Use Of Advanced Analytics Research Articles

Smart Agriculturing Based on KSK Approach: A Novel AI-Driven-IoT(AIIoT) Based Decision-Making Approach

An enormous change is taking place in the agriculture industry as a result of the introduction of the Internet of Things (AIIoT), which is powered by artificial intelligence and provides farmers with unparalleled automation capabilities and insights. The purpose of this research is to present a comprehensive review of artificial intelligence and the internet of things (AIIoT) in smart agriculture, focussing on its applications, benefits, and consequences for decision-making. The concept of smart agricultural decision-making refers to a breakthrough method that enables farmers to optimise their farm operations while also making decisions that are well-informed. Farmers are able to increase crop yields while simultaneously reducing costs and hazards when they make use of advanced analytics, real-time data, and decision-making tools. The development of smart agriculture will result in farmers having increased decision-making authority, which will ultimately lead to an agricultural sector that is more sustainable and productive. The Internet of Things (IoT) and artificial intelligence (AI) in smart agriculture is a cutting-edge technology that has the potential to be a game-changer in terms of how we produce and consume food. Farmers may be able to improve agricultural yields and quality, streamline operations, and contribute to a food supply chain that is more efficient and sustainable if they use artificial intelligence and the internet of things. In spite of the fact that there are still certain problems that need to be cleared up, the Internet of Things (IoT) offers numerous advantages for smart agriculture, and its implementation is anticipated to increase in the years to come. The KSK technique, also known as the Knowledge-Sensors-Knowledge approach, is a suggestion made by Dr. Kutubuddin S. Kazi, who is also using his name. The output of the KSK technique results in an accuracy of 99.9% and a recall of 97.9%, respectively.

Optimizing loss functions for improved energy demand prediction in smart power grids

In this paper, our aim is to improve the accuracy and effectiveness of energy demand forecasting, particularly within modern electricity transmission systems and smart grid technology. To achieve this, we developed a hybrid approach that combines machine learning, representation learning, and other deep learning techniques. This approach is based on extracting essential features, including time-based attributes, identifiable trends, and optimal lags. The outcome of our investigation is the observation that triplet losses demonstrate remarkable accuracy, particularly when employed with a larger margin size and for longer prediction lengths. This finding signifies a substantial improvement in the precision and reliability of energy demand forecasting within modern electricity transmission systems. Our research not only improves predictive modeling in the power grid but also demonstrates the practical use of advanced analytics in addressing renewable energy integration challenges, refining energy demand forecasting for efficient management, system operation, and market analysis.

Richardson’s Multilateral Military Expenditure Model Augmentation via Sentiment Analysis of News Data in the Framework of Complexity Eco-nomics

Complexity economics incorporates factors that are not accounted for in classical economic thought. With this paper, we intend to demonstrate the potential of using the complexity economics frame of reference by revisiting an existing model of the past, especially one that has been proven to be effective and, most importantly, could utilize the recent developments in advanced analytics as a tool to address a more realistic social milieu. We propose the introduction of more complex dynamics into an already existing model by using Deep-Learning-based NLP analysis of large news article data to fill in the action-reaction matrix for Richardson’s multilateral arms race model. By introducing said complex dynamics we also opened a discussion about the effect that media has on democratic societies’ military spending and concluded at the first level of analysis that media has a significant influence on the electorates’ decision-making of democratic nations in the matter of arms race and defense budgets. Also, we were able to demonstrate the final form of the augmented multilateral arms race model and its predictive capacity. We hope that our findings will encourage the use of advanced analytics in the framework of complexity analysis and improve the existing models’ performance via big data insights.

Revolutionizing Agriculture: A Case Study of IBM's AI Innovations

Background / Purpose: The development of computer systems that can carry out tasks that traditionally require human intelligence is referred to as artificial intelligence (AI). It entails the development of intelligent machines that can reason, learn, solve issues, and make judgements. A fast-developing topic, AI has enormous ramifications for many different businesses and facets of society. By leveraging advanced algorithms and data analysis techniques, AI systems can process and interpret large amounts of information in real-time, enabling them to extract valuable insights and patterns that may be difficult for humans to perceive.AI technologies have a wide range of applications across multiple domains, including healthcare, finance, transportation, manufacturing, education, entertainment, including agriculture. When referring to AI in the context of agriculture, we mean the use of advanced analytics and computational algorithms to analyse massive volumes of agricultural data, anticipate the future, and give farmers and stakeholders useful information. The main goal of using AI to agriculture is to increase efficiency, sustainability, and productivity across a range of farming operations, also to create smart and efficient systems that can monitor, analyze, and control water resources in real-time, leading to improved water management and sustainable agricultural practices, thereby addressing the challenges faced by the agricultural sector.AI offers significant potential to optimize water usage, enhance crop productivity, and mitigate environmental impact. In this paper, IBM, a significant provider of services in the sector of agriculture in recent years, is examined. Objective: In this case study, artificial intelligence is the main topic with particular emphasis on IBM's agricultural technology. Design/Methodology/Approach: Academic works published in a variety of peer-reviewed journals, conferences, and business websites provided the necessary information and specifics for this case study on IBM. Findings/Result: This study is primarily concerned with the usefulness and significance of AI in the modern world. The demand for and necessity of the numerous resources. provided by IBM, discussion topics include the company's business plan, varied results, top clientele, and numerous service types. Originality/Value: The analysis gives a concise description of IBM, the types of data collected and managed, information on artificial intelligence (AI), and the numerous AI services offered by IBM. Paper Type: Case study on the importance of storage and computing requirements for AI services offered by different service. providers, with a focus on IBM.

