Computers

34 pages, 28776 KB

Open AccessArticle

Global-Local Feature-Based Rice Leaf Disease Classification Using Two-Stream Deep Neural Network Feature Fusion

by Md Nahidur Rahaman, Abdullah Al Mamun, Md. Kamal Hossen, Abdur Rouf, Tumpa Rani Shaha, Jungpil Shin, Mohd Nizam Husen and Abu Saleh Musa Miah

Computers 2026, 15(8), 528; https://doi.org/10.3390/computers15080528 - 14 Aug 2026

Abstract

Rice leaf diseases significantly affect crop health and yield potential, creating a need for accurate and timely disease diagnosis. Many existing approaches still rely on single-stream feature extraction architectures, which may limit the ability to simultaneously capture global contextual information and fine-grained disease [...] Read more.

Rice leaf diseases significantly affect crop health and yield potential, creating a need for accurate and timely disease diagnosis. Many existing approaches still rely on single-stream feature extraction architectures, which may limit the ability to simultaneously capture global contextual information and fine-grained disease characteristics. Moreover, limited interpretability and decision-support capability hinder their practical deployment in real-world rice farming. To address these limitations, we employed a framework consists of two parallel feature extraction streams designed to capture different characteristics of disease patterns. The first stream uses a Swin Transformer to learn global contextual information and long-range spatial relationships across the leaf image. The second stream employs ConvNeXt to extract local texture features, including lesion details, spots, and color variations. By combining these complementary representations, the proposed framework effectively integrates global semantic information with local disease-specific features for improved classification performance. The extracted features from the two streams are fused through concatenation followed by an attention-based feature refinement module, enabling adaptive weighting of discriminative features. The refined representation is then used by a fully connected classifier for disease prediction. To enhance model interpretability, Grad-CAM visualization is incorporated to highlight disease-relevant regions and provide visual explanations for the model decisions. Furthermore, an LLM-based advisory module is integrated as a post-diagnosis decision-support component to provide contextualized disease management information and suggestions based on the predicted disease category. The generated suggestions are intended to support, rather than replace, expert agronomic recommendations and should be validated by agricultural professionals before practical application. The proposed framework was evaluated on two rice leaf disease datasets, achieving accuracies of 99.55% and 97.06% on Dataset-1 and Dataset-2, respectively, which are higher than those reported in previous studies. Additionally, five-fold cross-validation on Dataset-1 achieved an average accuracy of 98.91% ± 0.47, demonstrating the stability of the proposed approach. Cross-dataset evaluation using nine common disease classes across both datasets achieved 90.80% accuracy, indicating improved generalization across different data distributions. The proposed framework provides an accurate and explainable approach for rice leaf disease diagnosis in smart agriculture applications. Full article

(This article belongs to the Special Issue Advances in Computer Vision: Models, Learning, and Inference)

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30 pages, 1840 KB

Open AccessArticle

Weak Ridge-Flow Prior-Guided Fingerprint Reconstruction Under Severe Degradation

by Haiyong Xie, Lin Wang, Yonghao Dai and Yunqian Cheng

Computers 2026, 15(8), 527; https://doi.org/10.3390/computers15080527 - 14 Aug 2026

Abstract

Fingerprint enhancement plays an important role in recovering identity-related ridge structures from degraded fingerprints. However, existing methods primarily focus on local texture restoration and may struggle to preserve ridge continuity and structural consistency under severe degradation conditions, including ridge fragmentation, diffusion blur, and [...] Read more.

Fingerprint enhancement plays an important role in recovering identity-related ridge structures from degraded fingerprints. However, existing methods primarily focus on local texture restoration and may struggle to preserve ridge continuity and structural consistency under severe degradation conditions, including ridge fragmentation, diffusion blur, and partial information loss. In this paper, we observe that degraded fingerprints may retain incomplete ridge-flow information that can provide useful structural guidance for fingerprint reconstruction. Based on this observation, we propose a conditional generative adversarial network guided by a weak ridge-flow prior (WRP-cGAN) for degraded fingerprint enhancement. The proposed method treats the estimated ridge-flow information as a weak structural prior rather than an exact structural constraint and introduces prior-conditioned feature modulation to adaptively incorporate structural cues during reconstruction. The framework is jointly optimized using adversarial, image-space reconstruction, ridge-flow orientation-consistency, and gradient-consistency losses to improve ridge continuity, structural coherence, and local detail preservation. On the NIST SD301-derived test set, the proposed method increases the median NFIQ2 score from 9 to 44, improves the minutiae-restoration F1-score from 0.2507 to 0.5426, and increases the SourceAFIS Rank-1 identification rate from 39% to 86%. An additional qualitative evaluation on FVC2004 DB1 provides preliminary evidence of cross-dataset transferability without fine-tuning. These results suggest that weak ridge-flow priors provide useful structural guidance for degraded fingerprint reconstruction and improve recognition-oriented fingerprint quality under the degradation conditions considered in this study. Full article

