Displaying publications 1 - 20 of 27 in total

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  1. Fulltext A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem
    Odili JB, Noraziah A, Zarina M
    Comput Intell Neurosci, 2021;2021:6625438.
    PMID: 33986793 DOI: 10.1155/2021/6625438
    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.
    Matched MeSH terms: Heuristics
  2. Fulltext PRESIMS: Requirements and design for Preschool Management System using usability theory
    Nor Hasnul Azirah Abdul Hamid, Normalina Ibrahim@Mat, Nurul Najihah Mustopa
    ESTEEM Academic Journal, 2020;16(2):51-64.
    MyJurnal
    Student Information Management System (SIMS) is a computerized system for education that can be used to manage student information and data. PASTI An-Nur is chosen as a case study in developing the system. Thus, several problems are identified that PASTI An-Nur faces due to the
    implementation of a manual system in the admission process. The first problem is the paper-based registration form that is prone to lose, misplaced and less secure. As for the payment process, arise a problem in term of higher error rate when checking and calculating the payments. The biggest downfall for PASTI An-Nur is the amount of space used to store all the students' files.
    These problems bring inefficiency since the world is changing to computerized, where data management become one of the most significant issues nowadays. So, the aim of developing the Preschool Management System (PRESIMS) is for helping the staffs and teachers in managing the
    students' information. The Adapter Waterfall model was used in developing this system. Additionally, usability heuristics was used also as a theory to guide the development of this system. The system has been tested with the four (4) users and two (2) experts. The testing method is the ISO/IEC 9126- 4 approach to measure usability metrics, including efficiency, effectiveness, and satisfaction. Whereas, for the experts, heuristic evaluation is used to bring six (6) usability principles into implementation for testing. The result of the testing is very satisfying, which shows 75.5% of efficiency, 83.33% of effectiveness and three (3) out of four (4) users very satisfied with the system. The result of heuristic evaluation also shows a successful implementation of the system. The details of the result are discussed in this paper and expected to meet the users' specification and it is ready to go live.
    Matched MeSH terms: Heuristics
  3. A new meta-heuristic optimization-based MPPT control technique for green energy harvesting from photovoltaic systems under different atmospheric conditions
    Kishore DJK, Mohamed MR, Sudhakar K, Peddakapu K
    Environ Sci Pollut Res Int, 2023 Jul;30(35):84167-84182.
    PMID: 37358770 DOI: 10.1007/s11356-023-28248-8
    At present, a photovoltaic (PV) system takes responsibility to reduce the risk of global warming and generate electricity. However, the PV system faces numerous problems to track global maximum peak power (GMPP) owing to the nonlinear nature of the environment especially due to partial shading conditions (PSC). To solve these difficulties, previous researchers have utilized various conventional methods for investigations. Nevertheless, these methods have oscillations around the GMPP. Hence, a new metaheuristic method such as an opposition-based equilibrium optimizer (OBEO) algorithm is used in this work for mitigating the oscillations around GMPP. To find the effectiveness of the proposed method, it can be evaluated with other methods such as SSA, GWO, and P&O. As per the simulation outcome, the proposed OBEO method provides maximum efficiency against all other methods. The efficiency for the proposed method under dynamic PSC is 95.09% in 0.16 s, similarly, 96.17% for uniform PSC and 86.25% for complex PSC.
    Matched MeSH terms: Heuristics*
  4. Fulltext The efficacy of art therapy on mental health outcomes among breast cancer patients: the current state of evidence based on randomised controlled trials
    Azmawati M.N., Boekhtiar B., Zawiah M., Siti Aisah M., Chiew W.L., Dalila R.
    International Medical Journal Malaysia, 2018;17(2):71-82.
    MyJurnal
    Introduction: Breast cancer is the most common cancer among women. Psychosocial distress, in the forms of depression and anxiety, regularly accompanies breast cancer diagnosis and throughout its treatment. Hence, it is important to objectively assess the therapy options that may help this distress. One of the most flexible psychotherapy modalities is the art therapy as it may be incorporated in many settings. The purpose of this systematic review is to determine the efficacy of art therapy (via creative paintings) on the mental health outcomes of female breast cancer patients. Material and Methods: Related articles were identified from Medline (OvidSP), ScienceDirect, Pubmed, EbscoHost-Academic Search Premier and Wiley Online Library. Screenings were performed based on the criteria that the study must be a randomised control trial (RCT) that investigated the effect of art therapy in breast cancer. Data were extracted using standardised forms, followed by evaluation of quality of reporting using CONSORT Guidelines. Results: Six RCTs were identified. Based on the heuristic synthesis of data extraction results, none of the studies can be considered as studies with acceptable quality, although four of them showed promising results. Conclusion: There is inconclusive evidence of the efficacy of art therapy (via creative paintings) for the improvement of mental health outcomes among female breast cancer patients.
