Displaying publications 181 - 200 of 1460 in total

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  1. A hybrid climate model for rainfall forecasting based on combination of self-organizing map and analog method
    Natita Wangsoh, Wiboonsak Watthayu, Dusadee Sukawat
    Sains Malaysiana, 2017;46:2541-2547.
    A hybrid climate model (HCM) is a novel proposed model based on the combination of self-organizing map (SOM) and analog method (AM). The main purpose was to improve the accuracy in rainfall forecasting using HCM. In combination process of HCM, SOM algorithm classifies high dimensional input data to low dimensional of several disjointed clusters in which similar input is grouped. AM searches the future day that has similar property with the day in the past. Consequently, the analog day is mapped to each cluster of SOM to investigate rainfall. In this study, the input data, geopotential height at 850 hPa from the Climate Forecast System Reanalysis (CFSR) are training set data and also the complete rainfall data at 30-meteorological stations from Thai meteorological department (TMD) are observed. To improve capability of rainfall forecasting, three different measures were evaluated. The experimental results showed that the performance of HCM is better than the traditional AM. It is illustrated that the HCM can forecast rainfall proficiently.
    Matched MeSH terms: Algorithms
  2. Solution of third order ordinary differential equation using improved block hybrid collocation method
    Lee KC, Ummul Khair Salma Din, Rokiah Rozita Ahmad
    Sains Malaysiana, 2018;47:2179-2186.
    The improved block hybrid collocation method (KBHK(B)) with two off-step points and four collocation points is proposed
    for the direct solution of general third order ordinary differential equations. Modification is done by adding first
    derivative of third order function into the general form of block hybrid collocation method to yield KBHK(B). Both main
    and additional methods are derived via interpolation (power series function) and collocation of the basic polynomial.
    An improved block method is derived to provide the approximation at five points concurrently. Zero stability, consistency,
    convergence and absolute stability region of KBHK(B) are investigated. Some numerical examples with exact solution are
    tested to illustrate the efficiency of the method. KBHK(B) is compared by other block hibrid collocation method in term
    of global error and step number. It is shown that KBHK(B) generates minimum global error with minimum step number.
    Matched MeSH terms: Algorithms
  3. Fulltext The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements
    Feng Z, Hu X, Jiang Z, Song H, Ashraf MA
    Saudi J Biol Sci, 2016 Mar;23(2):189-97.
    PMID: 26980999 DOI: 10.1016/j.sjbs.2015.10.008
    The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.
    Matched MeSH terms: Algorithms
  4. Fulltext An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
    Gharaei N, Abu Bakar K, Mohd Hashim SZ, Hosseingholi Pourasl A, Siraj M, Darwish T
    Sensors (Basel), 2017 Aug 11;17(8).
    PMID: 28800121 DOI: 10.3390/s17081858
    Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network's lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network's lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs.
    Matched MeSH terms: Algorithms
  5. Fulltext Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier
    Memari N, Ramli AR, Bin Saripan MI, Mashohor S, Moghbel M
    PLoS One, 2017;12(12):e0188939.
    PMID: 29228036 DOI: 10.1371/journal.pone.0188939
    The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE) and Child Heart and Health Study in England (CHASE_DB1) datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively.
    Matched MeSH terms: Algorithms
  6. Fulltext Lattice Boltzmann Method of Different BGA Orientations on I-Type Dispensing Method
    Abas A, Gan ZL, Ishak MH, Abdullah MZ, Khor SF
    PLoS One, 2016;11(7):e0159357.
    PMID: 27454872 DOI: 10.1371/journal.pone.0159357
    This paper studies the three dimensional (3D) simulation of fluid flows through the ball grid array (BGA) to replicate the real underfill encapsulation process. The effect of different solder bump arrangements of BGA on the flow front, pressure and velocity of the fluid is investigated. The flow front, pressure and velocity for different time intervals are determined and analyzed for potential problems relating to solder bump damage. The simulation results from Lattice Boltzmann Method (LBM) code will be validated with experimental findings as well as the conventional Finite Volume Method (FVM) code to ensure highly accurate simulation setup. Based on the findings, good agreement can be seen between LBM and FVM simulations as well as the experimental observations. It was shown that only LBM is capable of capturing the micro-voids formation. This study also shows an increasing trend in fluid filling time for BGA with perimeter, middle empty and full orientations. The perimeter orientation has a higher pressure fluid at the middle region of BGA surface compared to middle empty and full orientation. This research would shed new light for a highly accurate simulation of encapsulation process using LBM and help to further increase the reliability of the package produced.
    Matched MeSH terms: Algorithms
  7. Fulltext Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms
    Singh OP, Vallejo M, El-Badawy IM, Aysha A, Madhanagopal J, Mohd Faudzi AA
    Comput Biol Med, 2021 Sep;136:104650.
    PMID: 34329865 DOI: 10.1016/j.compbiomed.2021.104650
    Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing (DSP) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA, which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10 × 10 cross-validation paired t-test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4 %, sensitivity of 96.2 %, and specificity of 98.2 %, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 s to compute the genome biomarkers, outperforming previous studies.
    Matched MeSH terms: Algorithms
  8. Fulltext Detecting industrial motor faults with current signatures
    Krishnan S, Vengadasalam V
    F1000Res, 2021;10:903.
    PMID: 36398279 DOI: 10.12688/f1000research.54266.1
    Background: A major player in industry is the induction motor. The constant motion and mechanical nature of motors causes much wear and tear, creating a need for frequent maintenance such as changing contact brushes. Unmannered and infrequent monitoring of motors, as is common in the industry, can lead to overexertion and cause major faults. If a motor fault is detected earlier through the use of automated fault monitoring, it could prevent minor faults from developing into major faults, reducing the cost and down-time of production due the motor repairs. There are few available methods to detect three-phase motor faults. One method is to analyze average vibration signals values of V, I, pf, P, Q, S, THD and frequency. Others are to analyze instantaneous signal signatures of V and I frequencies, or V and I trajectory plotting a Lissajous curve. These methods need at least three sensors for current and three for voltage for a three-phase motor detection. Methods: Our proposed method of monitoring faults in three-phase industrial motors uses Hilbert Transform (HT) instantaneous current signature curve only, reducing the number of sensors required. Our system detects fault signatures accurately at any voltage or current levels, whether it is delta or star connected motors. This is due to our system design, which incorporates normalized curves of HT in the fault analysis database. We have conducted this experiment in our campus laboratory for two different three-phase motors with four different fault experiments. Results: The results shown in this paper are a comparison of two methods, the V and I Lissajous trajectory curve and our HT instantaneous current signature curve. Conclusion: We have chosen them as our benchmark as their fault results closely resemble our system results, but our system benefits such as universality and a cost reduction in sensors of 50%.
    Matched MeSH terms: Algorithms
  9. Fulltext Next generation insect taxonomic classification by comparing different deep learning algorithms
    Ong SQ, Hamid SA
    PLoS One, 2022;17(12):e0279094.
    PMID: 36584101 DOI: 10.1371/journal.pone.0279094
    Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consuming, labor intensive, and expensive, and recently, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects-order, family, and genus-and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. The InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family, which is having F1-score of 0.75 and 0.79, respectively. Referring to the performance per class, Hemiptera (order), Rhiniidae (family), and Lucilia (genus) had the lowest performance, and we discuss the possible rationale and suggest future works to improve the generalization of a DL model for taxonomic rank classification.
    Matched MeSH terms: Algorithms
  10. Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models
    Elbeltagi A, Pande CB, Kumar M, Tolche AD, Singh SK, Kumar A, et al.
    Environ Sci Pollut Res Int, 2023 Mar;30(15):43183-43202.
    PMID: 36648725 DOI: 10.1007/s11356-023-25221-3
    Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.
    Matched MeSH terms: Algorithms
  11. Fulltext A new Multi Sine-Cosine algorithm for unconstrained optimization problems
    Rehman MZ, Khan A, Ghazali R, Aamir M, Nawi NM
    PLoS One, 2021;16(8):e0255269.
    PMID: 34358237 DOI: 10.1371/journal.pone.0255269
    The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify & intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA.
    Matched MeSH terms: Algorithms
  12. Fulltext An improved multi-strategy beluga whale optimization for global optimization problems
    Chen H, Wang Z, Wu D, Jia H, Wen C, Rao H, et al.
    Math Biosci Eng, 2023 Jun 09;20(7):13267-13317.
    PMID: 37501488 DOI: 10.3934/mbe.2023592
    This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems.
    Matched MeSH terms: Algorithms
  13. High Accuracy of a Simplified, Practical Algorithm in Differentiating Crohn's Disease from Intestinal Tuberculosis
    Hilmi IN, Nik Muhamad Affendi NA, Shahrani S, Thalha AM, Leow AH, Khoo XH
    Dig Dis, 2023;41(4):581-588.
    PMID: 36702102 DOI: 10.1159/000529238
    BACKGROUND: The differentiation between intestinal tuberculosis (ITB) and Crohn's disease (CD) remains a challenge, particularly in areas where tuberculosis is highly prevalent. Previous studies have identified features that favour one diagnosis over the other. The aim of the study was to determine the accuracy of a standardized protocol in the initial diagnosis of CD versus ITB.

