Search

The use of meta-heuristics in Combinatorial Interaction Testing (CIT) is becoming more and more popular due to their effectiveness and efficiency over the traditional methods especially in authenticating electronic payment (e-payment) transactions. Concomitantly, over the past two decades, there has been a rise both in the development of metaheuristics and their application to diverse theoretical and practical areas including CIT in e-payments. In the implementation of t-way strategies (the t is used to represent the interaction strength), mixed results have been reported; some very exciting but, in other cases, the performance of metaheuristics has been, to say the least, below par. This mixed trend has led many researchers to explore alternate ways of improving the effectiveness and efficiency of metaheuristics in CIT, hence this study. It must be emphasized, however, that available literature indicates that no particular metaheuristic testing strategy has had consistent superior performance over the others in diverse testing environments and configurations. The need for effectiveness, therefore, necessitates the need for algorithm hybridization to deploy only the component parts of algorithms that have been proven to enhance overall search capabilities while at the same time eliminating the demerits of particular algorithms in the hybridization procedure. In this paper, therefore, a hybrid variant of the African Buffalo Optimization (ABO) algorithm is proposed for CIT. Four hybrid variants of the ABO are proposed through a deliberate improvement of the ABO with four algorithmic components. Experimental procedures indicate that the hybridization of the ABO with these algorithmic components led to faster convergence and greater effectiveness superior to the outcomes of existing techniques, thereby placing the algorithm among the best when compared with other methods/techniques.

Data Clustering is an effective mechanism for the clustering of similar data objects. Fuzzy logic based or soft data clustering technique is very suitable for the formation of data clusters semantically when a data object is the member of more than one clusters unlike hard data clustering techniques. Fuzzy exponent variable is one of the most important variable in fuzzy logic based clustering technique. This variable has significant impact on the performance and effectiveness of the clustering results. An experimental evaluation is performed to analyze the impact of fuzzy exponent variable in this study. The experiments are performed on textual web document dataset through Fuzzy C-Means soft clustering technique. The results retrieved with various values of fuzzy exponent variable, are analyzed and presented to represent the performance and effectiveness.

Data replication has been one of the pathways for distributed database management as well as computational intelligence scheme as it continues to improve data access and reliability. The performance of data replication technique can be crucial when it involves failure interruption. In order to develop a more efficient data replication technique which can cope with failure, a fault tolerance approach needs to be applied in the data replication transaction. Fault tolerance approach is a core issue for a transaction management as it preserves an operation mode transaction prone to failure. In this study, a data replication technique known as Binary Vote Assignment on Grid (BVAG) has been combined with a fault tolerance approach named as Checkpoint and Rollback-Recovery (CR) to evaluate the effectiveness of applying fault tolerance approach in a data replication transaction. Binary Vote Assignment on Grid with Checkpoint and Rollback-Recovery Transaction Manager (BVAGCRTM) is used to run the BVAGCR proposed method. The performance of the proposed BVAGCR is compared to standard BVAG in terms of total execution time for a single data replication transaction. The experimental results reveal that BVAGCR improves the BVAG total execution time in failure environment of about 31.65% by using CR fault tolerance approach. Besides improving the total execution time of BVAG, BVAGCR also reduces the time taken to execute the most critical phase in BVAGCRTM which is Update (U) phase by 98.82%. Therefore, based on the benefits gained, BVAGCR is recommended as a new and efficient technique to obtain a reliable performance of data replication with failure condition in distributed databases.

This paper presents a comparative performance analysis of some metaheuristics such as the African Buffalo Optimization algorithm (ABO), Improved Extremal Optimization (IEO), Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO), Max-Min Ant System (MMAS), Cooperative Genetic Ant System (CGAS), and the heuristic, Randomized Insertion Algorithm (RAI) to solve the asymmetric Travelling Salesman Problem (ATSP). Quite unlike the symmetric Travelling Salesman Problem, there is a paucity of research studies on the asymmetric counterpart. This is quite disturbing because most real-life applications are actually asymmetric in nature. These six algorithms were chosen for their performance comparison because they have posted some of the best results in literature and they employ different search schemes in attempting solutions to the ATSP. The comparative algorithms in this study employ different techniques in their search for solutions to ATSP: the African Buffalo Optimization employs the modified Karp–Steele mechanism, Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO) employs the path construction with patching technique, Cooperative Genetic Ant System uses natural selection and ordering; Randomized Insertion Algorithm uses the random insertion approach, and the Improved Extremal Optimization uses the grid search strategy. After a number of experiments on the popular but difficult 15 out of the 19 ATSP instances in TSPLIB, the results show that the African Buffalo Optimization algorithm slightly outperformed the other algorithms in obtaining the optimal results and at a much faster speed.

