Employee Attrition System Prediction using Random Forest Classifier

Despite rising unemployment, most job coverage of the COVID-19 outbreak has concentrated on layoffs. Employees have been fired for reasons related to the epidemic, which has been a less prominent issue. COVID-19 is still doing damage to the country\u27s economy. Companies are in the midst of a recession, so they are beginning to fire off unproductive employees. Making critical decisions like laying off employees or cutting an employee\u27s compensation is a challenging undertaking that must be done with extreme attention and accuracy. Adding negligence would harm the employee\u27s career and the company\u27s image in the industry. In this paper, we have predicted employee attrition using Logistic Regression, Random Forest, and Decision Tree techniques. Random Forest Classifier has outperformed other algorithms in this work. After using different machine learning techniques, we can say that Random Forest gives the best performance with a recall of 70%, and also, we have found Precision, Accuracy, and F1- Score

Predicting Accurate Heart Attacks Using Logistic Regression

A heart attack is one of the leading causes of death today. According to a large data population used as a training set for the algorithm for machine learning, classification is a technique for predicting the target class from input data. A difficulty in clinical data analytics is predicting heart attacks with greater precision. The focal point of this work is to analyze the heart attack dataset (Kaggle repository) to find a Machine learning classifier technique that predicts if a person is prone to a heart attack with maximum accuracy based on various health factors. The efficacy of the three classifiers, namely Logistic Regression, Random Forest, and Decision Tree, is demonstrated for predicting heart attack. This work compares the three classification algorithms among various factors. Logistic Regression outperforms all for predicting the values from the dataset accurately

Empirical Forecasting Analysis of Bitcoin Prices: A Comparison of Machine learning, Deep learning, and Ensemble learning Models

Bitcoin has drawn a lot of interest recently as a possible high-earning investment. There are significant financial risks associated with its erratic price volatility. Therefore, investors and decision-makers place great significance on being able to precisely foresee and capture shifting patterns in the Bitcoin market. However, empirical studies on the systems that support Bitcoin trading and forecasting are still in their infancy. The suggested method will predict the prices of all key cryptocurrencies with accuracy. A number of factors are going to be taken into account in order to precisely predict the pricing. By leveraging encryption technology, cryptocurrencies may serve as an online accounting framework and a medium of exchange. The main goal of this work is to predict Bitcoin price. To address the drawbacks of traditional forecasting techniques, we use a variety of machine learning, deep learning, and ensemble learning algorithms. We conduct a performance analysis of Auto-Regressive Integrated Moving Averages (ARIMA), Long-Short-Term Memory (LSTM), FB-Prophet, XGBoost, and a pair of hybrid formulations, LSTM-GRU and LSTM-1D_CNN. Utilizing historical Bitcoin data from 2012 to 2020, we compared the models with their Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The hybrid LSTM-GRU model outperforms the rest with a Mean Absolute Error (MAE) of 0.464 and a Root Mean Squared Error (RMSE) of 0.323. The finding has significant ramifications for market analysts and investors in digital currencies

Influence of localized surface plasmons on Pauli blocking and optical limiting in graphene under femtosecond pumping

The Pauli blocking limit and optical limiting threshold have been found to be modified following silver-nanoparticle decoration of functionalized hydrogen induced exfoliated graphene. Femtosecond Z-scan experiments have been used to measure the Pauli blocking range, optical limiting threshold, and the third order nonlinear susceptibility (χ(3)) values. The observed results have been explained by modified band structure of graphene in the presence of silver nanoparticles and their localized surface plasmon resonances

Association between unhygienic menstrual management practices and prevalence of lower reproductive tract infections: a hospital-based cross-sectional study in Odisha, India.

BACKGROUND: The extent to which reproductive tract infections (RTIs) are associated with poor menstrual hygiene management (MHM) practices has not been extensively studied. We aimed to determine whether poor menstrual hygiene practices were associated with three common infections of the lower reproductive tract; Bacterial vaginosis (BV), Candida, and Trichomonas vaginalis (TV). METHODS: Non-pregnant women of reproductive age (18-45 years) and attending one of two hospitals in Odisha, India, between April 2015 and February 2016 were recruited for the study. A standardized questionnaire was used to collect information on: MHM practices, clinical symptoms for the three infections, and socio-economic and demographic information. Specimens from posterior vaginal fornix were collected using swabs for diagnosis of BV, Candida and TV infection. RESULTS: A total of 558 women were recruited for the study of whom 62.4% were diagnosed with at least one of the three tested infections and 52% presented with one or more RTI symptoms. BV was the most prevalent infection (41%), followed by Candida infection (34%) and TV infection (5.6%). After adjustment for potentially confounding factors, women diagnosed with Candida infection were more likely to use reusable absorbent material (aPRR = 1.54, 95%CI 1.2-2.0) and practice lower frequency of personal washing (aPRR = 1.34, 95%CI 1.07-1.7). Women with BV were more likely to practice personal washing less frequently (aPRR = 1.25, 95%CI 1.0-1.5), change absorbent material outside a toilet facility (aPRR = 1.21, 95%CI 1.0-1.48) whilst a higher frequency of absorbent material changing was protective (aPRR = 0.56, 95%CI 0.4-0.75). No studied factors were found to be associated with TV infection. In addition, among women reusing absorbent material, Candida but not BV or TV - infection was more frequent who dried their pads inside their houses and who stored the cloth hidden in the toilet compartment. CONCLUSION: The results of our study add to growing number of studies which demonstrate a strong and consistent association between poor menstrual hygiene practices and higher prevalence of lower RTIs

