Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Board Size, Board Independence, Board Expertise and the Financial Performance of Listed Manufacturing Firms in Ghana: Does Board Commitment Play a Role?
No full textThis study focused on examining how board size, board independence, and board expertise relate to the financial performance of manufacturing companies listed on the Ghana Stock Exchange, considering the moderating role of board commitment. The study population had to do with the listed manufacturing firms in Ghana. This study employed a quantitative research method along with a causal research design. Secondary data (panel) were gathered from the annual financial reports of seven listed manufacturing companies from 2010 to 2022. It was discovered that board size has an insignificant effect on the financial performance (return on asset and return on equity) of listed manufacturing companies in Ghana. Board independence and expertise positively and significantly affect the financial performance of listed manufacturing firms in Ghana. This study found a positive but insignificant moderating effect of board commitment on board size and return on asset nexus. However, board commitment positively and significantly affects board independence and return on asset nexus, board expertise and return on asset nexus, board size and return on equity nexus, board independence and return on equity nexus, and board expertise and return on equity nexus. This study is the first to examine the moderating effect of board commitment on how board size, independence, and expertise relate to the financial performance of listed manufacturing companies. Aligned with the findings, we recommend that the management of listed manufacturing companies implement effective measures to improve the independence, expertise, and commitment of the board of directors
Novel AI-Powered Dynamic Inventory Management Algorithm in the USA: Machine Learning Dimension
No full textDynamic inventory management revolves around the practice of progressively modifying inventory degrees to adapt to fluctuations in client demand, production, and supply chain dynamics. At the center, inventory management focuses on upholding enhanced levels of stock to balance consumer service via availability with the costs related to holding excess inventory. This research paper aimed to explore the dynamic inventory management activities employed by organizations in the USA, shedding light on the machine learning strategies that can be deployed and their implications. The performance of the algorithms was empirically evaluated in a Python program experiment utilizing real-world data. To facilitate the data for input into the Neural Network, feature engineering, and selection were imposed to affirm its suitability. This study proposes the Sequence-to-Sequence (Seq2Quant) algorithm, a neural network-powered technique for demand prediction in inventory management. The current experiment compared and contrasted the performance of the Neural Networks against the following baselines, most notably, Naïve Seasonal Forecast, Moving Average Forecast, ARIMA, Naïve Seasonal Forecast with Averaging over four periods, SARIMAX. From the experiment, it was evident that the Seq2Seq had the lowest MAE (17.44) and the lowest SMAPE (66.91), suggesting that it was the best-performing algorithm overall. Besides, SARIMAX and ARIMAX also performed well, with MAE values of 18.33 and 18.09, respectively
Extent of Electronic Gadget Usage in Learning English and Reading Comprehension of Grade Six Pupils
No full textIn an age where digital technology is deeply ingrained in our lives, children are becoming more immersed in electronic gadgets, often at the expense of traditional reading. Thus, this study sought to determine and understand how the proliferation of electronic gadget usage of the Grade 6 pupils of Loon South Central Elementary School impacts the level of their reading comprehension. With gadgets becoming a ubiquitous presence in their lives, this study aims to determine the significant relationship between the extent of electronic gadget usage of the respondents and the respondent’s level of reading comprehension in applied, literal and interpretive category. The descriptive survey research design was used within the study. Findings revealed that there is no significant relationship between the extent of electronic gadget usage and level of reading comprehension. This means that the amount of time children spent in using electronic gadgets does not affect the level of their reading comprehension. Thus, students are capable of maintaining their comprehension skills, regardless of their screen time
A Case Study of Implementation Strategy for Performance Optimization in Distributed Cluster System
No full textNowadays, many people spend their time on the Internet, and the number of people subscribed to mobile phones is 69.4% of the 5.61 billion population in the world. To handle this situation, we need to implement a high-performance Distributed Cluster System (DCS) in the correct architecture as well. We separated the cluster for each purpose and gave it a unique VLAN. This study uses a mix of methodologies between case study and system development with evaluation after implementation. We observe all aspects of built-in technologies. In this research, monolith spikes us for performance issues, and also, the infrastructure is messy implemented. Event Based System (EBS) helps DCS to absorb high processing tasks in peak situations. EBS can easily lose a couple as needed. Labeling the incoming data assists us in managing inconsistent distributed data in the environment. Our research was evaluated for two weeks. The result is very pleasant, and the requirements in this research were satisfied
