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Enhanced Colon Cancer Prediction Using Capsule Networks and autoencoder-Based Feature Selection in Histopathological Images

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 817-829页

作者： Ramesh, Janjhyam Venkata Naga Mary, F. Sheeja Balaji, S. Nimma, Divya Muniyandy, Elangovan Kranthi, A. Smitha El-Ebiary, Yousef A. Baker Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Pradesh India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Graph Era Deemed Be Univ Dept CSE Dehra Dun 248002 Uttarakhand India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept CSE Chennai India Panimalar Engn Coll Dept CSE Chennai India Univ Southern Mississippi Computat Sci Hattiesburg MS USA Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522302 AP India UniSZA Univ Fac Informat & Comp Kuala Terengganu Malaysia

The malignant development of cells in the colon or rectum is known as colon cancer, and because of its high incidence and possibility for death, it is a serious health problem. Because the disease frequently advances without symptoms in its early stages, early identification is essential. Improved survival rates and more successful therapy depend on an early and accurate diagnosis. The reliability of early detection can be impacted by problems with traditional diagnostic procedures, such as high false-positive rates, insufficient sensitivity, and inconsistent outcomes. This unique approach to colon cancer diagnosis uses autoencoder-based feature selection, capsule networks (CapsNets), and histopathology images to overcome these problems. CapsNets capture spatial hierarchies in visual input, improving pattern identification and classification accuracy. When employed for feature extraction, autoencoders reduce dimensionality, highlight important features, and eliminate noise, all of which enhance model performance. The suggested approach produced remarkable outcomes, with a 99.2% accuracy rate. The model's strong capacity to detect cancerous lesions with few mistakes is demonstrated by its high accuracy in differentiating between malignant and non-malignant tissues. This study represents a substantial development in cancer detection technology by merging autoencoders with Capsule Networks, so overcoming the shortcomings of existing approaches and offering a more dependable tool for early diagnosis. This method may improve patient outcomes, provide more individualized treatment regimens, and boost diagnostic accuracy.

关键词： Colon cancer prediction capsule network autoencoder histopathological images early cancer detection

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Enhancing Deep Learning-Based Channel Estimation: A Novel autoencoder-Based Approach

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第5期36卷

作者： Fola, Ephrem Luo, Chunbo Luo, Yang Jiang, Xiangyuan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Shandong Univ Inst Marine Sci & Technol Qingdao Peoples R China

Deep-learning (DL) methods have shown promising performance in pioneering studies on orthogonal frequency division multiplexing (OFDM) channel estimation challenges. Unlike typical DL-based channel estimation methods that mainly rely on separate real and imaginary inputs while ignoring the inherent correlation between the two streams, this paper proposes AE-DENet, a novel autoencoder (AE)-based data enhancement network to achieve robust channel estimation for OFDM systems. AE-DENet employs the classic least square (LS) channel estimation as input and proposes a data enhancement method to extract the interaction features from the real and imaginary parts of the complex channel estimation matrix, which are joined with the original real and imaginary streams to generate an enhanced input for better channel inference. Experimental findings in terms of the mean square error (MSE) results for a range of representative DL-based channel estimation methods demonstrate that the proposed AE-DENet-enhanced channel estimation framework achieves state-of-the-art channel estimation performance with trivial added computational complexity. Furthermore, the input dimensions of the DL-based channel estimation models can be adaptively adjusted to accommodate the dimension of the enhanced LS input. The proposed approach is also shown to be robust to channel variations and high user mobility.

关键词： AE-DENet autoencoder channel estimation least square estimation

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Few-shot traffic classification based on autoencoder and deep graph convolutional networks

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SCIENTIFIC REPORTS 2025年第1期15卷 1-16页

作者： Xu, Shengwei Han, Jijie Liu, Yilong Liu, Haoran Bai, Yijie Beijing Elect Sci & Technol Inst Informat Secur Res Inst Beijing 100070 Peoples R China Beijing Elect Sci & Technol Inst Dept Cryptog Sci & Technol Beijing 100070 Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China

