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Detection of anti-leptospiral antibodies in the serum of animals using recombinant Leptospira GroEL-based latex agglutination test

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Microbial Pathogenesis 2025年 205卷 107658页

作者： Kumar, K Vinod Bokade, Prajakta Prashant Pal, Archana Sowjanyakumari, S. Bharath, V. Shome, B.R. Balamurugan, V. ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI) Yelahanka Karnataka Bengaluru 560 119 India National Post-Doctoral Fellowship Science and Engineering Research Board Department of Science and Technology Government of India India

Leptospirosis is a global zoonosis with significant implications for public and animal health. Effective diagnosis and timely intervention are crucial for preventing severe complications and managing chronic carriage of pathogenic Leptospira infection in animals. The heat shock protein GroEL of Leptospira has been identified as a promising candidate due to its ability to elicit high antibody responses and provide long-lasting immunity in both acute and chronic phases of infection. This study explores the diagnostic potential of recombinant GroEL (rGroEL) protein in a Latex Agglutination Test (LAT) for detecting anti-leptospiral antibodies in the sera of animals. The groEL coding gene, devoid of its signal peptide, was amplified from pathogenic Leptospira interrogans serovar Hardjprajitno, cloned into a pETite vector, and expressed in Escherichia coli. Characterisation of the expressed rGroEL protein was carried out by SDS-PAGE and Western blot, which confirmed its specific molecular weight of ∼60 kDa protein. Ni-NTA purified rGroEL protein was used to adsorb latex beads, for preparing the sensitized beads which were then assessed as diagnostic antigen in LAT (rGroEL-LAT). Evaluation against the Microscopic Agglutination Test (MAT) revealed a relative diagnostic sensitivity (DSn) of 95.69 % and specificity (DSp) of 93.13 % with an accuracy of 94.33 % for detecting anti-leptospiral antibodies in the sera of animals. This is the first study to utilize the rGroEL protein in a latex agglutination test (rGroEL-LAT), which offers a simple, rapid, and effective screening test. With further exhaustive validation, rGroEL-LAT has the potential to be implemented as a diagnostic tool at field-level laboratories with resource-limited settings. © 2025 Elsevier Ltd

关键词： Diagnosis LAT Leptospira Leptospirosis rGroEL

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ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Zhang, Xiang Lu, Yan Yan, Huan Huang, Jinyang Gu, Yu Ji, Yusheng Liu, Zhi Liu, Bin University of Science and Technology of China School of Cyber Science and Technology Hefei230026 China Hefei University of Technology School of Computer and Information Hefei230601 China AI Lab Shanghai200030 China Guizhou Normal University School of Computer and Information Guiyang230601 China National Institute of Informatics Tokyo101-8430 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

Because of the ambiguous and subjective property of the facial expression, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current methods often directly predict whether the label is noised or not, aiming to reduce the contribution of the noised data. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning. In this paper, we propose a more reliable noise-label suppression method called ReSup. First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on popular datasets shows the effectiveness of ReSup. © 2010-2012 IEEE.

关键词： Affective computing facial expression label noise label noise modeling label noise suppression

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Enhancing CNN-based network intrusion detection through hyperparameter optimization

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Intelligent Systems with Applications 2025年 26卷

作者： Kaissar, Antanios Nassif, Ali Bou Soudan, Bassel Injadat, MohammadNoor College of Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science & AI Faculty of Information Technology Zarqa University Zarqa Jordan

This research investigates the optimization of hyperparameters in Convolutional Neural Networks (CNNs) to enhance the performance of Network Intrusion Detection Systems (NIDS). Four distinct optimization techniques, including Grid Search (GS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), are rigorously examined. Comprehensive experiments employ the UNSW-NB15 and CSE-CIC-IDS2018 datasets to determine the optimal hyperparameter configurations for each technique. Subsequently, detailed experiments evaluate the efficiency of the optimized models in detecting network attacks. The findings consistently reveal that optimized CNN models outperform their non-optimized counterparts across all optimization techniques. Notably, GWO emerges as the top-performing technique, achieving remarkable detection performance on both datasets, with 97.08 % accuracy, 96.95 % precision, 97.21 % recall, and 97.08 % F1 score on the UNSW-NB15 dataset, and 96.37 % accuracy, 96.13 % precision, 96.59 % recall, and 96.36 % F1 score on the CSE-CIC-IDS2018 dataset. Furthermore, hyperparameter optimization significantly reduces training and testing times. The GWO-optimized model achieved a reduction of >11 % in training time and 6.14 % in testing time on the UNSW-NB15 dataset. On the CSE-CIC-IDS2018 dataset, the GA-optimized model provided the best improvements, reducing training and testing times by 9.63 % and 8.61 %, respectively. In comparison to existing CNN models trained on the same datasets, the GWO-optimized CNN model consistently excels in all performance metrics, without the need for complex hybrid Deep Learning models. These results underscore the value of systematic hyperparameter optimization in enhancing CNN-based NIDS, with GWO standing out as a compelling technique for achieving optimal model configurations. © 2025 The Author(s)

