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Prediction of colorectal cancer based on taxonomic levels of microorganisms and discovery of taxonomic biomarkers using the Grouping-Scoring-Modeling (G-S-M) approach

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computers in Biology and Medicine 2025年 187卷 109813-109813页

作者： Bakir-Gungor, Burcu Temiz, Mustafa Canakcimaksutoglu, Beyza Yousef, Malik Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Electrical and Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Bioengineering Faculty of Life and Natural Science Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Zefat Academic College Zefat13206 Israel Zefat Academic College Israel

Colorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases. © 2025 Elsevier Ltd

关键词： Colorectal cancer Human gut microbiome Machine learning Taxonomic biomarkers MicroBiomeGSM

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MINER-RRT*: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments

arXiv

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arXiv 2024年

作者： Wang, Pengyu Tang, Jiawei Lin, Hin Wang Zhang, Fan Wang, Chaoqun Wang, Jiankun Shi, Ling Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada The School of Control Science and Engineering Shandong University Shandong China

—Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor’s differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments. (Video1) Note to Practitioners—The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in real scenarios and empo

关键词： Deep neural networks

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HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation

HR-APR: APR-agnostic Framework with Uncertainty Estimation a...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Changkun Liu Shuai Chen Yukun Zhao Huajian Huang Victor Prisacariu Tristan Braud Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Engineering Science Active Vision Laboratory University of Oxford United Kingdom Division of Integrated Systems Design The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Absolute Pose Regressors (APRs) directly estimate camera poses from monocular images, but their accuracy is unstable for different queries. Uncertainty-aware APRs provide uncertainty information on the estimated pose, alleviating the impact of these unreliable predictions. However, existing uncertainty modelling techniques are often coupled with a specific APR architecture, resulting in suboptimal performance compared to state-of-the-art (SOTA) APR methods. This work introduces a novel APR-agnostic framework, HR-APR, that formulates uncertainty estimation as cosine similarity estimation between the query and database features. It does not rely on or affect APR network architecture, which is flexible and computationally efficient. In addition, we take advantage of the uncertainty for pose refinement to enhance the performance of APR. The extensive experiments demonstrate the effectiveness of our framework, reducing 27.4% and 15.2% of computational overhead on the 7Scenes and Cambridge Landmarks datasets while maintaining the SOTA accuracy in single-image APRs.

关键词： Training Uncertainty Accuracy Databases Pipelines Estimation Network architecture

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Probabilistic Prediction for Start-Up Success through Deep Learning based Stacked DAE Model 3

Probabilistic Prediction for Start-Up Success through Deep L...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Syed, Shakir Sondinti, Lakshminarayana Reddy Kothapalli Sapra, Puneet Joshi, Amit Shobana, M. Deepan Chakravarthi, A.V. Shivam hills India Us Bank LB. Nagar India Rayat Bahara University Department of Computer Science Engineering Mohali India Isma University Department of Information Technology Rīga Latvia M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India Velammal College of Engineering and Technology Department of Civil Engineering Madurai India

ISBN: (纸本)9798331508456

Researchers and financial entities have long been fascinated by the ability to accurately predict the success of start-ups. Modern start-ups, which are creative, tech-focused, and quickly growing companies, face this challenge more than most. This study introduces a deep learning Stacked Denoising Autoencoder (SDAE) model that uses important technology domains to forecast the likelihood of a startup's success. The three primary steps of the proposed approach are as follows: preprocessing, feature extraction, and training the model. To ensure statistical independence of signal components in multivariate datasets, preprocessing steps include min-max normalisation for data standardisation and Independent Component Analysis (ICA) for feature extraction. The predictive power of the SDAE model is then enhanced by training. The proposed model outperforms traditional methods like Decision Trees (DT) and Denoising Autoencoders (DAE) with an average accuracy of 94.28%, according to the experimental data. This makes it a strong contender for predicting startup success in changing technical contexts. © 2025 IEEE.

关键词： Independent component analysis

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An Adaptive-Gain Recurrent Neural Network for Solving the Joint-Angle Drift Issues of Redundant Manipulators

An Adaptive-Gain Recurrent Neural Network for Solving the Jo...

