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Transformer-based language-independent gender recognition in noisy audio environments

Scientific Reports 2025年第1期15卷 1-16页

作者： Anidjar, Or Haim Yozevitch, Roi Faculty of Computer Science College of Management Rishon Le’Tzion Israel Department of Computer and Software Engineering Ariel University Ariel Israel Department of Electronic and Electrical Engineering Ariel University Ariel Israel

This study proposes an independent method for identifying the gender of the speaker from an audio clip in a noisy environment. In this paper are performed two different processes on audio clips: one as a Mel-Spectrogram and the other using the Wav2Vec2 acoustic model emission, examining the advantages and disadvantages of each method. A series of experiments are presented across five different languages-English, Arabic, Spanish, French, and Russian-containing male and female audio clips. An analysis of these languages is carried out, examining their independent characteristics against a five-language model. The goal of this study is to distinguish the gender of the speaker based on an audio clip, regardless of language or complex background noise such as nightclubs or stadiums. Additionally, this research addresses the critical issue of gender bias in voice recognition systems. It highlights the challenges posed by the over-representation of male voices in training datasets and the subsequent impact on the accuracy and fairness of gender classification, particularly for female voices. The approach in this paper involves maintaining an equivalent quantity of audio clips for both male and female voices to ensure balance and mitigate this bias. The experimental results indicate that the performance evaluation of the traditional spectrogram method achieved better results compared to the Wav2Vec transformer method. For the Russian language, the spectrogram method achieved an accuracy of 99%, while the Wav2Vec transformer¹ method achieved only 89% accuracy. Tests in various environments-noisy and silent-show that a model trained in both conditions exhibited better accuracy. The results also indicate that a model trained on data from a wide variety of languages yielded higher results. The research findings highlight important insights for developing more reliable, accurate, and equitable systems in acoustic gender detection. © The Author(s) 2025.

关键词： Automatic speech recognition Language independent gender recognition Wav2Vec 2.0

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Enhancing Elderly Care with Socially Assistive Robots: A Holistic Framework for Mobility, Interaction, and Well-Being

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IEEE Access 2025年 13卷 82698-82717页

作者： Naseer, Fawad Khan, Muhammad Nasir Tahir, Muhammad Addas, Abdullah Kashif, Hasnain Beaconhouse International College Computer Science and Software Engineering Department Faisalabad 38000 Pakistan Government College University Lahore Electrical Engineering Department Lahore Pakistan Sir Syed University of Engineering and Technology Department of Software Engineering Karachi Pakistan Prince Sattam Bin Abdulaziz University College of Engineering Department of Civil Engineering Alkharj 11942 Saudi Arabia Faculty of Architecture and Planning King Abdulaziz University Landscape Architecture Department P.O. Box 8 0210 Jeddah 21589 Saudi Arabia University of South Asia Department of Computer Science Lahore Pakistan

The global elderly population is projected to double by 2050, creating challenges in mobility, social isolation, and cognitive decline. Socially Assistive Robots (SARs) offer a promising solution, yet gaps remain in integrating mobility assistance with cognitive and emotional support. The research developed a comprehensive framework for SARs, emphasizing user-centered design, technological innovation, and cultural adaptability. A mixed-methods approach was employed with 200 participants aged 60+, from diverse settings, including rehab centers, retired groups, old age communities and nursing homes. Results demonstrated a 33.3% improvement in mobility scores and a 40% increase in social engagement. Health metrics revealed a 22% reduction in falls, a 15% rise in physical activity, and a 90% user satisfaction rate. Gender and age differences were evident, with males aged 71+ showing higher mobility improvements (35.6%) and females aged 71+ reporting the highest satisfaction (93%). Long-term data indicated sustained benefits, with participants retaining 80% of mobility gains and social engagement levels 38% above baseline after six months. These findings highlight the importance of culturally sensitive and user-centered SAR designs in promoting elderly well-being. The research offers a robust foundation for integrating SARs into elderly care systems, emphasizing adaptability, sustainability, and ethical considerations for future implementation. © 2013 IEEE.

关键词： Cognitive support elderly care human-robot interaction (HRI) mobility enhancement quality of life socially assistive robots (SARs) user-centered design

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Construction of PCOS Prediction Model Based on BP Neural Network

Construction of PCOS Prediction Model Based on BP Neural Net...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Xianqing Hu Amit Yadav Asif Khan Abhishek Pratap Sah Sami Azam School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP N eural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients.