Traffic classification is a crucial technique in network management that aims to identify and manage data packets to optimize network efficiency, ensure quality of service, enhance network security, and implement policy management. As graph convolutional networks (GCNs) take into account not only the features of the data itself, but also the relationships among sets of data during classification. Many researchers have proposed their own traffic classification methods based on GCN in recent years. However, most of the current approaches use two-layer GCN primarily due to the over-smoothing problem associated with deeper GCN. In scenarios with small samples, a two-layer GCN may not adequately capture relationships among traffic data, leading to limited classification performance. Additionally, during graph construction, traffic usually needs to be trimmed to a uniform length, and for traffic with insufficient length, zero-padding is typically applied to extension. This zero-padding strategy poses significant challenges in traffic classification with small samples. In this paper, we propose a method based on autoencoder (AE) and deep graph convolutional networks (ADGCN) for traffic classification for few-shot datasets. ADGCN first utilizes an AE to reconstruct the traffic. AE enables shorter traffic to learn abstract feature representations from longer traffic of the same class to replace zeros, mitigating the adverse effects of zero-padding. The reconstructed traffic is then classified using GCNII, a deep GCN model that addresses the challenge of insufficient data samples. ADGCN is an end-to-end traffic classification method applicable to various scenarios. According to experimental results, ADGCN can achieve a classification accuracy improvement of 3.5 to 24% compared to existing state-of-the-art methods. The code is available at https://***/han20011019/ADGCN.

关键词： Traffic classification autoencoder Graph convolutional networks Few-shot

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Design of autoencoder Algorithm for Compression of Lightweight EEG Signals Based on 2-D Rhythm Feature Maps

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2025年第3期9卷

作者： Ma, Peijun Yao, Cong Shi, Jiangyi Zhao, Gongzhi Ma, Mingyu Xidian Univ Sch MicroElect Xian 710000 Peoples R China

In recent years, with the development of brain science, the value of electroencephalography (EEG) data has become prominent. However, due to the characteristics of real-time transmission and large amounts of data, there is an urgent need for efficient and lightweight EEG compression algorithms. The existing EEG compression methods have many shortcomings, such as limited compression ratio (CR), poor reconstruction signal quality, and too large model scale, which cannot meet the developmental needs of portable wearable EEG detection devices. In this letter, a method of EEG signal compression based on 2-D rhythm feature maps is proposed. Through discrete wavelet transformation (DWT) extraction of the signal rhythm characteristics, the signal is compressed and reconstructed using encoding and reconstruction channels based on an autoencoder network. At the output end of the encoding channel, entropy coding is carried out to further compress the data volume. Through the discussion of several coding algorithms, JPEG2000 is selected as the local optimal coding algorithm. In addition, based on the idea of grouping convolution and void convolution kernel, a lightweight structure is designed to simplify the process of the proposed network and greatly reduce the number of model parameters. Experiments show that, compared with other similar algorithms, the percentage-root-mean-square distortion and mean squared error (MSE) of the proposed algorithm are 14.76% and 2.95%, respectively, at a relatively high CR (CR is about 16). And only 87.9-k parameters are used, which is more suitable for embedded scenarios and wearable devices.

关键词： Convolution Electroencephalography Feature extraction Transform coding Rhythm Kernel autoencoders Brain modeling Sleep Signal processing algorithms Sensor signal processing 2-D rhythm profile autoencoder electroencephalography (EEG) JPEG2000 lightweight signal compression

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Localized global models using autoencoder-based clustering to forecast related time series

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2025年 1-22页

作者： Abbasimehr, Hossein Noshad, Ali Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz Iran Politecn Milan Dept Elect Informat & Bioengn Milan Italy

In many industries, such as banking, retail, medical, and tourism, abundant time series data is generated. Global forecasting methods have emerged to train a single model by leveraging cross-series information. However, the performance of the global models may be decreased when applied to heterogeneous unequal-length time series datasets. To address heterogeneity in time series, this study contributes a comprehensive methodology for time series forecasting, incorporating three key components: (1) automatic feature extraction from unequal-length time series utilizing two novel autoencoder models based on long short-term memory and convolutional neural network methods, (2) time series clustering, (3) and global forecasting. To evaluate the forecasting accuracy of the proposed methodology, we conduct comprehensive experiments across various time series datasets including six datasets of M3, Tourism, and Hospital datasets. Across all datasets with 2620 time series, attained the lowest mean symmetric mean absolute percentage error (sMAPE) of 15.08, surpassing the baseline mean sMAPE of 15.42. It exhibits enhancements of 0.19 and 0.60 in mean sMAPE relative to ETS and ARIMA, respectively. Furthermore, it demonstrates improvements of 1.10, 0.52, and 2.11 compared to DeepAR, N-BEATS, and Transformer, respectively. A comparative analysis of our proposed methodology against clustering-based and ensembling-based models in the existing literature highlights its superiority in global time series forecasting.