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2023 International Annual Conference on Complex systems and Intelligent science, CSIS-IAC 2023

作者： Zheng, Boyu Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. School of Information Engineering Nanchang University Nanchang330031 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Engineering Peking University Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

ISBN: (纸本)9798350309003

This paper proposes a new adaptive-gain recurrent neural network (AG-RNN) to effectively cope with the joint-angle drift issues in redundant manipulators. Specifically, a joint-angle drift-free with the feedback control of the velocity layer motion equation (JADF-FC) is proposed via an optimization criterion for synchronously optimizing linear terms and quadratic. Then, the JADF-FC is reasonably formulated into a standard quadratic programming (QP) issue. Different from the previous recurrent neural networks (RNNs), the AG-RNN proposed in this paper constructs an error-based differential equation with a new adaptive-gain. It should be noted that the proposed adaptive-gain does not gradually approach infinity as time increases, which is more in line with actual hardware implementation requirements than the existing time-variant-gain. The adaptive-gain can reduce the joint-angle drift errors of the redundant manipulator. Therefore, the proposed AG-RNN can solve the QP problem of the manipulator more effectively and quickly. To validate the performance of the proposed AG-RNN, it is compared with representative RNNs. The experimental results indicate that smaller joint-angle drift errors can be get by the proposed AG-RNN solving JADF-FC scheme than the other solutions when solving the joint-angle drift issues. © 2023 IEEE.

关键词： Quadratic programming

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Oil Spill Identification using Deep Convolutional Neural Networks 14

Oil Spill Identification using Deep Convolutional Neural Net...

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Feinauer, David M. Latif, Ghazanfar Alenazy, Abeer M. Tayem, Nizar Alghazo, Jaafar Alzubaidi, Loay Virginia Military Institute Department of Electrical and Computer Engineering LexingtonVA United States Prince Mohammad Bin Fahd University Computer Science Department Khobar Saudi Arabia Texas A&m University Department of Engineering and Technology Commerce United States Higher Colleges of Technology Faculty of Computer Information Systems United Arab Emirates

ISBN: (纸本)9781665487719

Oil spill detection is an extremely important topic in which Machine Learning (ML) can be utilized because oil spills that go undetected can cause huge environmental negative impacts. The science of how an oil spill can cause devastation to wildlife has been widely viewed with sorrow and the early detection of oil spills can greatly reduce the negative impact oil spills have on the environment. If an optimal solution is found for oil spill detection, continuous monitoring can be achieved either through satellite images or images obtained from unmanned aerial vehicles such as drones. In this paper, we develop a dataset for oil spill detection collected from images from the Internet and other online resources. The dataset consists of 783 images of Oil Spills and 783 normal images. Since these are real-world images, research done on this dataset will produce a more realistic and practical solution. In this paper, we also propose an enhanced CNN model based on GoogleNet and VGG16 combined with transfer learning for the detection and classification of oil spills. The GoogleNet Transfer Learning model achieved better results of training accuracy of 97.5%, training loss of 0.0894, and validation accuracy of 95.6%. Since this is a new dataset, the results cannot be compared to anything in the extant literature. © 2022 IEEE.

关键词： Oil spills

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A 120GHz Receiver with $1/f$ Noise Mitigation Technique for Near-Field IoT

A 120GHz Receiver with $1/f$ Noise Mitigation Technique for ...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Ademola A. Mustapha Mihai Sanduleanu Department of Electrical Engineering and Computer Science Khalifa University of Science and Technology UAE Systems-on-Chip Lab Khalifa University of Science and Technology UAE

This paper presents a 120 GHz receiver for Near-Field IoT sensors, without the conventional LNA and mixers building blocks. A new technique for the reduction of the low-frequency 1/f noise is also presented. The technique is used to improve the sensitivity of the 120GHz receiver with a data rate of 100kb/s, by lowering the 1/f noise corner frequency. The technique involves the complementary switching of two MOSFET transistors in order to effectively act as a single MOSFET transistor. It also involves the isolation of the drain terminals of these identical MOSFET transistors in order to eliminate any electric field from the complementary transistors which may affect the de-trapping process. The complementary switching in the literature has only been applied to low-frequency circuits with the highest operating frequency being 120 kHz. The proposed technique leads to a 9dB reduction in the drain current noise at frequencies up to 100 kHz. Realized in a 65nm CMOS LPE technology from GlobalFoundries™, the receiver has a measured sensitivity of −46dBm, power consumption of $\mathbf{52}\ \boldsymbol{\mu} \mathbf{W}$ , and energy efficiency of 5.2pJ/bit. The occupied on-chip area is 0.56mm 2 .

