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Research Paper

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

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Research Paper

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

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Review Paper

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

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Review Paper

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

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Research Paper

Application of Catboost algorithm as a predictive tool in a CNC turning process

In this paper, an ensemble learning method, in the form of a Categorical boost (Catboost) algorithm is adopted as an effective predictive tool for envisaging values of average surface roughness and material removal rate during the CNC turning operation of C45 steel workpiece with a tungsten carbide cutting tool. In order to develop the related models, a grid with combinations of different hyperparameters is created and tested for all the possible hyperparametric combinations of the model. The configurations having the optimal values of the considered hyperparameters and yielding the lowest training error are finally employed for predicting the response values in the CNC turning process. The performance of the developed models is finally validated with the help of root mean squared percentage error. It can be observed that Catboost can be efficiently applied as a predictive tool with excellent accuracy in machining processes.

Published by: Lalitkishore N., Shriraam Manoharan

Author: Lalitkishore N.

Paper ID: V9I5-1153

Paper Status: published

Published: October 12, 2023

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Technical Notes

Forensic pathology and autopsy techniques

forensic medicine and pathology play a vital role in the justice system and society at large. The techniques and expertise of forensic pathologists are instrumental in determining the cause of death, solving crimes, and ensuring justice is served. This field continues to evolve with advancements in technology, making it a critical aspect of modern law enforcement and public health.

Published by: Dr. G. Panneer Selvam

Author: Dr. G. Panneer Selvam

Paper ID: V9I5-1160

Paper Status: published

Published: October 12, 2023

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