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Cloud technology is a utility where different hardware and software resources are accessed on pay-per-user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization. However, the tasks send by user to cloud may cause the VM to be under loaded or overloaded due to tasks allocation system in VM which lead to the failure of the system or delay the user tasks. Therefore, we propose an improved load balancing technique known as hybridizing artificial bee colony algorithm with Bat algorithm (HBAC). For searching food source employed bee’s use they share the information about to the food source to onlooker bee. In the initialization section equal number of employed bees and onlooker bees used for searching process with the same updation rule which make trapping in search process. Therefore for employed bee the Bat updation rule use in initialization section. When the employed bees share the information with onlooker bee with the help of dancing now it time for onlooker bee to prepare the candidate bee for searching process. Onlooker bees start searching for candidate bee using as technique in this technique it take cycle for searching bee if some tasks are missing in this cycle it take more cycle up to when all tasks are cover in the searching process. This technique take more time for that reason a new technique used in onlooker searching section which make the tasks are into equal part then start searching which was affective and take less time as compare to the previous one. The third modification took place at fitness value of artificial bee colony algorithm where the tasks distribution take more time due to overlapping which affect the tasks accuracy system. The proposed HBAC algorithm was tested and compared with other state-of-the-art algorithms on 200–2000 even tasks by using CloudSim on standard workload format (SWF) data sets file size (200 kb and 400 kb). The proposed HBAC showed an improved accuracy rate in tasks distribution and reduced the makespan of VM in a cloud data center. Based on the ANOVA comparison test results, a 1.25% improvement on accuracy and 0.98% reduced makespan on tasks allocation system of VM in cloud computing is observed with the proposed HBAC.

Query expansions are being used in many search applications for retrieving relevant documents. Although retrieving the relevant documents are important for search users, the complexity of Arabic morphology remains a challenge. As such, many irrelevant documents were still retrieved from the ranked results. To address this challenge, this paper proposes a new searching method for Arabic text semantic-based query expansion. The proposed method combines Arabic word synonyms and ontology to expand the query with additional terms. Specifically, the proposed method combined lexical words within the ranking algorithm and then improved with ontology links to expand query. The performance of Arabic text semantic-based query expansion was evaluated in terms of average precision, means average precision and means reciprocal rank. Experiments on Quran datasets show that the proposed method using Arabic text semantic-based query expansion approach outperforms the previous methods using other dataset which is called Tafsir dataset. The proposed method achieved 15.44% mean average precision.

Cloud computing is modern technology that has led to significant change in different fields of life by providing different kinds of servicer like hardware and software on user demands based on pay and gain rule. Due to the rapid growth of cloud computing it faces different kinds of issues, and resource allocation is one of them. For the improvement of resource allocation system in cloud computing different kinds of technique are used, and load balancing technique is one of them. In this paper we discuss resource allocation system in virtual machines (VM) because when a user sends data in VM then a situation may occur that some VM are overloaded and some become underloaded, which may lead to failure of system or request delay. For improvement of this situation different researchers used different algorithms in load balancing technique for cloud computing. This paper only focuses on BAT algorithms which are used for load balancing technique in improved resource allocation system for VM and also defines those rules which are used for improvement in load balancing technique in cloud data centre.

Over the past decade, the growth of the housing construction in Malaysia has been increase dramatically and the level of urbanization process in Malaysia is considered to be important in planning for low-cost housing needs. Unfortunately, there is a clear miss-match between the supply and the demand of low cost housing in Malaysia. Due to the problems faced, there have been several attempts in predicting housing demands using the artificial-neural networks (ANN) technique particularly back-propagation (BP). However, the training process of BP can result in slow convergence or even network paralysis and can easily get stuck at local minima. This paper presents a new approach to improve the training efficiency of BP algorithms to forecast low-cost housing demand in one of the states in Peninsular Malaysia. The proposed algorithm (BPM/AG) adaptively modifies the gradient based search direction by introducing the value of gain parameter in the activation function. The results show that the proposed algorithm significantly improves the learning process with more than 31% faster in term of CPU time and number of epochs as compared to the traditional approach. The proposed algorithm can forecast low-cost housing demand very well with 6.62% of MAPE value.

This paper presents the modeling the drying process of shell mould layering process for the ceramic shell mould fabrication process. As this process involves the repeated drying of the different wetted/saturated layers, therefore modeling of this phenomena involve a sequent approach to tackle the whole development of shell mould layering process. In this work an Ab-Initia approach was selected as the best simulation technique to map the drying mechanism. Saturation or moisture content was selected as the best parameter that will represent the drying of layering process. Using FEM with quadrilateral shape mapping of several interested points were selected to predict the moisture/saturation movement during the drying of shell layering stages. Standard drying time with 2 hours and early drying time were chosen to measure the moisture movement as layer added to the previous dried coated shell or layers. This complex mechanism of drying and penetration layering shell were then numerically solve with fully implicit backward time stepping scheme. Hopefully, this model and can be used to measure the complex movement of the main parameter i.e saturation/temperature in the drying process of multilayer system which is sometimes impossible directly measured under experimental technique