    Matched MeSH terms: Heuristics
  5. Fulltext Production decision support system for batch processes
    Chee, L.P., Wan Alwi, S.R., Lim, J.S.
    Journal of Advanced Research in Applied Sciences and Engineering Technology, 2016;5(1):7-21.
    MyJurnal
    Aggregate planning acts as a blueprint for all operational planning activities. Despite
    the substantial amount of research that has been done in determining methods to
    improve aggregate planning approaches, the industry is still at a loss when it comes
    to working on the tactical planning aspect, especially in aggregate production.
    Therefore, this research work aims to present a comprehensive and generalised
    framework that will formulate a realistic batch production environment using an
    interactive Production Decision Support System. This system consists of an aggregate
    planning framework that combines a simulation model and a Pinch Analysis graphical
    approach to improve the effectiveness and efficiency of the decision-making process.
    The target is to allow operational opportunities to be captured at first sight and thus,
    maximise organisational profit. The simplicity and practicality of this new Production
    Decision Support System is demonstrated through two illustrative examples where a
    total of four heuristics were identified and turned into the new strategies to avoid the
    stock-out scenarios.
    Matched MeSH terms: Heuristics
  6. A Heuristic Automatic and Robust ROI Detection Method for Medical Image Warermarking
    Mousavi SM, Naghsh A, Abu-Bakar SA
    J Digit Imaging, 2015 Aug;28(4):417-27.
    PMID: 25736857 DOI: 10.1007/s10278-015-9770-z
    This paper presents an automatic region of interest (ROI) segmentation method for application of watermarking in medical images. The advantage of using this scheme is that the proposed method is robust against different attacks such as median, Wiener, Gaussian, and sharpening filters. In other words, this technique can produce the same result for the ROI before and after these attacks. The proposed algorithm consists of three main parts; suggesting an automatic ROI detection system, evaluating the robustness of the proposed system against numerous attacks, and finally recommending an enhancement part to increase the strength of the composed system against different attacks. Results obtained from the proposed method demonstrated the promising performance of the method.
    Matched MeSH terms: Heuristics*
  7. Fulltext Probability or Reasoning: Current Thinking and Realistic Strategies for Improved Medical Decisions
    Nantha YS
    Korean J Fam Med, 2017 Nov;38(6):315-321.
    PMID: 29209469 DOI: 10.4082/kjfm.2017.38.6.315
    A prescriptive model approach in decision making could help achieve better diagnostic accuracy in clinical practice through methods that are less reliant on probabilistic assessments. Various prescriptive measures aimed at regulating factors that influence heuristics and clinical reasoning could support clinical decision-making process. Clinicians could avoid time-consuming decision-making methods that require probabilistic calculations. Intuitively, they could rely on heuristics to obtain an accurate diagnosis in a given clinical setting. An extensive literature review of cognitive psychology and medical decision-making theory was performed to illustrate how heuristics could be effectively utilized in daily practice. Since physicians often rely on heuristics in realistic situations, probabilistic estimation might not be a useful tool in everyday clinical practice. Improvements in the descriptive model of decision making (heuristics) may allow for greater diagnostic accuracy.
    Matched MeSH terms: Heuristics
  8. Fulltext Plant data visualisation using network graphs
    Mohamad-Matrol AA, Chang SW, Abu A
    PeerJ, 2018;6:e5579.
    PMID: 30186704 DOI: 10.7717/peerj.5579
    Background: The amount of plant data such as taxonomical classification, morphological characteristics, ecological attributes and geological distribution in textual and image forms has increased rapidly due to emerging research and technologies. Therefore, it is crucial for experts as well as the public to discern meaningful relationships from this vast amount of data using appropriate methods. The data are often presented in lengthy texts and tables, which make gaining new insights difficult. The study proposes a visual-based representation to display data to users in a meaningful way. This method emphasises the relationships between different data sets.

