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Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement

computers, Materials & Continua 2023年第1期74卷 1069-1091页

作者： Rashid Jahangir Muhammad Asif Nauman Roobaea Alroobaea Jasem Almotiri Muhammad Mohsin Malik Sabah M.Alzahrani Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science University of Engineering and Technology LahorePakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental *** common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and *** research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation *** this research,the performance of transformer and convolutional neural networks(CNN)are *** audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,***,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio *** evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced *** UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 *** experimental results reveal that the proposed technique can achieve the best performance for ESC problems.

关键词： Environmental sound classification convolutional neural network deep learning transformer data augmentation

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Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

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Tsinghua science and technology 2024年第2期29卷 305-324页

作者： Yiqiao Cai Zifan Lin Meiqin Cheng Peizhong Liu Ying Zhou Xiamen Key Laboratory of Data Security and Blockchain Technology College of Computer Science and TechnologyHuaqiao UniversityXiamen361021China College of Engineering Huaqiao UniversityQuanzhou362000China School of Computer Sciences Shenzhen Institute of Information TechnologyShenzhen518000China

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

关键词： MOVRPTW multiform optimization multiform construction knowledge transfer adaptive local search

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Adaptive VDI Session Placement via User Logoff Prediction

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Machine Intelligence Research 2025年第1期22卷 189-200页

作者： Wenping Fan Puhui Meng Yu Tian Min-Ling Zhang Yao Zhang School of Computer Science and Engineering Southeast UniversityNanjing210096China VMware Information Technology(China)Limited Beijing100190China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing210096China

After the global pandemic,DaaS(desktop as a service)has become the first choice of many companies’remote working *** the desktops are usually deployed in the public cloud when using DaaS,customers are more cost-sensitive which boosts the requirement of proactive power *** researches in this area focus on virtual desktop infrastructure(VDI)session logon behavior modeling,but for the remote desktop service host(RDSH)-shared desktop pools,logoff optimization is also *** systems place sessions by round-robin or in a pre-defined order without considering their logoff ***,these approaches usually suffer from the situation that few left sessions prevent RDSH servers from being powered-off which introduces cost *** this paper,we propose session placement via adaptive user logoff prediction(SODA),an innovative compound model towards proactive RDSH session ***,an ensemble machine learning model that can predict session logoff time is combined with a statistical session placement bucket model to place RDSH sessions with similar logoff time in a more centralized manner on RDSH ***,the infrastructure cost-saving can be improved by reducing the resource waste introduced by those RDSH hosts with very few hanging sessions left for a long *** on real RDSH pool data demonstrate the effectiveness of the proposed proactive session placement approach against existing static placement techniques.

关键词： Virtual desktop infrastructure(VDI)resource management remote desktop service logoff prediction adaptive modeling session placement

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Attention-based network embedding with higher-order weights and node attributes

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CAAI Transactions on Intelligence technology 2024年第2期9卷 440-451页

作者： Xian Mo Binyuan Wan Rui Tang Junkai Ding Guangdi Liu School of Information Engineering Ningxia UniversityYinchuanChina School of Cyber Science and Engineering Sichuan UniversityChengduChina Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Academy of Medical Sciences Zhengzhou UniversityZhengzhouChina

Network embedding aspires to learn a low-dimensional vector of each node in networks,which can apply to diverse data mining *** real-life,many networks include rich attributes and temporal ***,most existing embedding approaches ignore either temporal information or network attributes.A self-attention based architecture using higher-order weights and node attributes for both static and temporal attributed network embedding is presented in this article.A random walk sampling algorithm based on higher-order weights and node attributes to capture network topological features is *** static attributed networks,the algorithm incorporates first-order to k-order weights,and node attribute similarities into one weighted graph to preserve topological features of *** temporal attribute networks,the algorithm incorporates previous snapshots of networks containing first-order to k-order weights,and nodes attribute similarities into one weighted *** addition,the algorithm utilises a damping factor to ensure that the more recent snapshots allocate a greater *** features are then incorporated into topological ***,the authors adopt the most advanced architecture,Self-Attention Networks,to learn node *** results on node classification of static attributed networks and link prediction of temporal attributed networks reveal that our proposed approach is competitive against diverse state-of-the-art baseline approaches.

