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IEEE Transactions on artificial Intelligence 2025年第4期6卷 1038-1049页

作者： Xu, Yifan Shamsolmoali, Pourya Zareapoor, Masoume Yang, Jie Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China East China Normal University School of Communication Shanghai200240 China University of York YorkYO10 5DD United Kingdom

Visual place recognition (VPR) is a highly challenging task that has a wide range of applications, including robot navigation and self-driving vehicles. VPR is a difficult task due to duplicate regions and insufficient attention to small objects in complex scenes, resulting in recognition deviations. In this article, we present ClusVPR, a novel approach that tackles the specific issues of redundant information in duplicate regions and representations of small objects. Different from existing methods that rely on convolutional neural networks (CNNs) for feature map generation, ClusVPR introduces a unique paradigm called clustering-based weighted transformer network (CWTNet). CWTNet uses the power of clustering-based weighted feature maps and integrates global dependencies to effectively address visual deviations encountered in large-scale VPR problems. We also introduce the optimized-VLAD (OptLAD) layer, which significantly reduces the number of parameters and enhances model efficiency. This layer is specifically designed to aggregate the information obtained from scale-wise image patches. Additionally, our pyramid self-supervised strategy focuses on extracting representative and diverse features from scale-wise image patches rather than from entire images. This approach is essential for capturing a broader range of information required for robust VPR. Extensive experiments on four VPR datasets show our model's superior performance compared to existing models while being less complex. © 2024 IEEE.

关键词： Self-supervised learning

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A collaborative inference strategy for medical image diagnosis in mobile edge computing environment

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PEERJ COMPUTER SCIENCE 2025年 11卷 e2708-e2708页

作者： Zhang, Shiqian Cui, Yong Xu, Dandan Lin, Yusong Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Henan Peoples R China Zhengzhou Univ Collaborat Innovat Ctr Internet Healthcare Zhengzhou Henan Peoples R China Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou Henan Peoples R China Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou Henan Peoples R China

The popularity and convenience of mobile medical image analysis and diagnosis in mobile edge computing (MEC) environments have greatly improved the efficiency and quality of healthcare services, necessitating the use of deep neural networks (DNNs) for image analysis. However, DNNs face performance and energy constraints when operating on the mobile side, and are limited by communication costs and privacy issues when operating on the edge side, and previous edge-end collaborative approaches have shown unstable performance and low search efficiency when exploring classification strategies. To address these issues, we propose a DNN edge-optimized collaborative inference strategy (MOCI) for medical image diagnosis, which optimizes data transfer and computation allocation by combining compression techniques and multi-agent reinforcement learning (MARL) methods. The MOCI strategy first uses coding and quantization-based compression methods to reduce the redundancy of image data during transmission at the edge, and then dynamically segments the DNN model through MARL and executes it collaboratively between the edge and the mobile device. To improve policy stability and adaptability, MOCI introduces the optimal transmission distance (Wasserstein) to optimize the policy update process, and uses the long short-term memory (LSTM) network to improve the model's adaptability to dynamic task complexity. The experimental results show that the MOCI strategy can effectively solve the collaborative inference task of medical image diagnosis and significantly reduce the latency and energy consumption with less than a 2% loss in classification accuracy, with a maximum reduction of 38.5% in processing latency and 71% in energy consumption compared to other inference strategies. In real-world MEC scenarios, MOCI has a wide range of potential applications that can effectively promote the development and application of intelligent healthcare.

关键词： Mobile edge computing Medical imaging Collaborative inference Feature compression Reinforcement learning

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Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain

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SCIENTIFIC REPORTS 2025年第1期15卷 1-17页

作者： Yin, Yi Pan, Jianwei Wang, Fang Li, Peihang Cai, Zhen Xu, Xin Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol State Key Lab Refractories & Met Wuhan 430081 Peoples R China Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Univ Durham Comp Sci Durham DH1 3LE England Minist Educ Joint Int Res Lab Refractories & Met Wuhan 430081 Peoples R China

