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Attention-guided CenterNet deep learning approach for lung cancer detection

computers in Biology and Medicine 2025年 186卷 109613-109613页

作者： Dawood, Hussain Nawaz, Marriam Ilyas, Muhammad U. Nazir, Tahira Javed, Ali School of Computing Skyline University College Sharjah United Arab Emirates Department of Software Engineering University of Engineering and Technology-Taxila Punjab 47050 Pakistan School of Computer Science University of Birmingham Dubai United Arab Emirates Department of Software Engineering and Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection. The primary importance of this work lies in its innovative integration of ResNet-34 with an attention mechanism within the CenterNet architecture, addressing critical limitations identified in previous studies. By augmenting the base network with an attention mechanism, our framework offers improved feature extraction capabilities, enabling the model to learn relevant patterns associated with lung cancer amidst complex backgrounds and varying environmental conditions. This enhancement facilitates more accurate and interpretable predictions while reducing computational complexity and inference times. Through extensive experimental evaluations conducted on standard datasets, our proposed approach demonstrates promising results, highlighting its potential to advance the field of lung cancer detection and diagnosis. Specifically, we have acquired the precision, recall, and F1-Score of 99.89 %, 99.82 %, and 99.85 % on the LUNA-16 dataset, and 98.33 %, 98.02 %, and 98.17 % for the Kaggle data sample, respectively which is showing the efficacy of our approach. One limitation of the work is that it cannot effectively locate the samples with intense light variations. Therefore, future research work is focused on overcoming this challenge. © 2024 Elsevier Ltd

关键词： Lung cancer

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Enhancing Semantic Awareness by Sentimental Constraint with Automatic Outlier Masking for Multimodal Sarcasm Detection

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IEEE Transactions on Multimedia 2025年

作者： Yuan, Shaozu Wei, Yiwei Zhou, Hengyang Xu, Qinfu Chen, Meng He, Xiaodong Meituan Beijing China at Karamay College of Petroleum Engineering Karamay China Qingdao Institute of Software College of Computer Science and Technology China JD AI Research Beijing China

Multimodal sarcasm detection, aiming to uncover sarcastic sentiment behind multimodal data, has gained substantial attention in multimodal communities. Recent advancements in multimodal sarcasm detection (MSD) methods have primarily focused on modality alignment with pre-trained vision-language (V-L) model. However, text-image pairs often exhibit weak or even opposite semantic correlations in MSD tasks. Consequently, directly aligning these modalities can potentially result in feature shift and inter-class confusion, ultimately hindering the model's ability. To alleviate this issue, we propose the Enhancing Semantic Awareness Model (ESAM) for multimodal sarcasm detection. Specifically, we first devise a Modality-decoupled Framework (MDF) to separate the textual and visual features from the fused multimodal representation. This decoupling enables the parallel integration of the Sentimental Congruity Constraint (SCC) within both visual and textual latent spaces, thereby enhancing the semantic awareness of different modalities. Furthermore, given that certain outlier samples with ambiguous sentiments can mislead the training and weaken the performance of SCC, we further incorporate Automatic Outlier Masking. This mechanism automatically detects and masks the outliers, guiding the model to focus on more informative samples during training. Experimental results on two public MSD datasets validate the robustness and superiority of our proposed ESAM model. © 2025 IEEE.

关键词： Visual languages

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Towards Drought Modeling in South Asia: Machine Learning Approaches, Challenges and Opportunities

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IEEE Access 2025年

作者： Reshan, Mana Saleh Al Raza, Muhammad Owais Mahoto, Naeem Ahmed Rajab, Adel Shaikh, Asadullah Elmagzoub, M.A. Rajab, Khairan Najran University College of Computer Science and Information Systems Department of Information Systems Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran61441 Saudi Arabia Istanbul Sabahattin Zaim University Department of Computer Engineering Istanbul34303 Turkey Mehran University of Engineering and Technology Department of Software Engineering Sindh Jamshoro76062 Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Najran61441 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Network and Communication Engineering Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state- of-the-art in ML and DL (Complex Machine learning and deep learning models) for drought modeling, highlighting current challenges such as incomplete and inconsistent datasets, Lack of explainability and interpretability in complex ML/DL models, unavailability of benchmarks. Based on these challenges, this review suggests the following techniques to address these challenges: Data integration (Data fusion), distributed machine learning (Federated Learning) and explainable AI (XAI, SHAP, LIME, etc.). © 2013 IEEE.

