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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Kanimozhi T A. Thangam D. Sai Chaitanya Kishore Saranya R Abbarapu Ashok S. Kaliappan Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Department of Mathematics Pondicherry University Community College India Department of Mechanical Engineering Srinivasa Ramanujan Institute of Technology Anantapur India Department of Information Technology Nandha Engineering College Erode India Department of Mathematics VIT-AP Andhra Pradesh India Department of Research and Development Lovely Professional University Jalandhar-Delhi Phagwara India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The impact of an ERP system's implementation on a company's supply chain system is investigated in this study. Supply chain design is emerging as a key competency in supply chain management (SCM), which is expected to integrate the enterprise resource planning (ERP) system as a basic component. Although Enterprise Resource Planning (ERP) software offers a range of capabilities to facilitate supply chain integration, there are some features that hinder ERP from integrating with other business operations. Preprocessing, feature extraction, and training the model are the three steps that make up the proposed technique. data cleansing, data integrations, data transformation, data reduction, and data scaling are the five primary activities of data pre-processing. Principal component analysis (PCA) is utilized in component extraction. We used MACO-GBDT to train the model. This cutting-edge method outperforms MACO and GBDT with an average accuracy of 93.28 percent.

关键词： Training Ant colony optimization Supply chain management Accuracy Computational modeling Supply chains Feature extraction Enterprise resource planning System implementation Principal component analysis

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dataset for neutron and gamma-ray pulse shape discrimination

arXiv

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arXiv 2023年

作者： Wang, Kaimin Liu, Haoran Li, Peng Liu, Mingzhe Zuo, Zhuo State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu610059 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China Engineering & Technical College Chengdu University of Technology Leshan614000 China Southwest Institute of Physics Chengdu610225 China Chengdu University of Technology College of Engineering Technology Sichuan Leshan614000 China

This work provides a publicly accessible dataset includes neutron and gamma-ray pulse signals for conducting pulse shape discrimination experiments. Several traditional and recently proposed pulse shape discrimination algorithms are utilized to evaluate the performance of pulse shape discrimination under raw pulse signals and noise-enhanced datasets. These algorithms are zero-crossing (ZC), charge comparison (CC), falling edge percentage slope (FEPS), frequency gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient (LG), and heterogeneous quasi-continuous spiking cortical model (HQC-SCM). In addition to the pulse signals, this dataset includes the source code for all the aforementioned pulse shape discrimination methods. Moreover, the dataset provides the source code for schematic pulse shape discrimination performance evaluation and anti-noise performance evaluation. This feature enables researchers to evaluate the performance of these methods using standard procedures and assess their anti-noise ability under various noise conditions. In conclusion, this dataset offers a comprehensive set of resources for conducting pulse shape discrimination experiments and performance evaluation. It can be accessed at https://***/10.5281/zenodo.7754573. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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Improving freshness prediction in frozen fish burgers: A comparative study of propolis additives using ANN and RSM models

Agriculture Communications

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Agriculture Communications 2025年第1期3卷

作者： Koushki, Fatemeh Mokhtarian, Mohsen Dalvi-Isfahan, Mohsen Xiao, Hongwei Zhang, Weipeng Department of Food Science and Technology Azad University Roudehen 19585-466 Iran College of Food Science and Nutritional Engineering China Agricultural University Beijing 100085 China Department of Food Science and Technology Faculty of Agriculture Jahrom University Jahrom 74135-111 Iran College of Engineering China Agricultural University Beijing 100083 China School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing 100048 China

The freshness of fish burgers (FBs) declines during frozen storage. Here, we assessed FB freshness using quality control indicators (QCIs), including peroxide value (PV), total volatile basic nitrogen (TVB-N), and total viable count (TVC). Two predictive models were compared, one based on response surface methodology (RSM) and the other on an artificial neural network (ANN). Their accuracy was evaluated using frozen FBs that incorporated different concentrations of freeze-dried propolis (FDP;0%–0.4%) and stored for various durations (0, 30, 60, and 90 days). Both FDP and storage time (ST) had significant effects (p < 0.01) on the quality control indicators (QCIs) of frozen FBs, with ST having a more significant effect than FDP on the QCI changes. A numerical optimization process determined that the optimal values of ST and FDP were approximately 27 days and 0.30 g/[100 g of fish paste], respectively. The coefficient of determination (R²) values for the QCIs of frozen FBs in the ANN model were 0.9657 for PV, 0.9753 for TVB-N, and 0.9872 for TVC. These values were slightly lower in the RSM model, 0.9717 for PV, 0.9603 for TVB-N, and 0.9861 for TVC. Overall, the ANN model with a 2-13-3 topology (13 neurons in the first hidden layer) showed greater potential for prediction of FB quality during frozen storage and was found to be the more efficient method. © 2025 The Author(s)

关键词： artificial neural network Fish burgers Freshness modeling Propolis Response surface methodology