Foam ceramics are widely used in industrial applications due to their unique properties, including high porosity, lightweight, and high-temperature resistance. However, their complex microstructure presents significant challenges for image analysis. Traditional machine learning methods often fall short in capturing both global feature dependencies and detailed representations. To address this, a novel artificial intelligence recognition model, FD-Conv, is proposed, which combines the global information processing capabilities of Transformers with the local feature extraction strengths of convolutional neural networks. Additionally, a frequency domain block detail enhancement mechanism is introduced to improve recognition accuracy. Experimental results demonstrate that the FD-Conv model enhances recognition accuracy by at least 7.6% compared to state-of-the-art methods. Furthermore, the model effectively identifies foam ceramics with varying compositions and formulations and quantifies their microstructural phase characteristics. This research aims to advance the application of foam ceramic microstructure image analysis by improving recognition accuracy, particularly in multi-source microscopic image feature learning and pattern recognition.

关键词： Foam ceramics Multi-source Microstructure images FD-Conv Frequency domain block

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Deep Learning-Driven Analysis of Tumor Regions in Magnetic Resonance Imaging 24

Deep Learning-Driven Analysis of Tumor Regions in Magnetic R...

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5th International Symposium on artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Cao, Bingyu School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400717826

The early diagnosis of tumor is very important to improve the survival rate of patients. Magnetic resonance imaging (MRI), as a non-invasive advanced imaging technology, has a wide range of potential applications in tumor detection and evaluation. However, MRI images are often complex and susceptible to noise, and traditional manual analysis methods have limitations in accuracy and efficiency. In recent years, deep learning technology has made a breakthrough in the field of image recognition, which brings a new method and perspective for the intelligent analysis of MRI images. This paper studied the application of several classical deep learning models in MRI image tumor region recognition, including Convolutional neural Network (CNN) models such as VGG, GoogLeNet, ResNet, EfficientNet and Vision Transformer model, combined with data processing technology and parameter adjustment technology. The performance of these models on data sets is discussed, and a high classification accuracy is finally achieved. This study not only provides an effective solution for automated tumor region detection, but also provides important technical support and reference for clinical tumor diagnosis, and further promotes the intelligent development of imaging diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Comparative analysis of deep learning models for crack detection in buildings

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SCIENTIFIC REPORTS 2025年第1期15卷 1-33页

作者： Krishnan, S. Siva Rama Karuppan, M. K. Nalla Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Tandon, Saarthak Balusamy, Balamurugan Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 Tamilnadu India Sri Balaji Univ Balaji Inst Modern Management Pune 411033 India King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia Kebri Dehar Univ Dept Comp Sci Kebri Dehar Ethiopia Shiv Nadar Univ Delhi Ncr 201314 Delhi India Chennai Inst Technol Dept Comp Sci & Engn Chennai India Chitkara Univ Ctr Res Impact & Outcome Rajpura Punjab India King Abdulaziz Univ Ctr Res Excellence Artificial Intelligence & Data Jeddah Saudi Arabia

Life-time of the buildings is generally challenged by the act of nature. In-spite of the fact that the constructions provide minimum guarantee on quality and durability, certain mismatch in the composition of the materials, stress on the building, and chemical or physical imbalance of the materials, lead to surface crack. Cracks are also generated due to the shuffle of climatic conditions, which leads to the contraction and expansion of the building surfaces, and other damages. The guarantee on building safety and serviceability depends on how these buildings are successfully assessed and maintained. The development of artificial Intelligence (AI) techniques, provide favourable solutions in-order to handle, manage and solve building cracks, through analysis using deep image neural network models, that perform classification of the building with crack images. As a result, a critical challenge for many civil engineering applications is the precise, quick, and automated identification of cracks on structural surfaces is addressed with the solutions provided by the deep image neural networks. In this research, we tackle the research gap and data scarcity by developing and curating a novel deep learning image processing for detecting cracks in brickwork. We also train and validate several deep learning models to classify brickwork images as either cracked or normal. The dataset of the proposed work contains 24,000 images which are classified through binary classes. These classes are generated for crack and non-crack images. The various parameters such as Batch size, Pooling, Activation functions Learning-rate, Kernel-Size, Normalization, and Optimizers are used for the evaluation of the model. The proposed work performs a comparative analysis of four deep image models such as Inception V3, VGG-16, RESNET-50 VGG-19, Inception ResNetV2 and CNN-RES MLP. With the analysis of all these models, the Inception V3 provides the best of all with the accuracy value of 99.98%. The In