关键词： data mining deep neural networks social network

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Does Partner Asymmetry Facilitate Innovation Speed? Organizational Learning and Social Exchange Perspectives

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IEEE Transactions on engineering Management 2024年 71卷 4144-4159页

作者： Hsiao, Yung-Chang Wu, Ming-Ho National University of Tainan Department of Business and Management College of Management Tainan700301 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science Kaohsiung824005 Taiwan

The right partner and high innovation speed are crucial for a successful research and development (R&D) alliance in the high-tech industry. Does homogeneity or heterogeneity between partners benefit innovation speed? This study examined the links between partner asymmetries (technological heterogeneity and network resource asymmetry) and innovative speed from both economic and social dual perspectives by focusing on biopharmaceutical industries. The results revealed an inverse U-shaped relationship between technology heterogeneity and innovation speed. In addition, a positive linear relationship exists between network resource asymmetry and innovation speed for 1991-2000 alliances and for biotech-biotech partner-type alliances;significantly negative moderating effects of time to market occurred on these linear relations. Finally, this study makes important theoretical and practical contributions to partner selection literature on R&D alliances in the biopharmaceutical industry. © 1988-2012 IEEE.

关键词： Behavioral research

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A charge system search based watermarking technique for gray-scale images using DWT–SVD

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Multimedia Tools and Applications 2024年第18期83卷 53743-53757页

作者： Gupta, Ritu Singh, Abhilasha Department of Information Technology Bhagwan Parshuram Institute of Technology GGSIPU Delhi India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Ghaziabad India

This paper put forward an embedded scheme to execute image watermarking in light of the discrete wavelet transform (DWT), singular value decomposition (SVD) and Charge System Search (CSS) method. In the proposed scheme, Charge System Search technique is applied to advance the multiple scaling factors (MSFs). To approve the proposed scheme, six different images are used to inspect its performance and its outcomes are likewise contrasted with the past work announced in writing. From the results, it very well may be seen that proposed watermarking technique is a vigorous and proficient methodology in the field of grayscale image watermarking technology. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Discrete wavelet transforms

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Enhanced Growth Optimizer and Its Application to Multispectral Image Fusion

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computers, Materials & Continua 2024年第11期81卷 3033-3062页

作者： Jeng-Shyang Pan Wenda Li Shu-Chuan Chu Xiao Sui Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The growth optimizer(GO)is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social ***,the original GO algorithm is constrained by two significant limitations:slow convergence and high mem-ory *** restricts its application to large-scale and complex *** address these problems,this paper proposes an innovative enhanced growth optimizer(eGO).In contrast to conventional population-based optimization algorithms,the eGO algorithm utilizes a probabilistic model,designated as the virtual population,which is capable of accurately replicating the behavior of actual populations while simultaneously reducing memory ***,this paper introduces the Lévy flight mechanism,which enhances the diversity and flexibility of the search process,thus further improving the algorithm’s global search capability and convergence *** verify the effectiveness of the eGO algorithm,a series of experiments were conducted using the CEC2014 and CEC2017 test *** results demonstrate that the eGO algorithm outperforms the original GO algorithm and other compact algorithms regarding memory usage and convergence speed,thus exhibiting powerful optimization ***,the eGO algorithm was applied to image *** a comparative analysis with the existing PSO and GO algorithms and other compact algorithms,the eGO algorithm demonstrates superior performance in image fusion.

关键词： Growth optimizer probabilistic model Lévy flight image fusion

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A Novel Deep Neural Network Architecture based GAN-DRANet for DOA Sensing With an Enhanced Performance in Low SNR

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IEEE Internet of Things Journal 2025年第13期12卷 22610-22622页

作者： Bai, Jiatong Shu, Feng Gao, Wei Wu, Guilu Yang, Weiwei Chen, Riqing Zhuang, Zhihong Hainan University School of Information and Communication Engineering Haikou570228 China Hainan University School of Information and Communication Engineering Collaborative Innovation Center of Information Technology Haikou570228 China Tibet University China School of Information Science and Technology Lhasa850000 China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China College of Communications Engineering Army Engineering University of PLA Nanjing China Fujian Agriculture and Forestry University School of Computer Science and Information Engineering Fuzhou350002 China

