This study analyzes global contamination trends, emphasizing the contributions of industrial and electricity sectors to environmental degradation. Using the analysis of the temporal series, the study identifies the sectoral and regional disparities in pollution levels, highlighting the urgent need for specific mitigation strategies. A feedforward neural network (FFNN) model predicts pollution levels over the next 3 years, revealing the most affected countries and allowing proactive policy interventions. The model proposed in the present study achieves a Mean Squared Error (MSE) of 79.6%, a Root Mean Squared Error (RMSE) of 89.2%, and a Mean Absolute Error (MAE) of 75.8%, ensuring a reliable prognosis accuracy. The results provide information based on multi-country sectoral data of fifty nations by applying an FFNN-powered global emission model, combining time series. The framework enables political leaders and industry leaders to implement sustainable practices, optimize emission controls, and develop specific regulations through predictive scenario analysis, providing a reliable basis for decision-making in the field of pollution control and climate change mitigation.

Extreme weather events pose significant risks and threats to the Sindh region, shedding light on the potential havoc they can wreak on livestock, human lives, and public infrastructure. Unusual monsoon rains in 2022 transformed the geography of Sindh, leading to devastating floods and significant damage. The current flood management strategies are effective, but there is some gap in early warning systems. Different findings suggest a need for refined approaches to improve predictive accuracy and response efficiency. The primary goal is to assess and improve existing flood response measures, identify opportunities for improvement, and strengthen resilience against flood events. In response to these challenges, this study provides actionable insights for policymakers and emergency planners to refine flood management strategies, ultimately aiming to bolster community resilience and preparedness. This paper presents a novel framework for a hybrid predictive model that combines machine learning and statistical analysis to improve the prediction of flood-induced displacement, providing timely warnings and preparing for future flood events. The selected dataset used for this research is from the 'Sindh Flood Data Analysis and Prediction 2022', which includes both demographic and geographical variables. By integrating machine learning and statistical models, the research evaluates their effectiveness in predicting flood impacts and identifying the most affected Tehsils using Python language and Seaborn library for analysis. The Gradient Boosting Classifier achieved the highest accuracy at 97.67%, followed by Logistic Regression at 95%. In contrast, Decision Trees and Lasso Regression demonstrated lower accuracies of 25% and 55%, respectively.

Visualization through charts has become a crucial method for presenting complex data in a clear and structured format, surpassing traditional representation techniques in both clarity and interpretability. However, the automatic classification of chart images remains a significant challenge, particularly in the absence of labeled data. This study investigates the unsupervised classification of chart images using a combination of deep learning and clustering techniques. The primary objective is to develop a method capable of automatically categorizing four common chart types, histogram, bar, line, and pie charts without relying on annotated datasets. These chart types are frequently used in critical domains such as financial, socio-economic, and political analysis. To achieve this, a pre-trained Visual Geometry Group 16 (VGG16) model is employed for feature extraction, followed by principal component analysis (PCA) for dimensionality reduction. The k-means clustering algorithm is then applied to group visually similar chart images. For classification, three pre-trained models, Residual Network 50 (ResNet50), Residual Network 50 Version 2 (ResNet50V2), and Densely Connected Convolutional Network (DenseNet) are evaluated alongside a customized convolutional neural network (CNN) using the ChartVQA dataset. The models achieve classification accuracy of 85.40%, 80.76%, 82.46%, and 90.71%, respectively. By integrating transfer learning with clustering and CNN-based architecture, this study presents a robust and scalable framework for unsupervised chart image classification.

Most STEM (Science, Technology, Engineering, and Mathematics) subjects rely heavily on graphs and charts, which remain largely inaccessible to students who are blind or visually impaired. While text can often be made accessible through screen readers, complex visual structures such as charts are much harder to interpret non-visually. This study presents a proof-of-concept system that applies the DONUT (Document Understanding Transformer) model to STEM charts. The model was trained and evaluated on the Benetech STEM dataset and tested on multiple images, demonstrating promising results in extracting key information such as chart type, chart ID, and x–y coordinate values. Although no user-centered trials or formal educational studies have yet been conducted, this work establishes an initial technical foundation for converting chart data into accessible formats. By enabling interpretation of chart types and data trends, the proposed system has the potential to improve accessibility in STEM education for blind and visually impaired learners, pending further validation and integration with assistive technologies.

In the era of digital advancements, education has undergone a profound transformation through the integration of data science and predictive analytics. The application of data science methodologies in education revolves around the collection and analysis of extensive data from diverse sources, encompassing student demographics, attendance records, assessment scores, and more. This research offers an overview of the fundamental elements and implications of utilizing data science to predict student performance within educational institutions. The findings show that attitudes towards the current grading system are varied: 53% of respondents are satisfied with it, while 42% think there are many factors which need to be considered to determine academic success. Opinions differed as to whether the grading system reflects a child's true capabilities; 48% agreed, whereas 14% disagreed. There is also no consensus regarding how parents would like to be kept informed about their children’s progress at school; some would rather have regular meetings with teachers, others for example, prefer sending emails, etc., so that each parent can choose his/her preferred communication channel. Thus, according to 48% of those surveyed believe there should be Parent-Teacher Conferences (PTCs) while another 42% feel more resources will make prediction and improvement better respectively.