Early Breast Cancer Prediction using Machine Learning and Deep Learning Techniques

Breast Cancer (BC) is a considered as one of the utmost lethal diseases across the globe that has a very high morbidity and mortality rate. Accurate and early prediction along with diagnosis is one of the most crucial characteristics for the treatment of Breast Cancer. Doctors can have an edge over Breast cancer if they are able to predict it in its early stages using deep learning and machine learning techniques. This paper proposed consists of comparison between the and accuracy of various machine learning models like Support vector machine (SVM), K-Nearest Neighbours (KNN), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), XGB Classifier and deep learning model of Artificial neural networks (ANN) for the precise detection of breast cancer. The most crucial properties from the database have been chosen using one feature-selection technique. Correlation is also used to choose the most correlated features from the data. Implementing the ANN model consists of one input layer, two hidden layers, and one output layer. All Machine Learning models and ANN model are then applied to selected features. The results demonstrated that the SVM classifier achieved the highest performance with an accuracy of ~98.24%

Low temperature plasma-catalytic NOx synthesis in a packed DBD reactor: effect of support materials and supported active metal oxides

The direct synthesis of NOx from N2 and O2 by non-thermal plasma at an atmospheric pressure and low temperature is presented, which is considered to be an attractive option for replacement of the Haber-Bosch process. In this study, the direct synthesis of NOx was studied by packing different catalyst support materials in a dielectric barrier discharge (DBD) reactor. The support materials and their particle sizes both had a significant effect on the concentration of NOx. This is attributed to different surface areas, relative dielectric constants and particles shapes. The nitrogen could be fixed at substantially lowered temperatures by employing non-thermal plasma-catalytic DBD reactor, which can be used as an alternative technology for low temperature synthesis. The γ-Al2O3 with smallest particles size of 250–160 μm, gave the highest concentration of NOx and the lowest specific energy consumption of all the tested materials and particle sizes. The NOx concentration of 5700 ppm was reached at the highest residence time of 0.4 s and an N2/O2 feed ratio of 1 was found to be the most optimum for NOx production. In order to intensify the NOx production in plasma, a series of metal oxide catalysts supported on γ-Al2O3 were tested in a packed DBD reactor. A 5% WO3/γ-Al2O3 catalyst increased the NOx concentration further by about 10% compared to γ-Al2O3, while oxidation catalysts such as Co3O4 and PbO provided a minor (∼5%) improvement. These data suggest that oxygen activation plays a minor role in plasma catalytic nitrogen fixation under the studied conditions with the main role ascribed to the generation of microdischarges on sharp edges of large-surface area plasma catalysts. However, when the loading of active metal oxides was increased to 10%, NO selectivity decreased, suggesting possibility of thermal oxidation of NO to NO2 through reaction with surface oxygen species

Response of Two Sunflower (Helianthus Annuus L.) Genotypes to Va-mycorrhizal Inoculation and Phosphorus Levels

The performance of two sunflower genotypes (Morden and MSFH-8) with and without VA-mycorrhizal fungi at three P levels (38, 56 and 75 kg P2O5 ha-1) in vertisol of Dharwad was studied to determine the effect of mycorrhizal inoculation on plant growth, yield and P uptake. The results showed that the VAM inoculation increased sunflower yield (14%), total biomass (16%), oil content (3.1%) and P uptake (30.5%) over uninoculated control. The percent root colonization and chlamydo-spore count decreased with increasing P levels. The total biomass production, seed yield and P uptake of mycorrhizal plants at 38 kg P2O5 ha-1 more than the non-mycorrhizal plants at 75 kg P2O5 ha-1. The biomass and seed yield of mycorrhizal plants at same P level were more than the non-mycorrhizal plants. Mycorrhizal plants of Morden at 38 kg P2O5 ha-1 and MSFH-8 at 56 kg P2O5 ha-1 produced higher seed yield, oil content and total biomass than non-mycorrhizal plants supplied with 75 kg P2O5 ha-1. The results indicated that, VA-mycorrhizal inoculation helps in saving 25 and 50 percent of recommended dose of phosphatic fertilizer (75 kg P2O5 ha-1) in MSFH-8 (single cross hybrid) and Morden (open pollinated variety), respectively

Encapsulation of water insoluble drugs in mesoporous silica nanoparticles using supercritical carbon dioxide

Mesoporous silica nanoparticles MCM – 41 were synthesized with two dimensional hexagonal p6mm symmetry, high specific surface area(~ 980m2/g) narrow pore size and an average particle size of 186 nm. The produced nanoparticles were used to encapsulate carbamazepine through a supercritical carbon dioxide process combined with various organic solvents. Supercritical processing was found to provide increased drug encapsulation. The loaded MCM - 41 nanoparticles were analyzed using X–ray diffraction and differential scanning calorimetry (DSC) to investigate the crystalline state of the encapsulated carbamazepine and it was found to be dependent on the nature of the organic solvent. Carbamazepine showed increased dissolution rates under sink conditions. Viability studies of Caco – 2 cells demonstrated negligible cytotoxicity for the MCM–41 nanoparticles

Intraosseous acinic cell carcinoma

Acinic cell carcinoma is an uncommon low-grade malignant tumor of salivary glands. It was first described by Nasse in 1892, arising in parotid salivary gland. Salivary gland tumors are also known to develop within jaw bones, arising within the jaw as a primary central lesion, and are extremely rare with only a few cases reported. We present a rare case report of 65-year-old woman with intraosseous acinic cell carcinoma of left side of the mandible.Key words: Acinic cell carcinoma, central tumor of mandible, intraosseous acinic cell carcinom