An exhausted testing is one of the testing strategy that need more time taken due to test the whole test cases in the Software Under Test. Many techniques have been proposed to avoid this situation because the size of the Software Under Test is vary and need to have good testing strategy performance. One of the techniques is Adaptive Random Testing (ART). The ART is one of the enhanced random testing. Due to ART performance is better than pure random testing, it becomes motivation to implement the ART in Aspect Oriented Program (AOP). The ART and random testing are similar in which is selection the first test case with random manner. But, ART add another one characteristic which is the evenness test in domain area. Due to similar for first test case, we proposed a new strategy called StART. In StART, we use statistical testing technique to get the information before we test. This process we named it as code profiling. This code profiling helps in selection first test case in this technique. The result from this phase shows the area that we need to select for test activity.

Nowadays, many organizations deploy the database application systems in order to manage their business operation. Administrations is required to deliver up to date data to users who live distantly, thus replicated databases can be one solution to increase the business operations performance in the distributed environment. Even though data availability is increased, common existing replication strategies neglect the correlation among the different data files in a Distributed Database Systems. In this paper, Binary Vote Assignment on Grid Quorum with Association Rules (BVAGQ-AR) has been proposed to manage the database replication. This technique combines data replication and data mining approaches in order to decrease the job processing time. The result shows that BVAGQ-AR has the lowest job processing time compared to other techniques. BVAGQ-AR performs 66.548 ms to complete a transaction compared to BRS with 137.157 ms, HRS with 257.928 ms, and ROWA with 262.243 ms. From the results, it is proved that managing replication and transaction through the proposed BVAGQ-AR able to preserve data consistency with low job processing time.

Traditional information retrieval systems retrieve documents based on keyword based matching, thereby incapable to provide results according to user information needs. These systems do not consider the semantic relationships among the user query keywords, thus cannot interpret the query context accurately. Query Expansion technique enhances the performance of information retrieval process by expanding user query with relevant meaningful concepts. Ontology represents semantics of a domain, and emerges as an important resource for query expansion. In this paper, we propose a query expansion framework based on conceptual knowledge derived from ontology in the domain of computer science. The system prototype results show good average precision percentage of 89.2% over keyword based search.

This paper show the strength of cluster and load balancing. It is important to technical people to choose between cluster and load balancing. The cluster computing is becoming more and more popular. The state-of-the-art technological developments and research innovations are pushing clusters into mainstream computing. The high performance cluster provides an approach to parallel processing that yields super computer level performance. This trend makes it very promising to build high performance computing environments by clusters, with various performance measures from which high availability need to be address. However Network Load Balancing provides improved performance, the ability to add additional servers as necessary, and the ability to perform maintenance on any of the servers without any user impact or hardware configuration changes. By using the combination of Load Balancing and multiple, redundant clusters can add and remove clusters with minimal user impact. It is usually involves dedicated software or hardware, such as a multilayer switch or a Domain Name System server to mitigate some added layer for clustering weakness. The focus of this paper to get High availability 24 x 7 which is a characteristic of a system, which aims to ensure an agreed level of operational performance for a higher than normal period.

Replication is a topic of interest in the cloud computing, distributed systems, and database communities. It is also one of the practical methods in distributed systems for developing better reliability and availability. Replication is employed to reduce user waiting time, increasing data availability and decreasing cloud system bandwidth used by offering the user numerous replicas of a detailed service on multiple nodes in cloud computing. This paper presents an overview of replication techniques on distributed database in the cloud environment.

A data centre is a network of computer servers used by an organization to either save, process or distribute large amounts of data. A green data centre is a repository of data where electrical, mechanical, lighting and computer systems are designed for maximum energy efficiency and minimum environmental impact. Due to increase demands of data in the university, data centre not only consume high quantities of energy, but produce carbon dioxide along with other IT inefficiencies. With the dynamic changes in technology and the exponential rise in quantities of data, the need for implementing green data centre have become the need of the hour. Green technology in data centre will not only reduce costs and emissions, but will provide operational benefits as well. However, there are challenges that need to be faced in order to implement green technology in university data centre. This review will explore what is green technology is all about, its challenges that may hampered to the implementation in university Data Centre and solutions that may combat the limits.

Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Massive growth in the scale of data or big data generated through cloud computing has been observed. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. This paper introduces several Big Data processing techniques from system and application aspects. First, from the view of cloud data management and Big Data processing mechanisms, we present the key issues of Big Data. Following, we present the cloud computing for Big Data and related work. Furthermore, we also discuss, Big Data moving to the cloud. Finally, we present the conclusion and future work.

