Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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    Using Machine Learning Techniques to Forecast Mehram Company's Sales: A Case Study

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    Sales forecasting, situated at the intersection of art and science, is critical for inspiring managers toward achieving profitable outcomes. Its precision sustains production levels and capital and plays a pivotal role in the company's and its leaders' overall success and career progression. In the context of Mahram Food Industries, the challenge arises from diverse investor perspectives and the impactful nature of numerous variables. To address this, a new sales forecasting algorithm has been introduced to enhance accuracy. The aim is to predict future sales through a comprehensive approach, leveraging technical analysis, time series modeling, machine learning, neural networks, and random forest techniques. The research methodology integrates various advanced techniques to improve sales forecasting for Mahram Food Industries. Technical analysis, time series modeling, machine learning, neural networks, and random forest methods are combined to create a robust framework. The focus is on predicting sales for a future period within the artificial intelligence-based machine learning domain. The study employs metrics such as Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE) to evaluate and compare the performance of the proposed neural network against traditional methods like multiple variable regression and time series modeling. The study's results highlight the superior performance of the neural network in sales forecasting for Mahram Food Industries. The Mean Absolute Deviation (MAD) for the neural network is 28.33, outperforming multiple variable regression (28.54) and time series modeling (29.45). Additionally, the neural network demonstrates a better MAD Percentage (MADP) with a value of 10.2%, surpassing the values associated with multiple variable regression (10.35%) and time series modeling (10.30%). The Mean Squared Error (MSE) further confirms the neural network's superiority with a value of 6452 compared to 6472 and 7865 for multiple variable regression and time series modeling, respectively. In conclusion, the study showcases the effectiveness of integrating advanced techniques, particularly the neural network, in enhancing the accuracy of sales forecasting for Mahram Food Industries. The comprehensive approach, combining technical analysis, time series modeling, machine learning, neural networks, and random forest, is a valuable strategy for predicting future sales. The superior performance of the neural network, as evidenced by lower MAD, MADP, and MSE values, suggests its potential for guiding informed decision-making in goal setting, hiring, budgeting, and other critical aspects of business management

    Credit Risk Prediction Using Explainable AI

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    Despite advancements in machine-learning prediction techniques, the majority of lenders continue to rely on conventional methods for predicting credit defaults, largely due to their lack of transparency and explainability. This reluctance to embrace newer approaches persists as there is a compelling need for credit default prediction models to be explainable. This study introduces credit default prediction models employing several tree-based ensemble methods, with the most effective model, XGBoost, being further utilized to enhance explainability. We implement SHapley Additive exPlanations (SHAP) in ML-based credit scoring models using data from the US-based P2P Lending Platform, Lending Club. Detailed discussions on the results, along with explanations using SHAP values, are also provided. The model explainability generated by Shapely values enables its applicability to a broad spectrum of industry applications

    Unleashing Deep Learning: Transforming E-commerce Profit Prediction with CNNs

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    This research examines the potential of Convolutional Neural Networks (CNNs), including VGG16, ResNet50, and InceptionV3, in predicting ecommerce profits. Emphasizing the importance of high-quality datasets, the study showcases the superior performance of CNN models over traditional algorithms, particularly noting a notable accuracy rate of 92.55% with CNN (VGG16). These results highlight deep learning's capability to extract actionable insights from complex ecommerce data, offering significant opportunities for revenue optimization and operational efficiency improvement. The conclusion underscores the need for investment in infrastructure and expertise for successful CNN integration, alongside ethical and privacy considerations. This research contributes valuable insights to the discourse on deep learning in ecommerce, offering guidance to businesses navigating the competitive global market landscape

    Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective

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    Sales prediction plays a paramount role in the decision-making process for organizations across various industries. Nonetheless, accurately predicting sales is challenging because of trends and seasonality in sales data. The prime objective of this research paper was to explore machine learning methodologies and techniques that can efficiently address seasonality and trend detection in predictive sales forecasting. The research focused on pinpointing suitable features based on correlation coefficients, which were then adopted to train the three different models: random forests, linear regression, and gradient boosting. From the performance evaluation, gradient boosting displayed relatively superior performance compared to the other two regarding R2 score and accuracy. These results highlighted the capability of sales prediction through machine learning, offering vital insights for decision-making processes. The findings of this empirical research provide an extensive guideline for executing machine learning techniques in sales forecasting and addressing seasonality and trend detection, especially when working with large datasets. Furthermore, the study shed light on possible challenges and issues encountered in the process. By resolving these issues, retailers can reinforce the reliability and accuracy of their sales predictions, thereby enhancing their decision-making capabilities in the context of sales management

