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2025 ieee symposium on computational intelligence for Financial Engineering and Economics, CiFer 2025

作者： Chen, Ruxin Zhang, Zeqiang Nagoya University Department of Economics Nagoya Japan Institute of Neural Information Processing Ulm University Ulm Germany

ISBN: (纸本)9798331508319

This paper proposes a novel framework for applying reinforcement learning (RL) within agent-based models (ABMs) to study labor market dynamics in labor economics. ABMs provide a flexible platform for simulating economic systems, particularly by modelling heterogeneous agents and their complex interactions, which effectively capture non-linear and emergent phenomena in labor markets. We extend an existing labor market model by integrating it into an RL framework, using the Deep Deterministic Policy Gradient (DDPG) algorithm to train agents to maximize profits, and comparing their performance with bounded-rational agents governed by predefined policies. Our findings show that RL agents, depending on the level of competition and rationality in the market, spontaneously learn distinct strategies, which significantly impact outcomes, such as unemployment and wage distribution. This work underscores the importance of ABMs in analyzing labor market dynamics and illustrates how RL-equipped agents can learn optimal strategies in evolving economic environments, offering a robust tool for policy analysis and exploring the complexities of labor market behavior. © 2025 ieee.

关键词： Employment

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Mamba Collaborative Implicit Neural Representation for Hyperspectral and Multispectral Remote Sensing image Fusion

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ieee TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhu, Chunyu Deng, Shangqi Song, Xuan Li, Yachao Wang, Qi Xidian Univ Hangzhou Inst Technol Hangzhou 311231 Zhejiang Peoples R China Xi An Jiao Tong Univ Natl Key Lab Human Machine Hybrid Augmented Intell Natl Engn Res Ctr Visual Informat & Applicat Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

Hyperspectral remote sensing images (HSIs) capture detailed spectral characteristics of features, while multi- spectral remote sensing images (MSIs) provide clear spatial distribution. Fusing these two types of images can enhance feature identification and classification accuracy. Current deep learning algorithms achieve high fusion quality but struggle with balancing global effective perception and lightweight computation. Moreover, these algorithms typically discretely handle data mapping, which contrasts with the continuous nature of the world. Recently, the Mamba has shown significant potential for complex long-range modeling, addressing the computational complexity of global perception. Concurrently, implicit neural representation (INR) offers high-quality solutions for continuous domain modeling. To this end, this study introduces a novel network architecture that combines Mamba and INR, termed the Mamba cooperative INR fusion network (MCIFNet). MCIFNet effectively captures global image information and generates fused images in a continuous domain through pointto-point processing. The network comprises two main units: potential space projection and semantic extraction and fusion. The potential space projection unit performs shallow encoding of hyperspectral and MSIs, mapping them to a latent feature space. The semantic extraction and fusion unit (SEFU) uses scale adaptive residual state spatial and implicit spatial-spectral fusion (ISSF) modules to extract deep features from the bimodal images, generating fused images point-by-point. A series of fusion experiments with 4x, 8x, and 16x scale factors demonstrate that MCIFNet surpasses popular algorithms in both spatial detail and spectral information reconstruction, while also providing more lightweight performance. The code for MCIFNet will be shared on https://***/chunyuzhu/MCIFNet.

关键词： Feature extraction Accuracy Spatial resolution Matrix decomposition Optimization Deep learning Sparse approximation image fusion Hyperspectral imaging Transformers Hyperspectral and multispectral image fusion implicit neural representation (INR) Mamba Mamba cooperative INR fusion network (MCIFNet) state-space model (SSM)

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ieee SSCI 2025 Welcome Message

IEEE SSCI 2025 Welcome Message

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computational intelligence in Natural Language processing and Social Media (CI-NLPSoMe Companion), ieee symposium on

Dear Delegates, From the General Chairs, on behalf of the whole Organising Commi7ee, we extend a warm welcome to all of you to Trondheim, Norway for the 2025 ieee symposium Series on ComputaFonal intelligence (ieee SS...

ISBN: (数字)9798331519742

ISBN: (纸本)9798331519759

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A Review on the Applications of Machine Learning and Deep Learning Algorithms for image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 ieee.

关键词： Adversarial machine learning

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Rethinking Camouflaged Object Detection via Foreground-Background Interactive Learning

Rethinking Camouflaged Object Detection via Foreground-Backg...