关键词： Training Adaptation models Accuracy Biological system modeling Computational modeling Neural networks Predictive models Data models Robustness Tuning

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Adaptive Graph Structure Learning Neural Rough Differential Equations for Multivariate Time Series Forecasting

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IEEE Transactions on Big Data 2025年

作者： Su, Yuming Ma, Tinghuai Rong, Huan Wahab, Mohamed Magdy Abdel Nanjing University of Information Science & Technology The School of Cyber Science and Engineering School of Computer Science Nanjing210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang222005 China Nanjing University of Information Science & Technology School of Software Nanjing210044 China Nanjing University of Information Science & Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Cairo University Faculty of Science Cario 12613 Egypt

Multivariate time series forecasting has extensive applications in urban computing, such as financial analysis, weather prediction, and traffic forecasting. Using graph structures to model the complex correlations among variables in time series, and leveraging graph neural networks and recurrent neural networks for temporal aggregation and spatial propagation stage, has shown promise. However, traditional methods' graph structure node learning and discrete neural architecture are not sensitive to issues such as sudden changes, time variance, and irregular sampling often found in real-world data. To address these challenges, we propose a method called Adaptive Graph structure Learning neural Rough Differential Equations (AGLRDE). Specifically, we combine dynamic and static graph structure learning to adaptively generate a more robust graph representation. Then we employ a spatio-temporal encoderdecoder based on Neural Rough Differential Equations (Neural RDE) to model spatio-temporal dependencies. Additionally, we introduce a path reconstruction loss to constrain the path generation stage. We conduct experiments on six benchmark datasets, demonstrating that our proposed method outperforms existing state-of-the-art methods. The results show that AGLRDE effectively handles aforementioned challenges, significantly improving the accuracy of multivariate time series forecasting. © 2015 IEEE.

关键词： Graph neural networks

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Efficient clustering for NOMA with THz mMIMO D2D inspired 6G system model

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Telecommunication Systems 2025年第2期88卷 1-16页

作者： Jain, Preksha Gupta, Akhil Rakesh, Nitin Tanwar, Sudeep Pau, Giovanni Alqahtani, Fayez Tolba, Amr Indian Institute of Technology Maharashtra Bombay India Nagpur Campus Maharashtra Pune India Department of Computer Science and Engineering Institute of Technology Nirma University Gujrat Ahmedabad India Faculty of Engineering and Architecture Kore University of Enna Enna94100 Italy Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia

Terahertz (THz) communication, offering unprecedented bandwidth, is a key enabler for the high data rates envisioned in 6G wireless networks. To further enhance capacity and spectral efficiency, this paper proposes a novel 6G hybrid system model integrating THz communication with massive multiple-input multiple-output (mMIMO), cooperative non-orthogonal multiple access (NOMA), and device-to-device (D2D) communication within a heterogeneous network architecture comprising macrocells and small cells. This integrated approach aims to address the escalating demand for connectivity. A crucial aspect of our proposed system is the development of an Antenna Angle Bound (AAB) clustering technique. This innovative method leverages antenna parameters, specifically beam angle, and width, to intelligently group users. By forming clusters based on these spatial characteristics, the AAB technique minimizes inter-user interference, a critical challenge in mMIMO systems. This interference reduction directly contributes to significantly improving the overall system sum rate. Furthermore, we formulate an energy efficiency maximization problem to optimize power allocation coefficients across the various technologies employed. This optimization ensures a balance between achieving high data rates and minimizing energy consumption, a critical consideration for sustainable 6G deployments. The complex optimization problem is tackled using the alternating direction method of multipliers algorithm, which efficiently decomposes the problem into manageable sub-problems. The combination of THz communication, mMIMO, cooperative NOMA, D2D, the AAB clustering technique, and the optimized power allocation promises to deliver a substantial leap in performance for future 6G wireless communication systems, addressing the growing need for higher data rates and improved energy efficiency. The simulation shows that the proposed 6G hybrid system schemes perform better than the traditional models by attaini

关键词： Bandwidth

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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Faster Edge Coloring by Partition Sieving

arXiv

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arXiv 2025年

作者： Akmal, Shyan Koana, Tomohiro INSAIT Sofia University "St. Kliment Ohridski" Bulgaria Technische Universität Berlin Faculty IV Institute of Software Engineering and Theoretical Computer Science Algorithmics and Computational Complexity Germany

