Journal of Information Engineering and Applications最新文献

{"title":"The Superiority of the Ensemble Classification Methods: A Comprehensive Review","authors":"Silas Nzuva, L. Nderu","doi":"10.7176/jiea/9-5-05","DOIUrl":"https://doi.org/10.7176/jiea/9-5-05","url":null,"abstract":"The modern technologies, which are characterized by cyber-physical systems and internet of things expose organizations to big data, which in turn can be processed to derive actionable knowledge. Machine learning techniques have vastly been employed in both supervised and unsupervised environments in an effort to develop systems that are capable of making feasible decisions in light of past data. In order to enhance the accuracy of supervised learning algorithms, various classification-based ensemble methods have been developed. Herein, we review the superiority exhibited by ensemble learning algorithms based on the past that has been carried out over the years. Moreover, we proceed to compare and discuss the common classification-based ensemble methods, with an emphasis on the boosting and bagging ensemble-learning models. We conclude by out setting the superiority of the ensemble learning models over individual base learners. Keywords: Ensemble, supervised learning, Ensemble model, AdaBoost, Bagging, Randomization, Boosting, Strong learner, Weak learner, classifier fusion, classifier selection, Classifier combination. DOI : 10.7176/JIEA/9-5-05 Publication date : August 31 st 2019","PeriodicalId":440930,"journal":{"name":"Journal of Information Engineering and Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122665436","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":"Testing User Satisfaction Using End-User Computing Satisfaction (EUCS) Method in Hospital Management Information System (SIMRS) (Case Study at the Regional Public Hospital dr. A. Dadi Tjokrodipo)","authors":"A. Cucus, Ghufranil Halim","doi":"10.7176/jiea/9-5-06","DOIUrl":"https://doi.org/10.7176/jiea/9-5-06","url":null,"abstract":"Accuracy and timeliness of information are the main priority in hospital services. The purpose of this study is to determine the level of satisfaction of users of the Hospital Management Information System at the Regional Public Hospital dr. A. Dadi Tjokrodipo in order to obtain an overview of the level of user satisfaction as a reference for future evaluations and examine the factors that influence user satisfaction with the EUCS method of Hospital Management Information Systems in the hospital. The sampling technique in this study is purposive sampling technique. The variables used in this study are: Content (X 1 ), Accuracy (X 2 ), Format (X 3 ), Ease of Use (X 4 ), Timeliness (X 5 ), and User Satisfaction (Y). Variable measurement scale in the study used Likert Scale. The results of the study concluded in the Content (X 1 ), Accuracy (X 2 ), Format (X 3 ), Ease of use (X 4 ), and Timeliness (X 5 ) are in a quite good category, with an average percentage of 55.67%, 60.27 %, 62.50%, 64.83%, and 66.17%. The research results also conclude that based on the results of descriptive analysis, the variable User Satisfaction (Y) on Hospital Management Information System according to each question is in the Good category with an average value of 71.33%. Keywords: Hospital Management Information System; Information systems; User satisfaction DOI : 10.7176/JIEA/9-5-06 Publication date : September 30 th 2019","PeriodicalId":440930,"journal":{"name":"Journal of Information Engineering and Applications","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132793514","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":"Effect of Non-Optimal Amplitude Frequency Response on Transmission of Power Line Communication Signals","authors":"G. Ajenikoko, Muyiwa Arowolo, Lambe M. Adesina, O. Ogunbiyi, Wasiu Adebayo Eboda","doi":"10.7176/jiea/10-1-03","DOIUrl":"https://doi.org/10.7176/jiea/10-1-03","url":null,"abstract":"Power line Communication (PLC) systems represent a relatively recent and rapidly evolving technology, aimed at the utilization of the electricity power lines for the transmission of data. This is due to increasing demand of low cost telecommunication, broadband and access to internet services. Power lines are inherently the most attractive medium for home networking due to its universal existence in homes, the abundance of alternating current outlets and the simplicity of the power plug. This work presented the effect of non-optimal amplitude frequency on transmission of power line communication signals by utilizing Orthogonal Frequency Division Multiplexing (OFDM) system. The simulation was carried out using MATLAB/SIMULINK with additive white Gaussian noise (AWGN) in order to obtain correct simulation performance results. Two channels of PLC were considered, the worse channel was taken into account and the channel output signal power was obtained. Bit Error Rate (BER) of Binary Phase Shift Keying (BPSK) in conjunction with multipath channel was used for a comparative performance of the studies.  The results indicated that data transmission in PLC environment needed a signal to be amplified or transmitted at higher powers. The result also showed that non-optimal amplitude frequency response had no effect on transmission of the PLC signal in the frequency bands despite the low noise signal in the system. The result demonstrated that OFDM exhibited better BER performance for providing adequate transmission channel for information over a PLC system.  