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The Initial Screening of Laryngeal Tumors via Voice Acoustic Analysis Based on Siamese network Under Small Samples

Journal of Voice 2025年

作者： You, Zhenzhen Sun, Delong Shi, Zhenghao Du, Shuangli Hei, Xinhong Kong, Demin Du, Xiaoying Yan, Jing Ren, Xiaoyong Hou, Jin Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China Department of Otolaryngology Second Affiliated Hospital of Medical College Xi'an Jiaotong University Xi'an China

Objective: The initial screening of laryngeal tumors via voice acoustic analysis is based on the clinician's experience that is subjective. This article introduces a Siamese network with an auxiliary gender classifier for automated, accurate, and objective initial screening of laryngeal tumors based on voice signals. Methods: The study involved 71 tumor patients and 293 non-tumor subjects of Chinese Mandarin. This dataset was divided into a training set and a test set in a ratio of 4:1. We applied nine data augmentation techniques to enlarge the voice training set and extracted the corresponding mel-frequency cepstral coefficients (MFCC) maps. The MFCC maps were randomly paired and fed into the proposed Siamese network to achieve multitask classification for tumor and non-tumor, woman and man. The performance of the proposed model was compared with one machine learning method and six classical deep learning models with and without the auxiliary gender classifier. Results: Experiments demonstrate the superiority of the proposed network compared with the reference models. The proposed model achieved an overall accuracy of 0.9437, an F score of 0.8462, a precision of 0.9167, a sensitivity of 0.7857, and a specificity of 0.9825. Conclusion: The proposed network can assist in the initial screening of laryngeal tumors through voice acoustic analysis. The initial screening solely through voice acoustic analysis can help individuals seek medical assistance outside the hospitals and reduce the burden on doctors as well. © 2025

关键词： Initial screening of laryngeal tumors—Voice acoustic analysis—Mel-frequency cepstral coefficients—Siamese network

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Feature selection in linear support vector machines via a hard cardinality constraint: A scalable conic decomposition approach

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European Journal of Operational Research 2025年

作者： Bomze, Immanuel D'Onofrio, Federico Palagi, Laura Peng, Bo Faculty of Mathematics and Research Network Data Science University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy VGSCO University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria

In this paper, we study the embedded feature selection problem in linear Support Vector Machines (SVMs), in which a cardinality constraint is employed, leading to an interpretable classification model. The problem is NP-hard due to the presence of the cardinality constraint, even though the original linear SVM amounts to a problem solvable in polynomial time. To handle the hard problem, we first introduce two mixed-integer formulations for which novel semidefinite relaxations are proposed. Exploiting the sparsity pattern of the relaxations, we decompose the problems and obtain equivalent relaxations in a much smaller cone, making the conic approaches scalable. To make the best usage of the decomposed relaxations, we propose heuristics using the information of its optimal solution. Moreover, an exact procedure is proposed by solving a sequence of mixed-integer decomposed semidefinite optimization problems. Numerical results on classical benchmarking datasets are reported, showing the efficiency and effectiveness of our approach. © 2025

关键词： Mixed-integer linear programming

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Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Convolutional Recurrent Imputation network

Enhanced Prediction of Fuel Consumption and Carbon Emissions...

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： T. Lalitha Jerzy Ryszard Szymański Marta Żurek-Mortka Mithileysh Sathiyanarayanan K. R. Srilekha Computer Science School of Information Science Presidency University Bengaluru Karnataka India Department of Electrical Drives and Industrial Electronics Faculty of Transport Electrical Engineering and Computer Sciences Casimir Pulaski University of Radom Poland Europe Lukasiewicz Research Network Institute for Sustainable Technologies Kazimierza Pułaskiego Radom Poland Europe MIT Square London I M.Tech Integrated(Software Engineering) VIT University Vellore Tamilnadu India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Fuel utilization and emission estimation are essential for assessing the consequence of materials and strict emission control measures due to the startling rate of weather changes. The world's socioeconomic issues are significantly impacted by the growing worldwide average temperature and its effects on climate change. The melting of the polar ice caps, that is a direct result of this rise, leads to a number of other problems, such as the polar animal’s death, coastal flooding, and exposure to bacteria and earliest microorganisms that are frozen in the snow and could cause many more global pandemics and invisible illnesses. In this paper, Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Dynamic Graph Convolutional Recurrent Imputation network (EP-FCCE-DGCRIN) are proposed.. The effectiveness of the EP-FCCE-DGCRIN technique is compared to other existing methods. This research offers evidence-based suggestions to lessen the environmental effects of vehicles for both manufacturers and consumers depending upon the outcomes of the Descriptive Statistics analysis.

