Data-driven Choices Research Articles

The Role of Data Analytics and Sustainable Circular Economy in Developing Countries

In the twenty-first century, data analytics has become a vital tool in decision-making. Modern companies and organisations create enormous volumes of data, which need for sophisticated ways to analyse. Statistics help companies make data-driven decisions by organising, summarizing, and interpreting data. 21st-century enterprises, organisations, and people generate more data than ever. Due to rapid technological advancement and growing data, companies must make data-driven choices. As such, the place of data analytics is the era of digital economy cannot be overemphasize. This paper essentially review these roles in achieving sustainable circular economy in developing countries. The study was based solely on secondary data elicited from the review of previous studies on the subject being investigated. The examined the concepts and benefits of circular economy, the challenges in implementing circular economy in developing countries, as well as the concept, impact and role of data analytical impact on achieving sustainable circular economy. The overall findings from this study indicates that indeed, data analytics play important role in achieving sustainable circular economy in developing countries and thus recommends the system theory approach to circular economy where all parts are connected in somewhere to enhance effectiveness and reduce cost. Such connection can only be efficient through data analytics.

Optimization of Fuzzy Economic Order Quantity using Machine Learning and Weka

Inventory management is a crucial need for businesses because it requires a significant financial and human resource investment. Machine Learning models are used by e-commerce huge companies to manage their inventory to the demand for specific products. Businesses can benefit from inventory management as a service to increase sales and forecast product demand. Inventory Management might be revolutionised by AI and ML technologies, which would allow businesses to automate repetitive processes, make data-driven choices, and instantly optimise inventory levels. By analysing vast amounts of both historical and current data, these technologies can spot inclination, forecast, and recommend, all of which increase inventory accuracy and lower the chance of stockouts or overstocking. The most efficient solution is determined using the Kuhn-Tucker Method for Nonlinear Programming, which has an impact on the monthly cost. The proposed model employs the Signed distance approach for defuzzification and the Pentagonal fuzzy number to find the lowest cost and met the demand. To evaluate the inventory concept, purchased a dataset and calculated the profit and non profit using apriori algorithm and use the Weka software to analyse the average order quantity and total cost.

Movie Ticket Booking Application

In this digital age, buying movie tickets the old-fashioned way has been replaced by an efficient online experience. The main characteristics and advantages of a cinema ticket booking system are described in this abstract. This system provides a feature-rich online platform that may be accessed by mobile and web applications. Customers may quickly peruse a huge library of films, check the showtimes, and instantly select the seats they want. A seamless and safe transaction process is ensured by the system's integration of secure payment gateways. Customers can book tickets whenever and wherever they choose thanks to the system's simplicity and flexibility. Customized recommendations, dynamic pricing, and seat selection are examples of advanced features that improve the user experience overall. Theatre employees can effectively manage ticket inventory, track sales analytics, and optimise seat allocation with the system's comprehensive management interface. Administrators are informed and empowered to make data-driven choices thanks to real-time updates and notifications. Additionally, the system places a high priority on inclusion and accessibility, with features like multilingual help and seating accommodations for those with special needs. In conclusion, this technology offers a smooth, user-centric experience for both patrons and theatre operators, signalling a paradigm shift in the movie ticket booking industry. With its cutting-edge capabilities and user-friendly interface, it raises the bar for watching movies on a digital device

HR Analytics and its Impact on Organizations Performance

HR practices shift from intuition-based to evidence-based decision-making when HR Analytics uses data analysis to extract insights from employee information. The impact of HR analytics on organizational performance is examined in this abstract from a number of angles. Organizations can find patterns and trends in areas like hiring, training, retention, and performance by examining employee data. These revelations enable data-driven choices that enhance HR procedures, resulting in: • Better Talent Acquisition: HR analytics can find high-achieving applicants, cut expenses associated with hiring, and simplify the hiring procedure. • Enhanced Employee Development: Data analysis facilitates the customization of training curricula to meet the needs of staff members, promoting the growth of new skills and enhancing productivity. • Lower Turnover: HR analytics helps create focused retention plans by identifying variables that contribute to employee churn, which raises morale and satisfaction among staff Enhanced Productivity: Organizations may discover areas for improvement and optimize workflows with the use of data-driven insights, which results in a workforce that is more productive. All things considered, HR analytics is essential to coordinating HR strategy with overarching corporate objectives. HR analytics helps businesses gain a major competitive edge by turning people management from a cost center to a strategic asset. This article examines the diverse applications of HR analytics across various organizations, as well as the advantages associated with its utilization. By utilising the data that the business has accessible, analytical tools enable organisations to identify problems with performance, employee retention and turnover, employee conduct, etc. Many organizations undervalue the role of HR, but in this day and age of advanced technology, numerous analytical members.

