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A chiller plant is a centralized system used for air cooling systems, commonly, for covering a large area of building with various components such as chillers, cooling towers, pumps, and chilled water storage tanks. Each component has several sensors or indicators with status information. Users can use the information to plan for maintenance and as guidance during troubleshot if an event occurs. It is crucial to ensure the chiller plant is operating efficiently without any faulty especially in critical buildings such as a hospital. The main problem of the chiller plant is to conduct preventive maintenance for avoiding the chiller plant failure and breakdown unexpectedly. Based on the literature, approximately 80 components in the chiller plant has found as the possible reason for the chiller plant faulty. In the current research, modeling chiller plants has been done by several researchers, objectively for preventative maintenance purposes. Study case for this project is for a chiller plant at Hospital Raja Permaisuri Bainun, Ipoh, Perak, Malaysia. A model for the proposed chiller plant system is to be designed using System Identification (SI) technique based on Nonlinear Autoregressive with Exogenous Inputs (NARX). Validation result shows, the proposed chiller plant system can be modelled and to be used as One Step Ahead prediction tool with residual Mean Square Error (MSE) of 1.018E-3 for training set and 1.017E-3 for testing set.

Data replications effectively replicate the same data to various multiple locations to accomplish the objective of zero loss of information in case of failures without any downtown. Dynamic data replication strategies (providing run time location of replicas) in clouds should optimize the key performance indicator parameters, like response time, reliability, availability, scalability, cost, availability, performance, etc. To fulfill these objectives, various state-of-the-art dynamic data replication strategies has been proposed, based on several criteria and reported in the literature along with advantages and disadvantages. This paper provides a quantitative analysis and performance evaluation of target-oriented replication strategies based on target objectives. In this paper, we will try to find out which target objective is most addressed, which are average addressed, and which are least addressed in target-oriented replication strategies. The paper also includes a detailed discussion about the challenges, issues, and future research directions. This comprehensive analysis and performance evaluation based-work will open a new door for researchers in the field of cloud computing and will be helpful for further development of cloud-based dynamic data replication strategies to develop a technique that will address all attributes (Target Objectives) effectively in one replication strategy.

Nowadays, under the pressure of numerous research publications, researchers increasingly pay attention to writing survey papers to track and understand one research topic they are interested in, and then begin to conduct more in-depth research. Until this moment, there are two types of survey papers: traditional review analysis and bibliometric statistical analysis. Compared with traditional review analysis, due to the analysis of various bibliometric information that can be quickly summarized to assess and predict one research field’s development, the bibliometric statistical analysis is progressively proposed. However, no research relied on the bibliometric approach to explore fuzzy inference system (FIS) -based classifiers. More importantly, since the current open-ended bibliometric analysis approaches have different assessment focuses, choosing a suitable approach is problematic. Therefore, based on the extraction, integration, and expansion of previous bibliometric analysis theories, this research proposes a new systematic and time-saving bibliometric statistical analysis approach. It is worth noting that the proposed approach eliminates the need to read the internal content of all publications. Two core parts (Publication Information and TOP 20 SETs) are generated by the proposed analysis approach. Among them, analyzing Author Keywords and TOP 20 SETs are unprecedented guiding features to assist researchers in exploring research topic. Significantly, this research relies on the proposed approach to explore FIS-based classifiers. Various assessments cover the bibliometric information of the entire related publications. In addition, these results may need to be considered to increase the citation rate of future publications.

The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to identify phishing attacks noted in the web site applications. The study compares with five classifiers to find the best machine learning classifiers in detecting phishing attacks. In identifying the phishing attacks, it demonstrates that random forest is able to achieve high detection accuracy with true positive rate value of 94.79% using website features. The results indicate that random forest is effective classifiers for detecting phishing attacks.

Identifying which biodiversity species are more dominant than others in any area is a very challenging task. This is because of the abundant of biodiversity species that may become the majority species in any particular region. This situation create a large dataset with a complex variables to be analysed. Moreover, the responds of organisms and environmental factors are occurred in a non-linear correlation. The effort to do so is really important in order to conserve the biodiversity of nature. To understand the complex relationships that exist between species distribution and their habitat, we analysed the interactions among bird diversity, spatial distribution and land use types at Kenyir landscape in Terengganu, Malaysia by using artificial neural network (ANN) method of self-organizing map (SOM) analysis. SOM performs an unsupervised and non-linear analysis on a complex and large dataset. It is capable to handle the non-linear correlation between organism and environmental factors because SOM identifies clusters and relationships between variables without the fixed assumptions of linearity or normality. The result suggested that SOM analysis was suited for understanding the relationships between bird species assemblages and habitat characteristics.

Malware is an application that executes malicious activities to a computer system, including mobile devices. Root exploit brings more damages among all types of malware because it is able to run in stealthy mode. It compromises the nucleus of the operating system known as kernel to bypass the Android security mechanisms. Once it attacks and resides in the kernel, it is able to install other possible types of malware to the Android devices. In order to detect root exploit, it is important to investigate its features to assist machine learning to predict it accurately. This study proposes flying animal-inspired (1) bat, 2) firefly, and 3) bee) methods to search automatically the exclusive features, then utilizes these flying animal-inspired decision features to improve the machine learning prediction. Furthermore, a boosting method (Adaboost) boosts the multilayer perceptron (MLP) potential to a stronger classification. The evaluation jotted the best result is from bee search, which recorded 91.48 percent in accuracy, 82.2 percent in true positive rate, and 0.1 percent false positive rate.

