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  1. Spatial aspects of surface water quality in the Jakara Basin, Nigeria using chemometric analysis
    Mustapha A, Aris AZ
    J Environ Sci Health A Tox Hazard Subst Environ Eng, 2012;47(10):1455-65.
    PMID: 22571534 DOI: 10.1080/10934529.2012.673305
    Multivariate statistical techniques such as hierarchical Agglomerated cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) were applied to identify the spatial variation and pollution sources of Jakara River, Kano, Nigeria. Thirty surface water samples were collected: 23 along Getsi River and 7 along the main channel of River Jakara. Twenty-three water quality parameters, namely pH, temperature, turbidity, electrical conductivity (EC), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), Faecal coliform, total solids (TS), nitrates (NO(3)(-)), phosphates (PO(4)(3-)), cobalt (Co), iron (Fe), nickel (Ni), manganese (Mn), copper (Cu), sodium (Na), potassium (K), mercury (Hg), chromium (Cr), cadmium (Cd), lead (Pb), magnesium (Mg), and calcium(Ca) were analysed. HACA grouped the sampling points into three clusters based on the similarities of river water quality characteristics: industrial, domestic, and agricultural water pollution sources. Forward and backward DA effectively discriminated 5 and 15 water quality variables, respectively, each assigned with 100% correctness from the original 23 variables. PCA and FA were used to investigate the origin of each water quality parameter due to various land use activities, 7 principal components were obtained with 77.5% total variance, and in addition PCA identified 3 latent pollution sources to support HACA. From this study, one can conclude that the application of multivariate techniques derives meaningful information from water quality data.
  2. Fulltext Synthesis of new azo compounds based on N-(4-hydroxypheneyl)maleimide and N-(4-methylpheneyl)maleimide
    Mohammed IA, Mustapha A
    Molecules, 2010;15(10):7498-509.
    PMID: 20975631 DOI: 10.3390/molecules15107498
    Maleic anhydride was reacted with p-aminophenol and p-toluidine in the presence of di-phosphorus pentoxide (P₂O₅) as a catalyst to produce two compounds: N-(4-hydroxy-phenyl)maleimide (I) and N-(4-methylphenyl)maleimide (II). The new azo compounds I(a-c) and II(a-c) were prepared by the reaction of I and II with three different aromatic amines, namely aniline, p-aminophenol and p-toluidine. The structures of these compounds were confirmed by CHN, FT-IR, ¹H-NMR, ¹³C-NMR, mass spectrum and UV/Vis spectroscopy.
  3. Determination of physical processes influencing Chl a distribution using remotely sensed images
    Mustapha AM, Lihan T, Saitoh S
    Pak J Biol Sci, 2011 Jan 15;14(2):82-90.
    PMID: 21916257
    In management of the Japanese scallop Mizuhopecten yessoensis culture, it is important to understand the phytoplankton bloom development in the coastal region of the Okhotsk Sea. Variations in food available to this benthic bivalve are a primary environmental factor affecting growth in nature. This paper determined the seasonal variability of Chlorophyll a (Chl a) at the scallop farming region in the Okhotsk Sea from 1998 to 2004 using satellite imageries. Satellite images were processed using default NASA coefficients and community-standard algorithms as implemented by Sea DAS. Spatial and temporal variation of Chl a was determined by EOF analysis. The Chl a concentration showed high seasonal and interannual variability. Peak of Chl a concentration occurred in spring followed by autumn and summer. This was evident in the Empirical Orthogonal Function (EOF) analysis. The spatial pattern of the first mode of EOF analysis of Chl a revealed intensified Chl a at the shelf and offshore areas in spring and autumn (51.8% of variance). The second mode explained 14.2% of the variance indicating enhancement of spring (April-May) Chl a pattern in the frontal area along the coast. Meanwhile, the third mode captured 9.0% of the variability demonstrating high Chl a extending seaward from the shelf area during late autumn. These seasonal variability of Chl a resulted from the variability in occurrences of physical processes associated with retreat of sea ice in spring, advection of Soya Warm Current in summer and intrusion of East Sakhalin Current in autumn.
