Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2025
Volume
Vol. 26
Number
No. 1
Description
Journal Volume
Computer Science
Vol. 26 (2025)
Projects
Pages
Articles
Automatic indexation of cultural heritage 3D object
(Wydawnictwa AGH, 2025) Le-Tien, Mau; Nguyen-Tan, Khoi; Raffin, Romain
There has been significant evolution in the fields of 3D digitization thanks to the development of 3D reconstruction and geometry processing. The results of digitization researches have been widely applied in many fields, especially in Cultural Heritage and Archaeology. Reconstruction, characterization and annotation of components forming 3D objects have become an effective tool for research, conservation and promotion of archaeological relics. The aim of this paper is to propose a process of 3D model reconstruction, segmentation and annotation on the basis of a enhanced corresponding 2D dataset. A machine learning method is used for the semantic segmentation of 2D images, thereby label, annotate and reconstruct a 3D model based upon links between distinctive invariant features, orientation of images, and depth map of images. The initial result as a data basis for research, reconstruction and identification of parts in 3D objects is applied in the reconstruction of archaeological relics, object identification, 3D printing, etc. Our work uses the data collected from the Museum of Cham Sculpture DaNang and the Myson QuangNam sanctuary in VietNam, to carry out the proposed method.
Performance evaluation of a lightweight consensus protocol for blockchain in IoT networks
(Wydawnictwa AGH, 2025) Kaur, Manpreet; Gupta, Shikha
The consensus protocol is essential in practically every blockchain application. Most of these existing blockchain consensus protocols need massive computational capabilities, substantial energy consumption, and dependency on monetary stakes. These shortcomings in the mainstream consensus approach lead to their unsuitability for low-resource applications like IoT. As a result of this work, a lightweight consensus process referred as Delegated Proof of Accessibility (DPoAC) is implemented and evaluated. DPoAC makes use of Shamir secret sharing, Proof of Stake (PoS) with random selection, and the Inter-Planetary File System (IPFS). The DPoAC operation is composed of four modules: secret generation and distribution, retrieval of secret shares, block creation and verification, and block rewards and penalty. A detailed description of DPoAC has been provided and implemented in JavaScript and experimental results demonstrate that our solution meets the necessary performance and security requirements for a lightweight scalable protocol for IoT systems.
Enhanced cluster merging and deep learning techniques for entity name identification from biomedical corpus
(Wydawnictwa AGH, 2025) Das, Nilanjana; Dutta, Rakesh; Mondal, Uttam Kumar; Majumder, Mukta; Mandal, Jyotsna Kumar
For mining biomedical information identifying names is the prime task. Complex and uncertain naming styles of biomedical entities are the major setbacks here. Thus, state-of-the-art accuracy of biomedical name identification is reasonably inferior compared to general domain. This study includes Machine Learning and Deep Learning techniques to recognize names from biomedical corpus. In supervised classification, a classifier is built by finding required statistics from training corpus. Accordingly, performance of the system is primarily dependent on quantity and quality of training corpus. But manually preparing a large training dataset with enriched feature samples is laborious and time-taking. Therefore, various techniques were adopted in the literature to make effective use of raw corpora. We have incorporated a novel Cluster Merging technique and Attention Mechanism with BERT embedding for boosting Machine Learning and Deep Learning classifiers respectively. The suggested results outpour that profound techniques are competent and delineate signifying improvement over surviving methods.
The power of intelligence emerging from swarms
(Wydawnictwa AGH, 2025) Adrdor, Rachid
Swarm intelligence (SI) is a field of study that seeks to understand and model collective behaviors observed in natural social systems, such as ant colonies, bee hives, termite mounds, flocks of birds or schools of fish. The central principle of SI is that complex intelligent behaviors can emerge from the interactions of large numbers of simple individual entities, without any centralized control or monitoring. SI researchers aim to uncover the underlying principles and mechanisms behind this SI, with the aim of applying these concepts to solve complex problems in areas such as optimization, robotics, transport, IT, etc. As the field continues to evolve, SI is expected to have an increasingly significant impact on our understanding of biological systems and our ability to design intelligent systems capable of adapting and thriving in complex environments and dynamic. This article aims to introduce the reader to the field of SI, presenting its fundamental concepts, key principles, existing applications, and prospective future developments.
Energy efficient and QoS aware trustworthy routing protocol for MANET using hybrid optimization algorithms
(Wydawnictwa AGH, 2025) Veeramani, R.; MadhanMohan, R.; Mahesh, C.
The security challenges in MANETs are particularly difficult to address. To assess the reliability of each mobile node, factors such as location, mobility speed, energy usage, transmission count, and neighbor list are considered. This research proposes the Intelligent Dynamic Trust (IDT) paradigm to enhance security in wireless networks. For secure routing, IDT combines beta reputation trust with dynamic trust. Performance analysis was conducted using Network Simulator 3.36, with metrics such as throughput, energy consumption, packet delivery ratio, jitter, end-to-end delay, packet loss rate, detection rate, and routing overhead. The results show that the proposed approach outperforms existing methods.