Al-Kindi Center for Research and Development (KCRD) (E-Journals)
Not a member yet
6248 research outputs found
Sort by
The State of Fact-checking in Morocco: from Information Disorder to Information Integrity
No full textThis article examines the emergence of fact-checking in Morocco as a modern journalistic practice in response to a growing proliferation of mis/disinformation across social media platforms in recent years. The article specifically explores the factors contributing to the rise of fact-checking initiatives, assesses the efficacy of fact-checking in mitigating the spread of false and misleading information, and identifies key challenges encountered by practitioners in the field. For the purposes of this study, we have used semi-structured interviews and document analysis as data collection instruments. Based on the data collected, we have concluded that fact-checking has a crucial role to play in combatting mis/disinformation in Morocco, but that contribution could be greater if two conditions are met: 1) the government provides financial support to fact-checking efforts, and 2) schools devote resources to building young people’s media and digital literacy competence
The Role of Vocal Technique in Attracting Audiences in Digital Television and Podcasting
No full textThis study explores the role of vocal technique in enhancing audience engagement across digital television and podcasting platforms. It investigates how elements such as clear pronunciation, varied tone, appropriate speech pace, and distinct voice quality influence listener and viewer attraction. Employing a descriptive research methodology, the study utilizes electronic questionnaires and observational tools to gather data from a sample of digital television and podcast audiences. Key findings reveal that clear pronunciation (Statement 5) is the most critical factor, with a mean score of 4.88 and a low standard deviation (0.34), indicating near-universal agreement on its importance. Distinct voice (Statement 8) follows closely, with a mean of 4.73, as participants valued unique vocal qualities that foster emotional connection. While varied tone (Statement 6) and appropriate speech pace (Statement 7) were highly valued, they showed slightly lower means (4.63 and 4.59, respectively) and higher variability, suggesting context-dependent preferences. The study also highlights the importance of technical sound quality (Statement 9) and voice training (Statement 12) in media success, with high mean scores (4.52 and 4.63, respectively). These findings underscore the need for professional vocal skills and high-quality audio production. Additionally, participants expressed a moderate preference for podcasts due to their sound quality (3.65), while digital TV was favored for its audio-visual diversity (3.48). Recommendations include providing specialized vocal technique training for broadcasters, leveraging modern technology to enhance audio quality, encouraging thorough script preparation, and conducting further research with diverse samples. The study also suggests integrating vocal technique training into academic curricula and raising audience awareness about the importance of vocal quality. Significance of this study lies in its empirical examination of vocal technique in digital media, filling a gap in the literature and offering practical insights for broadcasters. Its contributions to both theory and practice underscore the enduring relevance of vocal skills in modern communication.  
Bankruptcy Prediction for US Businesses: Leveraging Machine Learning for Financial Stability
No full textThe economic ramifications due to the bankruptcy of businesses in the USA are exponentially huge and multi-dimensional. Starting from small businesses to huge and large-scale businesses, all declare bankruptcy every year, leading to massive sacking, reduced consumer confidence, and consequently a trickled effect throughout other sectors of the economy. The prime objective of the present study was to devise and execute machine learning techniques to predict bankruptcy in US businesses effectively. This research project intends to develop an efficient understanding of the factors leading to business failures using algorithms that learn from data. For the present study focusing on bankruptcy prediction, we used several datasets to enhance the quality and reliability of forecasts. The major data sources were financial statements, which include balance sheets, income statements, and cash flow statements, providing quantitative measures that enable analysts to perceive the financial health of a firm through various ratios and indicators. Machine learning model selection for the prediction of bankruptcy is based on the evaluation of various algorithms: Logistic Regression, Random Forest, Gradient, and Boosting. The models were evaluated against a set of overall metrics: accuracy, precision, recall, F1-score, and ROC-AUC. Random Forest and XG-Boost resulted in marginally better scores across all metrics as compared to Logistic Regression. Predictive insights determined from bankruptcy risk models give rise to valuable interpretations for decision-makers. An organization in the USA can, from model prediction analysis, identify firms that show a high risk of going into bankruptcy and thus enable appropriate interventions in time. Machine learning-driven bankruptcy prediction undoubtedly assists in integrating better risk management policies and procedures in financial institutions. Similarly, by using complex algorithms for pattern identification in historical data, an institution will go deeper in identifying patterns constituting distress in companie
The Role of HRM in Shaping Inclusive Cultures: Navigating Cross-Cultural D&I Challenges in U.S. Organizations
No full textThis paper looks at the stewardship duties of Human Resource Management (HRM) in the promotion of diverse workplace cultures across various organizations within the United States of America. In today’s diverse workplace across the world, organizations have wised up to the fact that Diversity and Inclusion (D&I) initiatives are strategic business imperatives for lasting organizational success. This paper looks at how the various HRM processes, including recruitment and selection, onboarding, leadership, and development, as well as performance management, may be used to support D&I efforts, leading to diverse and talented employees being welcomed and supported in organizations. Evaluating a variety of strategic D&I interventions, including unconscious bias training, the role of the mentorship program, and technology-supported monitoring, the paper shows that through HRM it is possible to manage the issues of cross-cultural D&I. On workplace diversity, it describes how organizations can create a culture that will not only increase the overall staff morale but also increase innovation as well as the overall performance of organizations. Moreover, the paper presents case studies that might illustrate how the HRM may become the key driver of successful D&I practices, cultural change, and business impact. The long-term advantages of D&I, which are better retention, creativity and relations with clients, are explained further. As highlighted in this paper, leadership support and the role of HR professionals in policing accountability remain key to the organizational change. This calls for commitment from HRM to ensure that D&I isn’t an afterthought in the business world but a major component that puts into practice organizational social responsibility and excellence. As a closing note, there is an appeal to HR professionals to embrace positive, evidence-based approaches to applied policies that contribute to Diversity and balance organization’s development
