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Adaptive fish school search optimized resnet for multi-view 3D objects reconstruction

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第32期83卷 77639-77666页

作者： Premalatha, V. Parveen, Nikhat Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India

Reconstruction of multi-view 3-dimensional images is essential in robotics and computer vision to obtain an accurate 3-dimensional representation of objects by analyzing the 2-dimensional input data. For reconstructing the 3-dimensional image, it is mandatory to analyze the 3-dimensional geometry features from multiple viewpoints. It includes feature extraction and transformation from 2-dimensional features to 3-dimensional volumetric meshes. However, the existing research cannot produce consistent reconstruction results for the same input images with different orders. Therefore, the deep learning-based Residual Network-50 model is developed for 3-dimensional image reconstruction from multi-view images in the present work. The proposed system model comprises a 2-dimensional and 3-dimensional network and a backpropagation layer. From the input image, 2-dimensional features are computed using a 2-dimensional network. Then, the metaheuristic Adaptive School of Fish Optimization is used to improve the neural network's output, determining the optimal weight that gives less classification error. Then, the testing process uses the deep autoencoder, which decodes the output of the training model. Residual Network-50 is used to reconstruct 2-dimensional images to 3-dimensional using single or multi-views. Finally, the experimental analysis is performed in Python. The experiment is performed on the ShapeNet dataset and compared with the existing works. The proposed model yields better accuracy, F-score and Intersection-over-Union values of 99.3%, 0.893 and 0.734, respectively, which is more efficient than other existing models.

关键词： Object reconstruction Fish school search optimization deep autoencoder Back projection layer 3D reconstructed images ResNet-50

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Predicting the Early Detection of Breast Cancer Using Hybrid Machine Learning Systems and Thermographic Imaging

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第6期34卷

作者： Hosseini, Mohammad Mehdi Mosahebeh, Zahra Chakraborty, Somenath Gharahbagh, Abdorreza Alavi Islamic Azad Univ Shahrood Branch Dept Comp Engn Shahrood Iran Univ Tehran Med Sci Med Informat Tehran Iran West Virginia Univ Comp Sci & Informat Syst Morgantown VA USA

Breast cancer is a leading cause of mortality among women, emphasizing the critical need for precise early detection and prognosis. However, conventional methods often struggle to differentiate precancerous lesions or tailor treatments effectively. Thermal imaging, capturing subtle temperature variations, presents a promising avenue for non-invasive cancer detection. While some studies explore thermography for breast cancer detection, integrating it with advanced machine learning for early diagnosis and personalized prediction remains relatively unexplored. This study proposes a novel hybrid machine learning system (HMLS) incorporating deep autoencoder techniques for automated early detection and prognostic stratification of breast cancer patients. By exploiting the temporal dynamics of thermographic data, this approach offers a more comprehensive analysis than static single-frame approaches. Data processing involves splitting the dataset for training and testing. A predominant infrared image was selected, and matrix factorization was applied to capture temperature changes over time. Integration of convex factor analysis and bell-curve membership function embedding for dimensionality reduction and feature extraction. The autoencoder deep neural network further reduces dimensionality. HMLS model development included feature selection and optimization of survival prediction algorithms through cross-validation. Model performance was assessed using accuracy and F-measure metrics. HMLS, integrating clinical data, achieved 81.6% accuracy, surpassing 77.6% using only convex-NMF. The best classifier attained 83.2% accuracy on test data. This study demonstrates the effectiveness of thermographic imaging and HMLS for accurate early detection and personalized prediction of breast cancer. The proposed framework holds promise for enhancing patient care and potentially reducing mortality rates.

关键词： breast cancer diagnosis convex factor analysis data dimensionality reduction deep autoencoder feature extraction thermal imaging

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DASVDD: deep Autoencoding Support Vector Data Descriptor for Anomaly Detection

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 3739-3750页

作者： Hojjati, Hadi Armanfard, Narges McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0C3 Canada Mila Quebec AI Inst QC H2S Montreal PQ Canada

One-Class anomaly detection aims to detect anomalies from normal samples using a model trained on normal data. With recent advancements in deep learning, researchers have designed efficient one-class anomaly detection methods. Existing works commonly use neural networks to map the data into a more informative representation and then apply an anomaly detection algorithm. In this paper, we propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hypersphere on its latent representation. We propose a novel anomaly score that combines the autoencoder's reconstruction error and the distance from the center of the enclosing hypersphere in the latent representation. Minimizing this anomaly score aids us in learning the underlying distribution of the normal class during training. Including the reconstruction error in the anomaly score ensures that DASVDD does not suffer from the hypersphere collapse issue since the DASVDD model does not converge to the trivial solution of mapping all inputs to a constant point in the latent representation. Experimental evaluations on several benchmark datasets show that the proposed method outperforms the commonly used state-of-the-art anomaly detection algorithms while maintaining robust performance across different anomaly classes.

