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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hengyu Zhang Hang Lv Yanchao Tan Guofang Ma Fan Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China School of Computer Science Zhejiang Gongshang University China College of Computer Science Zhejiang University China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Continuous diagnosis prediction based on multi-modal electronic health records (EHRs) of patients is a promising yet challenging task for AI in healthcare. Existing studies ignore abundant domain knowledge of diseases (e.g., specific medical terms and their interrelations) in textual EHRs, which fails to accurately predict disease progression and assist in sequential diagnosis prediction. To this end, we first propose an Expert enhanced neural Ordinary Differential Equations (ExpertODE) framework for continuous diagnosis prediction. In particular, we first propose a novel Mixture of Language Experts (MoLE) module to enhance disease embeddings with domain knowledge. Furthermore, we propose a Contrastive Neural Ordinary Differential Equation (CNODE) module to continuously model temporal correlations of disease progression, and implement a unified contrastive learning framework to jointly optimize the domain-based MoLE module and the temporal-based CNODE module. Extensive experiments on two real-world textual EHR datasets show significant performance gains brought by our ExpertODE, yielding average improvements of 3.91% for diagnosis prediction over state-of-the-art competitors.

关键词： Medical services Ordinary differential equations Predictive models Performance gain Mathematical models Sequential diagnosis Electronic medical records Optimization Medical diagnostic imaging Diseases

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Multiband Slotted Crescent-shaped Patch Antenna for K-band Satellite and mmWave Communications

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IEIE Transactions on Smart Processing and Computing 2022年第3期11卷 213-221页

作者： Hossain, Md Najmul Paul, Liton Chandra Rahim, Md Abdur Shin, Jungpil Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Fukushima965-8580 Japan

In this paper, a multiband miniaturized crescent-shaped patch antenna with circular slots is presented for ultra-wideband applications. The proposed antenna is constructed on a Flame Retardant 4 (FR-4) dielectric substrate. Next-generation wireless communication systems, such as beyond 5G (B5G) and 6G mobile communication systems require a wider bandwidth and higher data rates. The overall size of the proposed antenna is 11.4 mm3 operating in the frequency range of 17.73 GHz to 26.04 GHz and 29.6 GHz to 31.02 GHz for satellite communications and radar systems, respectively, as well as 35.40 GHz to 38.65 GHz for future mmWave B5G mobile applications. These frequency ranges usually fall into the K-band and mmWave frequency spectrums. Simulation results demonstrate that the peak gain of the proposed antenna is 4.68 dB, which is obtained at 25 GHz. © 2022 The Institute of Electronics and Information Engineers.

关键词： Microstrip antennas

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Cultivating Customer Purchase Intent: Leveraging Machine Learning for Precise Predictions 12

Cultivating Customer Purchase Intent: Leveraging Machine Lea...

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Krishna, R. Dhanush Mahadev, M. Hariprasad, S. Abhishek, S. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350369861

Insurance firms must employ cost-effective client engagement tactics in order to maximize the effectiveness of their marketing campaigns in the ever-changing business landscape of today. This work uses machine learning to solve the problem of effectively identifying future insurance subscribers. To build a prediction model, we utilize past marketing data from a modern insurance provider. The model makes use of historical campaign data, such as call duration, frequency of contact, and previous campaign outcomes, as well as client variables including age, job type, marital status, educational background, and contact communication style. Through the analysis of these characteristics, our machine learning model predicts the probability of a customer subscribing to the insurance product, allowing the business to focus on high-potential customers and reduce the expense of *** addition to offering insightful information about consumer behavior, the research helps to improve the general effectiveness of marketing tactics used by the insurance sector. © 2023 IEEE.

关键词： Insurance

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A Review on Breast Cancer Classification using Neural Networks 10

A Review on Breast Cancer Classification using Neural Networ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Rajput, Aashita Muskan Yadav, Sachin Chhabra, Megha Agarwal, Arun Prakash School of Engineering and Technology Sharda University Department of Computer Science and Application Greater Noida India

ISBN: (纸本)9789380544472

Breast cancer is the second most common type of cancer due to which females have such a high mortality rate. Several computer-assisted diagnostic systems have recently been proposed to operate with the least amount of data necessary due to the high number of unnecessary breast biopsies. The following review paper discusses the entire process of breast cancer classification along with various kinds of neural networks used to build such models. It further includes the various to-date research done in this domain and the different accuracy obtained by them. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Diseases

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OFDM with Generalized Cascade Index Modulation for Secure Communications

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China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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Q-Learning Energy Management System (Q-EMS) in Wireless Sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Ardiansyah, Deden Mustafid Mantoro, Teddy Universitas Diponegoro Doctoral Program of Information Systems School of Postgraduate Central Java Semarang Indonesia Universitas Pakuan Vocational School Computer Engineering Department West Java Bogor Indonesia Sampoerna University Media-Tech-Lab Computer Science Department DKI Jakarta Indonesia

ISBN: (纸本)9798350364101

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm. © 2024 IEEE.

