Sarcasm is employed widely on various social media platforms. Due to the potential for sarcasm to alter the intended meaning of a statement, the opinion analysis technique is susceptible to inaccuracies. Detecting sarcasm is one of the most challenging problems in analyzing sentiment and mining opinions in social media. Therefore, identifying sarcasm is crucial when making informed public opinion decisions. Preliminary research indicates that sarcastic statements alone have a substantial negative impact on the accuracy of automatic sentiment analysis. Several distinct natural language processing strategies have been previously suggested. However, each technique has limits in terms of textual context and proximity, and the accuracy of classifiers is affected by noise in the dataset. This research introduces SarcAE, a unique method for combining feature-level embedding fusion using an autoencoder and fuzzy logic-based reasoning to classify sarcasm. The evaluation experiments used two benchmark datasets: the News Headlines dataset and Ironic Tweet dataset, subjected to several preprocessing techniques. Extensive experiments conducted using the proposed SarcAE approach demonstrate that the proposed method outperforms other fusion models with an accuracy of 98.53% on the News Headlines dataset and 89.83% on the Ironic Tweet dataset, respectively, surpassing baseline methods by up to 3.7%. These results indicate the effectiveness of SarcAE in capturing contextual and semantic cues needed for sarcasm detection.

Cleaning and inspection of pipelines and gun barrels are crucial for ensuring safety and integrity to extend their lifespan. Existing automatic inspection approaches lack high robustness, as well as portability, and have movement restrictions and complexity. This study presents the design and development of a scalable, comprehensive automated inspection, cleaning, and evaluation mechanism (CAICEM) for large‐sized pipelines and barrels with diameters in the range of 105 mm–210 mm. The proposed system is divided into electrical and mechanical assemblies that are independently designed, tested, fabricated, integrated, and controlled with industrial grid controllers and processors. These actuators are suitably programmed to provide the desired actions through toggle switches on a simple housing subassembly. The stress analysis and material specifications are obtained using ANSYS to ensure robustness and practicability. Later, on‐ground testing and optimization are performed before industrial prototyping. The inspection system of the proposed mechanism includes barrel‐mounted and brush‐mounted cameras with sensors utilized to keep track of the pipeline deposits and monitor user activity. The experimental results demonstrate that the proposed mechanism is cost‐effective and achieves the desired objectives with minimum human efforts in the least possible time for both smooth and rifled large‐diameter pipes and barrels.

The digital era, while offering unparalleled access to information, has also seen the rapid proliferation of fake news, a phenomenon with the potential to distort public perception and influence sociopolitical events. The need to identify and mitigate the spread of such disinformation is crucial for maintaining the integrity of public discourse. This research introduces a multi-view learning framework that achieves high precision by systematically integrating diverse feature perspectives. Using a diverse dataset of news articles, the approach combines several feature extraction methods, including TF-IDF for individual words (unigrams) and word pairs (bigrams), and counts vectorization to represent text in multiple ways. To capture additional linguistic and semantic information, advanced features, such as readability scores, sentiment scores, and topic distributions generated by latent Dirichlet allocation (LDA), are also extracted. The framework implements a multi-view learning strategy, where separate views focus on basic text, linguistic, and semantic features, feeding into a final ensemble model. Models like logistic regression, random forest, and LightGBM are employed to analyze each view, and a stacked ensemble integrates their outputs. Through rigorous tenfold cross-validation, our proposed multi-view ensemble achieves a state-of-the-art accuracy of 0.9994, outperforming strong baselines, including single-view models and a BERT-based classifier. Robustness testing confirms the model maintains high accuracy even under data perturbations, establishing the value of structured feature separation and intelligent ensemble techniques.

With the large use of social media, the dissemination of intentionally altered and falsified information has become easy, thus posing negative effects on society. Detecting fake content is a non-trivial task as fake news has unique characteristics and challenges. Additionally, the wide use of artificial intelligence (AI) for fake content generation makes the detection of fake content further complicated. Fake news presents engineered content, making it difficult for traditional approaches to comprehend. Existing fake news detection approaches face four problems: lack of robustness, adaptability, limited or no use of auxiliary information, and inability to handle diversity. Fake content diversity introduces the models’ complexities and degrades their performance. Similarly, the accuracy of fake news detection approaches remains low for practical systems. This study focuses on detecting fake news by using an AI-based approach to obtain high accuracy and robustness by using the concept of text transformation into images. It transforms the text into a standard image format which enriches the feature space and boosts the performance of machine learning models. Extensive experiments using two different datasets involving binary and multi-class classification reveal that the proposed approach outperforms existing solutions by yielding superior accuracy. The use of AI approaches helps obtain higher accuracy of 99.70% and 92% for fake news detection using ISOT and LIAR datasets, respectively.

Social Network Analysis (SNA) has emerged as a new paradigm to effectively represent complex patterns of relationships between all categories of social groups. It helps to find the structure of ties and its impact on individuals, groups or even incidents. This article is a similar attempt to explore the vehicles and vehicle-related violations leading to accidents through the use of SNA. SNA is performed on accident data of New York for 2016. SNA measures including degree centrality, betweenness centrality and eigenvector centrality are used to probe into the impact of ties between actors of accidents. The empirical analysis shows that 'Passenger vehicle' with degree centrality of 7513 has the highest degree of accidents, explaining 41.12% of total accidents. In addition, it is involved in 57.42% of accidents when accidents occur between same types of vehicles. 'Sport utility/station wagon' and 'taxi' rank second and third in this category with degree values of 4657 and 1454 respectively. It is also found that 'driver inattention' holds the pivotal place when violations leading to accidents are concerned. It accounts for 19.09% accidents in general and 44.58% when accidents, where both parties commit the same violation, are considered. 'Failure to yield right-of-way' and 'following too closely' are ranked second and third. Research also finds that Manhattan area of New York is marked by elevated number of accidents.