关键词： Anomaly detection deep autoencoder deep learning support vector data descriptor deep autoencoder deep learning support vector data descriptor

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Multi-path long-term vessel trajectories forecasting with probabilistic feature fusion for problem shifting

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OCEAN ENGINEERING 2024年第Part1期312卷

作者： Spadon, Gabriel Kumar, Jay Eden, Derek van Berkel, Josh Foster, Tom Soares, Amilcar Fablet, Ronan Matwin, Stan Pelot, Ronald Dalhousie Univ Fac Comp Sci Halifax NS Canada Dalhousie Univ Ind Engn Dept Halifax NS Canada IMT Atlantique Dept Genie Math & Elect Brest France Linnaeus Univ Dept Comp Sci & Media Tech Vaxjo Sweden Polish Acad Sci Inst Comp Sci Warsaw Poland DHI Water & Environm Inc Ottawa ON Canada

This paper presents a deep auto-encoder model and a phased framework approach to predict the next 12 h of vessel trajectories using 1 to 3 h of Automatic Identification System data as input. The strategy involves fusing spatiotemporal features from AIS messages with probabilistic features engineered from historical AIS data to reduce forecasting uncertainty. The probabilistic features have an F1-Score of approximately 85% and 75% for the vessel route and destination prediction, respectively. Under such circumstances, we achieved an R2 Score of over 98% with different layer structures and varying feature combinations;the high R2 Score is a natural outcome of the well-defined shipping lanes in the study region. However, our proposal stands out among competing approaches as it demonstrates the capability of complex decision-making during turnings and route selection. Furthermore, we have shown that our model achieves more accurate forecasting with average and median errors of 11km and 6km, respectively, a 25% improvement from the current state-ofthe-art approaches. The resulting model from this proposal is deployed as part of a broader Decision Support System to safeguard whales by preventing the risk of vessel-whale collisions under the smartWhales initiative and acting on the Gulf of St. Lawrence in Atlantic Canada.

关键词： Feature fusion Probabilistic modeling deep autoencoder Spatiotemporal forecasting Phased framework approach Trajectory reconstruction

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Unsupervised deep Clustering With Hard Balanced Constraint: Application in Disciplinary-Focused Student Section Formation

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IEEE ACCESS 2024年 12卷 98239-98253页

作者： Chantamunee, Siripinyo Thamrongrat, Pornpon Thanathamathee, Putthiporn Chaisriya, Kannattha Nizam, Dinna Nina Mohd Walailak Univ Informat Innovat Ctr Excellence Nakhon Si Thammarat 80160 Thailand Walailak Univ Sch Engn & Technol Nakhon Si Thammarat 80160 Thailand Walailak Univ Sch Informat Nakhon Si Thammarat 80160 Thailand Univ Malaysia Sabah Fac Comp & Informat User Experience Res Grp Labuan 87008 Malaysia

Effective student group formation is crucial in higher education to foster collaborative learning environments. Grouping students by academic disciplines enhances peer-to-peer interactions and facilitates in-depth discussions on specialized topics. However, due to classroom space and resource constraints, it is challenging to accommodate all students from similar disciplines in one class. This necessitates a grouping method that can ensure a balanced distribution of students across available groups. Traditional K-means clustering, commonly used for this purpose, often results in inconsistent group sizes and fails to guarantee a balanced distribution of group members. Hard balanced clustering, which strictly enforces precise size limits on each cluster, offers a promising alternative for organizing balanced student sections to optimize classroom utilization. Nonetheless, most hard balanced clustering methods are limited in feature learning capability, which can lead to the overlooking of significant data patterns and result in ineffective clustering. To address this limitation, this paper introduces a new unsupervised model, deep Hard Balanced Clustering (DHBC), which integrates hard balanced clustering with a deep learning framework to enhance feature learning. DHBC incorporates a balanced clustering mechanism within the optimization process of an autoencoder architecture. It enhances the generated latent space representation by introducing a joint loss function that combines reconstruction and balanced clustering objectives, ensuring the embedded representation supports a balanced distribution of students. The model optimizes balanced clustering centroids during training. Comparative experiments conducted on real-world student enrollment datasets, evaluated by WCSS scores, demonstrate DHBC's superiority in creating more cohesive and balanced student groups compared to state-of-the-art methods.