(This article belongs to the Section AI-Driven Innovations)

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29 pages, 840 KB

Open AccessReview

Virtual Educational Agents in Immersive Virtual Reality Learning Environments: A Scoping Review of Terminology, Conceptualizations, and Taxonomy Requirements

by Panagiota Athanasiou and Emmanuel Fokides

Computers 2026, 15(8), 526; https://doi.org/10.3390/computers15080526 - 13 Aug 2026

Abstract

The growth of immersive virtual reality learning environments (IVRLEs) coincides with the emergence of numerous virtual educational agents that offer support to their users. Researchers suggested various terms to describe these agents; yet, this terminology fragmentation creates conceptual ambiguity, rendering them hard to [...] Read more.

The growth of immersive virtual reality learning environments (IVRLEs) coincides with the emergence of numerous virtual educational agents that offer support to their users. Researchers suggested various terms to describe these agents; yet, this terminology fragmentation creates conceptual ambiguity, rendering them hard to compare. The purpose of this scoping review was to map out the terms used to describe educational agents in IVRLEs and look at how they have been conceptualized, the characteristics that have been used to differentiate them, and the educational roles and associated learning functions they have. In addition, the goal was to identify the requirements for the development of a relevant taxonomy. Arksey’s and O’Malley’s methodological framework and the PRISMA Extension for Scoping Reviews were applied. Out of the 1867 articles found in the Scopus, Eric, and LearnTechLib databases, published between 2015 and 2026, 129 met the eligibility criteria. The findings revealed substantial conceptual fragmentation, with similar educational entities being described using different terminology while identical terms were frequently applied to conceptually distinct systems. Characteristics such as intelligence, embodiment, interaction modality, adaptivity, affective and social characteristics, and context awareness distinguished these agents rather than terminology alone. Advances in artificial intelligence have further blurred the boundaries of traditional categories. Overall, this review provides a theoretical foundation for future research aimed at developing more coherent conceptual frameworks and standardized approaches to the design, classification, and evaluation of virtual educational agents. Full article

(This article belongs to the Special Issue Emerging Technologies and 21st Century Learning)

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41 pages, 29978 KB

Open AccessArticle

Attention-Guided Cross-Connected Filters Convolutional Neural Network with Surrogate-Based Interpretability for Image Splicing Forgery Detection

by Aruna Srinivasan, Surabhi Narayan and Aarnav Sandeep Deshmukh

Computers 2026, 15(8), 525; https://doi.org/10.3390/computers15080525 - 13 Aug 2026

Abstract

Background: Image splicing forgery detection is one of the most challenging problems in the field of image forensics as it involves identifying and localizing suspicious regions that are created by integrating contents from one or more different sources. The accurate detection and classification [...] Read more.

Background: Image splicing forgery detection is one of the most challenging problems in the field of image forensics as it involves identifying and localizing suspicious regions that are created by integrating contents from one or more different sources. The accurate detection and classification of splicing forgery still remains a difficult task because of the existence of overlapping image regions, which makes it complex to differentiate authentic and tampered images. This overlap causes a lack of feature representation, making it difficult to precisely detect the tampering in images. Also, the decision-making process of the model is often a black box, which makes it challenging to interpret and understand the rationale behind its decisions. Methods: To address these challenges, a Convolutional Block Attention Module (CBAM)–U-Net with Cross-Connected Filters–Convolutional Neural Network (CCF-CNN) is proposed to achieve precise detection and localization of spliced regions. The CBAM enhances spatial and channel-wise attention, enabling accurate localization of forged regions. The dual-phase CCF-CNN is incorporated with cross-connected filters to differentiate between the authentic and tampered regions by extracting global and local features. Additionally, a surrogate heatmap mechanism is introduced using intermediate decoder features to generate patch-level visual explanations, enabling precise localization of the spliced regions, thereby improving the model’s transparency in decision-making. Results: The proposed CCF-CNN obtains a high accuracy of 99.84% on the CASIA 2.0 dataset and an accuracy of 95.63% on the MISD. Conclusions: Compared to traditional CNNs such as VGG, ResNet and attention-based interpretability algorithms, the proposed model obtains higher performance in terms of detection and interpretability. Full article

(This article belongs to the Section ICT Infrastructures for Cybersecurity)

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20 pages, 7625 KB

Open AccessHypothesis

Reducing Peak Load Underprediction Through Risk-Aware Upper Quantile Forecasting: A University Laboratory Case Study

by Marwa O. Al Enany, Mazen Hesham Elnahal and Amira M. Gaber

Computers 2026, 15(8), 524; https://doi.org/10.3390/computers15080524 - 13 Aug 2026

Abstract

Underprediction of high electrical demand can be more operationally consequential than an equally sized overprediction, yet standard point forecasting models are optimized primarily for average error. This case study evaluates a multivariate quantile Transformer as a safety-oriented next observation forecasting layer for a [...] Read more.

