Online Result Processing System for Secondary Schools
Research Paper | Journal Paper
Vol.10 , Issue.9 , pp.1-5, Sep-2022
Abstract
Result Processing is a core academic activity carried out in the secondary school setting. The development of an online result procedure for secondary schools was motivated by the need for accurate and timely result calculation for secondary schools student. With the digitized system, the schools are ensured of a centralized online storage system for management and dissemination of student result. After careful observation and analysis the existing manual process, the manual method was converted into an automated system. The manual method of result computation was modeled with an object-oriented methodology and this resulting system was implemented with a scripting language (PHP), also MySQL was employed as the database management systems for the project. This provided a robust admin interface for the various key players in this sector to actualize their duties in a convenient and responsive system. The final output of this research work produced a system that automates result processing in secondary schools, which can be used in any secondary school in Nigeria.
Key-Words / Index Term
Secondary School, Result Computation, Web-based, Test and Exam Score
References
[1] M.E Ekpenyong, “A Real-Time IKBS for Students Results Computation”, International Journal of Physical Sciences (Ultra Scientist of Physical Sciences) Volume 20, Number 3(M), pp 2-6, 2012
[2] T.M. Connolly, and Begg, “C.E. Database System A Practical Approach to Design, Implementation and Management”, Addison-Wesley, 2015
[3] J.Sulaiman, R.H Mat and N.K Mohd Noor “Electronic Student Academic System (E-SAS) For Secondary School”, International Business Information Management Association (IBIMA) Vol. 5 No.1, pp 34-3 2018.
[4] E.A. Añulika, “Design and Implementation of Result Processing System for Public Secondary Schools in Nigeria”, Department of Information Technology, National open university of Nigeria Lafia, Nasarawa state. [Unpublished] 2014.
[5] A.M Chidimma “Course Registration and Result Processing System in Computer Science Department University of Nigeria, Nsukka” Disertation Thesis, Computer Department UNN [Unpublished]2015.
[6] R. Chandrasekaran, S. Divya, A. Patil “Web Based College Information Management System” International Journal of Computer Engineering, Vol.7, Issue.14, pp.176-180, 2019
[7] D.S Pujare, M.S. Mir, S.M Melasagare “Android Based College Management System” International Journal of Computer Engineering, Vol.9, Issue.4, pp.115-118, 2020
[8] S.L Marcandy “Design and Implementation of Course Registration and Result Processing System”, Computer Department Caritas University, Amorji Nike Enugu. [Unpublished] 2018
[9] O.T. Akal, O.A. Margaret, “Effect of Online Registration on Exam Performance in Kenya Certificate of Secondary Education Enrolment”. American International Journal of Contemporary Research. Vol. 3 No.7 , pp 118-123 2013
Citation
Ezema Henry Kelechi, Blessing Ogbuokiri, "Online Result Processing System for Secondary Schools," International Journal of Computer Sciences and Engineering, Vol.10, Issue.9, pp.1-5, 2022.
Temperature and Humidity Monitoring System over Plant and Uploading into the Cloud
Research Paper | Journal Paper
Vol.10 , Issue.9 , pp.6-9, Sep-2022
CrossRef-DOI: https://doi.org/10.26438/ijcse/v10i9.69
Abstract
The ultimate goal of this project is to create an IoT (Internet of Things) based system that monitors temperature, humidity, and moisture from the farm. Temperature, humidity, and soil moisture sensors measure and process environmental conditions from the Arduino microcontroller. The actuator in this uses a pump to water the plants and lower their temperature. The obtained data from the sensor and the status of the actuator are sent to the Thing Speak server via the node MCU and can be remotely monitored. With the help of a smartphone or any other device. The collected data can be evaluated for various purposes. The results obtained are the effects of moisture, humidity, and pump water on the plants.
Key-Words / Index Term
Cloud Computing, Data Analysis, Temperature sensor, Moisture sensor.
References
[1]. Kidd, CoryD. et,al. “The Aware Home: A Living Laboratory for Ubiquitous Computing Research”. Springer Berlin Heidelberg, 1999.
