Other affiliations: Florida State University College of Arts and Sciences
Bio: Nursuriati Jamil is an academic researcher from Universiti Teknologi MARA. The author has contributed to research in topics: Image segmentation & Thresholding. The author has an hindex of 12, co-authored 97 publications receiving 706 citations. Previous affiliations of Nursuriati Jamil include Florida State University College of Arts and Sciences.
Papers published on a yearly basis
••26 Mar 2018
TL;DR: This paper applies Transfer Learning to the well-known AlexNet Convolution Neural Network (AlexNet CNN) for human recognition based on ear images and fine-tuned AlexNet CNN to suit the problem domain.
Abstract: Transfer Learning is an efficient approach of solving classification problem with little amount of data In this paper, we applied Transfer Learning to the well-known AlexNet Convolution Neural Network (AlexNet CNN) for human recognition based on ear images We adopted and fine-tuned AlexNet CNN to suit our problem domain The last fully connected layer is replaced with another fully connected layer to recognize 10 classes instead of 1000 classes Another Rectified Linear Unit (ReLU) layer is also added to improve the non-linear problem-solving ability of the network To train the fine-tuned network, we allocate 250 ear images taken from 10 subjects for training, and 50 ear images are used for validation and testing The proposed fine-tuned network works well in our application as we get 100% validation accuracy
TL;DR: Postural instability and gait imbalance in DPN may contribute to high risk of fall incidence, especially in the geriatric population, and further works are crucial to highlight this fact in the hospital based and community adults.
Abstract: Purpose. The aim of this paper is to review the published studies on the characteristics of impairments in the postural control and gait performance in diabetic peripheral neuropathy (DPN). Methods. A review was performed by obtaining publication of all papers reporting on the postural control and gait performance in DPN from Google Scholar, Ovid, SAGE, Springerlink, Science Direct (SD), EBSCO Discovery Service, and Web of Science databases. The keywords used for searching were “postural control,” “balance,” “gait performance,” “diabetes mellitus,” and “diabetic peripheral neuropathy.” Results. Total of 4,337 studies were hit in the search. 1,524 studies were screened on their titles and citations. Then, 79 studies were screened on their abstract. Only 38 studies were eligible to be selected: 17 studies on postural control and 21 studies on the gait performance. Most previous researches were found to have strong evidence of postural control impairments and noticeable gait deficits in DPN. Deterioration of somatosensory, visual, and vestibular systems with the pathologic condition of diabetes on cognitive impairment causes further instability of postural and gait performance in DPN. Conclusions. Postural instability and gait imbalance in DPN may contribute to high risk of fall incidence, especially in the geriatric population. Thus, further works are crucial to highlight this fact in the hospital based and community adults.
••26 Sep 2008
TL;DR: Six basic morphological operations are investigated to remove noise and enhance the appearance of binary images and showed that noise can be effectively removed from binary images using combinations of erode-dilate operations.
Abstract: Mathematical morphological operations are commonly used as a tool in image processing for extracting image components that are useful in the representation and description of region shape. In this paper, six basic morphological operations are investigated to remove noise and enhance the appearance of binary images. Dilation, erosion, opening, closing, fill and majority operations are tested on twenty-five images and subjectively evaluated based on perceived quality of the enhanced images. Results of the experiments showed that noise can be effectively removed from binary images using combinations of erode-dilate operations. Also, the binary images are significantly enhanced using combinations of majority-close operations.
04 Dec 2009
TL;DR: Outside surface colors of palm oil fresh fruit bunches are analyzed to automatically grade the fruits into over ripe, ripe and unripe, and two methods of color grading are compared.
Abstract: Automated fruit grading in local fruit industries are gradually receiving attention as the use of technology in upgrading the quality of food products are now acknowledged. In this paper, outer surface colors of palm oil fresh fruit bunches (FFB) are analyzed to automatically grade the fruits into over ripe, ripe and unripe. We compared two methods of color grading: 1) using RGB digital numbers and 2) colors classifications trained using a supervised learning Hebb technique and graded using fuzzy logic. A total of 90 images are used as the training images and 45 images are tested in the grading process. Overall, automated grading using RGB digital numbers produced an average of 49% success rate, while the neuro-fuzzy approach achieved an accuracy level of 73.3%.
10 Dec 2009
TL;DR: In this paper, a computer assisted photogrammetric methodology which correlates the color of the palm oil fruits to their ripeness and eventually sorts them out physically is presented. But the methodology consists of five main phases, i.e. image acquisition, image pre-processing, image segmentation, calculation of color Digital Numbers (DN) and finally the classification of the fresh fruit bunches according to their quality.
Abstract: Abstract— Conventional grading of oil palm Fresh Fruit Bunches (FFB) is still currently manually carried out in palm oil producing industries. The most critical part of the grading process is the categorization of the oil palm fruit bunches according to their ripeness. This paper presents a computer assisted photogrammetric methodology which correlates the color of the palm oil fruits to their ripeness and eventually sorts them out physically. The methodology consists of five main phases, i.e. image acquisition, image pre-processing, image segmentation, calculation of color Digital Numbers (DN) and finally the classification of the fresh fruit bunches according to their ripeness. The software and hardware essential s for the implementation of the methodology have been developed and tested. The design of system is geared towards four main characteristics: (i) affordable in comparison to the labor cost in palm oil mills, (ii) reliable grading process equivalent to the task carried out by a skilled grader, (iii) sufficiently robust to withstand the oil palm mill environment without human intervention and (iv) synergistic integration of hardware and software systems. The system and the methodology formulated in this work have developed a complete automation grading system of oil palm FFB and thus drastically increased the grading productivity.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
01 Jan 1995
TL;DR: In this article, Nonaka and Takeuchi argue that Japanese firms are successful precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies, and they reveal how Japanese companies translate tacit to explicit knowledge.
Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.
01 Jan 2016
01 Jan 2012