This paper presents a comparative study of some Neural Networks methods and the newly-designed African Buffalo Optimization in solving 12 popular benchmark symmetric Travelling Salesman’s Problems. Recently, researchers are exploring solutions to difficult combinatorial problems using the Neural Networks methods. So far, the experiments have been successful. On the other hand, the metaheuristic, African Buffalo Optimization has proven to be quite effective and efficient in providing solutions to some NP-hard and NP-Complete problems, including, of course, the Travelling Salesman’s Problems. After a number of experimental evaluations on the chosen dataset, the African Buffalo Optimization was found to be more successful in solving the symmetric Travelling Salesman’s Problems under consideration.

One of the mechanisms for managing data is replication since it improves data access and reliability. However, the amount of various data grows rapidly since technology is widely available at a low-cost. Problem arises when the database is packed with data, but it has lacked of knowledge. If the unreasonable data is used in database replication, it will cause waste of data storage and delay the time taken for a replication process. This paper proposes a new algorithm namely Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) to handle fragmented database replication. BVAGQ-AR algorithm is capable of partitioning the database into disjoint fragments. Handling fragmented database replication becomes challenging issue to administrator since the distributed database is disseminated into split partitions or fragments. This paper will discuss about how to build reliable system by using the proposed BVAGQ-AR algorithm for distributed database fragmentation. The result shows that managing fragmented database replication through proposed BVAGQ-AR algorithm able to preserve data consistency.

This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. The study reveals that the African Buffalo Optimization and the Ant Colony Optimization are the best in solving the symmetric TSP, which is similar to intelligence gathering channel in the strategic management of big organizations, while the Randomized Insertion Algorithm holds the best promise in asymmetric TSP instances akin to strategic information exchange channels in strategic management.

Objectives: This paper presents a review on the application of simulation software as a tool aiding in design and manufacturing aspect of wood plastic composites (WPCs). The scope of application of models present in literature by previous researchers is discussed in general. Methods/Statistical Analysis: A review on the simulation in wood plastic composites manufactured by compression moulding process is presented by analyzing the data present in literature. Important factors which affect the mechanical properties of final wood plastic composite products are stated. This paper also addresses the challenges of application of simulation models for prediction of mechanical properties of wood plastic composites by other researchers. Findings: Simulation models related to wood based composites are discussed and their applicability for wood plastic composites is reported. A need of simulation software for WPC prediction purpose and easy to use by other researchers is highlighted. Application/Improvements: Importance of collaborative efforts between material researchers and computer science researchers is also highlighted to fulfill the need of the simulation software in wood plastic composite area.

Replication is one of the mechanisms managing data since it improves data access and reliability. However, in recent years, with widely available, low-cost technology, the amount of various data grows rapidly. The problem is although we are packed with data, but we have lacked of knowledge. Nevertheless, if the impractical data is used in database replication, this will cause waste of data storage and the time taken for a replication process will be delayed. This paper propose a new algorithm namely Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) in order to handle fragmented database replication. BVAGQ-AR algorithm is capable of partitioning the database into disjoint fragments. Fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. Handling fragmented database replication becomes challenging issue to administrator since the distributed database is scattered into split replica partitions or fragments. We address how to build reliable system by using the proposed BVAGQ-AR algorithm for distributed database fragmentation by using Malaysian Greater Research Network (MyGRANTS) data as a case study. The result shows that managing fragmented database replication through proposed BVAGQ-AR algorithm able to preserve MyGRANTS data consistency.

Ontology engineering is an important aspect of semantic web vision to attain the meaningful representation of data. Although various techniques exist for the creation of ontology, most of the methods involve the number of complex phases, scenario-dependent ontology development, and poor validation of ontology. This research work presents a lightweight approach to build domain ontology using Entity Relationship (ER) model. Firstly, a detailed analysis of intended domain is performed to develop the ER model. In the next phase, ER to ontology (EROnt) conversion rules are outlined, and finally the system prototype is developed to construct the ontology. The proposed approach investigates the domain of information technology curriculum for the successful interpretation of concepts, attributes, relationships of concepts and constraints among the concepts of the ontology. The experts’ evaluation of accurate identification of ontology vocabulary shows that the method performed well on curriculum data with 95.75% average precision and 90.75% average recall.

K-Means is one of the popular methods for generating clusters. It is very well-known and commonly used for its convenience and fastness. The main disadvantage of these criteria is that user should specify the number of cluster in enhance. As a repetitive clustering strategy, a K-Means criterion is very delicate to the preliminary beginning circumstances. In this paper, has been proposed a clustering strategy known as Multi-dimensional K-Means clustering criteria. This algorithm auto generates preliminary k (the preferred variety of cluster) without asking input from the user. It also used a novel strategy of establishing the preliminary centroids. The experiment of the proposed strategy has been conducted using synthetic data, which is taken form LIyod’s K-means experiments. The algorithm is suited for higher education for calculating the student’s CGPA and extracurricular activities with graphs.

