Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). We also constructed ensemble models such as bagging (Random Forest) and boosting like AdaBoost and Extreme Gradient Boosting (XGBoost) to evaluate the performance of prediction models. The proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the disease. The combined ML models used majority voting to select the best outcomes among the models. The efficacy of the proposed and other models was evaluated for effective diabetes prediction. The investigation concluded that the proposed model, after hyperparameter tuning, outperformed other learning models with an accuracy of 84.42%, a precision of 85.12%, a recall rate of 65.40%, and a specificity of 94.11%.     

Bimetallic Nanoalloy Catalysts for Green Energy Production: Advances in Synthesis Routes and Characterization Techniques

Bimetallic Nanoalloy catalysts have diverse uses in clean energy, sensing, catalysis, biomedicine, and energy storage, with some supported and unsupported catalysts. Conventional synthetic methods for producing bimetallic alloy nanoparticles often produce unalloyed and bulky particles that do not exhibit desired characteristics. Alloys, when prepared with advanced nanoscale methods, give higher surface area, activity, and selectivity than individual metals due to changes in their electronic properties and reduced size. This review demonstrates the synthesis methods and principles to produce and characterize highly dispersed, well-alloyed bimetallic nanoalloy particles in relatively simple, effective, and generalized approaches and the overall existence of conventional synthetic methods with modifications to prepare bimetallic alloy catalysts. The basic concepts and mechanistic understanding are represented with purposely selected examples. Herein, the enthralling properties with widespread applications of nanoalloy catalysts in heterogeneous catalysis are also presented, especially for Hydrogen Evolution Reaction (HER), Oxidation Reduction Reaction (ORR), Oxygen Evolution Reaction (OER), and alcohol oxidation with a particular focus on Pt and Pd-based bimetallic nanoalloys and their numerous fields of applications. The high entropy alloy is described as a complicated subject with an emphasis on laser-based green synthesis of nanoparticles and, in conclusion, the forecasts and contemporary challenges for the controlled synthesis of nanoalloys are addressed.     

Electrical Contacts With 2D Materials: Current Developments and Future Prospects

Current electrical contact models are occasionally insufficient at the nanoscale owing to the wide variations in outcomes between 2D mono and multi-layered and bulk materials that result from their distinctive electrostatics and geometries. Contrarily, devices based on 2D semiconductors present a significant challenge due to the requirement for electrical contact with resistances close to the quantum limit. The next generation of low-power devices is already hindered by the lack of high-quality and low-contact-resistance contacts on 2D materials. The physics and materials science of electrical contact resistance in 2D materials-based nanoelectronics, interface configurations, charge injection mechanisms, and numerical modeling of electrical contacts, as well as the most pressing issues that need to be resolved in the field of research and development, will all be covered in this review.     

A novel hybrid requirement’s prioritization approach based on critical software project factors

Cognition, Technology & Work - Requirements prioritization is essential for development of quality software products. Requirements prioritization helps focus on the most important requirements...