Displaying publications 21 - 40 of 1459 in total

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  1. Fulltext A novel method for multifactorial bio-chemical experiments design based on combinational design theory
    Wang X, Sun B, Liu B, Fu Y, Zheng P
    PLoS One, 2017;12(11):e0186853.
    PMID: 29095845 DOI: 10.1371/journal.pone.0186853
    Experimental design focuses on describing or explaining the multifactorial interactions that are hypothesized to reflect the variation. The design introduces conditions that may directly affect the variation, where particular conditions are purposely selected for observation. Combinatorial design theory deals with the existence, construction and properties of systems of finite sets whose arrangements satisfy generalized concepts of balance and/or symmetry. In this work, borrowing the concept of "balance" in combinatorial design theory, a novel method for multifactorial bio-chemical experiments design is proposed, where balanced templates in combinational design are used to select the conditions for observation. Balanced experimental data that covers all the influencing factors of experiments can be obtianed for further processing, such as training set for machine learning models. Finally, a software based on the proposed method is developed for designing experiments with covering influencing factors a certain number of times.
    Matched MeSH terms: Algorithms
  2. Development and validation of a novel non-invasive test for diagnosing fibrotic non-alcoholic steatohepatitis in patients with biopsy-proven non-alcoholic fatty liver disease
    Gao F, Huang JF, Zheng KI, Pan XY, Ma HL, Liu WY, et al.
    J Gastroenterol Hepatol, 2020 Oct;35(10):1804-1812.
    PMID: 32246876 DOI: 10.1111/jgh.15055
    BACKGROUND AND AIM: There is an immediate need for non-invasive accurate tests for diagnosing liver fibrosis in patients with non-alcoholic steatohepatitis (NASH). Previously, it has been suggested that MACK-3 (a formula that combines homeostasis model assessment-insulin resistance with serum serum aspartate aminotransferase and cytokeratin [CK]18-M30 levels) accurately identifies patients with fibrotic NASH. Our aim was to assess the performance of MACK-3 and develop a novel, non-invasive algorithm for diagnosing fibrotic NASH.

    METHODS: Six hundred and thirty-six adults with biopsy-proven non-alcoholic fatty liver disease (NAFLD) from two independent Asian cohorts were enrolled in our study. Liver stiffness measurement (LSM) was assessed by vibration-controlled transient elastography (Fibroscan). Fibrotic NASH was defined as NASH with a NAFLD activity score (NAS) ≥ 4 and F ≥ 2 fibrosis.

    RESULTS: Metabolic syndrome (MetS), platelet count and MACK-3 were independent predictors of fibrotic NASH. On the basis of their regression coefficients, we developed a novel nomogram showing a good discriminatory ability (area under receiver operating characteristic curve [AUROC]: 0.79, 95% confidence interval [CI 0.75-0.83]) and a high negative predictive value (NPV: 94.7%) to rule out fibrotic NASH. In the validation set, this nomogram had a higher AUROC (0.81, 95%CI 0.74-0.87) than that of MACK-3 (AUROC: 0.75, 95%CI 0.68-0.82; P 

    Matched MeSH terms: Algorithms
  3. Fulltext New crystal structure prediction of fully hydrogenated borophene by first principles calculations
    Wang Z, Lü TY, Wang HQ, Feng YP, Zheng JC
    Sci Rep, 2017 04 04;7(1):609.
    PMID: 28377622 DOI: 10.1038/s41598-017-00667-x
    New crystal structures of fully hydrogenated borophene (borophane) have been predicted by first principles calculation. Comparing with the chair-like borophane (C-boropane) that has been reported in literature, we obtained four new borophane conformers with much lower total-energy. The most stable one, washboard-like borophane (W-borophane), has energy about 113.41 meV/atom lower than C-borophane. In order to explain the relative stability of different borophane conformers, the atom configuration, density of states, charge transfer, charge density distribution and defect formation energy of B-H dimer have been calculated. The results show that the charge transfer from B atoms to H atoms is crucial for the stability of borophane. In different borophane conformers, the bonding characteristics between B and H atoms are similar, but the B-B bonds in W-borophane are much stronger than that in C-borophane or other structures. In addition, we examined the dynamical stability of borophane conformers by phonon dispersions and found that the four new conformers are all dynamically stable. Finally the mechanical properties of borophane conformers along an arbitrary direction have been discussed. W-borophane possesses unique electronic structure (Dirac cone), good stability and superior mechanical properties. W-borophane has broad perspective for nano electronic device.
