IOSR Journal of Computer Engineering (IOSR-JCE)

Mar - Apr 2019 Volume 21 - Issue 2

Series - 1 Series - 2 Series - 3 Series - 4

Paper Type : Research Paper
Title : Radio Frequency Identification (RFID) Based Attendance System with Short Message Service (SMS) Backup
Country : Nigeria
Authors : Ukoima Kelvin Nkalo || Ekwe Ogbonnaya Agwu || Ezeonye Chinonso Stanley
: 10.9790/0661-2102010108     logo

Abstract: In this paper, we seek to address means of automatically registering students, recording attendance, saving students' data on the personal computer (PC) as well as backing this data via the global system for mobile communication (GSM) and finally making a decision on the eligibility of a student to sit for an examination course. Owing to the challenges of the manual method of taking attendance in Nigerian universities and colleges especially in the Michael Okpara University of Agriculture Umudike, an automated attendance system needs to be adopted. The...........

 

Keywords: Radio Frequency Identification (RFID) Tag and Reader, Liquid Crystal Display (LCD), ATMEGA 328P Microcontroller, and GSM Module.

[1]. C, Bardaki, P, Kourouthanassis and K, Pramatari. Deploying RFID-Enabled Services in the Retail Supply Chain, Lessons Learned toward the Internet of Things. Information Systems Management, 29(3), 233-245, 2012.
[2]. S, Chitresh and K, Amit. An efficient Automatic Attendance Using Fingerprint Verification Technique, International Journal on Computer Science and Engineering (IJCSE), 2(2), 264-269, 2010.
[3]. A. T, Dawes. Is RFID Right for Your Library, Journal of Access Services, 2(4), 7-13, 2004.

[4]. C. Geoffrey. Automatic Access Control System using Student's Identification Card based on RFID Technology, Thesis, Faculty of Electrical Engineering, University of Technology Malaysia, 2012
[5]. S, Lee, K, Ha and K, Lee. A pyroelectric infrared sensor-based indoor location-aware system for the smart home, IEEE Trans. Consumer Electron. 52(4), 311 -1317, 2006.


Paper Type : Research Paper
Title : A Decision Tree based Algorithm for Forecasting the Proposed Service Acceptance by Store Customers
Country : Iran
Authors : Ali Mirzapour
: 10.9790/0661-2102010914     logo

Abstract: Organizations are under intense pressure today to respond quickly to changing circumstances and innovation since business environment is increasingly becoming complex and changing. Furthermore, data analysis and data mining will create a competitive advantage. Hence, customer relationship management has been very much considered in recent years. Accordingly, in order to understand the effective criteria for accepting the proposed service to customers through the database, the present study aimed to classify the information about the store customers and extract the appropriate pattern using the decision tree algorithm so that it will be possible to properly predict the accepting the proposed service and ultimately, meet the advertising and marketing requirements for the society. According to the results obtained from implementing the proposed system on the collected store data, evaluated during 2016-2018 through the Clementine 12.0 software, the system accuracy level is at a desirable level.

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[2]. O. A. Gashteroodkhani, M. Majidi, M. Etezadi-Amoli, "A Fuzzy-based Control Scheme for Recapturing Waste Energy in Water Pressure Reducing Valves" IEEE Power and Energy Society General Meeting (PESGM), pp. 1-5, Portland, OR, Aug 2018.
[3]. O.R. Zaıane (2002),Web Usage Mining for a Better Web-Based Learning Environmen, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.21.799&rep=rep1&type=pdf.
[4]. O. A. Gashteroodkhani, M. Majidi, M. Etezadi-Amoli, A. F. Nematollahi, B. Vahidi, "A hybrid SVM-TT transform-based method for fault location in hybrid transmission lines with underground cables" Electric Power Systems Research, vol. 170, pp. 205-214, 2019.
[5]. S. Aznavi, P. Fajri and M. Rasheduzzaman, "Hierarchical Energy Management Strategy for a Community of Multi Smart Homes," in IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 2018, pp. 176-181..


Paper Type : Research Paper
Title : Predicting Emergency Department Services in Hospital Using Machine Learning
Country : India
Authors : Palakurthi Shashank || Annadi Jahnavi || Dr.Madugula Sujatha
: 10.9790/0661-2102011522     logo

Abstract: Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. To be able to predict, at the time of triage, whether a need for hospital admission exists for emergency department (ED) patients may constitute useful information that could contribute to system wide hospital changes designed to improve ED throughput. The objective of this study was to develop and validate a predictive model to assess whether a patient is likely to require inpatient admission at the time of ED triage, using routine hospital administrative..........