关键词： Particle swarm optimization (PSO)

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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A Critical Analysis of Deployed Use Cases for Quantum Key Distribution and Comparison with Post-Quantum Cryptography

arXiv

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arXiv 2025年

作者： Aquina, Nick Cimoli, Bruno Das, Soumya Hövelmanns, Kathrin Weber, Fiona Johanna Okonkwo, Chigo Rommel, Simon Škorić, Boris Monroy, Idelfonso Tafur Verschoor, Sebastian Department of Electrical Engineering Eindhoven University of Technology Flux Building Eindhoven5612 AZ Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology MetaForum Building Eindhoven5612 AZ Netherlands Eindhoven Quantum Hub Eindhoven University of Technology Qubit Building Eindhoven5612 AZ Netherlands Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands

Quantum Key Distribution (QKD) is currently being discussed as a technology to safeguard communication in a future where quantum computers compromise traditional public-key cryptosystems. In this paper, we conduct a comprehensive security evaluation of QKD-based solutions, focusing on real-world use cases sourced from academic literature and industry reports. We analyze these use cases, assess their security and identify the possible advantages of deploying QKD-based solutions. We further compare QKD-based solutions with Post-Quantum Cryptography (PQC), the alternative approach to achieving security when quantum computers compromise traditional public-key cryptosystems, evaluating their respective suitability for each scenario. Based on this comparative analysis, we critically discuss and comment on which use cases QKD is suited for, considering factors such as implementation complexity, scalability, and long-term security. Our findings contribute to a better understanding of the role QKD could play in future cryptographic infrastructures and offer guidance to decision-makers considering the deployment of QKD. © 2025, CC BY-SA.

关键词： Quantum computers

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Community-centric modeling of citation dynamics explains collective citation patterns in science, law, and patents

arXiv

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arXiv 2025年

作者： Kojaku, Sadamori Mahari, Robert Lera, Sandro Claudio Moro, Esteban Pentland, Alex Ahn, Yong-Yeol Department of Systems Science and Industrial Engineering Binghamton University New York United States Media Lab Massachusetts Institute of Technology Cambridge United States Harvard Law School Cambridge United States Connection Science Massachusetts Institute of Technology Cambridge United States Southern University of Science and Technology Shenzhen China Network Science Institute Department of Physics Northeastern University Boston United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Many human knowledge systems, such as science, law, and invention, are built on documents and the citations that link them. Citations, while serving multiple purposes [1–3], primarily function as a way to explicitly document the use of prior work and thus have become central to the study of knowledge systems [4–6]. Analyzing citation dynamics has revealed statistical patterns that shed light on knowledge production, recognition, and formalization [5, 7–14], and has helped identify key mechanisms driving these patterns [5, 15, 16]. However, most quantitative findings are confined to scientific citations, raising the question of universality of these findings. Moreover, existing models of individual citation trajectories fail to explain phenomena such as delayed recognition, calling for a unifying framework. Here, we analyze a newly available corpus of U.S. case law, in addition to scientific and patent citation networks, to show that they share remarkably similar citation patterns, including a heavy-tailed distribution of sleeping beauties. We propose a holistic model that captures the three core mechanisms driving collective dynamics and replicates the elusive phenomenon of delayed recognition. We demonstrate that the model not only replicates observed citation patterns, but also better predicts future successes by considering the whole system. Our work offers insights into key mechanisms that govern large-scale patterns of collective human knowledge systems and may provide generalizable perspectives on discovery and innovation across domains. © 2025, CC BY-NC-SA.

