Data Analysis Applications Research Articles

Artificial intelligence and advanced data analytics: Implications for higher education

AbstractRecently, the rise of generative AI tools such as ChatGPT have prompted deep and wide considerations about teaching and learning, student success, research and development, and the use of data for informed institutional decision making. In this volume, authors discuss specific concepts, considerations for use, and some specific tools and applications of advanced data analytics and artificial intelligence that are gaining prominence in higher education. Although these tools and our perceptions about the tools will continue to evolve, we believe that aspects of advanced data analytics and artificial intelligence will remain in our midst in the future. We believe that members of our community will become better educators and higher education managers as we engage deeply with these tools, stay vigilant about the risks and limitations, and establish policies that ensure student and institution success.Practical Takeaways The soaring interest in aspects of artificial intelligence (AI) prompts consideration of its potential applications, impact, and implications for the higher education community. AI‐supported tools hold the promise to help students learn more efficiently and effectively as well as assist faculty and staff with more mundane tasks, yet getting to the place where GenAI helps will take some effort. Since we must consider data privacy, ethical use, security, and accountability, the path may not be straight and narrow. Analytics through AI tools have a particular promise to support underrepresented and disabled students through focused websites, learning support, and customized tutoring. AI has the potential to narrow the gap in student success, but without focused attention, it could serve to widen the gap across some student groups. We acknowledge that perceptions about and implications due to artificial intelligence in higher education will change as we move forward; the articles in this volume seek to contribute to the current discussion and may prompt considerations for future conversations.

Application of Topological Data Analysis to Elucidate the Mechanical Behavior of Powders

Application of Topological Data Analysis to Elucidate the Mechanical Behavior of Powders

Operational Efficiency, The Risk of Finance and Liquidity Towards The Profitability of Syariah Banking

The background of the research begins in 2020. In that year a large number of clients withdrew massive amount of their money out of the bank. The banks had not fulfilled their obligation to their clients which, in return, caused issues with their liquidity along with other problems pushing them towards the brink of the bankcruptcy. The research employs a quantitative approach. It uses secondary data obtained from annual report on the website issued by the Bank Umum Syariah (General Syariah Bank) as seen on the official site of OJK and the bank site within the period of 2018 and 2022. The applied data analysis of the research is quantitative analysis using eviews statistics -10. The result of the research shows that first, the operatioanl efficiency does not significantly affect the profitability of Syariah Banks. On the other hand finance risks pose a significant influence towards the profitability of Syariah Banks. It can be concluded that the liquidity risk is denied and that it has positive yet insignificant effects to the profitability of Syariah Banks

Design of regression neural network model for estimating the remaining useful life of lithium-ion battery

Lithium-ion batteries are widely utilized in a variety of transportation sectors, including highways, airplanes, and defensive military applications because of their positive features, which include a low self-discharge rate, raised operating voltage, prolonged cycle life, and high energy density. Nevertheless, during operation, the battery undergoes adverse reactions that may eventually cause material aging and capacity deterioration. For this reason, the prediction of remaining useful life (RUL) for LiBs is important and necessary for ensuring reliable system operation. Moreover, accurate RUL prediction can effectively offer maintenance strategies to certify the system’s dependability and safety. The impedance of the battery plays a vital role in the degradation process and hence its measurement accounts for the changes in the battery’s internal parameters as aging occurs. The objective of this paper is to create a forecasting model for the lifespan of batteries through the application of data analysis. Moreover, it aims to use a Regression Neural Network (RNN) based mathematical model to assess the degradation of the battery across diverse operational scenarios. The co-efficient of the RNN model is found by solving the RNN equations. In this research, MATLAB is employed for data analysis, utilizing open-source battery data sourced from the NASA dataset. The conclusive prediction outcomes indicate the effectiveness of the proposed methodology in accurately forecasting the battery RUL.

