Computational Science and Technology

Programming knowledge is increasingly important to facilitate code reuse. Nevertheless, comprehending another programming language is not simple because of its complexity and clarification needs. Prior work focused on different learning styles to aid programming, but it was important to identify which ones were more effective. This research highlights findings in assessing the different documentation styles, including sequential and global documentation styles. Organizing an observation of 125 intermediate undergraduates participated in cloud hosting computation and file content programming exercises, this empirical investigation revealed that sequential documentation exhibits a positive impact in obtaining programming knowledge, significantly pertaining faster completion time, higher multiple choice comprehension, and fewer difficulties. This concludes that sequential documentation solutions can lead intermediate undergraduates with sequential learning styles to faster growth in gaining programming knowledge.

Vulnerability scanning is one of the vital process conducted by many penetration testers and security consultants as to assess the security of an organizational network. However, when multiple vulnerability scanners are used, reports of varied sources have to be compiled via manual means. It is an uncomplicated but lengthy process, where vulnerabilities of different reports have to be examined thoroughly in order to assess them. Thus, this paper describes an approach of creating a report consolidation tool in order to merge similar vulnerabilities and to unify results of differing scanner.

Document sentiment analysis is the task of determining whether a document has a positive, negative or neutral sentiment. It is made up of subtasks including feature extraction, feature selection and sentiment classification. Feature selection is the task of selecting relevant features that can aid the classifier to produce better results. This paper focuses on comparing the classification performances based on several feature selection methods used to select relevant features and also to minimize the document-term matrix representation of the documents. The purpose of applying feature selection besides selecting relevant features is also to reduce the number of features to preserve the efficiency of the whole system. In this work, the experiment setup is designed in order to investigate the effectiveness of several selected feature selection methods in improving the sentiment analysis results. Based on the findings from the experiment, although common feature selection methods such as Document Frequency (DF), Information Gain (IG) and Chi-Squared Statistics (CSS) are found to be able to produce high sentiment analysis accuracy, the Categorical Probability Proportional Difference (CPPD) method is found to be more effective as it produces higher performance accuracy in classifying the documents based on the sentiments. Although, the Categorical Proportional Difference (CPD) method produces acceptable classification results, it is weak in reducing the number of features. In short, the CPPD method enables the sentiment analysis task to be conducted with higher accuracy rate couples with high feature reduction rate too.

For no less than two decades, the development of autonomous systems has led to the development of embedded applications permitting to enhance the driving comfort and limit the hazard level of dangerous zones. One of the first embedded system is a lane detection system, which was implemented using road marking detection algorithms with the aim to produce a system that is able to detect various shapes of road markings on the images that are captured under various imaging conditions. Generally, the road images were captured using a camera, which has been placed inside a vehicle at a fixed position. In this paper, a road markings detection system that tackles the problems of detecting road markings on the images captured under various weather and illumination conditions is proposed. The proposed system consists of inverse perspective transform method, which is used to convert an image into a bird’s-eye view image, an image normalization method, namely CLAHE that tackle various illumination conditions and Sobel edge detection method for identifying the road marker. We demonstrate the usefulness of the constructed algorithm by performing experiments on our Large Variability Road Images database (LVRI) that consists of 22,500 road images with the accuracy of 96.53%.

The design of the control system is a crucial point for improving the thermal comfort level in the smart home environment. Compared to the conventional temperature control system, a thermal comfort control (TCC) system can provide a better human comfort. Due to system complexity, the TCC system is usually designed as a hybrid system. To ensure the design of a highly energy efficient thermal comfort control (EETCC) system, Cyber-Physical Systems (CPS) can offer numerous opportunities. This paper addresses the time delay modeling issues of the EETCC system. Following the execution model of the programming temporally integrated distributed embedded systems (PTIDES), the real-time requirements of execution tasks can be guaranteed. In addition, as the task dependency relations widely exist in the practical applications, by using a directed acyclic graph and the proposed schemes, these task dependency relations can also be dealt properly.

The vision of ubiquitous computing is based on the fact that future computers will merge up with the surrounding environment in IoT domain. Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSN) are two important cornerstones for pervasive computing as they combine the physical and virtual world, thereby bridging the gap between cyber space and physical world of real things. RFID enables the identification and detection of entities while WSN are used to sense the condition of the environment or object. The integration of RFID and WSN have paved way for the existence of RSN (RFID Sensor Networks), thereby providing extended capabilities, scalability, portability, lower cost and novel perspective towards a broad range of applications. This paper presents a brief introduction about the evolution of RSN, major issues in RSN and energy management with regards to Energy Harvesting (EH), energy request and transfer. It also investigates into the problems encountered for efficient energy transmission. Differently from the classic schemes in the literature that deals with scalability, security and communication protocol aspects, the proposed methodology focuses on energy management issue which is of utmost importance for wide area RSN. Furthermore, the paper provides insights into the preliminary experimental evaluation and its comparative analysis with existing schemes pertaining to performance metrics.

