{"title":"Sensing as a Service: Vision, Practices and Architecture","authors":"Maha Arooj, M. Asif","doi":"10.5815/ijieeb.2019.06.06","DOIUrl":"https://doi.org/10.5815/ijieeb.2019.06.06","url":null,"abstract":"The Internet of Things (IoT) is becoming pervasive and immersive due to the recent advancements in communication and sensing technologies. The proliferation of smart devices and their sensing capabilities has opened new opportunities and business models. The billions of connected sensing devices are generating enormous amount of data. The sensing as a service concept has the potential to provide a wide variety of services to citizens, companies and public administrations. This paper presents a sensing as service vision for IoT in different domains such as agriculture, waste management, supply chain, traffic management and others. Moreover, different applications of sensing as service model is analyzed and discussed in detail. In this paper, we specifically propose a service oriented sensing as service architecture to realize the vision of sensing as a service. The proposed service oriented architecture has the potential to address the challenges of heterogeneity, integration and interoperability of a sensing as service concept and can open new business opportunities.","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125136054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient Predictive Model for Determining Critical Factors Affecting Commodity Price: The Case of Coffee in Ethiopian Commodity Exchange (ECX)","authors":"Worku Abebe Degife, Dr.ing. Abiot Sinamo","doi":"10.5815/ijieeb.2019.06.05","DOIUrl":"https://doi.org/10.5815/ijieeb.2019.06.05","url":null,"abstract":"In this paper, we have focused on the data mining technique on market data to establish meaningful relationships or patterns to determine the determinate critical factors of commodity price. The data is taken from Ethiopia commodity exchange and 18141 data sets were used. The dataset contains all main information. The hybrid methodology is followed to explore the application of data mining on the market dataset. Data cleaning and data transformation were used for preprocessing the data. WEKA 3.8.1 data mining tool, classification algorithms are applied as a means to address the research problem. The classification task was made using J48 decision tree classification algorithms, and different experimentations were conducted. The experiments have been done using pruning and unpruning for all attributes. The developed models were evaluated using the standard metrics of accuracy, ROC area. The most effective model to determine the determinate critical factors for the commodity has an accuracy of 88.35% and this result is a good experiment result. The output of this study is helpful to support decisionmaking activities in the area of the Ethiopia Commodity Exchange. The study support commodity suppliers to take care of the determinant factors work towards maintaining quality. Ethiopia Commodity Exchange (ECX), as the main facilitator of commodity exchanges, can also use the model for setting price ranges and regulations.","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122010935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O.S.S. Alsharif, K. M. Elbayoudi, A.A.S. Aldrawi, K. Akyol
{"title":"Evaluation of Different Machine Learning Methods for Caesarean Data Classification","authors":"O.S.S. Alsharif, K. M. Elbayoudi, A.A.S. Aldrawi, K. Akyol","doi":"10.5815/ijieeb.2019.05.03","DOIUrl":"https://doi.org/10.5815/ijieeb.2019.05.03","url":null,"abstract":"Recently, a new dataset has been introduced about the caesarean data. In this paper, the caesarean data was classified with five different algorithms; Support Vector Machine, K Nearest Neighbours, Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. The dataset is retrieved from California University website. The main objective of this study is to compare selected algorithms’ performances. This study has shown that the best accuracy that was for Naïve Bayes while the highest sensitivity which was for Support Vector Machine.","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121108497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Optimized Model for Breast Cancer Prediction Using Frequent Itemsets Mining","authors":"Ankita Sinha, B. Sahoo, S. Rautaray, M. Pandey","doi":"10.5815/ijieeb.2019.05.02","DOIUrl":"https://doi.org/10.5815/ijieeb.2019.05.02","url":null,"abstract":"This presented research paper mainly studies the frequent itemsets mining approach for finding the most important attribute to overcome the existing problems in the extraction of relevant information by using data mining approaches from a huge amount of dataset. Firstly a state of art diagram for prediction is designed and data mining classifier like naive bayes, support vector machine, decision tree, knearest neighbour are compared and then proposed methodology with new techniques are proposed. Moreover, a new attribute filtering association frequent itemsets mining algorithm is presented. Then, by analyzing the feasibility of the proposed algorithm, the data mining classification classifier is compared. As a result, SVM produces the best result among all the classifier with attribute filtrating and without attribute filtrating. With attribute filtrating algorithm enhances the accuracy of all the other classifier.","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"15 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124773432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Embedding Stock Tracking Module into Electronic Fiscal Device Machine and its Management System to Reduce Tax Evasion: A case of Tanzania","authors":"Paul E. Shao, M. Dida","doi":"10.5815/ijieeb.2019.05.04","DOIUrl":"https://doi.org/10.5815/ijieeb.2019.05.04","url":null,"abstract":"This research article published by the International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.5, 2019","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129221921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Real Estate Recommendation Using Historical Data and Surrounding Environments","authors":"Uchchash Barua, Md. Sabir Hossain, M. Arefin","doi":"10.5815/ijieeb.2019.05.05","DOIUrl":"https://doi.org/10.5815/ijieeb.2019.05.05","url":null,"abstract":"Recommending appropriate things to the user by analyzing available data is becoming popular day by day. There are no sufficient researches on Real-estate recommendation with historical data and surrounding environments. We have collected real-estate, historical and point of interest (POI) data from the various sources. In this research, a hybrid filtering technique is used for recommending real-estate consisting of collaborative and content-based filtering. Generally, in every website user ratings