In extremely low signal-to-noise ratio (SNR) region, the useful features of the signal are weakened by higher-power noise, making it difficult for conventional direction-of-arrival (DOA) estimation methods to adequately exploit and extract the low-SNR signal features. Thus, a generative adversarial network (GAN) is presented to learn the underlying features and complex distributions of high-SNR covariance matrices. The introduced GAN establishes a mapping between low-SNR and high-SNR covariance matrices, thereby generating first-rate high-SNR covariance matrices that closely resemble real high-SNR matrices. Also, it effectively captures signal features that are overwhelmed by excessive noise power. Additionally, to improve the performance of convolutional neural network (CNN)-based DOA estimation models in medium-to-high SNR ranges, a deep residual attention network (DRANet) is designed to significantly enhance DOA estimation accuracy in such SNR region. By integrating residual and attention modules, the network effectively filters key features. This enhances feature learning and adaptability, allowing it to capture DOA-related features more proficiently. The experimental results indicate that the developed GAN-DRANet approach can approach the CRLB in the extremely-low SNR range and improves the estimation resolution limits of the other two DL-based methods, DNN and CNN, in medium to high SNR conditions. © 2014 IEEE.

关键词： Generative adversarial networks

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AMCB: Authenticated Medical Cyber-Physical Blockchain model

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Multimedia Tools and Applications 2024年第41期83卷 89079-89109页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundora Australia

At a time when technology is spreading rapidly and widely, technology has become a necessity in daily life and practical life, and this led to the emergence of many cyber-physical systems (CPS), among which the medical cyber-physical systems (MCPS) have emerged, which is one application of CPS that is particularly concerned with patients and health care providers. These systems generate a large amount of data that may be difficult to process and store, in addition to unauthorized access to these systems, which affects their protection. This paper presents a proposed Authenticated Medical Cyber-Physical Blockchain (AMCB) model in Medical Cyber-Physical Systems (MCPS) using Blockchain technology, machine learning, and physically unclonable function (PUF) to enhance the authentication process by controlling the access to electronic health records (EHR) that stored on the cloud server and physical authentication. In addition, it analyzes the data generated from the authorized devices to ensure that the devices do not contain malicious. This paper presents an experiment based on the medical Internet of Things (IoMT) dataset using K-nearest neighbors (k-NN), Random Forest (RF), Naive Bayes and Supporting Vector Machine (SVM) for malicious detection to test the proposed model’s accuracy. The Random Forest (RF) classifier gave more accurate results based on the preliminary results with a slight difference from k-NN. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Optimizing User Recommendations with Variational Autoencoders: Insights from MovieLens-1M and BookCrossing Datasets

Optimizing User Recommendations with Variational Autoencoder...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Samridhi, Kumari Sinha, Bam Bahadur Das, Trinanjan Indian Institute of Information Technology Electronics and Communication Engineering Ranchi India National Institute of Technology Computer Science and Engineering Sikkim India Indian Institute of Information Technology Computer Science and Engineering Ranchi India

ISBN: (纸本)9798350376876

Recommender systems utilize algorithms and data to predict user preferences based on their past choices. While these systems can be highly accurate, this increased accuracy can sometimes lead to predictability and monotony. In the realm of big data, recommender systems are both prevalent and valuable. Research on these systems has intensified in recent years, accompanied by a growing number of datasets. This paper examines the application of Variational Autoencoder (VAE)-based recommender systems using the MovieLens 1M and BookCrossing datasets. The study primarily evaluates the performance of the VAE model using Root Mean Square Error (RMSE) as the key metric. The VAE model was trained on these datasets to predict user preferences and generate personalized recommendations. Experimental results revealed an RMSE of less than 1 for both datasets, indicating the model's proficiency in accurately estimating user ratings for items. These findings suggest that VAE-based recommender systems are promising for enhancing recommendation accuracy and personalization. This research contributes to the understanding of VAE models within the context of recommender systems and provides insights for future research and potential improvements in this field. © 2024 IEEE.

关键词： Data accuracy

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