The last decade, numerous researches are still working on developing a robust and faster keypoints image descriptors algorithm. In this paper, we will review a few complex keypoint descriptor approaches that are well-known and commonly used in vision applications, and they are Scale Invariant Feature Transform (SIFT) and Speed-up Robust Features (SURF). These methods aim to make the descriptors faster to compute and robust to scale, rotation and noise. We will the results of the experiments on face image data. The extracted keypoints and the regions of interest are analysed and compared against the corresponding facial features. The results have shown SIFT outperformed SURF in terms of speed while the extracted keypoints using SURF descriptors are mainly located on the corners and distinct facial features.

VANETs are the next uprising technology in automotive industry to promote safety and valuable information to make a better driving experience. This technology allows vehicle to communicate directly to the next vehicle and exchange data. The main purpose of VANETs is to improve traffic safety and to provide efficient traffic management on road via safety and non-safety message. Safety and non-safety messages are disseminated instantly among the vehicles through broadcasting protocol. By broadcasting, message can be delivered to multiple vehicles at the same time within vicinity. When too many vehicles broadcasting in dense traffic, VANETs suffer network congestion due to excessive amount of broadcast messages consume the bandwidth of communication channel. When broadcast storm occurs, message could not be delivered properly due to packet loss in the transmission. To address this issue, congestion control mechanism was proposed to alleviate the congestion in the communication channel during broadcast storm. In this research, the congestion control is applied to Service Channel communication (SCH) for non-safety message. The proposed congestion control was also tested using Taguchi method to optimize packet loss reduction in the network. The experiment was conducted with and without Taguchi method in urban area. The obtained results from the experiment show packet loss was greatly reduced when applying congestion control with Taguchi method. The results have proven that integrating Taguchi method in congestion control mechanism could improve packet loss reduction when broadcast storm take place in high-density traffic.

Mobile devices have become an indispensable component in modern society. Many of these devices rely on personal identification numbers (PIN) as a form of user authentication. One of the main concerns in the use of mobile devices is the possibility of a breach in security and privacy if the device is seized by an outside party. Threats can possibly come from friends as well as strangers. Smart devices can be easily lost due to their small size, thereby exposing details of users’ private lives. User behavior authentication is designed to overcome this problem by utilizing user behavioral techniques to continuously assess user identity. This study proposed a behavioral data profiling technique that utilizes data collected from the user behavior application to verify the identity of the user in a continuous manner. By utilizing a combination of analytical hierarchy process and correlation coefficient method, the best experimental results were obtained by verifying the identity of six types of user behaviors to determine the different behaviors. Based on the results, this study proposes a new authentication technique that enables verification of a user’s identity through their application usage in a transparent manner. Behavioral data profiling is designed in a modular manner that will not reject user access based on a single application activity but on several consecutive abnormal application usages to balance the trade-off between security and usability. The proposed framework is evaluated using a PIN-based technique and achieved an overall 95% confidence level. Behavioral data profiling provides a significant improvement in the security afforded to the device and user convenience.

The ElGamal cryptosystem was originally proposed by Taher ElGamal in 1985, in which its security level is based on the Discrete Logarithm Problem (DLP). ElGamal cryptosystem is relatively an expensive algorithm. For security guarantees, ElGamal cryptosystem requires modulo operation of large prime integer whose size range approximately from 1,024 to 4,096 bits. As a consequence of such requirement, the application of ElGamal cryptosystem is limited for securing only small messages such as secret keys. This paper aims to propose an efficient variant of ElGamal cryptosystem. The proposed scheme is designed based on quotient ring of polynomial, $$ Z_{2} [x]/{ < }f (x ) { > } $$Z2[x]/<f(x)>, where $$ f\left( x \right) $$fx is an irreducible polynomial. The decryption algorithm was further optimized with the use of the multiplicative inverse of the generator g(x), which only generated once during the key generation algorithm, thus leading to a simpler and faster decryption process. The proposed scheme is as secure as the original ElGamal scheme, since both schemes are based on the DLP. The preliminary result shows that the proposed scheme minimizes complex arithmetic operations and achieves very practical performance compared to the classic ElGamal algorithm and its variants. The proposed $$ F_{2}^{n} $$F2n based ElGamal scheme outperforms the $$ F_{p} $$Fp based scheme by significantly reducing 69.74% of the numbers of required logic gates in the case study of VLSI implementation.

