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arXiv 2025年

作者： Ámon, Attila Miklós Fenech, Kristian Kovács, Péter Dózsa, Tamás Eötvös Loránd University Faculty of Informatics Department of Numerical Analysis Hungary Eötvös Loránd University Faculty of Informatics Department of Artificial Intelligence Hungary HUN-REN Institute for Computer Science and Control Systems and Control Laboratory Hungary University of Győr Hungary

In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a biomedical application, namely, the detection of ventricular ectopic beats (VEBs) in real ECG *** Codes 65D15 © 2025, CC BY.

关键词： Electrocardiography

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A Novel Double Pruning method for Imbalanced Data using Information Entropy and Roulette Wheel Selection for Breast Cancer Diagnosis

arXiv

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arXiv 2025年

作者： Soufiane, Bacha Ning, Huansheng Mostefa, Belarbi Sarwatt, Doreen Sebastian Dhelim, Sahraoui School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China of University of Tiaret University of Tiaret Faculty of Mathematics and Informatics Department of Computer Science BP 78 Zaâroura Tiaret14000 Algeria School of Computing Dublin City University Ireland

—Accurate illness diagnosis is vital for effective treatment and patient safety. Machine learning models are widely used for cancer diagnosis based on historical medical data. However, data imbalance remains a major challenge, leading to hindering classifier performance and reliability. The SMOTEBoost method addresses this issue by generating synthetic data to balance the dataset, but it may overlook crucial overlapping regions near the decision boundary and can produce noisy samples. This paper proposes RE-SMOTEBoost, an enhanced version of SMOTEBoost, designed to overcome these limitations. Firstly, RE-SMOTEBoost focuses on generating synthetic samples in overlapping regions to better capture the decision boundary using roulette wheel selection. Secondly, it incorporates a filtering mechanism based on information entropy to reduce noise, and borderline cases and improve the quality of generated data. Thirdly, we introduce a double regularization penalty to control the synthetic samples’ proximity to the decision boundary and avoid class overlap. These enhancements enable higher-quality oversampling of the minority class, resulting in a more balanced and effective training dataset. The proposed method outperforms existing state-of-the-art techniques when evaluated on imbalanced datasets. Compared to the top-performing sampling algorithms, RE-SMOTEBoost demonstrates a notable improvement of 3.22% in accuracy and a variance reduction of 88.8%. These results indicate that the proposed model offers a solid solution for medical settings, effectively overcoming data scarcity and severe imbalance caused by limited samples, data collection difficulties, and privacy constraints. Copyright © 2025, The Authors. All rights reserved.

关键词： Patient treatment

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DSLR-CNN: Efficient CNN Acceleration using Digit-Serial Left-to-Right Arithmetic

arXiv

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arXiv 2025年

作者： Nisar, Malik Zohaib Ibrahim, Muhammad Sohail Gorgin, Saeid Usman, Muhammad Lee, Jeong-A Department of Computer Engineering College of IT Convergence Chosun University Gwangju61452 Korea Republic of Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany Tehran33535-111 Iran

Digit-serial arithmetic has emerged as a viable approach for designing hardware accelerators, reducing interconnections, area utilization, and power consumption. However, conventional methods suffer from performance and latency issues. To address these challenges, we propose an accelerator design using left-to-right (LR) arithmetic, which performs computations in a most-significant digit first (MSDF) manner, enabling digit-level pipelining. This leads to substantial performance improvements and reduced latency. The processing engine is designed for convolutional neural networks (CNNs), which includes low-latency LR multipliers and adders for digit-level parallelism. The proposed DSLR-CNN is implemented in Verilog and synthesized with Synopsys design compiler using GSCL 45nm technology, the DSLR-CNN accelerator was evaluated on AlexNet, VGG-16, and ResNet-18 networks. Results show significant improvements across key performance evaluation metrics, including response time, peak performance, power consumption, operational intensity, area efficiency, and energy efficiency. The peak performance measured in GOPS of the proposed design is 4.37× to 569.11× higher than contemporary designs, and it achieved 3.58× to 44.75× higher peak energy efficiency (TOPS/W), outperforming conventional bit-serial designs. © 2025, CC BY.

