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Indian military strategy has tended to neglect the link between the conventional and nuclear domains in a nuclear weapons environment. We argue that this anomaly is evident in two broad areas: the conception of "war," and the complexity produced by new technologies that span the two domains. First, we show with empirical evidence that "limited war" in a nuclear environment is misnomer: the reality is more appropriately called "marginal conflict" owing to its extremely restricted nature. It follows that strategic planning and posture must be tailored accordingly. We then highlight the risk of escalation produced by conventional technologies that carry potential cross-domain nuclear effects, noteably with respect to cyber, artificial intelligence, missile defence and space. We note that the complex strategic effects produced also complicate military-strategic interactions traversing geographic domains, noteably South, Southeast Asia, and Northeast Asia. The paper concludes with some reflections on the reasons for these lacunae in Indian strategic thinking and what might be done about them. [ABSTRACT FROM AUTHOR]

Semantic analysis is a particular technique, which is an interesting area of research that associates with Natural Language Processing (NLP), artificial intelligence, opinion mining, text clustering, and classification. Numerous text processing techniques are being used to find out sentiments from the comments, such as social media tweets, hoax, fiction, nonfiction, novels, books, movies, health care, and stock exchange. Agrarian experts' opinions play a vital role in the agriculture sector that yields good crop productivity. This paper presents a descriptive analysis of agriculture experts' opinions through machine learning methods based on textual data collection. The data has been collected by surveying various academia, research institute, and industry of Punjab, Pakistan. The impact of various agricultural inputs such as seed quality, soil quality, soil-intensive tillage, climate changes, water shortage, synthetic fertilizer, and precision technologies on crop productivity have been collected through questionnaires. This research provides a descriptive analysis of collected agrarians experts opinions to increase the crop yield by providing awareness regarding current agriculture inputs to farmers by using machine learning. The current research provides a cohesive expert guideline for improving crop productivity, useful for agricultural policymaking, and conveys adequate farmers' knowledge. Consequently, the proposed method is an innovative way of discovering recommendations of agrarians through sentiment analysis in survey data using machine learning methods. Furthermore, to the best of our knowledge, agrarians experts opinions on enhancing crop productivity have been considered for the first time in Pakistan. [ABSTRACT FROM AUTHOR]