Recently, mining least association rule from the sequential data becomes more important in certain domain areas such as education, healthcare, text mining, etc. due to its uniqueness and usefulness. However, discovering such rule is a great challenge because it involves with a set of least items which usually holds a very low in term of support. Therefore, in this paper propose a model for mining sequential least association rule (2M-SELAR) that embedded with SELAR algorithm, and Critical Relative Support (CRS) and Definite Factor (DF) measures. The experimental results reveal that 2M-SELAR can successfully generate the desired rule from the given datasets.

Fault tolerance is a major concern to guarantee availability and reliability of critical services as well as application execution. Recently, due to rapid expansion of smart phone market and wireless communication environment, mobile devices are considered as resource for large scale distributed processing but there are several drawbacks, such as unstable wireless connection, limitation of data storage capacity, low communication bandwidth and frequent location changes. Cloud systems differ from these previous frameworks in that they are designed to support large numbers of customer-oriented applications, each with different quality of service (QoS) requirements and resource consumption characteristics. As resource providers, mobile devices can join and leave the distributed computing environment unpredictably. This interrupts the undergoing operation, and the delay or failure of completing the operation may cause a system failure. In this paper, we propose a monitoring technique based on Load Balancing.

This paper is the current approach database security of organization need layers of security around their data, with each layer protecting a specific area. The current trend of security attack is targeting any valuable data stored in a database such as financial data and student mark, including data theft, data modification or replay and data disruption. To understand how the system works, the system setting and the weaknesses of Enterprise Distributed Database. This paper is about the design and development to secure database access, a framework based on packet and content filtering on IP address, port and SQL statement. And simulate the framework and design database security framework. We address how to build the outcome of this is to reduce the risk of data manipulation and reduce the impact of internal threat and malicious insiders. The result shows that the proposed method succeeded in meeting those aspects of information security.

This paper present load balancing technique in a heterogeneous environment allows the usage for geographically widely distributed and multi-owner resources to solve large-level application, usage of load balancing algorithms was important to keep maintaining the balance of workload between emerged infrastructures like grid. This replication generally referred as mechanism to improve availability and performance in distributed databases especially handling fragmented database replication becomes demanding issue. Intended in this paper we address various kinds of load balancing algorithms for the heterogeneous network like grid, especially Binary Vote Assignment Grid Quorum (BVAGQ) and to identify various metric and gaps between them. Many load balancing algorithms are already implemented which work against various issues like heterogeneity, scalability, etc. Different load balancing algorithms for the grid environment work on various metrics such as make span, time, average resource utilization rate, communication overhead, reliability, stability, and fault tolerance. However the aim is to find improved query response time and overall throughput as compared to other scheme.

This paper presents the software namely Building Submission Checklist System (BUSCLIS). It has been developed to manage the submission of building checklist plans process in the construction industry. BUSCLIS helps to simplify the management for acquiescence data of building plan approval for the Local Authority (LA) and Country Planning in Malaysia through the web based system. BUSCLIS facilitates user through the computerization forms, which provides fast, efficient and effective service to the engineer, architect and contractor. Relevant and timely information manage by sophisticated BUSCLIS with the database management system MySQL. Keywords: BUSCLIS, RAD, building plan, ICT, construction industry.

The effectiveness of optimization in scientific and engineering applications has made optimization a popular area of scientific investigation in data generation and analytics leading to the design of several optimization algorithms. In view of the huge number of optimization algorithms in literature, there is the need for a thorough diagnostic evaluation so as to bring out the strengths and weaknesses of each technique: that way, assist researchers in making informed choices whenever they are confronted with an optimization problem. This paper aims to fill the gap in literature of the Flower Pollination Algorithm in terms of diagnostic assessment of the impact of the number of iteration and search agents in solving the popular benchmark Sphere function and the unpopular but complex multimodal Dejong 5 function, otherwise called Shekel Foxhole function. After a number of empirical evaluations, the study finds out that the Flower Pollination Algorithm is not only a fast technique but also obtained good results when the appropriate iteration and flower population is used.

This paper presents research findings on the application of swarm intelligence techniques in computational intelligence to solve the travelling salesman’s problem. The travelling salesman’s problem finds real-life application in post office mail delivery, school bus routing, delivery of food to home-bound people etc. After a number of experimental procedures, the study concludes that all the comparative algorithms are very efficient in providing solutions to the benchmark travelling salesman’s problems considered, though the Discrete Cuckoo Search and the African Buffalo Optimization have a slight edge in performance over the other comparative algorithms. In all, the study agrees with earlier studies in reaching the conclusion that swarm-based optimization techniques are not only effective but also are very efficient in providing solutions to the travelling salesman’s problems.

