812 Recent Advances in Computer Science and Communications, 2020, Vol. 13, No. 5 Editorial EDITORIAL Emerging Trends and Applications in Cognitive Computing Cognitive Computing focuses on mimicking human behavior and reasoning to solve complex problems where the answers may be ambiguous and uncertain. AI augments [1] human thinking to solve complex problems. It focuses on providing accurate results. The phrase cognitive computing is closely associated with IBM's cognitive computer system, Watson. The goal of cog- nitive computing is to simulate human [2] thought processes in a computerized model. Using self-learning algorithms that use data mining, pattern recognition and natural language processing, the computer can mimic the way the human brain works. Cognitive tools are generalizable computer tools that are intended to engage and facilitate cognitive processing. Cogni- tive computing systems can analyze and combine more information on a topic than any one person could ever be expected to understand. The special issue of the journal titled "Recent Advances in Computer Science and Communications" is an excellent collec- tion of review and research articles in the field of cognitive computing, its methodologies and applications. A call for the paper was issued for this special issue. The guest editors feel happy to announce this special issue of the most reputed journal of Ben- tham Science. From a wide range of interesting research papers on various aspects of cognitive computing, the guest editors, after under- going exhaustive peer-reviews from experienced and well-known reviewers, have carefully selected 26 research papers out of 43 submitted papers. The final decision for the inclusion of 26 research papers has been strictly based on the outcome of the rigorous peer-review process, shortlisting successful research papers by researchers as per reviewers' comments and guidelines. The list of contributors, along with research paper titles included in this special issue is enlisted as follows: 1. COMPARATIVE PERFORMANCE EVALUATION OF KEYWORD AND SEMANTIC SEARCH ENGINES US- ING DIFFERENT QUERY SET CATEGORIES Poonam Jatwani, Pradeep Tomar and Vandana Dhingra To check the semantics handling performance, four types of query sets consisting of 20 queries of agriculture domain are chosen. Different query sets are single term queries, two term queries, three term queries and NLP queries. Queries from differ- ent query sets were submitted to Google, DuckDuckGo and Bing search engines. Effectiveness of different search engines for different nature of queries is experimented and evaluated in this research using Grade relevance measures like Cumulative Gain, Discounted Cumulative Gain, Ideal Discounted Cumulative Gain, and Normalized Discounted Cumulative Gain in addi- tion to the precision metric. 2. DEEP LEARNING-BASED SENTIMENT CLASSIFICATION ON USER-GENERATED BIG DATA Akshi Kumar and Arunima Jaiswal In this paper, the authors propose the application of a deep learning architecture namely the Convolution Neural Network. The proposed model is implemented on benchmark Twitter corpus (SemEval 2016 and SemEval 2017) and empirically ana- lyzed with other baseline supervised soft computing techniques. The pragmatics of the work includes modelling the behavior of trained Convolution Neural Network on well-known Twitter datasets for sentiment classification. The performance efficacy of the proposed model has been compared and contrasted with the existing soft computing techniques like Naïve Bayesian, Sup- port Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Decision Tree using precision, accuracy, recall, and F- measure as key performance indicators. 3. INTUITIONISTIC LEVEL SET SEGMENTATION FOR MEDICAL IMAGE SEGMENTATION Jyoti Arora and Meena Tushir In the proposed method, the input image is simplified by the process of intuitionistic fuzzification of an image. Further seg- mentation is carried out by intuitionistic based clustering technique incorporated with local spatial information (S-IFCM). The controlling parameters of level set method are automated by S-IFCM for defining anatomical boundaries. 4. ASPECT-ORIENTED SYSTEM COUPLING METRIC AND ITS VALIDATION Amandeep Kaur, Preetam Singh Grover and Ashutosh Dixit The research aims to present a novel Aspect-oriented System Coupling Metric (COAO) that calculates the coupling for the complete aspect-oriented system as a whole, based on the properties of elements and the relationships among them. © 2020 Bentham Science Publishers 2666-2566/20