Wireless Sensor Networks (WSNs) are defined as networks of nodes that work in a cooperative way in order to sense and control the surrounding environment. Several WSNs algorithms have been proposed by utilizing the Fuzzy Logic technique to select the cluster heads (CHs). Each technique employs a different combination of input parameters such as nodes density, communication cost, and residual energy. CHs determination is critical towards this goal, whereas the combination of input parameters is expected to play an important role. Nevertheless, the received signal strength (RSSI) is one of the main criteria which get little attention from researchers on the topic of CHs selection. In this study, an RSSI based scheme was proposed which utilizes Fuzzy Logic approach in order to be combined with residual energy and centrality of the fuzzy descriptor. In order to evaluate the proposed scheme, the performance Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) were compared. The simulation results show that the proposed approach has significantly prolonged the survival time of the network against SEP and MAP, while effectively decelerating the dead process of sensor nodes.

The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to identify phishing attacks noted in the web site applications. The study compares with five classifiers to find the best machine learning classifiers in detecting phishing attacks. In identifying the phishing attacks, it demonstrates that random forest is able to achieve high detection accuracy with true positive rate value of 94.79% using website features. The results indicate that random forest is effective classifiers for detecting phishing attacks.

Cloud Computing is a technology that allows services of the communication, file transfer, infrastructure and platform usage to the user through the internet. In the era of globalisation, each of the countries raced to growth cloud technology as the main part of their industrial development. Similarly, with ASEAN countries, they start using cloud technology as a core part of the industrial revolution 4.0 (IR 4.0) to increase their economy and raise productivity. ASEAN already started using cloud computing, but some country has slow progress due to several challenges such as the broadband quality, lack of cloud knowledge and security issues. They require some strategies to address this problem in order to prove the ASEAN can be competitive in the use of technology as it can open up better industrial opportunities. This article reviews the published literature on the implementation of cloud computing in ASEAN. The author systematically analysed a total of 37 selected articles. There were 22 of the articles selected through restrictive search criteria while the other 15 were selected based on unrestrictive search criteria. The review shows the cloud implementation, expose the benefit and challenge to grow a cloud computing in general, and focusing the challenge of ASEAN in cloud computing development. This paper concludes with some recommended future works to construct the theory and perform empirical research.

Wireless Sensor Network (WSN) is a distributed wireless connection that consists many wireless sensor devices. It is used to get information from the surrounding activities or the environment and send the details to the user for future work. Due to its advantages, WSN has been widely used to help people to collect, monitor and analyse data. However, the biggest limitation of WSN is about the network lifetime. Usually WSN has a small energy capacity for operation, and after the energy was used up below the threshold value, it will then be declared as a dead node. When this happens, the sensor node cannot receive and send the data until the energy is renewed. To reduce WSN energy consumption, the process of selecting a path to the destination is very important. Currently, the data transmission from sensor nodes to the cluster head uses a single hop which consumes more energy; thus, in this paper the enhancement of previous algorithm, which is MAP, the data transmission will use several paths to reach the cluster head. The best path uses a small amount of energy and will take a short time for packet delivery. The element of Shortest Path First (SPF) Algorithm that is used in a routing protocol will be implemented. It will determine the path based on a cost, in which the decision will be made depending on the lowest cost between several connected paths. By using the MATLAB simulation tool, the performance of SPF algorithm and conventional method will be evaluated. The expected result of SPF implementation will increase the energy consumption in order to prolong the network lifetime for WSN.

Identifying which biodiversity species are more dominant than others in any area is a very challenging task. This is because of the abundant of biodiversity species that may become the majority species in any particular region. This situation create a large dataset with a complex variables to be analysed. Moreover, the responds of organisms and environmental factors are occurred in a non-linear correlation. The effort to do so is really important in order to conserve the biodiversity of nature. To understand the complex relationships that exist between species distribution and their habitat, we analysed the interactions among bird diversity, spatial distribution and land use types at Kenyir landscape in Terengganu, Malaysia by using artificial neural network (ANN) method of self-organizing map (SOM) analysis. SOM performs an unsupervised and non-linear analysis on a complex and large dataset. It is capable to handle the non-linear correlation between organism and environmental factors because SOM identifies clusters and relationships between variables without the fixed assumptions of linearity or normality. The result suggested that SOM analysis was suited for understanding the relationships between bird species assemblages and habitat characteristics.