Predictive Analytics for Customer Retention: Machine Learning Models to Analyze and Mitigate Churn in E-Commerce Platforms
No full textThe competitive e-commerce business environment in the USA now identifies customer retention as the critical factor in deciding long-term business achievement. Research shows that an organization reaps more benefits by retaining existing customers rather than spending money on customer acquisition. The main purpose of this research project was to develop highly precise machine learning algorithms that detect customers prone to leaving the company using multiple behavioral patterns combined with transaction histories and demographics. The dataset assembled for this analysis included a broad range of characteristics that reflect both static and dynamic facets of customer behavior in the online store. User attributes like age, gender, location, and account signup date give essential context regarding the profile of the customers. Adding depth to this are rich purchase behavior measures, such as frequency of purchase, basket size, overall spending, accepted methods of payment, and usage patterns for discounts. Order history is carefully documented, including the quantity of completed, canceled, and returned orders, and the time since the last orders. Top-level product category preferences are also monitored to discern preferences for types of merchandise (e.g., electronics, clothing, home, and garden), providing greater insight into changing interests. We used three very different models to best tackle the issues of churn prediction for customers. To ascertain the strength of our models, we adopted a systematic strategy for training and testing the models. XG-Boost generally has the best performance overall with the highest scores for all four measures, always above 0.9. Random Forest is second with scores slightly less than for XG-Boost but generally high (above 0.85). Implementing a machine learning-based churn alert system is a major advancement toward enabling customer retention tactics within e-commerce platforms. A churn alert system actively tracks user behavior and activity levels, using predictive algorithms to allocate the risk of churn within near real-time. Predictive analytics for churn is a key factor in safeguarding and forecasting revenue streams for Internet businesses, where even minor fluctuations in customer retention can have disproportionate effects on profitability. To provide richer, more practical insights from churn models, research and development must focus largely in the coming period on the incorporation of richer, more detailed data sources.
Overview of Data Warehouse architecture, Big Data and Green computing
No full textEnterprise today heavily invests in big data, data warehouses, and green computing to measure performance and make intelligent decisions. By enabling data warehouse architecture, enterprises can store structural and nonstructural data in defined backend systems and transform amounts of data to perform various analyses and make business decisions that put companies on top of their competitors. Data is increasing daily, and businesses want to use all their data to perform advanced business analytics and machine learning and distribute data to their backend algorithms to evaluate and make decisions faster. However, the carbon footprints and global warming have alarmed these organizations to move towards green computing for a better future. Green computing focuses on designing energy-efficient systems, optimizing resource utilization, and reducing the CO2 emissions of data centers and IT infrastructure. This paper reviews how data warehouse architecture, big data, and green computing relate and addresses the challenges and opportunities in achieving sustainable and scalable data management solutions. By integrating energy-efficient practices into data warehouses and big data systems, organizations can make a huge difference globally and set an example for other industries to follow the green computing path. This paper helps to understand the new paradigm of big data and green computing, which helps achieve the best performance, reduce environmental impacts, and achieve the best standards
Examining the Relationship between the Stressful Factors of the Work Environment and the Job Performance of Bank Employees: A Case Study of Private and Public Banks of Taloqan
