Implementation Of Association Technique In Identify The Frequent Pairing Subject From Private Tutor Data

In this project we are doing research on implementation data mining in big data. We have explored about Market Basket Analysis is that consist of the algorithm, technique and implementation method. In market basket analysis, normally we can identify the business study and research were focusing on the marketing and business domain. There are no research’s about education domain. So in this research, we would like to implement the market basket analysis in education domain. We would like to identify the frequent pairing request subject by customer. We are using real data that has been scrapped from online tuition website. The implementation process will be doing using rapid miner tools. An Apriori and Association algorithm was selected to implemented this project

Changing the focus: worker-centric optimization in human-in-the-loop computations

A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back to humans, and study different data analytics problems, by recognizing characteristics of the human workers, and how to incorporate those in a principled fashion inside the computation loop. The first contribution of this dissertation is to propose an optimization framework and a real world system to personalize worker’s behavior by developing a worker model and using that to better understand and estimate task completion time. The framework judiciously frames questions and solicits worker feedback on those to update the worker model. Next, improving workers skills through peer interaction during collaborative task completion is studied. A suite of optimization problems are identified in that context considering collaborativeness between the members as it plays a major role in peer learning. Finally, “diversified” sequence of work sessions for human workers is designed to improve worker satisfaction and engagement while completing tasks

Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations

Existing aspect extraction methods mostly rely on explicit or ground truth aspect information, or using data mining or machine learning approaches to extract aspects from implicit user feedback such as user reviews. It however remains under-explored how the extracted aspects can help generate more meaningful recommendations to the users. Meanwhile, existing research on aspect-based recommendations often relies on separate aspect extraction models or assumes the aspects are given, without accounting for the fact the optimal set of aspects could be dependent on the recommendation task at hand. In this work, we propose to combine aspect extraction together with aspect-based recommendations in an end-to-end manner, achieving the two goals together in a single framework. For the aspect extraction component, we leverage the recent advances in large language models and design a new prompt learning mechanism to generate aspects for the end recommendation task. For the aspect-based recommendation component, the extracted aspects are concatenated with the usual user and item features used by the recommendation model. The recommendation task mediates the learning of the user embeddings and item embeddings, which are used as soft prompts to generate aspects. Therefore, the extracted aspects are personalized and contextualized by the recommendation task. We showcase the effectiveness of our proposed method through extensive experiments on three industrial datasets, where our proposed framework significantly outperforms state-of-the-art baselines in both the personalized aspect extraction and aspect-based recommendation tasks. In particular, we demonstrate that it is necessary and beneficial to combine the learning of aspect extraction and aspect-based recommendation together. We also conduct extensive ablation studies to understand the contribution of each design component in our framework

Human-AI complex task planning

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, sub-tasks are points of interest (POIs) and constraints represent time and monetary budget, or user-specified requirements. Needless to say, task plans are to be individualized and designed considering uncertainty. When done manually, the process is human-intensive and tedious, and unlikely to scale. The goal of this dissertation is to present computational frameworks that synthesize the capabilities of human and AI algorithms to enable task planning at scale while satisfying multiple objectives and complex constraints. This dissertation makes significant contributions in four main areas, (i) proposing novel models, (ii) designing principled scalable algorithms, (iii) conducting rigorous experimental analysis, and (iv) deploying designed solutions in the real-world. A suite of constrained and multi-objective optimization problems has been formalized, with a focus on their applicability across diverse domains. From an algorithmic perspective, the dissertation proposes principled algorithms with theoretical guarantees adapted from discrete optimization techniques, as well as Reinforcement Learning based solutions. The memory and computational efficiency of these algorithms have been studied, and optimization opportunities have been proposed. The designed solutions are extensively evaluated on various large-scale real-world and synthetic datasets and compared against multiple baseline solutions after appropriate adaptation. This dissertation also presents user study results involving human subjects to validate the effectiveness of the proposed models. Lastly, a notable outcome of this dissertation is the deployment of one of the developed solutions at the Naval Postgraduate School. This deployment enables simultaneous route planning for multiple assets that are robust to uncertainty under multiple contexts

Data management in audiovisual business: Netflix as a case study

Big data has become an enormous asset for on-demand content distribution services, helping information supply and decision- making, regarding both the content of the database and suscribers to the database. In this article we describe and define big data and data management in a media company devoted to on-demand audiovisual content distribution: Netflix. This article suggests that big data is a prime strategy in media business and outlines the upcoming challenges that follow its global expansion