[2]. Antonio J. Jara, Miguel A. Zamora, and Antonio F. Skarmeta. “An internet of things—based personal device for diabetes therapy management in ambient assisted living (AAL).”. Personal Ubiquitous Comput. 15, 4, 431-440. April 2011.
[3]. M. Mancuso and F. Bustaffa, “TA Wireless Sensors Network for Monitoring Environmental Variables in a Tomato Greenhouse”.
[4]. Teemu Ahonen, Reino Virrankoski and Mohammed Elmusrati , “Greenhouse Monitoring with Wireless Sensor Network”. University of Vaasa.
[5]. TongKe, Fan. “Smart Agriculture Based on Cloud Computing and IOT.” Journal of Convergence Information Technology 8.2, 2013.
[6]. Zhang, Xiang Wen, Ran Chen, and Chun Wang. “Design for Smart Monitoring and Control System of Wind Power Plants.” Advanced Materials Research. Vol. 846. 2014.
[7]. Chen, Joy Iong Zong, Yuan-Chen Chen, and Shien-Dou Chung. “Implementation.
[8]. Utsav Gada; Bhavya Joshi; Siddhant Kadam; Nilakshi Jain; Srikant Kodeboyina; Ramesh Menon. “IOT based Temperature Monitoring System, 31st July 2021.
Citation
V.N. Ghodke, Athul Pillai, Sahil S., Karan Pillay, "Temperature and Humidity Monitoring System over Plant and Uploading into the Cloud," International Journal of Computer Sciences and Engineering, Vol.10, Issue.9, pp.6-9, 2022.
Flight Price Prediction Using Machine Learning Techniques
Research Paper | Journal Paper
Vol.10 , Issue.9 , pp.10-13, Sep-2022
CrossRef-DOI: https://doi.org/10.26438/ijcse/v10i9.1013
Abstract
This article will examine the issue of foreseeing air passages. To do this, a great deal of things has been distinguished, and you believe that the qualities of a typical airplane will influence the cost of aircraft tickets. Highlights are utilized in eight current AI strategies, used to foresee airplane costs, and model execution is thought about. As well as cautiously anticipating each model, this paper cautiously inspects the data used to distinguish carrier tickets.
Key-Words / Index Term
Machine Learning, Decision tree, Random Forest, K-Nearest Method.
References
[1] Tom Chitty, CMBC Business News, “This is how airplanes price tickets”, August 3, 2018.
[2] Moira McCormick, Black Curce, “Behind the Scenes of Airline Pricing Strategies”, September 19, 2017.
[3] K. Tziridis, Th. Kalampokas, G. A. Papakostas, “Airfare Prices Prediction Using Machine Learning Techniques”, 25th European Signal Processing Conference (EUSIPCO), IEEE, October 26, 2017.
[4] Tianyi Wang, Samira Pouyanfar, Haiman Tian, Yudong Tao, Miguel Alonso Jr., Steven Luis and Shu-Ching Chen, “A Framework for Airfare Price Prediction: A Machine Learning Approach”, 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), September 9, 2019.
[5] Tao Liu, Jian Cao, Yudong Tan, Quanwu Xiao, “ACER: An Adaptive Context- Aware Ensemble Regression Model for Airfare Price Prediction”, 2017 International Conference on Progress in Informatics and Computing (PIC), December, 2017.
[6] Supriya Rajankar, Neha Sakharkar, Omprakash Rajankar, “Predicting the price of a flight ticket with the use of Machine Learning algorithms”, international journal of scientific & technology research, vol.8, December, 2019.
[7] Juhar Ahmed Abdella, Nazar Zaki and Khaled Shuaib, “Automatic Detection of Airline Ticket Price and Demand: A Review”, 13th International Conference on Innovations in Information technology (IIT), January 10, 2019.
[8] Chaya Bakshi, Medium, “Random Forest Regression”, June 9, 2020.
[9] Zach, Statology, “How to calculate mean Absolute Error in Python”, January 8. 2021.
[10] Bickel, Peter J.; Doksum, Kjell A. Mathematical Statistics: Basic Ideas and Selected Topics. Vol. I (Second ed.). p. 20. 2015.