关键词： Related time series Global forecasting autoencoder LSTM CNN

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A Chemical Kinetic Mechanism Reduction Method with autoencoder for Hydrocarbon Fuels

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COMBUSTION SCIENCE AND TECHNOLOGY 2025年

作者： Xia, Xiaoqiao Gou, Xiaolong Chongqing Univ Key Lab Low Grade Energy Utilizat Technol & Syst Minist Educ 174 Shazheng St Chongqing Peoples R China Chongqing Univ Sch Energy & Power Engn Chongqing Peoples R China

A chemical kinetic mechanism reduction method based on autoencoder (CRAE) is proposed to obtain the skeletal mechanism of hydrocarbon fuels. Based on the network architecture of autoencoder, a computational network structure is constructed using the species molar concentration as the input and output layers. The training datasets are generated from numerical simulations of zero-dimensional homogeneous ignition process using detailed chemical kinetic mechanisms under diverse operating conditions, and the autoencoder is optimized by using back-propagation algorithm and gradient descent algorithm. Subsequently, the weight coefficients connecting the input layer to the first hidden layer are calculated to assess the importance of chemical species, which in turn determines whether a species and its associated elementary reactions can be removed. The skeletal mechanisms for methane and n-heptane generated by using the present CRAE method are compared to those produced by the path flux analysis (PFA) method and the detailed mechanism. The comparisons of the ignition delay time, temperature profiles, and the concentration of important species show that with significantly smaller number of species, the skeletal mechanisms generated by the present CRAE method are more accurate than those of PFA in a broad range of initial pressures, temperatures and equivalence ratios. The CRAE method provides a new and effective means for reduction complex combustion kinetic mechanism of hydrocarbon fuels.

关键词： Mechanism reduction autoencoder hydrocarbon fuels chemical kinetic mechanism

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Leveraging autoencoder for laser phase noise compensation in coherent optical OFDM systems

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JOURNAL OF OPTICS-INDIA 2025年 1-10页

作者： Al-Saedi, Ibtesam R. K. Alnaseri, Omar Rasool, Jamal Mohammed Li, Hongxiang Univ Technol Iraq Commun Engn Dept Baghdad Iraq DHBW Ravensburg Univ Dept Elect Engn Ravensburg Baden Wuerttemb Germany Univ Louisville Dept Elect & Comp Engn Louisville KY USA

This paper unveils a cutting-edge end-to-end learning approach to tackle laser phase noise in coherent optical orthogonal frequency-division multiplexing (CO-OFDM) fiber communications. Inspired by the autoencoder concept, a powerful deep neural network, the proposed approach tackles this challenge by learning resilient symbol sequences that can withstand laser phase noise impairments, even from low-cost distributed feedback lasers with linewidths up to 2 MHz. This makes it ideally suited for high-order modulation formats and large FFT sizes in CO-OFDM systems, where maximizing spectral efficiency is crucial. By eliminating the need for computationally intensive conventional techniques, this approach opens the door to a simpler, potentially more efficient solution.

关键词： Optical fiber communication Coherent detection OFDM Laser phase noise autoencoder

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A Channel Attention-Based Denoising autoencoder U-Net and Classifier (UNA-DAEC) for Signal Identification Under Challenging SNR Conditions

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IEEE ACCESS 2025年 13卷 77317-77333页

作者： Ebo, Ife Olalekan Ajayi, Idowu Mroueh, Lina Ziade, Youmni Azarian, Sylvain ISEP Lab Informat Signal & Image Elect & Telecommun LIS F-92130 Issy Les Moulineaux France Agence Natl Frequences F-94704 Maisons Alfort France SDR Technol F-78120 Rambouillet France

The use of Low-Power Wide Area Network (LPWAN) technologies, such as Long Range (LoRa), Sigfox, and IEEE 802.15.4g (ZigBee), has grown significantly, addressing a wide range of applications including smart metering, agriculture, smart homes, and healthcare. These technologies are valued for their simplicity, flexible connectivity, low power consumption, efficient modulation techniques, and moderate data rates. As a result, they can coexist within the same environment, serving either similar or distinct applications. However, the increasing deployment of devices and technologies has amplified the likelihood of interference between them, leading to performance degradation, particularly in real-world scenarios under challenging conditions where noise power surpasses signal power. The rapid proliferation of these technologies, especially within unlicensed Industrial, Scientific, and Medical (ISM) frequency bands, underscores the need for effective techniques to ensure seamless coexistence without disrupting communication. To address this challenge, we investigate the role of data representation and propose a Channel Attention-based Denoising autoencoder U-Net and Classifier (UNA-DAEC). This model is designed to denoise multi-label LPWAN signals affected by white Gaussian noise and accurately classify overlapping transmissions, specifically IEEE 802.15.4g, Sigfox, and LoRa signals, within the same environment. The primary objective of UNA-DAEC is to achieve reliable signal classification in low Signal-to-Noise Ratio (SNR) conditions. This is achieved by first denoising the noisy signals to obtain optimal representations, enabling high classification accuracy with a single forward and backward propagation. Our results further demonstrate that data representation plays a critical role in identifying and classifying LPWAN signals, particularly in challenging low-SNR environments, with a significant performance of 44%, 7%, and 26% over CNN-based IQ, CNN-based FFT and DAE+Cla