    Method: This study involves four main steps which translate text-based results from Extensible Markup Language (XML) serialisation format into graphs. The four steps include: (1) conversion of ontological dataset as graph model data; (2) query from graph model data; (3) transformation of text-based results in XML serialisation format into a graphical form; and (4) display of results to the user via a graphical user interface (GUI). Ontological data for plants and samples of trees and shrubs were used as the dataset to demonstrate how plant-based data could be integrated into the proposed data visualisation.

    Results: A visualisation system named plant visualisation system was developed. This system provides a GUI that enables users to perform the query process, as well as a graphical viewer to display the results of the query in the form of a network graph. The efficiency of the developed visualisation system was measured by performing two types of user evaluations: a usability heuristics evaluation, and a query and visualisation evaluation.

    Discussion: The relationships between the data were visualised, enabling the users to easily infer the knowledge and correlations between data. The results from the user evaluation show that the proposed visualisation system is suitable for both expert and novice users, with or without computer skills. This technique demonstrates the practicability of using a computer assisted-tool by providing cognitive analysis for understanding relationships between data. Therefore, the results benefit not only botanists, but also novice users, especially those that are interested to know more about plants.

    Matched MeSH terms: Heuristics
  9. Fulltext Usability of educational computer game (UsaECG): a quantitative approach
    Hasiah Mohamed@Omar, Rohana Yusoff, Azizah Jaafar
    Pertanika Journal of Science & Technology, 2013;21(1):247-260.
    MyJurnal
    Heuristic Evaluation (HE) is used as a basis in developing a new technique to evaluate usability or
    educational computer games known as Playability Heuristic Evaluation for Educational Computer Game (PHEG). PHEG was developed to identify usability problems that accommodate five heuristics, namely, interface, educational elements, content, playability and multimedia. In HE process, usability problems are rated based on severity score and this is followed by presentation of a mean value. The mean value is used to determine the level of usability problems; however, in some cases, this value may not accurate because it will ignore the most critical problems found in a specific part. In developing PHEG, a new quantitative approach was proposed in analyzing usability problems data. Numbers of sub-heuristics for each heuristic involved were taken into account in calculating percentage for each heuristic. Functions to calculate critical problems were also introduced. Evaluation for one educational game that was still in development process was conducted and the results showed that most of the critical problems were found in educational elements and content heuristics (57.14%), while the least usability problems were found in playability heuristic. In particular, the mean value in this analysis can be used as an indicator in identifying critical problems for educational computer games.
    Matched MeSH terms: Heuristics
  10. Fulltext A new heuristic method to solve straight assembly line balancing problem
    Mohd Khairol Anuar Mohd Ariffin, Masood fathi, Napsiah Ismail
    Pertanika Journal of Science & Technology, 2012;20(2):355-369.
    MyJurnal
    Assembly line balancing is well-known in mass production system but this problem is non-deterministicpolynomial-time(NP)-hard, even for a simple straight line. Although several heuristic methods havebeen introduced and used by researchers, knowing and using an effective method in solving these typesof problems in less computational time have a considerable place in the area of line balancing problem.In this research, a new heuristic approach, known as critical node method (CNM), was introduced andtested by solving several test problems available in the literature so as to solve straight assembly lines.Finally, the obtained results are compared with 9 other heuristic rules in some performance measures.Thus, it is concluded that the proposed CNM is better than the rest in all the measures.
    Matched MeSH terms: Heuristics
  11. Fulltext Robust artificial bee colony in the Hopfield network for 2-satisfiability problem
    Mohd. Shareduwan Mohd. Kasihmuddin, Mohd. Asyraf Mansor, Saratha Sathasivam
    Pertanika Journal of Science & Technology, 2017;25(2):453-468.
    MyJurnal
    Swarm intelligence is a research area that models the population of swarm that is able to self-organise effectively. Honey bees that gather around their hive with a distinctive behaviour is another example of swarm intelligence. In fact, the artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic algorithm introduced by Karaboga in order to optimise numerical problems. 2SAT can be treated as a constrained optimisation problem which represents any problem by using clauses containing 2 literals each. Most of the current researchers represent their problem by using 2SAT. Meanwhile, the Hopfield neural network incorporated with the ABC has been utilised to perform randomised 2SAT. Hence, the aim of this study is to investigate the performance of the solutions produced by HNN2SAT-ABC and compared it with the traditional HNN2SAT-ES. The comparison of both algorithms has been examined by using Microsoft Visual Studio 2013 C++ Express Software. The detailed comparison on the performance of the ABC and ES in performing 2SAT is discussed based on global minima ratio, hamming distance, CPU time and fitness landscape. The results obtained from the computer simulation depict the beneficial features of ABC compared to ES. Moreover, the findings have led to a significant implication on the choice of determining an alternative method to perform 2SAT.