Underprediction of high electrical demand can be more operationally consequential than an equally sized overprediction, yet standard point forecasting models are optimized primarily for average error. This case study evaluates a multivariate quantile Transformer as a safety-oriented next observation forecasting layer for a university laboratory. A timestamp-level audit identified 33,374 native measurements collected from 22 April to 12 December 2024 at a median interval of approximately 10 min. The final leakage-free pipeline uses only real observations, performs the chronological split before sequence generation, fits all scalers on training data only, and rejects windows containing gaps greater than 30 min. Persistence, fixed-order SARIMA, LSTM, GRU, CNN–LSTM, and an MSE-trained Transformer were evaluated on the same 6595-sample test period. GRU achieved the best deterministic accuracy (MAE 0.017744 kW; RMSE 0.023278 kW), whereas the proposed τ = 0.90 Transformer intentionally traded point accuracy (MAE 0.033830 ± 0.001133 kW) for asymmetric risk control. Across five independent runs, it achieved a pinball loss of 0.004453 ± 0.000069 kW, empirical coverage of 87.95 ± 1.01%, and a peak underprediction rate of 26.64 ± 5.32%, compared with 72.94–100% for the conventional benchmark outputs. Additional τ = 0.75 and τ = 0.95 experiments demonstrate the expected accuracy–safety trade-off. MAPE is not used as a primary metric because near-zero loads make percentage errors unstable. The results support the proposed model as a complementary upper quantile forecasting layer for this small, dynamic facility; they do not establish general performance at feeder or system scale. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid Energy Management and Industrial Electrical Systems)

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Graphical abstract

17 pages, 6118 KB

Open AccessArticle

Time-Aware Late Fusion for Multimodal Rice Growth-Rate Prediction from UAV Imagery and Weather Context

by Alaa O. Elhadi, Saad M. Darwish and Mahmoud A. Mahdi

Computers 2026, 15(8), 523; https://doi.org/10.3390/computers15080523 - 12 Aug 2026

Abstract

Accurate crop-growth estimation from unmanned aerial vehicle (UAV) imagery is important for precision agriculture, but image-only models can struggle to represent seasonal context. This study evaluates whether combining UAV imagery with weather variables and elapsed time improves continuous rice growth-rate prediction in a [...] Read more.

Accurate crop-growth estimation from unmanned aerial vehicle (UAV) imagery is important for precision agriculture, but image-only models can struggle to represent seasonal context. This study evaluates whether combining UAV imagery with weather variables and elapsed time improves continuous rice growth-rate prediction in a single-site, publicly available rice-seedling dataset spanning multiple growing seasons. A Time-Aware Late Fusion (TALF) model is introduced in which a convolutional branch encodes image features, a multilayer perceptron encodes contextual features, and the two streams are merged only at the regression head. Relative humidity, wind speed, and elapsed time are used as contextual inputs after season-aware preprocessing. Evaluation is reported on a chronological multi-season split using internal ablations rather than external generalization claims. TALF achieved a mean absolute error (MAE) of 0.031, compared with 0.1455 for the optimized image-only baseline and 0.0890 for an early-fusion image-and-weather baseline. A secondary tolerance-based metric reached 94.9% under the reported threshold. The results indicate that weather and elapsed-time context improve prediction on this dataset and that separating image and tabular encoders until the final layers is a competitive multimodal learning design under the reported protocol. Full article

(This article belongs to the Special Issue Intelligent Computing and Sensing Systems for Sustainable Precision Agriculture)

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Graphical abstract

33 pages, 4017 KB

Open AccessArticle

Energy-Efficient Distributed Flexible Job Shop Scheduling with Machine Degradation and State-Driven Imperfect Preventive Maintenance

by Li Liu, Chenhao Gu and Kaifeng Geng

Computers 2026, 15(8), 522; https://doi.org/10.3390/computers15080522 - 12 Aug 2026

Abstract

This study addresses a distributed flexible job shop scheduling problem with machine degradation and state-driven imperfect preventive maintenance, denoted as MD-SDIPM-DFJSP. A bi-objective model is developed to minimize makespan and total energy consumption by jointly optimizing job assignment, operation sequencing, machine selection, and [...] Read more.

This study addresses a distributed flexible job shop scheduling problem with machine degradation and state-driven imperfect preventive maintenance, denoted as MD-SDIPM-DFJSP. A bi-objective model is developed to minimize makespan and total energy consumption by jointly optimizing job assignment, operation sequencing, machine selection, and processing speed. The model links processing speed with processing time, power consumption, and degradation increment, and uses a unified degradation bound to represent both degradation and reliability constraints. Preventive maintenance is treated as an imperfect recovery action and is generated according to machine states and idle-window conditions. To solve the problem, a degradation-aware multi-objective memetic algorithm (DMA) is proposed, incorporating four-layer encoding, state-driven decoding, hybrid initialization, knowledge-guided neighborhood search, and a speed-based adjustment operator. Numerical experiments show that Gurobi solved the small instance to optimality with a 0% optimality gap, and the resulting schedule satisfied the modeled production, maintenance, degradation, reliability, and energy accounting requirements. Across 84 combinations of instances and factory sizes, DMA achieved the highest HV in 72 cases and the lowest IGD in 62 cases. The Wilcoxon tests further confirmed its overall advantages over the three comparison algorithms in terms of both HV and IGD. Full article