Forecasting Energy Consumption Trends with Machine Learning Models for Improved Accuracy and Resource Management in the USA
No full textAccurate prediction of energy consumption patterns is vital for attaining sustainability and efficient economic planning. In the USA energy consumption trends have evolved substantially because of factors such as population growth, technological advancements, and shifts in consumption behaviors. The prime objective of this research project was to develop and evaluate machine learning algorithms with accuracy in predicting trends in America\u27s consumption of energy. By employing complex methodologies such as neural networks, regression analysis, and ensemble approaches, this work aims to enhance the accuracy of forecasts in terms of demand for energy in residential, commercial, and industrial sectors. The U.S. consumption datasets covered a wide variety of information representing the consumption of electricity, use of fuel, and integration of renewable sources in residential, commercial, and transportation sectors. Most datasets used for analysis are taken from the U.S. Energy Information Administration (EIA) and the Department of Energy (DOE), offering in-depth statistics about production, consumption trends, and price trends. With our dataset consisting of continuous values for consumption of energy and many predictor factors, we compared a variety of machine algorithms, encompassing XG-Boost, Logistic Regression, and Random Forest. The implemented code compared three algorithms – Logistic Regression, Random Forest, and XG-Boost – in terms of performance via calculation and visualization of key evaluation metrics. It devised a function calculate-metrics to calculate accuracy, precision, recall, and F1-score for a prediction of each model over a test set. Retrospectively, comparing accuracy values, one can observe that Logistic Regression got a high score, with Random Forest following closely, and XG-Boost following a little behind them. Overall, through strategic plots, the comparative strengths and weaknesses of all three algorithms were seen, proving that Logistic Regression was the most reliable algorithm for predicting values in a dataset. Utility companies can benefit a lot through machine learning (ML)-based prediction in terms of distribution efficiency and overall operational efficiency. With ML algorithms, utility companies can utilize humungous volumes of consumption in the past to make future demand predictions with high accuracy.
The Influence of Google Analytics on E-commerce: Enhancing Customer Insights and Business Performance
No full textThis report is prepared for the purpose of sharing google analytics knowledge by incorporating a real time report from an online shopping website called google merchandising store. We can get real time data and analyze that data to get insights before taking a decision. An online store called the Google Merchandise Store offers products bearing the Google logo. The ecommerce site hosts both Universal Analytics and Google Analytics 4 properties. The web address for google merchandise store is https://shop.googlemerchandisestore.com/ The main objective of this project is to get used to and analyze any ecommerce website, here we took one example i.e., google merchandising store. The reason I choose this website is that google has kept the data specifically for this website free to access. The integration between this website and google analytics were already set up by default. After studying this report, we would be able to understand how google analytics work, how reports are being prepared, the importance of google analytics, what are the web analytics, how these web analytics are tracked and lastly the benefits of web analytics
The Dynamism of the Structure and Mechanism of the Alternative Dispute Resolution (ADR) System in Bangladesh for Dispute Resolution Outside the Formal Courts
No full textThis study aimed to investigate the dynamic nature of this hybrid system to serve society in contemporary Bangladesh\u27s changing circumstances effectively. Through a content analysis of documents and literature, it was found that the system is quite dynamic and effective in terms of structure and mechanism. Over the years, it has expanded in terms of scope and institutions to cover resolutions of conflicts relating to not only family affairs but civil disputes and even selected criminal cases. It is now compulsory for civil disputes to be addressed first through ADR. The system is gaining popularity in the country. The various ADR institutions entertain a large and increasing number of disputes of both natures every year. In most cases, the rate of success in resolving the cases submitted to these institutions is as high as 88%. However, handling civil disputes through ADR has yet to become popular. The record shows that in four districts, only 2.2% of civil suits were disposed of through ADR during 2012 and 2014. It suggests that adequate efforts need to be made to make ADR popular in this respect as well
The Curriculum Ideological and Political Reform of Cross-Border E-commerce Major in Higher Vocational Colleges Helps Chinese-Style Modernization
No full textThe "Chinese-style modernization" and "high-quality development" are the key themes of China\u27s current social and economic reforms. This development requires higher vocational colleges to proactively serve national strategic needs and provide high-quality, morally and professionally competent talents that are in line with the requirements of Chinese-style modernization. This article explores the compatibility and mutual promotion among the ideological and political education in the cross-border e-commerce major of higher vocational colleges, the high-quality development of higher vocational education, and the Chinese-style modernization of related industries. By analyzing the theoretical basis of ideological and political education in cross-border e-commerce courses and its role in value guidance, cultural dissemination, and innovative practice for relevant talents, the article holds that the reform of ideological and political education in cross-border e-commerce courses, by integrating ideological and political education into the entire teaching process of the major, significantly enhances the ideological quality and professional skills of the talents cultivated and delivered to related industries. Finally, the paper also proposes the logic and path for curriculum ideological and political reform of cross-border e-commerce major in higher vocational colleges to facilitate the transformation and upgrading of the cross-border e-commerce industry and serve Chinese-style modernization
Can ESL Students Identify Emphatic Features of Advertisements?