Advancing the marketing‐operations interface in omnichannel retail

Advancing the marketing‐operations interface in omnichannel retail

Multi-Source Data Interpretation For Field Scale Precision Management In Healthcare Industry

Precision management is a critical component of healthcare, where access to accurate and timely data is essential for effective decision-making. In the healthcare industry, precision management involves the use of multiple data sources to understand patient needs and improve health outcomes. Multi-source data interpretation, specifically at the field scale, is becoming increasingly popular as a means to obtain a comprehensive view of patient care. Field scale precision management in healthcare involves the integration of data from various sources, including electronic health records, patient-generated data, and social determinants of health. Through the use of advanced analytics and machine learning algorithms, this data can be processed and interpreted to identify patterns, trends, and insights that can help healthcare providers make more informed decisions. The application of multi-source data interpretation in healthcare is particularly important because it allows for a more holistic view of patient health, taking into account factors beyond traditional medical metrics. For example, social determinants of health, such as access to healthy food and safe housing, can have a significant impact on patient health outcomes, and these factors can be identified through the analysis of non-medical data sources. Overall, the use of multi-source data interpretation for field scale precision management in healthcare has the potential to significantly improve patient outcomes by providing healthcare providers with a more complete understanding of patient needs and circumstances. With advances in technology and analytics, healthcare providers can better harness the power of data to optimize patient care and improve health outcomes at scale.

Estimating Prevalence and Characteristics of Statin Intolerance among High and Very High Cardiovascular Risk Patients in Germany (2017 to 2020).

Statin intolerance (SI) (partial and absolute) could lead to suboptimal lipid management. The lack of a widely accepted definition of SI results into poor understanding of patient profiles and characteristics. This study aims to estimate SI and better understand patient characteristics, as reflected in clinical practice in Germany using supervised machine learning (ML) techniques. This retrospective cohort study utilized patient records from an outpatient setting in Germany in the IQVIA™ Disease Analyzer. Patients with a high cardiovascular risk, atherosclerotic cardiovascular disease, or hypercholesterolemia, and those on lipid-lowering therapies between 2017 and 2020 were included, and categorized as having "absolute" or "partial" SI. ML techniques were applied to calibrate prevalence estimates, derived from different rules and levels of confidence (high and low). The study included 292,603 patients, 6.4% and 2.8% had with high confidence absolute and partial SI, respectively. After deploying ML, SI prevalence increased approximately by 27% and 57% (p < 0.00001) in absolute and partial SI, respectively, eliciting a maximum estimate of 12.5% SI with high confidence. The use of advanced analytics to provide a complementary perspective to current prevalence estimates may inform the identification, optimal treatment, and pragmatic, patient-centered management of SI in Germany.

Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines.

Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.

Microfluidic Device for Patient-Centric Multiplexed Assays with Readout in Centralized Laboratories.

Patient-centric sampling strategies, where the patient performs self-sampling and ships the sample to a centralized laboratory for readout, are on the verge of widespread adaptation. However, the key to a successful patient-centric workflow is user-friendliness, with few noncritical user interactions, and simple, ideally biohazard-free shipment. Here, we present a capillary-driven microfluidic device designed to perform the critical biomarker capturing step of a multiplexed immunoassay at the time of sample collection. On-chip sample drying enables biohazard-free shipment and allows us to make use of advanced analytics of specialized laboratories that offer the needed analytical sensitivity, reliability, and affordability. Using C-Reactive Protein, MCP1, S100B, IGFBP1, and IL6 as model blood biomarkers, we demonstrate the multiplexing capability and applicability of the device to a patient-centric workflow. The presented quantification of a biomarker panel opens up new possibilities for e-doctor and e-health applications.