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A Privacy-Preserving Framework with Multi-Modal data for Cross-Domain Recommendation

arXiv

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arXiv 2024年

作者： Wang, Li Sang, Lei Zhang, Quangui Wu, Qiang Xu, Min School of Electrical and Data Engineering University of Technology Sydney 15 Broadway Ultimo SydneyNSW2000 Australia School of Computer Science and Technology Anhui University 111 Jiulong Road Economic and Technological Development District Hefei230601 China School of Artificial Intelligence Chongqing University of Arts and Sciences 319 Honghe Avenue Yongchuan District Chongqing402160 China

Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in a target domain with sparse data by leveraging rich information in a source domain, thereby addressing the data-sparsity problem. Some existing CDR methods highlight the advantages of extracting domain-common and domain-specific features to learn comprehensive user and item representations. However, these methods can't effectively disentangle these components as they often rely on simple user-item historical interaction information (such as ratings, clicks, and browsing), neglecting the rich multi-modal features. Additionally, they don't protect user-sensitive data from potential leakage during knowledge transfer between domains. To address these challenges, we propose a Privacy-Preserving Framework with Multi-Modal data for Cross-Domain Recommendation, called P2M2-CDR. Specifically, we first design a multi-modal disentangled encoder that utilizes multi-modal information to disentangle more informative domain-common and domain-specific embeddings. Furthermore, we introduce a privacy-preserving decoder to mitigate user privacy leakage during knowledge transfer. Local differential privacy (LDP) is utilized to obfuscate the disentangled embeddings before inter-domain exchange, thereby enhancing privacy protection. To ensure both consistency and differentiation among these obfuscated disentangled embeddings, we incorporate contrastive learning-based domain-inter and domain-intra losses. Extensive Experiments conducted on four real-world datasets demonstrate that P2M2-CDR outperforms other state-of-the-art single-domain and cross-domain baselines. © 2024, CC BY.

关键词： Sensitive data

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Adaptive finite-time backstepping control tracker for quadrotor UAV with model uncertainty and external disturbance

引用

Aerospace science and technology 2023年第1期133卷

作者： Wang, Jianghua Alattas, Khalid A. Bouteraa, Yassine Mofid, Omid Mobayen, Saleh Institute of Plasma Physics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia University of Sfax Sfax3038 Tunisia Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou640301 Taiwan

In this study, for the purpose of finite-time tracking control of quad-rotor Unmanned Aerial Vehicle (UAV) system under modeling uncertainties and external disturbances, an adaptive command-filtered backstepping sliding mode control scheme is presented. At first, a state-space model of quad-rotor's system is considered in the existence of bounded model uncertainties and exterior perturbations. Then, a command-filter according to the virtual control input is planned to reduce the computing complications. Subsequently, the output of command filter is considered instead of high-order derivative of virtual input. Besides, in order to remove the differential signals which are obtained during design of command-filter, the compensation error is used. Afterward, the sliding surfaces are designed according to tracking errors which are defined between actual states of the quad-rotor, desired values and compensation error system. Furthermore, backstepping control procedure combined with Lyapunov stability theory is used for finite-time convergence of switching surface. Besides, the adaptive control strategy is applied to estimate the upper bound of uncertainty and disturbance which is unknown in advance. At last, in order to validate the high efficacy of the designed control scheme, simulation results based on the actual parameters of quadrotor are provided. In addition, the comparison outcomes are provided to show proficiency and validity of the recommended technique compared with the existing method. © 2022 Elsevier Masson SAS

关键词： Sliding mode control

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Gated Recurrent Unit Neural Networks for Wind Power Forecasting based on Surrogate-Assisted Evolutionary Neural Architecture Search

Gated Recurrent Unit Neural Networks for Wind Power Forecast...

引用

data Driven Control and Learning Systems (DDCLS)

作者： Kehao Zhang Huaiping Jin Huaikang Jin Bin Wang Wangyang Yu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming China Huaneng Renewables Co. Ltd. Yunnan Branch Kunming China Wuhan Maritime Communication Research Institute Wuhan China

Wind energy has become an important part of national power systems due to its wide distribution, low cost, and non-polluting characteristics. However, the intermittence, randomness, and fluctuating of wind energy make it extremely difficult to connect wind power to the grid, which in turn affects the normal dispatch of power resources. Therefore, accurate wind power forecasting is crucial for power systems. Deep neural networks (DNNs) can efficiently capture high-dimensional nonlinear spatiotemporal features and are employed. The architectures of state-of-the-art DNNs are usually hand-designed by users with extensive expertise. In this paper, a gated recurrent unit neural networks for wind power forecasting approach based on surrogate-assisted evolutionary neural architecture search (SA-ENAS) is proposed. Firstly, SA-ENAS uses gated recurrent unit neural networks (GRU) to capture high-dimensional nonlinear spatiotemporal features, while incorporating delay variables into ENAS. Secondly, the GRU architecture is jointly encoded with delay variables. Then, the architecture search and delay variable selection are achieved using a surrogate model based ENAS approach. Finally, the effectiveness and superiority of the proposed method are verified through the case study of an actual wind farm dataset.