关键词： Patents and inventions

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Reliable Indoor Localization in Multi-Building Environments: Leveraging Environment-Invariant and Position-Related Features

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IEEE Internet of Things Journal 2025年

作者： Long, Wenhan Wen, Xinlong Liu, Hao Li, Mengyao Li, Songquan Wu, Yonghui Chen, Fuxiang Liu, Lu Zhu, Rongbo Huazhong Agricultural University College of Informatics Wuhan430070 China Ministry of Education Engineering Research Center of Intelligent Technology for Agriculture Wuhan430070 China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan430070 China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom University of Exeter Department of Computer Science Exeter United Kingdom

Received Signal Strength Indicator (RSSI)-based indoor localization offers a cost-effective solution for autonomous mobile robot navigation in 3D indoor environments, including cross-floor and multi-building structures. However, localization accuracy is fundamentally constrained by the low sampling density and unstable measurement of RSSI data. So far, existing methods neglect cross-environment RSSI coherence (e.g., repeated signal patterns in geometrically similar areas), resulting in unreliable fingerprint databases. What’s more, most approaches fail to model the spatial hierarchy of buildings, floors, and coordinates, which leads to lower accuracy in indoor positioning model predictions. To address these issues, we propose EP-3DLoc, a novel 3D indoor localization framework that combines an Environment-Invariant feature-based Data Completion (EIC) method with a Position-Related feature-based Localization (PRL) method. The EIC enhances data quality by filling in sparse RSSI data using environment-invariant features, which are recurring RSSI patterns found in similar environmental structures. The PRL module combines multi-scale RSSI signal processing (raw data and image-like data) with a multi-task network that analyzes location relationships, enhancing localization accuracy in 3D environments. Experimental results on public datasets (TUT2018, UTSIndoorLoc, and UJIIndoorLoc) have demonstrated that EP-3DLoc achieves state-of-the-art performance on indoor localization in multi-building environments. Further testing on the self-constructed dataset HZAUIndoorLoc have revealed that EP-3DLoc not only outperforms existing methods in localization accuracy but also maintains low energy consumption and strong resistance to interference. © 2014 IEEE.

关键词： Floors

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A Novel Double Pruning method for Imbalanced Data using Information Entropy and Roulette Wheel Selection for Breast Cancer Diagnosis

arXiv

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arXiv 2025年

作者： Soufiane, Bacha Ning, Huansheng Mostefa, Belarbi Sarwatt, Doreen Sebastian Dhelim, Sahraoui School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China of University of Tiaret University of Tiaret Faculty of Mathematics and Informatics Department of Computer Science BP 78 Zaâroura Tiaret14000 Algeria School of Computing Dublin City University Ireland

—Accurate illness diagnosis is vital for effective treatment and patient safety. Machine learning models are widely used for cancer diagnosis based on historical medical data. However, data imbalance remains a major challenge, leading to hindering classifier performance and reliability. The SMOTEBoost method addresses this issue by generating synthetic data to balance the dataset, but it may overlook crucial overlapping regions near the decision boundary and can produce noisy samples. This paper proposes RE-SMOTEBoost, an enhanced version of SMOTEBoost, designed to overcome these limitations. Firstly, RE-SMOTEBoost focuses on generating synthetic samples in overlapping regions to better capture the decision boundary using roulette wheel selection. Secondly, it incorporates a filtering mechanism based on information entropy to reduce noise, and borderline cases and improve the quality of generated data. Thirdly, we introduce a double regularization penalty to control the synthetic samples’ proximity to the decision boundary and avoid class overlap. These enhancements enable higher-quality oversampling of the minority class, resulting in a more balanced and effective training dataset. The proposed method outperforms existing state-of-the-art techniques when evaluated on imbalanced datasets. Compared to the top-performing sampling algorithms, RE-SMOTEBoost demonstrates a notable improvement of 3.22% in accuracy and a variance reduction of 88.8%. These results indicate that the proposed model offers a solid solution for medical settings, effectively overcoming data scarcity and severe imbalance caused by limited samples, data collection difficulties, and privacy constraints. Copyright © 2025, The Authors. All rights reserved.