Duplicate address detection (DAD) is an essential procedure of neighbor discovery protocol (NDP). Further, DAD process decides in case an IP address is in conflict with other nodes. In usual DAD process, the target address to be identified is multicast via the network, which provides an ability for malicious nodes to attack. A malicious node can send a spoofing reply to prevent the address configuration of a normal node, and thus, a denial of service (DoS) attack is launched. This study proposes a new mechanism to hide the target address in DAD, which prevents an attack node from reaching target node. If the address of a normal node is identical to the detection address, then its IP address should be able to decrypt the random word and compare the decryption with decryption in “DADmatch” tag. Consequently, DAD can be successfully completed. This process is called DAD-match. We expect DAD-match will provide a lightweight security resolution and less complexity as well as fully prevent of DoS attacks during DAD process in IPv6 link-local network.

Turbulent flow segmentation from image data is a challenging problem. This is due to the un-defined edge and the complex flow nature of turbulence. In this paper, an image-based technique is proposed for turbulent flow segmentation from image. The proposed technique segments the flow region based on enhancing the input image intensity at flow edges and by defining a thresholding value to differentiate between flow region and image background. To test the image-based segmentation technique, a turbulent buoyant jet was experimentally simulated at different nozzle flow rates which have a Reynolds numbers of 960, 1560, and 3210. Then, a video camera was used to record the jet flow data. Then, the image-based technique was applied to segment the flow region and estimate the jet penetration area. As a result, the turbulent flow region was segmented well for all cases of nozzle flow rates. Moreover, application of the image-based technique for jet penetration estimation showed a good agreement with the previous work, in which the jet propagated linearly over time.

Wireless Sensor Network has become one of the crucial and vital technology in environmental monitoring and tracking. The advancement of such technology had evolved WSN to transmit a heavy data as well as handled the high number of traffics which had increased the demand for studies and further research on the aspects of error control protocols. Based on the previous work, the existing error control techniques were not able to combat the issue of interference levels and excessive overhead properly. Thus, the problem regarding unnecessary overhead, interferences and error rates in changing conditions of the WSN becomes our motivation to propose a new method for extending the capability of HARQ error control algorithm in CDMA WSN. This paper evaluates the proposed HARQ based Multiple Error Correction in terms of Bit Length and Node Densities. This paper has demonstrated that the proposed methods show promising results in optimizing the energy consumption and error rates.

As more information becomes accessible online, users are faced with difficulties in digesting and selecting important information from longer text. A summary can serve as a condensed version of a text, where salient information can be presented. In order to improve a summary’s quality, a special task in the area of Automatic Text Summarization known as Sentence Compression (SC) can be applied. Existing SC techniques are highly dependent on syntactic knowledge applied on individual word or phrases to decide on the compressions decision. In contrast, this study introduces a new constrained Pattern-Growth SC (PGSC) technique inspired by the “divide and conquer” strategy tailored to the Malay language. The basic idea is to divide the sentences into segments where unimportant segments are removed while the important ones are conquered iteratively. Using a Malay news dataset, the application of PGSC have shown promising results where the compressed summaries reported an F-Measure score of 0.5752 agreements when evaluated against manual human summaries and perform better than the Baselines (uncompressed) model. Manual human evaluation produced readability score of 4.31 out of 5 and 4.1 for content responsiveness, suggesting a better quality and readability of the compressed summaries produced by the MYTextSum model.

The nature of computing paradigm has shifted from centralized, static and closed to distributed, dynamic and open due to the advent and popularity of Internet. Multi-agent system (MAS) gained popularity as its characteristic match with this paradigm shift. In order for MAS to interact efficiently and communicate meaningfully, agent communication language (ACL) plays an important role. FIPA-ACL is an ACL developed by FIPA that has become the de facto standard of ACL’s implementation in MAS. Another emerging trend due to Internet is the Semantic Web (SW). Semantic web is an extension of the current World Wide Web, which encodes the content of the web with well-defined meaning to allow it to be processed by machines such as computer (agents). Hence, combining the existing FIPA-ACL with semantic web can bring ACLs to another level to enhance the interoperability in MAS. In this paper, a FIPA-ACL ontology in OWL is proposed to enhance the communication between agents in MAS.

This paper explores the advantage of loyalty program in the domain of business, while Starbucks is chosen as a case study. It focuses mainly on the point system that provides a certain degree of gamification effect. It considers a game progress model of My Starbucks Rewards to derive a game refinement measure for the assessment of gamification impact. The assessment results indicate that the game element of point system in My Starbucks Rewards shows motivations towards the normal purchasing activities. On the other hand, the point system shows the decreasing of motivation effect towards customers’ purchases over the time. In short, customers are experiencing unsophisticated game experience in a point system which is proved to be a short term incentive that is useful to motivate customers in the early age for a short period of time. Starbucks incorporates both point system and tier system in its loyalty program, targeting to attract new customers as well as retain them for a long time to come. However, the current study only examines the point system of Starbucks. Further research might explore more on structure of loyalty program in restaurant or food industry.