No full textIn this research, the effective study of the stressful factors of the work environment on the job performance of bank employees in Talaqon City has been studied. This research has covered all private and public banks active in Taloqan city of Takhar province. The statistical population of this research includes 50 employees of public and private banks in this city; using Cochran\u27s sample size method, questionnaires were distributed to 46 people who were selected by random cluster sampling. In this research, Standard Nordic questionnaires and Hersey Gold Smith questionnaires were used. Descriptive and inferential statistical methods have been used to analyze the data collected by the questionnaires. Frequency tables, statistical tables, tables and graphs are used as parts of descriptive statistics and normality tests, correlation coefficients and regression models that are used to test the hypotheses of inferential statistics. Statistical Package for Social Sciences (SPSS) is the program used in this research to analyse data. The results obtained from the research show that the stressful factors of the work environment caused the job Performance of the employees of the banks of Taloqan City to decrease significantly, and this result indicates the meaningfulness of the relationship between the stressful factors of the work environment and the job Performance of the employees of the banks of Taloqan city
The Obstacles Facing Educational Security in the Faculties of Education at University of Benghazi from the Perspective of Faculty Members
No full textThis study aimed to identify the obstacles facing educational security in the faculties of education at the University of Benghazi from the perspective of faculty members. To achieve the goals of this study, the authors used the analytical descriptive method; the study sample consisted of (103) faculty members. The authors used the questionnaire as the instrument to collect data, which consisted of (25) statements divided into two parts: (obstacles to the educational process and obstacles to the surrounding environment). The results show that the most significant obstacles facing educational security are (the lack of a clear educational philosophy that highlights the importance and value of educational security, weak interest in the faculty members and their status as the lofty value in society, a decline in the role of the family and its preoccupation with living life, the cultural and value dependency that our society experiences, and the cultural conflict to which it is exposed). The results also confirmed that there is no difference in the responses of the sample members towards the obstacles facing educational security in faculties of education according to the variables of gender and academic qualification of faculty members. The authors applied suitable statistical methods in this study. At the end of the study, the authors came out with many results and stated a number of recommendations
Strengthening Digital Security: Dynamic Attack Detection with LSTM, KNN, and Random Forest
No full textDigital security is an ever-escalating concern in today\u27s interconnected world, necessitating advanced intrusion detection systems. This research focuses on fortifying digital security through the integration of Long Short-Term Memory (LSTM), K-Nearest Neighbors (KNN), and Random Forest for dynamic attack detection. Leveraging a robust dataset, the models were subjected to rigorous evaluation, considering metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The LSTM model exhibited exceptional proficiency in capturing intricate sequential dependencies within network traffic, attaining a commendable accuracy of 99.11%. KNN, with its non-parametric adaptability, demonstrated resilience with a high accuracy of 99.23%. However, the Random Forest model emerged as the standout performer, boasting an accuracy of 99.63% and showcasing exceptional precision, recall, and F1-score metrics. Comparative analyses unveiled nuanced differences, guiding the selection of models based on specific security requirements. The AUC-ROC comparison reinforced the discriminative power of the models, with Random Forest consistently excelling. While all models excelled in true positive predictions, detailed scrutiny of confusion matrices offered insights into areas for refinement. In conclusion, the integration of LSTM, KNN, and Random Forest presents a robust and adaptive approach to dynamic attack detection. This research contributes valuable insights to the evolving landscape of digital security, emphasizing the significance of leveraging advanced machine learning techniques in constructing resilient defenses against cyber adversaries. The findings underscore the need for adaptive security solutions as the cyber threat landscape continues to evolve, with implications for practitioners, researchers, and policymakers in the field of cybersecurity
Prediction of Translation Quality by Risk-Taking and Critical Thinking Among Translation Learners
No full textThe main objective of the present study was to investigate the relationship between Iranian translation learners’ critical thinking (CT), risk-taking (RT), and their English-to-Persian translation quality. To achieve this aim, a group of 120 male and female university students majoring in English translation (ET) were selected based on convenience sampling. Then, two questionnaires, namely CT and RT, were administered to them. Along with the questionnaires, two texts were given to all participants to translate. The quality of translations was assessed based on Farahzad’s (1992) model by two raters. After data collection and analysis, it was revealed that there was a positive and significant relationship between participants’ CT and RT and their translation quality. Moreover, further data analysis showed that RT was a better predictor of translation quality than CT. The findings of this study would be applicable to translation learners, teachers, and translation training courses