Institute of Engineering Mathematics
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Arau

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The Influence of Grasping Technique and Arm Posture on Shooting Performance in Traditional Archery

2021-01-01 , Fitriyani N. , Ahmad Faizal Salleh , Mohammad Shahril Salim , Kasim M.F. , Nurhidayah Omar , Maz Jamilah Masnan , Ghazali Z.

Traditional archery is becoming popular and has attracted many people at different age level to acquire skills and finally participate in competitive games. It differs from modern recurve archery where it could not rely on equipment for accuracy and stability. It depends on the skilled developed by the archer and adaptability to the environment for competition. Technique of archery shooting was written in many books and may require investigation to confirm their effect on performance of an archer. Therefore, this study aims to investigate the effect of arm posture and bow’s grasping techniques on the traditional archer’s shooting performance (higher score and minimal distance from bulls eye to the location where the arrow hits the target). Four experienced archers performed 5 m indoor shooting and their movement was recorded using motion capture system. The Square grasping technique resulted in more higher scores and minimal distance as compared to the other technique. Overall, the arm posture angle, Φ, did not influenced the shooting performance, except for the Oblique grasping technique.

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The Influence of Saracen Archery Grasping Techniques and Forearm Muscles Activation on Shooting Performance in Traditional Archery: A Pilot Study

2021-11-25 , Kamarudin N.F. , Ahmad Faizal Salleh , Mohammad Shahril Salim , Kasim M.F. , Nurhidayah Omar , Maz Jamilah Masnan , Lim C.C.

The purpose of this study is to understand the influence of four grasping techniques recommended by Saracen Archery and associated forearm muscles activation on traditional archer’s shooting performance. Each archer has shot 6 arrows in each grasping technique to the target, EMG activities of muscle Flexor Digitorum Superficialis (MFDS) and muscle Extensor Digitorum (MED) were collected in bow arm during aiming phase. The shooting performance was indicated by the distance from arrow hitting point on the target to the bull’s eye. The results revealed that each subject has specific grasping technique to obtain the best shooting performance. The grasping technique that generated the best performance is not as recommended by Saracen Archery. All subjects indicated that the best shooting performance was obtained when MED activated more than MFDS.

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Improved maturity and ripeness classifications of magnifera indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor

2012 , Ammar Zakaria , Ali Yeon Md Shakaff , Masnan, Maz Jamilah , Fathinul Syahir Ahmad Sa'ad , Abdul Hamid Adom , Mohd Noor Ahmad , Mahmad Nor Jaafar , Abu Hassan Abdullah , Latifah Munirah Kamarudin

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.

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A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration

2011-08 , Ammar Zakaria , Ali Yeon Md Shakaff , Masnan, Maz Jamilah , Norazian Subari , Nazifah Ahmad Fikri , Abdul Hamid Adom , Mohd Noor Ahmad , Mahmad Nor Jaafar , Latifah Munirah Kamarudin , Abdul Hallis Abdul Aziz , Abu Hassan Abdullah , Supri A. Ghani

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

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Evaluation of predictors for the development and progression of Diabetic Retinopathy among diabetes Mellitus Type 2 patients

2022-12 , Syafawati Ab Saad , Maz Jamilah Masnan , Karniza Khalid , Safwati Ibrahim

Diabetic retinopathy is one of the microvascular complications caused by prolonged uncontrolled diabetes. It is believed that diabetic retinopathy correlates with certain predictors and risk factors that might worsen the disease, eventually causing visual loss and blindness among diabetes patients. There are some predictors and risk factors that attribute to the development and progression of diabetic retinopathy, such as the duration of diabetes and HbA1c trends. This study aims to evaluate the predictors and risk factors associated with the development and/or progression of diabetic retinopathy. Retrospective data were collected from a single healthcare facility in the northwest of Peninsular Malaysia. Patients included in this study were those with type 2diabetes mellitus diagnosed with diabetic retinopathy. The total number of patients involved in this study were 197, where 161 of them were newly diagnosed or with progressive diabetic retinopathy. The characteristics of diabetes patients with complication of diabetic retinopathy were described through descriptive statistics. Characteristics include demographics data such as age, gender, race and clinical data such as HbA1c readings HbA1c, estimated glomerular filtration rate (eGFR), urea and haemoglobin concentration (Hb). The results show that 7 predictors and risk factors are significant to the development and progression of diabetic retinopathy among diabetes patients. By using multinomial logistic regression, this study offers better understanding of the significant predictors and risk factors related to diabetic retinopathy.

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Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors

2010 , Ammar Zakaria , Ali Yeon Md Shakaff , Abdul Hamid Adom , Mohd Noor Ahmad , Masnan, Maz Jamilah , Abdul Hallis Abdul Aziz , Nazifah Ahmad Fikri , Abu Hassan Abdullah , Latifah Munirah Kamarudin

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.

