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arXiv 2025年

作者： Haque, Ershadul Paul, Manoranjan School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia

Quantum image computing has emerged as a groundbreaking field, revolutionizing how we store and process data at speeds incomparable to classical methods. Nevertheless, as image sizes expand, so does the complexity of qubit connections, posing significant challenges in the efficient representation and compression of quantum images. In response, we introduce a modified Toffoli gate state connection (MTGSC) using a quantized transform coefficient preparation process. This innovative strategy streamlines circuit complexity by modifying state connection from the state connection information. In our operational control gates, only the input’1’ impacts the output, allowing us to modify the state connection and dramatically enhance the efficiency of the proposed circuit. As a result, the proposed approach significantly reduces the number of gates required for both image compression and representation. Our findings reveal that it requires an impressive 44.21% fewer gates than existing techniques, such as the Direct Cosine Transform Efficient Flexible Representation of Quantum Images (DCTEFRQI), all while maintaining a consistent peak signal-to-noise ratio (PSNR). For an image block size of 2Sx × 2Sy with q gray levels, the complexity of our approach can be succinctly expressed as: O[3q + log2 Sx + log2 Sy + q · 2(log2 Sx+log2 Sy)] Here, Sx and Sy represent the X and Y positional control gates, while q indicates the non-zero transform coefficients. Moreover, experimental evaluations strongly demonstrate that it excels in both compressing and representing quantum images compared to the DCTEFRQI approach, particularly excelling in the essential metrics of gate requirements and PSNR performance. Embrace the future of quantum imaging with our innovative solution—where efficiency meets excellence. Copyright © 2025, The Authors. All rights reserved.

关键词： Image compression

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PALQA: A NOVEL PARAMETERIZED POSITION-AWARE LOSSY QUANTUM AUTOENCODER USING LSB CONTROL QUBIT FOR EFFICIENT IMAGE COMPRESSION

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arXiv 2025年

作者： Haque, Ershadul Paul, Manoranjan Tohidi, Faranak Ulhaq, Anwaar Debnath, Tanmoy School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia

With the growing interest in quantum computing, quantum image processing technology has become a vital research field due to its versatile applications and ability to outperform classical computing. A quantum autoencoder approach has been used for compression purposes. However, existing autoencoders are limited to small-scale images, and the mechanisms of state compression remain unclear. There is also a need for efficient quantum autoencoders using standard representation approaches and for studying parameterized position-aware control qubits and their corresponding quality measurement metrics. This work introduces a novel parameterized position-aware lossy quantum autoencoder (PALQA) circuit that utilizes the least significant bit control qubit for image compression. The PALQA circuit employs a transformed coefficient block-based modified state connection approach to efficiently compress images at various resolutions. The method leverages compression opportunities in the state-label connection by applying position-aware least significant control qubit. Compared to JPEG and other enhanced quantum representation-based quantum autoencoders, the PALQA circuit demonstrates superior performance in terms of the number of gates required and PSNR metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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A Novel Approach for Performance Evaluation and Effectiveness of Data-Driven Heart Disease Diagnosis

A Novel Approach for Performance Evaluation and Effectivenes...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Islam, Md Aminul Nag, Anindya Das, Ayontika Faruque, Jobaer Shuvo, Shabbir Ahmed Nur, Abdullah Hafez Rahman, Md Habibur School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom Department of Computer Science and Engineering Adamas University Kolkata India IRD Dhaka Bangladesh Offenburg University of Applied Sciences Offenburg Germany International Islamic University Chittagong Sonaichhari Bangladesh Department of Mathematics University of Chittagong Chittagong Bangladesh

