International Journal of Computer (IJC) https://ijcjournal.org/index.php/InternationalJournalOfComputer <p>The <a title="International Journal of Computer (IJC) home page" href="/index.php/InternationalJournalOfComputer/index" target="_blank" rel="noopener"><strong>International Journal of Computer (IJC)</strong></a> is an open access International Journal for scientists and researchers to publish their scientific papers in Computer Science related fields. <a title="International Journal of Computer (IJC)" href="/index.php/InternationalJournalOfComputer/index" target="_blank" rel="noopener">IJC</a> plays its role as a refereed international journal to publish research results conducted by researchers.</p> <p>This journal accepts scientific papers for publication after passing the journal's double&nbsp;peer review process.&nbsp; For detailed information about the journal kindly check <a title="About the Journal" href="/index.php/InternationalJournalOfComputer/about">About the Journal</a>&nbsp;page.&nbsp;</p> <p>All <a title="International Journal of Computer (IJC)" href="/index.php/InternationalJournalOfComputer/index" target="_blank" rel="noopener">IJC</a> published papers in Computer Science will be available for scientific readers for free; no fees are required to download published papers in this international journal.</p> <p>&nbsp;</p> en-US <p style="text-align: justify;">Authors who submit papers&nbsp;with this journal agree to the following terms:</p> <ol start="1"> <li class="show" style="text-align: justify;">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a&nbsp;<a href="http://creativecommons.org/licenses/by/3.0/" target="_new">Creative Commons Attribution License</a>&nbsp;that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li> <li class="show" style="text-align: justify;">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li> <li class="show" style="text-align: justify;">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See&nbsp;<a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li> <li class="show" style="text-align: justify;">By submitting the processing fee, it is understood that the author has agreed to our terms and conditions which may change from time to time without any notice.</li> <li class="show" style="text-align: justify;">It should be clear for authors that the Editor In Chief is responsible for the final decision about the submitted papers; have the right to accept\reject any paper. &nbsp;The Editor In Chief will choose any option from the following to review the submitted papers:A. send the paper to two reviewers, if the results were negative by one reviewer and positive by the other one; then the editor may send the paper for third reviewer or he take immediately the final decision by accepting\rejecting the paper. The Editor In Chief will ask the selected reviewers to present the results within 7 working days, if they were unable to complete the review within the agreed period then the editor have the right to resend the papers for new reviewers using the same procedure. If the Editor In Chief was not able to find suitable reviewers for certain papers then he have the right to reject the paper.</li> <li class="show" style="text-align: justify;">Author will take the responsibility what so ever if any copyright infringement or any other violation of any law is done by publishing the research work by the author</li> <li class="show" style="text-align: justify;">Before publishing, author must check whether this journal is accepted by his employer, or any authority he intends to submit his research work. we will not be responsible in this matter.</li> <li class="show" style="text-align: justify;">If at any time, due to any legal reason, if the journal stops accepting manuscripts or could not publish already accepted manuscripts, we will have the right to cancel all or any one of the manuscripts without any compensation or returning back any kind of processing cost.</li> <li class="show" style="text-align: justify;">The cost covered in the publication fees is only for online publication of a single manuscript.</li> </ol> editor@ijcjournal.org (Dr. Mohamad Nasar) support@gssrr.org (Inquiries:) Sun, 11 Jul 2021 18:57:38 +0000 OJS 3.1.2.1 http://blogs.law.harvard.edu/tech/rss 60 Fuzzy Logic Based Dam Water Shutter Control System by Using Water Level and Rainfall Condition in Raining Season https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1881 <p>A robust water shutter management system ensures that the water does not overflow and destroy or damage the dam. During the rainy season, care must be taken when dams conserve water, if the reservoir volume is too high, the risk of dam failure may be increased. So water level control is a special matter in the rainy season. Fuzzy logic sets provide better control than binary logic-based methods because they are used to determine the meaning of qualitative values for controller inputs and outputs, such as small, medium, and large control actions. This system used the fuzzy logic control theory in the water shutter management system to get smoothness motor control values of small, very small, medium, large, and very large. The system uses ultrasonic sensors to detect water levels, rain sensors to detect rain, and fuzzy logic controls to control the PWM duty cycle to the shutter gate motor driver circuit based on the detection of these two sensors. This control strategy is implemented with Arduino Uno.