This approach provided accurate reliability, security and robustness for better management of available energy resources to overcome the limitations of existing Power line communication technology. Keywords: Power Line Communication, Bit Error Rate, Orthogonal Frequency Division Multiplexing, Gaussian Noise, Transmission Line, Binary Phase Shift Keying DOI : 10.7176/JIEA/10-1-03 Publication date: January 31 st 2020","PeriodicalId":440930,"journal":{"name":"Journal of Information Engineering and Applications","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116338818","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":"Optimization Design and Characterization of Helmholtz Coils","authors":"U. A. Sadiq, O. Oluyombo","doi":"10.7176/jiea/9-4-05","DOIUrl":"https://doi.org/10.7176/jiea/9-4-05","url":null,"abstract":"Earth’s magnetic field data from ground-based magnetometer observatories are important for studies related to geomagnetic storm. The absence of earth’s magnetic field data observatories results in a complex mysterious phenomena of the geomagnetic storm and remains as unexplained one. Magnetometer is used to monitor and record the earth’s magnetic field data at the geomagnetic observatory. It is also used to measure the three components of the field such as the horizontal component (H), the declination component (D), and the downward component (Z). Fluxgate magnetometer is contributing to the ongoing extensive research work dedicated to explanation of some of the complex phenomena related to the geomagnetic storm and solar terrestrial system. In order to examine the magnetic field sensing of a fluxgate sensor, a large area with uniform magnetic field is required. The advantage of having a large area is that it will allow easy access of the sensor during measurement. A laboratory design and characterization of Helmholtz coils is a better choice when Helmholtz coil with larger areas that are available in the market are very expensive. This paper presents the optimization design of Helmholtz coils to create magnetic fields, which could be used to null the earth’s magnetic field, calibrate magnetic sensors, and used for other experiments in which a controllable amount of uniform magnetic field is required. DOI : 10.7176/JIEA/9-4-05 Publication date :June 30 th 2019","PeriodicalId":440930,"journal":{"name":"Journal of Information Engineering and Applications","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124710164","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":"Feature Based Data Anonymization for High Dimensional Data","authors":"Esther Gachanga, Michael W. Kimwele, L. Nderu","doi":"10.7176/jiea/9-2-03","DOIUrl":"https://doi.org/10.7176/jiea/9-2-03","url":null,"abstract":"Information surges and advances in machine learning tools have enable the collection and storage of large amounts of data. These data are highly dimensional.  Individuals are deeply concerned about the consequences of sharing and publishing these data as it may contain their personal information and may compromise their privacy. Anonymization techniques have been used widely to protect sensitive information in published datasets. However, the anonymization of high dimensional data while balancing between privacy and utility is a challenge. In this paper we use feature selection with information gain and ranking to demonstrate that the challenge of high dimensionality in data can be addressed by anonymizing attributes with more irrelevant features. We conduct experiments with real life datasets and build classifiers with the anonymized datasets. Our results show that by combining feature selection with slicing and reducing the amount of data distortion for features with high relevance in a dataset, the utility of anonymized dataset can be enhanced. Keywords: High Dimension, Privacy, Anonymization, Feature Selection, Classifier, Utility DOI : 10.7176/JIEA/9-2-03 Publication date : April 30 th 2019","PeriodicalId":440930,"journal":{"name":"Journal of Information Engineering and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129611678","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":"Comparing the Capabilities of SVMC and MLC Using Contingency Matrix and a Novel Template","authors":"I. Ezeomedo, J. Igbokwe","doi":"10.7176/jiea/9-4-06","DOIUrl":"https://doi.org/10.7176/jiea/9-4-06","url":null,"abstract":"Since extraction of useful information from remote sensing data is important, scientists manage to propose efficient algorithms for automatic extraction of constructive information from the satellite imageries. To date, image classification has benefitted from advancements in improved computational power and algorithm development. Therefore, Satellite image classification using GeoEye-1, High Resolution Satellite Image (HRSI) of 2016, Support Vector Machine Classifier (SVMC) and Maximum Likelihood Classifier (MLC) were performed with a view to comparing the capabilities of SVMC and MLC using Post-processing Accuracy Assessment (PAA) and a Novel Template in producing urban land use and land cover map of the area. The objectives include performing supervised classification using SVM and MLC in ENVI Software, analysing the performance of SVM and MLC in mapping geometric features using error matrix and a new template. The methodology used comprise Image