Application Of Predictive Analytics Research Articles

Environmental data in epidemic forecasting: Insights from predictive analytics

Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, and geographical features, holds valuable insights for predicting and identifying areas prone to epidemics. This paper explores the integration of predictive analytics with environmental data to enhance epidemic forecasting capabilities. By leveraging predictive analytics techniques, researchers and public health officials can analyze environmental data to identify regions at higher risk of experiencing epidemic outbreaks. Through statistical modeling, machine learning algorithms, and computational simulations, predictive analytics utilize environmental indicators to forecast the likelihood and spread of diseases. For example, areas with high temperatures and humidity may be conducive to mosquito-borne diseases, while regions with poor air quality may experience increased rates of respiratory infections. Case studies highlight the application of predictive analytics in various contexts, including forecasting mosquito-borne diseases in tropical regions and tracking respiratory infections in urban areas with poor air quality. Early warning systems, informed by environmental data, provide timely alerts to potential epidemic threats, enabling proactive interventions and resource allocation. While the integration of environmental data into epidemic forecasting offers significant benefits, challenges remain, including data quality, availability, and ethical considerations. Continued research and collaboration are essential to address these challenges and further enhance the effectiveness of predictive analytics in identifying and mitigating epidemic risks. In conclusion, this paper underscores the importance of leveraging environmental data and predictive analytics for epidemic forecasting, emphasizing their potential to improve public health outcomes and enhance preparedness efforts in the face of emerging infectious diseases and climate change. Keywords: Environmental Data, Epidemic Forecasting, Predictive Analytics.

Predictive Analytics and Machine Learning in Disease Diagnosis: A Review of Recent Advances

Artificial intelligence (AI) refers to the utilisation of computer information to exhibit intelligent behaviour with minimal human intervention, whereas machine learning (ML) is seen as a subset of AI methodologies. Typically, this form of intelligence is widely recognised to have originated with the advent of robotics. Given the slow progression of diseases, it is crucial to make early predictions and administer appropriate medication. Hence, it is imperative to present a decision model that may aid in the diagnosis of chronic illnesses and forecast future patient prognoses. The primary objective of this study is to investigate the application of Predictive Analytics and ML in the field of Disease Diagnosis. The study will focus on reviewing the latest advancements in this area. This work specifically emphasises the significance of ML prediction models in disease diagnosis within the AI field, amidst other approaches available. The study utilises a qualitative research methodology. ML has been prominent in the medical field as it offers methods for analysing disease-related data, as indicated by this study. ML algorithms are crucial in attaining early disease detection. Another crucial finding in this research is that the accuracy and performance of the model can be enhanced by employing an alternative technique to generate a single ensemble model.

PREDICTIVE ANALYTICS FOR PROACTIVE SUPPORT IN TRAFFICKING PREVENTION AND VICTIM REINTEGRATION

Human trafficking is a pervasive and complex crime that affects millions of people worldwide. In recent years, there has been a growing recognition of the need for proactive approaches to trafficking prevention and victim reintegration. Predictive analytics, a data-driven technology that uses algorithms to analyze patterns and predict future outcomes, holds great promise in this regard. This review explores the application of predictive analytics in trafficking prevention and victim reintegration, highlighting its potential to enhance proactive support for victims and improve overall outcomes. Predictive analytics can play a crucial role in trafficking prevention by identifying patterns and trends that may indicate potential trafficking activities. By analyzing data from various sources, such as social media, financial transactions, and law enforcement records, predictive analytics can help identify high-risk areas and individuals, enabling law enforcement agencies and NGOs to take proactive measures to prevent trafficking. For example, predictive analytics can help identify vulnerable populations, such as runaway youth or migrants, and target prevention efforts accordingly. In the context of victim reintegration, predictive analytics can help improve outcomes by identifying factors that may influence a victim's likelihood of successful reintegration into society. By analyzing data on factors such as education, employment, and social support, predictive analytics can help identify interventions that are most likely to help victims rebuild their lives. For example, predictive analytics can help identify the types of support services, such as housing assistance or job training, that are most effective in helping victims reintegrate into society. Overall, predictive analytics has the potential to revolutionize trafficking prevention and victim reintegration efforts by enabling proactive support that is tailored to the specific needs of victims. However, it is important to recognize that predictive analytics is not without its challenges, including concerns about data privacy and ethical implications. Therefore, it is essential to ensure that predictive analytics is used responsibly and in accordance with ethical guidelines to maximize its benefits in trafficking prevention and victim reintegration. Keywords: Predictive Analytics, Human Trafficking, Prevention, Victim Reintegration, Systematic Review.