关键词： Federated learning

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A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network

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PeerJ computer science 2025年 11卷 e2866-e2866页

作者： Alzahrani, Ahmed Omar Alghamdi, Ahmed Mohammed Ashraf, M. Usman Ilyas, Iqra Sarwar, Nadeem Alzahrani, Abdulrahman Alarood, Alaa Abdul Salam Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Makkah Jeddah Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Makkah Jeddah Saudi Arabia Department of Computer Science Government College Women University Sialkot Punjab Sialkot Pakistan Department of Computer Science Bahria University Punjab Lahore Pakistan

Variations in domain targets have recently posed significant challenges for facial expression recognition tasks, primarily due to domain shifts. Current methods focus largely on global feature adoption to achieve domain-invariant learning;however, transferring local features across diverse domains remains an ongoing challenge. Additionally, during training on target datasets, these methods often suffer from reduced feature representation in the target domain due to insufficient discriminative supervision. To tackle these challenges, we propose a dynamic cross-domain dual attention network for facial expression recognition. Our model is specifically designed to learn domain-invariant features through separate modules for global and local adversarial learning. We also introduce a semantic-aware module to generate pseudo-labels, which computes semantic labels from both global and local features. We assess our model’s effectiveness through extensive experiments on the Real-world Affective Faces Database (RAF-DB), FER-PLUS, AffectNet, Expression in the Wild (ExpW), SFEW 2.0, and Japanese Female Facial Expression (JAFFE) datasets. The results demonstrate that our scheme outperforms the existing state-of-the-art methods by attaining recognition accuracies 93.18, 92.35, 82.13, 78.37, 72.47, 70.68 respectively. Copyright 2025 Alzahrani et al. Distributed under Creative Commons CC-BY 4.0

关键词： Deep learning

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Elastic Spectrum Sensing: An Adaptive Sensing Method for Non-Terrestrial Communication Under Highly Dynamic Channels

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3868-3881页

作者： Xu, Tianheng Xu, Yinjun Zhou, Ting Zhang, Haijun Hu, Honglin Leung, Victor C. M. Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Shanghai Frontier Innovation Research Institute Shanghai201100 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Shanghai University School of Microelectronics Shanghai200444 China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

With the rapid development of space technology, the role of satellite communications has become progressively significant. Non-terrestrial communication is deemed a critical scenario in the sixth generation (6G) communication systems, which showcases seamless connectivity, minimal geographic constraints and substantial communication capacity. Simultaneously, satellites and terminals spring up, spatial density increases, which further emphasizes the scarcity of spectrum resources. Consequently, to improve spectrum utilization for non-terrestrial communication is a significant concern. Spectrum sensing, which allows dynamic resource reuse, plays an important role in 6G. However, high mobility in non-terrestrial scenarios poses great challenges, such as fast time-varying channels, Doppler effect, etc., which seriously affect sensing accuracy and cannot well support optimal spectrum utilization. Motivated by such circumstances, this paper proposes an elastic sensing method for the downlink non-terrestrial communication scenario. Firstly, we design the system architecture and sensing workflow. To overcome the negative effects raised by high mobility, we propose the elastic sensing criterion and multi-area dividing scheme for the sensing zone. Thresholds affected by the elastic sensing are derived for different areas. Finally, the numerical results show that from -10 dB to -5 dB, the proposed method can improve the total performance by an average of 28.3% while stabilizing the false alarm probability around 0.1 typical level and demonstrating higher constancy compared with traditional technologies from −10 dB to −5 dB. © 2002-2012 IEEE.

关键词： Satellite communication systems

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DAISY: Dual-Granularity Contrastive Learning for Disease Similarity Prediction

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 757-768页

作者： Zihao Gao Huifang Ma Yike Wang Zhixin Li Liang Chang College of Computer Science and Engineering Northwest Normal University Lanzhou China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

The quantification of similarities among human diseases is crucial for enhancing our understanding of disease biology, which can aid in improving disease diagnosis, prognosis and treatment, and drug development. Recently, efforts have been devoted to quantifying disease similarity by integrating multi-view data sources from disparate biological data. However, disease data are often sparse due to their rarity or privacy, leading to suboptimal representation of disease when biological entity relationships and labeled disease data are not adequately modeled. To address this challenge, we propose DAISY (Dual-grAnularity contrastIve learning for diSease similaritY prediction), an effective deep learning model for disease representation modeling. DAISY leverages a novel dual-granularity contrastive learning mechanism to enhance the representation of diverse biological entities. Its ability to enable the collaborative supervision of diseases represented by both homogeneous and heterogeneous information is of great significance, leading to a high level of disease representation learning. Besides, this contrastive learning mechanism combines two independent and complementary components: a hierarchical biological entity relationship-aware module to capture disease features on other biological entities, and a disease association capturing module based on signed random walk with precious disease data. Experimental results demonstrate that DAISY achieves outstanding performance on the disease similarity prediction problem.