The information has evolved rapidly over the World Wide Web in the past few years. To satisfy information needs, users mostly submit a query via traditional search engines, which retrieve results on the basis of keyword matching principle. However, a keyword-based search cannot recognize the meanings of keywords and the semantic relationship among the terms in the user’s query; thus, this technique cannot retrieve satisfactory results. The expansion of an initial query with relevant meaningful terms can solve this issue and enhance information retrieval. Generally, query expansion methods consider concepts that are semantically related to query terms within the ontology as candidates in expanding the initial query. An analysis of the correct sense of query terms, rather than only considering semantic relations, is necessary to overcome language ambiguity problems. In this work, we proposed a query expansion framework on the basis of query sense analysis and semantics mining using computer science domain ontology, followed by working prototype of the system. The experts analyzed the results of system prototype over test dataset and Web data, and found a remarkable improvement in the overall search performance. Furthermore, the proposed framework demonstrated better mean average precision and recall values than the baseline method.

The increasing fame of biofeedback game has brought convenience to human life. More and more people rely on biofeedback game as an alternative medical treatment to overcome stress problems. Practitioners, meditators, healthcare clinicians and patients are among the most common people who use this biofeedback method to handle their stress in a better way. Basically, biofeedback game integrates biofeedback device in their therapy. Different biofeedback devices have unique way to patterned user’s physiological response towards the game. Biofeedback game implements the virtual game to collaborate with these biofeedback devices. Global issues such as the Electric Vehicles (EVs) issue which are part of the green technology solution are rarely found in the existing biofeedback virtual games themes. Therefore this project, GreenVec Game for Skin Conductivity Level (SCL) Biofeedback Performance Simulator using Galvanic Skin Response (GSR) Sensor or mainly known as Green Vehicle Biofeedback Game is a biofeedback game-based application that able to measure SCL of the player with the integration of GSR. The skin conductivity level corresponds to the player’s awareness level about the green Electric Vehicles (EVs) issues. The biofeedback game will also create a short report on the player’s skin conductivity level based on the GSR sensor data and the game data by the end of the game. From the end result, it shows that when the SCL is high, the awareness on the electric vehicles issues is high. While the SCL is low, the awareness on the electric vehicles issues is low.

One of the most popular and important studies in data mining is association rules mining. Generally, association rules can be divided into two categories called frequent and least. However, finding the least association rules is more complex and time consuming as compared to the frequent one. These rules are very useful in certain application domain such as determining the exceptional association between university’s programs being selected by students. Therefore in this paper, we apply our novel measure called Definite Factors (DF) to determine the significant least association rules from undergraduate’s program selection database. The dataset of computer science student for July 2008/2009 intake from Universiti Malaysia Terengganu was employed in the experiment. The result shows that our measurement can mine these rules and it is at par with the existing benchmarked Relative Support Apriori (RSA) measurement.

Computational models of the artificial intelligence such as rough set theory have several applications. Rough set-based data clustering can be considered further as a technique for medical decision making. This paper presents the results of an experimental study of a rough-set based clustering technique using Variable Precision Rough Set (VPRS). Here, we employ our proposed clustering technique [12] through a medical dataset of patients suspected diabetic. Our results indicate that the VPRS-based technique is better than that the standard rough set-based techniques in the process of selecting a clustering attribute.

Frequent Pattern Tree (FP-Tree) is one of the famous data structure to keep frequent itemsets. However when the content of transactional database is modified, FP-Tree must be reconstructed again due to the changes in patterns and items support. Until this recent, most of the techniques in frequent pattern mining are using the original database to determine the items support and not from their recommended trees data structure. Therefore in this paper, we proposed a technique called Fast Determination of Item Support Technique (F-DIST) to capture the items support from our suggested Disorder Support Trie Itemset (DOSTrieIT) data structure. Experiments with the UCI datasets show that the processing time to determine the items support using F-DIST from DOSTrieIT is outperformed the classical FP-Tree technique. Furthermore, the processing time to construct a complete tree data structure for DOSTrieIT is lesser than the benchmarked CanTree data structure.

Mining least association rules has received a great attention from the past decades. However, no current research has been specifically performed for visualizing these types of rules. In this paper, a model for visualizing critical least association rules is presented. The proposed model contains five main steps, including scanning dataset, constructing Least Pattern Tree (LP-Tree), applying Critical Relative Support (CRS), capturing Critical Least Association Rules and finally visualizing the respective rules.