acquisition, Image enhancement, Image Sub-setting, Extraction of Regions of Interests (ROIs) and its separability index analysis, supervised classification using SVMC and MLC, Post-Processing Accuracy Assessment, Statistical Analyses, and Preparation of maps. ENVI 5.1 software was used for image processing, masking, spatial data analysis and classification. Microsoft Excel, GraphPad Prism ver.7.0 and IBM SPSS ver.21 were used for statistical analysis. The result of image classification indicates that Nnewi-North L.G.A is having 13.52% of Built-up Areas, 24.23% of Vegetation, 22.05% of Water bodies, Farm lands is equal to 39.40% and open/bare surface is 0.81% using SVMC while MLC result shows that Built-up Areas is14.99%, Vegetation is 13.01%, Water bodies is 34.08%, Farm lands is 36.00% and open/bare surface is 1.32%. Error Matrix and Kappa Coefficient results revealed that SVMC is better than MLC as follows (SVMC overall Accuracy is 98.07% and Kappa Coefficient is 0.97 while MLC overall Accuracy is 82.50% and Kappa Coefficient is 0.76. Additional statistical testing with aggregate mean from SVM and MLC was used to determine the significance of the mean difference using the researcher’s developed template called “Post Confusion Matrix” (PoCoMa). The result showed that the t-statistics is 0.670 with probability value of -0.476 which is greater than 0.05, thus, the null hypothesis was accepted with a deduction that using any of the algorithms (SVM and MLC) yields no significant difference in performance and efficiency of result of the map produced. The overall study revealed that both classifiers are efficient and accurate statistically, without any significant difference but using error matrix analysis, the research revealed that ‘Support Vector Machine Classifier’ is robust in extracting urban landscape from HRSI, especially Built-up areas and open/bare surfaces. The research recommends there is need for periodic urban LULC analysis to guide stakeholders in Planning, Monitoring, and Management of ‘Urb","PeriodicalId":440930,"journal":{"name":"Journal of Information Engineering and Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132631268","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":"A hybrid approach to Enhancing Process Scheduling in multiple core Systems","authors":"Dr. Anthony Luvanda","doi":"10.7176/jiea/11-2-04","DOIUrl":"https://doi.org/10.7176/jiea/11-2-04","url":null,"abstract":"Process scheduling within computer systems and with regards to the CPU always encounters bottle necks due to over reliance on single processor scheduling techniques. The presence of multicore processor systems attempts to increase throughput without however operating at full optimum capacity, hence the need for the proposal of a more efficient scheduling approach for use in multiple core systems. This paper conducted a comparative analysis of the rate of efficiency of existing scheduling algorithms with the aid of secondary data. It then employed CPU user benchmark analysis to asses the effectiveness of the proposed approach. Quad-core processor systems are most suitable for the proposed approach which basically implements two approaches one where scheduling decisions are handled by a master processor while other processors execute the user code thus always ensuring that all processors are busy and always utilized.","PeriodicalId":440930,"journal":{"name":"Journal of Information Engineering and Applications","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134406676","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":"Design and Fabrication of Impact Strength Machine","authors":"G. AliemekeB.N., Ehibor","doi":"10.7176/jiea/11-2-01","DOIUrl":"https://doi.org/10.7176/jiea/11-2-01","url":null,"abstract":"The development of the impact strength testing machine is successfully presented. The impact strength testing machine is necessary for ascertaining the strength of metallic components in withstanding applied load. A comprehensive design analysis was carried out to ascertain the various component sizes of the Impact strength testing machine in order to create a path for precise construction. Majority of the materials used in this fabrication were obtained locally. The constructed machine yielded a maximum velocity of 4.9m/s on execution of Charpy and Izod impact test. Great stability was achieved as a result of thick base plate and column support used during construction. A crushing force of 68.05KN and an impactor head energy of about 35J was able to impart the requisite deformation on the metallic specimen needed for the Charpy and Izod tests.","PeriodicalId":440930,"journal":{"name":"Journal of Information Engineering and Applications","volume":"285 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120850514","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":"Feature Selection Techniques and Classification Accuracy of Supervised Machine Learning in Text Mining","authors":"Loise Makara, Kennedy Ogada, D. Njagi","doi":"10.7176/jiea/9-3-06","DOIUrl":"https://doi.org/10.7176/jiea/9-3-06","url":null,"abstract":"Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques.  Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification","PeriodicalId":440930,"journal":{"name":"Journal of Information Engineering and Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127982096","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}