Research Paper on Exploring the Landscape of Recommendation Systems: A Comparative Analysis of Techniques and Approaches

The field of recommendation systems has witnessed a profound evolution since its inception with Grundy, the first computer-based librarian, in 1979. From its humble beginnings, recommendation systems have become integral to various facets of daily life, particularly in e-commerce, thanks to breakthroughs like Amazon’s Collaborative Filtering in the late 1990s. This led to widespread adoption across diverse sectors, prompting significant research interest and investment, exemplified by Netflix’s renowned recommendation system contest in 2006. Today, recommendation systems employ various techniques such as Hybrid Filtering, Content-Based Filtering, Demographic Filtering, and Collaborative Filtering catering to personalized information needs across industries like entertainment, education, and healthcare. Moreover, emerging types of recommendation systems, including Knowledge-Based, RiskAware, Social-Networking, and Context-Aware, further broaden their applicability, addressing specific user needs and preferences. Leveraging machine learning and AI algorithms on big data, recommendation systems have become a quintessential application of big data analytics, enhancing user experience and engagement in domains like e-learning, tourism, and news dissemination. However, scaling recommendation systems present challenges due to the exponential growth of input data, necessitating strategies like Dimensionality Reduction and cluster-based methods. Integrating multiple recommendation algorithms enhances system complexity, requiring careful consideration of algorithm selection, performance monitoring, and maintenance. Transparency and explanation mechanisms become crucial in complex systems to foster user trust and understanding. Despite challenges, recommendation systems continue to drive innovation, delivering personalized recommendations and enriching user experiences across various domains.

Multiple Correspondence Analysis for Assessment of the Socioeconomic and Social Impact of the COVID-19 Pandemic in Gezira State, Sudan

Background COVID-19 spread in Sudan like other countries in the world. The first COVID-19 case in Sudan was confirmed on 13 March 2020. It has been shown that Sudan's economy was affected before the COVID-19 pandemic because of currency increases, high inflation, and the incapability of the authorities to propose support. Methods The study aimed to assess the economic and social influence of COVID-19 in Al Gazira State, Sudan. This study used the primary data collected from Gezira state in Sudan. A structured questionnaire was used for data collection in 2020-2021, and the sample size was 800 participants. An analysis of multiple correspondences was used to analyze the data concerning COVID-19. The study was validated by ensuring that the survey follows sound research methodologies. This includes clearly defining research questions, using appropriate data collection methods, and applying rigorous analytical techniques. Besides that, it is important to explore the impact of the socioeconomic, demographic, and geographic variables. Results The educational level distribution shows that 36.7% of urban and 26.6% of rural residents have completed secondary education. Furthermore, all participants, 100% in urban regions and 99% in rural areas, were aware of the COVID-19 epidemic. Likewise, all participants in urban areas and 99.5% in rural areas were informed about the lockdowns and measures to curb the spread of COVID-19. Concerning the decision to refrain from attending social gatherings amid COVID-19, 87.6% of participants in urban areas and 75.3% in rural regions opted to cancel such events. Similarly, 86.2% in urban areas and 73.4% in rural areas believe avoiding handshakes is necessary to mitigate the spread of COVID-19. Regarding concerns about job loss during the COVID-19 lockdown period, 52.4% of rural participants did not express anxiety about potential job loss. Conversely, 55.3% of respondents in rural areas were indeed anxious about possibly losing their jobs. The results obtained from the multiple correspondence analysis revealed a relationship between the socioeconomic and demographic variables concerning COVID-19 epidemics. Conclusion This study concluded that socioeconomic, demographic, and geographic variables have a combined influence on the COVID-19 epidemic. One potential technical contribution to the assessment of the economic and social impact of the COVID-19 pandemic could be the application of sophisticated data analysis and modeling techniques to comprehend the complex interrelationships among the numerous components impacted by the pathogen. Predictive models, machine learning, data analytics, simulation studies, and geographic analysis are a few examples of similar techniques.

Big Data Analysis and Machine Learning for Predicting Individual Differences and Learning Paths in Education

This study explores the application of big data analytics and machine learning in identifying individual learner differences and predicting learning paths within the educational sector. As education becomes increasingly digitized, personalized learning has emerged as a key strategy for enhancing educational quality. The rapid advancement of big data analytics and machine learning offers new tools and methods that provide precise identification of individual learner differences and predict learning trajectories. Employing a literature review and theoretical analysis, this study systematically examines the current status of these technologies in education. It focuses on their theoretical foundations and mechanisms for identifying learner differences and predicting learning paths. Through an in-depth analysis of relevant literature, the study summarizes the principal application scenarios and technical methods of these technologies in education and discusses their advantages and challenges in personalized learning. Findings indicate that big data analytics and machine learning can process and analyze massive volumes of educational data to precisely identify learners' styles, interests, and capability differences, thereby providing a scientific basis for personalized teaching. Moreover, these technologies can predict future learning trajectories by analyzing historical learning data, aiding educators in developing more effective teaching strategies. In conclusion, while big data analytics and machine learning hold great potential in education, they also face challenges related to data privacy and technical complexity, necessitating further research and practical exploration.

