Open Access

Article

Article ID: 2331

Design and implementation of an intelligent waste classification device

by Yung-Hsiang Chen, Chan-Hong Chao

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

This study presents a guideline for an intelligent waste classification device developed using a Raspberry Pi, a camera, and Google’s Teachable Machine (TM) for image recognition. The device is designed to identify waste and classify it into recyclable and non-recyclable categories to improve recycling efficiency. The system is primarily controlled by the Raspberry Pi, with the camera capturing images, which are then processed by TM for image model training to facilitate waste classification. This paper describes the hardware and software components as well as their applications and verifies the effectiveness of the device in practical use. The device is cost-effective, offers good scalability, and is practical for waste classification in households, offices, and public spaces. This study provides valuable insights for the design and future applications of intelligent waste classification systems.

Open Access

Article

Article ID: 1819

Multilevel rules mining association for processing big data using genetic algorithm

by Gebeyehu Belay Gebremeskel, Teshale Wubie Yilma

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

Data mining is a machine learning method and a subset of artificial intelligence that focuses on developing algorithms to enable a computer to learn from data and past experiences within its context. Multilevel association rules mining is a crucial area for discovering interesting relationships between data elements at various levels of abstraction. Many existing algorithms addressing this issue rely on exhaustive search methods such as Apriori and FP-growth. However, these methods incur significant computational costs when applied to big data applications searching for association rules. Therefore, we propose a novel genetic-based method with three key innovations to speed up the search for multilevel association rules and reduce excessive computation. Firstly, we utilize the category tree to describe multilevel application data sets as domain knowledge. Next, we introduce a unique tree-encoding schema based on the category tree to develop the heuristic multilevel association-mining algorithm. Lastly, we present a genetic algorithm based on the tree-encoding schema that greatly decreases the association rule search space. This method is valuable for mining multilevel association rules in big data applications.

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.