International Journal of Computations, Information and Manufacturing (IJCIM)

Stimulating E-Business Capabilities and Digital Marketing Strategies on Business Performance in E-Commerce Industry

Federico Del Giorgio Solfa — 2023-12-22

This study investigates how e-business capabilities and digital marketing strategies jointly influence business performance in the e-commerce industry, which has experienced unprecedented growth driven by technological advancements and changing consumer behavior. E-business capabilities encompass the use of technology and digital infrastructure, while digital marketing strategies are employed to attract and retain online customers. The study examines the effect of e-business capabilities through digital marketing strategies on the customer satisfaction and loyalty of UAE e-commerce industry. The research is descriptive and explanatory, and uses a structured 5-scale Likert questionnaire to collect data from the HR managers of 135 e-commerce companies based in UAE. The statistical analysis is performed using structural equation modeling. The findings highlight the significant relationship of the study variables. Consequently, the results of this study provide valuable insights for e-commerce businesses, elucidating the need to invest in robust e-business capabilities to support effective digital marketing efforts. Additionally, the research underscores the significance of tailoring digital marketing strategies to align with specific business objectives and customer segments. The study also contributes to the literature on digital transformation, by demonstrating how e-business capabilities and digital marketing strategies can enable e-commerce firms to adapt and thrive in the dynamic and competitive digital environment.

Utilizing Machine Learning for Predicting Software Faults Through Selenium Testing Tool

Ghada Alsuwailem — 2023-12-22

Software quality assurance, especially in the context of the testing phase, plays a pivotal role in ensuring the reliability and functionality of software systems. Automation testing is recognized as a valuable technique to enhance test coverage and accuracy. However, challenges such as diverse automation tools and unrealistic expectations can hold up its effectiveness. This research explores the integration of machine learning into the Selenium automation testing tool to predict faults based on UI and historical scenarios. The study aims to investigate the impact of machine learning on perceived task difficulty and time required for fault prediction during software testing. The literature review emphasizes the importance of software testing, automation testing, and the Selenium tool. The research methodology employs a mixed-methods approach, combining quantitative and qualitative analyses. The results show positive perceptions regarding the clarity of implementing machine learning-based Selenium but mixed opinions on the ease of implementation. The ML-based Selenium tool demonstrates increased effectiveness, reliability, and reduced testing duration. Interviews highlight the complementary roles of manual and automated testing. The discussion addresses improved test effectiveness, reliability, challenges, and future considerations, affirming the viability and advantages of incorporating machine learning into the Selenium framework for automation testing.

Rainfall Prediction using Big Data Analytics: A Systematic Literature Review

Mahwish Anwer Cheema — 2023-12-22

With major ramifications for agriculture, water resource management, and disaster planning, rainfall prediction is an essential component of weather forecasting. The use of big data analytics techniques has become more commonplace in recent years as a means of improving rainfall prediction models' accuracy and dependability. The goal of this systematic literature review is to present a thorough summary of the state of the art in the field of big data analytics-based rainfall prediction research. The first section of this paper provides a thorough examination of the basic ideas and procedures used in rainfall prediction models. It emphasises how crucial it is to incorporate data from a variety of sources into predictive models, such as social media, meteorological, and remote sensing data. This complete overview of the state-of-the-art in big data analytics-based rainfall prediction is provided by this systematic literature review. It highlights the need for multidisciplinary collaboration between meteorologists, data scientists, and domain specialists to further develop the subject of rainfall prediction and its applications. It also identifies gaps in the current research and recommends prospective directions for future studies.

Role of Feature Selection in Cross Project Software Defect Prediction- A Review

Muhammad Salman Saeed — 2023-12-22

Software Defect Prediction (SDP) is crucial for enhancing software quality and minimizing issues after release. The advent of machine learning, particularly in Cross-Project Defect Prediction (CPDP), has garnered significant attention for its potential to enhance defect predictions in one project by leveraging information from another. A critical factor influencing CPDP effectiveness is feature selection, the process of identifying the most relevant features from an available set. This review article thoroughly examines the role of feature selection in CPDP. Existing feature selection methods are systematically analyzed and classified within the CPDP context, encompassing both traditional and state-of-the-art approaches. The review delves into the challenges and opportunities presented by diverse project characteristics, data heterogeneity, and the curse of dimensionality. Additionally, the article underscores how feature selection impacts model performance, generalization, and adaptability across various software projects. Through synthesizing findings from multiple studies, trends, best practices, and potential research directions in the field are identified. In conclusion, this review article provides valuable insights into the significance of feature selection for enhancing the reliability and efficiency of CPDP models.

Software Defect Prediction Using Clustering: A Comprehensive Literature Review

Amna Batool — 2023-12-22

Anticipating software defects prior to the testing phase proves advantageous for efficient resource allocation to develop the high-quality software, a necessity for any organization. Machine learning (ML) methodologies play a pivotal role in addressing these issues, leading to the creation of numerous predictive models designed to categorize software modules as either defective or non-defective. Several obstacles hinder the analysis of software data that is defected, encompassing issues like redundancy, correlation, irrelevant features, missing data points, and an unbalance distribution between faulty and non-faulty classes. Both supervised and unsupervised machine learning techniques have garnered global attention from practitioners and researchers as viable approaches to tackle these challenges, yielding noticeable enhancements in defect prediction accuracy. This review paper examines clustering unsupervised machine learning technique developed for software defect prediction spanning the years 2017 to 2023 and covered the 15 researches.

Optimizing Algorithm Efficiency through Advanced Data Structures in C++: A Comparative Analysis of Performance, Scalability, and Complexity

Phool Fatima — 2023-12-31

The main objective of this research is to improve the efficiency of algorithms in C++ by utilizing data structures. It investigates how these structures impact the performance, scalability and complexity of algorithms. The study involves an examination that includes reviewing existing literature and implementing algorithms using various data structures. By exploring the effects of data structures, on execution speed, memory usage and scalability in software applications valuable insights are gained. These insights contribute significantly to optimizing algorithms in C++ and making decisions, about selecting data structures to enhance software performance and effectively manage complexity.