Guest editorial: Intelligent Information Processing: Techniques and Applications

Numerous intelligent techniques and applications have been developed in recent years for solving problems in computer science and engineering such as Internet of Things (IoT), smart and ambient environments, and sensor-based applications, context-aware systems, e-health systems, industrial information processing, image processing, data mining and many others. Intelligent information processing (e.g. gathering, aggregating, organizing and interpreting) became a key factor to justify the effectiveness of any intelligent system. Intelligent based solutions were not only changed the way in which information is gathered, aggregated, and stored but also play crucial role in the management and delivery of information.

Considering the recent developments of information processing techniques in intelligent systems and applications, this special section is to further promote researchers in the development of fundamental principles, innovative algorithms or applications of intelligent information acquisition, processing, aggregating, industrial application and analysis of data to benefit in various fields.

This special section is a collection of 7 papers selected from the 68 submissions. All the papers included here have gone through a rigorous peer-review and revision process for their originality and quality. Selected papers take the topic of advances in intelligent information processing: techniques and applications. Papers covered mostly theoretical research and various application domains, such as intelligent parallel programming, genetic algorithms for image segmentation, distributed information processing, cloud computing, trajectory stream clustering, and image processing.

The paper “Mobile Agent Group Communication Ensuring Reliability and Totally-ordered Delivery” by Jinho Ahn investigates methodologies for reliably providing group communication messages to mobile agents in an atomic order. It enables each agent to adaptively choose a small number of forwarders among its visiting nodes based on its decision. Also, it replicates paths on which messages should be transmitted to their targeting mobile agents effectively. Third, the mobile agent group location cache each sending agent keeps in our protocol can considerably speed up message delivery to a group of agents and lower message forwarding load imposing on forwarders. Lastly, it allows messages destined to a group of agents to be reliably delivered to all surviving agents in the same order despite their mobility and F forwarders’ failures.

The paper "A Cloud-Based Pervasive Serious Game Framework to Support Obesity Treatment" by Atif Alamri et al. explores novel serious game approaches to combat obesity. The authors proposed a gaming framework based on cloud infrastructure, which facilitates bio-signal monitoring of obese people while they play the games. The health and physical activity data, which is captured during the gaming session using several sensors, are shared with the therapist on-the-fly. The framework also allows the therapist to make on-the-spot activity recommendation and change the game complexity level for the obese. The authors in this paper claims that their approach has great potential in supporting obesity treatment.

The paper “Experimental and Theoretical Speedup Prediction of MPI-Based Applications“ by Alaa Elnashar and Sultan Aljahadli focuses on the conflicting parameters that affect the parallel programs execution experimentally and theoretically, especially for MPI-based applications, showing some recommendations to be followed to achieve a reasonable performance. An experimental method that aids in speedup prediction is also proposed.

The authors Zhenhua Tan, Guangming Yang, Wei Cheng and Xingwei Wang in the paper “Distributed Secret Sharing Scheme Based on Personalized Spherical Coordinates Space,” present an interesting threshold secret sharing scheme based on spherical coordinates for distributed networks. Verifiability and pro-activity secret sharing are considered during the procedures of distributing shadows and reconstructing secret. The proposed scheme has relatively advantage in computation complexity, storage space and communication amounts, and it can tolerate collusion attacks and detect dishonest participants.

The paper “Online Clustering for Trajectory Data Stream of Moving Objects” by Yanwei Yu, Qin Wang and Xiaodong Wang et al investigates methodologies for discovering trajectory clusters from trajectory data stream of mass moving objects used for information extraction in real time applications. The proposed method consists of two stages: line segment stream clustering and online trajectory cluster updating. The first stage processes quickly line segments based on previous line clusters to obtain line-segment-clusters on the fly, and then the current closed trajectory clusters are updated online based on TC-Tree according to the line-segment-clusters. The method effectively solves the problem of trajectory data stream clustering.

Authors Elnomery A. Zanaty and Ahmed S. Ghiduk present a new segmentation approach based on hybridization of the genetic algorithms (GAs) and seed region growing to produce accurate medical image segmentation, and to overcome the over segmentation problem. A new fitness function is presented and a chromosome representation suitable for the process of segmentation is proposed. Each chromosome contains three parts: control genes, gray-levels genes, and position genes. The region growing algorithm uses these values as an initial seeds to find accurate regions for each control gene.

The paper “Perception-driven resizing for dynamic image sequences” by Lingling Zi, Junping Du, Lisha Hou, Xiangda Sun, and Jangmyung Lee presents a new resizing method to acquire high quality resizing results in accordance with human visual perception. The implementation process of the method involves three steps: attention region establishment, the construction of a partition interpolation model, and region energy protection using seam carving. The authors use a coarse-to-fine detection approach to determine the important content of image sequences, and construct a partition interpolation model to improve the definitions of important content.

I would like to give my great thanks to the reviewers for their helpful comments and all of the authors for their contributions, efforts and enthusiasm. Thanks are also due to the ComSIS Consortium, and especially to the Editor-in-Chief of ComSIS, Mirjana Ivanović and other staff in the Editorial Office for their advice and help in making this special section possible.

Guest Editors
Dr. Mehedi Masud and Prof. Alaa Sheta
Faculty of Computers and Information Technology Taif University, Taif, Saudi Arabia