Search Results (2,381)

B-cell epitope prediction is crucial for advancing immunology, particularly in vaccine development and antibody-based therapies. Traditional experimental techniques are hindered by high costs, time consumption, and limited scalability, making them unsuitable for large-scale applications. Computational methods provide a promising alternative, enabling high-throughput screening and accurate predictions. However, existing computational approaches often struggle to capture the complexity of protein structures and intricate residue interactions, highlighting the need for more effective models. This study presents GraphEPN, a novel B-cell epitope prediction framework combining a vector quantized variational autoencoder (VQ-VAE) with a graph transformer. The pre-trained VQ-VAE captures both discrete representations of amino acid microenvironments and continuous structural embeddings, providing a comprehensive feature set for downstream tasks. The graph transformer further processes these features to model long-range dependencies and interactions. Experimental results demonstrate that GraphEPN outperforms existing methods across multiple datasets, achieving superior prediction accuracy and robustness. This approach underscores the significant potential for applications in immunodiagnostics and vaccine development, merging advanced deep learning-based representation learning with graph-based modeling. Full article

Background: The interaction network between the human host proteins and the proteins of the gut bacteria is essential for the establishment of human health, and its dysregulation directly contributes to disease development. Despite its great importance, experimental data on protein–protein interactions (PPIs) between these species are sparse due to experimental limitations. Methods: This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. The framework leverages graph-based protein representations and variational autoencoders (VAEs) to extract structural embeddings from protein graphs, which are then fused through a Bi-directional Cross-Attention module to predict interactions. The model addresses common challenges in PPI datasets, such as class imbalance, using focal loss to emphasize harder-to-classify samples. Results: The results demonstrated that this framework exhibits robust performance, with high precision and recall across validation and test datasets, underscoring its generalizability. By incorporating proteoforms in the analysis, the model accounts for the structural complexity within proteomes, making predictions biologically relevant. Conclusions: These findings offer a scalable tool for investigating the interactions between the host and the gut microbiota, potentially yielding new treatment targets and diagnostics for disorders linked to the microbiome. Full article

Addressing the noise in seismic signals, a prevalent challenge within seismic signal processing, has been the focus of extensive research. Conventional algorithms for seismic signal denoising often fall short due to their reliance on manually determined feature functions and threshold parameters. These limitations hinder effective noise removal, resulting in suboptimal signal-to-noise ratios (SNRs) and post-denoising waveform distortion. To address these shortcomings, this study introduces a novel denoising approach leveraging a DnCNN network. The DnCNN framework, which integrates batch normalization with residual learning, is adept at swiftly identifying and eliminating noise from seismic signals through its residual learning capabilities. To assess the efficacy of this DnCNN-based model, it was rigorously tested against a curated test set and benchmarked against other denoising techniques, including wavelet thresholding, empirical mode decomposition, and convolutional auto-encoders. The findings demonstrate that the DnCNN model not only significantly enhances the SNR and correlation coefficient of the processed seismic signals but also achieves superior noise reduction performance. Full article

Generative AI (Gen-AI) revolutionizes design by leveraging machine learning to generate innovative solutions. It analyzes data to identify patterns, creates tailored designs, enhances creativity, and allows designers to explore complex possibilities for diverse industries. This study uses a Gen-AI design generation process to develop an urban landscape fumigation service robot. This study proposes a machine-learned multimodal and feedback-based variational autoencoder (MMF-VAE) model that incorporates a readily available spraying robot dataset and includes design considerations from various research efforts to ensure real-time deployability. The objective is to demonstrate the effectiveness of data-driven and feedback-based approaches in generating design specifications for a fumigation robot with the targeted requirements of autonomous navigation, precision spraying, and an extended runtime. The design generation process comprises three stages: (1) parameter fixation, emphasizing functionality-based and aesthetic-based specifications; (2) design specification generation using the proposed MMF-VAE model with and without a spraying robot dataset; and (3) robot development based on the generated specifications. A comparative analysis evaluated the impact of the dataset-driven design generation. The design generated with the dataset proved more feasible and optimized for real-world deployment with the integration of multimodal inputs and iterative feedback refinement. A real-time prototype was then constructed using the model’s parametric constraints and tested in actual fumigation scenarios to validate operational viability. This study highlights the transformative potential of Gen-AI in robotic design workflows. Full article

This article addresses the impact of generative artificial intelligence on the creation of composite sketches for police investigations. The automation of this task, traditionally performed through artistic methods or image composition, has become a challenge that can be tackled with generative neural networks. In this context, technologies such as Generative Adversarial Networks, Variational Autoencoders, and Diffusion Models are analyzed. The study also focuses on the use of advanced tools like DALL-E, Midjourney, and primarily Stable Diffusion, which enable the generation of highly detailed and realistic facial images from textual descriptions or sketches and allow for rapid and precise morphofacial modifications. Additionally, the study explores the capacity of these tools to interpret user-provided facial feature descriptions and adjust the generated results accordingly. The article concludes that these technologies have the potential to automate the composite sketch creation process. Therefore, their integration could not only expedite this process but also enhance its accuracy and utility in the identification of suspects or missing persons, representing a groundbreaking advancement in the field of criminal investigation. Full article

To enhance maintenance endeavors, it is imperative to gain a deep understanding of system degradation. In systems with degradation-aware control, observing degradation becomes particularly challenging. Even with sensors, such controllers continuously mitigate deviations to ensure the system operates within optimal limits. Here, we propose a framework explicitly tailored for degradation-aware control systems, built upon two main components: (1) degradation modeling to estimate and track hidden degradation over time and (2) a Long Short-Term Memory Autoencoder-Degradation Stage Detector (A-LSTMA-DSD) to define alarm and failure thresholds for enabling condition-based maintenance. In degradation modeling, the framework utilizes actuator measurements to model hidden degradation. Next, an A-LSTMA-DSD model is developed to flag anomalies, based on which alarm and failure thresholds are assigned. These dynamic thresholds are defined to ensure sufficient time for addressing maintenance requirements. Working with real data from a boiler unit in an oil refinery and focusing on steam leakages, our proposed framework successfully identified all failures and on average triggered alarm and failure thresholds 15 and 8 days in advance of failures, respectively. In addition to triggering these thresholds, our system outperforms baseline models, such as CNN, LSTM, ANN, ARIMA, and Facebook Profit, in identifying failures by 60% and 95%, respectively. Full article

The plague of cyberbullying on social media exerts a dangerous influence on human lives. Due to the fact that online social networks continue to daily expand, the proliferation of hate speech is also growing. Consequentially, distressing content is often implicated in the onset of depression and suicide-related behaviors. In this paper, we propose an innovative framework, named as the trustable LSTM-autoencoder network (TLA NET), which is designed for the detection of cyberbullying on social media by employing synthetic data. We introduce a state-of-the-art method for the automatic production of translated data, which are aimed at tackling data availability issues. Several languages, including Hindi and Bangla, continue to face research limitations due to the absence of adequate datasets. Experimental identification of aggressive comments is carried out via datasets in Hindi, Bangla, and English. By employing TLA NET and traditional models, such as long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), the LSTM-autoencoder, Word2vec, bidirectional encoder representations from transformers (BERT), and the Generative Pre-trained Transformer 2 (GPT-2), we perform the experimental identification of aggressive comments in datasets in Hindi, Bangla, and English. In addition to this, we employ evaluation metrics that include the F1-score, accuracy, precision, and recall, to assess the performance of the models. Our model demonstrates outstanding performance across all the datasets by achieving a remarkable 99% accuracy and positioning itself as a frontrunner when compared to previous works that make use of the dataset featured in this research. Full article

In industrial settings, machinery components inevitably wear and degrade due to friction between moving parts. To address this, various maintenance strategies, including corrective, preventive, and predictive maintenance, are commonly employed. This paper focuses on predictive maintenance through vibration analysis, utilizing data-driven models. This study explores the application of unsupervised learning methods, particularly Convolutional Autoencoders (CAEs) and variational Autoencoders (VAEs), for anomaly detection (AD) in vibration signals. By transforming vibration signals into images using the Synchrosqueezing Transform (SST), this research leverages the strengths of convolutional neural networks (CNNs) in image processing, which have proven effective in AD, especially at the pixel level. The methodology involves training CAEs and VAEs on data from machinery in healthy condition and testing them on new data samples representing different levels of system degradation. The results indicate that models with spatial latent spaces outperform those with dense latent spaces in terms of reconstruction accuracy and AD capabilities. However, VAEs did not yield satisfactory results, likely because reconstruction-based metrics are not entirely useful for AD purposes in such models. This study also highlights the potential of ReLU residuals in enhancing the visibility of anomalies. The data used in this study are openly available. Full article

The Border Gateway Protocol (BGP) is the backbone of inter-domain routing on the internet, but its susceptibility to both benign and malicious anomalies creates substantial risks to both network reliability and security. In this study, we present a new approach for deep learning-based BGP anomaly detection utilizing duplicate announcements, which are known to be a symptom of routing disruptions. We developed our methodology based on public BGP data from RIPE and Route Views. We used the number of duplicate announcements as a baseline against which we checked for sporadic and time-based anomalies. Here, we propose a deep learning framework based on the Exponential Moving Average (EMA) model in combination with Autoencoder for anomaly identification. We also apply the Temporal-oriented Synthetic Minority Over-Sampling Technique (T-SMOTE) to overcome data imbalance. Comparative evaluations show that the Autoencoder model is significantly better than LSTM and that existing state-of-the-art methods have higher accuracy, precision, recall, and F1 scores. This study proposes a reliable, scalable, and rapid framework for real-time BGP adversary detection, which improves network security and resilience. Full article

The digitalization of shipbuilding processes has become an important trend in modern naval construction, enabling more efficient design, assembly, and maintenance operations. A key aspect of this digital transformation is traceability, which ensures that every component and step in the shipbuilding process can be accurately tracked and managed. Traceability is critical for quality assurance, safety, and operational efficiency, especially when it comes to identifying and addressing defects that may arise during construction. In this context, defect traceability plays a key role, enabling manufacturers to track the origin, type, and evolution of issues throughout the production process, which are fundamental for maintaining structural integrity and preventing failures. In this paper, we focus on the detection of defects in minor and simple pre-assemblies, which are among the smallest components that form the building blocks of ship assemblies. These components are essential to the larger shipbuilding process, yet their defects can propagate and lead to more significant issues in the overall assembly if left unaddressed. For that reason, we propose an intelligent approach to defect detection in minor and simple pre-assembly pieces by implementing unsupervised learning with convolutional autoencoders (CAEs). Specifically, we evaluate the performance of five different CAEs: BaseLineCAE, InceptionCAE, SkipCAE, ResNetCAE, and MVTecCAE, to detect overshooting defects in these components. Our methodology focuses on automated defect identification, providing a scalable and efficient solution to quality control in the shipbuilding process. Full article

The prediction of circular RNA (circRNA)-drug associations plays a crucial role in understanding disease mechanisms and identifying potential therapeutic targets. Traditional methods often struggle to cope with the complexity of heterogeneous networks and the high dimensionality of biological data. In this study, we propose a circRNA-drug association prediction method based on multi-scale convolutional neural networks (MSCNN) and adversarial autoencoders, named AAECDA. First, we construct a feature network by integrating circRNA sequence similarity, drug structure similarity, and known circRNA-drug associations. Then, unlike conventional convolutional neural networks, we employ MSCNN to extract hierarchical features from this integrated network. Subsequently, adversarial characteristics are introduced to further refine these features through an adversarial autoencoder, obtaining low-dimensional representations. Finally, the learned representations are fed into a deep neural network to predict novel circRNA-drug associations. Experiments show that AAECDA outperforms various baseline methods in predicting circRNA-drug associations. Additionally, case studies demonstrate that our model is applicable in practical related tasks. Full article

The detection of acoustic emission events from various failing mechanisms, such as plastic deformations, is a critical element in the monitoring and timely detection of structural failures in infrastructures. This study focuses on the detection of such failures in metal gates at rivers’ lifting dams aiming to increase the reliability of river transport compared to the current situation, thereby, increasing the resilience of transport corridors. During our study, we used lifting dams in both France and Italy where river transport is thriving. A methodology was developed, processing corresponding acoustic emission recordings originating from lifting dams’ metal gates, using advanced denoising—preprocessing, various decompositions, and spectral embeddings associated with various latest nonlinear processing clustering techniques—thus providing a detailed cluster label morphology and profile of water gates’ normal operating area. Latest machine learning outlier detection algorithms, like One-Class Support Vector Machine, Variational Auto-Encoder, and others, were incorporated, producing a vector of confidence on upcoming out-of-the-normal gate operation and failure prediction, achieving detection contrast enhancement on out-of-the-normal operation points up to 400%. Full article