Review of code blue system and audit

Background: Code Blue systems are communication systems that ensure the most rapid and effective resuscitation of a patient in respiratory or cardiac arrest. Code blue was established in Bharati Hospital and Research Centre in Sept 2011 in order to reduce morbidity and mortality in wards. The aim of the study was to evaluate the current code blue system and suggest possible interventions to strengthen the system.Methods: It was retrospective observational descriptive study. The study population included all consecutive patients above the age of 18 years for whom code blue had been activated. Data was collected using code blue audit forms. The data was analysed using SPSS (Statistical Package for social sciences) software.Results: A total of 260 calls were made using the blue code system between September 2011 to December 2012. The most common place for blue code activation was casualty. The wards were next, followed by dialysis unit and OPD. The indications for code blue team activation were cardio-respiratory arrest (CRA) (88 patients, 33.84%), change in mental status (52 patients, 20%), road traffic accidents RTA (21, 8.07%), convulsions (29 patients 11.15%), chest pain (19 patients, 8.46%), breathlessness (18 patients,6.92%) and worry of staff about the patient (17 patients, 6.53%), presyncope (10 patients, 3.84%), and others (6 patients, 2.30%). The average response time was 1.58±0.96 minutes in our study. Survival rate was more in medical emergency group 46.15% than in CRA group 31.61%. Initial success rate was 35.2% and a final success rate was 34.6%.Conclusions: Establishment of code blue team in the hospital enabled us to provide timely resuscitation for patients who had “out of ICU” CRA. Further study is needed to establish the overall effectiveness and the optimal implementation of code blue teams. The increasing use of an existing service to review patients meeting blue code criteria requires repeated education and a periodic assessment of site-specific obstacles to utilization

A New Dynamic Mathematical Modeling Approach of Zero Waste Management System

Dr. Paul Palmer coined the phrase "zero waste" in 1973 to describe thecomplete removal of municipal solid waste (MSW) from landfills. Through aliterature study and an interactive survey method, the conceptual basis and proceduresemployed in 25 cities throughout the world. The Battle Environment EvaluationSystem was used to study and analyse certain essential key issues involved in thepresent zero waste management system (BEES). The Delphi Technique wascommonly utilised in this study to evaluate the present municipal solid wastemanagement system (MSWMS) of the study area. The value of EnvironmentalQuality (EQ) determined for the area under study's existing MSWM system wasfound to be 238; however, the ideal value for a good rating of an existing MSWMsystem should be 521. Various conclusions were drawn based on the facts andfindings, including the development of innovative product design, a focus on the SolidLiquid Resource Management Centre (SLRMC), the Domestic Composting System(DCS), the design of optimal collection and transportation routes, the development oftransfer stations, the implementation of the 3R principles, and the monitoring of theMSWM system at various stages. A model for zero-waste management has also beencreated and presented

Obstetric and neonatal outcomes of the pregnancies complicated with thrombocytopenia

Background: Thrombocytopenia is second most common hematological abnormality in pregnancy after anemia (Incidence 8-10%). The aim of this study is to observe the obstetric and neonatal outcomes of pregnancies complicated with thrombocytopenia and to compare its maternal and fetal outcomes.Methods: The prospective observational study was conducted at tertiary care institute over period of one and half year and 100 cases of thrombocytopenia in present pregnancy were included after fulfilling inclusion and exclusion criteria and obtaining written informed valid consent. Complete history, physical examination and relevant investigations of the patient were documented. Patients were followed up to delivery and outcomes (obstetric, maternal, fetal, neonatal) were studied. The data obtained for all the patients was analyzed with SPSS (SPSS Inc, Chicago) software packages. Statistical comparisons were performed with Pearson’s Chi- square where appropriate with p-value of 37 weeks of gestation. Most (53%) had moderate thrombocytopenia. Incidence of maternal complications was statically significant (P-value 0.038) with most common complication being caesarian section site oozing (9%) followed by placental abruption (4%). There was no statistical significance in degree of thrombocytopenia and need for blood and blood product transfusion (P-value 0.67). Only (2%) neonates of thrombocytopenic mothers had thrombocytopenia and both required treatment.Conclusions: Most common cause of thrombocytopenia in pregnancy was gestational thrombocytopenia with uneventful pregnancy and perinatal outcomes. Few severe cases associated with medical or systematic causes leads to serious catastrophic events which can be avoided by increasing antenatal surveillance and appropriate management by multidisciplinary team of obstetrician, hematologist, anesthesiologist, neonatologist and physician

Thermal Decomposition of Ammonium Perchlorate in thePresence of Nanosized Ferric Oxide

The catalytic effect of two different sizes of a-Fe2O3 nanoparticles synthesised using an electrochemicalmethod was investigated on the thermal decomposition of ammonium perchlorate (AP) using differentialscanning calorimetry as a function of catalyst concentration.  The nanosized ferric oxide particles exhibitedmore of a catalytic effect on the thermal decomposition of AP than commercial Fe2O3  particles. A loweringof the high-temperature decomposition of AP by 59 oC was observed after mixing with 2 Wt per cent ofa-Fe2O3  particles with the very fine size of 3.5 nm. The mixture produced a high heat release of 4.574 kJ/g compared to 0.834 kJ/g of pure AP. The kinetic parameters were evaluated using Kissinger method. Thedecrease in the activation energy and increase in rate constant confirmed the catalytic activity of thesenanoparticles.Defence Science Journal, 2008, 58(6), pp.721-727, DOI:http://dx.doi.org/10.14429/dsj.58.169

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making