[11] Tianfeng Chai, R. R. Draxler, “Root-Mean Squared Error”, Geoscientific Model Development Discussions 7(1), DOI: 10.5194/gmdd-7-1525-2014.
Citation
B.S. Panda, B. Phanendra Varma, B. Chandini, R. Bhoomika, "Flight Price Prediction Using Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.10, Issue.9, pp.10-13, 2022.
Effect of GMAW on the Tensile Strength and Hardness of Commercial Steel
Research Paper | Journal Paper
Vol.10 , Issue.9 , pp.14-20, Sep-2022
CrossRef-DOI: https://doi.org/10.26438/ijcse/v10i9.1420
Abstract
Gas metal arc welding (GMAW) is very popular welding processes in the industry. The welding parameters that has significance in demonstrating the welding quality such as welding current, welding voltage, Gas flow rate, wire feed speed, welding speed and wire size. Taguchi`s design is considered an efficient and a powerful optimizing tool for better quality and higher output performance of manufacturing processes. In this study, GMAW has welded commercial steel under controlled parameters of base metal thickness, welding current and wire feed speed. The analysis using Taguchi`s design made on the influence of welding parameters on the strength of the welding, the tensile strength and hardness. Higher tensile strength and hardness were obtained at higher base metal thickness, lower voltage and wire feed speed (WFS). The hardness increased with the increased internal stresses. Higher base metal thickness obtained higher effect on the higher tensile strength and hardness according to the noise conditions analysis and also higher tensile strength and hardness results followed by lower wire feed speed. The optimal process parameters favorable for strong and effective welding are base metal groove shape V, 20 V and 5.9 m/min WFS. These settings are recommended when welding commercial steel (EN 10025-2) using mild steel filler in GMAW.
Key-Words / Index Term
GMAW, Commercial steel, Taguchi method, Tensile strength, Vickers microhardness
References
[1] S. Chavda, T. Patel, “A Review On Parametric Optimization of MIG Welding For Medium Carbon Steel using FEA-DOE Hybrid Modeling”, International Journal for Scientific Research & Development, Vol.1, Issue.9, pp.2321-0613, 2013.
[2] J. Shah, G. Patel, J. Makwana, P. Chauhan, “Optimization and Prediction of MIG Welding Process Parameters using ANN”, International Journal of Engineering Development and Research, Vol.5, Issue.2, pp.1487–1492, 2017.
[3] K. Kumar, A. Kumar, "Optimi-Zation of Critical Deliberations in a Product Type Industry Using Taguchi Method and Soft Computing Techniques", International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.80-84, 2020.
[4] S. Mahesh, V. Appalaraju, “Optimization of MIG Welding Parameters for Improving Strength of Welded Joints”, International Journal of Innovative Technology and Research, Vol.5, Issue.3, pp.6453-6458, 2017.
[5] N. Subramanian, “Steel structures-design and practice”, Oxford University Press, 2011.
[6] C. Hamzaçebi, “Taguchi Method as a Robust Design Tool”, In Quality Control - Intelligent Manufacturing, Robust Design and Charts, IntechOpen, UK, pp. 1-19, 2020.
[7] M.S. Phadke, “Quality Engineering using Robust Design”, Prentice Hall, New Jersey, 1989.
[8] R. Unal, E.B. Dean, “Design For Cost And Quality: The Robust Design Approach”, Journal Of Parametrics, Vol.11, Issue.1, 1991.
[9] P.J. Ross, “Taguchi Techniques For Quality Engineering”, Mc Graw Hill, Newyork, 1988.
[10] M. Tanco, E. Viles, L. Ilzarbe, M.J. Álvarez, “Manufacturing Industries Need Design of Experiments (DOE)”, In Proceedings of the World Congress on Engineering, World Congress on Engineering, UK, 2007.
[11] S. Nema, A. Sahay, K. Shivvedi, V. Rajput, “A Review on Optimization of MIG Welding Parameters Using Taguchi’s DOE Method”?, Journal for Advanced Research in Applied Sciences, Vol.6, Issue.1, pp.29-34, 2019.
[12] R. Raghu, S. Somasundaram, “An Optimization of Welding Parameters for MIG Welding”, International Journal of Engineering Research and Technology, Vol.6, Issue.14, pp.1-5, 2018.