Design and Implementation of an Iot-Based Safety and Energy Efficient System

The paper provides a safe and power-efficient Internet of Things (IoT)-based solution. With the growing concerns about environmental sustainability and the demand for improved safety measures in today’s society, it has become critical to focus on building systems that decrease energy consumption and assure the safety of humans and the environment. The proposed system employs cutting-edge technologies and novel approaches that will result in optimal energy usage with minimal or no human involvement thereby assuring the safety of people. The proposed system leverages on existing IoT technologies and is comprised of smart sensors, energy-efficient gadgets, and a centralized control unit. Therefore, it is able to demonstrate cutting edge power control strategies including power gating and dynamic central power regulation which control the usage of gadgets to operate at optimal power levels vis-a-vis the real-time demand to achieve energy efficiency. The system is also able to collect data on occupancy, lighting, temperature, and power consumption trends which can assist in making data-driven choices that improve the usage of energy. The proposed system provides large energy savings without sacrificing user comfort by dynamically altering lighting, and cooling systems based on occupants and ambient factors. Multiple simulations and real-world trials in homes and institutions’ buildings were used to assess the efficacy of the suggested system. The findings revealed substantial reductions in energy consumption without jeopardizing safety.

An Emerging Era Of Research In Agriculture Using AI

AI-driven precision agriculture, predictive analytics, robots, and market intelligence boost contemporary agriculture's production, efficiency, and sustainability. Precision agriculture, powered by AI algorithms, gives farmers detailed insights into crop health, soil conditions, and weather patterns for data-driven resource allocation and management. AI in agriculture's predictive analytics helps stakeholders forecast crop yields, market dynamics, and climate-related dangers, improving resilience and strategic planning. AI has great promise to solve agriculture's complicated problems. AI technologies allow computers to mimic human cognition and evaluate massive volumes of data to draw conclusions. AI can improve resource utilization, productivity, decision-making, and environmental effect in agriculture. AI-powered precision agriculture, crop monitoring, supply chain optimization, and market analysis are making agriculture more sustainable and resilient. To show AI's influence on farming, we explored precision agriculture, predictive analytics, robots, and supply chain optimization. Farmers may optimize resource usage, manage risks, and make data-driven choices using these tools, enhancing output, sustainability, and resilience.

Mathematical analysis of big data analytics under bipolar complex fuzzy soft information

The aim of this study is to develop new and efficient theories for handling complex and unreliable data in real-world scenarios. The proposed approach integrates two distinct theories: the Bipolar Complex Fuzzy Set (BCFS) and the Soft Set (SS), resulting in a novel and superior method compared to existing solutions. Furthermore, the value of big data analytics cannot be overstated as it provides businesses with the opportunity to use their data to find areas for development and progress. To gain insights from raw data, it must first be collected and organized. Data modeling represents these large, complex data sets in a visual format, such as a chart or diagram. However, the visualization process often results in uncertainties and inaccuracies, highlighting the need for a more robust approach to data analysis. To address this issue, this paper introduces the concept of Bipolar Complex Fuzzy Soft Relations (BCFSRs), a fuzzy decision-making method within a complex bipolar fuzzy environment that can have the advantages of both a Bipolar Complex fuzzy relation and a soft relation at the same time. In BCFSRs, an element can have degrees of membership in both the positive and negative directions, reflecting a more nuanced and versatile representation of uncertainty or ambiguity. These mathematical ideas are explained through the Cartesian product of two Bipolar Complex Fuzzy Soft Sets (BCFSSs) and serve to simplify the decision-making process by presenting the BCFSSs in a clear and concise manner. The inventive notion of the BCFSRs elucidates the combined positive and negative effects of anything with parameterization. To support organizations in making data-driven choices and obtain insights for strategic planning and inventiveness, the analysis of big data is essential in several industries, including financial services, marketing, and medical treatment, among others. To choose the optimal big data analytics for efficient operation, this research presents modeling methodologies based on BCFSRs, including the formulation and analysis of scoring functions. The validity of the proposed work is demonstrated through a comparison study with existing methods, providing valuable insights for effective data analysis.

