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Advancing differential diagnosis: a comprehensive review of deep learning approaches for differentiating tuberculosis, pneumonia, and COVID-19

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Multimedia Tools and Applications 2025年第13期84卷 11871-11906页

作者： Kansal, Kajal Chandra, Tej Bahadur Singh, Akansha School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

In the realm of medical diagnostics, particularly in differential diagnosis, where differentiating between illnesses or ailments with comparable symptoms is essential, deep learning has gained importance. Recent developments in deep learning have demonstrated considerable promise for revolutionizing medical diagnostics by using the ability of artificial intelligence (AI) to accurately interpret radiological images. We examine the most cutting-edge deep learning techniques currently being utilized for the differential diagnosis of tuberculosis, pneumonia, and COVID-19 in this in-depth review. The study presents an in-depth critical review of several SOTA (state-of-the-art) studies used for differential diagnosis of different respiratory abnormalities like TB, Pneumonia, and COVID-19. In addition, an overview of various approaches, datasets employed in each method, various diagnosis tests, used assessment measures, and obtained performance is summarized and comprehensively compared to assist future research. We suggest a pathway for future research and development of deep learning solutions for differential diagnosis by critically analyzing the current literature and outlining the limitations and potential in this sector. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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INVESTIGATION OF THE MISCIBILITY OF PMMA/F2604 BLENDING SYSTEMS

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UPB Scientific Bulletin, Series B: Chemistry and Materials science 2024年第3期86卷 169-184页

作者： Li, Xi Li, Jian Zhou, Mi Zhou, He Wang, Boliang Functional powder material laboratory of Bengbu City Bengbu University China Nanjing University of Science and Technology China Anhui Xiangyuan Science and Technology Co. Ltd Anhui Bengbu China School of Chemical Engineering Nanjing University of Science and Technology China

Fluoropolymer (F2604) is a random copolymer of vinylidene fluoride and hexafluoropropene, with a molar ratio of 4:1. F2604 has good thermal stability, flame retardancy, excellent mechanical properties, and high chemical resistance to many types of oil, fuel, and concentrated acids. To evaluate the potential use of polymethyl methacrylate (PMMA)/F2604 composites in mixed explosives and propellants, the miscibility of PMMA/F2604 blending systems with F2604 mass ratios of 0%, 20%, 50%, and 80% is investigated through molecular dynamics (MD) simulations and differential scanning calorimetry (DSC) experiments. The miscibility between PMMA and F2604 is indicated based on the results of the blending ability and the difference in the obtained solubility parameters (= 0.99 3)1/2). The radial distribution function (g(r)) results show that the PMMA/2604 80/20 blend is more miscible than the 50/50 and 20/80 blends. The strong hydrogen bonding interactions between PMMA and F2604 favor the formation of miscible blends. Our results show a decrease in the glass transition temperature (Tg) of PMMA whenever F2604 is added, as observed through MD simulations. The Tg results agree well with the DSC experiments. The PMMA/F2604 80/20 blend exhibits better tensile strength and elongation at break compared to pure PMMA and the 50/50 and 20/80 blends. © 2024, Politechnica university of Bucharest. All rights reserved.

关键词： Tensile strength

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A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

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computer Modeling in engineering & sciences 2024年第8期140卷 1405-1427页

作者： Sen-Hui Wang Xi Kang Cheng Wang Tian-Bing Ma Xiang He Ke Yang School of Mechatronics Engineering Anhui University of Science and TechnologyHuainan232001China Institute of Energy Hefei Comprehensive National Science CenterHefei230031China

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining *** research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected *** study proposes a hybrid predictive model to assess the RUL of rolling element *** proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain *** hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection ***,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal *** model is trained by optimizing its parameters via the grid search *** training datasets are adjusted to make them most suitable for the regression model using the cross-validation *** proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing *** comparison is constructed with other traditional *** experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

关键词： Bearing degradation remaining useful life estimation RReliefF feature selection extreme learning machine

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ONLINE PRIMAL DUAL MEETS ONLINE MATCHING WITH STOCHASTIC REWARDS: CONFIGURATION LP TO THE RESCUE

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SIAM Journal on Computing 2024年第5期53卷 1217-1256页