ISBN: (纸本)9789819795062

Cardiovascular disease (CVD) has emerged as a prominent cause of mortality globally during the past few decades. To receive medical care for heart illness in a clinic or medical center, it is necessary to undergo costly diagnostic tests. Implementing machine learning (ML) algorithms presents a viable approach to monitoring the health of individuals with cardiac conditions, offering a potential solution to mitigate the financial burden of medical expenses. This study employed ten ML algorithms to predict heart disease, which distinguishes itself from others by incorporating three distinct cross-validation approaches and three sample techniques, enhancing our findings’ accuracy. Three different cross-validation strategies have been implemented in this study: holdout cross-validation, tenfold cross-validation, and stratified tenfold cross-validation. Each technique has been applied to every sample technique used in the study. DTC, RFC, KNN, and XGB algorithms exhibited the most favorable outcomes. Our recommended model is the RF Classifier implemented with near-miss under-sampling out of the four models considered. The model under consideration demonstrates the maximum level of accuracy, specifically 97.54%, 97.11%, and 99%, while employing the Extra Tree, Random Forest Classifier, and XGB. The ML model's performance was assessed using metrics like accuracy, error rate, precision, recall, F1-score, ROC AUC curve etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cardiology

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ERROR ANALYSIS OF FRACTIONAL COLLOCATION METHODS FOR VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS WITH NONCOMPACT OPERATORS

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Journal of Computational mathematics 2025年第3期43卷 690-707页

作者： Zheng Ma Chengming Huang Anatoly A.Alikhanov School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China North-Caucasus Center for Mathematical Research North-Caucasus Federal UniversityStavropol 355017Russia North-Eastern Federal University Yakutsk 677000Russia

This paper is concerned with the numerical solution of Volterra integro-differential equations with noncompact *** focus is on the problems with weakly singular *** handle the initial weak singularity of the solution,a fractional collocation method is applied.A rigorous hp-version error analysis of the numerical method under a weighted H1-norm is carried *** result shows that the method can achieve high order convergence for such *** experiments are also presented to confirm the effectiveness of the proposed method.

关键词： Volterra integro-differential equation Noncompact operator Nonsmooth solution Collocation method Fractional polynomial hp-version error analysis

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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Framework for Drug User Analysis and Prediction 4

Framework for Drug User Analysis and Prediction

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

作者： Sethuraman, Dhanush Ananya, Ela Joseph Jeffrey, J. Thangavel, Senthil Kumar Rangasami, P. Somasundaram, K. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Amrita Vishwa Vidyapeetham Department of Social Work Coimbatore India Amrita School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

This paper provides a framework for knowledge and action related to and about substance abuse, that integrates both capacity development and data-driven analysis for drug use analysis and modeling. In line with the National Action Plan for Drug Demand Reduction (NAPDDR) the framework focuses on a multi-pronged strategy. It then gives examples of so-called capacity-building exercises, such as those carried out by Amrita Vishwa Vidyapeetham in Tamil Nadu in 2020. These programs targeted key stakeholders, including healthcare professionals, police personnel, teachers, and social workers, focusing on preventive education, medical management, psychosocial issues, and rehabilitation strategies related to substance abuse. Pre- and post-training evaluations showed a strong increase in participants knowledge and treatment skills for substance use disorders. Second, the paper presents a data-driven, quantitative analysis of drug use patterns and related factors. Using a Support Vector Machine (SVM) algorithm, the study utilizes a dataset of drug consumption patterns to determine the factors which are most important for drug consumption. Importantly, the framework also include Explainable AI (XAI) methods to increase the interpretability and tracibility of the machine learning model. XAI approaches are used to interpret the model's decision process and to identify the most important factors in predicting the use of drugs. Incorporating insights from the capacity-building program evaluation and from the XAI-aided machine learning analysis, this framework provides an integrated end-to-end framework for drug user analysis and the generation of actionable recommendations for the design of targeted interventions and prevention strategies. © 2025 IEEE.