    Matched MeSH terms: Algorithms
  4. Fulltext Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions
    Tan M, Aghaei F, Wang Y, Zheng B
    Phys Med Biol, 2017 01 21;62(2):358-376.
    PMID: 27997380 DOI: 10.1088/1361-6560/aa5081
    The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a 'scoring fusion' artificial neural network classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC  =  0.793  ±  0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions.
    Matched MeSH terms: Algorithms*
  5. NES-REBS: A novel nuclear export signal prediction method using regular expressions and biochemical properties
    Wu T, Wang X, Zhang Z, Gong F, Song T, Chen Z, et al.
    J Bioinform Comput Biol, 2016 06;14(3):1650013.
    PMID: 27225342 DOI: 10.1142/S021972001650013X
    A nuclear export signal (NES) is a protein localization signal, which is involved in binding of cargo proteins to nuclear export receptor, thus contributes to regulate localization of cellular proteins. Consensus sequences of NES have been used to detect NES from protein sequences, but suffer from poor predictive power. Some recent peering works were proposed to use biochemical properties of experimental verified NES to refine NES candidates. Those methods can achieve high prediction rates, but their execution time will become unacceptable for large-scale NES searching if too much properties are involved. In this work, we developed a novel computational approach, named NES-REBS, to search NES from protein sequences, where biochemical properties of experimental verified NES, including secondary structure and surface accessibility, are utilized to refine NES candidates obtained by matching popular consensus sequences. We test our method by searching 262 experimental verified NES from 221 NES-containing protein sequences. It is obtained that NES-REBS runs in 2-3[Formula: see text]mins and performs well by achieving precision rate 47.2% and sensitivity 54.6%.
    Matched MeSH terms: Algorithms*
  6. Fulltext Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation
    Illias HA, Zhao Liang W
    PLoS One, 2018;13(1):e0191366.
    PMID: 29370230 DOI: 10.1371/journal.pone.0191366
    Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.
    Matched MeSH terms: Algorithms
  7. Alzheimer's disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction
    Cheng J, Wang H, Wei S, Mei J, Liu F, Zhang G
    Comput Biol Med, 2024 Mar;170:108000.
    PMID: 38232453 DOI: 10.1016/j.compbiomed.2024.108000
    Alzheimer's disease (AD) is a neurodegenerative disease characterized by various pathological changes. Utilizing multimodal data from Fluorodeoxyglucose positron emission tomography(FDG-PET) and Magnetic Resonance Imaging(MRI) of the brain can offer comprehensive information about the lesions from different perspectives and improve the accuracy of prediction. However, there are significant differences in the feature space of multimodal data. Commonly, the simple concatenation of multimodal features can cause the model to struggle in distinguishing and utilizing the complementary information between different modalities, thus affecting the accuracy of predictions. Therefore, we propose an AD prediction model based on de-correlation constraint and multi-modal feature interaction. This model consists of the following three parts: (1) The feature extractor employs residual connections and attention mechanisms to capture distinctive lesion features from FDG-PET and MRI data within their respective modalities. (2) The de-correlation constraint function enhances the model's capacity to extract complementary information from different modalities by reducing the feature similarity between them. (3) The mutual attention feature fusion module interacts with the features within and between modalities to enhance the modal-specific features and adaptively adjust the weights of these features based on information from other modalities. The experimental results on ADNI database demonstrate that the proposed model achieves a prediction accuracy of 86.79% for AD, MCI and NC, which is higher than the existing multi-modal AD prediction models.
    Matched MeSH terms: Algorithms
  8. Design of interacting multi-stable nucleic acids for molecular information processing
    Ramlan EI, Zauner KP
    Biosystems, 2011 Jul;105(1):14-24.
    PMID: 21396427 DOI: 10.1016/j.biosystems.2011.02.006
    Despite an exponential increase in computing power over the past decades, present information technology falls far short of expectations in areas such as cognitive systems and micro robotics. Organisms demonstrate that it is possible to implement information processing in a radically different way from what we have available in present technology, and that there are clear advantages from the perspective of power consumption, integration density, and real-time processing of ambiguous data. Accordingly, the question whether the current silicon substrate and associated computing paradigm is the most suitable approach to all types of computation has come to the fore. Macromolecular materials, so successfully employed by nature, possess uniquely promising properties as an alternate substrate for information processing. The two key features of macromolecules are their conformational dynamics and their self-assembly capabilities. The purposeful design of macromolecules capable of exploiting these features has proven to be a challenge, however, for some groups of molecules it is increasingly practicable. We here introduce an algorithm capable of designing groups self-assembling of nucleic acid molecules with multiple conformational states. Evaluation using natural and artificially designed nucleic acid molecules favours this algorithm significantly, as compared to the probabilistic approach. Furthermore, the thermodynamic properties of the generated candidates are within the same approximation as the customised trans-acting switching molecules reported in the laboratory.