    METHODS: All patients with suspected ITB or CD were prospectively recruited. A standardized protocol was applied, and the diagnosis was made accordingly. The protocol consists of history and examination, ileocolonoscopy with biopsies, and tuberculosis workup. The diagnosis of probable ITB was made based on at least one positive finding. All other patients were diagnosed as probable CD. Patients were treated either with anti-tubercular therapy or steroids. Reassessment was then carried out clinically, biochemically, and endoscopically. In patients with suboptimal response, the treatment was either switched or escalated depending on the reassessment.

    RESULTS: 164 patients were recruited with final diagnosis of 30 (18.3%) ITB and 134 (81.7%) CD. 1 (3.3%) out of 30 patients with ITB was initially treated as CD. 16 (11.9%) out of 134 patients with CD were initially treated as ITB. The initial overall accuracy for the protocol was 147/164 (89.6%). All patients received the correct diagnosis by 12 weeks after reassessment.

    CONCLUSION: In our population, most patients had CD rather than ITB. The standardized protocol had a high accuracy in differentiating CD from ITB.

    Matched MeSH terms: Algorithms
  14. The recognition of anti-nuclear antibody's dense fine speckled pattern and the detection of anti-DFS70 antibodies in the laboratory practice: Its prevalence and clinical significance
    Wahab AA, Jauhary EJ, Ding CH
    Malays J Pathol, 2023 Aug;45(2):157-173.
    PMID: 37658526
    Anti-nuclear antibody test (ANA) is the test commonly requested for the working diagnosis of systemic autoimmune rheumatic diseases (SARDs) particularly in ANA-associated rheumatic diseases (AARDs) such as SLE, systemic sclerosis, Sjogren syndrome, mixed connective tissue diseases, and dermatomyositis. Dense fine speckled (DFS) pattern is an ANA fluorescence pattern that is commonly encountered in laboratories. This pattern is largely detected among the healthy population and in non-SARDs patients. Although this pattern is still can be observed among SARDs patients, the low prevalence of monospecific or isolated anti-DFS70 antibodies makes it useful for ruling out AARDs diagnosis. Thus, the inclusion of anti-DFS70 antibodies is perhaps logical for the exclusion of SARDs/AARDs. This review provides evidence of the prevalence of anti-DFS70 antibodies in different populations including healthy individuals, patients with SARDs and non- SARDs. The algorithm that includes the detection of anti-DFS70 antibodies during ANA screening is also suggested.
    Matched MeSH terms: Algorithms
  15. Fulltext Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection
    Zegarra Rodríguez D, Daniel Okey O, Maidin SS, Umoren Udo E, Kleinschmidt JH
    PLoS One, 2023;18(10):e0286652.
    PMID: 37844095 DOI: 10.1371/journal.pone.0286652
    Recent years have witnessed an in-depth proliferation of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems linked to Industry 4.0 technology. The increasing rate of IoT device usage is associated with rising security risks resulting from malicious network flows during data exchange between the connected devices. Various security threats have shown high adverse effects on the availability, functionality, and usability of the devices among which denial of service (DoS) and distributed denial of service (DDoS), which attempt to exhaust the capacity of the IoT network (gateway), thereby causing failure in the functionality of the system have been more pronounced. Various machine learning and deep learning algorithms have been used to propose intelligent intrusion detection systems (IDS) to mitigate the challenging effects of these network threats. One concern is that although deep learning algorithms have shown good accuracy results on tabular data, not all deep learning algorithms can perform well on tabular datasets, which happen to be the most commonly available format of datasets for machine learning tasks. Again, there is also the challenge of model explainability and feature selection, which affect model performance. In this regard, we propose a model for IDS that uses attentive mechanisms to automatically select salient features from a dataset to train the IDS model and provide explainable results, the TabNet-IDS. We implement the proposed model using the TabNet algorithm based on PyTorch which is a deep-learning framework. The results obtained show that the TabNet architecture can be used on tabular datasets for IoT security to achieve good results comparable to those of neural networks, reaching an accuracy of 97% on CIC-IDS2017, 95% on CSE-CICIDS2018 and 98% on CIC-DDoS2019 datasets.
