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Environmental sound classification using convolutional recurrent neural network and data augmentation

Multimedia Tools and Applications 2025年 1-23页

作者： Bansal, Anam Garg, Naresh Kumar Computer Science and Engineering GZS Campus College of Engineering and Technology Maharaja Ranjit Singh Punjab Technical University Punjab Bathinda151001 India

Environmental sound classification(ESC) is the trending research area. ESC categorizes sounds such as dog barking, gunshots, and children playing in the surroundings. Due to overlapping sound signals, the presence of several audio sources while recording audio, and different distances from audio sources to the microphone make this problem complex. This study proposes a robust model for ESC, which can help in crime investigation systems, security warning systems, and the development of smart homes and hearing aids. Researchers have designed numerous frameworks for classifying surrounding events. Various techniques for ESC have been used in the past, but they are either computationally intensive or provide less accuracy. A hybrid model consisting of Convolutional Neural Network and Recurrent Neural Network for ESC is proposed to provide an accuracy of 99.89%, which is the highest till now, as far as we know. The model is a combination of both models;it is called CRNN. CRNN has already been used in a few past studies, but raw waveforms are used, and the accuracy attained is quite low. The publicly available Dataset UrbanSound8 K is used. Augmentation techniques are used to overcome the scarcity of datasets. The cepstral features are extracted and input to the CRNN. CRNN is encouraged due to its ability to capture spatial and temporal dependencies of environmental sound waves. Various hyperparameters, such as the number of LSTM layers, number of filters, batch size, momentum, and number of neurons in the LSTM layer, are altered to find the best value for hyperparameters for ESC. It is found that 0.5 momentum, 128 filters, 512 neurons in the LSTM layer, 256 batch size, and one LSTM layer give the highest accuracy. Another dataset, ESC- 10, is used to validate the model. It is found that the proposed model provides considerable accuracy for ESC- 10, even though it is lower than in the case of UrbanSound8 K. In the future, the model can be applied to different applications

关键词： Recurrent neural networks

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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

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Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi directional optimization iot devices swarm deep learning

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Absorbing Time Synchronization Errors Using Shadow Queues in Time-Sensitive Networking

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IEEE Internet of Things Journal 2025年第9期12卷 12912-12926页

作者： Yang, Hao Zhang, Tong Feng, Xiaoqin Ma, Yanying Zhang, Liwei Ren, Fengyuan Lanzhou University School of Information Science and Engineering Lanzhou730000 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

Time-sensitive networking (TSN) is widely used in industrial automation and automotive applications due to its ability to provide deterministic transmission. To meet the stringent deterministic requirements of time-sensitive traffic, the mainstream traffic management mechanism time-aware shaper (TAS) relies on time synchronization among network devices. TAS schedules periodic traffic transmission using preallocated time windows. However, in real networks, time-sensitive traffic may not be precisely forwarded as scheduled, thus failing to achieve the expected performance. A fundamental reason is that the statically planned transmission schemes cannot accommodate dynamic time synchronization errors. To address this problem, we propose a novel TSN traffic scheduling strategy called robust TAS (RTAS), which utilizes a dual-queue gating structure to define a new scheduling rule for absorbing time synchronization errors to guarantee the real-time performance of scheduled traffic (ST). We conducted extensive simulations on OMNeT++ to study the performance of RTAS in industrial automation scenarios. The results show that RTAS enables deterministic transmission with imperfectly synchronized clocks and minimizes the impact of time synchronization errors on ST. © 2014 IEEE.

关键词： Queueing theory

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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Dual-Channel Multiplex Graph Neural Networks for Recommendation

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3327-3341页

作者： Li, Xiang Fu, Chaofan Zhao, Zhongying Zheng, Guangjie Huang, Chao Yu, Yanwei Dong, Junyu Ocean University of China Faculty of Information Science and Engineering Qingdao266005 China Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China The University of Hong Kong Department of Computer Science Hong Kong

Effective recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have started to shift their focus towards modeling various types of interactive relations between users and items in real-world recommendation scenarios, such as clicks, marking favorites, and purchases on online shopping platforms. Nevertheless, these approaches still grapple with two significant challenges: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations within behavior patterns on the target relation in recommender system scenarios. In this work, we introduce a novel recommendation framework, Dual-Channel Multiplex Graph Neural Network (DCMGNN), which addresses the aforementioned challenges. It incorporates an explicit behavior pattern representation learner to capture the behavior patterns composed of multiplex user-item interactive relations, and includes a relation chain representation learner and a relation chain-aware encoder to discover the impact of various auxiliary relations on the target relation, the dependencies between different relations, and mine the appropriate order of relations in a behavior pattern. Extensive experiments on three real-world datasets demonstrate that our DCMGNN surpasses various state-of-the-art recommendation methods. It outperforms the best baselines by 10.06% and 12.15% on average across all datasets in terms of Recall@10 and NDCG@10 respectively. The source code of our paper is available at https://***/lx970414/TKDE-DCMGNN. © 1989-2012 IEEE.