Big Data Analytics in Distributed Business Management

Large-scale data processing and analysis across several locations in a corporate setting is accomplished through the use of sophisticated analytics techniques, which is known as big data analytics in distributed business management. The data can be spread if it comes from several departments, business divisions, locations, or even outside services. Knowledge, data, and information have always been essential to business. Since there is an expanding quantity of data that may be gathered and stored, businesses require innovative approaches to data processing and evaluation. The notion of ‘Big Data’ is discussed in this article. The study aims to prove that big data analytics is a useful tool for business management. It also shows which tasks and domains may benefit businesses the most from the application of big data analytics.

Tensor Golub–Kahan method based on Einstein product

The Singular Value Decomposition (SVD) of matrices is a widely used tool in scientific computing. In many applications of machine learning, data analysis, signal and image processing, the large datasets are structured into tensors, for which generalizations of SVD have already been introduced, for various types of tensor–tensor products. In this paper, we present innovative methods for approximating this generalization of SVD to tensors in the framework of the Einstein tensor product. These singular elements are called singular values and singular tensors respectively. The proposed method uses the tensor Lanczos bidiagonalization applied to the Einstein product. In most applications, as in the matrix case, the extremal singular values are of special interest. To enhance the approximation of the largest or the smallest singular triplets (singular values and left and right singular tensors), a restarted method based on Ritz augmentation is proposed. Numerical results are proposed to illustrate the effectiveness of the presented method. In addition, applications to video compression and facial recognition are presented.

The rise of intelligent procurement: Embracing AI and data analytics for crossfunctional synergy

This paper delves into the transformative role of procurement in modern corporate structures, especially in North American businesses, highlighting its evolution from a transactional function to a strategic asset within organisations. The core aim is to explore how technological advancements, particularly in data analytics and cognitive artificial intelligence (AI), are redefining procurement’s role, facilitating cross-functional collaboration and driving innovative practices. The scope of this paper encompasses the historical perspective of procurement’s underestimated strategic potential, its current integration into various organisational facets and the profound impact of AI and data analytics in enhancing procurement’s efficacy and strategic value. Readers can expect to gain a comprehensive understanding of the strategic transformation of procurement, particularly how it has moved beyond traditional cost management to become a central player in innovation, risk management and sustainability efforts. The paper provides insights into the practical applications of data analytics and AI in procurement, showcasing how these technologies enable predictive decision making, enhance supplier relationship management and contribute to effective risk assessments and budget forecasting. The paper also underscores procurement’s growing influence in strategic decision making and its critical role in fostering cross-functional partnerships. Readers will also learn about the increasing importance of trust and ethical practices in procurement, reinforcing their position as a vital component of organisational success. Ultimately, the reader will be equipped with the essential knowledge to understand the evolving landscape of procurement, its strategic implications and the potential of AI and data analytics in shaping future procurement practices.

CNN-LSTM for Heartbeat Sound Classification

Cardiovascular disorders are among the primary causes of death. Regularly monitoring the heart is of paramount importance in preventing fatalities arising from heart diseases. Heart disease monitoring encompasses various approaches, including the analysis of heartbeat sounds. The auditory patterns of a heartbeat can serve as indicators of heart health. This study aims to build a new model for categorizing heartbeat sounds based on associated ailments. The Phonocardiogram (PCG) method digitizes and records heartbeat sounds. By converting heartbeat sounds into digital data, researchers are empowered to develop a deep learning model capable of discerning heart defects based on distinct cardiac rhythms. This study proposes the utilization of Mel-frequency cepstral coefficients for feature extraction, leveraging their application in voice data analysis. These extracted features are subsequently employed in a multi-step classification process. The classification process merges a convolutional neural network (CNN) with a long short-term memory network (LSTM), forming a comprehensive deep learning architecture. This architecture is further enhanced through optimization utilizing the Adagrad optimizer. To examine the effectiveness of the proposed method, its classification performance is evaluated using the "Heartbeat Sounds" dataset sourced from Kaggle. Experimental results underscore the effectiveness of the proposed method by comparing it with simple CNN, CNN with vanilla LSTM, and traditional machine learning methods (MLP, SVM, Random Forest, and k-NN).