关键词： Temperature measurement Pandemics Snow Sea measurements Carbon dioxide Imputation Fuels Vehicle dynamics Socioeconomics Meteorology

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Pre-Chirp-Domain Index Modulation for Full-Diversity Affine Frequency Division Multiplexing towards 6G

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Guangyao Mao, Tianqi Xiao, Zhenyu Wen, Miaowen Liu, Ruiqi Zhao, Jingjing Basar, Ertugrul Wang, Zhaocheng Chen, Sheng Beihang University School of Electronic and Information Engineering State Key Laboratory of CNS/ATM Beijing100191 China Greater Bay Area Innovation Research Institute of BIT Zhuhai519000 China Zhuhai519088 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China ZTE Corporation Wireless and Computing Research Institute Beijing100029 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China Tampere University Department of Electrical Engineering Tampere33720 Finland Koc University Department of Electrical and Electronics Engineering Sariyer Istanbul34450 Turkey Tsinghua University Department of Electronic Engineering Beijing100084 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

As a superior multicarrier technique utilizing chirp signals for high-mobility communications, affine frequency division multiplexing (AFDM) is envisioned to be a promising candidate for sixth-generation (6G) wireless networks. AFDM is based on the discrete affine Fourier transform (DAFT) with two adjustable parameters of the chirp signals, termed the pre-chirp and post-chirp parameters, respectively. Whilst the post-chirp parameter complies with stringent constraints to combat the time-frequency doubly selective channel fading, we show that the pre-chirp counterpart can be flexibly manipulated for an additional degree of freedom. Therefore, this paper proposes a novel AFDM scheme with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can implicitly convey extra information bits through dynamic pre-chirp parameter assignment, thus enhancing both spectral and energy efficiency. Specifically, we first demonstrate that the subcarrier orthogonality is still maintained by applying distinct pre-chirp parameters to various subcarriers in the AFDM modulation process. Inspired by this property, we allow each AFDM subcarrier to carry a unique pre-chirp signal according to the incoming bits. By such an arrangement, extra bits can be embedded into the index patterns of pre-chirp parameter assignment without additional energy consumption. We derive asymptotically tight upper bounds on the average bit error probability (BEP) of the proposed schemes with the maximum-likelihood detection, and validate that the proposed AFDM-PIM can achieve full diversity under doubly dispersive channels. Based on the derived result, we further propose an optimal pre-chirp alphabet design to enhance the bit error rate (BER) performance via intelligent optimization algorithms. Simulation results demonstrate that the proposed AFDM-PIM outperforms the classical benchmarks. © 2002-2012 IEEE.

关键词： Chirp modulation

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge Computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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Covert Communications with Enhanced Physical Layer Security in RIS-Assisted Cooperative networks

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Xingwang Liu, Musen Dang, Shuping Luong, Nguyen Cong Yuen, Chau Nallanathan, Arumugam Niyato, Dusit Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Jiaozuo Key Laboratory of Crow-Sensing Network Jiaozuo454003 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom Phenikaa University Faculty of Computer Science Hanoi12116 Viet Nam Nanyang Technological University Singapore School of Electrical and Electronics Engineering 639798 Singapore Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanyang Technological University Singapore College of Computing and Data Science 639798 Singapore

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) technologies are recognized for their programmability and high energy efficiency respectively, which will be the key parts of the future sixth generation (6G) mobile communication technology. The combination of the two technologies can improve communication security by reducing the probability of detection and decoding through enhanced transmission and backscatter transmission in different communication slots. In this paper, a dual-function RIS that supports cooperative relaying for covert communications is proposed. It operates in different communication slots (enhanced transmission slot and backscatter slot), but the performance is affected by phase errors due to function switching. A source covertly communicates with an intended destination via the help of RIS and cooperative relay. There is an illegal monitor aims to detect and eavesdrop the covert message. For this system, the outage probability (OP), intercept probability (IP), and detection error probability (DEP) in different communication slots are derived to examine the system reliability and security. Moreover, the system security probability (SSP) is proposed, and a block coordinated ascent (BCA)-based iterative algorithm is used to jointly optimize the power allocation coefficients to maximize the SSP. Simulation results show that increasing the number of elements can improve the security performance and mitigate the negative impact of RIS phase errors. © 2002-2012 IEEE.

关键词： Mobile telecommunication systems

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User-Centric Data Management in Decentralized Internet of Behaviors System

arXiv

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arXiv 2025年

作者： Zhang, Shiqi Wu, Dapeng Wang, Honggang Wang, Ruyan School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China China Chongqing Key Laboratory of Ubiquitous Sensing and Networking China Department of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University in New York City United States

The Internet of Behaviors (IoB) is an emerging concept that utilizes devices to collect human behavior and provide intelligent services. Although some research has focused on human behavior analysis and data collection within IoB, the associated security and privacy challenges remain insufficiently explored. This paper analyzes the security and privacy risks at different stages of behavioral data generating, uploading, and using, while also considering the dynamic characteristics of user activity areas. Then, we propose a blockchain-based distributed IoB data storage and sharing framework, which is categorized into sensing, processing, and management layers based on these stages. To accommodate both identity authentication and behavioral privacy, zero-knowledge proofs are used in the sensing layer to separate the correlation between behavior and identity, which is further extended to a distributed architecture for cross-domain authentication. In the processing layer, an improved consensus protocol is proposed to enhance the decision-making efficiency of distributed IoB by analyzing the geographical and computational capability of the servers. In the management layer, user permission differences and the privacy of access targets are considered. Different types of behavior are modeled as corresponding relationships between keys, and fine-grained secure access is achieved through function secret sharing. Simulation results demonstrate the effectiveness of the proposed framework in multi-scenario IoB, with average consensus and authentication times reduced by 74% and 56%, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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A Time-Driven UAV Deployment and Content Caching Approach for Multi-UAV-Enabled Consumer Electronics network

