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Fulltext Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Lye GX, Cheng WK, Tan TB, Hung CW, Chen YL

Sensors (Basel), 2020 Apr 08;20(7).
PMID: 32276431 DOI: 10.3390/s20072098

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge-desire-intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users' beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

Matched MeSH terms: Benchmarking
Fulltext Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid

Singh N, Elamvazuthi I, Nallagownden P, Ramasamy G, Jangra A

Sensors (Basel), 2020 May 25;20(10).
PMID: 32466240 DOI: 10.3390/s20102992

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman-Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%-43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.

Matched MeSH terms: Benchmarking
Fulltext DeepNC: a framework for drug-target interaction prediction with graph neural networks

Tran HNT, Thomas JJ, Ahamed Hassain Malim NH

PeerJ, 2022;10:e13163.
PMID: 35578674 DOI: 10.7717/peerj.13163

The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-based model for DTI prediction, we propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph Convolutional Networks (GCNConv), and Hypergraph Convolution-Hypergraph Attention (HypergraphConv). In short, our framework learns the features of drugs and targets by the layers of GNN and 1-D convolution network, respectively. Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs and targets. Moreover, compared to the results of baseline methods that worked on the same problem, DeepNC proves to improve the performance in terms of mean square error and concordance index.

Matched MeSH terms: Benchmarking
Fulltext Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation

Ali GA, Abubakar H, Alzaeemi SAS, Almawgani AHM, Sulaiman A, Tay KG

PLoS One, 2023;18(9):e0286874.
PMID: 37747876 DOI: 10.1371/journal.pone.0286874

This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EBkSAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EBkSAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EBkSAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business.

Matched MeSH terms: Benchmarking
Fulltext A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

Magsi A, Mahar JA, Maitlo A, Ahmad M, Razzaq MA, Bhuiyan MAS, et al.

Sci Rep, 2023 Sep 16;13(1):15381.
PMID: 37717081 DOI: 10.1038/s41598-023-41727-9

Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy.

Matched MeSH terms: Benchmarking
SCL: Self-supervised contrastive learning for few-shot image classification

Lim JY, Lim KM, Lee CP, Tan YX

Neural Netw, 2023 Aug;165:19-30.
PMID: 37263089 DOI: 10.1016/j.neunet.2023.05.037

Few-shot learning aims to train a model with a limited number of base class samples to classify the novel class samples. However, to attain generalization with a limited number of samples is not a trivial task. This paper proposed a novel few-shot learning approach named Self-supervised Contrastive Learning (SCL) that enriched the model representation with multiple self-supervision objectives. Given the base class samples, the model is trained with the base class loss. Subsequently, contrastive-based self-supervision is introduced to minimize the distance between each training sample with their augmented variants to improve the sample discrimination. To recognize the distant sample, rotation-based self-supervision is proposed to enable the model to learn to recognize the rotation degree of the samples for better sample diversity. The multitask environment is introduced where each training sample is assigned with two class labels: base class label and rotation class label. Complex augmentation is put forth to help the model learn a deeper understanding of the object. The image structure of the training samples are augmented independent of the base class information. The proposed SCL is trained to minimize the base class loss, contrastive distance loss, and rotation class loss simultaneously to learn the generic features and improve the novel class performance. With the multiple self-supervision objectives, the proposed SCL outperforms state-of-the-art few-shot approaches on few-shot image classification benchmark datasets.

Matched MeSH terms: Benchmarking
Consensus-Based Development of a Global Registry for Traumatic Brain Injury: Establishment, Protocol, and Implementation

Joannides AJ, Korhonen TK, Clark D, Gnanakumar S, Venturini S, Mohan M, et al.