  4. Fulltext Effect of temporal relationships in associative rule mining for web log data
    Khairudin NM, Mustapha A, Ahmad MH
    ScientificWorldJournal, 2014;2014:813983.
    PMID: 24587757 DOI: 10.1155/2014/813983
    The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.
  5. Fulltext Naive Bayes-guided bat algorithm for feature selection
    Taha AM, Mustapha A, Chen SD
    ScientificWorldJournal, 2013;2013:325973.
    PMID: 24396295 DOI: 10.1155/2013/325973
    When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.
  6. Fulltext Robust identification of polyethylene terephthalate (PET) plastics through Bayesian decision
    Zulkifley MA, Mustafa MM, Hussain A, Mustapha A, Ramli S
    PLoS One, 2014;9(12):e114518.
    PMID: 25485630 DOI: 10.1371/journal.pone.0114518
    Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.
  7. Fulltext A review of norms and normative multiagent systems
    Mahmoud MA, Ahmad MS, Yusoff MZ, Mustapha A
    ScientificWorldJournal, 2014;2014:684587.
    PMID: 25110739 DOI: 10.1155/2014/684587
    Norms and normative multiagent systems have become the subjects of interest for many researchers. Such interest is caused by the need for agents to exploit the norms in enhancing their performance in a community. The term norm is used to characterize the behaviours of community members. The concept of normative multiagent systems is used to facilitate collaboration and coordination among social groups of agents. Many researches have been conducted on norms that investigate the fundamental concepts, definitions, classification, and types of norms and normative multiagent systems including normative architectures and normative processes. However, very few researches have been found to comprehensively study and analyze the literature in advancing the current state of norms and normative multiagent systems. Consequently, this paper attempts to present the current state of research on norms and normative multiagent systems and propose a norm's life cycle model based on the review of the literature. Subsequently, this paper highlights the significant areas for future work.
  8. Spatial-temporal variation of surface water quality in the downstream region of the Jakara River, north-western Nigeria: A statistical approach
    Mustapha A, Aris AZ, Ramli MF, Juahir H
    J Environ Sci Health A Tox Hazard Subst Environ Eng, 2012;47(11):1551-60.
    PMID: 22702815 DOI: 10.1080/10934529.2012.680415
    The pollution status of the downstream section of the Jakara River was investigated. Dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH(3)), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO(3)), chloride (Cl) and phosphates (PO(3-)(4)) were evaluated, using both dry and wet season samples, as a measure of variation in surface water quality in the area. The results obtained from the analyses were correlated using Pearson's correlation matrix, principal component analysis (PCA) and paired sample t-tests. Positive correlations were observed for BOD(5), NH(3), COD, and SS, turbidity, conductivity, salinity, DS, TS for dry and wet seasons, respectively. PCA was used to investigate the origin of each water quality parameter, and yielded 5 varimax factors for each of dry and wet seasons, with 70.7 % and 83.1 % total variance, respectively. A paired sample t-test confirmed that the surface water quality varies significantly between dry and wet season samples (P < 0.01). The source of pollution in the area was concluded to be of anthropogenic origin in the dry season and natural origins in the wet season.
  9. Fulltext Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications
    Emambocus BAS, Jasser MB, Mustapha A, Amphawan A
    Sensors (Basel), 2021 Nov 13;21(22).
    PMID: 34833621 DOI: 10.3390/s21227542
    Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.
  10. Fulltext Temporal aspects of surface water quality variation using robust statistical tools
    Mustapha A, Aris AZ, Ramli MF, Juahir H
    ScientificWorldJournal, 2012;2012:294540.
    PMID: 22919302 DOI: 10.1100/2012/294540
    Robust statistical tools were applied on the water quality datasets with the aim of determining the most significance parameters and their contribution towards temporal water quality variation. Surface water samples were collected from four different sampling points during dry and wet seasons and analyzed for their physicochemical constituents. Discriminant analysis (DA) provided better results with great discriminatory ability by using five parameters with (P < 0.05) for dry season affording more than 96% correct assignation and used five and six parameters for forward and backward stepwise in wet season data with P-value (P < 0.05) affording 68.20% and 82%, respectively. Partial correlation results revealed that there are strong (r(p) = 0.829) and moderate (r(p) = 0.614) relationships between five-day biochemical oxygen demand (BOD(5)) and chemical oxygen demand (COD), total solids (TS) and dissolved solids (DS) controlling for the linear effect of nitrogen in the form of ammonia (NH(3)) and conductivity for dry and wet seasons, respectively. Multiple linear regression identified the contribution of each variable with significant values r = 0.988, R(2) = 0.976 and r = 0.970, R(2) = 0.942 (P < 0.05) for dry and wet seasons, respectively. Repeated measure t-test confirmed that the surface water quality varies significantly between the seasons with significant value P < 0.05.