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Jianshan Yang, Xu Ming, Rui Luo, Haibo Wang, Xiaoding Piran, Md. Jalil Minjiang University School of Computer and Big Data Fuzhou350108 China Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Chinese Academy of Tropical Agricultural Sciences National Key Laboratory for Tropical Crop Breeding Institute of Tropical Bioscience and Biotechnology Sanya Research Institute Hainan Sanya572024 China Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

To address the problem of high delay due to the explosive growth of mobile data traffic in consumer electronics network (CEN), in this paper, we investigate the multiple-unmanned aerial vehicle (UAV)-enabled CEN content caching problem, focusing on minimizing content access delay by jointly optimizing UAV deployment, ground user (GU) access, and content caching while satisfying other resource constraints. We propose an algorithm based on alternating optimization for the scenario where multiple UAVs and base station (BS) collaborate to provide caching services. The algorithm attains a sub-optimal solution with a convergence guarantee by iteratively solving the three sub-problems derived from the decomposition of the target problem. First, a convex difference-based algorithm solves the UAV deployment sub-problem. Second, a greedy-based algorithm is used to solve the content caching sub-problem. Then, a continuous convex approximation algorithm based on the penalty function is used to solve the user access sub-problem. Finally, the above procedure is repeated to obtain a sub-optimal solution to the target problem. The proposed joint optimization algorithm is shown to outperform the benchmark algorithms in terms of average content access delay and cache hit ratio, as evidenced by numerical results. © 1975-2011 IEEE.

关键词： Antennas

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Classification of Depression and Anxiety with Machine Learning Applying Random Forest Models 24

Classification of Depression and Anxiety with Machine Learni...

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Proceedings of the 2024 5th International Conference on Intelligent Medicine and Health

作者： Camila Britany Alvarez Espezua Jose Emmanuel Cruz de la Cruz Fabiana Alexandra Apaza Davila Trinidad Dorotea Cruz de la Cruz Saul Huaquipaco Encinas Wilson Antony Mamani Machaca Department of Psychology Universidad de San Martin de Porres Arequipa Arequipa Peru Department of Electronic Engineering Universidad Nacional del Altiplano Puno Puno Peru Department of Administration Universidad Catolica San Pablo Arequipa Arequipa Peru Tacna Health Network MINSA Francisco Bolognesi Health Center Tacna Tacna Peru Department of Electronic Engineering Universidad Nacional de Moquegua Moquegua Moquegua Peru Department of Computer Science Universidad de Alicante Alicante Alicante Spain

ISBN: (纸本)9798400709616

Anxiety and depression have a strong correlation; although they often differ in their symptoms, both disorders frequently coexist in individuals, leading to a significant impairment of their daily functioning. Anxiety is among the most common conditions that, over time, can develop into a depressive disorder, or vice versa. The simultaneous presence of these disorders contributes to a range of symptoms, including mood disturbances, anhedonia, sleep problems, persistent fear, suicidal thoughts or ideation, distress, irritability, and constant worry, which result in substantial suffering for affected individuals. Recognizing this complex interplay, the present research applied Machine Learning models, particularly Random Forest, which is renowned for its robustness in classification tasks and its ability to identify both linear and non-linear relationships. The study involved the use of 17 predictor variables to train two models, each focused on predicting different outcomes: “Depression Severity” and “Anxiety Severity.” By leveraging the strengths of Random Forest, the research achieved highly accurate classifications. Specifically, the model for “Depression Severity” attained an accuracy of 99.35% and an F1 Score of 99.34%, while the model for “Anxiety Severity” reached an accuracy of 98.04% and an F1 Score of 98.05%. These results underscore the effectiveness and precision of the Random Forest algorithm in accurately identifying the severity of these mental health conditions.

关键词： Anxiety

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A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

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IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 186-194页

作者： Yufei Tang Chao Luo Jun Yang Haibo He IEEE Department of Computer & Electrical Engineering and Computer Science and the Institute for Sensing and Embedded Network Systems EngineeringFlorida Atlantic University School of Electrical Engineering Wuhan University Department of Electrical Computer and Biomedical EngineeringUniversity of Rhode Island

The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming U+0028 CCSNLP U+0029, and then transformed into a deterministic nonlinear programming U+0028 NLP U+0029. To tackle this NLP problem, a three-stage framework consists of particle swarm optimization U+0028 PSO U+0029, sequential quadratic programming U+0028 SQP U+0029 and Monte Carlo simulation U+0028 MCS U+0029 is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach. © 2017 Chinese Association of Automation.

关键词： Constrained optimization Economics Electric load flow Electric power generation Intelligent systems Monte Carlo methods Nonlinear programming Optimization Particle swarm optimization (PSO) Problem solving Quadratic programming Scheduling Stochastic systems Wind power

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