No full textThe present study aimed at investigating ESL students’ ability to comprehend and identify emphatic structures in advertisements, to find out the emphatic features that are easy to identify, and those that are difficult to identify. Sixty ESL college students in the fifth semester of the translation program who were enrolled in a Stylistics course took a test which consisted of a Mazda advertisement. The students were asked to identify the emphatic features of the Mazda advertisement and give two examples to illustrate the feature they give. Analysis of the subjects’ correct responses showed that the emphatic structures that the students identified correctly are: Balanced sentence structure (53%), repeating key words (53%), arranging ideas in the order of climax, i.e. order of importance with the strongest idea last (45%), using active voice (33%), changing sentence length abruptly (33%), placing important words at the end of the sentence (32%), using periodic sentences (30%), placing emphatic words after a colon or a dash (27.5%), putting a word or phrase out of its usual order (23%) and identifying intensifiers, extraposition, exclamatory sentences, using anticipatory ‘it’, and changing sentence types together (20%). The emphatic structures in the advertisement proved to be difficult for the students to recognize because the advertisement draws an analogy between Mozart\u27s musical genius and the Mazda car-making philosophy, emphasizing how both creations are driven by emotion, vision, and craftsmanship. The advertisement employs multiple emphatic techniques simultaneously, which may have overwhelmed the students and constituted a cognitive load. The Mazda advertisement does not just inform, rather it immerses the reader in an emotional experience through syntactic and lexical emphasis. Instead of focusing on emphatic structures, the students were probably more engaged in decoding the poetic message and understanding the content rather than analyzing how structural elements shape emphasis. In other words, the analogy between Mozart’s music and the Mazda car, made the students less attentive to structural manipulations. Further causes of advertisement comprehension problems and recommendations for instructional techniques that would help enhance the students’ ability to comprehend and identify emphatic structures in genre-specific texts are given.  
AI Translation of Full-Text Arabic Research Articles: The Case of Educational Polysemes
No full textDue to the latest technological advancements, AI tools, assistants and chatbots have been used to perform tasks in a variety of domains, including translation. Some researchers and graduate students use ChatGPT, Google Translate, QuillBot, Smartling, and DeepL to translate research articles from Arabic to English for their theses or assignments as AI saves them time and effort. Although AI translation of full texts sounds natural uses good style and sentence structure, there are still contextual and semantic inaccuracies. There is insufficient research on the quality of AI translation of full-text articles from Arabic to English. Therefore, this study explores the problems that AI has in translating polysemes in research articles from Arabic to English. Mistranslated Arabic polysemes in full-text education articles were identified. Data analysis showed that AI has difficulty translating polysemes that have general and specialized meanings and two or more English equivalents (i.e., one to many), such as صدق which has the general equivalent “honesty” and the technical equivalent “validity” used in research; والمحكمون التحكيم are used in legal, sports and research contexts, but AI gave the equivalent used in legal contexts not the one used in an educational contexts. It gave “arbitration” & “arbitrators” rather than “peer reviewing” & “reviewers”. AI translated المنهج المحوري to “axial” instead of “spiral” curriculum. رسالة has 4 meanings in Arabic with 4 English equivalents (thesis, message, mission & letter) depending on the context. Most occurrences of رسالة were translated into “message”, rather than “thesis”. تصورات was translated into “visions” not “models”, خطة > “plan” not “proposal”, لجنة المناقشة > “discussion committee” not “defense committee”. Further mistranslations were given to junior and senior high school grades, الدليل الإرشادي, المادة العلمية, العبء التدريسي and others. It was noted that AI tends to give literal, not conceptual, equivalents to Arabic terms and those used in a particular domain. The study recommends that researchers use AI translation with caution, post-editing the translation, and using the technical terms commonly used in education. Results, causes of AI mistranslations and recommendations for improvement are given.