Classroom behaviour recognition is easily disrupted by visual confounders such as dense crowds, occlusions, viewpoint and illumination changes, and closely co-occurring actions. These factors hinder the learning of robust and interpretable behaviour representations. Causal inference has been introduced into this task, but existing methods typically treat spatio-temporal confounders as a single factor and ignore the distinct roles of spatial and temporal information. This contrasts with evidence from cognitive science that “where” and “when” information is processed by partly separate neural systems. Existing classroom behaviour datasets further focus on isolated actions and rarely capture the dynamics of interaction. To address these issues, we propose CausalCIBR, a causal-inference-based framework for classroom interactive behaviour recognition, together with a new dataset, BNU-SVIBD. CausalCIBR models spatial and temporal behaviour associations as decoupled confounders, whose occurrence probabilities are calibrated using behaviour co-occurrence statistics from educational practice. Within a structural causal model, we intervene on these confounder dictionaries and use a multi-scale gated feedforward module to couple intervened features with local and global spatio-temporal context, strengthening causal signals while suppressing spurious co-occurrences. BNU-SVIBD provides annotated interactive sequences that describe the dynamic evolution of classroom interaction events, enabling fine-grained behaviour analysis. Experiments on BNU-SVIBD and other challenging benchmarks show that CausalCIBR achieves state-of-the-art performance and improved robustness under distribution shifts and confounder perturbations, effectively capturing the complexity of classroom interactions.

Artificial intelligence (AI) is commonly framed as a technological force transforming economies, labor markets, scientific discovery, and social interaction. While these impacts are profound, they represent only the surface of a deeper historical and philosophical shift. We argue that AI constitutes a fourth major decentering revolution in human self-understanding. The Copernican revolution displaced humans from the center of the universe, the Darwinian revolution displaced humans from the pinnacle of biological life, the Freudian revolution displaced the conscious ego from the throne of the mind, and today AI initiates a cognitive decentering revolution, challenging the long-held assumption that humans occupy a unique and unassailable position at the apex of intelligence. By situating AI within historical, philosophical, and technological contexts, incorporating detailed case studies, illustrative examples, and policy analysis, this paper provides a comprehensive account of cognitive decentering and its societal implications.

Accidental falls have emerged as a major public health concern, especially among individuals aged 65 and older, due to their high incidence and severe consequences. Without timely intervention, such falls can result in fractures, traumatic brain injuries, and long-term complications. As a result, considerable research has focused on developing automated fall detection systems that integrate intelligent algorithms with sensor-based data acquisition to enable rapid response and medical assistance. This study follows the PRISMA framework to conduct a systematic literature review. A comprehensive search was performed across major databases including PubMed, Google Scholar, Scopus, and IEEE Xplore using fall detection–related keywords. From an initial pool of 596 articles, duplicates were removed and strict inclusion/exclusion criteria were applied, resulting in 182 relevant articles for in-depth analysis. This review examines a wide range of Artificial Intelligence (AI) and Machine Learning (ML) approaches applied to fall detection using diverse sensor modalities, including wearable, vision-based, ambient, and multimodal systems. Additionally, it summarizes the publicly available datasets and sensor configurations used in existing studies. This broad perspective is essential to compare trade-offs across sensing modalities and support effective system development for real-world deployment. This review provides a comparative overview of AI/ML-based fall detection approaches, highlighting differences in accuracy, sensitivity, and dataset usage as reported in existing literature. In the end, many open research challenges in using AI and ML to detect falls are outlined, along with potential future perspectives.

The desire for comprehending user behavior is significant, since social media data is growing quickly owing to user contributions, especially in light of the recent coronavirus outbreak. In the literature works, several semantic analysis methodologies are used for examining the user behavior and emotions from the corpus data. They continue to deal with the main issues of poor accuracy, a complex system, inadequate emotion recognition, and ineffectiveness. The main objective of this paper is to build a new model for sentiment analysis and opinion mining. When compared to the conventional works, the novel concept behind this work is, it adopts some unique computational methodologies for accurate sentiment prediction. Here, a new Cycle Generative Adversarial Network (GAN) based Term Memory integrated Sentiment Analysis (CyG-TMSA) framework is developed for analyzing and classifying user emotions from corpus data. The most accurate identification of emotions is made possible by the efficient generation of augmented data using the Cycle Generative Adversarial Network (GAN) model. Moreover, a classification technique based on Term Memory Allocation (TMA) is applied to accurately predict opinions derived from the data supplementation. The best way to approximate the weight value for classifying data is to use the Elite Golden Jackle Optimization (EGJO) technique, which enhances the classifier’s decision-making ability. To verify and contrast the effectiveness and results of the suggested CyG-TAMS model, multiple corpus data sets extracted from public repositories are employed. The proposed CyG-TMSA framework demonstrated great effectiveness in sentiment analysis via CycleGAN-based data augmentation, TMA for precise classification, and optimization of parameter tuning through GJO. In experimental evaluations conducted on datasets extracted from Amazon and Twitter, the model obtained high accuracy at 98% within a very short processing time of 10.12 s, significantly outclassing other conventional methods in terms of accuracy and computational efficiency.

Abstract In recent years, there has been a significant increase in research focused on Human Activity Analysis (HAA). This field has progressed from basic activity recognition tasks to addressing more challenging ones, such as predicting future human actions based on partially observed videos and even predicting actions before they happen. The evolution of HAA has been driven by recent advancements in attention-based models like Transformers, along with a wide range of applications from security surveillance to advanced monitoring systems, behaviour analysis, and more. A comprehensive review of HAA literature from 2017 to 2025, with a novel taxonomy emphasising activity recognition, prediction, and anticipation, is presented. We critically review and examine recognition methods from trimmed and untrimmed videos, context-aware and trajectory-based prediction, and short-term and long-term anticipation. Through a comprehensive analysis, we review and evaluate key aspects of this domain, including attention-based contextual comprehension, temporal dynamics modelling, and multi-model fusion methods. Furthermore, we critically examine and assess the public datasets utilised in driving this research forward, pinpointing limitations and primary challenges within this domain. Finally, the paper provides a summary of recent developments in HAA and suggests future directions, with the hope that it will serve as a valuable reference for researchers in the field.