With the increase number of opinionated content on the web, organizations and people have shown tremendous interest in knowing other’s opinions. This phenomena has attracted both the academic and the business world to pay a close attention towards the development of automated tools which helps in sentiment analysis (SA). While different well-defined approaches have been defined for English SA, the problem remains far from being solved for other languages such as Malay language, despite having more than 215 million Malay native speakers worldwide. To the author’s knowledge, most of researches on Malay language SA rely heavily on the use of bag-of-words model (BOW), which resulted Malay SA to have low accuracy, as BOW model disrupts word order, breaks the syntactic structures and discards some semantic information of the text. In this paper, we propose new feature sets that refine the traditional sentiment feature extraction method and take contextual valence shifters into consideration from a different perspective compared to the earlier research concerning Malay language. The most common valence shifter (VS) are considered in this paper, this includes negation, intensifier, diminisher and contrast. Negation is considered to be the most obvious and common valence shifter of all. A new technique is proposed in this paper to handle complex negation compared to the existing techniques where only simple negations (Bigram) are handled. The proposed system is then compared with existing techniques. The final result showed improvements in Malay SA after considering valence shifter. The discussion and implication of these findings are further elaborated.

Finger vein identification has becoming increasingly noticeable biometric trait. The finger vein pattern provides high distinguishing features that are difficult to counterfeit because it resides underneath the finger skin. The performance of finger vein identification is highly depending on the meaningful extracted features from feature extraction process. Previous works have developed new methods for better feature extraction. However, most of the works focus on how to extract the individual features and not presenting the individual characteristic of finger vein patterns with systematic representation. Therefore, in this paper we propose an improved scheme of finger vein feature extraction method by adopting Discretization method. The finger vein feature extraction is based on combination of Maximum Curvature and Directional Feature (MCDF) feature extraction. After the extraction, the MCDF features value are then fed into Discretization module. The extracted features will be represented systematically by discriminatory feature values. The features values are informative enough to reflect the identity of an individual. The experimental result shows that the proposed scheme using Discretization produce identification accuracy performance above 95.0%. This shows that the proposed scheme produce good performance accuracy compared to non-discretized features.

Imagined speech is a process where a person imagines the sound of words without moving any of his or her muscles to actually say the word. If the brain signals of a person imagining the speech can be used to recognize the actual words intended to be spoken, this could be a huge step towards helping people with physical disabilities such as locked-in syndrome to have effective communication with others. This can also prove to be useful in situation where visual or audible communication is undesirable, for instance in military situation. Recent advancement in technologies and devices for capturing brain signals, particularly electroencephalogram (EEG), has made the research in recognizing imagined speech possible. While these are still in early years, published studies have shown promising results in this particular area of research. Current approaches in recognizing imagined speech can generally be divided into two, syllable-based and word-based. In this paper, we proposed a simple word-based approach using Mel Frequency Cepstral Coefficients (MFCC) and k-Nearest Neighbor (k-NN) towards recognizing two simple words using EEG signals. Despite its simplicity, the results obtained show some improvements to other studies based on dry EEG electrode device.

Since early 2000, sentiment analysis has grown to be one of the most active research areas in Natural Language Processing (NLP). Since then, researchers have shown a tremendous interest in building automated Sentiment analysis applications for English language and non-English languages such as Arabic Language, French language, Deutsch language, Chinese language, Italian language, etc. Yet, very limited researches have been attributed to Malay opinionated social media text despite the big number of Malay native speakers which recorded to be approximately 215 million native speaker worldwide. In this paper, a framework is proposed to tackle some of the most common challenges posed by Malay social media text (informal text). Among the features discussed in this paper are the handling of Bahasa Rojak also known as Mix language (Malay-English language), the handling of Bahasa SMS, the proper handling of Emoticon and finally the handling of Valance shifter. As a result, RojakLex lexicon was constructed consists of 4 different lexicons combined together, namely (1) MySentiDic: a Malay lexicon, (2) English Lexicon: Translated version of MySentiDic, (3) Emoticon lexicon: a combination of 9 different well known lists of commonly used online emoticons, (4) Neologism lexicon: consists of common neologism words used in Malay social media text. The proposed system shows tremendous improvement in accuracy by recording 79.28% compared to baseline which recorded 51.38% only. Discussion and implication of these findings are further elaborated.

Dengue poses a large economic burden in Malaysia includes among other endemic countries. In order to detect the likely hotspots that breeds mosquito vectors, this study aims to formulate a contact network model of dengue transmission where the research scenario is characterised by spatial data that is complex and difficult to be modelled. The bipartite network modeling approach can address the homogenous limitation seen in deterministic models by projecting the research scenario into two sets of node: human hosts and locations visited by the human. The data of human movements are collected and aggregated from Sarawak State Health Department while the environmental data are obtained from Kuching Meteorological Department. All data are pre-processed and formulated into a targeted model which consists of eight human nodes and nineteen location nodes and a test model which consists of three human nodes and eight new incoming location nodes. The link weight between two sets of node is quantified using summation rule which combines the environmental predictors for instance temperature, precipitation, humidity, human and vector characteristics. The location nodes in targeted and validated models are ranked using a web-based search algorithm according to the respective ranking values. As a result, the ranking values between the targeted and validated model shows strong ranking similarity with good Spearman rank correlation coefficient (ρ > 0.80; p < 0.001). The ranked locations can help public health authorities to prioritize the locations for vector control to remove the hotspot which results in the reduction of the spread of dengue disease.