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13 pages, 3231 KB

Open AccessArticle

Mosquito Swarm Algorithm-Based Energy Minimization in Wireless Sensor Networks

by Amal Aabdaoui and Najlae Idrissi

Computers 2026, 15(8), 521; https://doi.org/10.3390/computers15080521 - 12 Aug 2026

Abstract

Wireless sensor networks (WSNs) are sophisticated monitoring systems that gather environmental data via wireless sensors. Numerous wireless sensors that have been placed to monitor and gather data on a particular environment make up these networks. Typically, a base station or central node wirelessly [...] Read more.

Wireless sensor networks (WSNs) are sophisticated monitoring systems that gather environmental data via wireless sensors. Numerous wireless sensors that have been placed to monitor and gather data on a particular environment make up these networks. Typically, a base station or central node wirelessly collects the data prior to analysis. As the battery in wireless sensors is both non-replaceable and non-rechargeable, it represents a key element. As a result, optimizing energy consumption in WSN has become a growing concern. One of the key challenges is consequently the creation of effective protocols for communication in WSNs. In this article, we provide a novel MSA (Mosquito Swarm Algorithm) technique for cluster formation and data routing. Simulations indicate that our proposed algorithm conserves the energy of the nodes and keeps them running for a greater number of survival rounds compared to LEACH (Low-Energy Adaptive Clustering Hierarchy) by a difference of 70.14%, PSO-R (Particle Swarm Optimization with routing) by a difference of 4.76%, and BA-R (Bat Algorithm with routing) by a difference of 2.52%. Our algorithm provides the highest throughput, surpassing LEACH by approximately 6.38%, PSO-R by almost 1%, and BA-R by 12.67%. Simulations indicate that our algorithm is highly effective at extending network longevity and increasing throughput, making it the preferred option to lower energy consumption in WSNs. Full article

(This article belongs to the Special Issue Wireless Sensor Networks in IoT)

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14 pages, 2227 KB

Open AccessArticle

A Source-Record Audit of DeepSeek-R1 Responses to Romanized Sindhi Prompts

by Irum Naz Sodhar, Dil Nawaz Hakro, Abdul Hafeez Buller, Umair Ramzan Sheikh, Suad Mohammed Al Qassabi, Osama Al Rahbi, Akhtar Hussain and Mohammed Izaan Kari

Computers 2026, 15(8), 520; https://doi.org/10.3390/computers15080520 - 11 Aug 2026

Abstract

Sindhi language models struggle with Romanized Sindhi as there is variation in the spellings of everyday words, and short prompts do not explicitly state the task or target language. In this study, the remaining source record of a small DeepSeek-R1 probe, which was [...] Read more.

Sindhi language models struggle with Romanized Sindhi as there is variation in the spellings of everyday words, and short prompts do not explicitly state the task or target language. In this study, the remaining source record of a small DeepSeek-R1 probe, which was based on seven canonical prompts and fourteen alleged A/B records, was audited. The screenshot captions were compared with the visible inputs and outputs, and duplication of images was checked. It was only relabeled when there was a single unambiguous canonical prompt that matched the visible input to a record. Task choice and exact target-string occurrence were then coded separately. Six records were auditable, three of which were recovered by relabeling; eight records were not auditable due to being duplicated, contaminated with a supplied answer, associated with a different phrase, or missing the user input or lost after label repair. Four of the six audited records adhered to the experimenter’s task, one provided a reasonably good English translation when the experimenter did not include the target language, and one resulted in an incompatible cross-linguistic reading. Five had the target (English/Sindhi) string and one did not. The screenshots do not demonstrate clear “standard” and “chain of thought” conditions: all audited B-labeled inputs are in the form of a simple prompt and there are no paired wrapper texts remaining. These are descriptive counts, not accuracy estimates and do not provide proof of causal benefit of prompt explicitness. The audit demonstrates the necessity of full prompt–response documentation, independently verified linguistic references, reruns, information-matched prompts, and preregistered evaluation plans in studies with low resources. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications: 2nd Edition)

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37 pages, 3573 KB

Open AccessArticle

A Multi-Stage Deep Learning Framework for Automated Brain Tumor Diagnosis and Clinical Report Generation

by Mohamed Eassa, Nagwa Yaseen Hegazy and Hussam Elbehiery

Computers 2026, 15(8), 519; https://doi.org/10.3390/computers15080519 - 11 Aug 2026

Abstract

Detection of brain tumors through MRI scans is a difficult and crucial process since medical imaging is complex and there are not enough experts to analyze these images in many countries. The timely detection of diseases such as gliomas, meningiomas, and pituitary tumors [...] Read more.