Telehealth and Type 2 Diabetes Management

The use of telehealth in healthcare has grown in recent years; however, little is known about the effectiveness of this delivery method in the management of Type 2 diabetes mellitus (T2DM). Guided by the chronic care model and telehealth in chronic disease model, the purpose of this systematic literature review was to explore evidence related to lowering hemoglobin A1c levels and managing T2DM using telehealth in the outpatient setting. The practice-focused questions explored telehealth interventions used in T2DM management and their effectiveness. The Joanna Briggs Institute (JBI) method for conducting systematic literature reviews was the process, and data were compiled using the PRISMA evidence-based minimum set for reporting. Eighteen studies met the inclusion criteria for this project. Data were extracted, analyzed, and synthesized using JBI tools for data extraction and critical appraisal. Article appraisals revealed numerous telehealth interventions for management of T2DM including telephone, Internet-based, clinical video, remote monitoring, and smart phones/applications. Overall, telehealth interventions showed statistically significant improvement in the hemoglobin A1c levels of participants compared to traditional outpatient care. Success of the interventions is associated with components of evidenced-based diabetes management such as education, self-management, support, and feedback loop. The implications of this project for positive social change include the integration of telehealth interventions in the outpatient setting to manage T2DM with enhanced access to care, reduction in health disparities, and improved health outcomes for society

Fairness in Algorithmic Multi-disciplinary Team Formation

Fairness in team creation is becoming an increasingly important subject of study in computer science and artificial intelligence. As algorithms increasingly automate decision-making processes, ensuring these systems are fair and unbiased has become a key concern. Team formation is one area of study where algorithms are used to match individuals with complementary talents and expertise to establish productive teams. When it comes to the process of forming teams, fairness is an essential component. Based on the relevant research, the thesis proposes the Rule-Based Expert Extraction Method and the Group-project distance and Unfairness Optimization Method to improve fairness during the team-formation process. Additionally, To assess the unfairness, the two proposed approaches are compared with the Pair-round selection method, which was previously examined by Machado and Stefanidis (2019). The fairness improvement is evaluated and compared. Several metrics were taken to assess the refined performance in the team formation process to create a balanced and fair team. The primary goal was to increase fairness when forming multidisciplinary teams. In terms of promoting fairness, the results reveal that the Group-project distance and Unfairness Optimization Method and the Rule-Based Expert Extraction Method perform slightly better than the Pair-round choosing method. The Rule-Based Expert Extraction Method has the most significant Group-project distance, followed by the Group-project distance and Unfairness Optimization Method, and the Pair-rounds choosing method. However, the new approaches have improved fairness and mitigated the increased Group-project distance. Overall, the experimental evaluation demonstrates the potential of the Group-project distance and Unfairness Optimization method and the Rule-Based Expert Extraction method to improve fairness in team formation, which has significant consequences for businesses and organizations that rely on team collaboration

Expanding Use of Sodium-Glucose Cotransporter-2 Inhibitor (SGLT2i) In Managing Patients with Diabetes and Chronic Kidney Disease in Primary Care

Practice Problem: In 2022, the addendum of standards of medical care in diabetes management was annotated to recommend the broader use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) to treat patients with Type 2 diabetes mellitus (DM) and diabetic nephropathy. Despite the Department of Veterans Affairs’ (VA) efforts to include SLGT2i as a formulary, non-restrictive prescription in the primary care ordering menu, the overall utilization rates of SGLT2i remained relatively low in primary care. PICOT: The PICOT question that guided this project was: In patients with DM and chronic kidney disease (CKD) (P), how does an evidence-based guideline algorithm bundle (I) compared to standard care (C) affect providers’ adherence and prescribing practices of including SGLT2 inhibitors (O) within 10 weeks (T)? Evidence: An extensive evidence literature review supported that the algorithm approach with current guidelines has allowed clinicians to identify patients eligible for SGLT2i was based on comprehensive risk assessment with various comorbidities and risk factors. The guideline-based algorithm was a quick reference guide to provide clarity and indication for patients with the most significant potential benefits from SGLT2i therapy. Intervention: The algorithm bundle, designed to reflect the current guidelines, was intended to enhance primary care clinicians\u27 prescribing confidence in SGLT2i and guide better decision-making. The algorithm bundle comprised the physical laminated algorithm card, embedded reminder in the e-prescribing menu, and a focused education session for the primary care providers. Outcome: The project outcomes reflected that the algorithm bundle has clinical significance in improving prescribers’ knowledge of SGLT2i agents and practice compliance, as evidenced by a rise in SGLT2i prescriptions. Conclusion: The algorithm bundle intervention in this project resonates with the American Diabetic Association’s (2022) latest recommendation to widen indications for using SGLT2 to optimize the management of DM and CKD patients. The evidence supports using a guideline-based algorithm to guide clinicians with a comprehensive assessment of high-risk patients and a better decision-making tool. Continued efforts to educate and audit primary care providers are essential to identify potential knowledge gaps and to sustain practice compliance of using SGLT2i as part of the standard of care

A Framework for Leveraging Artificial Intelligence in Project Management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business and change traditional ways of working. For the purpose of this study, it is essential to understand challenges and areas of project management and how artificial intelligence can contribute to them. A theoretical overview, applying the knowledge of project management, will show a holistic view of the current situation in the enterprises. The research is about artificial intelligence applications in project management, the common activities in project management, the biggest challenges, and how AI and ML can support it. Understanding project managers help create a framework that will contribute to optimizing their tasks. After designing and developing the framework for applying artificial intelligence to project management, the project managers were asked to evaluate. This study is essential to increase awareness among the stakeholders and enterprises on how automation of the processes can be improved and how AI and ML can decrease the possibility of risk and cost along with improving the happiness and efficiency of the employees