</p> Nay Thazin Htun, Moh Moh Nyunt Aung Copyright (c) 2021 International Journal of Computer (IJC) https://creativecommons.org/licenses/by-nc-nd/4.0 https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1881 Sun, 18 Jul 2021 15:14:24 +0000 Recognition of West African Indigenous Fruits using a Convolutional Neural Network Model https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1885 <p>The. Fruit recognition involves the extraction and processing of relevant features from fruit images in order to deduce the categories of that fruit. Due to its importance to human health and sustainability, various systems exist for recognition of fruits, although none exist for recognition of west Africa's indigenous fruits. This research developed a fruit recognition system using a convolutional neural network (CNN) based model. Five west Africa indigenous fruits were selected, while “images were directly used as input to CNN based model of (3 convolutional layers, 3 max pooling layers and 1 fully connected layer) for training and recognition without features extraction process. The study further presents a transfer learning on visual geometry group 16 and ResNet models for result comparison. Using the optimal training set, the proposed CNN based model produced a recognition rate of 96%.</p> Amarachi M. Udefi, Segun Aina, Samuel D. Okegbile, Aderonke R. Lawal, Adeniran I. Oluwaranti Copyright (c) 2021 International Journal of Computer (IJC) https://creativecommons.org/licenses/by-nc-nd/4.0 https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1885 Wed, 04 Aug 2021 07:44:53 +0000 Online Laundry Management System https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1895 <p>This study presents the automation of an online laundry management system (OLMS) for laundry organizations. Laundry firms usually have the challenges of keeping detailed records of customers clothing leading to disappointments on the side of customers. Issues arising include customer clothes mix-ups and untimely retrieval of clothes collected in relation to their owners. This system helps the users track progress on their clothing items, fixes date for collection or arranges drop-offs and communicates directly with business operators. Also, customers’ information remain available at all times as it is retained within the system. Each customer is assigned a unique ID on registration to avoid contrasting information. The implementation tools include PHP, JavaScript, HTML, MySQL, visual studio, WAMP server and a web browser. This solution brings ease to operating the business and controlling work flow; from managing customer information to managing service requests/orders as well as managing service rendition. The design also has a unique and user friendly interface. This affords the users and providers of the service an opportunity to enjoy seamless operations.</p> Olubukola D. Adekola, Stephen O. Maitanmi, Oyebola Akande, Olawale Somefun, Wumi Ajayi, Ayokunle Omotunde, Folusho S. Ayo-Fanibe, Iyanuoluwa T. Adeoye Copyright (c) 2021 International Journal of Computer (IJC) https://creativecommons.org/licenses/by-nc-nd/4.0 https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1895 Thu, 04 Nov 2021 20:03:05 +0000 Performance Analysis of Machine Learning Models for Sales Forecast https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1899 <p>Many supermarkets today do not have a strong forecast of their yearly sales. This is mostly due to the lack of the skills, resources and knowledge to make sales estimation. At best, most supermarket and chain store use adhoc tools and processes to analyze and predict sales for the coming year. The use of traditional statistical method to forecast supermarket sales has met a lot of challenges unaddressed and mostly results in the creation of predictive models that perform poorly.&nbsp; The era of big data coupled with access to massive compute power has made machine learning model the best for sales forecast. In this paper, we investigated the forecasting of sales with three machine learning algorithms and compare their predictive ability. Three different methods used are K-Nearest Neighbor, Gradient Boosting and Random forest. The data used to train the machine learning models are data provided by Data Science Nigeria on the Zindi platform, the data were collected from a supermarket chain called “Chukwudi Supermarkets”. The results show that the Random Forest algorithm performs slightly better than the other two models, we saw that Gradient Boosting models were prone to over-fitting easily and that K-Nearest Neighbor even though fast, performs poorest among the three.</p> Omogbhemhe Izah Mike, Odegua Rising Copyright (c) 2021 International Journal of Computer (IJC) https://creativecommons.org/licenses/by-nc-nd/4.0 https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1899 Sat, 27 Nov 2021 09:15:19 +0000