Detection of brain tumors through MRI scans is a difficult and crucial process since medical imaging is complex and there are not enough experts to analyze these images in many countries. The timely detection of diseases such as gliomas, meningiomas, and pituitary tumors is critical because delayed detection may have adverse impacts on the patient’s condition and result in incorrect treatment. These difficulties are why automated deep learning models were introduced to help diagnose brain tumors. In this research, we introduce a multi-stage sequential pipeline for brain tumor classification, localization, explainability, and report generation in a radiologist-style structured format based on MRI imaging data, with each stage trained and evaluated independently on its respective dataset. Specifically, an Xception-based classifier was used to classify MRI images into one of four classes, achieving 98.96% accuracy and an F1-score of 0.9876. To enhance interpretability, the Grad-CAM technique was used to visualize image patches the model used during prediction. If the tumor was present, U-Net was used to perform localization, giving a Dice score of 0.7835 and an IoU of 0.6919 with the use of T1-weighted MRI images. Finally, the obtained features were passed as input to a QLoRA fine-tuned language model that generates structured radiology reports, including such components as Technique, Findings, and Impression.The proposed framework was evaluated on a hold-out subset of publicly available T1-weighted MRI data, achieving LLM-as-a-Judge scores of 4.92/5 for accuracy and 4.95/5 for fluency. These results suggest that the proposed approach is promising and provide an initial proof of concept. However, additional validation on independent multi-center datasets and multimodal MRI data is still needed before considering its use in clinical practice. Full article

(This article belongs to the Section AI-Driven Innovations)

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44 pages, 508 KB

Open AccessSystematic Review

Bridging Two Worlds: Sensor Technologies and AI for Fruit Detection in Latin America and Beyond—A Scoping Review

by Franklin Parrales-Bravo, Joan Gracia-Chinga, Janio Jadán-Guerrero, Leonel Vasquez-Cevallos, Lorenzo Cevallos-Torres and Leili Lopezdominguez-Rivas

Computers 2026, 15(8), 518; https://doi.org/10.3390/computers15080518 - 10 Aug 2026

Abstract

This scoping review synthesizes 35 studies on sensor technologies and artificial intelligence for fruit detection, classification, and quality assessment, contrasting Latin American and international research traditions. The analysis suggests notable differences in technological approaches between the included Latin American and international studies: Latin [...] Read more.

This scoping review synthesizes 35 studies on sensor technologies and artificial intelligence for fruit detection, classification, and quality assessment, contrasting Latin American and international research traditions. The analysis suggests notable differences in technological approaches between the included Latin American and international studies: Latin American research tends to emphasize in developing accessible, practical solutions using classical computer vision and low-cost hardware, while international studies more frequently employ through deep learning architectures, multi-modal sensing, and complete robotic automation systems. Across the included studies, relatively limited attention was given to AI-assisted decision support for agricultural practitioners, insufficient consideration of inclusivity, and the scarce integration of environmental sustainability into intelligent sensing system design. The review identifies that only 8 of 35 studies originate from Latin America, suggesting an uneven geographical distribution of the available evidence. The included studies generally reported high accuracy values, yet these findings must be interpreted with caution given the reliance on curated datasets that may not represent real-world variability. The reviewed evidence suggests that future research may benefit not from one approach dominating the other, but from a thoughtful integration of complementary strategies, including knowledge transfer, edge computing democratization, and human-centered design. Overall, this review suggests that the ultimate goal extends beyond accuracy metrics to the transformation of agricultural practices that enhance food security, economic development, and environmental sustainability across the global agricultural landscape. It is important to note that this work does not propose or validate a new fruit detection algorithm but rather synthesizes and critically evaluates existing scientific evidence regarding sensor technologies and artificial intelligence applied to fruit detection and quality assessment. Full article

(This article belongs to the Special Issue Intelligent Computing and Sensing Systems for Sustainable Precision Agriculture)

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39 pages, 7486 KB

Open AccessArticle

A Collaborative Decision-Making Model Based on Blockchain-Driven Adaptive Consensus for Public Opinion Event Response

by Yuetong Chen, Yumei Wang, Yufu Ning, Fengming Liu and Mingrui Zhou

Computers 2026, 15(8), 517; https://doi.org/10.3390/computers15080517 - 10 Aug 2026

Abstract

Public opinion event response requires not only timely decisions but also transparent and trustworthy collaboration among multiple stakeholders. To address delayed responses, fragmented collaboration, and information opacity, this study first proposes a collaborative-decision model based on blockchain for public opinion event response and [...] Read more.