Amount of data generated and stored in relational databases has motivated numerous researchers to study and develop learning algorithms on learning relational data mining. One of the most important relational tasks is to discover knowledge from relational data for a better decision making. Despite that, various representations can be generated using the same data by applying the Self-Organizing Map (SOM) methods in clustering relational data. This can be achieved by tuning the parameters used in Self-Organizing Map (SOM), such as the number of clustering, weights, seeds, epoch and others. Thus, this paper proposes a summarization method that applies SOM as the main algorithm to cluster relational data and applies the concept of data fusion in order to get better results in learning relational data. Input data obtained from Dynamic Aggregation of Relational Attributes will be clustered using the SOM method by tuning the SOM parameters. Results generated will be fused and embedded into the target table to form a single representation. A few representations will be formed and fed into the classifiers (J48 Decision Tree and Naïve Bayes classification model) as input data. Throughout the experiments conducted, representations that are extracted by tuning the number of cluster produced better results compared to the representations that are extracted by tuning the other parameters. Overall, the data summarization approach based on individual data fusion is found to perform better compared to the other types of data fusion. In addition to that, the clusters based data fusion with average number of clusters provided better accuracy performances compared to clusters based data fusion with small and large number of clusters.

An efficient outdoor parking system is a crucial need for smart cities to monitor the occupancy of outdoor parking. Currently, there are many external sensors-based parking systems available for indoor parking. These external sensors either need to be installed at the parking slot or attached with vehicles at fixed positions. Hence, the deployment cost is very high for such implementation. Due to high cost and complex network configuration, external sensors are not preferred for Smart Outdoor Parking Systems (SOPS). Several SOPS have been deployed to solve outdoor parking problems. Understanding existing SOPS approaches is essential to develop a robust and effective outdoor parking system. In this paper, we present a review of the various SOPS. We have addressed the most important aspects including technical, economical, accuracy, open issues and challenges of the existing SOPS. Based on the review, a recommendation has been proposed to improve outdoor parking system.

A problem of selecting appropriate human VOCs (Volatile Organic Compound) emitted from sweat for human odor classification is presented in this paper. In this paper, all gases emitted by human through sweat have been collected and detected using the latest technology (High resolution GCMS/TOF) Gas Chromatograph Mass Spectrometry/Time of Flight. Due to the limitations of the experimental conducted, only a total of four different persons are required to have the samples of odor collected for twenty different times. 198 VOCs have been detected and feature selection methods have been applied to determine which VOCs are suitable to be used to classify human odor. Two feature selection methods based on Entropy and Chi Square test have been used to determine and decide the best and acceptable VOCs. Based on the results obtained, a total of 17 stable VOCs are extracted from 198 VOCs. In addition to that, there are 10 gases that are detected having zero values for both the entropy and chi-square test and these gases are considered the strongest candidates that can be used for odor detection and classification. The results obtained from this work can be used to assist the task of classifying specific VOCs for human detection through odor.

The aim of this paper is to investigate the effects of combining various sampling and ensemble classifiers on the prediction performance in addressing the multiclass imbalance data learning. This research uses data obtained from the Malaysian medicinal leaf images shape data and three other large benchmark datasets in which seven ensemble methods from Weka machine learning tool were selected to perform the classification task. These ensemble methods include the AdaboostM1, Bagging, Decorate, END, MultiboostAB, RotationForest, and stacking methods. In addition to that, five base classifiers were used; Naïve Bayes, SMO, J48, Random Forest, and Random Tree in order to examine the performance of the ensemble methods. Two methods of combining the sampling and ensemble classifiers were used which are called the Resample with ensemble classifier and SMOTE with ensemble classifier. The results obtained from the experiments show that there is actually no single configuration that is “one design that fits all”. However, it is proven that when using the sampling and ensemble classifier which is coupled with Random Forest, the prediction performance of the classification task can be improved on the multiclass imbalance dataset.

Reducing the electricity consumption is better for the planet and help to reduce harmful greenhouse releases. Hence, this paper proposes house appliances controller apps that can control and calculate the power usage of house appliances. Besides, the apps can control and set the auto-time based such as how long the light must be in switch on. This is important because the house might be a target for a thief when the light continuously on. The objectives of this paper are to identify and develop features of the mobile controller app and for the house appliances. The expected outcome would be a fully functional mobile application and web-based system namely as HomeBot.