作者： Huang, Zhiyi Zhang, Qiankun Department of Computer Science The University of Hong Kong Hong Kong School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Mehta and Panigrahi (FOCS 2012, IEEE, Piscataway, NJ, 2012, pp. 728-737) introduce the problem of online matching with stochastic rewards, where edges are associated with success probabilities and a match succeeds with the probability of the corresponding edge. It is one of the few online matching problems that have defied the randomized online primal dual framework by Devanur, Jain, and Kleinberg (SODA 2013, SIAM, Philadelphia, 2013, pp. 101-107) thus far. This paper unlocks the power of randomized online primal dual in online matching with stochastic rewards by employing the configuration linear program rather than the standard matching linear program used in previous works. Our main result is a 0.572 competitive algorithm for the case of vanishing and unequal probabilities, improving the best previous bound of 0.534 by Mehta, Waggoner, and Zadimoghaddam (SODA 2015, SIAM, Philadelphia, 2015, pp. 1388-1404) and, in fact, is even better than the best previous bound of 0.567 by Mehta and Panigrahi (FOCS 2012, IEEE, Piscataway, NJ, 2012, pp. 728-737) for the more restricted case of vanishing and equal probabilities. For vanishing and equal probabilities, we get a better competitive ratio of 0.576. Our results further generalize to the vertex-weighted case due to the intrinsic robustness of the randomized online primal dual analysis. © 2024 Society for Industrial and Applied Mathematics.

关键词： Consensus algorithm

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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

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Multimedia Tools and Applications 2025年第18期84卷 18733-18780页

作者： Pal, Saurabh Dutta Pramanik, Pijush Kanti Choudhury, Prasenjit Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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Exploring dark matter-gauge boson effective interactions at current and future colliders

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Chinese Physics C 2024年第6期48卷 58-68页

作者：张宇黄祎伟张伟涛宋昴丁然 Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Physics Hefei University of TechnologyHefei 230601China School of Physics and Optoelectronics Engineering Anhui UniversityHefei 230601China

In this study,we systematically investigate collider constraints on effective interactions between Dark Matter(DM)particles and electroweak gauge *** consider the simplified models in which scalar or Dirac fermion DM candidates couple only to electro weak gauge bosons through high dimensional effective *** the induced DM-quarks and DM-gluons operators from the Renormalization Group Evolution(RGE)running effect,we present comprehensive constraints on the effective energy scaleΛand Wilson coefficients CB(Λ),CW(Λ)from direct detection,indirect detection,and collider *** particular,we present the corresponding sensitivity from the Large Hadron Electron Collider(LHeC)and Future Circular Collider in the electronproton mode(FCC-ep)for the first time,update the mono-j and mono-γsearch limits at the Large Hadron Collider(LHC),and derive the new limits at the Circular Electron Positron Collider(CEPC).

关键词： dark matter effective field theory LHC CEPC LHeC FCC-ep

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Betweenness Approximation for Edge Computing with Hypergraph Neural Networks

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Tsinghua science and technology 2025年第1期30卷 331-344页

作者： Yaguang Guo Wenxin Xie Qingren Wang Dengcheng Yan Yiwen Zhang School of Management Hefei University of TechnologyHefei 230009China School of Computer Science and Technology Anhui UniversityHefei 230601China Global Cognition and International Communication Laboratory Anhui UniversityHefei 230601China

Recent years have seen growing demand for the use of edge computing to achieve the full potential of the Internet of Things(IoTs),given that various IoT systems have been generating big data to facilitate modern latency-sensitive *** Dismantling(ND),which is a basic problem,attempts to find an optimal set of nodes that will maximize the connectivity degradation in a ***,current approaches mainly focus on simple networks that model only pairwise interactions between two nodes,whereas higher-order groupwise interactions among an arbitrary number of nodes are ubiquitous in the real world,which can be better modeled as *** structural difference between a simple and a hypernetwork restricts the direct application of simple ND methods to a *** some hypernetwork centrality measures(e.g.,betweenness)can be used for hypernetwork dismantling,they face the problem of balancing effectiveness and ***,we propose a betweenness approximation-based hypernetwork dismantling method with a Hypergraph Neural Network(HNN).The proposed approach,called“HND”,trains a transferable HNN-based regression model on plenty of generated small-scale synthetic hypernetworks in a supervised way,utilizing the well-trained model to approximate the betweenness of the *** experiments on five actual hypernetworks demonstrate the effectiveness and efficiency of HND compared with various baselines.