Public opinion event response requires not only timely decisions but also transparent and trustworthy collaboration among multiple stakeholders. To address delayed responses, fragmented collaboration, and information opacity, this study first proposes a collaborative-decision model based on blockchain for public opinion event response and then develops a blockchain-driven adaptive consensus method to improve consensus efficiency and decision quality. In the proposed model, public opinion information is mined to identify the attribute categories and weights of response alternatives, while collaborative-decision quality is evaluated by integrating decision reliability, opinion convergence, and individual comprehensive weights derived from social network influence. On this basis, smart contracts are designed to support transparent, traceable, and automated consensus processes. The adaptive consensus method dynamically terminates the consensus process by considering public opinion crisis levels and individual consensus differentiation. A utility-maximizing feedback mechanism is further introduced to improve consensus quality, and smart contracts are used to detect the adjustment willingness of inconsistent individuals and implement an elastic incentive mechanism. Case analysis and simulation experiments verify the effectiveness and robustness of the proposed model and method, showing their potential to support trustworthy collaborative decision-making in public opinion event response under uncertain and time-sensitive conditions. Full article

(This article belongs to the Topic Decision Science Applications and Models (DSAM))

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27 pages, 26623 KB

Open AccessArticle

Beep and Show, Don’t Tell: Multimodal Feedback Strategies for Driver-Automation Coordination in Critical Driving Situations

by Stefan Reitmann, Tsvetomila Mihaylova, Dionysios Kritharoulas, Eelis Peltola, Elin A. Topp and Ville Kyrki

Computers 2026, 15(8), 516; https://doi.org/10.3390/computers15080516 - 8 Aug 2026

Abstract

In level-3 automated driving, control shifts between the system and the human driver, making timely and effective communication crucial in conflict situations. To explore how different communication modalities affect the driver’s understanding, trust, and performance in such situations, we conducted two complementary studies: [...] Read more.

In level-3 automated driving, control shifts between the system and the human driver, making timely and effective communication crucial in conflict situations. To explore how different communication modalities affect the driver’s understanding, trust, and performance in such situations, we conducted two complementary studies: a focus group and an interactive user study. The focus group revealed a preference for multimodal, tailored feedback, with visual information most frequently favored. The interactive user study tested these findings in practice by asking participants to confirm the system suggestion or take over. The results from the interactive study showed the importance of a pre-explanation signal that draws attention to the situation, and a clear visual marking of the proposed resolution. While most participants stated a preference for speech-augmented feedback, spoken and written explanations were frequently overlooked or reported as distracting during active conflict scenarios, suggesting a gap between stated preference and in-task utility. These results indicate that in urgent conflict situations, concise visual cues are more effective than detailed verbal explanations, offering guidance for the design of future level-3 vehicle interfaces. Full article

(This article belongs to the Special Issue Advanced Human–Robot Interaction 2026)

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21 pages, 1055 KB

Open AccessArticle

Beyond Traditional Metrics: Toward a Multifactorial Model for Measuring Productivity in Agile Software Teams

by Marcela Guerrero-Calvache, Giovanni Hernández and María Clara Gómez-Álvarez

Computers 2026, 15(8), 515; https://doi.org/10.3390/computers15080515 - 8 Aug 2026

Abstract

Measuring productivity in software development teams is a key process for evaluating their performance in agile environments, which are characterized by value creation, continuous adaptation, and incremental improvement. However, in the field of software engineering, although there are approaches focused on individual metrics, [...] Read more.

Measuring productivity in software development teams is a key process for evaluating their performance in agile environments, which are characterized by value creation, continuous adaptation, and incremental improvement. However, in the field of software engineering, although there are approaches focused on individual metrics, there remains a gap in the development of multifactorial models that integrate the various dimensions influencing team productivity. In response to this issue, this study proposes a multifactorial conceptual model designed to support the process of measuring productivity in agile teams. The development of the model was based on the measurement protocol proposed by Fenton and Bieman and included two main stages: design and validation. During the design phase, productivity factors were identified, and entities, properties, and empirical relationships were defined; these were represented using a Unified Modeling Language (UML) class diagram. The model was validated through expert judgment and an exploratory empirical application in higher education settings. The results demonstrate a high level of acceptance by experts, as well as the model’s viability for application in real-world scenarios, enabling the operationalization of the productivity construct in Scrum teams. In conclusion, the proposed model constitutes a significant advance in measuring productivity in agile teams by integrating multiple dimensions of performance, offering a structured, validated, and applicable framework that overcomes the limitations of traditional approaches and contributes to both the academic realm and professional practice. Full article

(This article belongs to the Special Issue Advanced Software Engineering in the Age of Artificial Intelligence: Best Practices, Challenges, and Opportunities)

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21 pages, 2796 KB

Open AccessArticle

FD-GhostFaceNet: Frequency-Decoupled Ghost Modules for Lightweight Face Recognition

by Abdalbasit Qadir, Bryar A. Hassan and Hozan Khalid

Computers 2026, 15(8), 514; https://doi.org/10.3390/computers15080514 - 7 Aug 2026

Abstract

Deploying accurate face recognition models on resource-constrained devices remains a significant challenge. Ghost modules mitigate feature-map redundancy by synthesizing half of each feature map through inexpensive linear transformations and form the basis of the lightweight GhostFaceNet and GhostFaceNet++ families. Nevertheless, the inexpensive generator [...] Read more.

