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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Keyan Jin Yapeng Wang Leonel Santos Tao Fang Xu Yang Sio Kei Im Faculty of Applied Sciences Macao Polytechnic University China Computer Science and Communication Research Centre Polytechnic of Leiria Leiria Portugal Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China Macao Polytechnic University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Large Language Models (LLMs) show strong performance in natural language processing tasks, but their application in the financial domain is limited. Current methods rely on large datasets and manual prompt engineering, resulting in high data demands, long inference times, and frequent hallucinations. To address these limitations, we propose a novel self-supervised prompt optimization framework tailored for the financial domain. Our approach involves training a critical model that evaluates and ranks generated outputs using both good and bad answers generated from various revised prompts. Experiments on a large Chinese financial corpus show that our framework significantly improves performance on tasks such as summarization and event-based question answering, as evidenced by higher scores on both automated metrics like ROUGE, BLEU, and BERTScore, and also through human evaluations. These results validate the effectiveness of our method in reducing hallucinations and improving the quality of financial text generation.

关键词： Training Measurement Large language models Contrastive learning Signal processing Question answering (information retrieval) Multilingual Prompt engineering Speech processing Optimization

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Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals

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Heliyon 2025年第3期11卷 e42257页

作者： Haq, Ihtisham Ul Husnain, Ghassan Ghadi, Yazeed Yasin Innab, Nisreen Alajmi, Masoud Aljuaid, Hanan Department of Computer Science Modeling Electronics and Systems (DIMES) University of Calabria CS Rende 87036 Italy Department of Computer Science CECOS University of IT and Emerging Sciences Peshawar 25000 Pakistan Department of Computer Science Al Ain University United Arab Emirates Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh 13713 Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Computer Sciences Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia

Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. The model was trained on a composite dataset consisting of local pediatric PCG signals and publicly accessible dataset. Preprocessing methods, such as low- and high-pass filtering (60–650 Hz), resampling, and noise reduction, were utilized to enhance signal quality. Data augmentation techniques, including chunking, padding, and pitch-shifting, were employed to rectify dataset imbalances and improve model efficacy. Experimental results indicate substantial enhancements, attaining an accuracy of 98.56 %, precision of 98.56 %, F1 score of 98.55 %, sensitivity of 0.98, and specificity of 0.99. The comparative analysis demonstrates the proposed approach's superiority over current methods regarding accuracy and reliability. The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. Future endeavors involve extending to multiclass categorization and assessing performance across a wider range of medical problems. This study represents a significant advancement in accessible, automated CHD diagnoses, enhancing clinical competence to elevate pediatric treatment. © 2025 The Authors

关键词： Congenital heart disease Electrocardiogram Phonocardiogram Phonocardiography

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Robust Feature Engineering Techniques for Designing Motor Imagery-Based Bci systems

SSRN

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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Empowering autonomous IoT devices in blockchain through gasless transactions

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Blockchain: Research and Applications 2025年第2期6卷

作者： Madhwal, Yash Yanovich, Yury Korotkevich, Aleksandra Parshina, Daria Seropian, Nshteh Gavrilov, Stepan Nikolaev, Alex Balachander, S. Murugan, A. Skolkovo Institute of Science and Technology Moscow121205 Russia Faculty of Computer Science HSE University Moscow109028 Russia Faculty of Information and Communication Technologies ITMO University St. Petersburg197101 Russia Moscow Institute of Physics and Technology Moscow141701 Russia Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India

The article introduces a proof-of-concept (PoC) that demonstrates the management of Internet of Things (IoT) devices' infrastructure via smart contracts, facilitating their interaction with the blockchain through gasless transactions. The focus is empowering IoT devices to autonomously sign transactions using their verified private keys, eliminating the necessity for external wallets and enabling blockchain interaction using Biconomy without incurring gas fees. In this PoC, managers can validate IoT devices, permitting them to transmit transactions securely without being able to manipulate measurements or risking losing crypto assets in case of hardware malfunctions. This innovative method ensures that devices with minimal funds can access sensor data and communicate with a smart contract on the blockchain to update information utilizing account abstraction. Detailed workflow and simulation results are provided to showcase the practicality and advantages of this approach in scenarios demanding seamless automated blockchain engagement through IoT devices. The PoC code is openly accessible on GitHub, enhancing the transparency and accessibility of our research outcomes. © 2025 The Author(s)

关键词： Account abstraction Blockchain Gasless transaction Internet of Things Mobile application Smart contract

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DPPAD-IE: Dynamic Polyhedra Permutating and Arnold Diffusing Medical Image Encryption Using 2D Cross Gaussian Hyperchaotic Map