关键词： Clustering algorithms Linear programming Feature extraction Representation learning Decoding deep learning Computer architecture Encoding Hard balanced clustering balanced clustering deep clustering deep autoencoder group formation

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Efficient quality enhancement of gastrointestinal endoscopic video by a novel method of color salient bilateral filtering

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第4期80卷 6235-6245页

作者： Das, Apurba Shylaja, S. S. PES Univ Comp Sci & Engn Bangalore Karnataka India

The recent advancements in bio-photonics enabled physicians to combine techniques such as narrow-band imaging, fluorescence spectroscopy, optical coherence tomography, with visible spectrum endoscopy video to provide in vivo microscopic tissue characterization in online optical biopsy (Ye et al.2015);(Wang and Van Dam2004). Despite the aforementioned advantages, it is challenging for gastroenterologists to retarget the optical biopsy sites during endoscopic examinations because of the degraded quality of endoscopic video which gets corrupted by haze, noise, oversaturated illumination, etc. Enhancement of video frames by considering color channels independently gives birth to unintended phantom color due to its ignorance of the psycho-visual correspondence. To address the aforementioned, we have proposed a novel algorithm to enhance video with faster performance. The proposedC(2)D(2)A(Cross Color Dominant deep autoencoder) uses the strength of (a) bilateral filtering both in spatial neighborhood domain and psycho-visual range;(b) deep autoencoder which learns salient patterns. The domain-based color sparseness has further improved the performance, modulating classical deep autoencoder to color dominant deep autoencoder. The work has shown promise towards not only a generic framework of quality enhancement of video streams but also addressing performance. The current work in turn improves the image and video analytics like segmentation, detection, and tracking the objects or regions of interest.

关键词： Endoscopy deep autoencoder Color sparseness Bilateral filtering

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D³FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging

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APPLIED INTELLIGENCE 2022年第7期52卷 7201-7217页

作者： Ghasemi, Majid Kelarestaghi, Manoochehr Eshghi, Farshad Sharifi, Arash Islamic Azad Univ Dept Comp Engn Farsan Branch Farsan *** Iran Kharazmi Univ Dept Elect & Comp Engn Tehran *** Iran

Providing accurate and speedy diagnosis and, in turn, treatment, automated medical image analysis plays a significant role in survival rate improvement. Inherent different kinds of uncertainties and complexities prove machine learning-based, particularly dictionary-learning-based classification approaches, very promisingly. This work concerns class-specific fuzzy discriminative dictionary learning using deep features on the continuum of our machine-learning-based medical image classifiers' evolution path. In (DFC)-F-3, a deep autoencoder generates a more relevant, representative, and compact features set. The distinctive-hidden information and inherent complexity and uncertainty of medical images are addressed using fuzzy-discriminative terms in the optimization function, simultaneously improving the inter-class-representation distance and intra-class-representation similarity. A comprehensive set of experiments on cancer tumor images from three different databases shows the outperformance of (DFC)-F-3 over related state-of-the-art competitions in accuracy, sensitivity, specificity, precision, convergence speed, and noise resilience. The meaningfulness of the experiments' results is statistically verified.

关键词： Class-specific deep autoencoder Discriminative sparse representation Fuzzy dictionary learning Intra- Inter-class distance

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Audio-visual feature fusion via deep neural networks for automatic speech recognition

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DIGITAL SIGNAL PROCESSING 2018年 82卷 54-63页

作者： Rahmani, Mohammad Hasan Almasganj, Farshad Seyyedsalehi, Seyyed Ali Amirkabir Univ Technol Biomed Engn Dept Hafez Ave Tehran Iran