    Matched MeSH terms: Heuristics
  12. Fulltext Heuristics to Evaluate Interactive Systems for Children with Autism Spectrum Disorder (ASD)
    Khowaja K, Salim SS, Asemi A
    PLoS One, 2015;10(7):e0132187.
    PMID: 26196385 DOI: 10.1371/journal.pone.0132187
    In this paper, we adapted and expanded a set of guidelines, also known as heuristics, to evaluate the usability of software to now be appropriate for software aimed at children with autism spectrum disorder (ASD). We started from the heuristics developed by Nielsen in 1990 and developed a modified set of 15 heuristics. The first 5 heuristics of this set are the same as those of the original Nielsen set, the next 5 heuristics are improved versions of Nielsen's, whereas the last 5 heuristics are new. We present two evaluation studies of our new heuristics. In the first, two groups compared Nielsen's set with the modified set of heuristics, with each group evaluating two interactive systems. The Nielsen's heuristics were assigned to the control group while the experimental group was given the modified set of heuristics, and a statistical analysis was conducted to determine the effectiveness of the modified set, the contribution of 5 new heuristics and the impact of 5 improved heuristics. The results show that the modified set is significantly more effective than the original, and we found a significant difference between the five improved heuristics and their corresponding heuristics in the original set. The five new heuristics are effective in problem identification using the modified set. The second study was conducted using a system which was developed to ascertain if the modified set was effective at identifying usability problems that could be fixed before the release of software. The post-study analysis revealed that the majority of the usability problems identified by the experts were fixed in the updated version of the system.
    Matched MeSH terms: Heuristics*
  13. Fulltext Word sense disambiguation using hybrid swarm intelligence approach
    Al-Saiagh W, Tiun S, Al-Saffar A, Awang S, Al-Khaleefa AS
    PLoS One, 2018;13(12):e0208695.
    PMID: 30571777 DOI: 10.1371/journal.pone.0208695
    Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset (SemCor 3.0, SensEval-2, and SensEval-3). Results show that the proposed method has superior performance in comparison with state-of-the-art approaches.
    Matched MeSH terms: Heuristics
  14. Fulltext Sidelobe reduction and capacity improvement of open-loop collaborative beamforming in wireless sensor networks
    Jayaprakasam S, Abdul Rahim SK, Leow CY, Ting TO
    PLoS One, 2017;12(5):e0175510.
    PMID: 28464000 DOI: 10.1371/journal.pone.0175510
    Collaborative beamforming (CBF) with a finite number of collaborating nodes (CNs) produces sidelobes that are highly dependent on the collaborating nodes' locations. The sidelobes cause interference and affect the communication rate of unintended receivers located within the transmission range. Nulling is not possible in an open-loop CBF since the collaborating nodes are unable to receive feedback from the receivers. Hence, the overall sidelobe reduction is required to avoid interference in the directions of the unintended receivers. However, the impact of sidelobe reduction on the capacity improvement at the unintended receiver has never been reported in previous works. In this paper, the effect of peak sidelobe (PSL) reduction in CBF on the capacity of an unintended receiver is analyzed. Three meta-heuristic optimization methods are applied to perform PSL minimization, namely genetic algorithm (GA), particle swarm algorithm (PSO) and a simplified version of the PSO called the weightless swarm algorithm (WSA). An average reduction of 20 dB in PSL alongside 162% capacity improvement is achieved in the worst case scenario with the WSA optimization. It is discovered that the PSL minimization in the CBF provides capacity improvement at an unintended receiver only if the CBF cluster is small and dense.
    Matched MeSH terms: Heuristics
  15. Fulltext Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment
    Madni SHH, Abd Latiff MS, Abdullahi M, Abdulhamid SM, Usman MJ
    PLoS One, 2017;12(5):e0176321.
    PMID: 28467505 DOI: 10.1371/journal.pone.0176321
    Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.
