The Ganga, a critical lifeline for nearly 400 million people, is increasingly threatened by heavy metal contamination from industrial effluents, agricultural runoff, and urban discharge. This study quantified eight heavy metals—iron (Fe), manganese (Mn), zinc (Zn), chromium (Cr), lead (Pb), nickel (Ni), copper (Cu), and cadmium (Cd)—in suspended solid particles collected from ten sites along a 210-kilometer stretch between Kanpur and Prayagraj, Uttar Pradesh, in July 2024. Suspended particulates were isolated by membrane filtration and analyzed using Atomic Absorption Spectrophotometry (AAS). Median concentrations (μg/g) were Fe (47,246), Mn (940), Zn (206), Cr (122), Pb (65.5), Ni (64.8), Cu (61.3), and Cd (1.61), with the highest values for Fe, Mn, Zn, Pb, and Cu observed at Nawabganj and Manikpur. Enrichment Factor (EF) analysis, using Fe as the reference element, indicated minor anthropogenic enrichment for Pb (1.32), Cd (1.27), Zn (1.20), Cr (1.13), and Ni (1.07), particularly at Dalmau Bridge, Unchahar, Nawabganj, and Manikpur. Mn and Cu showed EF values below 1, suggesting predominantly natural origins, although strong inter-metal correlations point to potential industrial sources for Cu. Multivariate statistical tools (PCA, clustering) revealed spatial contamination patterns and identified Nawabganj and Manikpur as consistent pollution hotspots. Compared to global rivers, the Ganga exhibited significantly higher suspended solid heavy metal loads (e.g., Pb: 65.5 μg g ⁻¹ versus Yangtze: 43 μg g ⁻¹ ). These findings underscore the urgent need for site-specific pollution control, stricter discharge regulations, and long-term ecological monitoring. This study provides a replicable framework for riverine metal pollution assessment and supports sustainable management initiatives like the Namami Gange program.

Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To identify the credibility of the posted claim, we have analyzed possible evidence from the news articles in the google search results. This paper proposes an intelligent and expert strategy to gather important clues from the top 10 google search results related to the claim. The N-gram, Levenshtein Distance, and Word-Similarity-based features are used to identify the clues from the news article that can automatically warn users against spreading false news if no significant supportive clues are identified concerning that claim. The complete process is done in four steps, wherein the first step we build a query from the posted claim received in the form of text or text additive images which further goes as an input to the search query phase, where the top 10 google results are processed. In the third step, the important clues are extracted from titles of the top 10 news articles. Lastly, useful pieces of evidence are extracted from the content of each news article. All the useful clues with respect to N-gram, Levenshtein Distance, and Word Similarity are finally fed into the machine learning model for classification and to evaluate its performances. It has been observed that our proposed intelligent strategy gives promising experimental results and is quite effective in predicting misleading information. The proposed work provides practical implications for the policymakers and health practitioners that could be useful in protecting the world from misleading information proliferation during this pandemic.

The escalating prevalence of hate speech on social media necessitates effective detection mechanisms to foster a safe and inclusive online community. This research paper aims to enhance hate speech detection accuracy by evaluating the performance of diverse machine learning algorithms: Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN). A diverse dataset comprising text samples from various online platforms, encompassing a wide spectrum of hate speech instances, was meticulously collected. The data underwent careful preprocessing involving tokenization, stemming, and stop-word removal to enhance data quality. Additionally, feature extraction techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) and word embeddings were employed to effectively represent the textual content. The dataset was divided into training and testing sets, and the selected machine learning algorithms were trained on the former. Fine-tuning of hyperparameters was performed using crossvalidation techniques to optimize their performance. Evaluation metrics, including accuracy, precision, recall, and F1-score, were employed to assess the models’ effectiveness. The experimental findings revealed promising outcomes for hate speech detection across all three algorithms. Notably, Count Vectorizer features demonstrated excellent performance, with Random Forest achieving an accuracy of 0.942 for binary hate speech analysis and Logistic Regression achieving an accuracy of 0.897 for multi-class hate speech analysis, followed by LR and KNN.