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Electronics
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Next-Generation Machine Learning and Deep Learning Models for Complex Data,...
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Next-Generation Machine Learning and Deep Learning Models for Complex Data, Vision, and Intelligent Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 355

Special Issue Editor

Dr. Yoichi Tomioka

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
Interests: image processing; computer vision; image forensics; fault-tolerant AI; circuit designs

Special Issue Information

Dear Colleagues,

The research community has shown a burgeoning interest in applying machine learning (ML) and deep learning (DL) methodologies to solve complex real-world problems across diverse fields. These advancements in machine learning continuously present new challenges and innovative solutions for a variety of intricate issues in applications, technologies, and theoretical constructs. Deep learning, a critical subset of machine learning, focuses on learning hierarchical representations of input data via multiple non-linear layers. In recent years, DL techniques have seen widespread application, achieving remarkable success across various domains.

In this Special Issue, we invite researchers and practitioners in the fields of machine learning and deep learning to disseminate their original and innovative ideas. We welcome submissions that include both theoretical advancements and practical applications aimed at solving complex data-related challenges using ML and DL algorithms. Topics of interest for this collection include, but are not limited to, the following:

  • Advanced machine learning models and deep learning architectures (e.g., CNN, RNN, LSTM, GNN, Transfer Learning, Attention, and GCN);
  • Novel methods for feature extraction and selection;
  • Image analysis techniques (segmentation, classification, retrieval, and generation);
  • Human action and gesture recognition;
  • Development of intelligent and user-friendly interfaces;
  • Handwriting analysis and recognition;
  • Applications in healthcare and medical image analysis;
  • Bioinformatics and computer vision;
  • Explainable artificial intelligence (XAI).

Dr. Yoichi Tomioka
Guest Editor

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • machine learning (ML)
  • deep learning (DL)
  • computer vision
  • image segmentation
  • feature extraction
  • explainable AI (XAI)

