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

作者： Yubo Huang Xin Lai Muyang Ye Anran Zhu Zixi Wang Jingzehua Xu Shuai Zhang Zhiyuan Zhou Weijie Niu School of Civil Engineering Southwest Jiaotong University China Zhenguan AI Lab ZhenGuan Innovation (Shenzhen) Technology Co. Ltd China SWJTU-Leeds Joint School Southwest Jiaotong University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Shenzhen International Graduate School Tsinghua University China Department of Data Science New Jersey Institute of Technology USA School of Economics and Management Southwest Jiaotong University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Singing Voice Conversion (SVC) has emerged as a significant subfield of Voice Conversion (VC), enabling the transformation of one singer’s voice into another while preserving musical elements such as melody, rhythm, and timbre. Traditional SVC methods have limitations in terms of audio quality, data requirements, and computational complexity. In this paper, we propose LHQ-SVC, a lightweight, CPU-compatible model based on the SVC framework and diffusion model, designed to reduce model size and computational demand without sacrificing performance. We incorporate features to improve inference quality, and optimize for CPU execution by using performance tuning tools and parallel computing frameworks. Our experiments demonstrate that LHQ-SVC maintains competitive performance, with significant improvements in processing speed and efficiency across different devices. The results suggest that LHQ-SVC can meet real-time performance requirements, even in resource-constrained environments.

关键词： Performance evaluation Computational modeling Static VAr compensators Parallel processing Signal processing Rhythm Real-time systems Timbre Speech processing Tuning

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Privacy Preservation in Gen AI Applications

arXiv

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arXiv 2025年

作者： Swetha, S. Shaju, Ram Sundhar K. Rakshana, M. Ganesh, R. Balavedhaa, S. Thiruvaazhi, U. Information Science and Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore India Computer Science and Engineering Kumaraguru College Of Technology Tamil Nadu Coimbatore India Artificial Intelligence and Data Science Kumaraguru College Of Technology Tamil Nadu Coimbatore India

The ability of machines to comprehend and produce language that is similar to that of humans has revolutionized sectors like customer service, healthcare, and finance thanks to the quick advances in Natural Language Processing (NLP), which are fueled by Generative artificial intelligence (AI) and Large Language Models (LLMs). However, because LLMs trained on large datasets may unintentionally absorb and reveal Personally Identifiable Information (PII) from user interactions, these capabilities also raise serious privacy concerns. Deep neural networks' intricacy makes it difficult to track down or stop the inadvertent storing and release of private information, which raises serious concerns about the privacy and security of AI-driven data. This study tackles these issues by detecting Generative AI weaknesses through attacks such as data extraction, model inversion, and membership inference. A privacy-preserving Generative AI application that is resistant to these assaults is then developed. It ensures privacy without sacrificing functionality by using methods to identify, alter, or remove PII before to dealing with LLMs. In order to determine how well cloud platforms like Microsoft Azure, Google Cloud, and AWS provide privacy tools for protecting AI applications, the study also examines these technologies. In the end, this study offers a fundamental privacy paradigm for generative AI systems, focusing on data security and moral AI implementation, and opening the door to a more secure and conscientious use of these tools. © 2025, CC BY.

关键词： Privacy by design

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Empirical Evaluation of Phishing Attack Identification Over Websites by Using Intelligent Deep Learning Principle

Empirical Evaluation of Phishing Attack Identification Over ...

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Emerging Trends in engineering and Medical sciences (ICETEMS), International Conference on

作者： Anitha G Mouleeswaran S K Dickson Irudayaraj V G B Santhi Rajkumar Chadge Hani Attar Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of CSE Dayananda Sagar University Bangalore India Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of CSE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Energy Engineer Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Phishing schemes keep on dominating the cyber threat landscape hence requiring enhancement of detection methods. This research sets out a new and novel idea for the detection of phishing website approach incorporating the XGBoost with ANN to boost the identification performance. The proposed model was compared with nine machine learning and deep learning models which includes Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), SVM, KNN, Naïve Bayes, XG Boost, artificial Neural Networks (ANN), CNN. It is apparent that the Ensembled XGBoost-ANN model was able to attain a higher accuracy rate of 9,463% and ranks on top of all the other models. It also showed great accuracy to prove precision, recall and F1-score metrics of the model. The findings reported herein support the suitability of the ensemble technique in modeling the intricate relationships characteristic of phishing websites, thereby holding the promise of being a practical solution in real-world environments. The reason why this methodology is promising for deployment in cybersecurity frameworks targeting the mitigation of phishing attacks is its capability of achieving high detection rates without making many false-positive detections.