    Matched MeSH terms: Algorithms
  16. Fulltext Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream
    Li X, Wang X, Ong P, Yi Z, Ding L, Han C
    Sensors (Basel), 2023 Oct 13;23(20).
    PMID: 37896537 DOI: 10.3390/s23208444
    Dragon fruit (Hylocereus undatus) is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient. Deep learning techniques have proven effective for object recognition tasks but limited research has been conducted on dragon fruit due to its unique stem morphology and the coexistence of flowers and fruits. Additionally, the challenge lies in developing a lightweight recognition and tracking model that can be seamlessly integrated into mobile platforms, enabling on-site quantity counting. In this study, a video stream inspection method was proposed to classify and count dragon fruit flowers, immature fruits (green fruits), and mature fruits (red fruits) in a dragon fruit plantation. The approach involves three key steps: (1) utilizing the YOLOv5 network for the identification of different dragon fruit categories, (2) employing the improved ByteTrack object tracking algorithm to assign unique IDs to each target and track their movement, and (3) defining a region of interest area for precise classification and counting of dragon fruit across categories. Experimental results demonstrate recognition accuracies of 94.1%, 94.8%, and 96.1% for dragon fruit flowers, green fruits, and red fruits, respectively, with an overall average recognition accuracy of 95.0%. Furthermore, the counting accuracy for each category is measured at 97.68%, 93.97%, and 91.89%, respectively. The proposed method achieves a counting speed of 56 frames per second on a 1080ti GPU. The findings establish the efficacy and practicality of this method for accurate counting of dragon fruit or other fruit varieties.
    Matched MeSH terms: Algorithms
  17. Subjective and objective financial toxicity among colorectal cancer patients: a systematic review
    Azzani M, Azhar ZI, Ruzlin ANM, Wee CX, Samsudin EZ, Al-Harazi SM, et al.
    BMC Cancer, 2024 Jan 05;24(1):40.
    PMID: 38182993 DOI: 10.1186/s12885-023-11814-1
    BACKGROUND: Colorectal cancer (CRC) is the third most common cancer type worldwide. Colorectal cancer treatment costs vary between countries as it depends on policy factors such as treatment algorithms, availability of treatments and whether the treatment is government-funded. Hence, the objective of this systematic review is to determine the prevalence and measurements of financial toxicity (FT), including the cost of treatment, among colorectal cancer patients.

    METHODS: Medline via PubMed platform, Science Direct, Scopus, and CINAHL databases were searched to find studies that examined CRC FT. There was no limit on the design or setting of the study.

    RESULTS: Out of 819 papers identified through an online search, only 15 papers were included in this review. The majority (n = 12, 80%) were from high-income countries, and none from low-income countries. Few studies (n = 2) reported objective FT denoted by the prevalence of catastrophic health expenditure (CHE), 60% (9 out of 15) reported prevalence of subjective FT, which ranges from 7 to 80%, 40% (6 out of 15) included studies reported cost of CRC management- annual direct medical cost ranges from USD 2045 to 10,772 and indirect medical cost ranges from USD 551 to 795.

    CONCLUSIONS: There is a lack of consensus in defining and quantifying financial toxicity hindered the comparability of the results to yield the mean cost of managing CRC. Over and beyond that, information from some low-income countries is missing, limiting global representativeness.