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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15 pages, 2389 KB  
Article
Diffmap: Enhancement Difference Map for Peripheral Prostate Zone Cancer Localization Based on Functional Data Analysis and Dynamic Contrast Enhancement MRI
by Roman Surkant, Jurgita Markevičiūtė, Ieva Naruševičiūtė, Mantas Trakymas, Povilas Treigys and Jolita Bernatavičienė
Electronics 2026, 15(3), 507; https://doi.org/10.3390/electronics15030507 (registering DOI) - 24 Jan 2026
Abstract
Dynamic contrast-enhancement (DCE) modality of MRI is typically considered secondary in prostate cancer (PCa) diagnostics, due to the common interpretation that its diagnostic power is lower than that of other modalities like T2-weighted (T2W) or diffusion-weighted imaging (DWI). To challenge this paradigm, this [...] Read more.
Dynamic contrast-enhancement (DCE) modality of MRI is typically considered secondary in prostate cancer (PCa) diagnostics, due to the common interpretation that its diagnostic power is lower than that of other modalities like T2-weighted (T2W) or diffusion-weighted imaging (DWI). To challenge this paradigm, this study introduces a novel concept of a difference map, which relies exclusively on DCE-MRI for the localization of peripheral zone prostate cancer using functional data analysis-based (FDA) signal processing. The proposed workflow uses discrete voxel-level DCE time–signal curves that are transformed into a continuous functional form. First-order derivatives are then used to determine patient-specific time points of greatest enhancement change that adapt to the intrinsic characteristics of each patient, producing diffmaps that highlight regions with pronounced enhancement dynamics, indicative of malignancy. A subsequent normalization step accounts for inter-patient variability, enabling consistent interpretation across subjects and probabilistic PCa localization. The approach is validated on a curated dataset of 20 patients. Evaluation of eight workflow variants is performed using weighted log loss, the best variant achieving a mean log loss of 0.578. This study demonstrates the feasibility and effectiveness of a single-modality, automated, and interpretable approach for peripheral prostate cancer localization based solely on DCE-MRI. Full article
(This article belongs to the Special Issue Next-Generation Machine Learning and Deep Learning Models for Complex Data, Vision, and Intelligent Applications)
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25 pages, 742 KB  
Article
Hybrid Poly Commitments for Scalable Binius Zero-Knowledge Proofs in Federated Learning
by Hasina Andriambelo, Hery Zo Andriamanohisoa and Naghmeh Moradpoor
Electronics 2026, 15(3), 500; https://doi.org/10.3390/electronics15030500 - 23 Jan 2026
Abstract
Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and [...] Read more.
Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and fast Fourier transform (FFT) intensive verification. Pairing-based schemes offer compact proofs but incur high prover and verifier overhead, while hash-based constructions reduce algebraic cost at the expense of rapidly growing proof sizes. This paper proposes Hybrid-Commit, a polynomial commitment architecture for Binius zero-knowledge proofs that aligns cryptographic primitives with the algebraic structure of federated learning workloads. The scheme separates verification into additive and multiplicative phases: linear aggregation is handled using batched additive commitments optimized for binary fields, while non-linear constraints are verified via hash-based commitments over sparsely selected FFT domains. Proofs from multiple clients are combined through recursive aggregation while preserving non-interactivity. Experiments demonstrate scalability in prover time and proof size (near-constant prover time across 4–11 clients; 160 bytes per client representing 341× and 813× reductions vs. FRI-PCS and Orion), although verification time (762 ms per client) does not scale favorably, making the scheme suitable for bandwidth-constrained scenarios. The scheme achieves under 2% end-to-end training overhead with no impact on model accuracy, indicating that workload-aware commitment design can improve specific scalability dimensions of zero-knowledge verification in federated learning systems. Full article
(This article belongs to the Special Issue Next-Generation Machine Learning and Deep Learning Models for Complex Data, Vision, and Intelligent Applications)
23 pages, 1503 KB  
Article
Hallucination-Aware Interpretable Sentiment Analysis Model: A Grounded Approach to Reliable Social Media Content Classification
by Abdul Rahaman Wahab Sait and Yazeed Alkhurayyif
Electronics 2026, 15(2), 409; https://doi.org/10.3390/electronics15020409 - 16 Jan 2026
Viewed by 165
Abstract
Sentiment analysis (SA) has become an essential tool for analyzing social media content in order to monitor public opinion and support digital analytics. Although transformer-based SA models exhibit remarkable performance, they lack mechanisms to mitigate hallucinated sentiment, which refers to the generation of [...] Read more.
Sentiment analysis (SA) has become an essential tool for analyzing social media content in order to monitor public opinion and support digital analytics. Although transformer-based SA models exhibit remarkable performance, they lack mechanisms to mitigate hallucinated sentiment, which refers to the generation of unsupported or overconfident predictions without explicit linguistic evidence. To address this limitation, this study presents a hallucination-aware SA model by incorporating semantic grounding, interpretability-congruent supervision, and neuro-symbolic reasoning within a unified architecture. The proposed model is based on a fine-tuned Open Pre-trained Transformer (OPT) model, using three fundamental mechanisms: a Sentiment Integrity Filter (SIF), a SHapley Additive exPlanations (SHAP)-guided regularization technique, and a confidence-based lexicon-deep fusion module. The experimental analysis was conducted on two multi-class sentiment datasets that contain Twitter (now X) and Reddit posts. In Dataset 1, the suggested model achieved an average accuracy of 97.6% and a hallucination rate of 2.3%, outperforming the current transformer-based and hybrid sentiment models. With Dataset 2, the framework demonstrated strong external generalization with an accuracy of 95.8%, and a hallucination rate of 3.4%, which is significantly lower than state-of-the-art methods. These findings indicate that it is possible to include hallucination mitigation into transformer optimization without any performance degradation, offering a deployable, interpretable, and linguistically complex social media SA framework, which will enhance the reliability of neural systems of language understanding. Full article
(This article belongs to the Special Issue Next-Generation Machine Learning and Deep Learning Models for Complex Data, Vision, and Intelligent Applications)
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