Clustering algorithm based on DINNSM and its application in gene expression data analysis.

Selecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions. This study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method. A new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson's or Spearman's correlation. It was then used to construct a nearest-neighbor table based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes. Experiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques. DINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules.

Trends of Physics Critical Thinking Skill Research in Indonesia: A Systematic Literature Review

Critical thinking skills are fundamental in 21st-century teaching. This study aims to identify trends in research on critical thinking skills in physics in Indonesia, as reported in journal articles published between 2016 and 2022. This is meta-analysis research using the Prisma model. There were 36 articles that met the selection criteria. We performed the data analysis manually and used VOSviewer software. The indicators used for data analysis include the year, type of research, research subject, topic, treatment, data collection instrument, and the application of data analysis in each article. The analysis of article reviews reveals that the research trend for critical thinking skills in physics in Indonesia has increased, with more publications expected in 2020. Temperature and heat are the physics topics that have become the focus of research. High school students in grade X are the most frequently selected research subjects. Research on physics education typically employs quantitative methods, employs a quasi-experimental design, and utilizes a test as a data collection instrument. The most common method of data analysis in research is the t-test.

Leveraging data analytics for informed product development from conception to launch

This review paper explores the impact of data analytics on guiding product development processes from conception to launch. It synthesizes findings from existing literature to outline how data-driven strategies can optimize each phase of product development, thereby enhancing efficiency and effectiveness in meeting market demands. The review spans various industries, highlighting the universality of data analytics applications in product innovation. The paper details how data analytics facilitates better decision-making through predictive insights into market trends and consumer preferences, which are crucial for defining product specifications and features. It also examines the role of data in refining production processes, ensuring quality control, and customizing marketing strategies to target potential customer segments effectively. Additionally, the review considers the benefits of continuous data evaluation during the product testing phase, enabling quicker adjustments and improvements. The findings indicate that data analytics significantly shortens the product development timeline and increases the likelihood of market success. Organizations leveraging data-driven insights from the outset of product development gain a competitive edge by creating more aligned and responsive products. The paper recommends broader adoption of robust data analytics tools and practices across industries to maximize product development outcomes.

An SME approach to data analytics by management accountants in the transition economy of Vietnam

Purpose Data analytics (DA) is an emerging topic in management science at large organizations; however, accountants in small and medium-sized enterprises (SMEs) are believed to be lagging far behind in the usage of DA. This study aims to provide a deep understanding of the actual DA activities undertaken by management accountants (MA) and to draw on SMEs’ characteristics to take advantage of DA applications. Design/methodology/approach This study used a qualitative approach by conducting in-depth interviews with 31 accounting and finance practitioners at senior levels of SMEs, following the use of the MAXQDA 2022 application for data analysis. Findings Findings in this study suggest variance and trend analysis as the two most popular tasks of DA, and advanced tasks such as contingency analysis, financial modeling, sentiment analysis and regression analysis are unfully performed in SMEs. The outcomes revealed that DA is not anticipated to affect the responsibilities but to expand the role and scope of management accounting. Practical implications This study simultaneously builds a theoretical framework about the antecedents, in terms of the external and internal drives and the characteristics of SMEs’ owner-managers, that are most common in all types of SMEs that encourage MA to use a specific technology, data analysis in a more advanced way instead of searching for determinants that affect the adoption of technology in general, as previous studies have conducted. Originality/value Although there are studies on DA usage, little has approached the mutual interconnections between how DA is applied by MA in SMEs and what changes in management accounting responsibilities by DA affect. Therefore, the point of this study was to look into how SMEs use DA and what activities MA actually do with DA to discover what traits SMEs need to use DA applications effectively as DA applications become more advanced.

A structured iterative division approach for non-sparse regression models and applications in biological data analysis.