Data-Driven Decision Making: Real-world Effectiveness in Industry 5.0 – An Experimental Approach

This empirical study on Industry 5.0 offers verifiable proof of the transformational potential of data-driven decision making. The validation of data-driven choices as a key component of Industry 5.0's performance is shown by a noteworthy 46.15% increase in decision outcomes. The fact that choice criteria are in line with pertinent data sources emphasizes how important data is in forming well-informed decision-making processes. Moreover, the methodical execution and oversight of choices showcase the pragmatic significance of data-driven methodologies. This empirical evidence positions data-driven decision making as a cornerstone for improving operational efficiency, customer happiness, and market share, solidifying its essential role as the industrial environment changes. These results herald in an age when data's revolutionary potential drives industrial progress by providing a compass for companies trying to navigate the complexity of Industry 5.0.

FINTECHS AS A GAME CHANGER IN BANKS-LITERATURE REVIEW AND RESEARCH AGENDA

Purpose:As technology and innovation advance, the world economy is always changing. Due to the recent upheaval in the banking sector brought about by digital technology, banks are now refocusing their business strategies in favor of e-banking. Fintech is the application of technological advances to create novel business models built on a wide range of creative concepts. It has developed additional services to more effectively address client demands at the most affordable pricing.Initially displacing banks due to their reduced operating costs, fintech startups are now collaborating with them to grow their revenue and clientele. Methodology/Approach:The literature reviews objective is to analyze the concept of fintech and how it works in the sector of banking. Findings:Fintech startups are now seen by banks as enablers rather than competitors. By collaborating with fintech firms, banks may instantly increase the services they offer. Banks should adopt Fintechs more proactively to continuously boost their profitability. It is essential to examine the profitability of banks utilizingfintech as it enables financial institutions to make data-driven choices, improve operational efficiency, manage risks, and develop a more customer-centric approach. Originality/Value: In the continuously changing financial landscape of today, assessing how fintech affects bank financial performance cannot be overlooked and it is crucialas it continues to gain popularity. Paper Type:Conceptual Research.

Navigating the Digital Marketplace: Strategies for Entrepreneurship in Electronic Commerce

This research explores the tactics used by e-commerce business owners in the ever-changing online industry. By addressing the difficulties faced by e-commerce business owners, the study closes a knowledge vacuum about the particular tactics they use. Through semi-structured interviews with entrepreneurs, this study uncovers important insights: a customer-focused strategy, flexibility in responding to market developments, data-driven choices, and strong cybersecurity defenses are essential for e-commerce success. These findings have obvious implications for entrepreneurs operating in the digital space: putting customer pleasure first, responding quickly to shifting market conditions, using data analytics, and putting strong cybersecurity in place are all critical. This report provides actionable advice for entrepreneurs to thrive as the digital market develops and stay competitive. This research is thought to be a beneficial tool for e-commerce business owners, scholars, and regulators, providing perceptions and direction in an everchanging e-commerce industry

Modern Agriculture: Smart Agriculture

Internet of Things (IOT) sensors are used in the fast developing sector of "smart agriculture" to improve agricultural practises. Farmers may receive real-time data from their fields through the use of IOT sensors, allowing them to make data-driven choices and optimise resource allocation. The feature of this paper includes development of a system which may monitor temperature, level of water, moisture and even the movement if any happens within the field which may destroy the crops in agricultural field through sensors using Arduino UNO board. In this article, the usage of IOT sensors in agriculture is examined, with an emphasis on user engagement and field monitoring in particular. It goes through the benefits of using IOT-enabled smart agriculture, the many types of sensors used, and how touch-based user input may improve farming operations

Multi-year energy performance data for an electrolysis-based hydrogen refueling station