    Matched MeSH terms: Heuristics*
  16. Fulltext A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem
    Zamli KZ, Din F, Ahmed BS, Bures M
    PLoS One, 2018;13(5):e0195675.
    PMID: 29771918 DOI: 10.1371/journal.pone.0195675
    The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.
    Matched MeSH terms: Heuristics*
  17. Fulltext A novel state space reduction algorithm for team formation in social networks
    Rehman MZ, Zamli KZ, Almutairi M, Chiroma H, Aamir M, Kader MA, et al.
    PLoS One, 2021;16(12):e0259786.
    PMID: 34855771 DOI: 10.1371/journal.pone.0259786
    Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF's. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts-resulting in the formation of more communicative teams with high expertise levels.
    Matched MeSH terms: Computer Heuristics
  18. Parameter estimation of stochastic differential equation
    Haliza Abd. Rahman, Arifah Bahar, Norhayati Rosli, Madihah Md. Salleh
    Sains Malaysiana, 2012;41:1635-1642.
    Non-parametric modeling is a method which relies heavily on data and motivated by the smoothness properties in estimating a function which involves spline and non-spline approaches. Spline approach consists of regression spline and smoothing spline. Regression spline with Bayesian approach is considered in the first step of a two-step method in estimating the structural parameters for stochastic differential equation (SDE). The selection of knot and order of spline can be done heuristically based on the scatter plot. To overcome the subjective and tedious process of selecting the optimal knot and order of spline, an algorithm was proposed. A single optimal knot is selected out of all the points with exception of the first and the last data which gives the least value of Generalized Cross Validation (GCV) for each order of spline. The use is illustrated using observed data of opening share prices of Petronas Gas Bhd. The results showed that the Mean Square Errors (MSE) for stochastic model with parameters estimated using optimal knot for 1,000, 5,000 and 10,000 runs of Brownian motions are smaller than the SDE models with estimated parameters using knot selected heuristically. This verified the viability of the two-step method in the estimation of the drift and diffusion parameters of SDE with an improvement of a single knot selection.
    Matched MeSH terms: Heuristics
  19. Fulltext GIS based assessment on landslide hazard zonation: case study of Cameron Highlands – Gua Musang road Kelantan, Malaysia
    Mohamed M. GahGah, Juhari Mat Akhir, Abdul Ghani M. Rafek, Ibrahim Abdullah
    Sains Malaysiana, 2009;38(6):827-833.
    The aim of this study is to investigate the factors that cause landslides in the area along the new road between Cameron Highlands and Gua Musang. Landslide factors such as lineaments have been extracted from remote sensing data (Landsat TM image) using ERDAS software. A soil map has been produced using field work and laboratory analysis, and the lithology, roads, drainage pattern and rainfall have been digitized using ILWIS software together with the slope angle and elevation from the Digital Elevation Model (DEM). All these parameters, which are vital for landslide hazard assessment, have been integrated into the geographical information system (GIS) for further data processing. Weightage for these landslide relevant factors related to their influence in landslide occurrence using the heuristic method has been carried out. The results from this combination through a modified ‘index overlay with multi class maps’ model was used to produce a landslide hazard zonation map. Five classes of potential landslide hazard have been derived as the following: very low hazard zone 17.27%, low hazard zone 39.35%, medium hazard zone 25.1%, high hazard zone 15.35% and very high hazard zone 2.93%. The results from this work have been checked through the landslide inventory using available aerial photos interpretation and field work, and show that the slope and elevation have the most direct affect on landslide occurrence.
    Matched MeSH terms: Heuristics
  20. Linear-time heuristic partitioning technique for mapping of connected graphs into single-row networks
    Ser LL, Shaharuddin Salleh, Nor Haniza Sarmin
    Sains Malaysiana, 2014;43:1263-1269.
    In this paper, a model called graph partitioning and transformation model (GPTM) which transforms a connected graph into a single-row network is introduced. The transformation is necessary in applications such as in the assignment of telephone channels to caller-receiver pairs roaming in cells in a cellular network on real-time basis. A connected graph is then transformed into its corresponding single-row network for assigning the channels to the caller-receiver pairs. The GPTM starts with the linear-time heuristic graph partitioning to produce two subgraphs with higher densities. The optimal labeling for nodes are then formed based on the simulated annealing technique. Experimental results support our hypothesis that GPTM efficiently transforms the connected graph into its single-row network.
    Matched MeSH terms: Heuristics
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