Dengue Fever (DF) is the leading cause of illness and death around the world. In order to take action against this issue the involvement of the public and society is very important. However the public awareness towards DF is very low due to the lack of attention for this issue. Therefore, this research explores the feasibility to develop a mobile application, which is an informative mobile application that is designed to attract the public and society’s attention and increase the awareness about DF. The mobile application, Dengue Fever Awareness using Mobile Application, DeFever, acts as an informative mobile application that can be used to provide more information about DF with some useful features. By using DeFever, all information regarding DF and infected areas will be accessible. The objectives of this research are to identify the level of public awareness based on the knowledge, attitudes and practices towards Dengue, to identify the suitable design features of DeFever and to develop the mobile app (DeFever) that helps to increase the public awareness towards DF. The method used in this research was by using the Appreciative Inquiry (AI) whereby the model used was the 5D Appreciative Inquiry Model for DeFever. AI is the cooperative, co-developmental search in finding the best in individuals of people, and the world around them. It includes deliberate revelation of what offers life to an association or a group when it is best and most skilled in financial, biological and human terms. Finally, hopefully DeFever can be used to assist efforts in increasing the public awareness level towards DF.

Different approaches have been used to determine, measure, and predict the performance of programmers to fit positions in the software team. In this study, we use a data mining approach to identify best-fit programmer to be appointed. A questionnaire was used to collect data from 470 programmers from different software companies. A best-fit programmer prediction model was developed to evaluate 10 performance attributes. This model incorporates the Bayes’ Theorem and uses Artificial Neural Network (ANN) with Multilayer Perceptron (MLP) algorithm to predict the most suitable candidates for appointment as programmers. The results have shown that programmers who scored high in all or most of the attributes match the predicted values of the Bayes’ probability values of the dataset. We conclude that the combination of the theorem and algorithm has proven to be effective in determining the best-fit programmers for appointment using the applied attributes.

In recent years, smartphones have grown exponentially in popularity, taking over the world by storm. While the mobile gaming industry has grown large due to the development, there is a deluge of mediocre games in the Android market which replicate the same formulas of popular games. Thus, it motivates us to try at a new game development and design. The Android game is developed using the jMonkeyEngine game engine for Android 2.2 versions and above. It utilizes simple 3D graphics, and use the accelerometer and touch screen for input. It is single one on one battle against an AI enemy, which uses a simple finite state machine and viewed using a third-person camera that stays behind the player’s character and follows the enemy’s movements. We believes that the virtual joystick, a popular method of control in current games is inadequate for use as a control method to control the player character in a 3D third-person game. As such the accelerometer is being used as the game’s method of control, which is not only more sensitive and removes unneeded elements on the screen, but also to increase the game’s uniqueness. The project is developed using a test-driven process model. The system could be divided into three sections. A section of the code handles the generation of the character models and others, while an input handling system take care of mapping the player’s inputs to the correct responses. A 3D world is created, its members positioned correctly to form the ground, walls, and the player and enemy characters. Next, physics was added to the 3D objects so that they may interact with each other. Next came the implementation of the accelerometer to move the character around, and the touch screen to produce actions. Finally, a state machine is built for the game AI and suitable actions for each state is coded in. The game is tested via test runs as each functionality was implemented.

This paper reports an exploratory study conducted to investigate the performance of topic modelling algorithms in aspect identification. Aspect identification is an important step in aspect-based sentiment analysis. Latent-Dirichlet Allocation model serves as the baseline of topic models in the experiments. One of the variations of LDA, namely Phrase-LDA was experimented to benchmark its performance against the original LDA. Although it was reported that PLDA performs better compared to LDA in aspect-based sentiment analysis, our experimental results indicate that LDA works better on dataset with short sentences. A new PLDA model was also proposed by using different types of dependencies to extract the phrases.

Biometrics is an advanced way of person recognition as it establishes more direct and explicit link with humans than passwords, since biometrics use measurable physiological and behavioral features of a person. In this paper, a gender recognition framework is proposed based on human odour. 20 samples of human odour from male and female are collected and only 16 out of 198 Volatile Organic Compounds (VOCs) are selected using the Chi-square test and entropy for gender detection and classification using artificial neural networks. In this paper, several different neural network activation functions were tested (e.g., Levenberg-Marquardt backpropagation, Gradient descent backpropagation and Resilient backpropagation) and several different neural network topologies are also tested with variety of hidden layers and number of neurons. It is also found that with 2 hidden layers having more number of neurons in the hidden layers (16 and 16 neurons in which hidden layer) was able to produce greater performance accuracy. The best learning algorithm that can be applied in gender detection shown in paper is the Gradient Descent learning algorithm. Also, it is notable that 8 out of 9 cases where all male samples are able to be detected or classified correctly compared to the 3 out of 9 cases in which all females are correctly detected or classified.