关键词： Graph neural networks

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MATM-SN: A Deep-Reinforcement-Learning Model for Ocean Sensing Task Allocation Based on Generative Social Network

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IEEE Internet of Things Journal 2025年第10期12卷 13477-13486页

作者： Guo, Shuai Xia, Menglei Xue, Huanqun Song, Yutong Zhang, Zhitian Liu, Chao Qingdao University of Technology College of Information and Control Engineering Qingdao266520 China Qingdao University of Technology College of Science Qingdao266520 China Ocean University of China Department of Computer Science and Technology Qingdao266100 China

Due to the complexity of ocean sensing tasks, buoy detection in traditional ocean observation methods has the disadvantages of high cost and insufficient real-time performance. Ocean mobile crowd sensing technology collects high-resolution data in real-time through shipborne sensors and has significant potential for future development in the ocean Internet of Things. In this article, we consider a cooperative vessel activity that performs sensing tasks, incentivizing multiple vessels through dynamic external task profits. To spontaneously incentivize the team to take part in tasks and maximize the total profit of the vessels, we propose a multiagent task allocation model (MATM-SN) based on deep reinforcement learning (DRL). This model uses a generative adversarial network to predict the trajectory of the vessels, and STGCN is used to extract synergistic features between vessels effectively. DRL training provides an optimal task allocation strategy for vessels. Finally, we compared four baselines and demonstrated that the MATM-SN model outperformed other baselines regarding task completion rate and total efficiency. © 2014 IEEE.

关键词： Deep reinforcement learning

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A High-Speed Compression and Secure Authentication Framework for Data Sharing Cloud-Enabled Big Data Environments

A High-Speed Compression and Secure Authentication Framework...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Abirami, I. Selvi, S. Lalitha, R. Julian, Anitha Rmk Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509828

With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-speed compression and secure authentication come forth playing a very crucial role. The two significant challenges addressed in this framework are data compression for efficient storage and transmission and robust authentication for secure access and integrity of shared data. This way, the framework compresses data size significantly without losing its quality, leading to efficient, faster data transfers and reduced considerably storage costs on cloud infrastructures. At the same time, secure authentication mechanisms [1] such as cryptography techniques and multi-factor authentication ensure that only authorized users access sensitive data, thus protected from potential unauthorized access or cyber-attacks. Integration of both capabilities speed compression and secure authentication-functionalizes big data environments to their peak performance and security. This framework is highly suitable for high-scale data-oriented industries like healthcare, finance, and IoT, which necessitate high-speed, secure, and reliable data sharing. Ultimately, this approach improves scalability and security in cloud-enabled big data systems by enabling organizations to keep pace with growing needs for highly efficient and secure data sharing in a highly dynamic environment. © 2025 IEEE.

关键词： Data integrity

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Fault Detection of Automotive Engine System Based on Canonical Variate Analysis Combined With Bhattacharyya Distance

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Journal of Dynamic Systems, Measurement and Control 2025年第4期147卷 041004页

作者： Cheng, Zhang Yun-Fei, Wang Yu-Yu, Lao Yuan, Li College of Science Shenyang University of Chemical Technology Liaoning Shenyang110142 China College of Computer Science and Technology Shenyang University of Chemical Technology Liaoning Shenyang110142 China College of Information Engineering Shenyang University of Chemical Technology Liaoning Shenyang110142 China

Aiming at the nonlinear and dynamic characteristics of data in automotive engine systems, a fault detection method based on canonical variate analysis combined with Bhattacharyya distance (CVA-BD) is proposed in this paper. First, CVA is utilized to calculate the state space of the system data. Second, a sliding window is introduced in the state space to quantify the difference in data distribution within the window using Bhattacharyya distance, thereby constructing a novel statistical indicator. Finally, the control limit for statistical indicator is determined to achieve process monitoring of automotive engine systems. CVA-BD effectively enhances the performance of process monitoring by capturing the sequential correlation of data through CVA and eliminating the nonlinear impact between samples using similarity measurement metrics. Simulation experiments are conducted using a numerical case and experimental data from turbocharged spark ignition (TCSI) engines. The simulation results further confirm that, compared with principal component analysis (PCA), dynamic principal component analysis (DPCA), canonical variable analysis (CVA), dissimilar canonical variable analysis (CVDA), auto-encoder (AE), and stacked auto-encoder (SAE) CVA-BD has demonstrated an improvement of at least 41%. Copyright © 2025 by ASME.

关键词： Fault detection

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A Hybrid Machine Learning Framework for Dynamic Resource Optimization in 5G Networks

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Wireless Personal Communications 2025年 1-29页

作者： Umamaheswaran, S. Gurulakshmi, A.B. Mannan, J. Mannar John, Rajan Rao, N. Lakshmana Nagarajan, S. Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering PES University EC Campus Bangalore India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Science Bluecrest University College Monrovia Liberia Department of Mechanical and Industrial Engineering Bahirdar Institute of Technology Bahirdar University Bahirdar Ethiopia

5G has boosted the possibility of ultra-high-speed, low-latency, and reliable wireless communication systems. With 5G networks, if efficient resource management is not properly looked at, then the full potential of such networking cannot be harnessed. This work proposes a new hybrid machine learning based framework for dynamic resource management in a 5G network based on a combination of deep and reinforcement learning. This model allocates resources with regard to the current state of the network and users' requirements to increase the level of network performance during usage. It includes a Deep Neural Network (DNN) as a feature learner and Reinforcement Learning (RL) as a decision maker. The DNN learns different levels of data that are associated with the network, including network performance, user behavior, and environmental data, in an attempt to identify features that define 5G network dynamics. The RL agent in contact with the 5G network environment acquires the know-how for resource allocation in order to maximize a reward signal derived from the network performance measures such as latency, throughput, and energy consumption. Due to variations in resource allocation, the RL agent enhances the network's efficiency over time. The strength of the proposed model is found in the DL part for capturing highly nonlinear patterns in the network data and the RL agent's flexibility to operate in real-time. The analytical and experimental studies prove the model's potency in attaining intelligent and efficient management of the resources, thus making it a potential solution for 5G network management. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Resource allocation

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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