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A study of extreme learning machine on small sample-sized classification problems

2021-12-01 , Ooi B.P. , Norasmadi Abdul Rahim , Maz Jamilah Masnan , Ammar Zakaria

Extreme learning machine (ELM) is a special type of single hidden layer feedforward neural network that emphasizes training speed and optimal generalization. The ELM model proposes that the weights of hidden neurons need not be tuned, and the weights of output neurons can be calculated by finding the Moore-Penrose generalized inverse method. Thus, the ELM classifier is suitable to use in a homogeneous ensemble model due to the untuned random hidden weights which promote diversity even with the same training data. This paper studies the effectiveness of the ELM ensemble models in solving small sample-sized classification problems. The research involves two variants of the ensemble model: the normal ELM ensemble with majority voting (ELE), and the random subspace method (RS-ELM). To simulate the small sample cases, only 30% of the total data will be used as the training data. Experiment results show that the RS-ELM model can outperform a multi-layer perceptron (MLP) model under the assumptions of a Friedman test. Furthermore, the ELE model has similar performance as an MLP model under the same assumptions.

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A Bio-Inspired herbal tea flavour assessment technique

2014 , Nur Zatul 'Iffah Zakaria , Masnan, Maz Jamilah , Ammar Zakaria , Ali Yeon Md Shakaff

Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the intended quality determined by the quality controller. One famous herbal-based product is herbal tea. This paper investigates bio-inspired flavour assessments in a data fusion framework involving an e-nose and e-tongue. The objectives are to attain good classification of different types and brands of herbal tea, classification of different flavour masking effects and finally classification of different concentrations of herbal tea. Two data fusion levels were employed in this research, low level data fusion and intermediate level data fusion. Four classification approaches; LDA, SVM, KNN and PNN were examined in search of the best classifier to achieve the research objectives. In order to evaluate the classifiers' performance, an error estimator based on k-fold cross validation and leave-one-out were applied. Classification based on GC-MS TIC data was also included as a comparison to the classification performance using fusion approaches. Generally, KNN outperformed the other classification techniques for the three flavour assessments in the low level data fusion and intermediate level data fusion. However, the classification results based on GC-MS TIC data are varied.

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Predictor selection for progression and development of diabetic nephropathy among diabetes mellitus type 2 patients

2023-04-24 , Syafawati Ab Saad , Maz Jamilah Masnan , Karniza K. , Safwati Ibrahim

The prevalence of diabetic nephropathy is thriving worldwide such as in the United States, Europe and Asia. Diabetic nephropathy or commonly known as "diabetic kidney disease"(DKD) is characterized by the present of albuminuria, hypertension and progressive renal failure. A variety of predictors are associated with the development, progression and severity of diabetic nephropathy. This study divides diabetes patients with diabetic nephropathy into three groups; Group 1 (diabetes patients who were diagnosed with diabetic nephropathy with the same stage for a certain period of time until the current follow-up), Group 2 (diabetes patients who were diagnosed with diabetic nephropathy without obvious clinical findings but have shown disease development) and Group 3 (diabetes patients who were diagnosed with diabetic nephropathy at a certain stage for a period time but progressively worsen over time during the current follow-up). The purpose of this study is to evaluate the predictors that are associated to patients in Groups 2 and 3. Retrospective data were collected from a healthcare center located in northern peninsular Malaysia. A total of 194 patients were included in this study. Characteristics of data include demographics information such as age, gender, race and clinical data such as glycosylated haemoglobin (HbA1c), estimated glomerular filtration rate (eGFR), urea and haemoglobin concentration (Hb). Findings show that few predictors and risk factors are significant to the development and progression of diabetic nephropathy. This study is important to reveal the significant predictors and risk factors as healthcare professionals can identify patients with risk for diabetic nephropathy and may reduce the morbidity and mortality among patients.

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Future leaders in reshaping an organization

2012 , Noormaizatul Akmar Ishak , Salmah Ayub , Evawaynie Valquis Md. Isa , Masnan, Maz Jamilah , Mohd Faizal Mohd Isa , Mohd Fisol Osman

It is the interest of the researchers to find how the benchmarking of the future leaders in done in the Government-linked Companies GLCs. A mixed method approach has been selected as the research strategy to study the issues under investigations and the relationship between them. Six interviews with prominent experts are carried out to gather the items for the questionnaire. Then sets of constructed questionnaire are distributed to the managerial staff of Universiti Malaysia Perlis as a pilot study. To gather the data from GLCs, three Government-linked Investment Companies (GLICs) and fifteen GLCs have been identified as the samples. Then an interview with a prominent statesman is carried out, and focus group discussions with the top management representative are conducted to support the survey. The findings of the study indicate that the choice for future leadership dimensions are traits, behaviours, performance functions, competence, skills and background. These six leadership dimension qualities are desired in GLCs as the future leaders are expected to use their best endeavours to prompt or reshape the organizations.

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