关键词： Energy management systems

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Transfer Learning and Custom CNNs to Advance Traffic Sign Detection 3

Transfer Learning and Custom CNNs to Advance Traffic Sign De...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mithra, Kadiyala Monish George, Prince Abraham Abit Sai, Srungarapu Jatin Abhishek, S. Anjali, T. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita Amritapuri India

ISBN: (纸本)9798350319125

For autonomous driving technologies to work effectively and increase road safety, traffic sign detection is essential. The use of transfer learning and customised convolutional neural networks (CNNs) for this crucial task is examined in this paper. As a starting point for transfer learning, we use the pre-trained VGG16 model and evaluate its performance against a CNN architecture that we created. data preprocessing and data gathering are two aspects of our research that entail a large dataset of traffic signs. We employ strategies for data augmentation to improve the model's generalisation capabilities. Both models exhibit tremendous potential in our studies. While the bespoke CNN architecture succeeds in achieving amazing accuracy, the transfer learning strategy based on VGG16 shows its capacity to produce competitive outcomes. These models' advantages and disadvantages are clarified by our thorough examination and comparison. This study has implications for improving traffic sign detecting systems and laying a strong foundation for practical applications. Our findings show the potential of using pretrained models and creating unique CNNs, paving the path for safer and more effective navigation systems for vehicles. These models' advantages and disadvantages are clarified by our thorough examination and comparison. This study has implications for improving traffic sign detecting systems and laying a strong foundation for practical applications. Our findings show the potential of using pre-trained models and creating unique CNNs, paving the path for safer and more effective navigation systems for vehicles. These models' advantages and disadvantages are clarified by our thorough examination and comparison. This study has implications for improving traffic sign detecting systems and laying a strong foundation for practical applications. Our findings show the potential of using pre-trained models and creating unique CNNs, paving the path for safer and more effective na

关键词： CNN Traffic Sign Detection Transfer Learning VGG16

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Multimedia Tools and Applications 2024年第13期84卷 12141-12162页

作者： Darraz, Nossayba Karabila, Ikram El-Ansari, Anas Alami, Nabil El Mallahi, Mostafa IPI Laboratory Department of Mathematics and Computer Sciences High Normal School Sidi Mohamed Ben Abdellah University Fez Morocco MASI Laboratory Computer Science Department Polydisciplinary Faculty Mohammed 1st University Nador Morocco MASI Laboratory Mohammed First University Higher School of Technology Nador Morocco

Trend detection is the process of identifying patterns in data over time. It is used to identify trends in various fields, such as business, finance, marketing, healthcare, and others. For example, in e-commerce, trend detection can be used to identify changes in consumer purchasing behavior, such as an increase in demand for a particular product. Combining trend detection with Sentiment Analysis (SA) can give e-commerce businesses a deeper understanding of customer sentiment over time, allowing them to make more informed decisions about how to improve the customer experience. Our proposed system integrates both the Machine Learning approach and Moving Average (MA) method for sentiment classification of product reviews and trend detection, respectively. To begin, we preprocess the product reviews to facilitate sentiment analysis. We compare the effectiveness of Lexicon-based analysis with machine learning algorithms to identify the optimal approach. The preprocessed reviews are transformed into a vector representation to enable the application of Machine Learning (ML) techniques. Multiple ML algorithms are trained and evaluated based on metrics such as accuracy, F1 score, precision, and recall to determine the best-performing model for sentiment classification. Additionally, we employ clustering techniques to identify the most favorable product. Finally, the MA technique is applied to detect trending products. Our findings indicate that the SVM classifier with BOW achieved an impressive accuracy of 89.26% for sentiment classification. Furthermore, by utilizing K-Means clustering, we obtained a high silhouette score, outperforming other clustering methods, in identifying trending products. Our study highlights that the combination of sentiment analysis and trend detection can provide valuable insights for e-commerce businesses. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Machine Learning-Based Predictive Maintenance for Industrial Equipment Optimization 2

Machine Learning-Based Predictive Maintenance for Industrial...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Narayanan, Lakshmi Kanthan Loganayagi, S. Hemavathi, R. Jayalakshmi, D. Vimal, V.R. Saveetha Institute of Medical and Technical Sciences Simats Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha University Saveetha School of Engineering Department of Cse Thandalam Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Thandalam Chennai India

ISBN: (纸本)9798350384277

Industrial equipment optimization is crucial for industries that want to improve operating efficiency, reduce downtime, and cut maintenance costs. Predictive maintenance is a major strategy to achieving these goals, since it uses advanced data analytics and machine learning techniques to foresee equipment breakdowns and schedule maintenance in advance. This research investigates the use of machine learning-based predictive maintenance in the industrial sector. It discusses the approach for gathering and preprocessing data from various sensors and equipment logs, as well as the critical step of feature engineering to extract useful insights from the data. The selection of relevant machine learning algorithms and the model training method are thoroughly explored. Furthermore, the research emphasizes the importance of anomaly detection approaches in early failure detection, allowing for timely interventions to avoid equipment breakdowns. This research focuses on the creation of predictive maintenance models that estimate equipment's remaining useful life (RUL). The selection of thresholds for maintenance operations based on RUL projections is examined, with the goal of balancing cost-effectiveness and equipment reliability. The report underlines the need of continual monitoring and feedback loops in the real-time implementation of predictive maintenance systems, which will drive continued optimization. Real-world case studies demonstrate the usefulness of predictive maintenance in industrial equipment optimization, resulting in significant cost savings and improved equipment reliability. Challenges and future possibilities in this subject are also discussed, emphasizing the importance of scalable and explainable AI solutions. © 2024 IEEE.

关键词： Predictive maintenance

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