    Matched MeSH terms: Algorithms
  18. Fulltext IoT Workflow Scheduling Using Intelligent Arithmetic Optimization Algorithm in Fog Computing
    Abd Elaziz M, Abualigah L, Ibrahim RA, Attiya I
    Comput Intell Neurosci, 2021;2021:9114113.
    PMID: 34976046 DOI: 10.1155/2021/9114113
    Instead of the cloud, the Internet of things (IoT) activities are offloaded into fog computing to boost the quality of services (QoSs) needed by many applications. However, the availability of continuous computing resources on fog computing servers is one of the restrictions for IoT applications since transmitting the large amount of data generated using IoT devices would create network traffic and cause an increase in computational overhead. Therefore, task scheduling is the main problem that needs to be solved efficiently. This study proposes an energy-aware model using an enhanced arithmetic optimization algorithm (AOA) method called AOAM, which addresses fog computing's job scheduling problem to maximize users' QoSs by maximizing the makespan measure. In the proposed AOAM, we enhanced the conventional AOA searchability using the marine predators algorithm (MPA) search operators to address the diversity of the used solutions and local optimum problems. The proposed AOAM is validated using several parameters, including various clients, data centers, hosts, virtual machines, tasks, and standard evaluation measures, including the energy and makespan. The obtained results are compared with other state-of-the-art methods; it showed that AOAM is promising and solved task scheduling effectively compared with the other comparative methods.
    Matched MeSH terms: Algorithms
  19. Fulltext A novel clustering algorithm for mobile ad hoc networks based on determination of virtual links' weight to increase network stability
    Karimi A, Afsharfarnia A, Zarafshan F, Al-Haddad SA
    ScientificWorldJournal, 2014;2014:432952.
    PMID: 25114965 DOI: 10.1155/2014/432952
    The stability of clusters is a serious issue in mobile ad hoc networks. Low stability of clusters may lead to rapid failure of clusters, high energy consumption for reclustering, and decrease in the overall network stability in mobile ad hoc network. In order to improve the stability of clusters, weight-based clustering algorithms are utilized. However, these algorithms only use limited features of the nodes. Thus, they decrease the weight accuracy in determining node's competency and lead to incorrect selection of cluster heads. A new weight-based algorithm presented in this paper not only determines node's weight using its own features, but also considers the direct effect of feature of adjacent nodes. It determines the weight of virtual links between nodes and the effect of the weights on determining node's final weight. By using this strategy, the highest weight is assigned to the best choices for being the cluster heads and the accuracy of nodes selection increases. The performance of new algorithm is analyzed by using computer simulation. The results show that produced clusters have longer lifetime and higher stability. Mathematical simulation shows that this algorithm has high availability in case of failure.
    Matched MeSH terms: Algorithms*
  20. Fulltext New ray tracing method to investigate the various effects on wave propagation in medical scenario: an application of wireless body area network
    Islam MJ, Reza AW, Kausar AS, Ramiah H
    ScientificWorldJournal, 2014;2014:306270.
    PMID: 25133220 DOI: 10.1155/2014/306270
    The advent of technology with the increasing use of wireless network has led to the development of Wireless Body Area Network (WBAN) to continuously monitor the change of physiological data in a cost efficient manner. As numerous researches on wave propagation characterization have been done in intrabody communication, this study has given emphasis on the wave propagation characterization between the control units (CUs) and wireless access point (AP) in a hospital scenario. Ray tracing is a tool to predict the rays to characterize the wave propagation. It takes huge simulation time, especially when multiple transmitters are involved to transmit physiological data in a realistic hospital environment. Therefore, this study has developed an accelerated ray tracing method based on the nearest neighbor cell and prior knowledge of intersection techniques. Beside this, Red-Black tree is used to store and provide a faster retrieval mechanism of objects in the hospital environment. To prove the superiority, detailed complexity analysis and calculations of reflection and transmission coefficients are also presented in this paper. The results show that the proposed method is about 1.51, 2.1, and 2.9 times faster than the Object Distribution Technique (ODT), Space Volumetric Partitioning (SVP), and Angular Z-Buffer (AZB) methods, respectively. To show the various effects on received power in 60 GHz frequency, few comparisons are made and it is found that on average -9.44 dBm, -8.23 dBm, and -9.27 dBm received power attenuations should be considered when human, AP, and CU move in a given hospital scenario.
    Matched MeSH terms: Algorithms*
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