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Research Article
Prognosticate the Analogous Region in Bangladesh Utilizing an Unsupervised Machine Learning Technique
Md. Habibur Rahman*
,
Humayra Sadia
Issue:
Volume 11, Issue 3, June 2025
Pages:
46-62
Received:
28 March 2025
Accepted:
20 May 2025
Published:
13 June 2025
DOI:
10.11648/j.ijdsa.20251103.11
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Abstract: Climate regionalization provides valuable insights into the climatic challenges faced by a country, enabling better preparedness for climate change impacts and the development of targeted strategies. In this study, the climate regionalization of Bangladesh was performed based on nine climatic factors from 34 weather stations using unsupervised machine learning techniques. The exploratory data analysis was performed to assess the characteristics of the parameters, revealing distributional patterns. Principal Component Analysis (PCA) was then applied to reduce the dimensionality of the data and extract significant climate patterns. Following this, the non-hierarchical k-means clustering algorithm was used to group the locations into homogeneous clusters. The optimal number of clusters was determined using three widely recognized methods: the average silhouette score, the gap statistic, and the elbow method, before applying the clustering. While both the Silhouette Method and Gap statistic suggested three clusters, the elbow method identified nine clusters, which provided a more detailed regionalization. The locations Barisal, Jessore, Khepupara, Khulna, Mongla, Potuakhali, Satkhira from the south-west region form a significant cluster with Faridpur. The second largest cluster includes Bogra, Dinajpur, Ishurdi, Rajshahi, Rangpur, and Saidpur from the North-West region of Bangladesh. The findings of this study demonstrate that clustering offers a systematic approach to understanding the spatial distribution of climatic characteristics, facilitating informed decision making, resource allocation, and the development of policies tailored to the specific needs of different geographic regions in Bangladesh.
Abstract: Climate regionalization provides valuable insights into the climatic challenges faced by a country, enabling better preparedness for climate change impacts and the development of targeted strategies. In this study, the climate regionalization of Bangladesh was performed based on nine climatic factors from 34 weather stations using unsupervised mach...
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Research Article
Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks
Ndegwa Ruth Wambui*,
Mwalili Samuel,
Wamwea Charity
Issue:
Volume 11, Issue 3, June 2025
Pages:
63-75
Received:
22 April 2025
Accepted:
7 May 2025
Published:
16 June 2025
DOI:
10.11648/j.ijdsa.20251103.12
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Abstract: The ongoing humanitarian crises in Sudan, which are being exacerbated by escalating climate change, persistent conflict, and migratory waves, highlight the critical need for predictive, data-driven models to guide efficient response plans. In order to investigate and predict the intricate links between climate variability, conflict intensity, and migration patterns in Sudan at the subnational level, this study combines Spatio-Temporal Neural Networks (STNNs) with Generalized Additive Models (GAMs). The hybrid modeling framework provides strong insights into displacement patterns and conflict dynamics by capturing both spatial-temporal dependencies and nonlinear effects. GAMs showed a high positive correlation between conflict severity and precipitation levels, as well as statistically significant nonlinear relationships between food prices and relocation. In the meantime, the STNNs performed better than traditional modeling techniques, with R2 values of 0.89 and 0.84 for conflict intensity and regional displacement prediction, respectively. These excellent performance measures show how well the model captures real-world dynamics and provide a useful tool for humanitarian predictions. Prototype app for visualizing migration and conflict forecasts are included in the study, but they are still in the early stages of development. Future studies aim to improve operational utility and decision-making support in the field through real-time application. The results highlight how crucial it is for humanitarian research to combine machine learning and statistical modeling. This study offers practical insights that can enhance early warning systems, policymaking, and disaster preparedness in climate-vulnerable areas like Sudan by identifying important drivers of displacement and violence.
Abstract: The ongoing humanitarian crises in Sudan, which are being exacerbated by escalating climate change, persistent conflict, and migratory waves, highlight the critical need for predictive, data-driven models to guide efficient response plans. In order to investigate and predict the intricate links between climate variability, conflict intensity, and m...
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Research Article
Analysing Concerns and Expressions of Using ChatGPT on Social Media and Educational Platform: An Application of Natural Language Processing and Machine Learning
Farhana Bina*
Issue:
Volume 11, Issue 3, June 2025
Pages:
76-98
Received:
30 April 2025
Accepted:
14 May 2025
Published:
19 June 2025
DOI:
10.11648/j.ijdsa.20251103.13
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Views:
Abstract: The advancement in Artificial Intelligence technology revolutionizes new opportunities and challenges, particularly with large language model ChatGPT, in various domains, especially in the educational platform. This research endeavors a comprehensive analysis to explore the concerns and expressions associated with this AI tool on the social media platform X and in academic contexts. Two distinct datasets, comprising X data and survey responses from academics, were utilized to achieve the objectives. This research examines the valuable concerns regarding ChatGPT among X users on social media platform. To implement the Natural Language Processing (NLP) techniques which included Sentiment Analysis and Topic Modeling using Latent Dirichlet Analysis (LDA), the study aimed to identify the significant insights expressed by the social media users. The analysis obtained that, most frequent discussed topic was “ChatGPT”. The majority of discussions among the X users were positive in sentiment (49%), focusing on the utility of ChatGPT. Comparatively, negative discussions (47%) were also expressed by the users (47%) about students’ cheating in exams, and the generation of inaccurate information, which could affect students’ learning skills, and their critical thinking. Furthermore, approximately 27% of the discussions were expressed neutral sentiment regarding the generation of contents by ChatGPT. Various machine learning models were implemented to predict the classification of sentiment labels correctly. The Random Forest model performed well to classify all the sentiment labels correctly compared to others with highest accuracy of 62%. This research also unveiled the academics’ opinion in the context of education. A case study was conducted among the academics, where approximately 59% reported using ChatGPT for academic purposes and academics (24%) use this tool occasionally. In terms of its usefulness, 32% academics consider it is as useful, especially for generating writing contents. Additionally, 29% of them believed that this tool primarily improves students’ language and writing skills but they also expressed the concerns about overreliance potentially impacting their critical thinking and violating academic integrity. The major concerned keywords for academics include “research”, “accuracy of information”, and “critical thinking”, while for students, “academic integrity”, “critical thinking”, “risk”, “copy-paste”, and “creativity skills”. The majority of the sentiments regarding the concerns were negative for students (38%), and minority for academics (28%). Overall, academics expressed positive sentiments about the utility of using ChatGPT. This research highlights these findings and recommends further exploration of using this tool in educational practices with a focus on the identified concerns to guide future implementation.
Abstract: The advancement in Artificial Intelligence technology revolutionizes new opportunities and challenges, particularly with large language model ChatGPT, in various domains, especially in the educational platform. This research endeavors a comprehensive analysis to explore the concerns and expressions associated with this AI tool on the social media p...
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