Emotion Detection Using Facial Expression Involving Occlusions and Tilt

<a class="ep_document_link" href="/17885/1/s41598-025-26052-7.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images

Mango is one of the most beloved fruits and plays an indispensable role in the agricultural economies of many tropical countries like Pakistan, India, and other Southeast Asian countries. Similar to other fruits, mango cultivation is also threatened by various diseases, including Anthracnose and Red Rust. Although farmers try to mitigate such situations on time, early and accurate detection of mango diseases remains challenging due to multiple factors, such as limited understanding of disease diversity, similarity in symptoms, and frequent misclassification. To avoid such instances, this study proposes a multimodal deep learning framework that leverages both leaf and fruit images to improve classification performance and generalization. Individual CNN-based pre-trained models, including ResNet-50, MobileNetV2, EfficientNet-B0, and ConvNeXt, were trained separately on curated datasets of mango leaf and fruit diseases. A novel Modality Attention Fusion (MAF) mechanism was introduced to dynamically weight and combine predictions from both modalities based on their discriminative strength, as some diseases are more prominent on leaves than on fruits, and vice versa. To address overfitting and improve generalization, a class-aware augmentation pipeline was integrated, which performs augmentation according to the specific characteristics of each class. The proposed attention-based fusion strategy significantly outperformed individual models and static fusion approaches, achieving a test accuracy of 99.08%, an F1 score of 99.03%, and a perfect ROC-AUC of 99.96% using EfficientNet-B0 as the base. To evaluate the model’s real-world applicability, an interactive web application was developed using the Django framework and evaluated through out-of-distribution (OOD) testing on diverse mango samples collected from public sources. These findings underline the importance of combining visual cues from multiple organs of plants and adapting model attention to contextual features for real-world agricultural diagnostics.

Producción Científica

Muhammad Mohsin mail , Muhammad Shadab Alam Hashmi mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Helena Garay mail helena.garay@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,

Mohsin

<a href="/17862/1/sensors-25-06419.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2

Forest fires pose significant threats to ecosystems, human life, and the global climate, necessitating rapid and reliable detection systems. Traditional fire detection approaches, including sensor networks, satellite monitoring, and centralized image analysis, often suffer from delayed response, high false positives, and limited deployment in remote areas. Recent deep learning-based methods offer high classification accuracy but are typically computationally intensive and unsuitable for low-power, real-time edge devices. This study presents an autonomous, edge-based forest fire and smoke detection system using a lightweight MobileNetV2 convolutional neural network. The model is trained on a balanced dataset of fire, smoke, and non-fire images and optimized for deployment on resource-constrained edge devices. The system performs near real-time inference, achieving a test accuracy of 97.98% with an average end-to-end prediction latency of 0.77 s per frame (approximately 1.3 FPS) on the Raspberry Pi 5 edge device. Predictions include the class label, confidence score, and timestamp, all generated locally without reliance on cloud connectivity, thereby enhancing security and robustness against potential cyber threats. Experimental results demonstrate that the proposed solution maintains high predictive performance comparable to state-of-the-art methods while providing efficient, offline operation suitable for real-world environmental monitoring and early wildfire mitigation. This approach enables cost-effective, scalable deployment in remote forest regions, combining accuracy, speed, and autonomous edge processing for timely fire and smoke detection.

Producción Científica

Dilshod Sharobiddinov mail , Hafeez Ur Rehman Siddiqui mail , Adil Ali Saleem mail , Gerardo Méndez Mezquita mail , Debora L. Ramírez-Vargas mail debora.ramirez@unini.edu.mx, Isabel de la Torre Díez mail ,

Sharobiddinov

<a class="ep_document_link" href="/17849/1/1-s2.0-S2590005625001043-main.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence

Gender classification plays a vital role in various applications, particularly in security and healthcare. While several biometric methods such as facial recognition, voice analysis, activity monitoring, and gait recognition are commonly used, their accuracy and reliability often suffer due to challenges like body part occlusion, high computational costs, and recognition errors. This study investigates gender classification using gait data captured by Ultra-Wideband radar, offering a non-intrusive and occlusion-resilient alternative to traditional biometric methods. A dataset comprising 163 participants was collected, and the radar signals underwent preprocessing, including clutter suppression and peak detection, to isolate meaningful gait cycles. Spectral features extracted from these cycles were transformed using a novel integration of Feedforward Artificial Neural Networks and Random Forests , enhancing discriminative power. Among the models evaluated, the Random Forest classifier demonstrated superior performance, achieving 94.68% accuracy and a cross-validation score of 0.93. The study highlights the effectiveness of Ultra-wideband radar and the proposed transformation framework in advancing robust gender classification.

