International Journal of Computational Intelligence Studies 

About: International Journal of Computational Intelligence Studies is an academic journal published by Inderscience Enterprises Ltd.. The journal publishes majorly in the area(s): Computer science & Deep learning. It has an ISSN identifier of 1755-4977. Over the lifetime, 79 publications have been published receiving 194 citations. The journal is also known as: IJCIStudies.

Application of cuckoo search in water quality prediction using artificial neural network

Abstract: Domestic and industrial pollution affected the water quality to a greater extent. Recent research studies have achieved reasonable success in predicting the water quality using several machine learning based techniques. In the current work, a proposed cuckoo search (CS) has been applied to improve the support in the classification process during the water quality prediction. The proposed model (NN-CS) gradually minimises an objective function; namely the root mean square error (RMSE) in order to find the optimal weight vector for the artificial neural network (ANN). The proposed model was compared with three other well-established models, namely NN-GA (ANN trained with genetic algorithm) and NN-PSO (ANN trained with particle swarm optimisation) in terms of accuracy, precision, recall, f-measure, Matthews correlation coefficient (MCC) and Fowlkes-Mallows index (FM index). The simulation results established superior accuracy of NN-CS over the other models.

Intelligent information system to ensure quality in higher education institutions, towards an automated e-university

Abstract: Despite great efforts to assure quality in higher education institutions, the ambiguity of its related concepts and requirements constitute a big challenge when trying to implement it as an intelligent information system. The present work introduces a framework for an intelligent information system that manages the quality assurance in higher education's institutions. The aim of designing such a system is to provide an automation tool that avoids unnecessary and redundant tasks associated to quality in higher education institutions. In addition, the proposed system helps all higher education stockholders to handle and monitor their tasks. Moreover, it aims to help the quality assurance units in a higher education institution to apply their quality's standards and to make sure that they are being maintained and enhanced. This information system contains a core module and 18 sub-modules, which are described in detail. Finally, the characteristics and components of each of these sub-modules are also discussed.

Application of computational intelligence techniques for internet of things: an extensive survey

Abstract: The application of computational intelligence (CI) techniques to internet of things (IoT) is gaining popularity due to its capability of providing human-like knowledge, such as cognition, recognition, understanding, learning, and others. This paper attempts to provide an exhaustive survey of the available literature on IoT using CI techniques. In addition, detailed categorisation has been provided on the basis of different CI tools and their hybridisations used to tackle different problems of IoT. The potential benefits and utility of CI techniques in IoT are highlighted. The possible mapping of CI techniques to the real-world IoT problems is presented. The advantages and disadvantages of CI algorithms over traditional IoT solutions are discussed. A general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for IoT. Finally, some considerations regarding the recent trends and potential research directions are presented. An extensive bibliography is also included.

Intelligent systems based on loud computing for healthcare services: a survey

Abstract: Cloud computing plays an important role in healthcare services (HCS) due to its the ability to retrieve patients' data, diagnosis of diseases and other medical fields in less time and less cost. This paper presents a survey of intelligent systems based on cloud environment for HCS. It reviews the uses of intelligent techniques such as genetic algorithm (GA), particle swarm optimisation (PSO) and parallel particle swarm optimisation (PPSO) on cloud computing environment to enhance task scheduling, reduce execution time of requests from stakeholders (patients, doctors, nurses, e.g.) and maximise of resources utilisation on clouds. This paper presents evaluation criteria of selected researches based on accuracy, usability, agility and applied method. Selected researches in this field were reviewed, analysed, summarised and compared according to the used intelligent techniques in cloud computing, healthcare systems and the concluded results. This paper also proposes architecture for intelligent healthcare systems based on cloud computing environment.

Automatic classification of Punjabi poetries using poetic features

Abstract: Automatic classification of poetic content is very challenging from the computational linguistic point of view For library suggestion framework, poetries can be grouped on different measurements, for example, artist, day and age, assumptions, and topic In this work, content-based Punjabi poetry classifier was built utilising Weka toolset Four unique classes were manually populated with 2,034 poetries NAFE, LIPA, RORE, PHSP classes comprises of 505, 399, 529 and 601 number of poems, individually These poems were passed to different pre-processing sub stages, for example, tokenisation, noise removal, stop word removal, special symbol removal An aggregate of 31,938 tokens was separated, after passing through pre-processing layer, and weighted using term frequency (TF) and term frequency-inverse document frequency (TF-IDF) weighting plan Depending upon poetic elements of poetry, two different poetic features (orthographic and phonemic) were experimented to build a classifier using machine learning algorithms Naive Bayes, support vector machine, hyper pipes, and K-nearest neighbour algorithms experimented with two poetic features The results revealed that addition of poetic features does not boost the performance of Punjabi poetry classification task Using poetic features, the best performing algorithm is SVM and highest accuracy (7198%) is achieved considering orthographic features