International Journal of Computer (IJC) <p>The <a title="International Journal of Computer (IJC) home page" href="" 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="" 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 peer review process (within 4 weeks). For detailed information about the journal kindly check <a title="About the Journal" href="">About the Journal</a> page. </p> <p>All <a title="International Journal of Computer (IJC)" href="" 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> </p> Mohammad Nassar for Researches (MNFR) en-US International Journal of Computer (IJC) 2307-4523 <p style="text-align: justify;">Authors who submit papers with this journal agree to the <a title="Copyright Notice" href="" target="_blank" rel="noopener">following terms</a>.&nbsp;</p> Realistic Sketch-based Face Photo Synthesis using Generative Adversarial Networks (GANs) <p>Facial photo-image synthesis and sketch-based face recognition are highly advantageous, particularly in the fields of security forces and forensics. Furthermore, it makes it more feasible for law enforcement to reduce the number of possible suspects in criminal identification operations. However, since pencil drawings and photographs have different properties by nature, creating a synthesis of photographs based on sketches presents a difficult topic. In the last few decades, generative adversarial network-based systems have achieved enormous advances towards improving the performance of image synthesis. It can speed up identification times while improving matching outcomes by reducing gaps among sketch and photo representations. We perform investigations on the well-known photo-sketch pair database CUHK. First, we demonstrate how a generative adversarial network transforms hand-drawn sketches into realistic photos. Secondly, we employ suspect identification by using the pre-trained VGG16-based feature extractor network and KNN classifier. Our technique focuses on the use of deep learning-based networks, which are well-known for their capacity to process data and extract hierarchical features. The presented image-to-image translation framework minimizes the modality differences between hand-drawn face sketches and color images while improving visual quality. Tests on sketch-photo matching demonstrate significant improvements over current state-of-the-art methods on the challenging task of matching sketches with corresponding photos.</p> Hnin Ei Ei Cho Aye Min Myat Copyright (c) 2024 Hnin Ei Ei Cho, Aye Min Myat 2024-06-15 2024-06-15 51 1 17 32 Optimisation of University Examination Timetable Using Hybridised Genetic and Greedy Algorithms: A Case Study of Computer Science Department, University of Ibadan <p>Timetable scheduling is an important aspect of decision-making in any organisation, particularly in academia. An examination timetable is expected to coordinate students, invigilators, courses, examination hall allocation, and time slots. However, <em>the</em> problem could be viewed as a Nondeterministic Polynomial (NP); NP-hard problem, scheduling problem has plagued humanity since its inception. Due to the complex structure of the problem in terms of hard and soft-constraints, most organisations schedule time inefficiently using manual approach. This study introduced an algorithms hybridisation method of genetic and greedy algorithms to automate the timetable scheduling process efficiently. A genetic algorithm is a heuristic search technique based on Charles Darwin's theory of natural evolution. The fitness of each course, venue, and faculty content is determined by the probabilistic optimisation which is the solution candidate in the initial population of all the objects. Subsequently, the greedy algorithm's activities selector selects the best solution. The output demonstrates that the method effectively handled all the constraints associated with timetable scheduling. Hybridising the two algorithms to build a scheduling system, such as the examination timetable. Therefore, it is a viable option to combine genetic and greedy algorithms to have an optimised examination timetable that is flexible to any situation.</p> Sunday J. Agbolade Babatunde l. Ayinla Lateefat A. Odeniyi Akinola S. O. Copyright (c) 2024 Sunday J. Agbolade, Ayinla , Lateefat A. Odeniyi, Akinola S. O. 2024-06-15 2024-06-15 51 1 1 16