    The Recognition, Measurement and Disclosure of Biological Assets of Selected Agritourism Farms in Region IV-A, Philippines

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    The accounting standard for agriculture was introduced to harmonize the accounting procedures of entities engaged in agricultural production. This accounting standard for agriculture specifies the accounting treatment for biological assets and their biological transformation as well as how it must be reflected in the financial statement. However, there has been little awareness of applying the standard, especially in the agritourism industry. Agritourism is a young industry in the Philippines and is seen as a profitable revenue-generating investment. Therefore, education on financial management and recordkeeping must be given importance. This study assessed the accounting practices of selected agritourism farms in Region IV-A, Philippines, in accordance with the Accounting Standard for Agriculture, 41. The International Accounting Standard 41 and Philippine Accounting Standard 41 cover the recognition and measurements of biological assets and the presentation and disclosure of biological transformation. Interviews were conducted with 17 farms and their financial records were reviewed. The results show that most farms do not recognize and measure their biological assets per accounting standards. The recognition and measurement of biological assets are based on their current practices and are not influenced by the provision of the standards. Moreover, the type of business registration, i.e., corporation or sole proprietorship, affects the accounting practice of the farm. Their records do not accurately reflect the presentation and disclosure of biological assets and biological transformation. Thus, it is recommended that training, seminars, and workshops on IAS/PAS 41 must be conducted, and an application guideline must be developed to improve the accounting practices of agritourism farms and their compliance with the accounting standard

    Extent of Electronic Gadget Usage in Learning English and Reading Comprehension of Grade Six Pupils

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    In 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

    Advancements in Early Detection of Lung Cancer in Public Health: A Comprehensive Study Utilizing Machine Learning Algorithms and Predictive Models

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    Lung cancer stands as the leading cause of death in the United States, attributed to factors such as the spontaneous growth of malignant tumors in the lungs that can metastasize to other parts of the body, posing severe threats. Notably, smoking emerges as a predominant external factor contributing to lung problems and ultimately leading to lung cancer. Nevertheless, early detection presents a pivotal strategy for preventing this lethal disease. Leveraging machine learning, we aspire to develop robust algorithms capable of predicting lung cancer at its nascent stage. Such a model could prove instrumental in aiding physicians in making informed decisions during the diagnostic process, determining whether a patient necessitates an intensive or standard level of diagnosis. This approach holds the potential to significantly reduce treatment costs, as physicians can tailor the treatment plan based on accurate predictions, thereby avoiding unnecessary and costly interventions. Our goal is to establish a sustainable model that accurately predicts the disease, and our findings reveal that XGBoost outperformed other models, achieving an impressive accuracy level of 96.92%. In comparison, LightGBM, AdaBoost, Logistic Regression, and Support Vector Machine achieved accuracies of 93.50%, 92.32%, 67.41%, and 88.02%, respectively

    Factors Affecting Computer System Maintenance Skills Improvement of Information Technology Students

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    The purpose of this study was to identify the variables that may influence how well students at particular Chinese computer schools are able to maintain their computer systems. It also looked into the types of technology-related leadership behaviors program administrators demonstrated how those behaviors affected and possibly even predicted the various ways that technology was used in schools. Based on the findings, it was determined that the factors that can affect the improvement of information technology students' skills in computer system maintenance were not significantly influenced by time management, test preparation, or reading in terms of sex, monthly family income, or academic performance

    ENG Students’ Attitude towards Peer Review

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    This paper discusses the action research carried out in the ENG 207 course at the American University of Sharjah. The action research was conducted to find out the students’ attitudes toward using external, non-corrected samples in review training sessions and computer-mediated peer review to enhance their experience as reviewers. The findings revealed positive attitudes from the students regarding the training session and computer-mediated peer review; however, some suggestions were provided for improvements

    Coping Mechanisms and Strategies in Overcoming Second Language Speaking Anxiety

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    This research presents a descriptive correlational study of coping mechanisms and strategies for overcoming second language speaking anxiety. This study aims to determine the coping mechanisms and strategies to overcome second language speaking anxiety. Using the random sampling method, the researcher conducted a research survey of 271 bona-fide ISU-CAS students. The questionnaire used is adapted and contextualized from the FLSA questionnaire from He (2013). The gathered data was analyzed using the frequency distribution and mean. It was found in this research study that the positive attitude of the teacher is the most preferred coping mechanism and strategy of the respondents

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