关键词： Deep learning Support vector machines Radio frequency Measurement Accuracy Phishing Prevention and mitigation artificial neural networks Nearest neighbor methods Random forests

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Effect of spatial heterogeneity on level of rejuvenation in Ni80P20 metallic glass

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Chinese Physics B 2022年第9期31卷 435-443页

作者： Tzu-Chia Chen Mahyuddin KM Nasution Abdullah Hasan Jabbar Sarah Jawad Shoja Waluyo Adi Siswanto Sigiet Haryo Pranoto Dmitry Bokov Rustem Magizov Yasser Fakri Mustafa A.Surendar Rustem Zalilov Alexandr Sviderskiy Alla Vorobeva Dmitry Vorobyev Ahmed Alkhayyat Dhurakij Pundit University Bangkok 10210Thailand Data Science and Computational Intelligence Research Group Universitas Sumatera UtaraMedanIndonesia Optical Department College of Health and Medical TechnologySawa UniversityMinistry of Higher Education and Scientific ResearchAl-MuthanaaSamawahIraq College of Health&Medical Technology Al-Ayen UniversityIraq Faculty of Engineering Universitas Muhammadiyah SurakartaJawa Tengah 57102Indonesia Department of Mechanical Engineering Faculty of Science and TechnologyUniversitas Muhammadiyah Kalimantan TimurSamarinda 75124Indonesia Institute of Pharmacy Sechenov First Moscow State Medical University8 Trubetskaya St.Bldg.2Moscow 119991Russia Kazan Federal University KazanRussia Department of Pharmaceutical Chemistry College of PharmacyUniversity of MosulMosul-41001Iraq Department of Pharmacology Saveetha Dental College and HospitalSaveetha Institute of Medical and Technical SciencesChennaiIndia Nosov Magnitogorsk State Technical University MagnitogorskRussia Innovative University of Eurasia PavlodarRepublic of Kazakhstan K.G.Razumovsky Moscow State University of Technologies and Management(The First Cossack University) Moscow 109004Russia College of Technical Engineering The Islamic UniversityNajafIraq

Understanding the relation between spatial heterogeneity and structural rejuvenation is one of the hottest topics in the field of metallic glasses(MGs).In this work,molecular dynamics(MD)simulation is implemented to discover the effects of initial spatial heterogeneity on the level of rejuvenation in the Ni_(80)P_(20)*** this purpose,the samples are prepared with cooling rates of 10^(10) K/s-10^(12) K/s to make glassy alloys with different atomic ***,it is found that the increase in the cooling rate leads the Gaussian-type shear modulus distribution to widen,indicating the aggregations in both elastically soft and hard *** the primary evaluations,the elastostatic loading is also used to transform structural rejuvenation into the atomic *** results indicate that the sample with intermediate structural heterogeneity prepared with 10^(11) K/s exhibits the maximum structural rejuvenation which is due to the fact that the atomic configuration in an intermediate structure contains more potential sites for generating the maximum atomic rearrangement and loosely packed regions under an external *** features of atomic rearrangement and structural changes under the rejuvenation process are discussed in detail.