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2025 ieee International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Chenxi Zhang, Qing Wu, Jiayun Faculty of Intelligence Technology Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350368741

Camouflaged object detection focuses on the challenge of segmenting objects that visually blend into their background. The effectiveness of camouflage strategies hinges on how well objects interact with their background to minimize their visibility. Based on this insight, we propose a novel Foreground-Background Interactive Learning Network (FBINet), which independently decouples foreground and background information, and facilitates bi-directional interactions. This allows the network to progressively complement each other, leading to high-quality predictions with clear boundaries. To the best of our knowledge, this is the first attempt to tackle the camouflaged object detection task through interactive learning between foreground and background, which not only better reveals camouflage patterns but also offers a new perspective in this field. Experimental results on three datasets demonstrate that our proposed FBINet outperforms current state-of-the-art methods in performance while maintaining a low computational cost, making it applicable for real-world scenarios. The code will be available at https://***/bbdjj/FBINet. © 2025 ieee.

关键词： Camouflaged object detection foreground-background interaction image segmentation

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Investigating Fairness with FanFAIR: is Pre-processing Useful Only for Performances?

Investigating Fairness with FanFAIR: is Pre-processing Usefu...

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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

作者： Rispoli, Michele Nobile, Marco S. Manzoni, Luca D’Onofrio, Alberto Confalonieri, Marco Salton, Francesco Confalonieri, Paola Ruaro, Barbara Gallese, Chiara Department of Mathematics Informatics and Geosciences University of Trieste Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy Pulmonology Unit University Hospital of Cattinara Trieste Italy Department of Medical Surgical and Health Sciences University of Trieste Italy Department of Law University of Turin Italy Bicocca Bioinformatics Biostatistics and Bioimaging research center Monza Italy

ISBN: (纸本)9798331508333

Artificial intelligence, and Machine Learning systems in general, are becoming pervasive in our society, from the industry to the public administration. AI can often provide a very efficient means to support decision-making, but it can represent a danger for high-risk applications such as bio-medicine and healthcare. In particular, biased datasets might lead to inaccurate or discriminatory ML systems, undermining the accuracy of their predictions and putting patients’ health at risk. FanFAIR is a python tool that provides the community with a semiautomatic tool for datasets’ fairness assessment. FanFAIR is designed to integrate qualitative considerations – such as ethics, human rights assessment, and data protection – with quantitative indicators of dataset’s fairness, such as balance, the presence of invalid entries, or outliers. In this work, we extend FanFAIR to deal with categorical data, and introduce a new algorithm for outlier detection in the presence of missing values. We then provide a case study on the data collected from COVID patients admitted to pneumology departments in Italy. We show how the successive steps of data cleaning and variable selection improve the indicators provided by FanFAIR. This shows that data cleaning procedures are not only necessary to improve the performance of the machine learning algorithm using the data for learning, but are also a way to improve (a measure of) fairness. Hence, the proposed case study provides an example in which performance and fairness are not in contrast, like it is commonly believed to be, but they improve together. © 2025 ieee.

关键词： Health risks

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Robust Pre-processing Strategies for Early Lung Cancer Diagnosis with Low-Dose CT Scans 2

Robust Pre-Processing Strategies for Early Lung Cancer Diagn...

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2nd International Conference on computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Thakral, Gagan Kumar, Umesh Gambhir, Sapna J. C. Bose University of Science and Technology YMCA Faridabad India KIET Group of Institutions Delhi-NCR Ghaziabad India George Mason University FairfaxVA United States

ISBN: (纸本)9798331530389

Cancer that starts in the lungs due to the growth of abnormal cell in uncontrolled way is known as lung cancer. Tumors that prevent the lung from supplying blood with oxygen may develop because of it. So, it is important to detect it in its early stages. Low-Dose Computed Tomography (LDCT) scans play very important role to detect it in early stages. This work represents the various pre-processing techniques which are applied to the dataset of LDCT scan images to enhance the efficacy and accuracy of the system to detect Lung cancer. The several pre-processing techniques, including Gaussian Filter, TV (Total Variation) Method, Bilateral Method, Wavelet Method, Invariant Wavelet Method, NLM (Non-Local Means) Method and BM3D (Block-Matching & 3D filtering) Methods are used in this study. These methods are able to perform positive results for image enhancement, noise reduction, and feature extraction. The results of the study show that these pre-processing techniques not only improve the clarity of LDCT scan images but also enable more precise identification of suspected tumor locations. These pre-processing methods also improve specificity and sensitivity of the system. Through the ability to intervene earlier and increase the chances of patient survival, this discovery holds the potential to save lives. It also has a great deal of potential to advance the field of early lung cancer detection. © 2025 ieee.

