Babar Shazad¹, Hikmat Ullah khan², Zahoor-ur-Rehman¹, Muhammad Farooq², Ahsan Mahmood¹, Irfan Mehmood^3,*, Seungmin Rho³, Yunyoung Nam^4,*

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 87-98, 2020, DOI:10.31209/2019.100000130

Abstract The social media has become an integral part of our daily life. The social web users interact and thus influence each other influence in many aspects. Blogging is one of the most important features of the social web. The bloggers share their views, opinions and ideas in the form of blog posts. The influential bloggers are the leading bloggers who influence the other bloggers in their online communities. The relevant literature presents several studies related to identification of top influential bloggers in last decade. The research domain of finding the top influential bloggers mainly focuses on feature centric models. This… More >

Soo Kyun Kim^*, Mario Köppen, Ali Kashif Bashir, Yuho Jin

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 83-85, 2020, DOI:10.31209/2019.100000129

Abstract This article has no abstract. More >

Dehua Shi^1,4, Shaohua Wang^1,2,*, Yingfeng Cai¹, Long Chen¹, ChaoChun Yuan¹, ChunFang Yin³

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 27-39, 2020, DOI:10.31209/2018.100000062

Abstract Model predictive control (MPC), owing to the capability of dealing with nonlinear and constrained problems, is quite promising for optimization. Different MPC strategies are investigated to optimize HEV nonlinear energy management for better fuel economy. Based on Bellman’s principle, dynamic programming is firstly used in the limited horizon to obtain optimal solutions. By considering MPC as a nonlinear programming problem, sequential quadratic programming (SQP) is used to obtain the descent directions of control variables and the current control input is further derived. To reduce computation and meet the requirements of real-time control, the nonlinear model of the system is approximated… More >

Julius Beneoluchi Odili^1,*, A. Noraziah², Mohd Helmy Abd Wahab³

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 41-51, 2020, DOI:10.31209/2018.100000059

Abstract This paper presents the African Buffalo Optimization algorithm for collision avoidance among electric fishes. Collision-avoidance in electric fish finds correlation with the Travelling Salesman avoiding the cities he has earlier visited. Collision avoidance in electric is akin to collision-avoidance in modern day driverless cars being promoted by Google Incorporation and other similar companies. The concept of collision-avoidance is also very useful to persons with visual impairment as it will help them avoid collision with objects, vehicles, persons, especially other visually-impaired. After a number of experimental procedures using the concept of the travelling salesman’s problem to simulate collision-avoidance in electric fish,… More >

Muhammad Abdullah^1,*, Saad Ahmad Khan¹, Mamdouh Alenez², Khaled Almustafa³, Waheed Iqbal¹

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 13-25, 2020, DOI:10.31209/2018.100000047

Abstract An efficient placement of virtual machines (VMs) in a cloud datacenter is important to maximize the utilization of infrastructure. Most of the existing work maximises the number of VMs to place on a minimum number of physical machines (PMs) to reduce energy consumption. Recently, big data applications become popular which are mostly hosted on cloud datacenters. Big data applications are deployed on multiple VMs and considered data and communication intensive applications. These applications can consume most of the datacenter bandwidth if VMs do not place close to each other. In this paper, we investigate the use of different VM placement… More >

Qinqin Fan^1,*, Yilian Zhang², Zhihuan Wang¹

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 1-12, 2020, DOI:10.31209/2018.100000042

Abstract Weak global exploration capability is one of the primary drawbacks in teaching learning based optimization (TLBO). To enhance the search capability of TLBO, an improved TLBO (ITLBO) is introduced in this study. In ITLBO, a uniform random number is replaced by a normal random number, and a weighted average position of the current population is chosen as the other teacher. The performance of ITLBO is compared with that of five meta-heuristic algorithms on a well-known test suite. Results demonstrate that the average performance of ITLBO is superior to that of the compared algorithms. Finally, ITLBO is employed to estimate parameters… More >

Mahiye Uluyagmur Ozturk^a, Ayse Rodopman Arman^b, Gresa Carkaxhiu Bulut^c, Onur Tugce Poyraz Findik^b, Sultan Seval Yilmaz^d, Herdem Aslan Genc^b, M. Yanki Yazgan^e,f, Umut Teker^a, Zehra Cataltepe^a

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 891-905, 2018, DOI:10.31209/2018.100000058

Abstract Emotion recognition behavior and performance may vary between people with major neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences for early diagnosis and individual treatment purposes. This study represents a methodology by using statistical data analysis and machine learning to provide help to psychiatrists and therapists on the diagnosis and individualized treatment of participants with ASD and ADHD. In this paper we propose an emotion recognition experiment environment and collect eye tracker fixation data together with the application log data (APL). In order to detect… More >

Moazam Ali, Susmit Bagchi^*

Intelligent Automation & Soft Computing, Vol.24, No.4, 2018, DOI:10.31209/2018.100000043

Abstract Due to the rapid advancements and developments in wide area networks and powerful computational resources, the load balancing mechanisms in distributed systems have gained pervasive applications covering wired as well as mobile distributed systems. In large-scale distributed systems, sharing of distributed resources is required for enhancing overall resource utilization. This paper presents a comprehensive study and detailed comparative analysis of different load balancing algorithms employing fuzzy logic and mobile agents. We have proposed a hybrid architecture for integrated load balancing and monitoring in distributed computing systems employing fuzzy logic and autonomous mobile agents. Furthermore, we have proposed a smooth and… More >

M. Carmel Sobia¹, A. Abudhahir²

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 869-881, 2018, DOI:10.31209/2018.100000014

Abstract In this paper, an efficient facial expression recognition system using ANFIS-MHS (Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking) has been proposed. The features were extracted using MLDA (Modified Linear Discriminant Analysis) and then the optimized parameters are computed by using mGSO (modified Glow-worm Swarm Optimization).The proposed system recognizes the facial expressions using ANFIS-MHS. The experimental results demonstrate that the proposed technique is performed better than existing classification schemes like HAKELM (Hybridization of Adaptive Kernel based Extreme Learning Machine), Support Vector Machine (SVM) and Principal Component Analysis (PCA). The proposed approach is implemented in MATLAB. More >

H. Anandakumar^a, K. Umamaheswari^b

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 843-849, 2018, DOI:10.1080/10798587.2017.1364931

Abstract Cognitive radio systems necessitate the incorporation of cooperative spectrum sensing among cognitive users to increase the reliability of detection. We have found that cooperative spectrum sensing is not only advantageous, but is also essential to avoid interference with any primary users. Interference by licensed users becomes a chief concern and issue, which affects primary as well as secondary users leading to restrictions in spectrum sensing in cognitive radios. When the number of cognitive users increases, the overheads of the systems, which are meant to report the sensing results to the common receiver, which becomes massive. When the spectrum, which is… More >