    Matched MeSH terms: Algorithms*
  9. 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: Algorithms
  10. Fulltext Stochastic process and tutorial of the African buffalo optimization
    Odili JB, Noraziah A, Alkazemi B, Zarina M
    Sci Rep, 2022 Oct 15;12(1):17319.
    PMID: 36243886 DOI: 10.1038/s41598-022-22242-9
    This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm's basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithms.
    Matched MeSH terms: Algorithms*
  11. Block backward differentiation formulas for solving first order fuzzy differential equations under generalized differentiability
    Iskandar Shah Mohd Zawawi, Zarina Bibi Ibrahim
    Sains Malaysiana, 2016;45:989-998.
    In this paper, the fully implicit 2-point block backward differentiation formula and diagonally implicit 2-point block
    backward differentiation formula were developed under the interpretation of generalized differentiability concept for
    solving first order fuzzy differential equations. Some fuzzy initial value problems were tested in order to demonstrate the
    performance of the developed methods. The approximated solutions for both methods were in good agreement with the
    exact solutions. The numerical results showed that the diagonally implicit method outperforms the fully implicit method
    in term of accuracy.
    Matched MeSH terms: Algorithms
  12. Fulltext Block backward differentiation formulas for solving second order fuzzy differential equations
    Tiaw, Kah Fookand, Zarina Bibi Ibrahim
    MATEMATIKA, 2017;33(2):215-226.
    MyJurnal
    In this paper, we study the numerical method for solving second order Fuzzy
    Differential Equations (FDEs) using Block Backward Differential Formulas (BBDF)
    under generalized concept of higher-order fuzzy differentiability. Implementation of
    the method using Newton iteration is discussed. Numerical results obtained by BBDF
    are presented and compared with Backward Differential Formulas (BDF) and exact
    solutions. Several numerical examples are provided to illustrate our methods.
    Matched MeSH terms: Algorithms
  13. Numerical algorithm of block method for general second order ODES using variable step size
    Nazreen Waeleh, Zanariah Abdul Majid
    Sains Malaysiana, 2017;46:817-824.
    This paper outlines an alternative algorithm for solving general second order ordinary differential equations (ODEs). Normally, the numerical method was designed for solving higher order ODEs by converting it into an n-dimensional first order equations with implementation of constant step length. Nevertheless, this involved a lot of computational complexity which led to consumption a lot of time. Consequently, a direct block multistep method with utilization of variable step size strategy is proposed. This method was developed for computing the solution at four points simultaneously and the derivation based on numerical integration as well as using interpolation approach. The convergence of the proposed method is justified under suitable conditions of stability and consistency. Five numerical examples are considered and some comparisons are made with the existing methods for demonstrating the validity and reliability of the proposed algorithm.
    Matched MeSH terms: Algorithms
  14. Fulltext Optimum Design of PIλDμ Controller for an Automatic Voltage Regulator System Using Combinatorial Test Design
    Ahmed BS, Sahib MA, Gambardella LM, Afzal W, Zamli KZ
    PLoS One, 2016;11(11):e0166150.
    PMID: 27829025 DOI: 10.1371/journal.pone.0166150
    Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly.
    Matched MeSH terms: Algorithms
  15. Mitotic nuclei analysis in breast cancer histopathology images using deep ensemble classifier
    Sohail A, Khan A, Nisar H, Tabassum S, Zameer A
    Med Image Anal, 2021 08;72:102121.
    PMID: 34139665 DOI: 10.1016/j.media.2021.102121
    Mitotic nuclei estimation in breast tumour samples has a prognostic significance in analysing tumour aggressiveness and grading system. The automated assessment of mitotic nuclei is challenging because of their high similarity with non-mitotic nuclei and heteromorphic appearance. In this work, we have proposed a new Deep Convolutional Neural Network (CNN) based Heterogeneous Ensemble technique "DHE-Mit-Classifier" for analysis of mitotic nuclei in breast histopathology images. The proposed technique in the first step detects candidate mitotic patches from the histopathological biopsy regions, whereas, in the second step, these patches are classified into mitotic and non-mitotic nuclei using the proposed DHE-Mit-Classifier. For the development of a heterogeneous ensemble, five different deep CNNs are designed and used as base-classifiers. These deep CNNs have varying architectural designs to capture the structural, textural, and morphological properties of the mitotic nuclei. The developed base-classifiers exploit different ideas, including (i) region homogeneity and feature invariance, (ii) asymmetric split-transform-merge, (iii) dilated convolution based multi-scale transformation, (iv) spatial and channel attention, and (v) residual learning. Multi-layer-perceptron is used as a meta-classifier to develop a robust and accurate classifier for providing the final decision. The performance of the proposed ensemble "DHE-Mit-Classifier" is evaluated against state-of-the-art CNNs. The performance evaluation on the test set suggests the superiority of the proposed ensemble with an F-score (0.77), recall (0.71), precision (0.83), and area under the precision-recall curve (0.80). The good generalisation of the proposed ensemble with a considerably high F-score and precision suggests its potential use in the development of an assistance tool for pathologists.