Producción Científica

Adil Ali Saleem mail , Hafeez Ur Rehman Siddiqui mail , Muhammad Amjad Raza mail , Sandra Dudley mail , Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Isabel de la Torre Díez mail ,

Saleem

<a class="ep_document_link" href="/17864/1/s10115-025-02588-y.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Advancing fake news combating using machine learning: a hybrid model approach

The digital era, while offering unparalleled access to information, has also seen the rapid proliferation of fake news, a phenomenon with the potential to distort public perception and influence sociopolitical events. The need to identify and mitigate the spread of such disinformation is crucial for maintaining the integrity of public discourse. This research introduces a multi-view learning framework that achieves high precision by systematically integrating diverse feature perspectives. Using a diverse dataset of news articles, the approach combines several feature extraction methods, including TF-IDF for individual words (unigrams) and word pairs (bigrams), and counts vectorization to represent text in multiple ways. To capture additional linguistic and semantic information, advanced features, such as readability scores, sentiment scores, and topic distributions generated by latent Dirichlet allocation (LDA), are also extracted. The framework implements a multi-view learning strategy, where separate views focus on basic text, linguistic, and semantic features, feeding into a final ensemble model. Models like logistic regression, random forest, and LightGBM are employed to analyze each view, and a stacked ensemble integrates their outputs. Through rigorous tenfold cross-validation, our proposed multi-view ensemble achieves a state-of-the-art accuracy of 0.9994, outperforming strong baselines, including single-view models and a BERT-based classifier. Robustness testing confirms the model maintains high accuracy even under data perturbations, establishing the value of structured feature separation and intelligent ensemble techniques.

Producción Científica

Zahid Aslam mail , Malik Muhammad Saad Missen mail , Arslan Abdul Ghaffar mail , Arif Mehmood mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Imran Ashraf mail ,

Aslam

<a class="ep_document_link" href="/17865/1/International%20Journal%20of%20Intelligent%20Systems%20-%202025%20-%20Shafi%20-%20Scalable%20Comprehensive%20Automatic%20Inspection%20%20Cleaning%20%20and.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Scalable Comprehensive Automatic Inspection, Cleaning, and Evaluation Mechanism for Large‐Diameter Pipes

Cleaning and inspection of pipelines and gun barrels are crucial for ensuring safety and integrity to extend their lifespan. Existing automatic inspection approaches lack high robustness, as well as portability, and have movement restrictions and complexity. This study presents the design and development of a scalable, comprehensive automated inspection, cleaning, and evaluation mechanism (CAICEM) for large-sized pipelines and barrels with diameters in the range of 105 mm–210 mm. The proposed system is divided into electrical and mechanical assemblies that are independently designed, tested, fabricated, integrated, and controlled with industrial grid controllers and processors. These actuators are suitably programmed to provide the desired actions through toggle switches on a simple housing subassembly. The stress analysis and material specifications are obtained using ANSYS to ensure robustness and practicability. Later, on-ground testing and optimization are performed before industrial prototyping. The inspection system of the proposed mechanism includes barrel-mounted and brush-mounted cameras with sensors utilized to keep track of the pipeline deposits and monitor user activity. The experimental results demonstrate that the proposed mechanism is cost-effective and achieves the desired objectives with minimum human efforts in the least possible time for both smooth and rifled large-diameter pipes and barrels.

Producción Científica

Imran Shafi mail , Imad Khan mail , Jose Breñosa mail josemanuel.brenosa@uneatlantico.es, Miguel Ángel López Flores mail , Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Jin-Ghoo Choi mail , Imran Ashraf mail , Richard Murray mail ,

Shafi