Building Adaptive Capacity: The Role of Advanced Analytics in Risk Management

In this study, we theoretically argue for a potential trade-off between risk information integrated into the strategy and the use of advanced analytics for risk management purposes with regard to the adaptive capacity of organizations. Furthermore, we investigate whether the adoption of an advanced analytics strategy helps overcome this suggested substitutive interrelationship by giving direction and communicating a clear strategic focus to employees, including senior management. To test our hypotheses, we collected survey data in summer 2020 from 174 non-financial companies that allows us to exploit the challenging setting of the Covid-19 pandemic for organizations and their risk management. Our results support both the substitutive relationship between strategy integration of risk management and the use of advanced analytics for risk management purposes, as well as the three-way complementarity between the two risk management design choices and advanced analytics strategy with regard to adaptive capacity. Thus, we contribute to the literature with new insights into the potential benefits of linking risk management practices to business intelligence and analytics in light of better decision-making and control.

Smart seru production system for Industry 4.0: a conceptual model based on deep learning for real-time monitoring and controlling

ABSTRACT Seru production system is an innovative assembly system and combines the flexibility of job shop production and high efficiency of assembly lines. This production system easily adapts to product replacements, reduces product wastages by eliminating work-in-process inventories and delay time, and provides enterprises with a competitive advantage through reducing the operating costs, required workforce, and space. Besides these advantages, the disadvantage of seru type production is that, in assembly lines, specific tasks are completed in specified stations, whereas in seru production, the tasks required for the assembly of a product are completed in a yatai by a cross-trained worker. This, in turn, results in a higher risk of production errors. Accordingly in this research, a conceptual model is proposed to monitor and control multiple factors of the production process like worker, environment, assembly tools, ergonomics, storage and inventory and issue warning for prevention of process and quality errors in seru production by use of advanced analytics based on deep learning. In addition to providing support to the worker, the proposed Smart Seru Production System Model will assist system participants in obtaining and understanding data about the production processes and reacting quickly based on this information.

Balancing fraud analytics with legal requirements: Governance practices and trade-offs in public administrations

Abstract Fraud analytics refers to the use of advanced analytics (data mining, big data analysis, or artificial intelligence) to detect fraud. While fraud analytics offers the promise of more efficiency in fighting fraud, it also raises legal challenges related to data protection and administrative law. These legal requirements are well documented but the concrete way in which public administrations have integrated them remains unexplored. Due to the complexity of the techniques applied, it is crucial to understand the current state of practice and the accompanying challenges to develop appropriate governance mechanisms. The use of advanced analytics in organizations without appropriate organizational change can lead to ethical challenges and privacy issues. The goal of this article is to examine how these legal requirements are addressed in public administrations and to identify the challenges that emerge in doing so. For this, we examined two case studies related to fraud analytics from the Belgian Federal administration: the detection of tax frauds and social security infringements. This article details 15 governance practices that have been used in administrations. Furthermore, it highlights the complexity of integrating legal requirements with advanced analytics by identifying six key trade-offs between fraud analytics opportunities and legal requirements.

Organisational enablers of advanced analytics adoption for supply chain flexibility and agility

Flexible and agile supply chains (SCs) add value by facilitating cost optimisation. Exceptional opportunities are being offered by smartening the SCs. The key is to further enhance the intelligence and intellect of the SCs by using analytics. Use of advanced analytics warrants proficient SC management that primes SC performance and helps to attain competitive advantage. It is worth noting that while this topic, in general, has attracted attention, there is a dearth of related work especially in the context of emerging markets. Many questions are yet unanswered and many aspects also remain unexplored, such as, what are the enablers for the adoption of advanced analytics in SC and the effects of the implementation of advanced analytics on SC performance indicators like flexibility and agility. Through this research work, with the help of detailed and structured literature review, prudent and viable organisational enablers for the adoption of advanced analytics in SC have been proposed. This research work other than adding to the existing knowledge base is intended to aid in acquiring a comprehensive understanding of the topic by the policy and decision makers.

The Role of Analytics Governance to Promote Health Care Transformation

Abstract Objectives Rapid digitization in health care during the 21st century has created significant data and analytics challenges for our providers and health systems. Just as information technology (IT) governance has helped manage exploding demand for IT services and increased efficiencies, analytics governance promises to bring these same benefits to data and analytics efforts. Potential governance models exist in other industries yet have not significantly penetrated health care. Methods and Results Geisinger has implemented analytics governance throughout our enterprise. We identified and accomplished six core goals toward the establishment of analytics governance, including developing a vision; defining the organizational structure, roles, and responsibilities; managing our data assets; implementing robust data governance; establishing standardized analytics processes; and utilizing metrics to evaluate our progress. Early outcomes include improved tracking and intelligence around data/analytics requests, decreases in duplicative data/analytics efforts, the creation of the Enterprise Analytics Hub for employees to consume data, and initial steps toward self-service analytics. Conclusion Our experiences support the proposition that analytics governance can provide meaningful benefits to health systems. It is clear from the experiences in other industries that health systems who can best manage their data and analytics will have a significant competitive advantage. Analytics governance will also provide a proper foundation for the use of advanced analytics, machine learning, and visualization tools, and prepare our workforce to utilize these tools for the benefit of patients.