Predictive HR Analytics to Optimize Decision-Making Processes and Enhance Workforce Performance

This paper explores the application of predictive analytics in Human Resource Management (HRM) to optimize decision-making processes and enhance workforce performance. The primary objective is to develop a predictive model that identifies key factors influencing employee retention within our organization. Utilizing historical HR data, machine learning algorithms are employed to analyze patterns and forecast potential attrition risks. The methodology involves data pre-processing, feature selection, and model training using a comprehensive dataset spanning employee demographics, performance metrics, and engagement indicators. The model's predictive accuracy is assessed through cross-validation, and the final model is validated using a separate test dataset. Results indicate a significant improvement in the accuracy of attrition predictions compared to traditional methods. Identified risk factors include job satisfaction, career development opportunities, and team dynamics. The project concludes with actionable insights for HR practitioners to proactively address potential retention challenges. This paper demonstrates the transformative potential of data-driven decision-making in HRM. By leveraging predictive analytics, organizations can strategically allocate resources, implement targeted interventions, and foster a more engaged and satisfied workforce

Theoretical perspectives on predictive analytics in it service management: Enhancing service quality

This paper explores the theoretical perspectives underpinning the application of predictive analytics in IT service management (ITSM) to enhance service quality. It begins with an introduction to predictive analytics in the context of ITSM and the significance of improving service quality in IT operations. Theoretical frameworks such as Systems Theory, Information Theory, Decision Theory, and Machine Learning Theory are discussed to provide a comprehensive understanding of the underlying principles guiding predictive analytics in ITSM. The paper examines the practical applications, challenges, and benefits of predictive analytics in ITSM, emphasizing its role in anticipatory problem resolution, proactive service improvements, and predictive maintenance. Case studies and examples of successful implementations are presented to illustrate real-world applications and best practices. Additionally, future directions and emerging trends in predictive analytics technology are explored, along with their potential impact on ITSM practices and ethical considerations. Overall, this paper contributes to the theoretical foundation and practical insights for leveraging predictive analytics to enhance service quality in ITSM.

Moral Engagement and Disengagement in Health Care AI Development

Background Machine learning (ML) is utilized increasingly in health care, and can pose harms to patients, clinicians, health systems, and the public. In response, regulators have proposed an approach that would shift more responsibility to ML developers for mitigating potential harms. To be effective, this approach requires ML developers to recognize, accept, and act on responsibility for mitigating harms. However, little is known regarding the perspectives of developers themselves regarding their obligations to mitigate harms. Methods We conducted 40 semi-structured interviews with developers of ML predictive analytics applications for health care in the United States. Results Participants varied widely in their perspectives on personal responsibility and included examples of both moral engagement and disengagement, albeit in a variety of forms. While most (70%) of participants made a statement indicative of moral engagement, most of these statements reflected an awareness of moral issues, while only a subset of these included additional elements of engagement such as recognizing responsibility, alignment with personal values, addressing conflicts of interests, and opportunities for action. Further, we identified eight distinct categories of moral disengagement reflecting efforts to minimize potential harms or deflect personal responsibility for preventing or mitigating harms. Conclusions These findings suggest possible facilitators and barriers to the development of ethical ML that could act by encouraging moral engagement or discouraging moral disengagement. Regulatory approaches that depend on the ability of ML developers to recognize, accept, and act on responsibility for mitigating harms might have limited success without education and guidance for ML developers about the extent of their responsibilities and how to implement them.

Efficiency Unleashed: Harnessing AI for Agile Project Management

This research paper explores the application of predictive analytics for real-time monitoring and feedback in project management. Through case studies of leading organizations such as Amazon, Uber, and NASA, the paper examines how predictive analytics enables organizations to optimize operations, improve efficiency, and achieve business objectives. The paper discusses the implementation of predictive analytics in various industries, including e-commerce, transportation, and space exploration, highlighting the benefits of real-time monitoring and feedback for enhancing decision-making, reducing costs, and improving customer satisfaction. By leveraging predictive analytics, organizations can anticipate trends, identify issues early, and make data-driven decisions to drive continuous improvement and innovation in project management practices.