关键词： metalic glasses mechanical properties molecular dynamics simulation disordered solids

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Raman response, piezoelectricity, and transport properties of the two-dimensional Janus HfSiX3H (X=N/P/As) semiconductors: A first-principles study

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Physical Review B 2024年第23期110卷 235403-235403页

作者： Tuan V. Vu Nguyen T. Hiep Huynh V. Phuc Bui D. Hoi A. I. Kartamyshev Nguyen N. Hieu Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City 70000 Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City 70000 Vietnam Institute of Research and Development Duy Tan University Da Nang 550000 Vietnam Faculty of Natural Sciences Duy Tan University Da Nang 550000 Vietnam Division of Physics School of Education Dong Thap University Cao Lanh 870000 Vietnam Faculty of Physics University of Education Hue University Hue 530000 Vietnam

Exploring advanced materials with extraordinary properties is necessary for developing high-performance devices. In this work we construct two-dimensional Janus HfSiX3H (X=N/P/As) monolayers and systematically examine their physical properties by utilizing first-principles calculations. Specifically, we focus on the stabilities, Raman spectra, electronic structures, piezoelectricity, and transport properties of the monolayers. As a result, the studied HfSiN3H, HfSiP3H, and HfSiAs3H monolayers are found as semiconductors with good structural, mechanical, dynamic, and thermodynamic stabilities. The piezoelectric coefficients (e11 and e31) are calculated to examine the piezoelectricity of the HfSiX3H monolayers. It is noted that besides the in-plane piezoelectricity, the three monolayers show additional out-of-plane piezoelectric effects because of their noncentrosymmetric structures. We also include fully anisotropic acoustic deformation potential, ionized impurity, piezoelectric, and polar electron–phonon scattering to calculate the total carrier mobilities of Janus HfSiX3H. It is demonstrated that Janus HfSiX3H monolayers exhibit low carrier mobilities at room temperature (<100cm2V−1s−1). Scattering from polar optical phonons is found to dominate the total mobilities of both electrons and holes.

关键词： Electronic structure Piezoelectricity Transport phenomena Vibrational states Layered semiconductors First-principles calculations

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Assisted Design of High-Performance Electrochromic Devices Using Multilayer Perceptron Neural Network with High R2

SSRN

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SSRN 2024年

作者： Li, Muyun Cai, Qingyue Lai, Huayi Ning, Honglong Kong, Sifan Guo, Chenxiao Jiang, Bocheng Su, Guoping Yao, Rihui Peng, Junbiao Guangdong Basic Research Center of Excellence for Energy & Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices School of Materials Sciences and Engineering South China University of Technology Guangzhou510640 China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Foshan528225 China School of Software South China Normal University Foshan528225 China

Electrochromic devices (ECDs), serving as thermal insulation and decorative components, have been extensively employed across various domains. To meet diverse application demands in different contexts, the product parameters of ECDs exhibit continual variations, notably within the electrochromic layer. In this work, a multilayer perceptron neural network was leveraged to comprehend the transition from material parameter alterations to device performance parameter changes. Specifically, 336 groups of electrochromic solutions containing different concentrations and voltages were prepared. Subsequently, a model comprising three hidden layers and three output neurons was developed, expanding the dataset simulation to 2000 groups, yielding an R2 of 0.975 on modulation amplitude. Through this model, accurate estimations of performance parameters of numerous unmanufactured liquid-based ECDs were achieved, with projected outcomes highlighting a peak modulation amplitude of 78.38%@620nm. It has been verified that the modulation amplitude prediction error was below 5% and demonstrated stable cyclic performance over 1000 times (97.93%@1.45V) , thereby underscoring the feasibility of our approach. This research serves as a pertinent instance of the synergy between multilayer perceptron neural networks and ECDs, thereby advancing the deployment of neural network in the innovation and application of chemical engineering, materials and other fields. © 2024, The Authors. All rights reserved.

关键词： Thermal insulation

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A Structural Feature-Based Approach for Comprehensive Graph Classification

arXiv

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arXiv 2024年

作者： Islam, Saiful Hasan, Md Nahid Khanra, Pitambar Institute for Artificial Intelligence and Data Science University at Buffalo State University of New York BuffaloNY14260 United States Department of Mathematics University of Dhaka Dhaka1000 Bangladesh Department of Mathematics Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh Jacobs School of Medicine & Biomedical Sciences University at Buffalo State University of New York BuffaloNY14203 United States

