https://revistas.usal.es/cinco/index.php/2255-2863 <p dir="ltr">The <a title="adcaij" href="http://adcaij.usal.es" target="_blank" rel="noopener">Advances in Distributed Computing and Artificial Intelligence Journal</a> (ISSN: 2255-2863) is an open access (OA) journal that publishes articles which contribute new results associated with distributed computing and artificial intelligence, and their application in different areas, such as the Deep Learning, Generative AI, Electronic commerce, Smart Grids, IoT, Distributed Computing and so on. These technologies are changing constantly as a result of the large research and technical effort being undertaken in both universities and businesses. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of computing.</p> <p dir="ltr">ADCAIJ focuses attention in the exchange of ideas between scientists and technicians. Both, academic and business areas, are essential to facilitate the development of systems that meet the demands of today's society. The journal is supported by the research group <a title="bisite" href="http://bisite.usal.es/en/research/research-lines" target="_blank" rel="noopener">BISITE</a>.</p> <p dir="ltr">The journal commenced publication in 2012 with quarterly periodicity and has published more than 300 articles with peer review. All the articles are written in scientific English language.</p> <p dir="ltr">From volume 12 (2023) onwards, the journal will be published in continuous mode, in order to advance the visibility and dissemination of scientific knowledge.</p> <p dir="ltr">ADCAIJ is indexed in Scopus and in the Emerging Sources Citation Index (ESCI) of Web of Science, in the category COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE. It also appears in other directories and databases such as DOAJ, ProQuest, Scholar, WorldCat, Dialnet, Sherpa ROMEO, Dulcinea, UlrichWeb, BASE, Academic Journals Database and Google Scholar.</p> Universidad de Salamanca en-US ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 2255-2863 https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31737 Diabetes, hypertension, obesity, glaucoma, etc. are severe and common retinopathy diseases today. Early age detection and diagnosis of these diseases can save human beings from many life threats. The retina’s blood vessels carry details of retinopathy diseases. Therefore, feature extraction from blood vessels is essential to classify these diseases. A segmented retinal image is only a vascular tree of blood vessels. Feature extraction is easy and efficient from segmented images. Today, there are existing different approaches in this field that use RGB images only to classify these diseases due to which their performance is relatively low. In the work, we have proposed a model based on machine learning that uses segmented retinal images generated by different efficient methods to classify diabetic retinopathy, glaucoma, and multi-class diseases. We have carried out extensive experiments on numerous images of DRIVE, HRF, STARE, and RIM-ONE DL datasets. The highest accuracy of the proposed approach is 90.90 %, 95.00 %, and 92.90 % for diabetic retinopathy, glaucoma, and multi-class diseases, respectively, which the model detected better than most of the methods in this field. Sushil Kumar Saroj Rakesh Kumar Nagendra Pratap Singh Copyright (c) 2024 Sushil Kumar Saroj https://creativecommons.org/licenses/by-nc-nd/4.0/ 2025-02-27 2025-02-27 14 e31737 e31737 10.14201/adcaij.31737 https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31698 The current computing era enables the generation of vast amounts of data, which must be processed to extract valuable insights. This processing often requires multiple query operations, where hashing plays a crucial role in accelerating query response times. Among hashing techniques, Cuckoo Hashing has demonstrated greater efficiency than conventional methods, offering simplicity and ease of integration into various real-world applications. However, Cuckoo Hashing also has limitations, including data collisions, data loss due to collisions, and the potential for endless loops that lead to high insertion latency and frequent rehashing. To address these challenges, this work introduces a modified Matrix hashing technique. The core concept of the proposed scheme is to utilize both a 2D array and an additional 1D array with random probing to create a more robust technique that competes effectively with Cuckoo Hashing. This study also introduces degree of dexterity as a new performance metric, in addition to the traditional load factor. Furthermore, the Even-Odd hash function is proposed to ensure a more balanced load distribution. Through rigorous experimental analysis in a single-threaded environment, this modified Matrix hashing with random probing in the 1D array is shown to effectively resolve key issues associated with Cuckoo Hashing, such as excessive data migration, inefficient memory usage, and high insertion latency. Rajeev Ranjan Kumar Tripathi Pradeep Kumar Singh Sarv Pal Singh Copyright (c) 2024 Rajeev Ranjan, Prof.P.K.Singh, Prof.S.P.Singh https://creativecommons.org/licenses/by-nc-nd/4.0/ 2025-02-27 2025-02-27 14 e31698 e31698 10.14201/adcaij.31698 https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31736 In an era where digital transformation is accelerating rapidly, artificial intelligence and machine learning have emerged as transformative forces, especially in e-commerce. This paper presents a comprehensive literature review that delves into the fundamentals of e-commerce, artificial intelligence, and machine learning, highlighting their key advantages and practical applications. By examining a broad array of studies, this research evaluates the critical role of artificial intelligence and machine learning in reshaping e-commerce and explores the potential these technologies hold for enhancing customer engagement and driving sales. The paper underscores how e-commerce companies leverage artificial intelligence-driven innovations to influence customer behaviour, enhance personalised marketing, and streamline purchasing pathways. However, the path to successful artificial intelligence integration is not without obstacles. Challenges such as organisational resistance, skills shortages, technical limitations, and awareness gaps are notable barriers. Despite these hurdles, the findings suggest that adopting artificial intelligence and machine learning tools positions e-commerce companies for long-term success, offering significant competitive advantages and fostering sustainable growth in an increasingly digital world. Fedorko Richard Kráľ Štefan Kráľová Lenka Copyright (c) 2025 Richard Fedorko, Štefan Kráľ, Lenka Štofejová https://creativecommons.org/licenses/by-nc-nd/4.0/ 2025-02-27 2025-02-27 14 e31736 e31736 10.14201/adcaij.31736 https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31609 Blockchain technology has experienced significant growth across various industries. However, challenges such as scalability, high transaction fees, and resource inefficiencies continue to limit its full potential. This paper presents a novel approach using a multilayer graph to model and analyze blockchain transactions, with a focus on resource consumption—specifically opcode execution and gas usage. By categorizing accounts into distinct layers—Externally Owned Accounts (EOAs), smart contracts, oracles, and cross-chain bridges—the graph-based model captures interactions across these account types. Through transaction trace analysis, we extract opcode usage and gas consumption, applying graph-theoretical metrics such as node scoring and edge weighting to identify critical nodes and resource-intensive transactions. Our findings provide new insights into resource-heavy behaviors, revealing optimization opportunities to reduce transaction costs and improve scalability. Additionally, the approach aids in anomaly detection and smart contract optimization, enhancing the cost-effectiveness and performance of blockchain systems. Inas Hasnaoui Maria Zrikem Raja Elassali Copyright (c) 2025 Inas Hasnaoui, Maria Zrikem, Rajaa Elassali https://creativecommons.org/licenses/by-nc-nd/4.0/ 2025-02-27 2025-02-27 14 e31609 e31609 10.14201/adcaij.31609