Neurosurgery, 2024 Feb 01;94(2):278-288.
PMID: 37747225 DOI: 10.1227/neu.0000000000002661

BACKGROUND AND OBJECTIVES: Global disparity exists in the demographics, pathology, management, and outcomes of surgically treated traumatic brain injury (TBI). However, the factors underlying these differences, including intervention effectiveness, remain unclear. Establishing a more accurate global picture of the burden of TBI represents a challenging task requiring systematic and ongoing data collection of patients with TBI across all management modalities. The objective of this study was to establish a global registry that would enable local service benchmarking against a global standard, identification of unmet need in TBI management, and its evidence-based prioritization in policymaking.
METHODS: The registry was developed in an iterative consensus-based manner by a panel of neurotrauma professionals. Proposed registry objectives, structure, and data points were established in 2 international multidisciplinary neurotrauma meetings, after which a survey consisting of the same data points was circulated within the global neurotrauma community. The survey results were disseminated in a final meeting to reach a consensus on the most pertinent registry variables.
RESULTS: A total of 156 professionals from 53 countries, including both high-income countries and low- and middle-income countries, responded to the survey. The final consensus-based registry includes patients with TBI who required neurosurgical admission, a neurosurgical procedure, or a critical care admission. The data set comprised clinically pertinent information on demographics, injury characteristics, imaging, treatments, and short-term outcomes. Based on the consensus, the Global Epidemiology and Outcomes following Traumatic Brain Injury (GEO-TBI) registry was established.
CONCLUSION: The GEO-TBI registry will enable high-quality data collection, clinical auditing, and research activity, and it is supported by the World Federation of Neurosurgical Societies and the National Institute of Health Research Global Health Program. The GEO-TBI registry ( https://geotbi.org ) is now open for participant site recruitment. Any center involved in TBI management is welcome to join the collaboration to access the registry.

Matched MeSH terms: Benchmarking
Impact of Article Page Count and Number of Authors on Citations in Disability Related Fields: A Systematic Review Article

Ahmed A, Adam M, Ghafar NA, Muhammad M, Ebrahim NA

Iran J Public Health, 2016 Sep;45(9):1118-1125.
PMID: 27957456

BACKGROUND: Citation metrics and total publications in a field has become the gold standard for rating researchers and viability of a field. Hence, stimulating demand for citation has led to a search for useful strategies to improve performance metric index. Meanwhile, title, abstract and morphologic qualities of the articles attract researchers to scientific publications. Yet, there is relatively little understanding of the citation trend in disability related fields. We aimed to provide an insight into the factors associated with citation increase in this field. Additionally, we tried to know at what page number an article might appear attractive to disability researchers needs. Thus, our focus is placed on the article page count and the number of authors contributing to the fields per article.
METHODS: To this end, we evaluated the quantitative characteristics of top cited articles in the fields with a total citation (≥50) in the Web of Science (WoS) database. Using one-way independent ANOVA, data extracted spanning a period of 1980-2015 were analyzed, while the non-parametric data analysis uses Kruskal-Walis test.
RESULTS: Articles with 11 to 20 pages attract more citations followed by those within the range of zero to 10. Articles with upward 21 pages are the least cited. Surprisingly, articles with more than two authors are significantly (P<0.05) less cited and the citation decreases as the number of authors increased.
CONCLUSION: Collaborative studies enjoy wider utilization and more citation, yet discounted merit of additional pages and limited collaborative research in disability field is revealed in this study.

Matched MeSH terms: Benchmarking
An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Sukumarran D, Hasikin K, Khairuddin ASM, Ngui R, Sulaiman WYW, Vythilingam I, et al.

Parasit Vectors, 2024 Apr 16;17(1):188.
PMID: 38627870 DOI: 10.1186/s13071-024-06215-7

BACKGROUND: Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease's spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model-but with improved accuracy-for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector.
METHODS: The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3-C5) Res-block bodies of the backbone architecture's C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed.
RESULTS: The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone.
CONCLUSIONS: The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model's performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision.

Matched MeSH terms: Benchmarking
BIA-Obesity (Business Impact Assessment-Obesity and population-level nutrition): A tool and process to assess food company policies and commitments related to obesity prevention and population nutrition at the national level

Sacks G, Vanderlee L, Robinson E, Vandevijvere S, Cameron AJ, Ni Mhurchu C, et al.