Reviewing the role of AI and machine learning in supply chain analytics

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain analytics has emerged as a transformative force in reshaping traditional logistics and operations. This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands. AI and ML technologies have revolutionized data analytics by enabling the extraction of actionable insights from vast and complex datasets. The application of predictive analytics, powered by machine learning algorithms, allows supply chain professionals to forecast demand more accurately, identify potential disruptions, and optimize inventory levels. This not only improves overall efficiency but also reduces costs and minimizes the risk of stockouts or overstock situations. Furthermore, the integration of AI-driven automation in supply chain management has streamlined routine tasks, such as order processing, inventory replenishment, and route optimization. This automation not only accelerates processes but also mitigates the risk of human errors, enhancing overall reliability. The ability of AI to continuously learn from historical data and adapt to evolving market conditions contributes to a more agile and responsive supply chain ecosystem. In the context of supply chain risk management, AI and ML play a pivotal role in identifying vulnerabilities and providing proactive strategies to mitigate potential disruptions. Sentiment analysis and predictive modeling enable organizations to assess geopolitical, economic, and environmental factors, thereby enhancing the resilience of their supply chains. However, the adoption of AI and ML in supply chain analytics is not without challenges. This review explores the ethical considerations, data security concerns, and the need for skilled personnel in managing these advanced technologies. Additionally, it delves into the importance of explainability and transparency in AI-driven decision-making processes, emphasizing the need for a balance between automation and human oversight. This review underscores the transformative impact of AI and ML on supply chain analytics, emphasizing their potential to revolutionize traditional practices, enhance efficiency, and fortify resilience in an increasingly complex and dynamic business environment.

Data science in public health: A review of predictive analytics for disease control in the USA and Africa

This scholarly paper delves into the realm of data science in public health, with a specific focus on the transformative role of predictive analytics in disease control across the United States and Africa. Set against a backdrop of rapidly evolving healthcare challenges, the study aims to dissect and synthesize the advancements, applications, and hurdles associated with data-driven health strategies in these diverse geographical contexts. Employing a qualitative analysis of peer-reviewed literature, the paper meticulously examines the evolution of predictive analytics, comparing public health structures, and scrutinizing key diseases and health challenges prevalent in both regions. The scope of the study extends to exploring the ethical considerations and technological advancements in health data utilization, offering a panoramic view of the current and potential landscape of data science in public health. The findings reveal a significant surge in the application of predictive analytics, particularly in the USA for chronic disease management and in Africa for infectious disease control. The study highlights the successes and challenges in implementing data-driven health policies, emphasizing the need for a balanced approach that addresses technological, ethical, and cultural barriers. The future of AI and machine learning in disease control is identified as a promising domain, with potential for further innovation and integration into healthcare and public policy. Conclusively, the paper recommends continued investment in data science applications in public health, advocating for collaborative efforts to overcome implementation challenges and ethical considerations. The study underscores the transformative potential of data science in enhancing healthcare delivery, advocating for more effective, efficient, and equitable healthcare systems globally.

A study on predictive analytics in talent acquisition in Indian companies: An Indian context perspective

