler velocimetry was done by PHILIPS HD11XE transabdominal ultrasound machine using a 4-6 MHz probe with the same sonographer.Results: The present study revealed a significant rise of inhibin A in preeclamptic women when compared to normotensive women (p<0.0001). The mean levels of 1st and 2nd trimester uterine artery PI significantly high in women who subsequently developed PE when compared to those who did not develop preeclampsia (p<0.0001). Study results showed a strong association between gestational age at delivery and neonatal outcome (neonatal birth weight and APGAR) with preeclampsia. The maternal serum inhibin A, and uterine artery PI found to have good sensitivity and specificity for early prediction of PE.Conclusions: Study concluded that the women who are prone to develop PE subsequently, had high levels of MAP, UAPI, inhibin A. In our setting, MAP, UAPI, inhibin A, appeared to be better screening modalities. Combination of the biochemical markers with the biophysical markers, demographic characteristics, and other novel markers will establish the effective screening models for early prediction of PE. Early identification of high-risk cases will offer an opportunity for prophylactic therapy, such as Low- dose Aspirin in selected groups of high-risk women screened in the first trimester, thus improving the maternal and perinatal outcome

Impact of environmental hazards on sense of hearing - A conceptual study

The high burden of deafness globally and in India is largely preventable and avoidable. The prevalence of deafness in Southeast Asia ranges from 4.6% to 8.8%. In India, 63 million people (6.3%) suffer from significant auditory loss. In Ayurveda we get many references of Karna, Karna is one among Pancha Jnanendriya and predominance of Akasha Mahabhuta, perceives Shabda. Asatmyendriyartha Samyoga of Indriya i.e. Ayoga, Atiyoga and Mithya Yoga of Srotrendriya causes Roga. Karna Shoola, Karna Nada, Karna Kshweda, Badhiryam are some diseases caused by Asatmyendriyartha Samyoga. Ayurvedic system of medicine gives more importance to preventive measures. Identifying (early screening) the Nidana and Nidana Parivarjana plays the key role in preventing most of the Karna Rogas. In all Karna Rogas Vata Prakopa to be the chief cause, hence Ghrutapana is told as Rasayana

Expression of nestin - a stem cell associated intermediate filament in human CNS tumours

Background &amp; objectives: Nestin is an intermediate filament protein expressed in undifferentiated cells during the development of brain and is considered as a marker for neuroepithelial stem cells. Expression of this protein in various CNS tumour cells suggests the possibility of existence of tumour stem cell modulating the evolution. We carried out an immunohistochemical study to demonstrate the expression of nestin and its co-expression with neuronal and glial intermediate filament and correlate with the grade of malignancy. Methods: Formalin fixed, paraffin processed sections from two human foetuses, 16 brain tumours of both neuronal and glial lineage and two metastatic tumours were immunostained with polyclonal antibody to nestin. Serial sections from primary brain tumours were also stained with monoclonal antibody to neurofilament (NF) and glial fibrillary acidic protein (GFAP). Fluorescent double labeling was carried out on four cases using laser confocal microscopy, to document co-localization of nestin with other intermediate filaments in the tumour cells. Results: Nestin expression was observed along the paraventricular zone of human foetuses and in brain tumours of both glial and neuronal lineage, of both high and low grades of malignancy. In addition, mature dysplastic spinal motor neurons adjacent to tumour and cerebellar Purkinje cells also expressed nestin along with neurofilament. Interpretation &amp; conclusion: Nestin expression was noted in both low and high grade brain tumours and dysplastic neurons and did not parallel the malignant grade of the tumour. The expression of nestin in tumour cells and dysplastic neurons suggests aberrant expression of antigenically primitive proteins in cells to facilitate remodelling of the cell and migration. More studies are needed to elucidate the concept

Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble: A Survey

Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods

Air Pollution Detection and Control System Using ML Techniques

In present times, air pollution is increasing day by day, depriving the health of many people due to the various toxic components in air. So, it is necessary to monitor and detect the levels of pollution in various areas and try to control it by taking precautionary actions. Air pollution detection and control system is all about detecting the level of pollution in a particular area based on the amount of polluting components and proposing the measures to control the pollution. Analysis is made on the regions of Visakhapatnam city in Andhra Pradesh, India and grouped based on their pollution and displayed along with each component level, reasons for the pollution depending on each component and measures that can be followed. Apart from this, we also display list of top 10 regions with the highest values for each component which can be used to identify the harmful regions based on the toxic components

Temporal patterns in biodiversity and health status of reef corals of Palk Bay

A detailed study aimed at identifying the changes in biodiversity, live coral cover as well as health status of the Palk Bay Reef corals was carried out over a period of 4 years. The live coral percent cover was measured using Line Intercept Transect method at fixed sites in the reefs of Palk Bay in 2008 in order to study and make comparisons with the surveys conducted in 2004. Substantial decrease in live coral cover was observed over the last four years with a live coral cover of 13.65% and 12.9% in Velapertumuni and Kathuvallimuni Reefs respectively. Acropora cytherea and Favites abdita were the dominant and abundant species respectively in Velapertumuni Reef with relative abundance values of 21.08 and 10.85 respectively. However, in Kathuvallimunai Reef, Acropora lamarcki was found to be the most abundant species with a relative abundance value of 12.68. All other species belonged either to common/uncommon species status. Variations in community structure were also noticed in both the reefs. Even though, the total live coral cover was found to be reduced, the increased recruitment of fast growing species like Acropora has contributed to a fair diversity as indicated by the diversity indices. Studies on the disease prevalence in hard corals indicated more incidences of diseases in massive corals as compared to branching corals. Disease conditions such as brown band disease, porites ulcerative white spot syndrome and pink line syndrome/porites pinking were recorded

Flood Prediction using MLP, CATBOOST and Extra-Tree Classifier

Flooding can be one of the many devastating natural catastrophes, resulting in the annihilation of life and damaging property. Additionally, it can harm farmland and kill growing crops and trees. Nowadays, rivers and lakes are being destroyed, and the natural water reservoirs are converted into development sites and buildings. Due to this, even just a bit of rain can cause a flood. To minimize the number of fatalities, property losses, and other flood-related issues, an early flood forecast is necessary. Therefore, machine learning methods can be used for the prediction of floods.To forecast the frequency of floods brought on by rainfall, a forecasting system is built using rainfall data. The dataset is trained using various techniques like the MLP classifier, the CatBoost classifier, and the Extra-Tree classifier to predict the occurrence of floods. Finally, the three models' performances are compared and the best model for flood prediction is presented. The MLP, Extra-Tree, and CatBoost models achieved accuracy of 94.5%, 97.9%, and 98.34%, respectively, and it is observed that CatBoost performed well with high accuracy to predict the occurrence of floods

GBJOF: Gradient Boosting Integrated with Jaya Algorithm to Optimize the Features in Malware Analysis

Malware analysis is used to identify suspicious file transferring in the network. It can be identified efficiently by using the reverse engineering hybrid approach. Implementing a hybrid approach depends on the feature selection because the dataset contains static and dynamic parameters. The given dataset contains 85 attributes with 10 different class labels. Since it has high dimensional and multi-classification data, existing approaches of ML could be more efficient in reducing the features. The model combines the enhanced JAYA genetic algorithm with a gradient boosting technique to identify the efficiency and a smaller number of features. Many existing approaches for feature selection either implement correlation analysis or wrapper techniques. The major disadvantages of these issues are that they are facing fitting problems with a very small number of features. With the Usage of the genetic approach, this paper has achieved 95% accuracy with 12 features, approximately 7% greater than ML approaches