Obes Rev, 2019 11;20 Suppl 2:78-89.
PMID: 31317645 DOI: 10.1111/obr.12878

Addressing obesity and improving the diets of populations requires a comprehensive societal response. The need for broad-based action has led to a focus on accountability of the key factors that influence food environments, including the food and beverage industry. This paper describes the Business Impact Assessment-Obesity and population-level nutrition (BIA-Obesity) tool and process for benchmarking food and beverage company policies and practices related to obesity and population-level nutrition at the national level. The methods for BIA-Obesity draw largely from relevant components of the Access to Nutrition Index (ATNI), with specific assessment criteria developed for food and nonalcoholic beverage manufacturers, supermarkets, and chain restaurants, based on international recommendations and evidence of best practices related to each sector. The process for implementing the BIA-Obesity tool involves independent civil society organisations selecting the most prominent food and beverage companies in each country, engaging with the companies to understand their policies and practices, and assessing each company's policies and practices across six domains. The domains include: "corporate strategy," "product formulation," "nutrition labelling," "product and brand promotion," "product accessibility," and "relationships with other organisations." Assessment of company policies is based on their level of transparency, comprehensiveness, and specificity, with reference to best practice.

Matched MeSH terms: Benchmarking/methods*
Fulltext Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects

Albahri OS, Zaidan AA, Albahri AS, Zaidan BB, Abdulkareem KH, Al-Qaysi ZT, et al.

J Infect Public Health, 2020 Oct;13(10):1381-1396.
PMID: 32646771 DOI: 10.1016/j.jiph.2020.06.028

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.

Matched MeSH terms: Benchmarking*
Fulltext A Review of CFD Modelling and Performance Metrics for Osmotic Membrane Processes

Toh KY, Liang YY, Lau WJ, Fimbres Weihs GA

Membranes (Basel), 2020 Oct 15;10(10).
PMID: 33076290 DOI: 10.3390/membranes10100285

Simulation via Computational Fluid Dynamics (CFD) offers a convenient way for visualising hydrodynamics and mass transport in spacer-filled membrane channels, facilitating further developments in spiral wound membrane (SWM) modules for desalination processes. This paper provides a review on the use of CFD modelling for the development of novel spacers used in the SWM modules for three types of osmotic membrane processes: reverse osmosis (RO), forward osmosis (FO) and pressure retarded osmosis (PRO). Currently, the modelling of mass transfer and fouling for complex spacer geometries is still limited. Compared with RO, CFD modelling for PRO is very rare owing to the relative infancy of this osmotically driven membrane process. Despite the rising popularity of multi-scale modelling of osmotic membrane processes, CFD can only be used for predicting process performance in the absence of fouling. This paper also reviews the most common metrics used for evaluating membrane module performance at the small and large scales.

Matched MeSH terms: Benchmarking
Fulltext An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Jameel SM, Hashmani MA, Rehman M, Budiman A

Sensors (Basel), 2020 Oct 14;20(20).
PMID: 33066579 DOI: 10.3390/s20205811

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.

Matched MeSH terms: Benchmarking
Fulltext A dataset of tree heights in mangrove and non-mangrove trees in Malaysia derived from multiple measurement methods

Saliu IS, Wolswijk G, Satyanarayana B, Fisol MAB, Decannière C, Lucas R, et al.

Data Brief, 2020 Dec;33:106386.
PMID: 33102654 DOI: 10.1016/j.dib.2020.106386

The dataset contains tree height data collected in 200 mangrove and non-mangrove trees sampled in various sites in Malaysia. Different height measurement methods were performed, including visual measurements (stick, thumb rule) and precision field instruments (clinometer, laser rangefinder and altimeter), which were compared against benchmark values obtained using an unmanned aerial vehicle (UAV) and a Leica distometer. The core data have been analysed and interpreted in the paper by Saliu et al. ''An accuracy analysis of mangrove tree height mensuration using forestry techniques, hypsometers and UAVs '' [1], in which the accuracy of each method for tree height measurement was discussed.

