Forthcoming articles

International Journal of Services Operations and Informatics

International Journal of Services Operations and Informatics (IJSOI)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Services Operations and Informatics (4 papers in press)

Regular Issues

  • Hybrid Resource Allocation and Task Scheduling Scheme in Cloud Computing Using Optimal Clustering Techniques   Order a copy of this article
    by Manikandan N., Pravin A. 
    Abstract: In diverse parallel and distributed computing systems, resource allocation is the progression of distributing consumer tasks for processing elements during execution in which some performance intentions are optimized. This document can be explain about the innovative resource allocation algorithm for the computing grid environment. In the scheduling problem of independent task in cloud computing, summarize other scheduling algorithms introduce a modified fuzzy c-means clustering algorithm (MFCM) Our algorithm abstract resource into a model to analyze these characteristics of resources with the MFCM algorithm. From that our proposed technique could decrease a execution time and memory space allocation of the system. For the optimal selection of virtual machines hybrid whale genetic (HWGA) optimization algorithm is used. Since the virtual machines are optimally selected on the basis of feature values, our proposed method provides reduced load balancing as well as improved parallel execution of tasks
    Keywords: Resource Allocation; Scheduling; Fuzzy C-Means Clustering; Whale Optimization; Virtual Machine; Parallel Execution.

  • Optimal distributed intelligent traffic system for road safety, emergency vehicle clearance using hybrid optimization algorithm   Order a copy of this article
    by Bhavani Sundar Raj, Srimathi Chandrasekaran 
    Abstract: Nowadays, the population growth and their choice raise the number of vehicles, which raises traffic congestion in critical condition across the developing Nations like India. In consequence, emergency vehicles are stuck in traffic and waste their valuable time. Many authors focus on traffic problems in terms of intelligent traffic system without the consideration of emergency cases. In this paper, we propose an optimal distributed intelligent traffic system (ODITS) using hybrid optimization algorithm. The proposed system designed by two phases are collision gathering and decision making. The clustered Jaya algorithm is used to gather the vehicle traffic information, which also differentiate emergency from normal vehicles. Then the modified multi-objective evolutionary based decision-making algorithm is used to compute the best route under the critical conditions. The simulation results shows that the proposed system reduces congestion in traffic, and waiting time of emergency vehicles without compromising normal vehicle speed, waiting time, and number of stopped vehicles.
    Keywords: ODITS; hybrid optimization; collision gathering; decision making; emergency vehicle clearance.

  • Enhancing Neural Network for Thailand Port Throughput Prediction   Order a copy of this article
    by Siwaporn Kunnapapdeelert 
    Abstract: Shipping volume in Thailand have significantly increased in last four years. It is important to pay attention to the trend of Thailand port throughput and use as the guideline to prepare for the needs of supporting facilities, infrastructures, financial and human resources. An effective forecasting technique called particle swarm based neural network (PSONN) is developed to estimate Thailand port throughput. The prediction results from PSONN and classical BP training algorithm, backpropagation neural networks (BPNN) are compared. The results show that PSONN provides more accurate results than BPNN when apply to predict port throughput of Thailand. The mean squared error obtained from PSONN are about 10 times lower than that of BPNN. This confirms that neural network based on PSO training algorithm has better performance and better ability to escape local optimum than that of BPNN.
    Keywords: port throughput; particle swarm optimization; neural network; logistics; prediction.

  • Implementation of Security in Multi Trading Activities of Wireless Sensor Networks associated with Big Data IoT   Order a copy of this article
    by Ahmed K. Al-Ani, Ahmed K. Al-Ani, Mustafa Hassan, Mustafa Hassan, Salwa Mahmood, Salwa Mahmood 
    Abstract: Big data analytics are playing predominant role in the multi-trading activities to take appropriate decisions. Big data analytics are conducted with the help of low-control processors, smart remote systems, and low-control sensors to transform into an impacting excitement for the industrial internet of things (IIoT). A wireless sensor network (WSN) is used to work with lowcontrol use bringing about low register and capacity limit. WSNs are widely used by organisations in the operations of IoT associated with big data analytics. In this connection WSN is transmitting enormous data through the network. These huge data transmissions have attracted the cyber criminals to eavesdrop the data with their techniques. The security challenges against the data transmissions in WSN while the multi-trade activities are performed needs a suitable admeasures. In this paper the security features have been implemented for gateways access in WSNs used for multi-trading activities of big data.
    Keywords: Big Data; Internet of Things; Wireless Sensor Network; time sensor data; service-oriented computing.
    DOI: 10.1504/IJSOI.2019.10023003