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5G-Enabled V2X Communication with Cloud-Powered XGBoost Algorithm for Electric Transportation

5G-Enabled V2X Communication with Cloud-Powered XGBoost Algo...

引用

International Conference on Communications and Signal Processing

作者： J Gnana Jeslin M Sujatha S. Aghalya C. Jehan M.V Ishwarya P Vedasundara Vinayagam Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering St. Joseph’s College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Multitech Dr.Rangarajan Dr.Sagunthala Engineering College Chennai Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The opportunity for electric transportation system optimization has never been greater with the combination of cloud-based machine learning algorithms and 5G-enabled Vehicle-to-Everything (V2X) connectivity. To improve the effectiveness, reliability, and longevity of electric vehicle (EV) networks, it provides a new architecture combining V2X communication enabled by 5G and an XGBoost algorithm driven by the cloud. Vehicles may exchange data in real time and make decisions with the help of infrastructure and the cloud via the seamless communication made possible by 5G networks’ high-speed, low-latency connection. The XGBoost algorithm, hosted on cloud servers, can use this data to forecast several metrics, including traffic flow, energy usage, and the best way to charge. Cloud computing allows more sophisticated analysis and prediction models by moving the processing load from individual cars to large, powerful servers. It improves transportation efficiency, decreases congestion, minimizes energy usage, and optimizes charging infrastructure via simulation and real-world experiments.

关键词： Measurement Cloud computing Machine learning algorithms Transportation Signal processing algorithms Prediction algorithms Real-time systems

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Relational Prompt-based Pre-trained Language Models for Social Event Detection

arXiv

引用

arXiv 2024年

作者： Pu, L.I. Xiaoyan, Y.U. Peng, Hao Xian, Yantuan Wang, Linqin Li, S.U.N. Zhang, Jingyun Philip, S.Y.U. Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Cyber Science and Technology Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM (Relational prompt-based Pre-trained Language Models for Social Event Detection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the RPLM on three real-world datasets, demonstrating that the RPLM model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Brain Tumor Segmentation using Convolutional Neural Networks based Visual Geometry Group 19

Brain Tumor Segmentation using Convolutional Neural Networks...

引用

Ambient intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： R Archana Reddy Nam Vasundhara Abbas Hameed Abdul Hussein M Anjan Kumar R. Ranjana School of Computer Science and Artificial Intelligence SR University Warangal India Department of CSE (Data Science) CMR Technical campus Hyderabad India College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of CSE Jyothishmathi Institute of Technology and Science Vachunur Inida ECE Panimalar Engineering College Chennai India

A Brain Tumour (BT) is an abnormal cell growth that can cause damage to surrounding tissues in the brain. The ability of doctors to treat cancer patients and increase their chances of survival depends on the early detection of brain tumours. Many deep learning techniques are created and recognised for effective brain tumour identification and categorization. This study develops a Deep Learning-based method to deal with the overfitting issue in the classification. In this study, the BraTS - 2015 dataset is used for segmentation and classification. After batch normalisation, the CNN convolutional layer receives the pre-processed data and uses it to extract features from Magnetic Resonance Images (MRIs). After that, the Simulated Annealing (SA) technique is used to effectively pick features. In order to achieve effective classification, this research presents a segmentation method based on Convolutional Neural Networks (CNNs) and a Visual Geometry Group 19 (VGG-19) technique. The performance of the developed CNN-VGG-19 is assessed using several tumour locations, including Whole Tumour (WT), Tumour Core (TC), and Enhancing Tumour (ET). The developed strategy delivers improved results in terms of sensitivity, specificity, and accuracy when compared to the current techniques.

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Improving Emergency Departments: Simulation-Based Optimization of Patients Waiting Time and the Number of Staff Present in A Hospital

Improving Emergency Departments: Simulation-Based Optimizati...

引用

Electrical, Energy, and artificial intelligence (EICEEAI), International engineering Conference on

作者： Hani Attar Ahmed Solyman Rania A. Sayed Dana M. Mohamed Mohamed Hafez Mohanad A. Deif Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK Department of Bioelectronics Modern University of Technology and Information (MTI) University Cairo Egypt Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia Department of Artificial Intelligence College of Information Technology Misr University for Science & Technology (MUST) Egypt

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The emergency department (ED), operating around the clock every day of the year, serves a diverse range of patients with varying medical conditions, making it the vital core of a hospital. Consequently, optimizing and simulating the ED's processes becomes essential to enhance the quality of care provided. This study offers a case analysis employing simulation to assess patient flows in a hospital's emergency department. Our objective is to evaluate the impacts of system enhancements within the ED. This model aims to measure patients' time from their ED entry, determine daily patient numbers, and calculate the overall patient movement time within the department. If the patient's condition is serious, he will be presented immediately to the doctor without waiting. A doctor will be added to the unit if the number of patients exceeds the standard limit.

关键词： Medical conditions Hospitals Process modeling Emergency services Discrete event simulation Resource management artificial intelligence

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