关键词： Convolutional neural networks

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Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks

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Discover Oncology 2025年第1期16卷 1-27页

作者： Khan, Muhammad Amir Mazhar, Tehseen Ali, Muhammad Danish Khattak, Umar Farooq Shahzad, Tariq Saeed, Mamoon M. Hamam, Habib School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam 40450 Malaysia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science COMSATS University Islamabad Abbottabad Campus Abbottabad 22060 Pakistan School of Information Technology UNITAR International University Kelana Jaya Petaling Jaya 47301 Malaysia Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Sana’a 00967 Yemen Faculty of Engineering University de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Hodmas University College Taleh Area Mogadishu Banadir 521376 Somalia Bridges for Academic Excellence—Spectrum Tunis Centre-Ville 1002 Tunisia Department of Computer Science School Education Department Government of Punjab Layyah 31200 Pakistan

Skin Cancer is an extensive and possibly dangerous disorder that requires early detection for effective treatment. Add specific global statistics on skin cancer prevalence and mortality to emphasize the importance of early detection. Example: “Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. Manual skin cancer screening is both time-intensive and expensive. Deep learning (DL) techniques have shown exceptional performance in various applications and have been applied to systematize skin cancer diagnosis. However, training DL models for skin cancer diagnosis is challenging due to limited available data and the risk of overfitting. Traditionally approaches have High computational costs, a lack of interpretability, deal with numerous hyperparameters and spatial variation have always been problems with machine learning (ML) and DL. An innovative method called adaptive learning has been developed to overcome these problems. In this research, we advise an intelligent computer-aided system for automatic skin cancer diagnosis using a two-stage transfer learning approach and Pre-trained Convolutional Neural Networks (CNNs). CNNs are well-suited for learning hierarchical features from images. Annotated skin cancer photographs are utilized to detect ROIs and reset the initial layer of the pre-trained CNN. The lower-level layers learn about the characteristics and patterns of lesions and unaffected areas by fine-tuning the model. To capture high-level, global features specific to skin cancer, we replace the fully connected (FC) layers, responsible for encoding such features, with a new FC layer based on principal component analysis (PCA). This unsupervised technique enables the mining of discriminative features from the skin cancer images, effectively mitigating overfitting concerns and letting the model adjust structural features of skin cancer images, facilitating effective detection of skin cancer features. The sys

关键词： Advanced adaptive fine-tuned Convolution neural networks Melanoma and non-melanoma skin cancer

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SEIMR: Secure and Ethical IoT Mixed Reality Integration Framework for Metaverse Experiences

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IEEE Transactions on Consumer Electronics 2025年

作者： Uddin, Mueen Abdelhaq, Maha Alsufyani, Hamed Alanazi, Fuhid Alsaqour, Raed University of Doha for Science and Technology College of Computing and IT 24449 Qatar Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology Riyadh11671 Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Computer Science Riyadh11673 Saudi Arabia Islamic University of Madinah Faculty of Computer and Information Systems Madinah42351 Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Information Technology Riyadh93499 Saudi Arabia

Mixed Reality (MR) environments are increasingly integral to various applications, necessitating robust security and privacy measures. Current frameworks often fail to address the comprehensive integration of privacy preservation, security resilience, interoperability efficiency, and ethical compliance. This study aims to develop the SEIMR framework, which seamlessly integrates the Contextual Data Anonymization Protocol (CDAP), the Dynamic Threat Response Algorithm (DTRA), the Unified IoT-MR Interaction Protocol (UIMRIP), and the Adaptive Ethical Decision-Making Algorithm (AEDMA). CDAP ensures that user data are contextually anonymized, while DTRA dynamically identifies and mitigates security threats. UIMRIP facilitates the seamless integration of heterogeneous IoT devices with MR platforms, and AEDMA maintains ethical standards through adaptive decision making. SEIMR has practical applications in healthcare and education Metaverse systems, addressing privacy and interoperability challenges effectively. The framework was evaluated using the CICIDS2017 dataset, demonstrating superior performance with a 95% detection rate, 90% privacy preservation, 88% integration efficiency and 85% ethical compliance. © 1975-2011 IEEE.