are collected for the recommendation. But we have considered historical data and surrounding environments of a real-estate location for recommendation by which it will be easy for a user to decide that which place would be better for him/her. If any user request for any specific location then the system will find the POI data using google map API. Then the system will consider historical data of that area, got from the trusted sources. So considering the minimum price and optimal facilities, our system will recommend top-k real-estate. After extensive experiments on real and synthetic data, we have proved the efficiency of our proposed recommender system.","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130368969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"New Metrics for Effective Detection of Shilling Attacks in Recommender Systems","authors":"T. Srikanth, M. Shashi","doi":"10.5815/IJIEEB.2019.04.04","DOIUrl":"https://doi.org/10.5815/IJIEEB.2019.04.04","url":null,"abstract":"Collaborative filtering techniques are successfully employed in recommender systems to assist users counter the information overload by making accurate personalized recommendations. However, such systems are shown to be at risk of attacks. Malicious users can deliberately insert biased profiles in favor/disfavor of chosen item(s). The presence of the biased profiles can violate the underlying principle of the recommender algorithm and affect the recommendations. This paper proposes two metrics namely, Rating Deviation from Mean Bias (RDMB) and Compromised Item Deviation Analysis (CIDA) for identification of malicious profiles and compromised items, respectively. A framework is developed for investigating the effectiveness of the proposed metrics. Extensive evaluation on benchmark datasets has shown that the metrics due to their high Information Gain lead to more accurate detection of shilling profiles compared to the other state of the art metrics.","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128580153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modeling and Optimizing Patients’ Flows Inside Emergency Department based on the Simulation Model: A Case Study in an Algerian Hospital","authors":"Oussama Derni, Fatma Boufera, M. Khelfi","doi":"10.5815/IJIEEB.2019.04.03","DOIUrl":"https://doi.org/10.5815/IJIEEB.2019.04.03","url":null,"abstract":"In Algeria, as in many other countries, the Emergency Department (ED) of the hospital, is the main entrance to the hospital, which provides Healthcare to patients threatened with death, and which faces several issues, emphasized by resource limitation. Our work presents a description of patient flow inside the ‘ED’ of Chalabi Abdelkader Hospital, Mascara, Algeria. This study aims to prevent the care complication scheme by adopting a workflow approach in order to design the patient flow in the chosen ‘ED’. The objective is to enhance patients’ flows, to improve the quality of the patient supervision, by targeting the minimization of the total and waiting times. A simulation model of the study system will be built based on the acquired data, and it will be validated by domain experts for a maximal rapprochement to the reality. Then, many simulations instances will be realized using Rockwell ARENA simulator to evaluate the impact of the proposed solutions. As a result of this study, we provided to ‘ED’ supervisors many improvement solutions and recommendations to the issues identified in the modeling phase.","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130220246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Natural Language Processing based Hybrid Model for Detecting Fake News Using Content-Based Features and Social Features","authors":"Shubham Bauskar, Vijay Badole, Prajal Jain, Meenu Chawla","doi":"10.5815/IJIEEB.2019.04.01","DOIUrl":"https://doi.org/10.5815/IJIEEB.2019.04.01","url":null,"abstract":"Internet acts as the best medium for proliferation and diffusion of fake news. Information quality on the internet is a very important issue, but web-scale data hinders the expert’s ability to correct much of the inaccurate content or fake content present over these platforms. Thus, a new system of safeguard is needed. Traditional Fake news detection systems are based on content-based features (i.e. analyzing the content of the news) of the news whereas most recent models focus on the social features of news (i.e. how the news is diffused in the network). This paper aims to build a novel machine learning model based on Natural Language Processing (NLP) techniques for the detection of ‘fake news’ by using both content-based features and social features of news. The proposed model has shown remarkable results and has achieved an average accuracy of 90.62% with F1 Score of 90.33% on a standard dataset.","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115474555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development of a Mobile-Based Hypertension Risk Monitoring System","authors":"Ngozi Chidozie Egejuru, O. Ogunlade, P. Idowu","doi":"10.5815/IJIEEB.2019.04.02","DOIUrl":"https://doi.org/10.5815/IJIEEB.2019.04.02","url":null,"abstract":"Hypertension is a silent killer, which gives no warning signs to alert a patient and can only be detected through regular blood pressure checkups. Uncontrolled and unmonitored hypertension contributed to stroke, chronic kidney disease, eye problem, and heart failure. It is an ongoing challenge to health care systems worldwide. Early detection of hypertension and creating awareness will greatly reduce the effect of hypertension and its related diseases. Also, having a mobile-based system will help patients to know their status, relate with Doctor and enjoy the quick response from the Doctor on hypertension diagnostic effect on their health. The mobile application will help in monitoring patients anytime, anywhere and provide services for each patient based on their personal health condition. The mobile application was designed using unified modeling language and implemented using the Extensible Mark-Up Language and Java programming language for the mobile layout and content, while JavaScript Object Notation was used to implement the data storage and retrieval mechanism of the system. The system was tested using data collected from hospital, which yielded an accuracy of 100%. In conclusion, the system will assist in providing timely, efficient, accurate and comprehensive information about hypertension, which is useful for Doctors and patients in detecting, diagnosing, classifying and managing hypertension and its risk.","PeriodicalId":427770,"journal":{"name":"International Journal of Information Engineering and Electronic Business","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125240907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}