The brain-like functionality of the artificial neural networks besides their great performance in various areas of scientific applications, make them a reliable tool to be employed in Audio-Visual Speech Recognition (AVSR) systems. The applications of such networks in the AVSR systems extend from the preliminary stage of feature extraction to the higher levels of information combination and speech modeling. In this paper, some carefully designed deep autoencoders are proposed to produce efficient bimodal features from the audio and visual stream inputs. The basic proposed structure is modified in three proceeding steps to make better usage of the presence of the visual information from the speakers' lips Region of Interest (ROI). The performance of the proposed structures is compared to both the unimodal and bimodal baselines in a professional phoneme recognition task, under different noisy audio conditions. This is done by employing a state-of-the-art DNN-HMM hybrid as the speech classifier. In comparison to the MFCC audio-only features, the finally proposed bimodal features cause an average relative reduction of 36.9% for a range of different noisy conditions, and also, a relative reduction of 19.2% for the clean condition in terms of the Phoneme Error Rates (PER). (C) 2018 Elsevier Inc. All rights reserved.

关键词： Audio-visual speech recognition deep autoencoder deep neural networks Feature extraction Multimodal information processing

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Self-Tuning Transfer Dynamic Convolution autoencoder for Quality Prediction of Multimode Processes With Shifts

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第9期20卷 11295-11305页

作者： Yang, Chao Liu, Qiang Wang, Chen Ding, Jinliang Cheung, Yiu-ming Cheung Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Tsinghua Univ Natl Engn Res Ctr Big Data Software Beijing 100190 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Kowloon Tong Hong Kong 999077 Peoples R China

Process shift of multimode process involving data distribution and dynamic relation makes traditional transfer learning methods be intractable and even result in negative transfer. To tackle this issue, this article proposes a novel self-tuning transfer dynamic modeling method for quality prediction of multimode processes. First, in order to capture domain-invariant spatiotemporal (DIST) features, a transfer dynamic convolution autoencoder (TDCAE) with a feature decomposition structure is established. Meanwhile, a first-order vector autoregressive constraint is embedded to extract consistent inner dynamics for DIST features. Then, a shared regression network is established to extract the relations with quality variables. Furthermore, by making full use of private spatiotemporal information from target labeled samples in response to the process shift, the self-tuning TDCAE (STDCAE) aided by a fine-tuning strategy is established for online compensation. Finally, the efficacy of the proposed TDCAE and STDCAE is demonstrated by a comprehensive study of a three-phase flow facility process.

关键词： Convolutional neural networks deep autoencoder dynamic process modeling multimode processes quality prediction transfer learning (TL)

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Sparse deep dictionary learning identifies differences of time-varying functional connectivity in brain neuro-developmental study

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NEURAL NETWORKS 2021年 135卷 91-104页

作者： Qiao, Chen Yang, Lan Calhoun, Vince D. Xu, Zong-Ben Wang, Yu-Ping Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Georgia Inst Technol Triinst Ctr Translat Res Neuroimaging & Data Sci Atlanta GA 30303 USA Emory Univ Atlanta GA 30303 USA Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA Tulane Univ Ctr Genom & Bioinformat New Orleans LA 70112 USA

Recently, the focus of functional connectivity analysis of human brain has shifted from merely revealing the inter-regional functional correlation over the entire scan duration to capturing the time-varying information of brain networks and characterizing time-resolved reoccurring patterns of connectivity. Much effort has been invested into developing approaches that can track changes in re-occurring patterns of functional connectivity over time. In this paper, we propose a sparse deep dictionary learning method to characterize the essential differences of reoccurring patterns of time varying functional connectivity between different age groups. The proposed method combines both the interpretability of sparse dictionary learning and the capability of extracting sparse nonlinear higher-level features in the latent space of sparse deep autoencoder. In other words, it learns a sparse dictionary of the original data by considering the nonlinear representation of the data in the encoder layer based on a sparse deep autoencoder. In this way, the nonlinear structure and higher-level features of the data can be captured by deep dictionary learning. The proposed method is applied to the analysis of the Philadelphia Neurodevelopmental Cohort. It shows that there exist essential differences in the reoccurrence patterns of function connectivity between child and young adult groups. Specially, children have more diffusive functional connectivity patterns while young adults possess more focused functional connectivity patterns, and the brain function transits from undifferentiated systems to specialized neural networks with the growth. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： deep dictionary learning deep autoencoder Sparsity Dynamic functional connectivity Reoccurring pattern Brain development

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