Abstract Inventory control of spare parts is very important to many organizations. Excess inventory and overstocks lead to high holding costs. On the other side stock outs can have a great impact on production or service. The problem of this paper is the shortcomings of the current systems of inventory management and the lack of proper regulation of spare parts using most common manual... Inventory control of spare parts is very important to many organizations. Excess inventory and overstocks lead to high holding costs. On the other side stock outs can have a great impact on production or service. The problem of this paper is the shortcomings of the current systems of inventory management and the lack of proper regulation of spare parts using most common manual methods to manage them in inventory. This paper was conducted at Laptop sale company known as UPS company and electrical appliances supply Company in Amman, Jordan as a case study. In this paper, the EOQ cost management model and XYZ analysis were implemented using a software system that helps to make the inventory management automatically prepared and organized. Using EOQ model with XYZ analysis and following the procedures of entering the data into the system consequently lead to a process of getting the necessary reports in consistent shape. Using the software system such as the one used in this paper helps facilitating the access to the history of any items that could be managed by such system. Read More Read Less

Abstract One indicator of the severity of diabetic retinopathy is the existence of lesion characteristics in the eyes such as microaneurysm, haemorrhages, exudates, and neovascularization. Without proper early medical attention, this lesion could lead to blindness. Considering its importance, a system that could detect such lesion will be beneficial. This paper investigates lesion characteristics of diabetic retinopathy from fundus images such... One indicator of the severity of diabetic retinopathy is the existence of lesion characteristics in the eyes such as microaneurysm, haemorrhages, exudates, and neovascularization. Without proper early medical attention, this lesion could lead to blindness. Considering its importance, a system that could detect such lesion will be beneficial. This paper investigates lesion characteristics of diabetic retinopathy from fundus images such as microaneurysm (redsmalldots), exudates, haemorhages, and neovascularization. In this study, we present three of feature extraction methods, i.e., Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM) and Segmentation Fractal Texture Analysis (SFTA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) chosen as classifiers for classifying five classes (redsmalldots, haemorrhages, hard exudates, soft exudates, and neovascularization). The data used in this research obtained from DiaretDB0 database. The experimental results show that our proposed method can detect the lesion characteristics of diabetic retinopathy with a higher accuracy of 86,84% and 96% for SVM and KNN respectively. Read More Read Less

Abstract Due to continuous growth of recent applications such as, telecommunication, sensor data, financial applications, analyzing of data streams, conceptually endless sequences of data records, frequently arriving at high rates is important task among the data mining community. Among the various task involved in data stream analysis, the classification of data streams pose various challenging issues compared to popular data classification algorithms. Since the... Due to continuous growth of recent applications such as, telecommunication, sensor data, financial applications, analyzing of data streams, conceptually endless sequences of data records, frequently arriving at high rates is important task among the data mining community. Among the various task involved in data stream analysis, the classification of data streams pose various challenging issues compared to popular data classification algorithms. Since the classification algorithm performs endlessly, it must be able to adapt the classification model to handle the change of concept or boundaries between classes. In order to handle these issues, we have developed a new fuzzy system called, HRFuzzy to classification of evolving data streams. Here, rough set theory and holoentropy function are utilized to construct the dynamic classification model. In the fuzzy system, the rules are generated using k-means clustering and membership functions are dynamically updated using holoentropy function. The experimentation of the proposed HRFuzzy is performed using two different databases such as, skin segmentation dataset and localization data and the performance is compared with adaptive k-NN classifier in terms of accuracy and time. From the outcome, we proved that the proposed HRFuzzy outperformed in both the metrics by giving the maximum performance. Read More Read Less