Financing, sizing, operating, or upgrading a hydrogen refueling station (HRS) is challenging and may be complex, much more so in today's rapidly changing and growing hydrogen industry. There is a significant information gap regarding experimental hydrogen station activities. A high-level perspective on such data and information may facilitate the transition between present and future HRS operations. To address the need for such high-level perspective, this paper presents a comprehensive data set on the performance of the California State University Los Angeles Hydrogen Research and Fueling Facility, based on multi-year operational data. The analysis of over 4500 refueling events and over 8800 kg of hydrogen dispensed, as well as the operation of the facility electrolyzer and of both storage and refueling compressors from 2016 to 2020, reveals a comprehensive picture of HRS energy performance and the identification of useful key performance indicators. In 2016, the station's energy efficiency was 25%, but in 2017 and the first three quarters of 2018, it dropped to 15%. Station-specific energy consumption increased during these quarters. The 2020 first quarter energy consumption was between 70 and 80 kWh/kg. At this time, the energy efficiency of the station reached 40%.This research is based on an unprecedented and unique dataset of an HRS operating under real-world conditions, with an approach that can be informative for modeling the performance of other stations, providing a dataset that HRS designers, operators, and investors may utilize to make data-driven choices regarding HRS components and their specs and size, as well as operating strategies.

Data acquisition for digitisation in the paraffin candle production process

By utilising digital technology, digitisation helps businesses streamline procedures, increase efficiency and cut costs. Moreover, it enables businesses to gather knowledge and make data-driven choices that enhance product quality, customers’ satisfaction, and market competitiveness. Data acquisition, which permits the collection and analysis of real-time data from equipment and processes, is a crucial component of the digitalisation of industrial automation. Predictive maintenance may, therefore, be utilised to decrease downtime, enhance quality control, and optimise operations. The paraffin candle production process is being discussed, and its key variables are being identified for the purpose of digitisation. The study proposes data collection and processing methods from a candle production line and uses an IoT-enabled controller to collect and analyse data. The PLC software that reads, processes, and saves data in a SQL database on a remote PC is then built based on these techniques. An MSSQL database is used to store information on process variables, including temperatures, cycle times, and downtimes, for the different candle production stages, including the paraffin preparation tank, extruder, and conveyor for cutting candles. The database acts as a storage location for the gathered information, which can then be utilised to analyse and improve the manufacturing process. Python and the open-source package Streamlit were used to provide a user-friendly interface for operators. The interface allows the user to select historical data for each item of equipment by time and relevant variables, and it shows the filtered data in an understandable graphical style. This allows the operator to evaluate the production process, spot trends, and optimise it. With the use of support and assistance technologies, the data may be used for decision-making in future.

An integrated outlook of Cyber–Physical Systems for Industry 4.0: Topical practices, architecture, and applications

Industry 4.0 requires a strong understanding of Cyber-Physical systems (CPS). An Industry 4.0-enabled manufacturing environment that offers real-time data gathering, transparency and analysis across all parts of a manufacturing process is known as cyber–physical systems, also known as cyber manufacturing. Data analytics enables executives to make data-driven choices and boost productivity, while automation speeds up manufacturing and decreases machine downtime. The main objective of deploying Industry 4.0 solutions is to enable manufacturing organisations to increase collaboration by making the correct information accessible to the right people in real time. The aim of encouraging optimal decision-making at the appropriate moment is to improve efficiency and production further. The critical terms in Industry 4.0, such as the CPS, Internet of Things (IoT), and Digital Twin, are widely used interchangeably in conversations about smart manufacturing. These are essential to Industry 4.0 and smart manufacturing because they give users access to real-time operating data of the equipment they represent. Cyber components and physical components make up the two elements of CPS. The main aim of this paper is to brief CPS and its need for Industry 4.0. Embedded Processes and Smart 5C diagrammatically elaborate architecture of CPS for Industry 4.0. Finally, the paper identifies and discusses significant applications of CPS in Industry 4.0. For this paper, we identified and then studied relevant literature on CPS for establishing an Industry 4.0 environment. CPS is integrated into several items, including vehicles and other equipment, to carry out particular functions. CPS can be utilised in any industry, including engineering, manufacturing, transportation, and even health care, because they are all easy to use. CPS network collaborative systems are built for communication. Furthermore, cutting-edge network technologies like cloud solutions are used by CPS. • Industry 4.0 requires a strong understanding of Cyber-Physical systems (CPS). • Industry 4.0 enable manufacturing increase collaboration through CPS. • This paper brief about CPS and its need for Industry 4.0. • Embedded Processes and Smart 5C Architecture of CPS for Industry 4.0 are elaborated. • Finally, paper identifies and discusses significant applications of CPS for Industry 4.0.