关键词： Building cracks Inception V3 VGG-16 ResNet-50 InceptionV2ResNet CNN

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Recognition and evaluation of cutaneous condition through assorted artificial intelligence reliant algorithms

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Mishra, Manmohan Yadav, Ajay Kumar Mazumdar, Bireshwar Dass Gupta, Prashant K. Panwar, Arvind Bharadwaj, Shivam Department of Computer Application United Institute of Management Prayagraj India School of Computer Science & Engineering Technology Bennett University Plot Nos 8-11 TechZone II Uttar Pradesh Greater Noida 201310 India Galgotias University Plot No. 2 Yamuna Expy opposite Buddha International Circuit Sector 17A Uttar Pradesh Prayagraj 203201 India

Our skin is the hefty organ that envelops and shields body. It prevents us from numerous fatal and non fatal diseases. It is observed that due to bacteria or other causes of infection, skin faces certain minor or life threatening diseases. The most prioritized step toward restoring health is early illness signs identification. Identifying Cutaneous Condition from clinical images is one of the foremost challenges in medical image investigation. In the presented study we will enlighten the various artificial Intelligence techniques falling under the categories of supervised machine learning including Probabilistic classifier (Naïve Bayes), Statistical algorithm (Logistic Regression), Ensemble learning (Random Decision Trees), Data analysis technique (Convolutional neural Network) and Kernel approach (Support Vector Machine) to identify and classify the cutaneous condition appropriately so that corrective measures of skin treatment can be endow with. The proposed approach entails collecting images as input, preprocessing, segmenting, feature extraction and lastly applying the classification algorithms to derive the Cutaneous Condition categories. Additional trials are conducted using the different approaches as indicated and it was discovered from the suggested tests that the Convolutional neural Network strategy yields the best results overall. The proposed model is trained, tested, and evaluated using the International Skin Imaging Collaboration (ISIC) 2019 challenge dataset and Human Against Machine with 10,000 training images (HAM10000) for the detection of manifold Cutaneous Condition. © Bharati Vidyapeeth's Institute of Computer applications and Management 2025.

关键词： artificial intelligence Confusion matrix Convolutional neural networks Cutaneous condition image processing Machine learning

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Development of Enhance-Net Deep Learning Approach for Performance Boosting on Medical images 5th

Development of Enhance-Net Deep Learning Approach for Perfor...

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5th International Conference on Data Science, Machine Learning and applications

作者： Manoharan, Geetha Solanke, D. R. Acharjee, Purnendu Bikash Nayak, Chinmaya Kumar Sharma, Mukesh Kumar Sahu, Dillip Narayan SR Univ Sch Business Warangal Telangana India SGB Amravati Univ Dept Appl Elect Amravati 444602 Maharashtra India CHRIST Univ Pune Maharashtra India Sri Sri Univ Fac Emerging Technol Cuttack Odisha India Rungta Coll Pharmaceut Sci & Res Bhilai 490024 Chhattisgarh India Gangadhar Meher Univ Dept MCA Sambalpur Odisha India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. The A rapidly expanding area of study is the Deep Learning Approach (DLA) in medical image processing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. The musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated.

关键词： Deep learning Medical image Enhance- net Dataset

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neural networks and Their applications in Smart Systems 9th

Neural Networks and Their Applications in Smart Systems

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9th International Conference on Smart City applications, SCA 2024

作者： Atmani, Aziz Boumhamdi, Mounir Laboratoire de Recherche Business Intelligence Gouvernance des Organisations Finance et Criminalité Financière Université Hassan II Faculté des Sciences Juridiques Économiques et Sociales de Casablanca No8110 Oasis Casablanca Morocco

ISBN: (纸本)9783031886522

This work investigates the potential of neural networks in the processing of artificial intelligence, especially in smart systems, and explores their applications regarding advancing automation, user experience, and resource management. The methodology will include collecting data from public datasets and international organizations, preprocessing or cleaning, normalization and augmentation of data, followed by the development of different neural network models: Convolutional neural networks for image recognition;Recurrent neural networks and Long Short-Term Memory for speech recognition and time series prediction;hybrids for such complicated tasks as energy consumption forecasting andhealth data analysis. Results are very high for a wide variety of datasets, with CNNs attaining state-of-the-art in image recognition, LSTMs doing the same in speech recognition with very low word error rates, and the hybrids quite effective in predictive analytics and health data analysis. The discussion has been about how neural networks are impacting smart systems in a big way while also presenting the challenges regarding the requirements around data and computational resources and model interpretability. Future directions in this regard may include transfer learning, explainable AI, edge computing, and quantum computing to further enhance neural network capabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Long short-term memory

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Learning Accurate Low-bit Quantization towards Efficient Computational Imaging

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年第4期133卷 1611-1643页

作者： Xu, Sheng Li, Yanjing Liu, Chuanjian Zhang, Baochang Beihang Univ Beijing Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China Hangzhou Res Inst Inst Artificial Intelligence Hangzhou Peoples R China Zhongguancun Lab Beijing Peoples R China NanChang Inst Technol Nanchang Peoples R China

Recent advances of deep neural networks (DNNs) promote low-level vision applications in real-world scenarios, e.g., image enhancement, dehazing. Nevertheless, DNN-based methods encounter challenges in terms of high computational and memory requirements, especially when deployed on real-world devices with limited resources. Quantization is one of effective compression techniques that significantly reduces computational and memory requirements by employing low-bit parameters and bit-wise operations. However, low-bit quantization for computational imaging (Q-Imaging) remains largely unexplored and usually suffer from a significant performance drop compared with the real-valued counterparts. In this work, through empirical analysis, we identify the main factor responsible for such significant performance drop underlies in the large gradient estimation error from non-differentiable weight quantization methods, and the activation information degeneration along with the activation quantization. To address these issues, we introduce a differentiable quantization search (DQS) method to learn the quantized weights and an information boosting module (IBM) for network activation quantization. Our DQS method allows us to treat the discrete weights in a quantized neural network as variables that can be searched. We achieve this end by using a differential approach to accurately search for these weights. In specific, each weight is represented as a probability distribution across a set of discrete values. During training, these probabilities are optimized, and the values with the highest probabilities are chosen to construct the desired quantized network. Moreover, our IBM module can rectify the activation distribution before quantization to maximize the self-information entropy, which retains the maximum information during the quantization process. Extensive experiments across a range of image processing tasks, including enhancement, super-resolution, denoising and dehazing, vali

关键词： Quantization Computational imaging Differentiable quantization search Information entropy

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Advanced Detection of Copy-Move Forgery Using Deep Convolutional neural Network Features Integrated with Machine Learning-Based Classification Techniques 3rd

Advanced Detection of Copy-Move Forgery Using Deep Convoluti...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Al-khuzaie, Mohammed Y. Chohan, Jasgurpreet Singh Sabah, Hawraa Ali Dey, Protyay Verma, Deeksha Hussein, Laith College of Technical Engineering National University of Science and Technology Dhi Qar Nasiriyah64001 Iraq School of Mechanical Engineering Rayat Bahra University Mohali India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Rajasthan Jaipur India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri- Punjab Mohali140307 India College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9789819624676

Technological advancements have significantly contributed to the creation and processing of digital images through various image manipulation software available today. Consequently, the need for effective image forgery detection techniques has become paramount to distinguish between authentic and manipulated images. image forensics is utilized in various fields, including national intelligence agencies, scientific publications, and social networks. One specific type of image manipulation, known as copy-move forgery, involves copying and pasting a portion of an image within the same image, resulting in an altered version of the original. In this study, we introduce a lightweight convolutional neural network (CNN) architecture combined with Support Vector Machine (SVM) classification for copy-move forgery detection (CMFD). This approach uses a similarity measure across feature patches to identify regions containing forgeries. We evaluated the effectiveness of our model using the challenging MICCF600 dataset commonly employed in CMFD research. The proposed model effectively reduces false positives, thereby enhancing pixel-level accuracy and reliability in CMFD applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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