关键词： hypernetwork dismantling Graph Neural Network(GNN) betweenness approximation edge computing

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Visualization of metabolite distribution based on matrix-assisted laser desorption/ionization-mass spectrometry imaging of tea seedlings(Camellia sinensis)

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园艺研究（英文） 2024年第10期11卷 103-117页

作者： Maoyin Fu Liying Tian Dongqiao Zheng Yang Gao Chenyi Sun Shihua Zhang ZhaoLiang Zhang Xiaochun Wan Qi Chen State Key Laboratory of Tea Plant Biology and Utilization School of Tea and Food Science & TechnologyAnhui Agricultural UniversityHefei 230036China College of Computer Science South-Central Minzu UniversityWuhan 430074China State Key Laboratory of Tea Plant Biology and Utilization School of Tea and Food Science & TechnologyAnhui Agricultural UniversityHefei 230036China Key Laboratory of Food Nutrition and Safety Anhui Engineering Laboratory for Agro-products ProcessingSchool of Tea and Food Science & TechnologyAnhui Agricultural UniversityHefei 230036China

Tea seedlings(Camellia sinensis)have a well-developed root system with a strong taproot and lateral *** with ordinary cuttings,tea has stronger vitality and environmental adaptability,thus facilitating the promotion of good ***,there is less of detailed research on the rooting and germination process of tea *** this study,matrix-assisted laser desorption ionization time-of-flight-mass spectrometry was used to conduct non-targeted spatial mass spectrometry imaging of the main organs during growth of tea seedlings.A total of 1234 compounds were identified,which could be divided into 24 *** them,theanine,as the most prominent nitrogen compound,was synthesized rapidly at the early stage of embryo germination,accounting for＞90％of the total free amino acids in the radicle,and it was then transferred to each meristem region through the mesocolumnar sheath,indicating that theanine-based nitrogen flow plays a decisive role in organ formation during the development of tea *** stored in the cotyledon were rapidly hydrolyzed to dextrin and 3-phosphoglyceraldehyde at the early stages of germination,and subsequently converted to other forms that provided carbon and energy for development,such as raffinose and D-galactose(glucose),which were mainly distributed in the growing zones of the root apex and the apical meristems of the *** study provides a new perspective on the synthesis and metabolism of substances during the development of tea seedlings and contributes to a better understanding of the biological characteristics of tea varieties.

关键词： distribution camellia imaging laser based desorption ionization-mass matrix-assisted metabolite seedlings

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Optimisation of Camera Calibration Parameters Based on Multiconditional Constraints 2

Optimisation of Camera Calibration Parameters Based on Multi...

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Zhang, Yuexin Zhang, Deyu Wang, Yue Wang, Xiaoqing Shenyang Ligong University School of Information Science and Engineering Shenyang China Anhui University of Technology School of Computer Science and Technology Maanshan China

ISBN: (纸本)9798350368888

Camera calibration is an important step based on binocular stereo vision technology, and the accuracy of calibration directly affects the accuracy of this visual measurement. In view of the problems of camera calibration, a camera parameter optimisation method based on multiple constraints is proposed. The internal and external parameter values of the camera are calculated as the initial values using Zhang Zhengyou camera calibration method, the parameter optimisation objective function is established through multiple constraints, and the LM algorithm is used to find the optimal parameters. The findings of the experiments indicate a significant enhancement in the precision of camera calibration achieved by the enhanced algorithm, as compared to the traditional approach of the Zhang Zhengyou calibration method.. © 2024 IEEE.

关键词： Stereo vision

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Machine Learning-Based Chronic Kidney Cancer Prediction Application: A Predictive Analytics Approach

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Biomedical Materials and Devices 2024年第2期2卷 1028-1048页

作者： Uddin, Khandaker Mohammad Mohi Nahid, Md. Nuzmul Hossain Ullah, Md. Mehedi Hasan Mazumder, Badhan Khan, Md. Saikat Islam Dey, Samrat Kumar Department of Computer Science and Engineering Dhaka International University Faculty of Computer Science and Engineering Patuakhali Science and Technology University School of Science and Technology Bangladesh Open University

Chronic Kidney Cancer (CKC) is a disease that hindrances the blood-filtering mechanism of the kidney and is increasing at an alarming rate in the recent few years. As CKC does not show any earlier symptoms, the earlier prediction will be very effective to elevate its effect. In this paper, a machine learning-based method for diagnosing CKC at an early stage is proposed. SVM, Decision Tree, Random Forest, KNN, Native Bayes, Logistic Regression, LGBM, CatBoost, and AdaBoost are employed for classification purposes, and among these algorithms, Random Forest, AdaBoost, and LGBM give 99% accuracy. Data pre-processing and feature selection help improve the accuracy of the proposed model. The outcome of the comparative analysis of our proposed work with eight existing approaches proves the robustness and supremacy of the work. Furthermore, the best model is also used to develop a user-friendly web application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2023.

关键词： AdaBoost Chronic kidney cancer Machine learning Random forest and LGBM Web application

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