How to Generate Store Loyalty? Exploring the Role of Preferential Treatment and Salesperson Trust: Mediating role of Commitment to Salesperson

This article describes how much the importance and impact of preferential treatment by salespersons & customers-salespersons relationship & trust for store loyalty. Providing preferential treatment and customer trust to salesperson can be very useful for managers so that customers can be loyal. This long-term relationship helps retailers gain profits and survive in business. A questionnaire was used to collect data. Most of the data were collected from Faisalabad University students by using convenience sampling, but some of the respondents responsible for different spheres were also included in generalizability. A sufficient number of females have also been included according to the needs of the research. Both preferential treatment and trust in salesperson have a positive impact on the build-up of store loyalty. The results also that trust in the salesperson is more affecting the customer's commitment to the salesperson and thus creates loyal customers.&nbsp

Customer churn prediction using composite deep learning technique

Customer churn, a phenomenon that causes large financial losses when customers leave a business, makes it difficult for modern organizations to retain customers. When dissatisfied customers find their present company\u27s services inadequate, they frequently migrate to another service provider. Machine learning and deep learning (ML/DL) approaches have already been used to successfully identify customer churn. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results for detecting client churn. Previous research on estimating customer churn revealed unexpected forecasts when utilizing machine learning classifiers and traditional feature encoding methodologies. Deep neural networks were also used in these efforts to extract features without taking into account the sequence information. In view of these issues, the current study provides an effective method for predicting customer churn based on a hybrid deep learning model termed BiLSTM-CNN. The goal is to effectively estimate customer churn using benchmark data and increase the churn prediction process\u27s accuracy. The experimental results show that when trained, tested, and validated on the benchmark dataset, the proposed BiLSTM-CNN model attained a remarkable accuracy of 81%

Partial Pair Programming: Link between Solo and Pair Programming

Leaving of key team members can make an unrecoverable loss to an organization. It means knowledge of a task should be in mind of two or more than two programmers. Working of two people on same task on different machine is an awkward practice. So alternatively pair programming practice is the best solution for above problem. Besides many advantages of pair programming, it has certain drawbacks such as personality clashes, and these issues may dominant pair programming over solo programming. Here authors suggested a practice Partial Pair Programming, which will work as bridge between solo and pair programming practices. In partial pair programming, three drivers can make a pair with a navigator of their own group and navigator can make a pair with navigator of other group. This practice will get all advantages of pair programming as well as its own benefits. And also partial pair programming will remove almost all demerits of pair programming

An efficient deep learning technique for facial emotion recognition

Emotion recognition from facial images is considered as a challenging task due to the varying nature of facial expressions. The prior studies on emotion classification from facial images using deep learning models have focused on emotion recognition from facial images but face the issue of performance degradation due to poor selection of layers in the convolutional neural network model.To address this issue, we propose an efficient deep learning technique using a convolutional neural network model for classifying emotions from facial images and detecting age and gender from the facial expressions efficiently. Experimental results show that the proposed model outperformed baseline works by achieving an accuracy of 95.65% for emotion recognition, 98.5% for age recognition, and 99.14% for gender recognition

Refactoring for Multi-Dimensional Reusability

Source code should be simpler, easy to read and easy to understand. This slogan is not only relates to change the existing code for current service, but also has an association with reusability. Refactoring is a best idea for above issues i.e. keeping the code simple and support the emergent design practice. Many refactoring techniques have been produced related to code simplicity and understandability for maintainability & extensibility. Here author enforced to make the method with the division of three sections and each section should have an argument as a signal. Such technique will be the pillar of reusability from many directions

An Efficient Supervised Machine Learning Technique for Forecasting Stock Market Trends

Background/introduction: In recent years, stock market forecasting has received a lot of attention from researchers. This attention and the growing stock market investments have highlighted this as an important and emerging application of machine learning.Methods: In this research work, we present a stock trend forecasting system with a focus on reducing the amount of sparseness in the data collected using machine learning. We conduct an outlier detection of the data available for reducing dimensionality and implement a K-nearest neighbor algorithm to classify stock trends.Results and conclusions: The experimental results show the performance and effectiveness of the proposed trend forecasting system compared to the existing systems. The proposed system’s model (i.e., KNN classifier) gives better results of low error (MSE = 0.00005, MAE = 0.005 and Logcosh = 0.004) on KSE dataset as compared to previous works

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. The experimental results indicate that the CNN Model outperforms the competitors on a number of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases