https://hspublishing.org/JRECS/issue/feed 2025-04-10T17:03:00+01:00 Faruk Soban office@headstartnetwork.org Open Journal Systems <p><em><strong>Journal of Research in Engineering and Computer Sciences (JRECS) </strong></em>ISSN-3049-7590 is a peer-reviewed academic journal published on bi-monthly bases that publishes high-quality research in the fields of engineering and computer sciences. The journal provides a platform for researchers, engineers, and scientists from around the world to share their latest research findings, ideas, and innovations.</p> <p>Engineering and computer sciences are two fields that are constantly evolving and pushing the boundaries of what is possible. They are integral to the development of new technologies and innovations that have transformed the way we live and work. Research in these fields seeks to understand the underlying principles that govern complex systems, as well as to develop new tools and techniques for solving complex problems. From artificial intelligence and machine learning to robotics and biotechnology, engineering and computer science research are at the forefront of many cutting-edge fields. As the demand for new technologies and innovative solutions continues to grow, the importance of research in these fields cannot be overstated.</p> https://hspublishing.org/JRECS/article/view/889 2025-03-22T15:18:44+00:00 Ameen M. Salami ameen.selami@uokirkuk.edu.iq Layla Salih layla-salih@uokirkuk.edu.iq Osama Alluhaibi osake@uokirkuk.edu.iq

<p>Osteoporosis is a disease that affects bones, it makes them fragile and susceptible to break. Recently, Dual-Energy X-ray Absorptiometry (DEXA) technology is being used to detect osteoporosis with low levels of radiation compared to X-Ray and CT-Scans. In this article, evaluation of the effectiveness of features extracted from DEXA images in detecting osteoporosis in the cervical vertebrae was tested. Machine learning (ML) and artificial intelligence (AI) techniques represented by fuzzy logic and feed forward back-propagation neural networks (FFBNN) are used to achieve an accurate detection of Osteoporosis. Fourteen features representing (histogram, texture, statistical, and Haar wavelet) types were tested. Three membership functions (MFs) namely: (Gaussian, Bell and Triangle) and three de-fuzzification techniques were used using Mamdani Fuzzy Inference System (FIS). In this phase of classification, a success rate achieved was 90%. Artificial Neural Network (ANN) represented by FFBNN is depended to complete the proposed classification system. Depending on (11, 7, 4, and 2) neurons having two output neurons and based on the Tansig function, the training succeeded with a performance of 10^-8. The training passed for 28 inputs, and for 5 test cases among 7 cases to reach a success rate of 94.28% and the overall classification (Fuzzy + FFBPNN) succeeded by a rate of 99.4% by classifying 349 images among 351.</p>

2025-04-15T00:00:00+01:00 Copyright (c) 2025 Journal of Research in Engineering and Computer Sciences https://hspublishing.org/JRECS/article/view/890 2025-03-23T03:14:06+00:00 Joseph Bikoun Mousi josephbikoun@gmail.com Rolande Aurelie Tchouateu Kamwa josephbikoun@gmail.com Pacôme Talla Oumbe josephbikoun@gmail.com Monique Makamyou josephbikoun@gmail.com Emmanuel Yamb josephbikoun@gmail.com Jacques Etame josephbikoun@gmail.com

<p>In developing countries such as Cameroon, the sale and use of second-hand tyres from European and Asian countries is booming; most vehicle owners cannot afford new tyres. These tyres obviously have a very short life (less than 3 months) and end up in the wild or in fire, with for both cases, a serious environmental impact: this often means that these countries are the garbage of the West. In order to make a useful valorization of these wastes, this work makes a contribution by incorporating the steel fibers from worn-out tyres into concrete. The study showed the impact of fiber lengths and their proportion, in quantity, on the mechanical behavior of concrete. To do this, the concrete was reinforced with steel fibers from used tyres collected from tyre operators in Douala 5^th specifically in Ndogbong - Douala. The sand used in this work comes from the river Sanaga - Edéa. We made test pieces according to four fiber grades (0%, 0.4%, 0.6% and 0.8%) and three different lengths of steel fibers (2 cm, 3.5 cm and 5 cm), in accordance with previous works. Subsequently, three-points bending tests, compression tests, water absorption rate and density tests were carried out on concrete samples at 7, 14 and 28 days of cure. Test pieces of dimensions 40 x 40 x 160 (in accordance with EN 196) were used to determine the rate of water absorption, density and tensile strength by three points bending in a first step; then those of dimensions 40 x 40 x 80 (following the NF P 18-406 standard) allowed to determine the compressive strengths. Analysis have shown that the density of steel fiber reinforced concrete is higher than that of control concrete in all cases. The introduction of steel fibers into concrete reduces the rate of absorption of concrete. In addition, compared with the control concrete, the bending limits and compression stresses of the different proportions increased for all lengths and fiber contents.&nbsp; However, it was observed that concrete specimens with a dosage of 0.6% steel fibers and a length of 2 cm (BFA2-6) had an optimum physical and mechanical properties and was more elastic.</p>

2025-04-10T00:00:00+01:00 Copyright (c) 2025 Journal of Research in Engineering and Computer Sciences