Phoenix Systems (United States)

Telemarketing, while often perceived as intrusive, remains a potent tool for banks to acquire new customers, cross-sell products, and deepen customer relationships. However, the old-style, unsystematic telemarketing continues to record low response rates and is damaging to a brand image. This is where machine learning comes into play in the picture. The collected customer data blended with the tools of the predictive analysis provide the opportunity to drastically enhance the telemarketing campaign results, focusing on the right customer at the right time with the right products. In this research, the following classification models are employed with the intention of evaluating their predictive accuracy in the context of customer response to telemarketing: XGBoost (XGB), Gradient Boosting (GB), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbors (KNN). By assessment of classification reports, confusion matrices, and ROC curves, it was concluded that the XGBoost model has the highest accuracy, precision, recall, and F1-score. Gradient boosting also identified good results, but logistic regression gave a more balanced approach though less accurate. As it will be recalled, the decision tree, while considered adequate, was observed to overtrain while K-Nearest Neighbors faced severe problems with misclassification where the minority class was involved. These results also indicate the importance of model selection in addressing the challenges associated with telemarketing data and for making the best strategies for allocating resources for the best performing model, which is XGBoost, when it comes to improving predictive precision and customer targeting in telemarketing.

Accurate crop yield prediction is pivotal in precision agriculture, enabling optimized resource allocation and enhanced decision-making for farmers. Despite significant advancements in machine learning (ML) models, existing approaches often lack interpretability and insufficient integration of diverse environmental data. This study fills in these gaps by creating a strong crop recommendation system using a random forest classifier (RFC). It achieves this by utilizing the RFC’s ensemble nature to improve prediction accuracy and understanding of which features are most important. The proposed model works with a dataset that includes environmental factors and soil nutrients. It does this by using the interquartile range (IQR) method to find outliers and logarithmic transformations to fix skewed data. The RFC’s capability to manage high-dimensional data and prevent overfitting is validated through rigorous evaluation, achieving an impressive accuracy of 99.13% on the testset. This performance surpasses other non-linear models, such as Gaussian Naive Bayes (GNB) and XGBoost, which attained accuracies of 99.2% and 98.5%, respectively. The model’s high accuracy and interpretability offer a practical real-time crop recommendation tool, facilitating sustainable agricultural practices. This research contributes to the field of crop yield prediction and sets a precedent for integrating ML with practical agricultural applications.

Two existing Ada tools AdaSAGE and AYACC were combined to produce a system that parses International Society for Analytical Cytology, ISAC, Flow Cytometry Standard 2.0 files and stores the data in AdaSAGE tables. There are significant differences in the way manufacturers interpret and conform to Flow Cytometry Standard 2.0. AdaSAGE is employed to analyze and plot the data from multiple experiments. This data is used to assess the stability of flow cytometers. The initial release will be for DOS. The utilization of AdaSAGE, which is a flexible database tool, will facilitate subsequent development of other products. The software engineer, whose previous professional experience was with C and C++, had very few problems with Ada syntax. The interface to the compiler and other tools was immature compared to those available for C++. The DOS text based user interface environment provided by AdaSAGE limited the functionality of the user interface. However, the present DOS 386 program can be directly ported to the newly released version of AdaSAGE for Microsoft Windows 95. Ada's strong type checking and package structure have significantly facilitated the development of the product.

The sperm chromatin structure assay (SCSA) measures the susceptibility of sperm nuclear DNA to acid-induced denaturation in situ, and was developed on two Ortho flow cytometers, an FC200 [Becton Dickinson Immunocytometry Systems (BDIS), Westwood, MA] and a Cytofluorograf 30 (BDIS), both having orthogonal axes of fluorochrome excitation, emission, and sample flow. Sperm cells are first treated with a pH 1.4 buffer to denature DNA in situ and then stained with the metachromatic dye acridine orange (AO). The metachromatic fluorescence measured reflects relative amounts of denatured (red fluorescence) and native (green fluorescence) DNA present per cell. The extent of DNA denaturation is quantified by the calculated parameter alpha t [alpha t = red/(red+green) fluorescence]. Alpha t variables important for correlations with fertility and toxicant-induced chromatin damage include mean (X alpha t), standard deviation (SD alpha t), and cells outside the main population (COMP alpha t). Mean green fluorescence intensity is an important measure for DNA content and/or degree of sperm chromatin condensation. This study showed that the SCSA can be successfully run on two epiillumination-type instruments, an Ortho ICP22A (BDIS, San Jose, CA) and Skatron Argus (Tranby, Norway), and two additional orthogonal axes instruments, a Becton Dickinson FACScan (BDIS) and a Coulter Elite (Coulter Corporation, Hialeah, FL). Epiillumination instruments produced a different fluorescence distribution than orthogonal instruments, but the resulting alpha t values showed strong conformity and interpretation of results was the same. SCSA values obtained on the Coulter Elite were most similar to the Cytofluorograf 30; the FACScan green fluorescence distribution was narrower and allowed resolution of cell doublets.(ABSTRACT TRUNCATED AT 250 WORDS)

Abstract Ceramic analysis with an electron microprobe and thin-section petrography are used to investigate prehistoric exchange among the Hohokam of the Salt River valley, Arizona. These complimentary techniques can be useful for resolving the special problems that arise when studying ceramic exchange over short distances.

Book reviewed in this article: Archeology: Small Site Architecture of Chaco Canyon, New Mexico . Peter J. McKenna and Marcia L. Truell. Archeology: Great Pueblo Architecture of Chaco Canyon, New Mexico . Stephen H. Lekson.

The aim of this paper is to develop a top-down design automation tool for digital system design such as microprogrammed processors. The package contains a hardware description language to specify the design, a microcode development module to generate an efficient microprogam for the microprogrammed processor's control, and a functional simulator module to verify the validity of the design. The goal of this project is to develop an interactive computer-aided design environment for specification, design and verification of instruction set processors.

The control of time-dependent temperature distribution of a dynamic high temperature IR target critically affects its radiance distribution. Two methods of controlling the target temperature through appropriate power generation and deposition are discussed. The application of the thermal analysis to a resolved, unresolved, and striped high temperature dynamic target is shown.