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Computational intelligence has been traditionally associated with neural networks, fuzzy systems, and genetic algorithms. Over the years there have been many developments in computational intelligence. At present, many other fields are part of the study and research in computational intelligence. With advances in cognitive sciences, more techniques of information processing by machines that show characteristics closely associated with human intelligence are being found. Some of these techniques have been studied for a long time, but in recent years there has been some maturity in the understanding and use of these techniques. One such technique is the use of semantics in computational intelligence. There has been a long-drawn-out philosophical debate between lingualism, which claims that there is no human thought without language, and “language of thought” theories, which believe that natural language is inessential to private thought. In an attempt to create intelligent machines, the use of semantics for knowledge representation and knowledge-based creation in a system follows the philosophy of lingualism. Different knowledge representations are used in a knowledge-based clinical decision support system. This chapter makes a study of various knowledge representations. The different theories behind the techniques used in the knowledge representations are discussed. The philosophy of lingualism and the use of semantics in computational intelligence are explained, while a study on semantic knowledge representation in clinical decision support systems is made. The conclusion is the explanation as to how ontological engineering can be used to create computational intelligence.

Ontologies continue to emerge in performing a greater function in various business processes and knowledge sharing in the field of modern information systems. They help to exchange and extend, from syntax to semantic, data and knowledge. They are viewed as a remedy for interoperable semantics which power semantic web. Since the turn of the millennium, ontology learning (OL) from text has received a considerable attention with a sudden surge of textual information for promising research. Remarkably, with intermingling of various disciplines like text mining (TM), statistical analysis techniques (SAT), machine learning (ML), knowledge representation (KR), natural language processing (NLP), etc., more research is on in the area of Ontological Engineering and OL from text. This survey brings out several outcomes of foregone research, discusses challenges and limitations of research in the past decade and points to new challenges in future.

The chapter introduces the history of the clinical decision support system beginning with the history of the system of decision making. It is an overview of how the clinical decision support systems developed through the years. The current technology used in decision making are also discussed. With the use of artificial intelligence, the clinical decision support systems have moved to the realm of predictive analysis to find out the possibilities of diseases rather than just the diagnosis and treatment. The chapter also elaborates the various types of clinical decision supports systems. Although the decision support systems are widely regard as an important and integral part of healthcare there has been a notable reluctance in the use of clinical decision support systems. The chapter also discusses the practical challenges in the implementation of the clinical decision support systems in healthcare organisations. Each of the topics in the chapter is dealt with summarily and the reference to a detailed study is provide. The idea is to provide a clear understanding of the system rather than to fully elaborate the system.

Let G be a graph and some pebbles are distributed on its vertices. A pebbling move (step) consists of removing two pebbles from one vertex, throwing one pebble away, and moving the other pebble to an adjacent vertex. The t-pebbling number of a graph G is the least integer m such that from any distribution of m pebbles on the vertices of G, we can move t pebbles to any specified vertex by a sequence of pebbling moves. In this paper, we determine the t-pebbling number of Lamp graphs.

Modern science and technology (S&T) has been present in India almost as long as it has anywhere else in the world. But the nature of its blossoming in India was substantially different, due to the huge (if not sole) role played by India’s colonial experience—especially the British colonial rule. The colonial state used modern S&T in practical and ideological ways to control the territory and its resources, and to keep colonial subjects in awe and submission. Correspondingly, the local intelligentsia’s interest in science was marked by ideological and instrumental concerns. The compulsions of colonialism did not allow for an easy flow of knowledge and expertise. Yet, with limited openings in education and scientific professions, Indians were able to acquire a measure of proficiency that could even lead to a Nobel Prize. The engagement, however, was not marked by one-way diffusion and passive acceptance, but by active appropriation and redefinition according to local imperatives. There was also an active critique of modern S&T—especially in its “big” forms and violent faces. After independence, the new nation state opted for a path of massive development of industry and agriculture through deployment of modern S&T, whereby world-class institutions, infrastructure industries, and research laboratories were opened in different parts of the country. While these have produced remarkable results, the meeting of science and state has led to stark ironies and difficulties. Also, continuing critiques of the authority of modern S&T, the undesirable economic, social, and ecological effects produced by it, and the renewed interest in “traditional alternatives” pose serious challenges to any uncontested or triumphalist march of modern S&T in India.

A graph G is said to be SD-harmonious labeling if there exists an injection f: V(G) -> {0,1,2,...,q} such that the induced function f*: E(G) ->{0,2,...,2q-2} defined by f(uv)=S+D (mod 2q) is bijective, where S=f(u)+f(v) and D=|f(u)-f(v)|, for every edge uv in E(G). A graph which admits SD-harmonious labeling is called SD-harmonious graph. In this paper, we investigate SD-harmonious labeling of path related graphs, tree related graphs, star related graphs and disjoint union of graphs.

To study the effect of Pusa hydrogel incorporated growing media on morphological characters of Syngonium podophyllum under various watering frequencies, an experiment was conducted in the Horticultural college and Research Institute, Periyakulam, Theni (Tamil Nadu). The treatments were eleven with the composition of basic pot mixture (sand, red soil and FYM at the ratio of 1:1:1), coco peat and Pusa hydrogel. The results revealed that, the media with a composition of 75% pot mixture + 25% coco peat+3g hydrogel /kg of potting media is found to be the optimum medium for potted foliage plant of Syngonium podophyllum. The watering frequency has been altered corresponding to potting media composition and in the other hand the potting media also influenced the morphological character of the plant. This study has proved that hydrogels in potting media improves the water availability and decreases the watering frequency.

Ontology is an emerging technology in building knowledge based information retrieval systems. It is used to conceptualize the information in human understandable manner. Over the years, much importance was given to the systematic development of ontologies. As a result many methods and strategies to build ontology in different domains came up in the field of ontological engineering. These methods contained different steps to plan, design and build ontology efficiently. Most of these methods provided a generic structure to construct ontology. The development of ontology has cut across different domains and applications and this has arisen a need to develop methods to build ontologies in various applications. Nowadays, multilingual ontology applications are developed in various domains to have Knowledge based information retrieval according to the language preferred by user. Multilingual ontology allows sharing of information and data in different languages and thus facilitates multi-language users. A method to develop multilingual ontologies is in need now. In this paper, we propose an ontology mediation method to develop multilingual ontology applications. It explains the steps involved in the development process. A six step method is followed which is done iteratively to develop multilingual ontologies. Merging and mapping techniques are also used in mediation of multilingual ontologies. New algorithms are proposed for merging and mapping multilingual ontologies. The proposed method is experimented by developing multilingual ontology application for Thirukkural, the famous Tamil literature. The experimental results show that multilingual ontologies can be developed using the proposed method and semantic knowledge can be retrieved from the multilingual ontology.

Given a bijection ʄ: V(G) → {1,2, …,|V(G)|}, we associate 2 integers S = ʄ(u)+ʄ(v) and D = |ʄ(u)-ʄ(v)| with every edge uv in E(G). The labeling ʄ induces an edge labeling ʄ' : E(G) → {0,1} such that for any edge uv in E(G), ʄ '(uv)=1 if gcd(S,D)=1, and ʄ ' (uv)=0 otherwise. Let eʄ ' (i) be the number of edges labeled with i ∈ {0,1}. We say ʄ is SD-prime cordial labeling if | eʄ ' (0)- e ʄ' (1)| ≤ 1. Moreover G is SD-prime cordial if it admits SD-prime cordial labeling. In this paper, we investigate the SD-prime cordial labeling of some derived graphs.