Grace Kobusinge1,2, 1Gothenburg University, Gothenburg, Sweden and 2Makerere University, P.O. Box 7062 Kampala, Uganda
Due to their renowned great information processing and dissemination power, Health information systems (HIS) can readily avail past patient medical information across the continuum of care in order to facilitate on going treatment. However, a number of existing HIS are designed as vertical silos with no interoperability onuses and therefore, cannot exchange patient information. At the same time little knowledge is known about the intricacies & factors that surround HIS’ interoperability implementations. This study therefore, employs an institutional lens perspective to investigate contextual factors with an impact on HIS’ interoperability designing. Through this perspective the following seven contextual factors were arrived at: institutional autonomism, intended system goals, existing health-information-systems, national HIS implementation guidelines, interoperability standards, policy and resources. A further study implication is the use of institutional lens in making sense of the systems’ context of integration in order to discover salient factors that might impact Health-information-systems’ interoperability designing.
Health Information Systems’ Interoperability, Contextual Factors, Health Information Systems’ Designing.
Nwanneka Eya1 and George Weir2, 1Department of Computer and Information Science, University of Strathclyde, Glasgow, Scotland and 2University of Strathclyde,G1 1XH, Glasgow, Scotland, United Kingdom
Enterprise information systems plays an important role in manufacturing companies by integrating the firm’s information, operating procedure and its functions in all department; resulting in a better operation in the global business environment. In developing countries like Nigeria, most manufacturing firms have been facing the need to compete efficiently in the global markets. This is because of the Nigerian dynamic business environment that continues evolving and the enormous government support for indigenous manufacturers. Therefore, the need for an enterprise information system cannot be underemphasized, but because an enterprise information system is a major investment that is expensive and time consuming; the need to assess if a company is ready for such a major transition becomes very important. In assessing the readiness of Nigerian manufacturing companies for ERP implementation, thereare many factors to consider. This study assesses the readiness level of Nigerian manufacturing companies’ base on the survey responses from a wide spectrum of Nigerian manufacturing firms. The findings showed that “readiness level” are mainly influenced by technological , organizational and environmental factors which basically involved assumed benefits, assumed difficulty, technological architecture, technological skills, competitive pressure, organization size and information management priority. It was observed that technological factors had more impact in determining the readiness level of any firm. This paper suggests a structure or standard that Nigerian manufacturers could use to ascertain their company’s readiness level before embarking on an investing in enterprise information system.
Enterprise information system, Readiness analysis, Nigeria, Manufacturing, Company.
Merve Duman, Roya Choupani and Faris Serdar Tasel, Department of Computer Engineering, Cankaya University, Ankara, Turkey
In this study, an automatic identification method of antibiogram analysis is implemented, existing methods are investigated and results are discussed. In an antibiogram analysis, inhibition zones of drugs read by humans might be measured with some mistakes. These mistakes such as misreading during the analysis process or the conditions like imperfect or partial seeding inhibition zones can be solved with automatic identification methods. Also, there is a need for periodically reading or a tracking system because inhibition zones change with time. To overcome antibiogram analysis problems, some improvements are made on the image. As pre-processing operations, Otsu Thresholding, largest object finding, binary image mask, morphological erosion and closing operations are applied. Circular Hough Transform is used to find drugs and profile lines are drawn to find inhibition zones. The Otsu thresholding is used to determine the zone borders. The results obtained from the algorithm are evaluated and discussed.
Antibiogram Analysis, Image Processing, Feature Extraction, Object Detection, Image Segmentation
Firas Gerges and Frank Y.Shih, Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA
Acne and Rosacea are two common skin diseases that affect many people in United States. These two skin conditions can result in similar symptoms, which leads patients to mistake their case. In this paper, we aim to develop a model that can efficiently differentiate between these two skin conditions. A deep learning model is proposed in order to automatically distinguish Rosacea from Acne cases using infected skin images. Using image augmentation, the size of the original dataset can be enlarged. Experimental results show that our model achieves a very high performance with an average testing accuracy of 99%.
Acne, Rosacea, Deep Learning, Image Processing, Convolutional Neural Networks
Ammar K Alazzawi1*, Helmi Md Rais1, Shuib Basri1, Yazan A. Alsariera4, Abdullateef Oluwagbemiga Balogun1,2, Abdullahi Abubakar Imam1,3, 1Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia, 2Department of Computer Science, University of Ilorin, PMB 1515, Ilorin, Nigeria, 3Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria and 4Department of Computer Science, Northern Border University, Arar 73222, Saudi Arabia
t-way interaction testing is a systematic approach for exhaustive test set generation. It is a vital test planning method in software testing which generates test sets based on interaction amongst parameters to cover every possible test sets combinations. t-way strategy clarifies the interaction strength between the number of parameters. However, there are some test sets combinations that should be excluded when generating the final test set as a result of invalid outputs, impossible or unwanted test sets combinations (e.g. system requirements set). These types of set combinations are known as constraints combinations or forbidden combinations. From existing studies, several t-way strategies have been proposed to address the test set combination problem, however, generating the optimal test set is still open research being an NP-hard problem. Therefore, this study proposed a novel hybrid artificial bee colony (HABC) t-way test set generation strategy with constraints support. The proposed approach is based on a hybrid artificial bee colony (ABC) algorithm with a particle swarm optimization (PSO) algorithm. PSO was integrated as the exploratory agent for the ABC hence the hybrid nature. The information sharing ability of PSO via the Weight Factor is used to enhance the performance of ABC. The output of the hybrid ABC is a set of promising optimal test set combinations. The results of the experiments showed that HABC outperformed and yielded better test sets than existing methods (HSS, LAHC, SA_SAT, PICT, TestCover, mATEG_SAT).
Software testing, t-way testing, hybrid artificial bee colony, meta-heuristics, optimization problem.
Aurora Y. Mu, Department of Mathmatics, Western Connecticut State University, Danbury, Connecticut, United States of America
This paper establishes a methodology to build hybrid machine learning models, aiming to combine the power of different machine learning algorithms on different types of features and hypothesis. A generic cost-based outlier removal algorithm is introduced as a step of preprocess of training data, we implement a hybrid machine learning model for a crediting problem, and experiment combination of three types of machine learning algorithms SVM, DT and LR. The new hybrid models shows improvement in performance compared to the traditional single SVM, DT, and LR. This new methodology can be further explored with other algorithms and applications.
Machine Learning, Outlier Removal, Credit Score Modelling, Hybrid Learning Model
MAHAMAT Moussa Dogoumi Department of Electrical engineering University of Quebec in Trois-Rivieres Quebec, CANADA and Adam Skorek, Department of Electrical engineering, University of Quebec in Trois-Rivieres Quebec, CANADA
The FANET networks have seen some progress recently. Solutions for positioning network nodes are numerous. One in particular offers a comprehensive and interesting approach. It is VBCA. This method has the particularity of positioning the nodes in 3-D. In general, the 3-D positioning becomes an NP-Hard problem. But with VBCA positioning is relatively simple.In this paper we present an optimization of communication in a topology based on the VBCA (Virtual forces Based Clustering Algorithm). The topology is optimized by the VBCA algorithm for a better coverage area. This method is 40% more efficient than existing approaches in terms of area coverage. In the first versions of VBCA, the performances in terms of communication between nodes have not been tested. The resulting network performances are very encouraging, in terms of throughput, delay and packets loss. Therefore, this work provides a first answer to the performances in terms of network.
VBCA, FANET, Communication, Topology, Positioning, Clustering