Optimizing Regulatory Compliance in Accounting: A Holistic Approach through Audits, Training, and Technology
With the constantly evolving regulatory landscape, organizations face high financial, legal, and reputational risks. To cope with these risks effectively, a holistic approach needs to be implemented, which includes periodic audits, targeted employee training, and cutting-edge regulatory technology. In this paper, we present a framework that employs machine learning techniques to predict regulatory violation rates. By using advanced algorithms and data analytics, our model not only identifies potential compliance breaches but also facilitates proactive decision-making and risk prevention. The use of machine learning enhances the accuracy and efficiency of compliance predictions, thereby enabling organizations to be a step ahead of regulatory challenges. We conduct a detailed analysis of real-world data from different sectors, employing a range of machine-learning algorithms to develop a predictive model. The results of the model demonstrate the efficacy of our approach in accurately forecasting regulatory violations. Additionally, we explore the effects of periodic audits, employee training programs, and regulatory technology to enhance overall compliance. This paper contributes valuable insights to the field of regulatory compliance and machine learning applications. The findings from the research provide a path for companies to proactively prevent financial losses, legal complications, and reputational damage. By embracing this holistic approach, organizations can create a culture of compliance, ensuring sustainable growth and resilience in the face of regulatory challenges. It also emphasizes the importance of continuous improvement, suggesting that a dynamic approach to compliance, informed by real-time data and machine learning insights, is pivotal in maintaining robust regulatory adherence and safeguarding organizational integrity.
Published by: Vaishnav Bhujbal, Dheeraj Nale
Author: Vaishnav Bhujbal
Paper ID: V9I5-1178
Paper Status: published
Published: October 18, 2023
Assessing a set of policies and the usage of smart tech to mitigate and minimize the risk of school shootings in the United States
Analyze how the implementation of smart guns, along with other policies to enhance the efficiency of this technology, should mitigate the risk of public and private school shootings, as well as the possible psychological ramifications it might have on students and the quality of the education that they receive.
Published by: Dia Bagla, Jiho Choi, Aditya Gupta, Ammu Santosh
Author: Dia Bagla
Paper ID: V9I5-1169
Paper Status: published
Published: October 18, 2023
Spatiotemporal variability time-series analysis of North American wildfire intensity on vegetation recovery using NDVI, EVI, and GPP
Wildfires are major disturbances that can leave lasting impacts on the ecosystem, biodiversity, and our society. Just this past year, over 4 million acres of land were burned across California, making the 2020 fire season the largest ever recorded in the state. Using three indices derived from satellite data, NDVI, EVI, and GPP, the post-fire recovery values in the indices were analyzed, and the results were used to determine whether vegetation type could affect the recovery. Three fires from the 2004 fire season in Alaska were selected to mimic the forest ecosystems in California without direct disturbances from human activities. MODIS-derived images were extracted from the Earth Explorer database every year from 2000-2018 and individually processed in QGIS to calculate NDVI, EVI, and GPP. The data of pre-fire areas from 2000-2003 was averaged and used as a control and reference area. NDVI and EVI values in post-fire recovery were extremely similar in areas with dense conifer populations and took an average of 8 years to recover, while GPP values show quicker recovery at only 3 years. Results also demonstrate that an area with a balance of 46% shrubs and 40% conifers recovered much faster, at around 3 years for all indices, in comparison to areas with 76% dense conifer populations. Using only one index is not enough for the most accurate results and it is critical to implement a variety of remote-sensing techniques in forest planning and recovery.
Published by: Amy Zheng
Author: Amy Zheng
Paper ID: V9I5-1174
Paper Status: published
Published: October 17, 2023
A study on the impact of binge-watching on dissociation
To study the impact of Binge-watching on Dissociation Binge-watching is a relatively new phenomenon that has gained popularity recently. Due to Covid 19 Lockdown, OTT platforms have seen a 65% increase in new subscriptions. Many studies have looked upon binging as a behavior, but minimal studies investigate the specific bingeing aspect, of binge-watching and the effects it might be causing. This study analyses binge-watching and its impact on dissociation-normative dissociation. The study consists of a survey design that helps to understand the relationship between binge-watching and dissociation. It comprised 125 responses divided into two age groups viz. 18-25 years and 25-30 years. The individuals were compared using the Binge Watching Engagement and Symptom Questionnaire (BWESQ) and the Cambridge Depersonalization Scale (CDS). It was hypothesized that there is no correlation between binge-watching and dissociation, there is no difference between excessive and non-excessive binge-watchers concerning dissociation, and there is no difference between the two age groups 18-25 and 25-30 concerning binge-watching and dissociation. Post correlation analysis, it was found that Binge-watching correlated positively with Dissociation. A difference is observed with respect to excessive binge-watchers and non-excessive binge-watchers for dissociation. It was also found that there is no difference between the age groups 18-25 and 26-30 on binge-watching and dissociation. These findings suggest that further research can be done on neuropsychological, executive functioning, and structural aspects of the same.
Published by: Arushi Aniruddha Bhorkar
Author: Arushi Aniruddha Bhorkar
Paper ID: V9I5-1152
Paper Status: published
Published: October 14, 2023
Ethical Guideline for Use of AL and ML Algorithms in Decision Making
Decision-making is a structured process involving the identification of objectives, the collection of relevant information, and the evaluation of potential solutions. This process fosters an environment conducive to innovation. Artificial Intelligence (AI) and Machine Learning (ML) algorithms comprise a set of instructions that enable machines to learn and make decisions based on acquired knowledge. The rapid evolution of research, development, and application of these technologies has led to their increasing integration into decision-making processes. To ensure the ethical use of AI and ML algorithms, comprehensive guidelines have emerged. These guidelines provide a framework for the responsible development and deployment of technology. By incorporating principles of transparency, fairness, accountability, and accuracy into AI and ML algorithms, these guidelines aim to build trust and mitigate bias. Despite the existence of various guidelines, they often lack specific applicability to particular use cases. To address these challenges and provide practical guidance, we have derived actionable guidelines for the ethical use of AI and ML algorithms in decision-making from existing ethics. This review paper analyzes these guidelines and provides a detailed overview of the ethical principles underpinning them.
Published by: Diksha Gaikwad, Aditee Thute, Akanksha Jadhav, Apurva Shelke
Author: Diksha Gaikwad
Paper ID: V9I5-1173
Paper Status: published
Published: October 14, 2023
Therapeutic use of psilocybin to treat alcohol use disorder: New York University
Alcoholism, a pervasive global health issue, demands innovative and effective treatment modalities. Recent interest in psychedelic drug therapy has opened new possibilities for addressing the complexities of alcohol use disorder (AUD). This research paper provides a comprehensive analysis of the groundbreaking trial conducted by New York University, which investigated the therapeutic potential of psilocybin, a classic psychedelic substance, in treating alcoholism. The trial's methodology included a double-blind randomized clinical trial measuring the efficacy of psilocybin against a placebo.Results indicated that 50% of the patients who received psilocybin stopped drinking altogether. These findings signify the potential of psychedelic drug therapy as a transformative intervention for alcoholism and highlight the need for further research in this promising field.
Published by: Yuti
Author: Yuti
Paper ID: V9I5-1167
Paper Status: published
Published: October 12, 2023
