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Algorithms
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Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications (2nd Edition)
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Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Analysis of Algorithms and Complexity Theory".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 487

Special Issue Editors

Prof. Dr. Carolina Del-Valle-Sot

E-Mail Website
Guest Editor
Academy of Computing, School of Engineering, Álvaro del Portillo 49, Universidad Panamericana, Zapopan 45010, Jalisco, Mexico
Interests: algorithm design; optimization techniques; wireless sensor networks; jamming detection; artificial intelligence; routing in complex networks; wearable and IoT systems; energy-efficient protocols
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ramiro Velázquez

E-Mail Website
Guest Editor
School of Engineering, Josemaría Escrivá de Balaguer 101, Universidad Panamericana, Aguascalientes 20296, Mexico
Interests: assistive technology; mechatronic systems; sensors and actuators; wearables; haptics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, “Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications (2nd Edition)”, invites high-quality contributions that explore the design, analysis, and application of novel algorithms. Its aim is to unify diverse computational approaches—ranging from theoretical models to real-world deployments—across fields such as sensor networks, artificial intelligence, robotics and mechatronics, healthcare systems, and smart environments.

We welcome submissions that demonstrate methodological rigor and practical relevance, particularly those that address optimization under constraints, performance analysis, or cross-disciplinary integration. Studies that leverage metaheuristics, neural network-based models, or algorithmic frameworks for emergent applications such as IoT, smart healthcare, environmental monitoring, and resilient networks are especially encouraged.

This Special Issue also aligns with the Algorithms journal’s mission to support reproducibility and interdisciplinary impact. As such, detailed methodological documentation and openly shared resources (e.g., source code, datasets) are highly encouraged.

Prof. Dr. Carolina Del Valle Soto
Prof. Dr. Ramiro Velázquez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • algorithm design and optimization
  • energy-aware computing
  • artificial intelligence and machine learning algorithms
  • robotics and mechatronics
  • routing protocols
  • wireless sensor networks and IoT
  • metaheuristics and hybrid algorithms
  • resilient and secure networks
  • interdisciplinary algorithm applications

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

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Research

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21 pages, 2014 KB  
Article
A Machine Learning-Driven CRM Approach for Identifying Member Churn in a Brazilian Agro-Industrial Cooperative: A Practical Case Study
by Sergio Akio Tanaka, João Vitor da Costa Andrade, Alessandro Botelho Bovo, Attilio Converti, Danilo Sipoli Sanches and Hugo Valadares Siqueira
Algorithms 2026, 19(3), 180; https://doi.org/10.3390/a19030180 - 27 Feb 2026
Viewed by 327
Abstract
This study addresses member churn in a Brazilian agro-industrial cooperative by operationalizing a leakage-aware, governance-aligned machine-learning protocol within the organization’s Customer Relationship Management (CRM) system. Using real-world CRM data under confidentiality constraints, we followed a KDD-based workflow. This workflow includes: (i) multi-source integration; [...] Read more.
This study addresses member churn in a Brazilian agro-industrial cooperative by operationalizing a leakage-aware, governance-aligned machine-learning protocol within the organization’s Customer Relationship Management (CRM) system. Using real-world CRM data under confidentiality constraints, we followed a KDD-based workflow. This workflow includes: (i) multi-source integration; (ii) targeted preprocessing with explicit handling of severe class imbalance via undersampling; (iii) a unified validation scheme with stratified cross-validation, hyperparameter search, and controlled AutoML benchmarking; (iv) comparison of tabular learners (Random Forest, XGBoost, and Support Vector Classifier) and a voting ensemble; and (v) SHAP-based explainability to support transparent decision-making. Class rebalancing substantially improved minority-class performance; for instance, the “Inactive” recall increased from 0.27 to 0.74 with SVC. Across ten folds, AutoML achieved competitive mean ROC-AUC (0.8844), followed by XGBoost (0.8690) and Random Forest (0.8660); global metrics supported operational feasibility (accuracy 0.79–0.80; ROC-AUC up to 0.8876), while the ensemble delivered comparable discrimination (ROC-AUC 0.8845) with a modest precision gain. SHAP analyses yielded business-coherent drivers and enabled actionable, instance-level communication in the CRM. The resulting microservices-based module exposes ranked churn propensities and explanations in dashboards for risk stratification and prioritization of retention actions. Overall, the work provides an interpretable, reproducible, and production-ready methodological blueprint for predictive CRM in seasonal cooperative environments under governance and confidentiality constraints. Full article
(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications (2nd Edition))
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Review

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25 pages, 3673 KB  
Review
Recent Advances in Multi-Camera Computer Vision for Industry 4.0 and Smart Cities: A Systematic Review
by Carlos Julio Fierro-Silva, Carolina Del-Valle-Soto, Samih M. Mostafa and José Varela-Aldás
Algorithms 2026, 19(4), 249; https://doi.org/10.3390/a19040249 (registering DOI) - 25 Mar 2026
Abstract
The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and [...] Read more.
The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures. Full article
(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications (2nd Edition))
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