Frequent Pattern Tree (FP-Tree) is a compact data structure of representing frequent itemsets. The construction of FP-Tree is very important prior to frequent patterns mining. However, there have been too limited efforts specifically focused on constructing FP-Tree data structure beyond from its original database. In typical FP-Tree construction, besides the prior knowledge on support threshold, it also requires two database scans; first to build and sort the frequent patterns and second to build its prefix paths. Thus, twice database scanning is a key and major limitation in completing the construction of FP-Tree. Therefore, this paper suggests scalable Trie Transformation Technique Algorithm (T3A) to convert our predefined tree data structure, Disorder Support Trie Itemset (DOSTrieIT) into FP-Tree. Experiment results through two UCI benchmark datasets show that the proposed T3A generates FP-Tree up to 3 magnitudes faster than that the benchmarked FP-Growth.

Replication in distributed database is the process of copying and maintaining database objects in multiple databases that make up a distributed database system. In this paper, we will manage fragmented database replication and transaction management for Malaysian Greater Research Network (MyGRANTS) using a new proposed algorithm called Binary Vote Assignment on Cloud Quorum (BVACQ). This technique will combine replication and fragmentation. Fragmentation in distributed database is very useful in terms of usage, efficiency, parallelism and also for security. This strategy will partition the database into disjoint fragments. The result shows that managing replication and transaction through proposed BVACQ able to preserve data consistency. It also increases the degrees of parallelism. This is because by using fragmentation, replication and transaction can be divided into several subqueries that operate on the fragments.

This communication presents experimental research findings on the application of the flower pollination algorithm (FPA) and the African buffalo optimization (ABO) to implement the complex and fairly popular benchmark Dejong 5 function. The study aims to unravel the untapped potential of FPA and the ABO in providing good solutions to optimization problems. In addition, it explores the Dejong 5 function with the hope of attracting the attention of the research community to evaluate the capacity of the two comparative algorithms as well as the Dejong 5 function. We conclude from this study that in implementing FPA and ABO for solving the benchmark Dejong 5 problem, a population of 10 search agents and using 1000 iterations can produce effective and efficient outcomes.

Data replication is an important technique in peer to peer network, data grid architecture, clustering and distributed system, where it increases data availability and enhances data access and reliability and minimizes the cost of data transmission. In this paper, we proposed a framework and structure of synchronous replication from the persistence layer that supports heterogeneous system. In this framework, we developed the multithreading based persistence layer. Our objective is to make the persistence layer more adaptive. In this adaptive persistency system, the replication server will not depend on the main server, so forth, adding a new replication server will be easier than ever, easy to cope with heterogeneous system, cost minimizing and finally there will be no down time.

Objectives: The nonlinearity of the stock market is widely accepted all over the world and to reveal such non-linearity the most effective technique has proved to be constructed through application of either data mining or neural network. Pharmaceutical sector is a rapidly growing in Bangladeshi stock market. The objective of this paper is to investigate whether the hybridization of data mining and neural network technique can be applied in predicting the stock price for Pharmaceutical sector of Dhaka Stock Exchange (DSE). Methods/Analysis: This study uses daily trade data for Pharmaceutical sector of DSE. We have analysed the behaviour of daily average price for Pharmaceutical sector of DSE. For this study, 6 top listed pharmaceutical companies have been selected to perform the analysis and selected time frame for the research is 15 years (2000-2015). The analysis is performed in two stages where first stage performs the K-means clustering of data mining method to discover the stock with most useful pattern and second stage applies the nonlinear autoregressive with Exogenous Input neural network method to predict the closing price for the selected stock. Findings: The prediction performance through the hybridization of data mining and neural network technique is evaluated and positive performance improvement of prediction is observed which is very encouraging for investors. The research also depicts that hybridization of data mining and neural network technique can be applied in determining the stock investment decision for Pharmaceutical sector of DSE though the impact of many different information has greater influence in determining the stock price. Novelty/Improvement: We intend to apply the data mining and optimized neural network in predicting stock market. We would like to work with the parameter and learning of the neural network to achieve better result. We will further investigate the effect of various factors viz. dollar price, gold price, FDI, bank interest rate etc. on stock price and index movement.

This paper presents the convergence analysis of the newly-developed African Buffalo Optimization algorithm. African Buffalo Optimization is a simulation of the organizational skills of the African buffalos using two basic sounds: /waaa/ and /maaa/ as they transverse the African landscape in search of grazing pastures. The African Buffalo Optimization has proven to be quite successful since its development hence the need to examine its convergence behavior. The analysis of the convergence of Nature-inspired optimization algorithms is necessary to help researchers and practitioners understand the workings of the algorithms in the algorithms’ attempts at solutions. After a number of evaluations, this study discovered that the convergence of African Buffalo Optimization is a function of the population size, communication topology, parameter-set, landscape topology and the objective function being optimized.

