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Dive into the research topics where Abdul Hallis Abdul Aziz is active.

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Featured researches published by Abdul Hallis Abdul Aziz.


Sensors | 2011

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

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

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.


international conference on intelligent systems, modelling and simulation | 2012

Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques

Zulkifli Husin; Ali Yeon Md Shakaff; Abdul Hallis Abdul Aziz; Rohani S. Mohamed Farook

Producing chili is a daunting task as the plant is exposed to the attacks from various micro-organisms and bacterial diseases and pests. The symptoms of the attacks are usually distinguished through the leaves, stems or fruit inspection. This paper discusses the effective way used in performing early detection of chili disease through leaf features inspection. Leaf image is captured and processed to determine the health status of each plant. Currently the chemicals are applied to the plants periodically without considering the requirement of each plant. This technique will ensure that the chemicals only applied when the plants are detected to be effected with the diseases. The image processing techniques are used to perform hundreds of chili disease images. The plant chili disease detection through leaf image and data processing techniques is very useful and inexpensive system especially for assisting farmers in monitoring the big plantation area.


Sensors | 2010

Improved Classification of Orthosiphon stamineus by Data Fusion of Electronic Nose and Tongue Sensors

Ammar Zakaria; Ali Yeon Md Shakaff; Abdul Hamid Adom; Mohd Noor Ahmad; Maz Jamilah Masnan; Abdul Hallis Abdul Aziz; Nazifah Ahmad Fikri; A. H. 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.


INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2014 (ICoMEIA 2014) | 2015

Understanding Mahalanobis distance criterion for feature selection

Maz Jamilah Masnan; Nor Idayu Mahat; Ali Yeon Md Shakaff; A. H. Abdullah; Nur Zawatil Ishqi Zakaria; Nurlisa Yusuf; Norazian Subari; Ammar Zakaria; Abdul Hallis Abdul Aziz

Distance criteria are widely applied in cluster analysis and classification techniques. One of the well known and most commonly used distance criteria is the Mahalanobis distance, introduced by P. C. Mahalanobis in 1936. The functions of this distance have been extended to different problems such as detection of multivariate outliers, multivariate statistical testing, and class prediction problems. In the class prediction problems, researcher is usually burdened with problems of excessive features where useful and useless features are all drawn for classification task. Therefore, this paper tries to highlight the procedure of exploiting this criterion in selecting the best features for further classification process. Classification performance for the feature subsets of the ordered features based on the Mahalanobis distance criterion is included.


international conference on intelligent systems, modelling and simulation | 2012

Data Mining on Climatic Factors for Harumanis Mango Yield Prediction

Rohani S. Mohamed Farook; Abdul Hallis Abdul Aziz; A. Harun; Zulkifli Husin; Ali Yeon Md Shakaff; Mahmad Nor Jaafar; Ndzi. D.L.; Ammar Zakaria; Latifah Munirah Kamarudin

Yield Prediction is an essential task to be achieved in order to implement effective forward marketing. Forward marketing is a contract that will be signed between supplier and client based on the amount of delivery and the price of delivery in future. To be able to sign such a contract the supplier should be very confident that the yield could be achieved. The yield sustainability is a challenging process in agriculture. Mango cultivar Harumanis is one of the best table tropical fruit due to its aroma and sweetness. Despite its overwhelming local demand in Malaysia and also internationally, the fruit supply never meets the demand. The flowering phase is identified as an important stage as plant reproductive physiology. Currently, Harumanis mango flowering only happens once a year that restricts the yield. In this paper, data mining is used to quantify the climatic effects on Harumanis mango yield to enable yield prediction.


international conference on electronic design | 2008

Harum Manis Mango weevil infestation classification using backpropagation neural network

Y.M. Yacob; A.R.A.M. Shaiful; Z. Husin; R.S.M. Farook; Abdul Hallis Abdul Aziz

Postharvest non-destructive detection methods in fruit quality have been widely studied ever since. This include studies of maturity, bruises and detection of pests or weevil existence in fruits such as apple, banana, zucchini including mango. Regarding fruit grading, the non-destructive methods which can be used are image processing and dielectric properties. Either technique has its own benefits and drawbacks. As for image processing technique, the cost is high since suitable device to acquire the images are by using MRI or X-ray. Whereas for dielectric method, permittivity is difficult to record because the reading is very small and are prone to environment and temperature influence. This paper analyze about classification of Harum Manis mango infestation using dielectric sensor which was trained and tested using back-propagation neural network. In addition, reviews regarding neural network design is also discussed.


international conference on electronic design | 2014

Design of a capacitive sensor for oil palm fresh fruit bunch maturity grading

Abdul Hallis Abdul Aziz; A. H. Ismail; Rohanin Ahmad; C. M. N. C. Isa; Rohani S. Mohamed Farook; Zulkifli Husin; A. A. M. Ezanuddin; A. Y. Md. Shakaff

Automatic grading of oil palm fresh fruit bunch is desired. In this study, a capacitive sensing system was designed and developed for the purpose of grading oil palm fresh fruit bunch. In this method, oil palm fresh fruit bunch placed between capacitive plates as a dielectric material and then measure the resulting capacitance voltage. Experiments were carried out using oil palm bunches of Tenera variety. Ripe and unripe mature bunches tested with 100 kHz sinusoidal frequencies. Correlation of capacitive response observed to be linear to bunch weight and negative linear to bunch ripeness. This result suggest that this method be explored for automatic grading of oil palm fresh fruit bunch.


Proceedings IMCS 2012 | 2012

P2.9.27 Development of a Decision Support System for Brackish Aquafarm Management Using Electronic Tongue

Abdul Hallis Abdul Aziz; Ali Yeon Md. Shakaff; Rohani S. Mohamed Farook; Mohd Noor Ahmad; Mahmad Nor Jaafar; Maz Jamilah Masnan; Abdul Hamid Adom

This paper presents the findings in developing a decision support in assessing brackish water quality using an electronic taste sensing system made up of sensor array and signal processing tools. Seven ionic sensors and precision centigrade temperature sensor fused together as an array. Developed graphical user interface displays real time measurement and pattern based prediction. Results of periodic test on prepared brackish water solutions showed that the system differentiates samples of varying salinity (0.01 to 0.5 Mol of NaCl per liter), supporting the system usage as salinity detection instrument in aquafarms. Incorporation of temperature sensor increases system stability to temperature drift. System robustness showed that three ionic sensors sufficiently differentiate sixdecade dilution of brackish solution.


Computers and Electronics in Agriculture | 2012

Embedded portable device for herb leaves recognition using image processing techniques and neural network algorithm

Zulkifli Husin; Ali Yeon Md Shakaff; Abdul Hallis Abdul Aziz; Rohani S. Mohamed Farook; Mahmad Nor Jaafar; U. Hashim; A. Harun


international conference on computational intelligence, modelling and simulation | 2011

Agent-based Decision Support System for Harumanis Mango Flower Initiation

Rohani S. Mohamed Farook; Zulkifli Husin; Abdul Hallis Abdul Aziz; Ali Yeon Md Shakaff; Ammar Zakaria; Latifah Munirah Kamarudin; Mahmad Nor Jaafar

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Ammar Zakaria

Universiti Malaysia Perlis

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Mahmad Nor Jaafar

Universiti Malaysia Perlis

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Zulkifli Husin

Universiti Malaysia Perlis

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Abdul Hamid Adom

Universiti Malaysia Perlis

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A. H. Abdullah

Universiti Malaysia Perlis

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Mohd Noor Ahmad

Universiti Sains Malaysia

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