  11. Fulltext Design and development of a content-based medical image retrieval system for spine vertebrae irregularity
    Mustapha A, Hussain A, Samad SA, Zulkifley MA, Diyana Wan Zaki WM, Hamid HA
    Biomed Eng Online, 2015;14:6.
    PMID: 25595511 DOI: 10.1186/1475-925X-14-6
    Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities.
  12. Production and modification of nanofibrillated cellulose using various mechanical processes: a review
    Abdul Khalil HP, Davoudpour Y, Islam MN, Mustapha A, Sudesh K, Dungani R, et al.
    Carbohydr Polym, 2014 Jan;99:649-65.
    PMID: 24274556 DOI: 10.1016/j.carbpol.2013.08.069
    Nanofibrillated cellulose from biomass has recently gained attention owing to their biodegradable nature, low density, high mechanical properties, economic value and renewability. Although they still suffer from two major drawbacks. The first challenge is the exploration of raw materials and its application in nanocomposites production. Second one is high energy consumption regarding the mechanical fibrillation. However, pretreatments before mechanical isolation can overcome this problem. Hydrophilic nature of nano-size cellulose fibers restricts good dispersion of these materials in hydrophobic polymers and therefore, leads to lower mechanical properties. Surface modification before or after mechanical defibrillation could be a solution for this problem. Additionally, drying affects the size of nanofibers and its properties which needs to study further. This review focuses on recent developments in pretreatments, nanofibrillated cellulose production and its application in nanopaper applications, coating additives, security papers, food packaging, and surface modifications and also for first time its drying.
  13. Fulltext Evaluation of factors influencing the groundwater chemistry in a small tropical island of Malaysia
    Kura NU, Ramli MF, Sulaiman WN, Ibrahim S, Aris AZ, Mustapha A
    Int J Environ Res Public Health, 2013 May;10(5):1861-81.
    PMID: 23648442 DOI: 10.3390/ijerph10051861
    Groundwater chemistry of small tropical islands is influenced by many factors, such as recharge, weathering and seawater intrusion, among others, which interact with each other in a very complex way. In this work, multivariate statistical analysis was used to evaluate the factors controlling the groundwater chemistry of Kapas Island (Malaysia). Principal component analysis (PCA) was applied to 17 hydrochemical parameters from 108 groundwater samples obtained from 18 sampling sites. PCA extracted four PCs, namely seawater intrusion, redox reaction, anthropogenic pollution and weather factors, which collectively were responsible for more than 87% of the total variance of the island's hydrochemistry. The cluster analysis indicated that three factors (weather, redox reaction and seawater intrusion) controlled the hydrochemistry of the area, and the variables were allocated to three groups based on similarity. A Piper diagram classified the island's water types into Ca-HCO3 water type, Na-HCO3 water type, Na-SO4-Cl water type and Na-Cl water type, indicating recharge, mixed, weathering and leached from sewage and seawater intrusion, respectively. This work will provide policy makers and land managers with knowledge of the precise water quality problems affecting the island and can also serve as a guide for hydrochemistry assessments of other islands that share similar characteristics with the island in question.
  14. A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application
    Mostafa SA, Mustapha A, Mohammed MA, Ahmad MS, Mahmoud MA
    Int J Med Inform, 2018 04;112:173-184.
    PMID: 29500017 DOI: 10.1016/j.ijmedinf.2018.02.001
    Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls.
  15. River water quality assessment using environmentric techniques: case study of Jakara River Basin
    Mustapha A, Aris AZ, Juahir H, Ramli MF, Kura NU
    Environ Sci Pollut Res Int, 2013 Aug;20(8):5630-44.