In this paper, we present the African Buffalo Optimization (ABO) approach for the metaheuristic tuning of the Proportional, Integral & Derivative (PID) Controller parameters of Automatic Voltage Regulators in electrical power generation and transmission. ABO simulates the movement of African Buffalos as they migrate from place to place all over Africa in search of grazing pastures. The buffalos use just two basic vocalizations, namely the waaa and the maaa to organize the entire herd in their migration and search for food a nd water. Though a recently-designed algorithm, the ABO has been effective and efficient in solving a number of search optimization problems. The highpoints of the ABO include its use of few parameters, constant interactions among the buffalos and the deployment of the exploration and exploitations mechanisms of the algorithm in every iteration. The simulation outcome of the application of ABO to tune the parameters of a PID-Controller parameters of Automatic Voltage Regulators has been very competitive when compared similar outcomes of other metaheuristics tuners: BFO-PID, PSO-PID, GA-PID, PID-PSO, PID Tuner and ACO-PID.

A data grid is compose of hundreds of geographically distributed computers and storage resources usually locate under different places and enables users to share data and other resources. Problem statement: Data replication is one of the mechanisms in managing data grid architecture that receive particular attention since it can provide efficient access to data, fault tolerance, reduce access latency and also can enhance the performance of the system. However, during transaction deadlock may occur that can reduce the throughput by minimizing the available resources, so it becomes an important resource management problem in distributed systems. Approach: The Neighbor Replication on Grid Deadlock Detection (NRGDD) transaction model has been developed to handle two deadlock cycle problems on grid. By deploying this method, the transactions communicate with each other by passing the probe messages. The victim message has been used to detect the deadlock when the number of waiting resource by other transaction is highest and become as the cause of deadlock occurs. In addition, this transaction must be aborted to solve the problem. Results: NRGDD transaction model are able to detect and solve more than one cycle of deadlocks. Conclusion: NRGDD has resolve the deadlock problem by sending the minimum number of probes message to detect the deadlock and it can resolve the deadlock to ensure the transaction can be done smoothly.

The ability for a product developer to successfully launch useful products is tied to the company’s product design strategies. Due to the complexity of perception and expectations on new product design from customers and the diverse perspectives of the product developer, any approaches without systematically analyzing these complex criteria to assess decision making on product design strategies are therefore deemed as inappropriate. A systematic approach to determine appropriate product design strategies based on the attributes of product design and customer expectations should thus be considered. This research focuses on the investigation of shape formulation process using evolutionary grammars (EG) and non-linear product design analysis with Kano model to refine product design strategies. Through the strategic integration of evolutionary grammars and Kano model, the complex effects on the evolutionary design process and product design strategies are revealed. In order to demonstrate the viability of the approach, experiments are described and a comparison analysis on three EG-Kano reference models is performed. Upon determining the appropriate EG-Kano reference model, the product developer can refine the product design strategies to suit the targeted market.

Cloud computing is fundamentally altering the expectations for how and when computing, storage, and networking resources should be allocated, managed, consumed, and allow users to utilize services globally. Due to the powerful computing and storage, high availability and security, easy accessibility and adaptability, reliable scalability and interoperability, cost and time effective cloud computing is the top, needed for current fast growing business world. A client, organization or a trade that adopting emerging cloud environment can choose a well suitable infrastructure, platform, software, and a network resource, for any business, where each one has some exclusive features and advantages. The authors first develop a comprehensive classification for describing cloud computing architecture. This classification help in survey of several existing cloud computing services developed by various projects globally such as Amazon, Google, Microsoft, Sun and Force.com and by using this survey’s results the authors identified similarities and differences of the architecture approaches of cloud computing.

Innovation and creativity are the key successful factors and a global priority in engineering industries. This paper describes the development of an evolutionary grammars based design framework for product innovation. A new genetic representation of shape grammars is developed in an evolutionary computing environment. The results generated by the genetic algorithm define a new combination of shape features for alternative designs. In this way, traditional shape grammar is extended to an interactive context in which generative and evolutionary computing methods are combined. Both product component design as well as product configuration are supported in this framework. Two important design issues are addressed in this research, 1) Product form exploration, and 2) Product design strategies. To tackle the issues, this research demonstrates the efficient use of evolutionary grammars to support the exploration of innovative product form designs.