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Quanjun Li, Qian Ling, Bingo Wing-Kuen Pun, Chi-Man Huang, Guoheng Yuan, Xiaochen Zhong, Guo Ayouni, Sarra Chen, Jianwu Guangdong University of Technology School of Advanced Manufacturing Jieyang522000 China Wuyi University School of Electronics and Information Engineering Jiangmen510006 China Guangdong University of Technology School of Information Engineering Guangzhou510006 China University of Macau Faculty of Science and Technology Macau China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Macau Polytechnic University Faculty of Applied Sciences Macau China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510420 China Princess Nourah bint Abdulrahman University College of Computer and Information sciences Department of Information Systems Riyadh11671 Saudi Arabia Fujian Medical University Union Hospital Department of Radiation Oncology Fujian province Fuzhou China Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors Fujian Medical University Fujian province Fuzhou China Fujian province Fuzhou China

With the rapid improvement of medical consumer electronics (CE), the transmission of multiple images has become increasingly common in the medical field. During the transmission and storage of image data, sensitive information, including medical images, may be accessed by unauthorized parties, resulting in possible privacy violations. Consequently, several multi-image encryption (MIE) algorithms have been proposed to solve this real-world problem. Nevertheless, current MIE schemes exhibit several drawbacks, including insufficient security in permutation and diffusion algorithms, as well as the insensitivity of chaotic maps. This paper presents a new encryption algorithm called DPPAD-IE. The algorithm primarily consists of three components. First, we developed an innovative hyperchaotic map called the 2D Cross Gaussian Hyperchaotic Map (2D-CGHM), which has an expanded chaotic interval and is proficient in producing chaotic sequences with improved pseudorandomness. Second, we introduce a new Dynamic Polyhedra Permutation algorithm (DPP) that successfully alters pixel locations of the image, hence diminishing the connection between neighboring pixels. Third, we propose an Arnold Diffusion algorithm (AD), using chaotic sequences to further encrypt the permuted image. The DPPAD-IE is capable of encrypting images of any quantity and size concurrently. The experimental results confirm that this approach is both robust and effective, capable of defending against various security attacks. The code are available at:https://***/QuincyQAQ/DPPAD-IE. © 1975-2011 IEEE.

关键词： Medical imaging

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Characterization and comparative profiling of piRNAs in serum biopsies of pediatric Wilms tumor patients

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Cancer Cell International 2025年第1期25卷 1-15页

作者： Mohamed, Fatma S. Jalal, Deena Fadel, Youssef M. El-Mashtoly, Samir F. Khaled, Wael Z. Sayed, Ahmed A. Ghazy, Mohamed A. Biotechnology Program Institute of Basic and Applied Science Egypt-Japan University of Science and Technology Alexandria New Borg El-Arab City Egypt Biochemistry ProgramFaculty of Science Minia University El-Minia Egypt Genomics and Metagenomics Program Department of Basic Research Children’s Cancer Hospital Egypt Cairo 57357 Egypt Bioinformatics Group Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt Leibniz Institute of Photonic Technology Albert Einstein-Straße 9 Jena 07745 Germany Department of Pediatric Oncology National Cancer Institute Cairo Egypt Department of Pediatric Oncology Children’s Cancer Hospital Egypt Cairo 57357 Egypt Consultant Pediatric Oncology Mouwasat Hospital Dammam Saudi Arabia Department of Biochemistry Faculty of Science Ain Shams University Cairo Egypt

Piwi-interacting RNAs (piRNAs) are small non-coding RNAs involved in transposon silencing and linked to cancer progression. However, their role in Wilms tumors (WT) remains unexplored. We conducted a thorough analysis and characterization of piRNAs in serum liquid biopsies of WT patients. Our study examined their expression patterns and functional annotations related to WT pathogenesis, as well as their clinical potential for diagnosis and monitoring. We identified 307 piRNAs expressed in WT serum samples, with 4% classified as repeat-related and 96% as non-repeat-related. The most abundant repeat-related piRNAs originated from LINEs retrotransposon, while tRNA-derived piRNAs were the most prevalent among non-repeat-related piRNAs. Furthermore, a distinct profile of 34 piRNAs showed significant differential expression in WT patients compared to healthy controls—22 downregulated and 12 upregulated. The target genes of differentially expressed piRNAs exhibited significant enrichment in biological pathways related to cytokine activity, inflammatory responses, TGF-beta signaling, p38 MAPK, and ErbB signaling. These genes are also involved in DNA damage response, DNA methylation, cell cycle regulation, as well as kidney development and function. Low expression levels of several piRNAs, especially piR-hsa-1,913,711, piR-hsa-28,190, piR-hsa-28,849, piR-hsa-28,848, and piR-hsa-28,318, showed significant diagnostic potential as non-invasive biomarkers for WT (AUC > 0.8, p < 0.05). Their expression levels also significantly correlated with adverse pathological features, including metastasis, anaplasia, and bilateral WT development. In conclusion, non-transposon-related piRNAs may serve as reliable biomarkers for WT and possess potential non-germline functions, particularly in regulating DNA methylation, cell growth, immune responses, and immune responses. Further studies are warranted to elucidate their functional significance. © The Author(s) 2025.