YouTube has become a popular social media among the users. Due to YouTube popularity, it became a platform for spammer to distribute spam through the comments on YouTube. This has become a concern because spam can lead to phishing attack which the target can be any user that click any malicious link. Spam has its own features that can be analyzed and detected by classification. Hence, enhancement features are proposed to detect YouTube spam. In order to conduct the experiments, a YouTube Spam detection framework that consists of five (5) phases such as data collection, pre-processing, features selection and extraction, classification and detection were developed. This paper, proposed the YouTube detection framework, examined and validate each of the phases by using two types of data mining tool. The features are constructed from analysis by using data collected from YouTube Spam dataset by using Naïve Bayes and Logistic Regression and tested in two different data mining tools which is Weka and Rapid Miner. From the analysis, thirteen (13) features that had been tested on Weka and RapidMiner shows high accuracy, hence is being used throughout the experiment in this research. Result of Naïve Bayes and Logistic Regression run in Weka is slightly higher than RapidMiner. In addition, result of Naïve Bayes is higher than Logistic Regression with 87.21% and 85.29% respectively in Weka. While in RapidMiner there is slightly different of accuracy between Naïve Bayes and Logistic Regression 80.41% and 80.88%. But, precision of Naïve Bayes is higher than Logistic Regression.

In Wireless Sensor Network (WSN), cluster head selection is a critical issue. It can drastically affect the whole performance of network lifetime when the characteristic of sensor node are not well defined. In order to deal with this problem, this paper proposed a fuzzy-based system for cluster head selection and new data routing to minimize the energy usage of sensor node. The proposed algorithm called Multi-tier Algorithm (MAP). It is constructed with 2-tiers of network design where the cluster head selections is based on three parameters using Fuzzy Logic technique. The primary nodes at each tier are introduced to transmit data to the base station. Then, the algorithm was tested by 2000 and 4000 bits of data using random energy to predict the lifetime of the sensor nodes in the network. It is concluded that, the implementation of Fuzzy Logic used all node evenly and nodes died sequentially as the data increased. This causes the sensor nodes utilize their energy effectively hence prolonging the network’...

A wireless sensor network (WSN) organizes collected data at a central location using a set of specialized transducers with a communications infrastructure for monitoring and recording conditions at various locations. A sensor network consists of multiple detection stations called sensor nodes, each of which is small, lightweight and portable. A WSN node contains several technical components which include the radio, microcontroller, analog circuit, sensor interface and an energy source, usually a battery. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. This paper presents an improvement of clustering method by implementing two-tiers of network design for efficiency of data transmission and fuzzy logic approach for cluster head selection which minimizes the energy usage of sensor nodes hence can maximize the network lifetime. The two-tiers clustering approach used in this paper covers the average of 100M×100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. The parameters have shown that the selection of cluster head can minimize the energy usage for the whole network lifetime. It is concluded that, the implementation of Fuzzy Logic used all node evenly and the nodes died sequentially as the data keep increasing. This causes the sensor nodes to use their energy effectively hence prolonging the network's lifetime. For the future work, the stability of this algorithm can be tested using random energy.

Wireless sensor network and its applications are interesting research that have been focused recently. Battery consumption of sensor nodes is the main problem in the family of wireless sensor that should be solved. So, to increase the scalability of the network, and to reduce the energy usage for overall sensor operations, clustering techniques and data aggregation are the main focus in this paper. The multi tier techniques has been designed precisely and the selection of the cluster head using Fuzzy Logic based on the three selected parameters are well used along with its limited resources of wireless sensor network. In this study, the main primary and secondary cluster head are the important entities of the algorithm for receiving and transmitting data to the base station. The contribution of this paper is mainly on the selection of a secondary cluster head and the routing protocol which the data transmission will involved the nearest cluster head for both tier one and tier two. Due to multi tier clustering in sensor network, the operations of the sensor network will eventually increase the lifetime of the network compared to LEACH and SEP protocols.

In Wireless Sensor Network, parameter selection is one of the important considerations to help the usage of the energy while selecting the cluster head in clustering scheme. This paper proposed the right combination of the parameter to select the cluster head in order to minimize the total energy consumption required for data collection and aggregation. We proposed three parameters which are residual energy, centrality and communication cost and blended all together using Fuzzy Logic method. By using these proposed parameters, we will able to compare the energy usage for each of the nodes with LEACH and SEP algorithm. Simulation results show that, the right combination of parameters may produce good results and tends to prolong the lifetime of the sensor network.

In wireless sensor network, nodes has limited energy resources in its battery. The battery also is impossible to recharge once being deployed. In this paper, we consider the problem of energy efficiency in wireless sensor network. We implemented clustering method by choosing appropriate cluster head based on two effectives parameters; which are residual energy and centrality. This method also was being improved by adopted Fuzzy Logic approach to combines these parameters in order to increase the lifetime of wireless sensor network. We show reasonable results that obtained from several simulation test cases which can drastically effect the lifetime of Wireless Sensor Network.

Power consumption is always an important element for Wireless Sensor Network (WSN). The energy is the main constraint should be considered when designing the algorithm, protocol or the methods. Clustering is one of the effective methods to use in designing low energy usage in WSN. In the clustering, the appropriate cluster head selection can drastically reduce the energy consumption and prolong the lifetime of the network. This paper proposes the use of ranking table to select cluster head based on the Fuzzy Logic approach. The multi-tier clustering will be used to improve the energy consumption of WSN. Thus, this approach will increase the network lifetime and reduce the energy consumption of nodes while selecting the cluster head.

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy.