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Multi-domain feature-enhanced attribute updater for generalized zero-shot learning

Neural Computing and Applications 2025年第14期37卷 8397-8414页

作者： Shi, Yuyan Jiang, Chenyi Song, Feifan Ye, Qiaolin Long, Yang Zhang, Haofeng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China School of Information Science and Technology Nanjing Forestry University Jiangsu Nanjing210037 China Department of Computer Science Durham University DurhamDH1 3LE United Kingdom

The goal of generalized zero-shot learning (GZSL) is to transfer knowledge from seen classes to unseen classes. However, a significant challenge is the single-category attributes are often inadequate to capture the intra-class variability of diverse image samples. During training, the model tends to cluster samples of seen categories based solely on semantic descriptions, overlooking subtle yet important visual differences. To address these challenges, this paper introduces an instance-level attribute updating method, termed as multi-domain feature-enhanced attribute updater (MDAU), which establishes a one-to-one alignment between visual and semantic by obtaining multiple instance-level attributes that closely correspond to the natural intra-class distribution of the image, in contrast to the many-to-one alignment typically used in traditional GZSL approaches. Specifically, we initially compute feature weights for each individual image, and then, the visual information is incorporated as conditions to facilitate the personalized updating of class attributes. We evaluate our model on three benchmark datasets, the results show that our model can achieve the state-of-the-art performance and detailed analysis also illustrates the effectiveness of each proposed module. Code is available at https://***/S-Silvia/MDAU. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Zero-shot learning

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Intelligent Healthcare Applications Based on GraphModo: A Comparative Study of Deep Learning Models for Lung Anomaly Detection 24

Intelligent Healthcare Applications Based on GraphModo: A Co...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Yang, Peng Zhou, Yanyang Liu, Yiliang Zeng, Zhifeng Department of Computer Science Zhuhai College of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798400717826

The aim of this study was to build ten novel deep learning models using the GraphModo system and then evaluate their ability to detect lung abnormalities in medical *** research employs accuracy, precision, recall, F1 score, and AUC value as evaluation metrics for a comprehensive assessment of model performance. Experiments are based on the ChestX-ray14 and LUNA16 datasets. The results indicate that DenseNet and EfficientNet achieve high accuracy in identifying lung diseases, while the Vision Transformer (ViT) excels in capturing global features of images. Additionally, U-Net demonstrates outstanding performance in CT image segmentation tasks, and MobileNetV3 maintains a high accuracy level in resource-constrained environments. These findings not only confirm the feasibility of model construction using the GraphModo system but also reveal its potential in assisting clinical decision-making, providing robust data support for model selection in medical image analysis. © 2024 Copyright held by the owner/author(s).

关键词： Lung cancer

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A Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities 12th

A Customizable Virtual Assistant Enhanced with Vision and Sp...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Naveen Kumaran, R. Sanjay Abhishek, J. Bhuvana, S. Karthick, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SRM Institute of Science and Technology Uttar Pradesh Delhi–NCR Campus Ghaziabad India

ISBN: (纸本)9789819788354

Recent advancements in Smart Assistants (SAs) as well as home automation have captured the attention of both researchers and consumers. Virtual Assistants (VAs) that are speech-enabled are commonly referred to as smart speakers. They offer a wide range of network-based services. In some cases, they seamlessly link to smart environments, introducing innovative and efficient user interfaces. Nevertheless, these devices present their own set of challenges and limitations. The distinctive features of this technology, particularly its hand-free operation handled by voice and implementation of voice-user interface create a unique landscape. Existing technology adoption models, however, fall short in offering a comprehensive explanation for the adoption of this innovative technology. To gain deeper insights into the motivation behind approving and using in-home voice assistants, this work shifts its focus towards examining voice interactions. Utilization of speech recognition for converting speech into text enables constructing an attractive personalized assistant. This development facilitates seamless tasks such as sending emails without manual typing, conducting Google searches without opening the browser, and accomplishing various daily activities including playing music or launching a preferred IDE, all with a single voice command. To assess its performance, the system is seamlessly integrated with home automation (open-source) environment and operated continuously for some days. During this period, users are encouraged to interact with it, and the results demonstrate increased accuracy, reliability and appeal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks

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computers, Materials & Continua 2025年第3期82卷 4181-4218页

作者： Haydar Abdulameer Marhoon Rafid Sagban Atheer Y.Oudah Saadaldeen Rashid Ahmed Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-Qar64011Iraq College of Computer Sciences and Information Technology University of KerbalaKarbala56001Iraq Enginerring Technical College Al-Ayen UniversityThi-Qar64011Iraq Information Technology College University of BabylonHilla51002Iraq Department of Computer Science College of Education for Pure ScienceUniversity ofThi-QarNasiriyah64001Iraq Artificial Intelligence Engineering Department College of EngineeringAl-Ayen UniversityThi-Qar64001Iraq Department of Computer Science Bayan UniversityErbilKurdistan44001Iraq

In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning *** applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational *** proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic *** approach not only improves the *** are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning *** are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble *** implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline *** train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor *** used Monte Carlo simulations to extract these *** on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.

关键词： Intrusion detection system(IDS) hybrid models of CNN-LSTM WSN extreme gradient boosting(XGBoost)regressor ensemble model

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SESDP:A Sentiment Analysis-Driven Approach for Enhancing Software Product Security by Identifying Defects through Social Media Reviews

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computers, Materials & Continua 2025年第4期83卷 1327-1345页

作者： Farah Mohammad Saad Al-Ahmadi Jalal Al-Muhtadi Center of Excellence in Information Assurance(CoEIA) King Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and TechnologyArab East CollegesRiyadh11583Saudi Arabia College of Computer&Information Sciences King Saud UniversityRiyadh11543Saudi Arabia

Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial *** the increasing reliance on user-generated content,social media reviews have emerged as a valuable source of real-time feedback,offering insights into potential software defects that traditional testing methods may ***,existing models face challenges like handling imbalanced data,high computational complexity,and insufficient inte-gration of contextual information from these *** overcome these limitations,this paper introduces the SESDP(Sentiment Analysis-Based Early Software Defect Prediction)*** employs a Transformer-Based Multi-Task Learning approach using Robustly Optimized Bidirectional Encoder Representations from Transformers Approach(RoBERTa)to simultaneously perform sentiment analysis and defect *** integrating text embedding extraction,sentiment score computation,and feature fusion,the model effectively captures both the contextual nuances and sentiment expressed in user *** results show that SESDP achieves superior performance with an accuracy of 96.37%,precision of 94.7%,and recall of 95.4%,particularly excelling in handling imbalanced datasets compared to baseline *** approach offers a scalable and efficient solution for early software defect detection,enhancing proactive software quality assurance.

关键词： Software defect data balancing feature extraction RoBERTa transformer

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Function Scheduling with Data Security in Serverless Computing Systems 17

Function Scheduling with Data Security in Serverless Computi...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Saha, Srestha Pandey, Arvind Addya, Sourav Kanti Brata Nath, Shubha Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331531195

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user's QoS. © 2025 IEEE.

关键词： Simulated annealing

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Degradation-Decoupling Vision Enhancement for Intelligent Underwater Robot Vision Perception System

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IEEE Internet of Things Journal 2025年第11期12卷 17880-17895页

作者： Zhou, Jingchun Liu, Chunjiang Long, Bing Zhang, Dehuan Jiang, Qiuping Muhammad, Ghulam Dalian Maritime University College of Information Science and Technology Dalian116026 China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province Sichuan Luzhou64600 China Ningbo University School of Information Science and Engineering Zhejiang Ningbo315211 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

Underwater robots rely on high-quality visual data for precise monitoring and manipulation, yet complex underwater environments often degrade image quality through color distortion, texture blurring, and detail loss. Existing enhancement methods partially address these issues, but fail to effectively decouple nonlinear relationships among degradation factors, leading to inconsistent performance. To address these challenges, we propose a degradation-content decoupling-based underwater image enhancement network (DCDN). The framework integrates a super-fusion cascade module for dynamic feature weighting, reducing artifacts, and combines multi-channel color space transformation with texture-guided correction to decouple and optimize degradation factors. This approach improves color fidelity and texture detail restoration by refining color information and adapting local textures. Experiments on public datasets demonstrate that DCDN outperforms existing methods in various underwater scenarios. This work enhances the visual capabilities of underwater robots, supporting intelligent transportation applications such as marine logistics and underwater inspections. © 2014 IEEE.

关键词： Robot vision

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8 17

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. This system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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LWFDTL: lightweight fusion deep transfer learning for oral Squamous cell Carcinoma diagnosis using Histopathological oral Mucosa

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Multimedia Tools and Applications 2025年 1-25页

作者： Shabir, Aiza ahmed, Khawaja Tehseen Mujahid, Muhammad Kanwal, Khadija Department of Computer Science Bahauddin Zakariya University Multan Pakistan Institute of Computer Science and Information Technology The Women University Multan60000 Pakistan Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia

Oral squamous cell carcinoma (OSCC) is among the most lethal types of cancers, especially realizing the significance of timely diagnosis. Insufficient training and awareness among primary healthcare practitioners might result in more difficult treatments, longer hospital stays, and worse survival rates. This study aims to develop a lightweight model that diagnoses cancer in its early stages and incorporates deep transfer training methods with strategies based on convolution networks and domain-specific features of OSCC samples. This work proposes a new light sequential fusion model based on histopathological data for the diagnosis of OSCC with oral mucosa images. To boost detection performance, EfficientNet is incorporated with convolutional neural networks (CNNs) as well as with max-pooling layers. The application of max-pooling also brings down the spatial features and takes care of edges and texture which proves to be both curtailing the computational workload and enhancing the performance of the model. The legitimacy of the developed framework was assessed on 1224 histopathological images where images were categorized under normal and OSCC classes depending on malignancy grades. Moreover, to improve the model training, the framework applies fine preprocessing and augmentation of input parameters such as rotations, zooms, flips, contrasts, and crops. These augmentations help the model learn from different views and hence boost its detection capability across all the aspects of the data. Experimental findings shows that the proposed framework had an accuracy before enhancement of 91.0% and an accuracy after enhancement of 98.9%, which is even higher than such methods as the traditional single-model and the majority of modern methods. Additionally, our model is lightweight, requiring significantly less time than 0.05 segments on an average GPU. It is also conclude that the proposed model be run in clinical settings with modest computational capabilities. These resu

关键词： Diseases

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On Reliability of the Extrema Propagation Technique in Random Environment 28

On Reliability of the Extrema Propagation Technique in Rando...

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28th International Conference on Principles of Distributed Systems, OPODIS 2024

作者： Cichoń, Jacek Dworzański, Dawid Gotfryd, Karol Department of Fundamentals of Computer Science Faculty of Information and Communication Technology Wroclaw University of Science and Technology Poland

ISBN: (纸本)9783959773607

We study the reliability of the following simple mechanism for spreading information in a communication network in the presence of random message loss. Initially, some nodes have information that they want to distribute throughout the network. Each node that has received the information tries to broadcast it to all its neighbors. However, due to interference or communication failures, each transmission between two nodes is broken independently with some fixed probability. This transmission mechanism is the basis for the extrema propagation technique, proposed and analyzed in [2, 3, 10]. Extrema propagation is a simple but powerful method of spreading the extreme values stored by the nodes. In a fully reliable environment, only the number of rounds equal to the network diameter is required for all nodes to be informed. It was shown empirically in [2] that it also performs well in the presence of link failures and message loss. Extrema propagation is an algorithmic framework that was adopted for designing efficient method for distributed data aggregation, such as estimation of cardinalities, sums, and averages, estimation of data distribution via histograms and random sampling (cf. [3, 19]). In this paper, we propose a formal network model in which random transmission failures occur and analyze the operation time of the extrema propagation technique for any connected network graph. We provide new general probabilistic upper bounds on the number of rounds needed to spread the extreme values that depend only on the number of nodes, the diameter of the network, and the probability of successful transmission. For some special families of graphs, we also derive specific, more accurate estimates. Our theoretical and experimental results confirm the reliability and efficiency of the extrema propagation technique in faulty or noisy environments. © Jacek Cichoń, Dawid Dworzański, and Karol Gotfryd.

关键词： Mobile ad hoc networks

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