关键词： Blood-based biomarkers Diagnostic potential Non-germline functions Non-transposon-related PiRNAs PiRNAome profiling Wilms tumor

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A hybrid of an automated multi-filter with a spatial bound particle swarm optimization for gene selection and cancer classification

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Heliyon 2025年第5期11卷

作者： Arram, Anas Ayob, Masri Albadr, Musatafa Abbas Abbood Albashish, Dheeb Sulaiman, Alaa Department of Computer Science Birzeit University Birzeit Palestine CAIT Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Department of Petroleum Project Management College of Industrial Management of Oil and Gas Basrah University for Oil and Gas Al-Basrah Al-Basrah Iraq Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

Cancer is one of the most dangerous diseases and a leading cause of death globally. Therefore, early detection of cancer is critical for effective treatments. However, the main challenge in disease identification and classification, such as cancer microarray dataset, is the large number of genes. Extracting meaningful information from these genes is a challenging task. This study proposes a new hybrid gene selection approach for cancer classification that combines three multi-filters in an automated manner with the proposed spatial bound particle swarm optimization (AMF-SBPSO) to effectively select the most important genes. The proposed AMF-SBPSO selects genes through two phases. In the first phase, the method uses three multi-filters to remove the most irrelevant and noisy genes in an automated manner. This process combines multiple filters based on various metrics and filter size ratios, then votes for the best one. This phase aims to select the best filter type and optimal ratio size for each dataset, taking into account that each dataset has unique gene sizes and characteristics. What may work for one dataset might not be effective for others. This phase makes the filtering process more robust. In the second phase, a new variant of particle swarm optimization (PSO), called spatial bound PSO (SBPSO), based on a spatial bounding strategy is proposed to reduce the remaining number of genes without losing the classifier's performance. The proposed AMF-SBPSO's performance was evaluated through experimental studies on nine microarray gene datasets. The results demonstrate that AMF-SBPSO yields a 100% classification accuracy on all tested datasets, outperforming state-of-the-art methods. These findings indicate the superiority of the proposed method in reducing the number of genes and achieving the highest accuracy for cancer classification. © 2025

关键词： Cancer classification Gene selection Microarray Particle swarm optimization Spatial bound particle swarm optimization

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Enhancing Performances of Deep Neural Networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable Sensors

Enhancing Performances of Deep Neural Networks with Ensemble...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing.

关键词： Deep learning Performance evaluation Accuracy Time series analysis Bidirectional long short term memory Performance gain Human activity recognition Ensemble learning Older adults Wearable sensors

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Consumer Technology in Task Offloading and Edge Resource Allocation: AIoT and Edge Computing for Next-Generation Communication

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IEEE Transactions on Consumer Electronics 2025年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Alghamdi, Azzah Ahanger, Tariq Ahamed Keshta, Ismail Xalikovich, Pardayev Abdunabi Soni, Mukesh Bhatt, Mohammed Wasim Convergence Department Korea University of Technology and Education Cheonan Korea Republic of Department of Computer Sciences College of Science University of Al Maarif Al Anbar31001 Iraq M.D. Research Intervention Treatment Institute HoustonTX United States Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Department of Management Information Systems CoBA Prince Sattam Bin Abdulaziz University Saudi Arabia Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia The Department of Accounting and Reporting Tashkent State University of Economics Tashkent Uzbekistan Division of Research and Development Lovely Professional University Phagwara India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

Artificial Intelligence of Things (AIoT) edge computing has emerged as a critical enabler for consumer technology, easing the load on distribution grid networks through enhanced data transmission and processing capabilities. The Internet of Things (IoT), a modern network paradigm, connects devices using sensors and communication mediums to enable seamless information exchange. However, the limited computational capacity of edge nodes poses challenges in optimizing AIoT edge resources compared to cloud computing. To address this, a cloud-edge three-layer framework for task offloading and edge resource allocation is proposed in the context of consumer technology and distribution grids. This model incorporates random tasks, limited resources, unequal processing power, and high latency requirements. It features two key phases: a resource auction using a multi-round iterative process and compute offloading managed by a Deep Reinforcement Learning (DRL) approach. Enhanced algorithms such as Double DQN and Dueling DQN are integrated into the job offloading process to improve performance. Simulations validate the convergence of the task offloading algorithm and demonstrate that the proposed methods significantly enhance computational efficiency and resource utilization for edge nodes. These advancements offer promising solutions for the dynamic needs of consumer technology and future communication networks. © 2025 IEEE.

关键词： Computation offloading

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SmarTeeth: Augmenting Manual Toothbrushing with In-ear Microphones 25

SmarTeeth: Augmenting Manual Toothbrushing with In-ear Micro...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Qiang Yang Yang Liu Jake Stuchbury-Wass Kayla-Jade Butkow Emeli Panariti Dong Ma Cecilia Mascolo Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Faculty of Dentistry Oral and Craniofacial Sciences King's College London London United Kingdom School of Computing and Information Systems Singapore Management University Singapore Singapore

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