In the Edge Coloring problem, we are given an undirected graph G with n vertices and m edges, and are tasked with finding the smallest positive integer k so that the edges of G can be assigned k colors in such a way that no two edges incident to the same vertex are assigned the same color. Edge Coloring is a classic NP-hard problem, and so significant research has gone into designing fast exponential-time algorithms for solving Edge Coloring and its variants exactly. Prior work showed that Edge Coloring can be solved in 2m poly(n) time and polynomial space, and in graphs with average degree d in 2(1−Εd)m poly(n) time and exponential space, where Εd = (1/d)Θ(d3). We present an algorithm that solves Edge Coloring in 2m−3n/5 poly(n) time and polynomial space. Our result is the first algorithm for this problem which simultaneously runs in faster than 2m poly(m) time and uses only polynomial space. In graphs of average degree d, our algorithm runs in 2(1−6/(5d))m poly(n) time, which has far better dependence in d than previous results. We also consider a generalization of Edge Coloring called List Edge Coloring, where each edge e in the input graph comes with a list Le ⊆ {1, . . ., k} of colors, and we must determine whether we can assign each edge a color from its list so that no two edges incident to the same vertex receive the same color. We show that this problem can be solved in 2(1−6/(5k))m poly(n) time and polynomial space. The previous best algorithm for List Edge Coloring took 2m poly(n) time and space. Our algorithms are algebraic, and work by constructing a special polynomial P based off the input graph that contains a multilinear monomial (i.e., a monomial where every variable has degree at most one) if and only if the answer to the List Edge Coloring problem on the input graph is YES. We then solve the problem by detecting multilinear monomials in P. Previous work also employed such monomial detection techniques to solve Edge Coloring. We obtain faster algor

关键词： Undirected graphs

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Federated transfer learning for distributed drought stage prediction

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Discover Artificial Intelligence 2025年第1期5卷 1-13页

作者： Raza, Muhammad Owais Umar, Aqsa Rasheed, Jawad Asuroglu, Tunc Alsubai, Shtwai Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Computer Science Sindh Madressatul Islam University Karachi74000 Pakistan Department of Software Engineering Istanbul Nisantasi University Istanbul34398 Turkey Applied Science Research Center Applied Science Private University Amman Jordan Faculty of Medicine and Health Technology Tampere University Tampere33720 Finland VTT Technical Research Centre of Finland Tampere33720 Finland Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Due to the uncertain nature of drought, it is one of the most menacing natural disasters. Drought modeling (Prediction, Detection, Forecasting, and Stage Prediction) is very essential for efficient policy making. But one of the key problems with drought modeling is the limited availability of centralized datasets. To address this problem, we are a novel proposing federated learning based transfer learning models for the prediction of drought stages. In this study, satellite image dataset was collected from the Tharparkar district (prone to drought) of Pakistan. We trained the dataset using traditional and federated learning approaches, comparing centralized ML models, pre-trained models, and their respective federated learning models (FL-ResNet, FL-DenseNet, FL-MobileNet). The development of these models is the novel aspect of the study specifically for the use case of drought stage prediction. Based on the final evaluation, FL-MobileNet achieved 82% precision while baseline MobileNet scored 68%. The results show the effectiveness of novelty (federated learning), that our proposed framework improves the performance of the drought stage classification task. © The Author(s) 2025.

关键词： Federated learning

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MODEL CHECKING FOR MULTI-AGENT SYSTEMS MODELED BY EPISTEMIC PROCESS CALCULUS

arXiv

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arXiv 2025年

作者： Yu, Qixian Cao, Zining Hui, Zong Zhou, Yuan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Faculty of Computer and Software Engineering Huaiyin Institute Of Technology Huaian223001 China

This paper presents a comprehensive framework for modeling and verifying multi-agent systems. The paper introduce an Epistemic Process Calculus for multi-agent systems, which formalizes the syntax and semantics to capture the essential features of agent behavior interactions and epistemic states. Building upon this calculus, we propose ATLE, an extension of Alternating-time Temporal Logic incorporating epistemic operators to express complex properties related to agent epistemic state. To verify ATLE specifications, this paper presents a model checking algorithm that systematically explores the state space of a multi-agent system and evaluates the satisfaction of the specified properties. Finally, a case study is given to demonstrate the method. © 2025, CC BY.

关键词： Multi agent systems

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Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory Data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

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