Deploying accurate face recognition models on resource-constrained devices remains a significant challenge. Ghost modules mitigate feature-map redundancy by synthesizing half of each feature map through inexpensive linear transformations and form the basis of the lightweight GhostFaceNet and GhostFaceNet++ families. Nevertheless, the inexpensive generator is a strictly local 3 × 3 depthwise convolution: the synthesized features remain near-duplicates of the intrinsic ones, and deeper stages lack the global receptive field required for cross-pose and cross-age matching. This paper introduces FD-GhostFaceNet, built upon the proposed Frequency-Decoupled Ghost (FD-Ghost) module, which replaces the local generator with a complementary-band global counterpart. A learnable, input-conditioned partition of the two-dimensional Discrete Cosine Transform spectrum decomposes the intrinsic features into a global low-frequency band that encodes pose- and age-stable identity structure and a locally refined high-frequency detail band. Nyquist-consistent anti-aliased downsampling, which band-limits each feature map before subsampling so that the decoupled high-frequency band survives decimation, completes the frequency-decoupled design. Applied to both published trunks, FD-GhostFaceNet-V1-2 and FD-GhostFaceNet-V2-2 require 4.60 M and 7.39 M parameters, 83.1 and 97.1 MFLOPs, and 9.19 and 14.77 MB of storage, respectively, and thus fall within the sub-100 MFLOPs category of lightweight face recognition models. Across six standard benchmarks, both variants surpass their corresponding GhostFaceNet baselines in 23 of 24 trunk–benchmark comparisons and are competitive with or superior to the GhostFaceNet++ variants, with the largest gains on the pose-sensitive evaluations. When trained with ArcFace on UMDFaces, FD-GhostFaceNet raises the best reported CP-LFW accuracy from 84.65% to 86.12% and CFP-FP from 87.60% to 89.56%; when trained on CASIA-WebFace, it improves the best reported CFP-FP from 90.10% to 90.94%. These results confirm that frequency decoupling strikes a favorable balance between compactness and accuracy, making both models strong candidates for deployment on resource-constrained devices. Full article

(This article belongs to the Section AI-Driven Innovations)

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24 pages, 27960 KB

Open AccessArticle

A Lightweight Shallow CNN-Based Approach for Ring-Neck Disease Detection in Avocado Fruits

by Anibal Flores, Ruso Morales-Gonzales, Jose Guzman-Valdivia, Saul Huaquipaco, Carlos Silva-Delgado, Hugo Tito-Chura, Mario Gauna-Chino and Honorato Ccalli-Pacco

Computers 2026, 15(8), 513; https://doi.org/10.3390/computers15080513 - 7 Aug 2026

Abstract

Ring-neck disease in avocado cultivation is a critical issue due to its significant impact on production, including economic losses caused by premature fruit drop, postharvest rejection, and export limitations, among other factors. To enable the automated detection of ring-neck symptoms, this study proposes [...] Read more.

Ring-neck disease in avocado cultivation is a critical issue due to its significant impact on production, including economic losses caused by premature fruit drop, postharvest rejection, and export limitations, among other factors. To enable the automated detection of ring-neck symptoms, this study proposes a computer vision-based framework. As an initial step, a dataset of 622 Fuerte avocado images was established and annotated, consisting of 361 healthy samples and 261 samples affected by ring-neck. During the experimental stage, convolutional neural network (CNN) models with different architectures, comprising four and five convolutional layers, were developed and systematically evaluated. In addition, a region-of-interest (ROI)-based strategy was employed to focus the analysis on the fruit peduncle, thereby enhancing the detection of ring-neck symptoms. The performance of the proposed model was benchmarked against widely adopted deep learning architectures, namely VGG-16, VGG-19, ResNet-18, MobileNetV3, and GhostNet. The experimental evaluation demonstrated the superiority of the proposed approach, achieving an F1-score of 0.9610, compared with 0.5667 for VGG-16, 0.7164 for VGG-19, 0.0952 for ResNet-18, 0.6176 for MobileNetV3, and 0.7733 for GhostNet. Furthermore, statistical significance was assessed using the McNemar test, which confirmed that the observed performance improvements over the benchmark models were statistically significant, thereby supporting the robustness and reliability of the proposed approach. Full article

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25 pages, 1597 KB

Open AccessArticle

From Classical to Deep Learning: A Hybrid CNN–Ensemble Framework for Intrusion Detection in Internet of Medical Things

by Faris Kateb, Owais Khan and Fazal Qudus Khan

Computers 2026, 15(8), 512; https://doi.org/10.3390/computers15080512 - 7 Aug 2026

Abstract

With the rapid expansion of the Internet of Medical Things (IoMT), the risks of cybersecurity have increased exponentially in healthcare settings, exposing patients’ safety. Three fundamental issues that existing intrusion detection systems (IDS) are challenged by are: (1) limited cross-domain generalization, (2) high [...] Read more.