关键词： Support vector machines

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Fine-Grained Features-based Code Search for Precise Query-Code Matching 31

Fine-Grained Features-based Code Search for Precise Query-Co...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Xinting Cheng, Mengqiu Wang, Mengzhen Gong, Songwen Cai, Yi Xie, Jiayuan Li, Qing Department of Computing The Hong Kong Polytechnic University Hong Kong School of Software Engineering South China University of Technology China Department of Mathematics The University of Hong Kong Hong Kong Guangdong Neusoft University China

ISBN: (纸本)9798891761964

Code search aims to quickly locate target code snippets from databases using natural language queries, which promotes code reusability. Existing methods can effectively obtain aligned token-level and query word-level features. However, these studies usually represent the semantics of code and query by averaging the features of each token and word respectively, which makes it difficult to accurately capture the code details that are closely related to the query. To address this issue, we propose a fine-grained code search model that consists of a cross-modal encoder, a mapping layer, and a classification layer. Specifically, we utilize a pre-trained model, GraphCodeBERT, in the cross-modal encoder to align features. In the mapping layer, we introduce a co-attention network to capture the fine-grained interactions between code and query, ensuring a model can precisely identify key code segments relevant to the query. Finally, in the classification layer, we incorporate instruction learning techniques that leverage contextual reasoning to improve the accuracy of query-code matching. Experimental results show that our proposed model significantly outperforms existing methods across multiple programming language datasets. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Performance evaluation of different deep learning models used for the purpose of healthy and diseased leaves classification of Cherimoya (Annona Cherimola) plant

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Neural computing and Applications 2025年第6期37卷 4531-4544页

作者： Chouhan, Siddharth Singh Singh, Uday Pratap Jain, Sanjeev School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Department of Mathematics Central University of Jammu Jammu and Kashmir Samba181143 India Department of Computer Science and Information Technology Central University of Jammu Jammu and Kashmir Samba181143 India

Controlling plant leaves disease helps in upholding their health. This augments the overall strength of the plant productivity both in terms of higher quality and quantity. Recently, with the expansion of high-end computing devices, Artificial Intelligence (AI) techniques have allied in almost all applications. In agriculture, AI has been used in crop surveillance, soil health monitoring, nutrient deficiency estimation, flower and fruits quality assessment, weed estimation, and crop health diagnosis among all. Therefore, in this work, five deep learning approaches, namely EfficientNet, MobileNetV2, BiT, EANet, and Swin Transformers, have been used for the classification of healthy and diseased leaves of Cherimoya plant. EANet model with precision = 0.9929%, recall = 0.9536%, F1-score = 0.9846%, training accuracy = 0.9775%, and testing accuracy = 0.9689% achieved superior performance among all. We believe that the proposed work can be very useful in providing an accurate and timely plant disease diagnosing system. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fruits

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Estimating Soil Organic Carbon from Multispectral Images Using Physics-Informed Neural Networks 17th

Estimating Soil Organic Carbon from Multispectral Images Us...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Sargeant, James Wei Teng, Shyh Murshed, Manzur Paul, Manoranjan Brennan, David Institute of Innovation Science and Sustainability Federation University Ballarat Australia School of Information Technology Deakin University Burwood Australia School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia Wimmera Catchment Management Authority Horsham Australia

ISBN: (纸本)9789819609628

Understanding the amount of Soil Organic Carbon (SOC) at farm and field scale is a necessary precursor to effective management, important for both agricultural productivity and to reduce CO2 emissions. To avoid the prohibitive cost of measurement, SOC can be estimated by using multispectral images. In this study, we propose a novel Physics-Informed Convolutional Neural Network (CNN) to model well-known but noisy relationship between a soil index and SOC using the network’s loss function. This study is also conducted by resampling the European Land Use/Classification Area Survey (LUCAS) dataset to Sentinel-2 bands. Our experimental results show that our proposed network converges more quickly, has a lower root mean squared error (RMSE) and is more robust (as measured by the standard deviation of RMSE over multiple trials) than a compatible standard CNN. The operation of the novel Physics-Informed CNN is explained in terms of the components of the loss function. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon sequestration

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