关键词： Lung cancer

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High Spatio-Temporal Imaging Reconstruction for Hybrid Spike-RGB Cameras

IEEE Transactions on Computational Imaging

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ieee Transactions on computational Imaging 2025年 11卷 586-598页

作者： Xia, Lujie Xiong, Ruiqin Zhao, Jing Wang, Lizhi Zhu, Shuyuan Fan, Xiaopeng Huang, Tiejun Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Beijing Normal University School of Artificial Intelligence Beijing100875 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Department of Computer Science and Technology Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China

The acquisition of high-resolution image sequence for dynamic scenes of fast motion remains challenging due to motion blur caused by fast object movement. As a novel neuromorphic sensor, spike camera records the changing light intensity via spike stream of ultra-high temporal resolution, excelling in motion recording but limited in spatial resolution. This paper proposes a method for high spatio-temporal resolution (HSTR) imaging with a hybrid Spike-RGB camera, utilizing the information from spike stream to enhance the temporal resolution and the information from RGB images to enhance the spatial resolution of texture details. For this purpose, we present HSTR-Net, a dedicated network to process the spike and RGB data, which incorporates three key innovations: 1) A temporal control encoder enabling flexible temporal reconstruction through spike stream processing with embedded time parameters, eliminating the requirement to train multiple inference models;2) Motion-aware feature projection that aligns RGB frame details to target timestamps using spike-derived motion offsets;3) An adaptive transformer-based fusion strategy establishing cross-modal spatial correlations through mutual attention mechanisms. Extensive experiments demonstrate state-of-the-art performance on synthetic benchmark datasets with 5.23 dB PSNR and 6.94% SSIM improvement. It also shows visually impressive performance on real-world captured spike dataset. © 2015 ieee.

关键词： High speed cameras

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MULTITASK AI FOR ONBOARD intelligence IN EARTH OBSERVATION MISSIONS

MULTITASK AI FOR ONBOARD INTELLIGENCE IN EARTH OBSERVATION M...

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2024 Small Satellites Systems and Services symposium, 4S 2024

作者： Inzerillo, Gabriele Valsesia, Diego Magli, Enrico Politecnico di Torino C.so Duca degli Abruzzi 24 Torino Italy

ISBN: (纸本)9781510688940

There is a growing interest towards moving intelligence from the ground segment to onboard satellites. However, efficient use of the limited onboard computational resources is not trivial, especially in the case of AI systems employing neural networks for state-of-the-art inference on several problems. In this work, we study how a potential mission could address its AI computational payload to provide low-latency responses to multiple tasks with efficient use of resources and flexible design. We envision the use of a neural network composed of a backbone to extract features from multispectral input images at a relatively fine grained spatial resolution. This feature extractor is trained in a self-supervised manner to exploit large collections of unlabeled imagery by the mission prime designer. The features are then used by lightweight neural network heads, working in parallel, each specialized for one task. These heads can be designed independently by third-party contractors, with domain knowledge of the tasks. We show a lightweight neural network design for the backbone and a sample application head that is competitive with state-of-the-art methods at a fraction of the complexity and report interesting processing throughput results on a 7W System-on-Chip. © 2025 The Authors.

关键词： image acquisition

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Latent Weight Quantization for Integerized Training of Deep Neural Networks

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE intelligence 2025年第4期47卷 2816-2832页

作者： Fei, Wen Dai, Wenrui Zhang, Liang Zhang, Luoming Li, Chenglin Zou, Junni Xiong, Hongkai Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China Zhejiang Univ Key Lab Biomed Engn Minist Educ Hangzhou 310027 Peoples R China

Existing methods for integerized training speed up deep learning by using low-bitwidth integerized weights, activations, gradients, and optimizer buffers. However, they overlook the issue of full-precision latent weights, which consume excessive memory to accumulate gradient-based updates for optimizing the integerized weights. In this paper, we propose the first latent weight quantization schema for general integerized training, which minimizes quantization perturbation to training process via residual quantization with optimized dual quantizer. We leverage residual quantization to eliminate the correlation between latent weight and integerized weight for suppressing quantization noise. We further propose dual quantizer with optimal nonuniform codebook to avoid frozen weight and ensure statistically unbiased training trajectory as full-precision latent weight. The codebook is optimized to minimize the disturbance on weight update under importance guidance and achieved with a three-segment polyline approximation for hardware-friendly implementation. Extensive experiments show that the proposed schema allows integerized training with lowest 4-bit latent weight for various architectures including ResNets, MobileNetV2, and Transformers, and yields negligible performance loss in image classification and text generation. Furthermore, we successfully fine-tune Large Language Models with up to 13 billion parameters on one single GPU using the proposed schema.

关键词： Quantization (signal) Training Perturbation methods Memory management Hardware Trajectory Random access memory Graphics processing units computational modeling Noise Integerized training deep neural network quantization latent weight dual quantizer large language models

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