关键词： Diseases Biological information theory Contrastive learning Biological system modeling Biology Semantics Ontologies Bioinformatics Data models Biomedical measurement

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Joint Energy and Computation Workload Management for Geo-distributed Data Centers

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Wang, Ran Wu, Rixin Liu, Linfeng Yi, Changyan Zhu, Kun Wang, Ping Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China York University Dept. of Electrical Engineering and Computer Science Lassonde School of Engineering Canada Nanyang Technological University School of Computer Science and Engineering Singapore

The increasing demands of data computation and storage for cloud-based services motivate the development and deployment of large-scale data centers (DCs). The energy demand of these devices is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the load pressure from DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, which can benefit SGs and DCs, as well as society at large. However, the geo-distributed property of DCs and SGs and the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such a tripartite coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, a three-layer game is formulated to model the iterations among entities including the energy market, data center operators (DCOs), and SGs. The market includes a certain amount of RES that must be dispatched. The SG offers the DCO an electricity selling price while simultaneously importing RES from the market at a buying price in order to maximize the benefit. The DCO allocates the workload to different DCs, aiming to minimize the costs of energy consumption and carbon emissions. The interactive processes between different entities are further decomposed into two coupling Stackelberg games. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the joint energy and computation workload management scheme and show its superiority over counterparts in utilizing renewable energy and reducing emissions. Furthe

关键词： Smart power grids

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Environment-Tolerant Trust Opportunity Routing based on Reinforcement Learning for Internet of Underwater Things

引用

IEEE Transactions on Mobile Computing 2025年

作者： He, Yu Han, Guangjie Hou, Yun Lin, Chuan Hohai University College of Information Science and Engineering Changzhou213200 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Changzhou213200 China Hohai University College of Computer Science and Software Engineering Nanjing211100 China Northeastern University Software College Shenyang110819 China

The Internet of Underwater Things (IoUT) has garnered significant interest due to its potential applications in monitoring underwater environments. However, the unique characteristics of acoustic communication, such as long propagation delays and high attenuation, present considerable obstacles for achieving efficient and dependable data transmission. Opportunistic routing is a crucial technique for enhancing packet delivery ratios by selecting a set of forwarding nodes and utilizing their cooperative forwarding to boost network throughput. Nevertheless, choosing an excessive number of forwarding nodes can lead to wasteful energy usage and extended communication delays. Moreover, the overlooked trustworthiness of forwarded nodes in most research works can undermine the effectiveness of opportunistic routing. Therefore, this study presents a novel trust opportunistic routing scheme that employs reinforcement learning to achieve resilience in constantly changing underwater settings. The combination of reinforcement learning and trust management enables the proposed opportunistic routing scheme to adapt to the unstable underwater environment and unknown malicious attacks. Initially, a method is introduced for measuring environmental fitness by considering multiple trust factors, including communication success rate, data reliability, and location dynamics. The proposed scheme then uses reinforcement learning to develop a reliable opportunistic routing method based on quantified state information. This component employs the obtained state to formulate action strategies and obtains reward values from environmental inputs. The reward update equation integrates these qualities to optimize the deployment of superior action strategies, finally achieving trust opportunistic routing for underwater data collection. Fundamental experimental results demonstrate that the proposed protocol performs exceptionally well in demanding underwater conditions, outperforming existing method

关键词： Reinforcement learning

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HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving

arXiv

引用

arXiv 2025年

作者： Lin, R.D. Weng, Pengcheng Wang, Yinqiao Ding, Han Han, Jinsong Wang, Fei School of Software Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China College of Computer Science and Technology Zhejiang University China

LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by LiDAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To leverage these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to LiDAR+Camera multimodal approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Autonomous Computing Emerging Networks Mobile devices Smart network Trusted Data Management

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