Modeling Municipal Heritage Management

The resources medium-sized municipalities allocate in the process of both creating and maintaining instruments for heritage management have increased in the last decades. There are first and foremost legal reasons for this, as European national laws translate societal concerns on the preservation and commodification of the past. Sustainable solutions may be achieved through the understanding of heritage assets, and their use in the support of responsible, data-driven choices. This paper analyses a case of local tangible heritage administration in Oliveira de Azeméis, Portugal.

A Penalized Synthetic Control Estimator for Disaggregated Data

Synthetic control methods are commonly applied in empirical research to estimate the effects of treatments or interventions on aggregate outcomes. A synthetic control estimator compares the outcome of a treated unit to the outcome of a weighted average of untreated units that best resembles the characteristics of the treated unit before the intervention. When disaggregated data are available, constructing separate synthetic controls for each treated unit may help avoid interpolation biases. However, the problem of finding a synthetic control that best reproduces the characteristics of a treated unit may not have a unique solution. Multiplicity of solutions is a particularly daunting challenge when the data includes many treated and untreated units. To address this challenge, we propose a synthetic control estimator that penalizes the pairwise discrepancies between the characteristics of the treated units and the characteristics of the units that contribute to their synthetic controls. The penalization parameter trades off pairwise matching discrepancies with respect to the characteristics of each unit in the synthetic control against matching discrepancies with respect to the characteristics of the synthetic control unit as a whole. We study the properties of this estimator and propose data-driven choices of the penalization parameter.

Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network

Skills gained from vocational or higher education form an essential component of country’s economy, determining the structure of the national labor force. Therefore, knowledge on how people’s education converts to jobs enables data-driven choices concerning human resources within an ever-changing job market. Moreover, the relationship between education and occupation is also relevant in times of global crises, such as the COVID-19 pandemic. Healthcare system overload and skill shortage on one hand, and job losses related to lock-downs on the other, have exposed a necessity to identify target groups with relevant education backgrounds in order to facilitate their occupational transitions. However, the relationship between education and employment is complex and difficult to model. This study aims to propose the methodology that would allow us to model education-to-skill mapping. Multiple challenges arising from administrative datasets, namely imbalanced data, complex labeling, hierarchical structure and textual data, were addressed using six neural network-based algorithms of incremental complexity. The final proposed mathematical model incorporates the textual data from descriptions of education programs that are transformed into embeddings, utilizing transformer neural networks. The output of the final model is constructed as the hierarchical classification task. The effectiveness of the proposed model is demonstrated using experiments on national level data, which covers whole population of Lithuania. Finally, we provide the recommendations for the usage of proposed model. This model can be used for practical applications and scenario forecasting. Some possible applications for such model usage are demonstrated and described in this article. The code for this research has been made available on GitHub.

COVID-19 Highlights Critical Need for Public Health Data Modernization to Remain a Priority.

COVID-19 Highlights Critical Need for Public Health Data Modernization to Remain a Priority.

Detecting selected haplotype blocks in evolve and resequence experiments

Shifting from the analysis of single nucleotide polymorphisms to the reconstruction of selected haplotypes greatly facilitates the interpretation of evolve and resequence (E&R) experiments. Merging highly correlated hitchhiker SNPs into haplotype blocks reduces thousands of candidates to few selected regions. Current methods of haplotype reconstruction from Pool‐seq data need a variety of data‐specific parameters that are typically defined ad hoc and require haplotype sequences for validation. Here, we introduce haplovalidate, a tool which detects selected haplotypes in Pool‐seq time series data without the need for sequenced haplotypes. Haplovalidate makes data‐driven choices of two key parameters for the clustering procedure, the minimum correlation between SNPs constituting a cluster and the window size. Applying haplovalidate to simulated E&R data reliably detects selected haplotype blocks with low false discovery rates. Importantly, our analyses identified a restriction of the haplotype block‐based approach to describe the genomic architecture of adaptation. We detected a substantial fraction of haplotypes containing multiple selection targets. These blocks were considered as one region of selection and therefore led to underestimation of the number of selection targets. We demonstrate that the separate analysis of earlier time points can significantly increase the separation of selection targets into individual haplotype blocks. We conclude that the analysis of selected haplotype blocks has great potential for the characterization of the adaptive architecture with E&R experiments.

Evidence-Based Medicine in Otolaryngology, Part XI: Modeling and Analysis to Support Decisions.

To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices. Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions. Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations. Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care. Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.