In the contemporary Indian business landscape, talent acquisition stands as a pivotal challenge for organizations striving to maintain competitiveness and drive innovation. With the proliferation of data and advancements in analytics technology, predictive analytics has emerged as a promising tool to revolutionize talent acquisition practices. This conceptual paper endeavors to explore the application of predictive analytics specifically within the unique context of Indian companies' talent acquisition endeavors. Beginning with an exploration of the theoretical foundations of predictive analytics in talent acquisition, this paper delves into the intricacies of its implementation in the Indian business ecosystem. By synthesizing existing literature and empirical evidence, it elucidates the potential benefits and challenges associated with predictive analytics adoption in the Indian context. In particular, the study investigates the socio-economic and cultural nuances that shape talent acquisition strategies in India, including the significance of factors such as regional diversity, educational disparities, and socio-economic backgrounds. It examines how these contextual factors influence the design, implementation, and outcomes of predictive analytics initiatives in talent acquisition. Furthermore, the paper provides insights into the current landscape of predictive analytics utilization in Indian companies' talent acquisition processes. It discusses notable trends, case studies, and success stories, shedding light on the transformative impact of predictive analytics on recruitment efficiency, candidate quality, and workforce diversity. Moreover, the study addresses the ethical and legal considerations inherent in the deployment of predictive analytics in talent acquisition within the Indian regulatory framework. It emphasizes the importance of ethical data practices, transparency, and accountability to mitigate risks related to privacy infringement and algorithmic bias. Additionally, the paper identifies key challenges hindering the widespread adoption of predictive analytics in Indian talent acquisition, such as data quality issues, talent scarcity in analytics expertise, and organizational resistance to change. It offers strategic recommendations for overcoming these barriers, including investment in data infrastructure, capacity-building initiatives, and stakeholder engagement. In conclusion, this study contributes to the advancement of knowledge in both academic and practical domains by offering a comprehensive understanding of predictive analytics in talent acquisition within the Indian context. By addressing the unique challenges and opportunities specific to the Indian business environment, this paper aims to inform strategic decision-making and drive organizational success in talent acquisition endeavors.

A Study of Impact and Applications of Predictive Analytics in Sales Forecasting

Abstract: Predictive analysis has become an efficient tool and procedures now a day for many industries, organization, firms and service sectors. In sales, predictive analytics refers to the software and/or procedures that evaluate past and present sales data in order to forecast sales results and enhance efficiency. A vast amount of first-party and third-party customer/prospect data is available these days to sellers and marketers. Indeed, sellers and marketers frequently feel overpowered by the sheer amount of data when deciding how to leverage it to get desired outcomes. That issue is resolved by predictive analytics in sales, which uses artificial intelligence systems to assist sales representatives in understanding customer and sales pipeline information. Advances in data analytics are being used by an ever-increasing number of organizations to identify patterns and trends, increase productivity, and make more informed business decisions. In this paper, we will discuss and analyze the role and impact of the predictive analytics in sales.

A methodological framework for the design of efficient resilience in supply networks

Disruptions can adversely affect profitability, service level, and even the viability of supply networks (SNs). Natural disasters, the COVID-19 pandemic, and geopolitical tensions heightened disruption risks for SNs. We propose a methodological framework to guide the design of resilient SNs, with a focus on efficient resilience. In fact, despite an increasing economic and social interest in SN resilience and a large variety of qualitative resilience frameworks, there is a lack of model-based frameworks to guide decision makers in the complex task of designing resilient SNs. Differently to the existing literature, that mostly focuses on industry- or disruption-specific approaches, the framework of this paper is general, independent from sectors and causes. It consists of three steps to guide practitioners through model-based stress testing of their SNs: it integrates and organises methods from the literature, from the selection of data to be collected to the validation of the resilient and efficient SN. We illustrate the application of descriptive, predictive, and prescriptive analytics at each phase using real-life case studies data supplemented by literature references. Finally, we present an industrial case study with the step-by-step application of the framework highlighting its strengths and objectives.

Modeling independent sales representative performance: application of predictive analytics in direct selling for improved outcomes

Modeling independent sales representative performance: application of predictive analytics in direct selling for improved outcomes

Application of Predictive Analytics in Built Environment Research: A Comprehensive Bibliometric Study to Explore Knowledge Domains and Future Research Agenda

Application of Predictive Analytics in Built Environment Research: A Comprehensive Bibliometric Study to Explore Knowledge Domains and Future Research Agenda

Effective Implementation of Predictive Sales Analytics

Sales managers are unlikely to reap the benefits of implementing predictive analytics applications when salespeople show aversion to or lack understanding of these applications. For managers, it is essential to understand which factors mitigate or exacerbate these challenges. This article investigates these factors by studying the implementation of an application that predicts customer churn. Using 9.7 million transactions from a business-to-business company, the authors develop a predictive model of customer churn, implement it in a field experiment, and study its treatment effects using causal forests. Furthermore, the authors manipulate one specific mitigation strategy proposed by prior literature: the fostering of users’ realistic expectations regarding the accuracy of an algorithm. The results show that the effectiveness of the churn prediction application strongly depends on customer characteristics (most importantly the predicted churn probability and prior revenue) and salesperson characteristics (technology perceptions, abilities, and selling orientations). Fostering realistic expectations improves the effectiveness of the churn prediction only under very specific circumstances. Two follow-up stimuli-based experiments conceptually replicate key results of the field study. Therefore, this article helps build theory on predictive sales analytics and provides specific guidance to managers aiming to increase their return on analytics investments.

