An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

Current redistribution model of anomalous resistance behaviour in superconductor-topological insulator heterostructures

Anomalous resistance upturn and downturn have been observed on the topological insulator (TI) surface in superconductor-TI (NbN-Bi1.95Sb0.05Se3) heterostructures at ~ mm length scales away from the interface. Magnetotransport measurements were performed to verify that the anomaly is caused due to the superconducting transition of the NbN layer. The possibility of long range superconducting proximity effect due to the spin-polarized TI surface state was ruled out due to the observation of similar anomaly in NbN-Au and NbN-Al heterostructures. It was discovered that the unusual resistance jumps were caused due to current redistribution at the superconductor-TI interface on account of the geometry effects. Results obtained from finite element analysis using COMSOL package has validated the proposed current redistribution (CRD) model of long range resistance anomalies in superconductor-TI and superconductor-metal heterostructures.Comment: 37 pages (including references), 17 figure

Systemically administered central nervous system drugs induced ocular side effects: a review

Several systemic drugs have reported ocular and visual side effects that affect patient management. It is imperative to be familiar with the associated side effects which can be mild or transient and may seriously threaten vision. This article deals briefly with the mechanisms and reasons that account for the impact that systemically administered central nervous system (CNS) drugs can exert on the visual or ocular system. The eye care practitioner can be instrumental in detecting and reporting ocular side effects, advising patients and collaborating with other members of the patient’s healthcare team. One of the difficulties include becoming familiar with the countless systemic medications prescribed to patients. Another is being able to correlate a particular side effect with a suspected drug. Several of the ocular adverse effects such as glaucoma, cataract, blurred vision, color vision, optic neuritis, maculopathy, dry eye, etc., are vision threatening and often patients fail to recognize or describe the symptoms appropriately. Therefore, physicians and paramedical members like staff nurses, clinical pharmacists and other members must make adequate observations while recommending these drugs to patients

The Murchison Widefield Array Transients Survey (MWATS). A search for low frequency variability in a bright Southern hemisphere sample

We report on a search for low-frequency radio variability in 944 bright (> 4Jy at 154 MHz) unresolved, extragalactic radio sources monitored monthly for several years with the Murchison Widefield Array. In the majority of sources we find very low levels of variability with typical modulation indices < 5%. We detect 15 candidate low frequency variables that show significant long term variability (>2.8 years) with time-averaged modulation indices M = 3.1 - 7.1%. With 7/15 of these variable sources having peaked spectral energy distributions, and only 5.7% of the overall sample having peaked spectra, we find an increase in the prevalence of variability in this spectral class. We conclude that the variability seen in this survey is most probably a consequence of refractive interstellar scintillation and that these objects must have the majority of their flux density contained within angular diameters less than 50 milli-arcsec (which we support with multi-wavelength data). At 154 MHz we demonstrate that interstellar scintillation time-scales become long (~decades) and have low modulation indices, whilst synchrotron driven variability can only produce dynamic changes on time-scales of hundreds of years, with flux density changes less than one milli-jansky (without relativistic boosting). From this work we infer that the low frequency extra-galactic southern sky, as seen by SKA-Low, will be non-variable on time-scales shorter than one year.Comment: 19 pages, 11 figure

Numerical scoring for the Classic BILAG index

Objective. To develop an additive numerical scoring scheme for the Classic BILAG index

High-energy sources at low radio frequency : the Murchison Widefield Array view of Fermi blazars

This is the accepted version of the following article: Giroletti, M. et al., A&A, 588 (2016) A141, which has been published in final form at DOI: http://dx.doi.org/10.1051/0004-6361/201527817. This article may be used for non-commercial purposes in accordance with the EDP Sciences self-archiving policies.Low-frequency radio arrays are opening a new window for the study of the sky, both to study new phenomena and to better characterize known source classes. Being flat-spectrum sources, blazars are so far poorly studied at low radio frequencies. We characterize the spectral properties of the blazar population at low radio frequency compare the radio and high-energy properties of the gamma-ray blazar population, and search for radio counterparts of unidentified gamma-ray sources. We cross-correlated the 6,100 deg^2 Murchison Widefield Array Commissioning Survey catalogue with the Roma blazar catalogue, the third catalogue of active galactic nuclei detected by Fermi-LAT, and the unidentified members of the entire third catalogue of gamma-ray sources detected by \fermilat. When available, we also added high-frequency radio data from the Australia Telescope 20 GHz catalogue. We find low-frequency counterparts for 186 out of 517 (36%) blazars, 79 out of 174 (45%) gamma-ray blazars, and 8 out of 73 (11%) gamma-ray blazar candidates. The mean low-frequency (120--180 MHz) blazar spectral index is $\langle \alpha_\mathrm{low} \rangle=0.57\pm0.02$: blazar spectra are flatter than the rest of the population of low-frequency sources, but are steeper than at $\sim$GHz frequencies. Low-frequency radio flux density and gamma-ray energy flux display a mildly significant and broadly scattered correlation. Ten unidentified gamma-ray sources have a (probably fortuitous) positional match with low radio frequency sources. Low-frequency radio astronomy provides important information about sources with a flat radio spectrum and high energy. However, the relatively low sensitivity of the present surveys still misses a significant fraction of these objects. Upcoming deeper surveys, such as the GaLactic and Extragalactic All-Sky MWA (GLEAM) survey, will provide further insight into this population.Peer reviewedFinal Published versio

The use of Systemic Lupus Erythematosus Disease Activity Index-2000 to define active disease and minimal clinically meaningful change based on data from a large cohort of systemic lupus erythematosus patients

Objectives. To examine SLEDAI-2000 cut-off scores for definition of active SLE and to determine the sensitivity to change of SLEDAI-2000 for the assessment of SLE disease activity and minimal clinically meaningful changes in score

Crop–livestock-integrated farming system: a strategy to achieve synergy between agricultural production, nutritional security, and environmental sustainability

IntroductionClimate change, nutritional security, land shrinkage, and an increasing human population are the most concerning factors in agriculture, which are further complicated by deteriorating soil health. Among several ways to address these issues, the most prominent and cost-effective means is to adopt an integrated farming system (IFS). Integrating farming systems with livestock enables a way to increase economic yield per unit area per unit of time for farmers in small and marginal categories. This system effectively utilizes the waste materials by recycling them via linking appropriate components, thereby minimizing the pollution caused to the environment. Further integrating livestock components with crops and the production of eggs, meat, and milk leads to nutritional security and stable farmer's income generation. So, there is a dire need to develop an eco-friendly, ecologically safe, and economically profitable IFS model.MethodsAn experiment was conducted to develop a crop–livestock-based integrated farming system model for the benefit of irrigated upland farmers in the semi-arid tropics for increasing productivity, farm income, employment generation, and food and nutritional security through efficient utilization of resources in the farming system.Results and discussionThe IFS model has components, viz., crop (0.85 ha) + horticulture (0.10 ha) + 2 cattles along with 2 calves in dairy (50 m2) + 12 female goats and 1 male goat (50 m2) + 150 numbers of poultry birds (50 m2) + vermicompost (50 m2) + kitchen garden (0.02 ha) + boundary planting + supporting activities (0.01 ha) in a one-hectare area. The model recorded a higher total MEY (162.31 t), gross return (689,773), net return (317,765), and employment generation (475 mandays). Further negative emissions of −15,118 CO2-e (kg) greenhouse gases were recorded under this model. The study conclusively reveals that integration of crop, horticulture, dairy, goat, poultry, vermicompost production, kitchen garden, and boundary planting models increases the net returns, B:C ratio, employment generation, nutritional security, and livelihoods of small and marginal farmers