Open Access

Article

Article ID: 1884

The potential role of domain vectors in optimizing digital data structure

by Wolfgang Orthuber

Computing and Artificial Intelligence, Vol.3, No.1, 2025;

Each piece of information represents a selection from a set of possibilities. This set is the “domain of information”, which must be uniformly known before any information transport. The components of digital information are also sequences of numbers that represent a selection from a domain of information. So far, however, there is no guarantee that this domain is uniformly known. There is still no infrastructure that makes it possible to publish the domain of digital information in a uniform manner. Therefore, a standardized machine-readable online definition of the binary format and the domain of digital number sequences is proposed. These are uniquely identified worldwide as domain vectors (DVs) by an efficient Internet address of the online definition. As a result, optimized, language-independent digital information can be uniformly defined, identified, efficiently exchanged and compared worldwide for more and more applications.

Open Access

Article

Article ID: 1786

Enhancing data curation with spectral clustering and Shannon entropy: An unsupervised approach within the data washing machine

by Erin Chelsea Hathorn, Ahmed Abu Halimeh

Computing and Artificial Intelligence, Vol.3, No.1, 2025;

In the realm of digital data proliferation, effective data curation is pivotal for extracting meaningful insights. This study explores the integration of spectral clustering and Shannon Entropy within the Data Washing Machine (DWM), a sophisticated tool designed for unsupervised data curation. Spectral clustering, known for its ability to handle complex and non-linearly separable data, is investigated as an alternative clustering method to enhance the DWM’s capabilities. Shannon Entropy is employed as a metric to evaluate and refine the quality of clusters, providing a measure of information content and homogeneity. The research involves rigorous testing of the DWM prototype on diverse datasets, assessing the performance of spectral clustering in conjunction with Shannon Entropy. Results indicate that spectral clustering, when combined with entropy-based evaluation, significantly improves clustering outcomes, particularly in datasets exhibiting varied density and complexity. This study highlights the synergistic role of spectral clustering and Shannon Entropy in advancing unsupervised data curation, offering a more nuanced approach to handling diverse data landscapes.

Open Access

Article

Article ID: 1577

Advancements in nutty quality: Segmentation for enhanced monitoring and determination

by P. Saranya, R. Durga

Computing and Artificial Intelligence, Vol.3, No.1, 2025;

 Segmentation of nut images plays a vital role in computer vision and agricultural applications. Precise segmentation enables the extraction and analysis of essential information about the nuts, supporting quality evaluation, yield estimation, and automated sorting processes. This study explores nuts image segmentation utilizing the cuckoo search algorithm. The cuckoo search algorithm, a nature-inspired optimization technique, is introduced to enhance the segmentation process, potentially optimizing parameters or guiding the segmentation algorithms. Performance evaluation emphasizes metrics such as MSE, IoU, and dice coefficient. CSA (cuckoo search algorithm) demonstrates superior results, showcasing its effectiveness in automated nuts segmentation. This research contributes to the advancement of nut image analysis, providing insights into segmentation methodologies that can enhance automated processes in agriculture and food industry applications. The findings underscore the significance of employing advanced algorithms like CSA for accurate and efficient segmentation of nuts in images.

Open Access

Article

Article ID: 2018

Intelligent process migration in heterogeneous distributed systems

by Terecio Diosnel Marecos Brizuela, David Luis La Red Martínez, Federico Agostini, Jorge Tomás Fornerón Martínez

Computing and Artificial Intelligence, Vol.3, No.1, 2025;

In distributed processing environments, multiple groups of processes are found sharing resources and competing for access. These processes may or may not require synchronization and it is essential to reach a consensus to manage access to resources in a way that establishes a strict order, thus ensuring mutual exclusion. The proposal presented is an innovative and effective solution for the management of shared resources in distributed systems, which allows solving problems related to overload and workload balancing. The evaluation of the state of computational loads and the final comparison using standard deviation are useful tools to detect and correct imbalances in the system. In addition, the possibility of establishing initial configurations of the algorithm for each particular situation allows adapting the solution to the specific needs of each system.

Open Access

Article

Article ID: 1987

Development of a system for creating and recommending combination collections in the e-commerce clothing industry

by Erdem Çetin, Murat Berker Özbek, Sezin Biner, Ceren Ulus, M. Fatih Akay

Computing and Artificial Intelligence, Vol.3, No.1, 2025;

In the clothing sector, matching the right demand with the appropriate user is of great significance. Combination suggestions emerge as an innovative strategy for e-commerce platforms operating in the clothing sector. By providing suitable combination suggestions tailored to the right user, the profit margin of sales increased, and the brand image strengthened. The aim of this study is to develop a recommendation system based on image processing and machine learning that generates combinations from products that may interest users and recommends these combinations to them. 90 million possible combinations have been obtained using a dataset consisting of products detected from images of items sold in the clothing category on Trendyol. These combinations have been trained using the Prod2Vec algorithm to create new pairings. Subsequently, collections have been developed for purchasing looks using image processing methods. In this context, the You Only Look Once (YOLO) model has been selected for clothing classification, while the Convolutional Network Next (ConvNext) model has been employed for calculating image similarity. Models have also been developed for estimating click performance using Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Regression (LR). The prediction performances of the developed models have been evaluated using Coefficient of Determination (R²), Mean Squared Error (MSE), and Mean Absolute Error (MAE) metrics. When the developed models have been examined, it has been observed that RF had superior performance. The developed system provided a 5% increase in the time spent on the Trendyol mobile application.

Open Access

Article

Article ID: 1934

A narrative literature review on the economic impact of cloud computing: Opportunities and challenges

by Surajit Mondal, Shankha Shubhra Goswami

Computing and Artificial Intelligence, Vol.3, No.1, 2025;

This paper focuses on assessing the Economic Impact (EI) of Cloud Computing (CC), which has emerged as a powerful technology that can transform business operations and enhance economic growth. This paper employs a narrative literature review methodology to assess the EI of CC, which has emerged as a transformative technology. It begins by examining the economic benefits of CC, including cost savings, improved efficiency, and increased innovation. Subsequently, it explores the challenges associated with assessing the EI of CC, such as data privacy and security concerns, interoperability issues, and the need for new regulatory frameworks. The paper also provides insights into the opportunities and challenges that CC presents for different sectors of the economy, including healthcare, finance, and government. Ultimately, the paper emphasizes the importance of a holistic approach to assessing the EI of CC that considers both its benefits and challenges in order to make informed decisions about its adoption and use.