Matched MeSH terms: Benchmarking
Fulltext Chaos-embedded particle swarm optimization approach for protein-ligand docking and virtual screening

Tai HK, Jusoh SA, Siu SWI

J Cheminform, 2018 Dec 14;10(1):62.
PMID: 30552524 DOI: 10.1186/s13321-018-0320-9

BACKGROUND: Protein-ligand docking programs are routinely used in structure-based drug design to find the optimal binding pose of a ligand in the protein's active site. These programs are also used to identify potential drug candidates by ranking large sets of compounds. As more accurate and efficient docking programs are always desirable, constant efforts focus on developing better docking algorithms or improving the scoring function. Recently, chaotic maps have emerged as a promising approach to improve the search behavior of optimization algorithms in terms of search diversity and convergence speed. However, their effectiveness on docking applications has not been explored. Herein, we integrated five popular chaotic maps-logistic, Singer, sinusoidal, tent, and Zaslavskii maps-into PSOVina[Formula: see text], a recent variant of the popular AutoDock Vina program with enhanced global and local search capabilities, and evaluated their performances in ligand pose prediction and virtual screening using four docking benchmark datasets and two virtual screening datasets.
RESULTS: Pose prediction experiments indicate that chaos-embedded algorithms outperform AutoDock Vina and PSOVina in ligand pose RMSD, success rate, and run time. In virtual screening experiments, Singer map-embedded PSOVina[Formula: see text] achieved a very significant five- to sixfold speedup with comparable screening performances to AutoDock Vina in terms of area under the receiver operating characteristic curve and enrichment factor. Therefore, our results suggest that chaos-embedded PSOVina methods might be a better option than AutoDock Vina for docking and virtual screening tasks. The success of chaotic maps in protein-ligand docking reveals their potential for improving optimization algorithms in other search problems, such as protein structure prediction and folding. The Singer map-embedded PSOVina[Formula: see text] which is named PSOVina-2.0 and all testing datasets are publicly available on https://cbbio.cis.umac.mo/software/psovina .

Matched MeSH terms: Benchmarking
Fulltext Personal Injury Claims (PIC) database modelling in Malaysia

Syadatul Syaeda Mat Saleh, Najihan Awang @ Ali, Nik Ruslawati Nik Mustapa, Nurul Husna Jamian, Hussin bin Abdul Hamid

Jurnal Inovasi Malaysia, 2020;3(2):75-86.
MyJurnal

Road accident is not stranger matter in Malaysia. Subsequently, often leads to a claim for personal injury by the persecuted party. In Malaysia, the method for calculating claims applies a multiplier-multiplicand approach. This approach is no longer relevant and unfair to the claimant as it excludes personal status in the quantum calculation of damages. Hence, this study uses the Ogden Table as introduced in the United Kingdom as benchmarking guidelines, by taking into account of all aspect of claimant's personal condition for the purpose of such calculation. This study is built upon a proposed framework of data modelling system known as Entity Relationship Diagram (ERD) and the created process modelling known as data flow diagram (DFD). Doing so, the claimants will insert their input data, run it through the first process, and store the information in the claim injury part database. They can also edit and store to claim injury part database on their own. This will generate a report with the information in claim injury part database and can be viewed by claimant, court and lawyer as target users. It is hoped that it will facilitate the calculation of injury claim which would serve justice and accuracy of personal injury in road accidents

Matched MeSH terms: Benchmarking
Benchmarking the performance of MM/PBSA in virtual screening enrichment using the GPCR-Bench dataset

Yau MQ, Emtage AL, Loo JSE

J Comput Aided Mol Des, 2020 Nov;34(11):1133-1145.
PMID: 32851579 DOI: 10.1007/s10822-020-00339-5