    PMID: 23443942 DOI: 10.1007/s11356-013-1542-z
    Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.
  16. Fulltext Removal of 4-chloro-2-methylphenoxyacetic acid from water by MIL-101(Cr) metal-organic framework: kinetics, isotherms and statistical models
    Isiyaka HA, Jumbri K, Sambudi NS, Zango ZU, Saad B, Mustapha A
    R Soc Open Sci, 2021 Jan;8(1):201553.
    PMID: 33614087 DOI: 10.1098/rsos.201553
    Effective removal of 4-chloro-2-methylphenoxyacetic acid (MCPA), an emerging agrochemical contaminant in water with carcinogenic and mutagenic health effects has been reported using hydrothermally synthesized MIL-101(Cr) metal-organic framework (MOF). The properties of the MOF were ascertained using powdered X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, thermal gravimetric analysis (TGA), field emission scanning electron microscopy (FESEM) and surface area and porosimetry (SAP). The BET surface area and pore volume of the MOF were 1439 m2 g-1 and 0.77 cm3 g-1, respectively. Artificial neural network (ANN) model was significantly employed for the accurate prediction of the experimental adsorption capacity (qe ) values with minimal error. A rapid removal of the pollutant (99%) was recorded within short time (approx. 25 min), and the reusability of the MOF (20 mg) was achieved up to six cycles with over 90% removal efficiency. The kinetics, isotherm and thermodynamics of the process were described by the pseudo-second-order, Freundlich and endothermic adsorption, respectively. The adsorption process is spontaneous based on the negative Gibbs free energy values. The significant correlation between the experimental findings and simulation results suggests the great potential of MIL-101(Cr) for the remediation of MCPA from water matrices.
  17. Fulltext Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review
    Mustapha A, Ishak I, Zaki NNM, Ismail-Fitry MR, Arshad S, Sazili AQ
    Heliyon, 2024 Jun 30;10(12):e32189.
    PMID: 38975107 DOI: 10.1016/j.heliyon.2024.e32189
    Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.
  18. Fulltext The every woman study™ low- and middle-income countries edition protocol: A multi-country observational study to assess opportunities and challenges to improving survival and quality of life for women with ovarian cancer
    Reid F, Adams T, Adel RS, Andrade CE, Bajwa A, Bambury IG, et al.
    PLoS One, 2024;19(5):e0298154.
    PMID: 38809901 DOI: 10.1371/journal.pone.0298154
    BACKGROUND: Ovarian cancer is a challenging disease to diagnose and treat effectively with five-year survival rates below 50%. Previous patient experience research in high-income countries highlighted common challenges and opportunities to improve survival and quality of life for women affected by ovarian cancer. However, no comparable data exist for low-and middle-income countries, where 70% of women with the disease live. This study aims to address this evidence gap.

    METHODS: This is an observational multi-country study set in low- and middle-income countries. We aim to recruit over 2000 women diagnosed with ovarian cancer across multiple hospitals in 24 countries in Asia, Africa and South America. Country sample sizes have been calculated (n = 70-96 participants /country), taking account of varying national five-year disease prevalence rates. Women within five years of their diagnosis, who are in contact with participating hospitals, are invited to take part in the study. A questionnaire has been adapted from a tool previously used in high-income countries. It comprises 57 multiple choice and two open-ended questions designed to collect information on demographics, women's knowledge of ovarian cancer, route to diagnosis, access to treatments, surgery and genetic testing, support needs, the impact of the disease on women and their families, and their priorities for action. The questionnaire has been designed in English, translated into local languages and tested according to local ethics requirements. Questionnaires will be administered by a trained member of the clinical team.

    CONCLUSION: This study will inform further research, advocacy, and action in low- and middle-income countries based on tailored approaches to the national, regional and global challenges and opportunities. In addition, participating countries can choose to repeat the study to track progress and the protocol can be adapted for other countries and other diseases.

  19. Fulltext Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants
    Wang A, Shen J, Rodriguez AA, Saunders EJ, Chen F, Janivara R, et al.
    Nat Genet, 2023 Dec;55(12):2065-2074.
    PMID: 37945903 DOI: 10.1038/s41588-023-01534-4
    The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.
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