The use of artificial neural networks and big data infrastructure for predictive analytics in solar energy

Abstract Renewable energy appears to be the solution to both the continuously growing energy demand, and pollution from fossil-based fuels. Recent advances in big data means that crucial areas of the energy supply chain are of interest to the use of advanced analytics. Solar energy is one of the most important renewable energy sources; however, it is stochastic, leading to production volatility and making it difficult to dispatch. The European Commission provides the legal framework and guidelines for increasing the adoption of renewable technologies in the European Union (EU). Meanwhile, the research community must provide solutions for increasing the predictability of solar energy: successful integration depends on how well solar energy production is predicted. Working under the Cross-Industry Standard Process for Data Mining, using real word operational data, this research focuses on providing a foundation of the analytics capabilities needed for reducing, or even removing, the disadvantages of solar energy, demonstrating that a world-class predicative tool can be obtained. Using weather and production data from photovoltaic cells installed in Romania, as a case study, coupled with the powerful artificial neural networks (ANN) architecture, results in a benchmark prediction performance. Currently, there is no research addressing photovoltaic energy production prediction by integrating the impact of artificial intelligence and big data.

Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design.

Interest in real‐world data (RWD) and real‐world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third‐party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single‐arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness “big” RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient‐derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.

Data-driven occupant-behavior analytics for residential buildings

Abstract Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. In this paper, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k-means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics.

Facebook and Pandora’s box: How using Big Data and Artificial Intelligence in advertising resulted in housing discrimination

In 2019, the US Department of Housing and Development charged Facebook with violating the Fair Housing Act 1968. This followed an investigation into the use of ethnically targeted advertising practices on Facebook. To understand Facebook’s targeting methods and the cause of the problematic outcomes, this paper follows the journey of an advertisement through Facebook’s platform. In this way, Facebook’s regulatory missteps can serve as a case study to illustrate how Big Data analytics can, when informed by human and machine bias, cross the line into discriminatory practices. This case study underscores how it is vital — in advertising as in other industries — not to treat advanced analytics like artificial intelligence as black boxes. Indeed, to inform the design and use of advanced analytics, it is essential for companies to consistently develop a comprehensive understanding of their data, in addition to the legal and ethical implications of their operations.

Business Data Ethics: Emerging Trends in the Governance of Advanced Analytics and AI

Advanced analytics and artificial intelligence are powerful technologies that, along with their benefits, create new threats to privacy, equality, fairness and transparency. Existing law does not yet protect sufficiently against these threats. This has led some organizations to pursue what they call “data ethics” or “AI ethics” in an attempt to bring advanced analytics and AI more into line with societal values and so legitimate their growing use of these technologies. To date, much of the scholarship on data ethics has sought either to define the ethical principles to which organization should aspire, or to map out the laws and regulations needed to push organizations towards these ethical goals. While these two lines of inquiry are important, the literature is missing a critical third dimension: empirical work on how organizations are actually governing the threats that their use of advanced analytics and AI can generate. Good regulatory design requires such knowledge. Yet, while there have been important studies of how organizations manage privacy “on the ground” (Bamberger and Mulligan 2015), there has been little such work on the governance of advanced analytics and AI. This report begins to fill this gap. Focusing on private sector organizations, the authors interviewed corporate privacy managers deemed by their peers to be leaders in the governance of advanced analytics and AI, as well as the lawyers, consultants and thought leaders who advise them on this topic. They also surveyed a wider range of privacy mangers. The study sought to answer three, fundamental questions about business data ethics management: (1) How do leading companies conceptualize the threats that their use of advanced analytics and AI pose for individuals, groups and the broader society? (2) If it is true that the law does not yet require companies to reduce these risks, then why are they pursuing data ethics? and (3) How are companies pursuing data ethics? What substantive benchmarks, management processes and technological solutions do they use towards this end? The authors previously shared on SSRN their preliminary findings. This final report provides a much fuller picture. The report should provide legislators and policymakers with an empirical foundation for their efforts to regulate advanced analytics and AI, at the same time as it gives interested organizations ideas on how to improve their data ethics management.

Improving Health Care with Advanced Analytics: Practical Considerations.

Artificial intelligence (AI) is becoming ubiquitous in health care, largely through machine learning and predictive analytics applications. Recent applications of AI to common health care scenarios, such as screening and diagnosing, have fueled optimism about the use of advanced analytics to improve care. Careful and objective considerations need to be made before implementing an advanced analytics solution. Critical evaluation before, during, and after its implementation will ensure safe care, good outcomes, and the elimination of waste. In this commentary we offer basic practical considerations for developing, implementing, and evaluating such solutions based on many years of experience.