Tacit knowledge is perceived as the most strategically important resource of competitiveness. The rise of web-based applications such as social media also gives rise to the question on whether these applications can facilitate tacit knowledge sharing in a collaborative work environment. Previous studies have indicated that such notion is indeed possible, but there is still a lack of understanding of how social media could facilitate tacit knowledge sharing as well as the condition that is most effective in transferring this type of knowledge. Hence, it is crucial to understand the individual and technical characteristics involved in tacit knowledge sharing using social media. This research attempts to bridge this gap as there is a need to develop a holistic tacit knowledge sharing model. Towards this end, before the model is developed, the conceptual model and its instruments are validated by three field experts. A pilot study is conducted to determine the reliability and validity of the measurement indicators as well as an analysis using SPSS. The findings of the pilot study are hence presented in this paper. The results confirmed the validity of the proposed model as well as the validity and reliability of the instrument. This pilot study investigated on whether the proposed research model is viable for further research, or whether pertinent changes to the model or the methodology need to be done before the model can be used on a larger sample. Recommendations for a follow-up study concludes the paper.

Video surveillance is a well researched area of study with substantial work done in the aspects of object detection, tracking and behavior analysis. With the abundance of video data captured over a long period of time, we can understand patterns in human behavior and scene dynamics through data-driven temporal analytics. In this work, we propose two schemes to perform descriptive and predictive analytics on long-term video surveillance data. We generate heatmap and footmap visualizations to describe spatially pooled trajectory patterns with respect to time and location. We also present two approaches for anomaly prediction at the day-level granularity: a trajectory-based statistical approach, and a time-series based approach. Experimentation with one year data from a single camera demonstrates the ability to uncover interesting insights about the scene and to predict anomalies reasonably well.

This paper aims to survey on the existing research works done on facial recognition and acknowledge their differences. Understanding facial recognition processes such as facial normalization, facial detection, facial extraction and facial recognition methods and algorithms are part of the essence of this paper. This paper outlines the purposes of existing techniques as well as its challenges. This paper also looks at the idea whether combining several techniques is feasible in order to produce a better synergy result. Methods are evaluated based on their classification rate percentages as well as the numbers of dimensionality reductions. Based on the literature reviews conducted, the facial recognition algorithm is made up of two steps. The first step is when an individual model is modeled in the database based on the color appearance and geometrical information provided by the available images whereby each model characterizes an individual like a bar code or a unique serial number and discriminates it from the other people in the database. The second step is to carry out the identification using a classifier, related with the standard Gaussian distribution, to decide whether a face image belongs to one person in the database or not. This paper has performed a comparative analysis of previously conducted experiments and based on the findings obtained, a schema of the framework for face recognition is proposed.

Recently, many organizations require security tools to maintain their network or IoT environment from DDoS attacks. Most security tools today, do not have enough power to detect whether the incoming packet is a normal packet or DDoS packet. The purpose of the DDoS attack is to undermine the web server of an organization that may run a business. Therefore, this research is conducted to design a technique called Packet Threshold Algorithm (PTA) coupled with SVM in order to detect four types of DDoS attacks such as TCP SYN flood, UDP flood, Ping of Death and Smurf. The results of this research on the use of this technique is claimed enable the action of minimizing false positive rates and increases the detection accuracy in comparison to the other three current techniques. The TPA-SVM technique has the capability of detecting incoming packets as normal packets or DDoS attacks. The DDoS attack type of detection is based on the packet threshold.

Student academic evaluation is part of academic information system (AIS) performance, in order to control student learning progress is necessary. Furthermore, the evaluation showing whether the student will pass or fail would benefit the student/instructor and act as a guide for future recommendations/evaluations on performance. An in depth study on the student academic evaluation technique by using Decision Tree C4.5 has been conducted. Specific parameters including age, place of birth, gender, high school status (public or private), department in high school, organization activeness, age at the start of high school level, and progress GPA (pGPA) and Total GPA (tGPA) from semester 1–4 with three times graduation criteria (i.e., fast, on, and delay) times have been defined and tested. The scope of the paper has been set for undergraduate programs. The experimental results show that accuracy algorithm (AC) of 78.57% with true positive rate (TP) of 76.72% by using quality training data of 90% have best performance accuracy value.

Finding complex roots of a system of nonlinear equations is not an easy numerical computation problem. A method of locating and finding all real and complex roots of systems of nonlinear equations in a single run is proposed here. The method that was first proposed for finding all real roots of systems of nonlinear equations is now slightly modified and adapted so that it can be used also for finding complex roots of the corresponding system. The root finding problem is transformed to optimization problem and then a spiral optimization algorithm of Tamura and Yasuda is used to solve the optimization problem. In order to locate the position of the roots, we proposed a certain clustering technique. Several test problems have been examined. This combination of technique enables ones to locate and find all real and complex roots within a bounded domain in all test cases.