This study presents a Generative AI-Enhanced Cybersecurity Framework designed to strengthen enterprise data privacy management while improving threat detection accuracy and scalability. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic datasets that mimic real-world data, ensuring privacy and regulatory compliance. At its core, the anomaly detection engine integrates machine learning models, such as Random Forest and Support Vector Machines (SVMs), alongside deep learning techniques like Long Short-Term Memory (LSTM) networks, delivering robust performance across diverse domains. Experimental results demonstrate the framework’s adaptability and high performance in the financial sector (accuracy: 94%, recall: 95%), healthcare (accuracy: 96%, precision: 93%), and smart city infrastructures (accuracy: 91%, F1 score: 90%). The framework achieves a balanced trade-off between accuracy (0.96) and computational efficiency (processing time: 1.5 s per transaction), making it ideal for real-time enterprise deployments. Unlike analog systems that achieve > 0.99 accuracy at the cost of higher resource consumption and limited scalability, this framework emphasizes practical applications in diverse sectors. Additionally, it employs differential privacy, encryption, and data masking to ensure data security while addressing modern cybersecurity challenges. Future work aims to enhance real-time scalability further and explore reinforcement learning to advance proactive threat mitigation measures. This research provides a scalable, adaptive, and practical solution for enterprise-level cybersecurity and data privacy management. Full article

The phenotype (P) of a crop is determined by the genotype (G), environment (E), and genotype-by-environment (G × E) interaction, expressed as P = G + E + G × E. Thus, studying G × E interactions is essential for phenotypic research. Traditional methods of crop phenotypes and adaptability based on G × E interaction analysis, based on large ecological regions, fail to account for year-to-year environmental changes and the blurring of region boundaries, leading to inaccurate insights into the relationship between genotypes and environmental factors. To address these issues, this study divided the research area into small ecological regions through the clustering of meteorological data, providing a more accurate framework for studying G × E interactions in maize. To ascertain the optimal method for ecological region delineation, the yield variance (SYV), the Davies–Bouldin Index (DBI), and the Silhouette Index (SI) were used to evaluate and compare the performance of the K-Means, Autoencoder K-Means (Ae-KM), and Deep K-Means Clustering Neural Network (DKMCNN) methodologies. The DKMCNN surpassed other methodologies and was selected for delineation. Based on this delineation result, the interactions between genotypes and the environment on maize were investigated and clarified using genome-wide association analysis (GWAS) and analysis of variance (ANOVA). Ultimately, through the analysis of maize field trial data from 2020 to 2021, we identified up to 108 single-nucleotide polymorphisms (SNPs) in 2020 and 153 SNPs in 2021 that exerted significant effects on maize yield and exhibited strong correlations with environmental factors, including temperature, cumulative precipitation, and cumulative sunshine duration. Full article

As the acceptance of Internet of Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT systems can result in resource inefficiency, high energy consumption, reduced security, and operational downtime, obstructing sustainability goals. Thus, blockchain (BC) technology, known for its decentralized and distributed characteristics, can offer significant solutions in IoT networks. BC technology provides several benefits, such as traceability, immutability, confidentiality, tamper proofing, data integrity, and privacy, without utilizing a third party. Recently, several consensus algorithms, including ripple, proof of stake (PoS), proof of work (PoW), and practical Byzantine fault tolerance (PBFT), have been developed to enhance BC efficiency. Combining fault detection algorithms and BC technology can result in a more reliable and secure IoT environment. Thus, this study presents a sustainable BC-Driven Edge Verification with a Consensus Approach-enabled Optimal Deep Learning (BCEVCA-ODL) approach for fault recognition in sustainable IoT environments. The proposed BCEVCA-ODL technique incorporates the merits of the BC, IoT, and DL techniques to enhance IoT networks’ security, trustworthiness, and efficacy. IoT devices have a substantial level of decentralized decision-making capacity in BC technology to achieve a consensus on the accomplishment of intrablock transactions. A stacked sparse autoencoder (SSAE) model is employed to detect faults in IoT networks. Lastly, the Piranha Foraging Optimization Algorithm (PFOA) approach is used for optimum hyperparameter tuning of the SSAE approach, which assists in enhancing the fault recognition rate. A wide range of simulations was accomplished to highlight the efficacy of the BCEVCA-ODL technique. The BCEVCA-ODL technique achieved a superior FDA value of 100% at a fault probability of 0.00, outperforming the other evaluated methods. The proposed work highlights the significance of embedding sustainability into IoT systems, underlining how advanced fault detection can provide environmental and operational benefits. The experimental outcomes pave the way for greener IoT technologies that support global sustainability initiatives. Full article