How do emotions shape ecological activism in the global South? Despite growing interest in researching ecological activism in International Relations (IR), there hasn't been much work that draws insights from the global South due to the predominant focus on western societies. Against the backdrop of the recent ‘relational’, ‘emotional’ and ‘Anthropocene’ turns in IR scholarship, this article examines how emotions can contribute to ecological activism in the global South. The article seeks to move beyond the dominant rationalist technocratic fixes to (...) the Anthropocene condition and conceptualize the ‘affective relations’ as a frame for situating and sustaining relations between humans and nature. It argues that the ‘international’ in IR is a relational sphere of society—and emotions act as the purveyor of those social relations. Empirically, this article examines historical insights from ecological activism in the Chipko (tree-hugging) movement in India. The article argues that feelings as the non-violent practice (in their hugging of trees, silent demonstrations, protest slogans, street plays, songs and performances) become sites of resistance in ecological activism in India's Chipko movement. Finally, the article explains how affective relations sustain humans, nature and the state. (shrink)

Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated by deep (...) belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm. (shrink)