关键词： Differential privacy

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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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International Journal of Advanced computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad 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 Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： Students

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain Technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important computer science, Engineering, and Decision sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Unmanned aerial vehicle based multi-person detection via deep neural network models

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Frontiers in Neurorobotics 2025年 19卷 1582995页

作者： Alshehri, Mohammed Zahoor, Laiba AlQahtani, Yahya Alshahrani, Abdulmonem AlHammadi, Dina Abdulaziz Jalal, Ahmad Liu, Hui Department of Computer Science King Khalid University Abha Saudi Arabia Faculty of Computer Science Air University Islamabad Pakistan Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of Cognitive Systems Lab University of Bremen Bremen Germany Guodian Nanjing Automation Company Ltd. Nanjing China Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing China

Introduction: Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from UAV-recorded videos becomes more challenging as the task presents unique challenges, including motion blur, dynamic background, lighting variations, and varying viewpoints. The presented work develops a deep learning system that recognizes multi-person behaviors from data gathered by UAVs. The proposed system provides higher recognition accuracy while maintaining robustness along with dynamic environmental adaptability through the integration of different features and neural network models. The study supports the wider development of neural network systems utilized in complicated contexts while creating intelligent UAV applications utilizing neural networks. Method: The proposed study uses deep learning and feature extraction approaches to create a novel method to recognize various actions in UAV-recorded video. The proposed model improves identification capacities and system robustness by addressing motion dynamic problems and intricate environmental constraints, encouraging advancements in UAV-based neural network systems. Results: We proposed a deep learning-based framework with feature extraction approaches that may effectively increase the accuracy and robustness of multi-person action recognition in the challenging scenarios. Compared to the existing approaches, our system achieved 91.50% on MOD20 dataset and 89.71% on Okutama-Action. These results do, in fact, show how useful neural network-based methods are for managing the limitations of UAV-based application. Discussion: Results how that the proposed framework is indeed effective at multi-person action recognition under difficult UAV conditions. Copyright © 2025 Alshehri, Zahoor, AlQahtani, Alshahrani, AlHammadi, Jalal and Liu.

关键词： Drones

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Generative AI Training and Copyright Law

arXiv

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arXiv 2025年

作者： Dornis, Tim W. Stober, Sebastian Institute of Legal Informatics Faculty of Law Leibniz University Hannover Königsworther Platz 1 Hannover30167 Germany Artificial Intelligence Lab Faculty of Computer Science Ottovon-Guericke-University Magdeburg Universitätsplatz 2 Magdeburg39106 Germany

Training generative AI models requires extensive amounts of data. A common practice is to collect such data through web scraping. Yet, much of what has been and is collected is copyright protected. Its use may be copyright infringement. In the USA, AI developers rely on "fair use" and in Europe, the prevailing view is that the exception for "Text and Data Mining" (TDM) applies. In a recent interdisciplinary tandem-study, we have argued in detail that this is actually not the case because generative AI training fundamentally differs from TDM. In this article, we share our main findings and the implications for both public and corporate research on generative models. We further discuss how the phenomenon of training data memorization leads to copyright issues independently from the "fair use" and TDM exceptions. Finally, we outline how the ISMIR could contribute to the ongoing discussion about fair practices with respect to generative AI that satisfy all stakeholders. © 2025, CC BY.

关键词： Generative adversarial networks

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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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