Most of the retrieved documents from the Information Retrieval (IR) System are irrelevant to the user because the IR cannot determine the user’s context. One of the main issues is that the relevancy of the retrieved documents is based on personal assessment that depends on the task to be done and its context. This paper provides the review of prior researches (2003–2016) and concludes the review by providing the summary of the research’s current trends, future direction and opportunity and defining the research gap. First, the findings show that in prior studies, there is no identification of contextual aspect has been done in optimizing the ranking function of the Malay IR. Second, in optimizing the ranking function, the integration process of context representation and document ranking must be done. This approach also has not been done yet in the development of Malay Document Retrieval. If it still stays in the current status, the Malay Document Retrieval system cannot be improved compared to the traditional languages of Context Aware IR System (English).

Single loop feedback control is commonly used in process control. The main drawback of single loop feedback control is its less effectiveness in rejecting the external disturbances. In order to improve speed of disturbance rejection and stability of closed-loop system, cascade control was studied and analyzed. To design the cascade control, first-order plus deadtime (FOPDT) models of both inner and outer loop were developed and applied for both sequential and simultaneous tuning methods. For sequential tuning, an IMC-based tuning was used whereas for simultaneous tuning method, Multiscale and Enhanced Cascade Control tunings were chosen. Relative performance of various controller settings for single and cascade control were compared. Moreover, recommendation for optimized tuning used “Step Response Checker” from System Design Toolbox was also elaborated and tested. Performance results were evaluated through Minimum Integral Error measurement. The effectiveness of the tuning methods was compared and evaluated using a lab-scale air flow rig.

In this paper, we demonstrate a novel approach in topic model exploration by applying word co-occurrence graph or graph-of-words (GOW) in order to produce more informative extracted latent topics from a large document corpus. According to the LatentDirichletAllocation (LDA) algorithm, it only considers the words occurrence independently via probabilistic distributions. It leads to the failure in term’s relationship recognition. Hence in order to overcome this disadvantage of traditional LDA, we propose a novel approach, called GOW-LDA. The GOW-LDA is proposed that combines the GOW graph used in document representation, the frequent subgraph extracting and distribution model of LDA. For evaluation, we compare our proposed model with the traditional one in different classification algorithms. The comparative evaluation is performed in this study by using the standardized datasets. The results generated by the experiments show that the proposed algorithm yields performance respectably.

The topic modeling has long been used to check and explore the content of a document in dataset based on the search for hidden topics within the document. Over the years, many algorithms have evolved based on this model, with major approaches such as “bag-of-words” and vector spaces. These approaches mainly fulfill the search, statistics the frequency of occurrences of words related to the topic of the document, thereby extracting the topic model. However, with these approaches the structure of the sentence, namely the order of words, affects the meaning of the document is often ignored. In this paper, we propose a new approach to exploring the hidden topic of document in dataset using a co-occurrence graph. After that, the frequent subgraph mining algorithm is applied to model the topic. Our goal is to overcome the word order problem from affecting the meaning and topic of the document. Furthermore, we also implemented this model on a large distributed data processing system to speed up the processing of complex mathematical problems in graph, which required many of times to execute.

Discovering and understanding the development of research topics in the community is useful for identifying important milestones and prominent researches. Recent works related to detect topics from scientific corpus also used the latent Dirichlet Allocation (LDA) to explore topics of papers. These systems usually used abstract of papers as the corpus instead of full papers. However, the LDA is based on the bag-of-words model so with such short texts it will give low accuracy. The tendency for improvement is to add prior knowledge to the analysis process with the latest algorithm, Source-LDA, which was presented by Justin Wood et al. at UCLA in 2017. We found that the Source-LDA has some shortcomings to overcome. Firstly, it is also based on counting method as LDA so short text will decrease the accuracy. Secondly, the knowledge source mentioned in the algorithm is constructed manually from labeled text data. This make Source-LDA becomes a supervised method. Therefore, we propose an approach to automatically construct knowledge source for Source-LDA from unlabeled data with an assumption that a specific paper will often cite papers which contain related topics. This approach both helps to integrate source knowledge in an unsupervised manner and resolve the issue of short text by using information from citation network. In the first stage, the propound method has achieved encouraging results.

This research presents the metaheuristic strategy to solve educational timetabling problem. The metaheuristic described in this research highlight the role of Genetic Algorithm (GA) when the algorithm improves the quality of solution by performing genetic operators. Two datasets of university course timetabling are used whereby the datasets are obtained from Universiti Malaysia Sabah Labuan International Campus (UMSLIC). The research experiment is conducted by comparing the quality of solutions produced by Genetic Algorithm with other metaheuristics which have been done in the past researches. The experimental results suggest that Genetic Algorithm manages to produces good solutions in this domain although other algorithms are able to improve the quality of the solutions.