Problem statement: Organizations critically needed to supply recent data to users who may be geographically remote, while at the same time handle a volume request of distributed data around multiple sites. The storage, availability and consistency are important issues to be addressed in order to allow distributed users efficiently and safely access data from many different sites. Approach: Data replication is a way to deal with this problem since it provides user with fast, local access to shared data and protects availability of applications because alternate data access options exist. Handling fragmented database replication becomes challenging issue to administrator since the distributed database was scattered into split replica partitions or fragments. Results: This study presented a new mechanism on how to handle the fragmented database replication through the Binary Vote Assignment on Grid Quorum (BVAGQ). We address how to build reliable system by using the proposed BVAGQ for distributed database fragmentation. Conclusion: The result shows that managing fragmented database replication and transaction through proposed BVAGQ is able to preserve the data consistency.

Internet has brought a major drift in user community. Apart from its well-known usage, it also promotes social networking. Research on such social networking has advanced significantly in recent years which have been highly influenced by the online social websites. People perceive the web as a social medium that allows larger interaction among people, sharing of knowledge, or experiences. Internet or social web forums act as an agent to reproduce some general information that would benefit the users. A product review by the user is a more accurate representation of its real-world performance and web-forums are generally used to post such reviews. Though commercial review websites allow users to express their opinions in whatever way they feel, the number of reviews that a product receives could be very high. Hence, opinion mining techniques can be used to analyze the user-reviews, classify the content as positive or negative, and thereby find out how the product fares. This paper focuses its attention on providing a recommendation to the products available on the web by analyzing the context to score the sentences for each review by identifying the opinion and feature words using a novel algorithm.

Problem statement: In this study, the channel allocation problem in direct sequence spread spectrum was investigated. First, Coloring in channel allocation has been discussed in detail. Second; a new graph model is proposed that namely Vertex Merge Algorithm (VMA). Approach: VMA try to solve channel allocation based on graph theory. Results: The problem is to assign channel to Access Point (AP) in such away that interference among access point is eliminated and the total number of channel is minimized. The proposed algorithm is also compared with Dsatur algorithm by input three graphs that are graph1, graph2 and graph3. Conclusion/Recommendations: Simulation results shows that the new graph model can provide minimum needed channel compared to Dsatur algorithm.

This paper presents a new algorithm namely Vertex Merge Algorithm(VMA) for channel allocation in Direct Sequence Spread Spectrum(DSSS). VMA try to solve channel allocation based on graph theory. Results from the simulation study reveal that the new graph model can provide reduce the channel required.

The rapid progress in education impacts the significant requirement to manage the educational globalization resources through the use of modern technology. In addition, an online system is the electronic interactive medium that delivers information to users via telephone lines to personal computers (PCs) or via cables to terminals. This paper presents on how to manage the registration of new student intake by using Online Registration New Student Intake System (ORNSIS). Integrated barcode technology with ORNSIS has been developed in order to overcome the manual system weakness, which involves the registration through the traditional methods and time consuming. Understanding the importance and limitations the register process, ORNSIS is a collaborative of web application concept that provides a platform for University Malaysia Pahang administrator to monitor the progress of each new student who did the register online. It has been developed by using the PHP language, Dreamweaver cs4 and database MySQL in the Windows 7. By using ORNSIS, the actual number of student will register in university can be obtain, thus decrease the time-consuming in registration event.

Significant and urgent solutions are required in order to manage educational resource efficient and effective. Data replication is one of the key components to manage the huge of resources in data grid architecture as it enhances data access and reliability. Replication provides user with fast, local access to shared data, and increased availability of applications because alternate data access options exist. In this paper, we present on how to manage fragmented database replication and transaction for educational resources-Student Information System (SIS) by using Binary Vote Assignment on Grid Quorum (BVAGQ) fragmented database replication model. Furthermore, fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. In particular, BVAGQ able to partition the database into disjoint fragments. The result shows that managing replication and transaction through proposed BVAGQ able to preserve data availability and consistency. It also increases the degrees of parallelism. This is because by using fragmentation, replication and transaction can be divided into several subqueries that operate on the fragments.

Competence is a very important characteristic to all area of activity, especially in an emergency situation. This is because people must react according to what supposed to do. In particular, if an individual lacking competence in the job or life, the person may take wrong action and make more loss in the area of work or situation such loss of life, injured, and others. This paper presents a new formal specification framework of individual Emergency, Response and Preparedness (ERP). A chemical company in Pahang, Malaysia has been selected as a case study to model the framework of emergency, response and preparedness. We design the framework of ERP according to the company's competency by considering three elements which are the skill, knowledge and attitude. Furthermore, this framework results the specification becomes clear, sharp, precise and non-multi-interpretation.