In this paper, we focus on the modeling problem of estimating data with non-sparse structures, specifically focusing on biological data that exhibit a high degree of relevant features. Various fields, such as biology and finance, face the challenge of non-sparse estimation. We address the problems using the proposed method, called structured iterative division. Structured iterative division effectively divides data into non-sparse and sparse structures and eliminates numerous irrelevant variables, significantly reducing the error while maintaining computational efficiency. Numerical and theoretical results demonstrate the competitive advantage of the proposed method on a wide range of problems, and the proposed method exhibits excellent statistical performance in numerical comparisons with several existing methods. We apply the proposed algorithm to two biology problems, gene microarray datasets, and chimeric protein datasets, to the prognostic risk of distant metastasis in breast cancer and Alzheimer's disease, respectively. Structured iterative division provides insights into gene identification and selection, and we also provide meaningful results in anticipating cancer risk and identifying key factors.

Influence of the Merchandise Inventory Accounting Information System on Internal Control of Merchandise Inventory

This study aims to examine the extent to which the Merchandise Inventory Accounting Information System impacts the Internal Control of Merchandise Inventory. The variables examined in this study are the Accounting Information System as the independent variable and Internal Control as the dependent variable. The population for this study consisted of the inventory department of a company operating in the service sector in Bandung. A sample of 40 individuals was selected for the investigation. The research employed a descriptive-analytic approach and verification analysis, utilizing questionnaires for data collection. The data analysis application employs the Statistical Package for Social Science (SPSS) Version 25 for Windows. The research employed the Correlation Coefficient Test, Simple Linear Regression, Determination Coefficient, and Normality Test for analysis. The Pearson Product Moment Correlation calculation indicates a "Very Strong" association between the Merchandise Inventory Accounting Information System and Internal Control of Merchandise Inventory. The merchandise inventory accounting information system significantly impacts 74.9% of the internal control of goods inventory. The remaining 25.1% is influenced by external factors that were not included in the variables evaluated by the author. The results of hypothesis testing indicate that the merchandise accounting information system significantly impacts the internal control of goods inventory.

Research on the Application of Cloud Computing Technology in Computer Big Data Analysis

This paper discusses the application of cloud computing technology in big data analysis. With the explosive growth of data volume, traditional data processing methods have been unable to meet the demand. Cloud computing technology provides a new solution for big data service analysis with its powerful computing power, elasticity and cost-effectiveness. This paper will introduce the basic concepts and characteristics of cloud computing, explore its specific application in big data analysis, and analyze its challenges and future development direction.

Optimizing Decision-Making: Data Analytics Applications in Management Information Systems

This study delves into integrating data analytics applications within Management Information Systems (MIS), exploring their impact on decision-making processes in organizational settings. The discussion synthesizes qualitative and quantitative methodologies, presenting insights from scholarly literature, surveys, and interviews. Scholarly discourse highlights the transformative potential of data analytics tools in facilitating informed decision-making, aligning with practical applications showcased in empirical studies. However, inherent challenges surface, primarily concerning data quality, as revealed by 62\% of respondents, underscoring the need for organizations to address these obstacles. Despite challenges, substantial adoption rates of data analytics tools (78\%) affirm their growing recognition in decision-making within diverse industries. Reported enhancements in operational efficiency (35\%) and competitive advantage (22\%) among organizations leveraging data analytics validate their efficacy in driving organizational performance metrics within MIS. Further research should address ethical implications, longitudinal analyses of data analytics efficacy, and interdisciplinary collaborations exploring the nexus between data analytics and managerial decision-making. This study is a foundational step, providing empirical evidence and future research trajectories essential for organizations aiming to optimize decision-making through data analytics applications within Management Information Systems.

PACMHCI V8, ETRA, May 2024 Editorial

We are excited to provide the third issue of the Proceedings of the ACM on Human-Computer Interaction focusing on contributions from the ACM Eye Tracking Research and Applications (ETRA) community. ETRA is the premier eye-tracking conference bringing together researchers from across disciplines to present advances in eye-tracking systems and methods, oculomotor research, eye movement data analysis, gaze-based interaction, and eye-tracking applications. A total of 25 full papers were accepted for ETRA 2024 (June 4 - 7, 2024, in Glasgow, U.K.), selected from 68 submissions after a rigorous reviewing process (37% acceptance rate). Depending on the topic fit and authors' preferences, accepted papers were split into two issues. 19 accepted papers are presented in this issue, and 6 will be included in an issue of the Proceedings of the ACM on Computer Graphics and Interactive Techniques. We want to thank all members of the Editorial Board and all external reviewers for their effort and dedication, as well as all authors for their high-quality contributions.