    Matched MeSH terms: Algorithms
  16. A review on the computational approaches for gene regulatory network construction
    Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z
    Comput Biol Med, 2014 May;48:55-65.
    PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011
    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
    Matched MeSH terms: Algorithms
  17. Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction
    Roslan R, Othman RM, Shah ZA, Kasim S, Asmuni H, Taliba J, et al.
    Comput Biol Med, 2010 Jun;40(6):555-64.
    PMID: 20417930 DOI: 10.1016/j.compbiomed.2010.03.009
    Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
    Matched MeSH terms: Algorithms
  18. SPlitSSI-SVM: an algorithm to reduce the misleading and increase the strength of domain signal
    Kalsum HU, Shah ZA, Othman RM, Hassan R, Rahim SM, Asmuni H, et al.
    Comput Biol Med, 2009 Nov;39(11):1013-9.
    PMID: 19720371 DOI: 10.1016/j.compbiomed.2009.08.002
    Protein domains contain information about the prediction of protein structure, function, evolution and design since the protein sequence may contain several domains with different or the same copies of the protein domain. In this study, we proposed an algorithm named SplitSSI-SVM that works with the following steps. First, the training and testing datasets are generated to test the SplitSSI-SVM. Second, the protein sequence is split into subsequence based on order and disorder regions. The protein sequence that is more than 600 residues is split into subsequences to investigate the effectiveness of the protein domain prediction based on subsequence. Third, multiple sequence alignment is performed to predict the secondary structure using bidirectional recurrent neural networks (BRNN) where BRNN considers the interaction between amino acids. The information of about protein secondary structure is used to increase the protein domain boundaries signal. Lastly, support vector machines (SVM) are used to classify the protein domain into single-domain, two-domain and multiple-domain. The SplitSSI-SVM is developed to reduce misleading signal, lower protein domain signal caused by primary structure of protein sequence and to provide accurate classification of the protein domain. The performance of SplitSSI-SVM is evaluated using sensitivity and specificity on single-domain, two-domain and multiple-domain. The evaluation shows that the SplitSSI-SVM achieved better results compared with other protein domain predictors such as DOMpro, GlobPlot, Dompred-DPS, Mateo, Biozon, Armadillo, KemaDom, SBASE, HMMPfam and HMMSMART especially in two-domain and multiple-domain.
    Matched MeSH terms: Algorithms*
  19. Fulltext A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks
    Goudarzi S, Haslina Hassan W, Abdalla Hashim AH, Soleymani SA, Anisi MH, Zakaria OM
    PLoS One, 2016;11(7):e0151355.
    PMID: 27438600 DOI: 10.1371/journal.pone.0151355
    This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.
    Matched MeSH terms: Algorithms*
  20. Fulltext Chaos-Based Simultaneous Compression and Encryption for Hadoop
    Usama M, Zakaria N
    PLoS One, 2017;12(1):e0168207.
    PMID: 28072850 DOI: 10.1371/journal.pone.0168207
    Data compression and encryption are key components of commonly deployed platforms such as Hadoop. Numerous data compression and encryption tools are presently available on such platforms and the tools are characteristically applied in sequence, i.e., compression followed by encryption or encryption followed by compression. This paper focuses on the open-source Hadoop framework and proposes a data storage method that efficiently couples data compression with encryption. A simultaneous compression and encryption scheme is introduced that addresses an important implementation issue of source coding based on Tent Map and Piece-wise Linear Chaotic Map (PWLM), which is the infinite precision of real numbers that result from their long products. The approach proposed here solves the implementation issue by removing fractional components that are generated by the long products of real numbers. Moreover, it incorporates a stealth key that performs a cyclic shift in PWLM without compromising compression capabilities. In addition, the proposed approach implements a masking pseudorandom keystream that enhances encryption quality. The proposed algorithm demonstrated a congruent fit within the Hadoop framework, providing robust encryption security and compression.
    Matched MeSH terms: Algorithms
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