8 research outputs found
Development of an inventory management system using association rule
Get PDFStores today still make use of manual approaches to keeping inventory which could be cumbersome. Having a computerized inventory system would make inventory management more efficient and effective. In this chapter, an Inventory Management System using Association Rule was developed which will ensure proper record keeping and keep items in stocks updated. ANGULARJS, a JavaScript framework, was used for the implementation of the system, PHP (hypertext pre-processor) was used for the backend of the system development as well as the database management, HTML was used alongside CSS for the system interface design and NoSQL database was the database used for this research. In conclusion, a computerized inventory system that had been improved using the association rule method was the resulting product useful for creating transactions, updating items in stock, record keeping, generating reports for decision making, and lastly, the system will make the stores more effective
Datasets on Malaria Disease
No full textA survey was conducted in Kwara State using questionnaires for data collection and these are the datasets gathered from the survey</p
Hand geometry recognition: an approach for closed and separated fingers
Get PDFHand geometry has been a biometric trait that has attracted attention from several researchers. This stems from the fact that it is less intrusive and could be captured without contact with the acquisition device. Its application ranges from forensic examination to basic authentication use. However, restrictions in hand placement have proven to be one of its challenges. Users are either instructed to keep their fingers separate or closed during capture. Hence, this paper presents an approach to hand geometry using finger measurements that considers both closed and separate fingers. The system starts by cropping out the finger section of the hand and then resizing the cropped fingers. 20 distances were extracted from each finger in both separate and closed finger images. A comparison was made between Manhattan distance and Euclidean distance for features extraction. The support vector machine (SVM) was used for classification. The result showed a better result for Euclidean distance with a false acceptance ratio (FAR) of 0.6 and a false rejection ratio (FRR) of 1.2
A Multiple Algorithm Approach to Textural Features Extraction in Offline Signature Recognition
No full textSclera boundary localization using circular hough transform and a modified run-data based algorithm
Full text linkSecurity challenges over the years has led to the need for an improvement in the traditional security approaches. This led to the advent of biometrics. Recently, among the biometric approaches, sclera has been an area of imense study. This is due to its accuracy; however, segmentation of the sclera has been a limiting factor to the application of this biometric trait. Several approaches have been proposed in literature but there is still the need to improve the segmentation accuracy. This study proposes the use of circular hough transform and a modified run-data based algorithm. The study also presented a sclera recognition system using the compound local binary pattern for features extraction and Manhattan distance for classification. The system produced a segmentation accuracy of 99.9% for sclera blood vessels, periocular and iris (SBVPI) sclera database and 100% for manually captured sclera database. The system produced an accuracy of 99.98 for SBVPI sclera database and 99.99% for manually captured sclera database
A Deep Convolutional Encoder-Decoder Architecture for Retinal Blood Vessels Segmentation
Full text linkOver the last decades, various methods have been employed in
medical images analysis. Some state-of-the-arts techniques such as deep learn�ing have been recently applied to medical images analysis. This research pro�poses the application of deep learning technique in performing segmentation of
retinal blood vessels. Analyzing and segmentation of retina vessels has assisted
in diagnosis and monitoring of some diseases. Diseases such as age-related
fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arterioscle�rosis and choroidal neovascularization can be effectively managed by the
analysis of retinal vessels images. In this work, a Deep Convolutional Encoder�Decoder Architecture for the segmentation of retinal vessels images is proposed.
The proposed method is a deep learning system composed of an encoder and
decoder mechanism allows a low resolution image set of retinal vessels to be
analyzed by set of convolutional layers in the encoder unit before been sent into
a decoder unit for final segmented output. The proposed system was evaluated
using some evaluation metrics such as dice coefficient, jaccard index and mean
of intersection. The review of the existing works was also carried out. It could be
shown that the proposed system outperforms many existing methods in the
segmentation of retinal vessels images
A Mobile Palmprint Authentication System Using a Modified MNT Algorithm, Circular Local Binary Pattern, and CNN (mobileNet)
Full text linkA few approaches have been proposed for hand segmentation in palmprint recognition. Skin-color information does not process sufficient information for discrimination in complex backgrounds and variable illumination. The use of guides has also been proposed, which restricts hand placement during capturing. Contour tracing algorithms have also been proposed in the literature. This worked in an even background scenario with no objects or patterns around the hand. In the case of uneven background with objects present, the traditional contour tracing algorithm cannot accurately segment the hand from the background. Hence, this paper proposes a modified Moore Neighbor Tracing (MNT) algorithm for hand detection and key-point extraction in complex backgrounds. The hand image is converted to grey, and the edges in the hand image are detected. The modified algorithm then transverses selected edges and returns the peak and valleys of each finger. This is then used to crop the palm. The modified algorithm improves the accuracy of hand detection in complex backgrounds with an F-Score of 0.8657. A mobile palmprint biometric system was also presented using Circular Local Binary Pattern (CLBP) and Convolutional Neural Network (CNN). The system showed an accuracy of 98.3% for hands captured with the mobile device and the CASIA online database. An accuracy of 99.0% was also recorded for GPDS and PolyU online databases