Directed Self Assembly Of Copper-Based Hierarchical Nanostructures on Nitrogen-Doped Graphene and Their Field Emission Studies

We report a large scale root to assemble hierarchical copper-based nanostructures on nitrogen-doped graphene sheets by a pH followed by a temperature-directed self-assembly process and their electron field emission studies. Starting with a controlled pH directed self-assembly root, we assembled Cu­(OH)₂ NRs on NGS and further reassembled it to 1D Cu NPAs and CuO NRs by thermal annealing in H₂ and Ar atmosphere, respectively. The field emission characteristics are precisely studied, which depicts a significantly lower threshold and turn-on field for 1D Cu NPAs-NGS-based emitter compared to CuO NRs-NGS and Cu­(OH)₂ NRs-NGS. Also it exhibited about 3 and 27 times higher emission current density than its oxide and hydroxide counterpart under a moderate field of 1 V/μm. The enhanced field emission behavior of 1D Cu NPAs-NGS is attributed to the low work function, the easy electron tunneling from the one-dimensional arrangement of Cu NPs, which increases the emission sites and hence the FE current density. On the basis of easy large-scale synthesis techniques and better FE performances, these hierarchical nanostructures offer prospects for understanding the effect of linear arrangement of nanoparticles on FE, which can be envisioned for design, fabrication, and optimization of cold cathode devices

Electro‐oxidation of Titanium Carbide Nanoparticles in Aqueous Acid Creates TiC@TiO2 Core‐Shell Structures

Titanium carbide (TiC) is an attractive support material used in electro‐catalysis and sensing. We report the electrochemistry of TiC nanoparticles (NPs, 35–50 nm in diameter) in different electrolytes in the pH range of 0 to 8. The TiC NPs undergo irreversible oxidation in acidic, basic, and neutral media, attributed to the partial conversion into titanium dioxide (TiO2) with the amount of oxidation highly dependent on the pH of the solution. In H2SO4 (pH 0), multiple voltammetric scans revealed the conversion to be partial but repeated scans allowed a conversion approaching 100 % to be obtained with 20 scans generating a ca 60 % level of oxidation. The process is inferred to lead to the formation of TiC@TiO2 core‐shell nanoparticles (∼12.5 nm core radius and ∼5 nm shell width for a 60 % conversion) and this value sharply decreases with an increase of pH. Independent measurements were conducted at a single NP level (via nano‐impact experiments) to confirm the oxidation of the NPs, showing consistent agreement with the bulk measurements

Enhanced Electron Field Emission of One-Dimensional Highly Protruded Graphene Wrapped Carbon Nanotube Composites

We report the enhanced field emission studies of one-dimensional highly wrinkled graphene wrapped carbon nanotubes measured at a base pressure of 10^–6 mbar. The combined advantage of high aspect ratio and protruded graphene layers on CNT surface are envisioned to improve the field emission current density. Furthermore, the emission characteristics are precisely studied by decorating metal/metal oxide (M/MO like Ru, ZnO, and SnO₂) nanoparticles over GWCNTs. It was found that the incorporation of M/MO NPs lowers the work function, which leads to easy electron tunneling and considerably improves the FE performance. The results depict the best FE performance for Ru-GWCNTs based emitters, with a turn-on field of 0.61 V/μm, a current density of 2.5 mA/cm² at a field of 1 V/μm, and a field enhancement factor of 6958. The enhanced FE behavior of GWCNTs based emitters is attributed to the easy electron tunneling from the protrusions created on CNT surface which increases the emission sites and hence the FE current density. In addition, the surface decorated M/MO NPs could lower the work function, which contributes to local field enhancement, and hence the low turn-on field. This high performance results for GWCNTs based field emitters are potentially useful for design, fabrication, and optimization of field emission devices

Single-entity Ti3C2Tx MXene electro-oxidation

We quantify the innate electro-oxidation behavior of a single Ti3C2Tx MXene particle by Single-Entity Electrochemistry. MXene undergoes irreversible oxidation at potentials greater than 0.3 V (vs. SCE), with the extent of oxidation dependant on the applied potential. A close correlation is seen with ensembles of drop-cast particles with submonolayer coverage