Recent breakthroughs in G protein-coupled receptor (GPCR) crystallography and the subsequent increase in number of solved GPCR structures has allowed for the unprecedented opportunity to utilize their experimental structures for structure-based drug discovery applications. As virtual screening represents one of the primary computational methods used for the discovery of novel leads, the GPCR-Bench dataset was created to facilitate comparison among various virtual screening protocols. In this study, we have benchmarked the performance of Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) in improving virtual screening enrichment in comparison to docking with Glide, using the entire GPCR-Bench dataset of 24 GPCR targets and 254,646 actives and decoys. Reranking the top 10% of the docked dataset using MM/PBSA resulted in improvements for six targets at EF1% and nine targets at EF5%, with the gains in enrichment being more pronounced at the EF1% level. We additionally assessed the utility of rescoring the top ten poses from docking and the ability of short MD simulations to refine the binding poses prior to MM/PBSA calculations. There was no clear trend of the benefit observed in both cases, suggesting that utilizing a single energy minimized structure for MM/PBSA calculations may be the most computationally efficient approach in virtual screening. Overall, the performance of MM/PBSA rescoring in improving virtual screening enrichment obtained from docking of the GPCR-Bench dataset was found to be relatively modest and target-specific, highlighting the need for validation of MM/PBSA-based protocols prior to prospective use.

Matched MeSH terms: Benchmarking
Fulltext Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh

Yaseen ZM, Ali M, Sharafati A, Al-Ansari N, Shahid S

Sci Rep, 2021 Feb 09;11(1):3435.
PMID: 33564055 DOI: 10.1038/s41598-021-82977-9

A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949-2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07-0.85, 0.08-0.76, 0.062-0.80 and 0.042-0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.

Matched MeSH terms: Benchmarking
Fulltext BOOSTING STUDENTS’ PERFORMANCE WITH THEAID OF SOCIAL NETWORK ANALYSIS

GOBITHAASAN RUDRUSAMY, NURUL SYAHEERA DIN, LINGESWARAN RAMACHANDRAN, ROSLAN HASNI

UMT Journal of Undergraduate Research, 2019;1(3):28-35.
MyJurnal

There are various teaching methods developed in order to attainsuccessful delivery of a subject without prior knowledge of the interaction among the students in a class. Social network analysis (SNA) can be used to identify individual, intermediate and group measures of interaction in a classroom. The idea is on identifying ways to boost the students’ performance by means of lecturer’s intervention based on their interaction. The case study was conducted involvingthird year batchthat consistedof 76 female and 24 male students. A friendship network was drawn based on the information obtained at the end of semester 5 and it wasinvestigated based on two metrics–centralitymeasures and Girvan-Newman algorithm. At the end of semester 5, grades were added asthe attributes of the network.12 clusters were found in this batch and a distinct pattern was identified between performing and poor achieving students. At the beginning of the 6th semester, the studentsweregiven the option to choose between 2 groups. Group 1 was unperturbed without any lecturer’s intervention whereas the performing students’ clusters in Group 1 were preserved but the students in poor performing clusters were distributed among performing clusters. The students were then asked to carry out assignments/quizzesin their respective groups. The final grades indicatedthat the performance of the students of Group 1 wasmuch superior and there wasclear evidence that those poor performing students in the 5th semester performed much better in semester 6. This shows that by understanding the students’ interaction and incorporatiniginstructor’s minimal intervention, the performance of the students can be improved by creating a social contagion effect through group assignment clustering.

Matched MeSH terms: Benchmarking
Fulltext A review of cosmetic and personal care products: halal perspective and detection of ingredient

Hashim, P., Mat Hashim, D.

Pertanika Journal of Science & Technology, 2013;21(2):281-292.
MyJurnal

The term halal refers to what ispermitted by Islamic law. It is a basic need for Muslims and encompasses all materials used in everyday life including cosmetics.Muslims want to be assured that the ingredients,handling, processing, distribution, transportation and types of cosmetic used are halal compliant. The halal aspects of cosmetic and personal care products cover ingredients, all the processes involved in production right up to delivery to consumers, safety and product efficacy evaluations. In order to verify halal compliance of cosmetic products, a method of detecting halal and non-halal ingredients is very important and critically needed. Halal cosmetic standards, halal certification and the halal logo can be used as benchmarks for halal compliance. In view of the importance of cosmetic and personal care products from the halal perspective, this review will cover the halal principles, halal cosmetic and personal care products, ingredients, standard and certification as well as safety. The development of the process of detecting non-halal ingredients and authenticating halal ingredients for potential cosmetic applications in recent years are included in this paper.

Matched MeSH terms: Benchmarking

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