The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity often hinders practical implementation. In this article, we address this challenge by proposing a method that constructs feature vectors based on fundamental graph structural properties. We demonstrate that these features, despite their simplicity, are powerful enough to capture the intrinsic characteristics of graphs within the same class. We explore the efficacy of our approach using three distinct machine learning methods, highlighting how our feature-based classification leverages the inherent structural similarities of graphs within the same class to achieve accurate classification. A key advantage of our approach is its simplicity, which makes it accessible and adaptable to a broad range of applications, including social network analysis, bioinformatics, and cybersecurity. Furthermore, we conduct extensive experiments to validate the performance of our method, showing that it not only reveals a competitive performance but in some cases surpasses the accuracy of more complex, state-of-the-art techniques. Our findings suggest that a focus on fundamental graph features can provide a robust and efficient alternative for graph classification, offering significant potential for both research and practical *** Codes 68R10, 05C85, 68R10 © 2024, CC BY.

关键词： Contrastive Learning

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Third Generation Security System for Face Detection in ATM Machine Using Computer Vision

Third Generation Security System for Face Detection in ATM M...

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Computer, Power and Communications (ICCPC), International Conference on

作者： S Sridevi K.M. Monica G. A Senthil R. Prabha A. Sathya J Ramya Dept. of Computer Science and Engineering Sri Ramachandra Engineering and Technology Chennai India Dept. of Computer Science and Engineering VIT Chennai Campus Chennai India Dept. of Information Technology Agni College of Technology Chennai India Dept. of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Dept. of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Dept. of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

A workflow is proposed for the detection of unauthorized persons in an ATM. Certain assumptions, including the context of identification are made by training their features and storing the datasets. However, not every individual will be a suspect. Suspicious activity on ATMs that are located in remote areas and to reduce the risk of fraudulent transactions. such as using another person's card to withdraw money etc., Various video survey and image processing have been discussed in relation to surveillance methods. It discusses the various processes, preprocessing, classification, feature extraction and the corresponding video processing methods.

关键词： Training Online banking Shape Face recognition Surveillance Prediction algorithms Feature extraction

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Optimized Cardiac Disease Prediction Using Cuckoo-Harmony Enhanced Convolutional Neural Networks

Optimized Cardiac Disease Prediction Using Cuckoo-Harmony En...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： S. Jayasankari Polaki Sujatha R. Vinitha Rama Prabha K.P K. Gayathri Bharathi Ramesh Kumar Department of CSE School of Computing Sastra Deemed to be University Thanjavur India Department of CSE(AI&ML) Prasad V Potluri Siddhartha Institute of Technology Kanuru Krishna district Andhra Pradesh India Department of ECE Karpagam Academy of Higher Education Coimbatore India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology Chennai Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Heart disease prediction is a critical challenge in healthcare, and accurate early diagnosis can improve outcomes. A new model is proposed for cardiac disease prediction using swarm intelligence algorithms based on the Cuckoo Search Algorithm and Harmony Search, in combination with Convolutional Neural Networks. The proposed model uses multiple hyperparameter optimization techniques to enhance the accuracy of cardiac disease prediction. A public dataset of 1,025 patient records with 14 attributes was used to test the proposed method. A CNN optimized for cuckoo harmony outperforms more conventional machine learning approaches. The F1-score was 98%, recall was 95%, precision was 100%, and the ROC-AUC score was 100%. The results show that the proposed method was the most accurate model in terms of classification accuracy. This model allows for early detection of cardiac disease in clinical environments.

关键词： Heart Accuracy Cardiac disease Medical services Predictive models Prediction algorithms Hyperparameter optimization Convolutional neural networks Particle swarm optimization Diseases

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An Enhanced Aquila-Based Resource Allocation for Efficient Indoor IoT Visible Light Communication

An Enhanced Aquila-Based Resource Allocation for Efficient I...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Selma Yahia Yassine Meraihi Sylia Mekhmoukh Taleb Seyedali Mirjalili Amar Ramdane-Cherif Tu Dac Ho Hossien B. Eldeeb Sami Muhaidat LIST Laboratory University of M’Hamed Bougara Boumerdes Boumerdes Algeria Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane Australia LISV Laboratory University of Versailles St-Quentin-en-Yvelines Velizy France Department of Electrical Engineering Faculty of Engineering Science and Technology UiT Arctic University of Norway Electrical Engineering Computer Science Center of Cyber-Physical Systems Khalifa University UAE

Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.

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