关键词： Low-power wide area networks Wireless sensor networks Signal to noise ratio LoRa Interference Deep learning Internet of Things Noise reduction Wireless networks Sensors autoencoder channel attention convolutional neural network deep learning denoising auto-encoder low-power wireless area network technology classification time-frequency analysis

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Seq2Seq-based GRU autoencoder for anomaly detection and failure identification in coal mining hydraulic support systems

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Zhan, Kai Wang, Cong Zheng, Xigui Kong, Chao Li, Guangming Xin, Wei Liu, Longhe Shandong Succeed Min Safety Engn Co LTD Jinan Peoples R China Chengdu Univ Technol Chengdu Peoples R China China Univ Min & Technol Sch Mines Xuzhou Peoples R China China Univ Min & Technol State Key Lab Geo Mech & Deep Underground Engn Xuzhou Peoples R China Liupanshui Normal Univ Sch Mines & Civil Engn Liupanshui Peoples R China Guizhou Guineng Investment Co Ltd Guiyang Peoples R China

In coal mining operations, the stable operation of hydraulic supports is crucial for ensuring mine safety. However, the nonlinear, non-stationary characteristics and noise interference in hydraulic support pressure data pose significant challenges for anomaly detection and fault diagnosis. This study proposes an anomaly detection and failure identification method based on Gated Recurrent Unit autoencoder (GRU-AE), aimed at achieving anomaly detection in hydraulic support pressure data and equipment failure early warning. Through in-depth analysis of data from two coal mines in China, we systematically evaluated the model's key parameters. The study revealed that window size had a limited impact on model performance, with a window length of 144 demonstrating optimal comprehensive performance in both anomaly detection and failure mode identification. The study also investigated the effectiveness of teacher forcing techniques. Although this technique can accelerate model convergence, it may lead to training instability and reduced generalization capability, requiring careful consideration in practical applications. Our proposed Recurrent Reconstruction Network model demonstrated excellent performance in complex coal mine hydraulic support data, effectively identifying anomalous regions and potential equipment failure characteristics while revealing potential deviations between model predictions and actual data, demonstrating its superior learning capability for periodic data patterns and equipment failure characteristics. Experimental results validated the effectiveness of the GRU-AE model in hydraulic support pressure anomaly detection and equipment fault diagnosis, providing an innovative technical solution for coal mine safety monitoring.

关键词： Mining engineering Hydraulic support Anomaly detection Gated recurrent unit autoencoder

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Robust biochar yield and composition prediction via uncertainty-aware ResNet-based autoencoder

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BIOCHAR 2025年第1期7卷 1-16页

作者： Zhang, Yali Lei, Bowen Mahdaviarab, Amirhossein Wang, Xiao Liu, Zong Texas A&M Univ 375 Olsen Blvd College Stn TX 77845 USA

With growing demand for energy and fossil fuels, biomass and biochar are gaining attention due to their abundance and sustainability. However, there is a crucial need for optimizing production conditions and minimizing environmental risks associated with biochar production. Machine learning is an emerging strategy for predicting biochar yield and composition, optimizing production conditions and minimizing environmental risks. This paper presents a ResNet-based autoencoder model that utilizes biomass properties and pyrolysis conditions to more accurately and robustly predict biochar yield and composition. The developed model has the advantage of addressing the common data uncertainty concerns in training data. Our model outperforms commonly used baseline methods, including MLP-NN (mean R2 = 0.907), Random Forest (RF, mean R2 = 0.798), XGBoost (XGB, mean R2 = 0.826), and Gaussian Process (GP, mean R2 = 0.786), by achieving a mean R2 of 0.974. The performance of the model was further improved by incorporating previously discarded data with high missing rates, achieving an average R2 of 0.983. The addition of the three newly collected covariates resulted in an average R2 of 0.985. Additionally, robust sensitivity analyses of the input covariates revealed the impact of data uncertainty on the performance of the model, emphasizing the robustness of the model. In advancing the application of machine learning in biochar research, this study provides a reliable method to determine optimal production conditions.

关键词： Biochar yield Data uncertainty Robustness ResNet autoencoder

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