With the rapid expansion of the Internet of Medical Things (IoMT), the risks of cybersecurity have increased exponentially in healthcare settings, exposing patients’ safety. Three fundamental issues that existing intrusion detection systems (IDS) are challenged by are: (1) limited cross-domain generalization, (2) high computation requirements not suitable for edge deployment, and (3) absence of systematic comparison between classical machine learning (ML) and deep learning (DL) approaches on IoMT-specific data. In this paper, we propose a multi-dataset evaluation framework that covers six models (Random Forest, XGBoost, DNN, CNN, LSTM, and CNN-LSTM) across three different datasets: WUSTL-EHMS-2020, Edge-IIoTset, and UNSW-NB15. We show that there is a scale-dependent pattern: classical ensemble methods work best when the data is small (F1 = 0.914 ± 0.013 on WUSTL-EHMS-2020); the proposed hybrid CNN–Ensemble framework performs best when the data is large (F1 = 0.968 ± 0.002 on UNSW-NB15 with 62.8% fewer features). The proposed framework achieves a total model size of 2.11 MB and an inference latency of 111.6 ms, with seven out of the top 15 discriminative features being patient vital signs, giving the first quantitative evidence that physiological data systematically contributes to IoMT attack detection, which is demonstrated through an explainability analysis using the SHAP approach. Cross-dataset generalization experiments across six transfer scenarios expose fundamental limitations in domain transfer, establishing an important baseline for future research. Full article

(This article belongs to the Special Issue IoT: Security, Privacy and Best Practices (3rd Edition))

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13 pages, 8118 KB

Open AccessOpinion

Digital White Spaces: A Cyberpsychology-Informed Framework to Mobile Phone Addiction

by Leandros A. Maglaras, Christina Kyritsi, Helge Janicke and Konstantinos Karantzalos

Computers 2026, 15(8), 511; https://doi.org/10.3390/computers15080511 - 7 Aug 2026

Abstract

Mobile-phone overuse and attention fragmentation have become pressing societal and public-health concerns. Cyberpsychology research highlights addictive engagement loops driven by intermittent rewards, persuasive design, and habit formation. In this article we synthesize current evidence on mobile-phone addiction and propose “Digital White Spaces” (DWSs), [...] Read more.

Mobile-phone overuse and attention fragmentation have become pressing societal and public-health concerns. Cyberpsychology research highlights addictive engagement loops driven by intermittent rewards, persuasive design, and habit formation. In this article we synthesize current evidence on mobile-phone addiction and propose “Digital White Spaces” (DWSs), a socio-technical framework that combines privacy-preserving monitoring, AI-driven detection of addictive loops, device-mode interventions, and physical signal-limited zones to restore user autonomy. Full article

(This article belongs to the Section ICT Infrastructures for Cybersecurity)

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40 pages, 2833 KB

Open AccessArticle

Agentic Shadow Infrastructure: How AI Supply-Chain Drift Creates Unmanaged Enterprise Infrastructure

by Robert Campbell

Computers 2026, 15(8), 510; https://doi.org/10.3390/computers15080510 - 6 Aug 2026

Abstract

Agent identity governance governs an agent’s identity, credentials, and lifecycle, but assumes the composition it was approved with is the composition it runs with. That stability assumption is unenforced: no lifecycle mechanism evaluates an agent’s evolving composition against its approved baseline. An agent’s [...] Read more.

Agent identity governance governs an agent’s identity, credentials, and lifecycle, but assumes the composition it was approved with is the composition it runs with. That stability assumption is unenforced: no lifecycle mechanism evaluates an agent’s evolving composition against its approved baseline. An agent’s effective composition—tools, data sources, delegated authorities, child agents—is a runtime supply chain of capability, and it drifts. We introduce composition drift, the departure of effective composition from the terms of approval, and isolate its sharpest form, compositional drift: individually approved changes accumulating into a capability none authorized alone. We formalize this with a two-stage operator: a component diff detects that the composition changed; a capability-closure stage detects when it authorized something new. The contribution is a temporal governance model linking emergent capability to reauthorization and inventory reconciliation. Drift produces shadow infrastructure: resources provisioned outside any inventory through benign, individually approved pathways. We propose composition attestation, a runtime control complementary to identity governance, and evaluate it in a pre-registered study. Across five thousand trajectories the detector separates compositional drift from authorized growth where a static analyzer cannot; an ablation isolates the primitives that cause it; and, in controlled live agent runs across three models (9B to 70B parameters, Llama and Qwen lineages), agents given only benign tasks provision unauthorized shadow infrastructure in 59% to 99% of drift-conducive trials against 0% to 19% of matched controls. Full article

(This article belongs to the Section ICT Infrastructures for Cybersecurity)

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