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A Random Memory Length Adaptive Algorithm for Vertex Cover Problem

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IFAC-PapersOnLine 2023年第2期56卷 5913-5918页

作者： Shaoyuan Niu Xiang Li Department of Control Science and Engineering College of Electronics Information Engineering Tongji University Shanghai 201804 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai 201210 China

The vertex cover (VC) problem has been studied for past few years, and some centralized and distributed algorithms have been proposed. In this paper, we propose the so-called random memory length adaptive (RMLA) algorithm for the VC problem in networks. Any initial state can reach the stable pure strict Nash equilibrium state through finite iterations by the RMLA algorithm. We find that by setting the minimum edge-keeping probability reasonably, the convergence rapidity and accuracy can be improved simultaneously. Our algorithm also removes the requirement for consistent node memory length. Through theoretical analysis and intensive numerical simulations, we verify the convergence and effectiveness of the RMLA algorithm.

关键词： Vertex cover problem Evolutionary game Adaptive algorithm Memory-based Nash equilibrium

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Non-euclidean monotone operator theory and applications

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 14872-14904页

作者： Alexander Davydov Saber Jafarpour Anton V. Proskurnikov Francesco Bullo Center for Control Dynamical Systems and Computation University of California Santa Barbara Santa Barbara CA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder CO Department of Electronics and Telecommunications Politecnico di Torino Turin Italy

While monotone operator theory is often studied on Hilbert spaces, many interesting problems in machine learning and optimization arise naturally in finite-dimensional vector spaces endowed with non-Euclidean norms, such as diagonally-weighted ℓ1 or ℓ1 norms. This paper provides a natural generalization of monotone operator theory to finitedimensional non-Euclidean spaces. The key tools are weak pairings and logarithmic norms. We show that the resolvent and reected resolvent operators of non-Euclidean monotone mappings exhibit similar properties to their counterparts in Hilbert spaces. Furthermore, classical iterative methods and splitting methods for finding zeros of monotone operators are shown to converge in the non-Euclidean case. We apply our theory to equilibrium computation and Lipschitz constant estimation of recurrent neural networks, obtaining novel iterations and tighter upper bounds via forward-backward splitting.

关键词： non-Euclidean norms monotone operator theory fixed point equations nonexpansive maps

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Fashion Recommendation System for E-Commerce using Deep Learning Algorithms

Fashion Recommendation System for E-Commerce using Deep Lear...

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International Conference on Computing and Networking Technology (ICCNT)

作者： M S Arunkumar R Gopinath M Chandru R Suguna S Deepa V Omprasath Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu Department of Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchgode Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the increasingly competitive outlook of ecommerce, personalized recommendation systems have emerged as pivotal tools to enhance user experience and drive sales. This paper proposes a novel Fashion Recommendation System (FRS) leveraging deep learning techniques to offer personalized fashion suggestions to users. The system utilizes a combination of convolutional neural networks (CNNs) for image feature extraction. Through a comprehensive analysis of user interactions and preferences, the FRS generates tailored recommendations that align with individual style preferences and current trends. The work proposes a novel approach that utilizes the ResNet50 algorithm to analyze images of fashion items, extracting meaningful features that capture the essence of style, color, and texture. By training the ResNet50 model on vast datasets of fashion images, thereby enhancing its ability to make accurate recommendations. Through the integration of user preferences and historical data, this system can generate personalized fashion recommendations tailored to each user’s unique style and preferences, thus enhancing user satisfaction and engagement. By harnessing the power of deep learning and the ResNet50 algorithm, fashion retailers can offer personalized shopping experiences, increase customer retention, and drive sales.

关键词： Deep learning Training Image color analysis Feature extraction Market research User experience Electronic commerce Convolutional neural networks Recommender systems Residual neural networks

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