[13] S.R. Patil, C.A. Waghmare, “Optimization of MIG Welding Parameters for Improving Strength of Welded Joints”, International Journal of Advanced Engineering, pp.14-16, 2013.
[14] S.S.S. Elfallah, “Influence of GMAW parameters on the tensile strength of commercial steel”, International Science and Technology Journal, Vol.29, pp.1-16, 2022.
[15] S. Jeet, A. Barua, B. Parida, B.B. Sahoo, D.K. Bagal, “Multi-Objective Optimization of Welding Parameters in GMAW for Stainless Steel and Low Carbon Steel using Hybrid RSM-TOPSIS-GA-SA Approach”, International journal of technical innovation in modern science, Vol.4, pp.683-692, 2018.
[16] T. Tawfeek, “Study The Influence of Gas Metal Arc Welding Parameters on the Weld Metal and Heat Affected Zone Microstructures of Low Carbon Steel”, International Journal of Engineering and Technology, Vol.9, Issue.3, pp.2013-2019, 2017.
[17] P.K. Yadav, M.D. Abbas, S. Patel, “Analysis of Heat Affected Zone of Mild Steel Specimen Developed Due to MIG Welding”, International Journal of Mechanical Engineering and Robotics Research, Vol.3, Issue.3, pp.399, 2014.
[18] Y. Purwaningrum, M. Wirawan Pu, F. Alfarizi, “Effect of Shielding Gas Mixture on Gas Metal Arc Welding (GMAW) of Low Carbon Steel (LR Grade A)”, Key Engineering Materials, Trans Tech Publications, Vol.705, pp.250-254, 2016.
[19] N. Sankar, S. Malarvizhi, V. Balasubramanian, “Mechanical Properties and Microstructural Characteristics of Rotating Arc-Gas Metal Arc Welded Carbon Steel Joints”, Journal of the Mechanical Behavior of Materials, Vol.30, Issue.1, pp.49-58, 2021.
[20] S.K. Bhatti, G. Kocher, M. Singh, "Influence of Number of Weld Passes on Microhardness and Impact Toughness of Gas Metal Arc (GMA) welded AISI 1020 joints", International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.278-285, 2018.
[21] M.N. Sultana, M.F. Hasan, M. Islam, “Analysis of mechanical properties of mild steel applying various heat treatment”, In Proceedings of the International Conference on Mechanical, Industrial and Energy Engineering (ICMIEE-PI-140160), Bangladesh, 2014.
[22] M.A. Bodude, I. Momohjimoh, “Studies on Effects of Welding Parameters on the Mechanical Properties of Welded Low-Carbon Steel”, Journal of Minerals and Materials Characterization and Engineering, Vol.3, Issue.3, pp.142-153, 2015.
[23] N.A. Abd Razak, S.S. Ng, “Investigation of Effects of MIG Welding on Corrosion Behaviour of AISI 1010 Carbon Steel”, Journal of Mechanical Engineering and Sciences, Vol.7, Issue.2, pp.1168-1178, 2014.
[24] P. Marimuthu, T.T.M. Kannan, G. Balaji, “Evaluation of Hardness and Tensile Strength of Micro Spot Welded Joints on Different Materials”, International Journal of Applied Engineering Research Vol.10, Issue.3, pp. 2412-2414, 2015.
[25] S.I. Talabi, O.B. Owolabi, J.A. Adebisi, T. Yahaya, “Effect of Welding Variables on Mechanical Properties of Low Carbon Steel Welded Joint”, Advanced Production Engineering Management, Vol.9, Issue.4, pp.181-186. 2014.
[26] Y.R. Ratiwi, S.S. Wibowo,. “The effect of electrode and number of passes on hardness and micro structure of shielded metal arc welding”, IOP Conference Series, Materials Science and Engineering, IOP Publishing, Vol.515, Issue.1, pp.012072, 2019.
Citation
Saleh Suliman Elfallah, "Effect of GMAW on the Tensile Strength and Hardness of Commercial Steel," International Journal of Computer Sciences and Engineering, Vol.10, Issue.9, pp.14-20, 2022.