 

Keywords: Triage Systems, emergency department, hospitals, machine learning, predictive models.

[1]. Wright SP, Verouhis D, Gamble G, Swedberg K, Sharpe N, Doughty RN. Factors influencing the length of hospital stay of patients with heart failure. Eur J Heart Fail. 2003; 5(2):201–209.
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[4]. Chang KC, Tseng MC, Weng HH, Lin YH, Liou CW, Tan TY. Prediction of length of stay of first-ever ischemic stroke. Stroke. 2002; 33(11):2670–2674.
[5]. Jiang X, Qu X, Davis L. Using data mining to analyze patient discharge data for an urban hospital. In : Proceedings of the 2010 International Conference on Data Mining; 2010 Jul 12-15; Las Vegas, NV. p. 139–144..


Paper Type : Research Paper
Title : Gender Disparity And Its Implications In Farming Set Up. Case Study of Fish Farming Projects Of 2009 Up To 2011 in Kirinyaga County Kenya
Country : Kenya
Authors : Godfrey Thuranira
: 10.9790/0661-2102012367     logo

Abstract: Despite the Government of Kenya injecting Ksh1.12 billion in Economic stimulus program little had been done to assess gender disparity not withstanding that among conditions set for fish farmers to benefit from the program, gender issues were not considered. This study assessed gender disparity in fish farming and its implication in fish farming under the Economic Stimulus Program in kirinyaga, County Kenya. A total of 124 questionnaires were administered to selected ESP fish farmers. Five sub-county fisheries officers were interviewed and documents regarding the project reviewed to support the survey data. Descriptive and Pearson chi-squarer was done using SPSS version 20.0.Economic stimulus program (ESP) fish farmers beneficiaries who were sampled comprised were men..........

 

Keywords: Gender disparity; gender equality, fish farming; Economic stimulus program; Fish farming enterprise productivity program...

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[2]. African Journal of Agricultural Research. Social capital and knowledge for the transition towards sustainable use of aquatic ecsystems. 7 (8), pp. 1324-1330. (ISSN 1991-637X), PP 32.
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[4]. Brugere, C. (2014). Mainstreaming gender in transboundary natural resources projects – the experience of the Bay of Bengal Large Marine Ecosystem (BOBLME) project. Environmental Development (11), 84–97.
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Paper Type : Research Paper
Title : Comparitive Study and Review for Development of Disease Prediction System for Indian Crops
Country : India
Authors : Mr. Balwant J Gorad || Dr. S. Kotrappa
: 10.9790/0661-2102016873     logo

Abstract: Current Agriculture faces many challenges such as climate change, environmental pollution, Infection of various diseases on plants, water shortages and increased societal demand of food production and many more. Many farmers in India commit suicide because of these challenges. Disease Prediction System for Brinjal crop using machine learning is the promising solution, which is based on predicting and responding to disease symptoms variations on Indian Brinjal plant.

 

Keywords: ML: Machine Learning, IOT: Internet of Thing; DL: Deep Learning, MDP: Massive Data Processing, AI: Artificial Intelligence, DPS- Disease Prediction System.

[1]. Konstantinos P. Ferentinos, Deep learning models for plant disease detection & diagnosis – Science Direct, Elsevier Journal Sept 2017
[2]. Fuentes A, Kim S C, Yoon S, Park DS, 2017. A robust deep learning based detector for real-time tomato plant diseases & pest recognition, http://dx.doi.org/10.3390/s17092022.
[3]. Johannes A, Picon A, Alvarez-Gila A, Echazarra J, Rodriguez-Vaamonde S, Navajas A D, Ortiz-Barredo A, 2017. Automatic plant disease diagnosis using mobile capture devices, applied on wheat. Compute Electron. Agric. 138, 200–209.
[4]. Vijai Singh, A.K. Misra, Detection of plant leaf diseases using image segmentation and soft computing techniques – Science Direct, Elsevier Journal, Nov 2016
[5]. Yun Hwan Kim, Seong Joon Yoo, Yeong Hyeon Gu, Jin Hee Lim, Dongil Han, Sung Wook Baik - Crop Pests Prediction Method using Regression and Machine Learning Technology, Nov 2014.



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