关键词： Patient treatment

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Multi-Label Movie Genre Classification with Attention Mechanism on Movie Plots

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Computers, Materials and Continua 2025年第3期83卷 5595-5622页

作者： Faheem Shaukat Naveed Ejaz Rashid Kamal Tamim Alkhalifah Sheraz Aslam Mu Mu Department of Computing and Technology Iqra University H-9 Campus Islamabad 04436 Pakistan School of Computing Queens University Kingston ON K7L2N8 Canada School of Computing Ulster University Belfast BT15 1ED Northern Ireland UK Faculty of Arts Science and Technology University of Northampton Waterside Campus Northampton Northamptonshire NN1 5PH UK Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science CTL Eurocollege Limassol 3077 Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol 3036 Cyprus

Automated and accurate movie genre classification is crucial for content organization, recommendation systems, and audience targeting in the film industry. Although most existing approaches focus on audiovisual features such as trailers and posters, the text-based classification remains underexplored despite its accessibility and semantic richness. This paper introduces the Genre Attention Model (GAM), a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots for multi-label genre classification. In order to assess its effectiveness, we assess multiple transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT), A Lite BERT (ALBERT), Distilled BERT (DistilBERT), Robustly Optimized BERT Pretraining Approach (RoBERTa), Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA), eXtreme Learning Network (XLNet) and Decoding-enhanced BERT with Disentangled Attention (DeBERTa). Experimental results demonstrate the superior performance of DeBERTa-based GAM, which employs a two-tier hierarchical attention mechanism: word-level attention highlights key terms, while sentence-level attention captures critical narrative segments, ensuring a refined and interpretable representation of movie plots. Evaluated on three benchmark datasets Trailers12K, Large Movie Trailer Dataset-9 (LMTD-9), and MovieLens37K. GAM achieves micro-average precision scores of 83.63%, 83.32%, and 83.34%, respectively, surpassing state-of-the-art models. Additionally, GAM is computationally efficient, requiring just 6.10 Giga Floating Point Operations Per Second (GFLOPS), making it a scalable and cost-effective solution. These results highlight the growing potential of text-based deep learning models in genre classification and GAM’s effectiveness in improving predictive accuracy while maintaining computational efficiency. With it

关键词： Multi-label classification artificial intelligence movie genre classification hierarchical attention mechanisms natural language processing content recommendation text-based genre classification explainable AI (Artificial Intelligence) transformer models BERT

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Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach

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Scientific Reports 2025年第1期15卷 1-33页

作者： Hassan, Osama F. Ibrahim, Ahmed F. Gomaa, Ahmed Makhlouf, M.A. Hafiz, B. Information Systems Department Faculty of Computers and Informatics Suez Canal University Ismailia 41522 Egypt Artificial Intelligence Department Faculty of Computer Science and Engineering King Salman International University (KSIU) South Sinai 46511 Egypt Computer Science and Engineering Department Egypt Japan University of Science and Technology (E-JUST) Alexandria 21934 Egypt Computer Science and Engineering Department National Research Institute of Astronomy and Geophysics (NRIAG) Helwan 11731 Egypt

Driver drowsiness is a leading cause of road accidents, resulting in significant societal, economic, and emotional losses. This paper introduces a novel and robust deep learning-based framework for real-time driver drowsiness detection, leveraging state-of-the-art transformer architectures and transfer learning models to achieve unprecedented accuracy and reliability. The proposed methodology addresses key challenges in drowsiness detection by integrating advanced data preprocessing techniques, including image normalization, augmentation, and region-of-interest selection using Haar Cascade classifiers. We employ the MRL Eye Dataset to classify eye states into “Open-Eyes” and “Close-Eyes,” evaluating a range of models, including Vision Transformer (ViT), Swin Transformer, and fine-tuned transfer learning models such as VGG19, DenseNet169, ResNet50V2, InceptionResNetV2, InceptionV3, and MobileNet. The ViT and Swin Transformer models achieved groundbreaking accuracy rates of 99.15% and 99.03%, respectively, outperforming all other models in precision, recall, and F1-score. To ensure the generalization and robustness of the proposed models, we also evaluate their performance on the NTHU-DDD and CEW datasets, which provide diverse real-world scenarios and challenging conditions. This represents a significant advancement over existing methods, demonstrating the effectiveness of transformer-based architectures in capturing complex spatial dependencies and extracting relevant features for drowsiness detection. The proposed system also incorporates a real-time drowsiness scoring mechanism, which triggers alarms when prolonged eye closure is detected, ensuring timely intervention to prevent accidents. A key novelty of this work lies in the integration of Class Activation Mapping (CAM) for enhanced model interpretability, allowing the system to focus on critical eye regions and improve decision-making transparency. The system was rigorously tested under varying lighting condit

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