Predicting young imposter syndrome using ensemble learning

Background. Imposter syndrome (IS), associated with self-doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well-being. This study aimed to predict the students' IS using the machine learning ensemble approach. Methods. This study was a cross-sectional design among medical students in Bangladesh. Data were collected from February to July 2020 through snowball sampling technique across medical colleges in Bangladesh. In this study, we employed three different machine learning techniques such as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. Results. In total, 500 students completed the questionnaire. We used the YIS scale to determine the presence of IS among medical students. The ensemble model has the highest accuracy of this predictive model, with 96.4%, while the individual accuracy of random forest and neural network is 93.5% and 96.3%, respectively. We used different performance matrices to compare the results of the models. Finally, we compared feature importance scores between neural network and random forest model. The top feature of the neural network model is Y7, and the top feature of the random forest model is Y2, which is second among the top features of the neural network model. Conclusions. Imposter syndrome is an emerging mental illness in Bangladesh and requires the immediate attention of researchers. For instance, in order to reduce the impact of IS, identifying key factors responsible for IS is an important step. Machine learning methods can be employed to identify the potential sources responsible for IS. Similarly, determining how each factor contributes to the IS condition among medical students could be a potential future direction

Safety Measures of Journalists during Corona Pandemic in Bangladesh

Among other frontline fighters journalists have been the first responders to the pandemic of the COVID-19 virus Because of following professional responsibilities they have become highly vulnerable to get exposed to the risk As a result providing safety measures to them has received the highest priority at this time It has been urged by national and international organizations and associations to media employers to provide safety measures to their respective journalists This study aims to examine the management of media employers of Bangladesh in providing safety measures to journalists The study interviews 48 journalists of 12 newspapers and 12 television channels selecting one reporter and one copy editor from each media The results reveal that the majority of journalists received inadequate nonstandard irregular imbalanced and improper safety measures while the rest got nothing because of the employer s total negligence and financial crisis The study also shows that the media employers failed to distribute safety measures between reporters and copy editors equally Based on the findings the study concludes by calling for a proper safety plan to protect journalists from health risk

Processed Radio Frequency towards Pancreas Enhancing the Deadly Diabetes Worldwide

Diabetes is a chronic and debilitating disease, which is associated with a range of complications putting tremendous burden on medical, economic and socio-technological infrastructure globally. Yet the higher authorities of health services are facing the excruciating cumulative reasons of diabetes as a very imperative worldwide issue in the 21st century. The study aims to relook at the misapplication of the processed radio frequency that frailties in the pancreas within and around the personal body boundary area. The administered sensor data were obtained at laboratory experiments from the selected specimens on dogs and cats in light and dark environments. The study shows the frequent urine flow speed varies with sudden infection due to treated wireless sensor networks in active open eyes. The overweight and obese persons are increasingly affected in diabetes with comprehensive urinary pressure due to continuous staying at dark environment. The findings replicate the increasing tide of diabetes globally. The study also represents the difficulties of physicians to provide adequate diabetic management according to their expectancy due to insecure personal area network control unit.Dynamic sensor network is indispensable for healthcare but such network is at risk to health security due to digitalized poisoning within GPS positions. The study recommends the anti-radiation integrated system policy with user’s security alternative approach to inspire dealing with National Health Policy and Sustainable Development Goals 2030

Impact of Sensor Networks on Aquatic Biodiversity in Wetland: An Innovative Approach

Aquatic biodiversity is in the central field of environmental conservation issues in a wetland. Yet it determinately faced aquatic conservation authorities the loss of biodiversity as a very important global issue for several years due to misuse wireless sensor technology. The study attempts to re-look at the sensor networks that affect the aquatic biodiversity within and around the Tanguar Haor- wetland study at Sunamganj district in Bangladesh. Key aquatic conservation tools provided at the Tanguar Haor and its challenges with gaps in policies for wetland management practices are highlighted. The study shows the aquatic biodiversity-related rules and regulations amended were apex in Bangladesh from 2010 to 2018. The study represents the impact of processed sensor networks on aquatic biodiversity in a wetland to be compared to larger, medium, and smaller animals in a bright, dark and optimum environment, facilitating the design and misuse of wireless sensor networks within GPS locations. Approximately 64% of the respondents agreed on the development of aquatic biodiversity for managing the wetland at Sunamganj with secure peripheral sensor networks. The research also found that the Tanguar Haor is at risk due to misuse of wireless sensor networks compared to other wetlands in the Sylhet Division. Scientific knowledge is indispensable in wetland resource management but it poorly identified such knowledge while various performances are still below par. The research is unique and represents the innovative idea to improve the existing wetland policy linking with the appropriateness for the Ramsar Wetland Conservation Strateg

Rank Your Summaries: Enhancing Bengali Text Summarization via Ranking-based Approach

With the increasing need for text summarization techniques that are both efficient and accurate, it becomes crucial to explore avenues that enhance the quality and precision of pre-trained models specifically tailored for summarizing Bengali texts. When it comes to text summarization tasks, there are numerous pre-trained transformer models at one's disposal. Consequently, it becomes quite a challenge to discern the most informative and relevant summary for a given text among the various options generated by these pre-trained summarization models. This paper aims to identify the most accurate and informative summary for a given text by utilizing a simple but effective ranking-based approach that compares the output of four different pre-trained Bengali text summarization models. The process begins by carrying out preprocessing of the input text that involves eliminating unnecessary elements such as special characters and punctuation marks. Next, we utilize four pre-trained summarization models to generate summaries, followed by applying a text ranking algorithm to identify the most suitable summary. Ultimately, the summary with the highest ranking score is chosen as the final one. To evaluate the effectiveness of this approach, the generated summaries are compared against human-annotated summaries using standard NLG metrics such as BLEU, ROUGE, BERTScore, WIL, WER, and METEOR. Experimental results suggest that by leveraging the strengths of each pre-trained transformer model and combining them using a ranking-based approach, our methodology significantly improves the accuracy and effectiveness of the Bengali text summarization.Comment: Accepted in International Conference on Big Data, IoT and Machine Learning 2023 (BIM 2023

Barriers to Electric Vehicle Adoption in Thailand

Electric vehicles (EVs) are considered to be a solution for sustainable transportation. EVs can reduce fossil fuel consumption, greenhouse gas emissions, and the negative impacts of climate change and global warming, as well as help improve air quality. However, EV adoption in Thailand is quite low. Against this backdrop, this study investigates barriers and motivators for EV adoption and their public perception in Thailand. A total of 454 responses were collected through an online questionnaire. The results indicate that the top three concerns of respondents about EVs are public infrastructure and vehicle performance in terms of charge range and battery life. Respondents with more than five years of driving experience in the age range of 26–35 years old could be key targets for early EV adoption