A theory of predictive sales analytics adoption

Given the pervasive ubiquity of data, sales practice is moving rapidly into an era of predictive analytics, using quantitative methods, including machine learning algorithms, to reveal unknown information, such as customers’ personality, value, or churn probabilities. However, many sales organizations face difficulties when implementing predictive analytics applications. This article elucidates these difficulties by developing the PSAA model—a conceptual framework that explains how predictive sales analytics (PSA) applications support sales employees’ job performance. In particular, the PSAA model conceptualizes the key contingencies governing how the availability of PSA applications translates into adoption of these applications and, ultimately, job performance. These contingencies determine the extent to which sales employees adopt these applications to revise their decision-making and the extent to which these updates improve the decision outcome. To build the PSAA model, we integrate literature on predictive analytics and machine learning, technology adoption, and marketing capabilities. In doing so, this research provides a theoretical frame for future studies on salesperson adoption and effective utilization of PSA.

Explore the Application of Predictive Analytics and Machine Learning Algorithms in Identifying and Preventing Cyber Threats and Vulnerabilities within Computer Systems

Explore the Application of Predictive Analytics and Machine Learning Algorithms in Identifying and Preventing Cyber Threats and Vulnerabilities within Computer Systems

Application of Predictive Analytics in Agriculture

Predictive analytics is one the most helpful techniques in business which provides the opportunity to forecast the upcoming risks and challenges in business. The assistance of predictive analytics provides the scope towards an enterprise to perform business with more effective strategies and decisions to avail higher success rate in business surroundings. As the competition in agriculture has been increasing day by day, business companies in the agricultural sector have started to implement predictive analytics in their operational area. It helps to identify upcoming issues and take steps to mitigate those risk factors in business. The study has been focused on analyzing the impact of predictive analytics in agriculture. Thus, various concepts and theories related with predictive analytics have been discussed in detailed manner within the entire study. An inductive approach has been taken while performing the entire task, cross sectional design has been used to perform the study effectively. On the other hand, qualitative data has been collected using secondary data collection methods within this particular study. The entire study has found that the application of predictive analytics mostly helps to prepare to face upcoming challenges and gain higher consumer satisfaction, competitive advantage and superior business growth in the agricultural industry throughout the global periphery.

Application of Predictive Analytics for Early Warning System for Healthcare Management

Coronavirus disease (COVID-19) has become a pandemic after its outbreak in January 2020. The majority of the countries have witnessed peak effects of the disease, and they need to learn from their past experience of dealing with and pro-actively controlling the future waves. Thus, this article aims to analyse the effect of the COVID-19 pandemic on some of the key states in India and provide analytical actionable insight for better epidemic management. In this article, we have focused on Maharashtra and tried to show how other states can also incorporate pro-active pandemic management to reduce the number of causalities in the following waves. The key objectives of this article are to provide a scenario base forecasted number of patients for Maharashtra with sufficient lead time, review the current capacity of the health infrastructure, analyse the risk of the current health infrastructure, identify the breaking point of system failure in advance, measure the gap between capacity (supply) vs demand and raise alarms and device an early warning system (EWS) pro-actively. Quantitative analysis of the statistics related to COVID-19 has been done based on their official (government) data and some of the previous research work. Prophet Model has been used to forecast and then the forecasted values are combined with the daily load in hospitals to measure the extra load on the healthcare system.

Weakening the Bullwhip Effect in the Supply Chain Based on Data Fusion

In the increasingly competitive society, as the third profit center of the enterprise, the supply chain makes scholars and industry related with the supply chain more interested in the study of relevant issues. Some scholars believe that the application of predictive analytics could have a tremendous impact on the supply chain. The uncertainty of supply chain demand and bullwhip effect challenge the supply chain. Data fusion can effectively reduce the uncertainty of demand and amplification effect. In this study, a new conceptual model was established on the traditional supply chain based on data fusion. Results show that the conceptual model refers to data fusion for solving the uncertain and inconsistent multi-source data by Bayesian estimation to provide reasonable decision information for supply chain managers.