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DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

Intelligent Automation & Soft computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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Optimization Research of Particle Swarm Optimization Algorithm in BP Neural Network Model 2

Optimization Research of Particle Swarm Optimization Algorit...

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2nd International Conference on Artificial Intelligence and Intelligent information Processing, AIIIP 2023

作者： Cai, Yongheng Guo, Jiadong Zhong, Junhao Zhuhai College of Science and Technology School of Computing Zhuhai China Zhuhai College of Science and Technology Electronic Information Engineering College Zhuhai China Zhuhai College of Science and Technology School of Pharmacy and Food Science Zhuhai China

ISBN: (纸本)9798350371451

In today's big data era, the application of artificial neural network (ANN) in the field of machine learning has attracted increasing attention. But there are still some problems in the research of BP neural network, such as slow convergence speed, over-fitting and so on. As an optimization algorithm, particle swarm optimization algorithm can overcome some problems existing in BP neural network at present. Based on this background, PSO optimization algorithm is used to optimize BP neural network in order to improve the performance of neural network. Our results show that the average absolute error (MAE) of PSO-BP model is 0.005 and 0.023 lower than that of GA-BP model and single BP model. In terms of root mean square error (RMSE), PSO-BP model shows a smaller RMSE (0.105), while GA-BP model and single BP model have RMSE of 0.128 and 0.208. In addition, PSO-BP model achieved a higher R2 value (0.96), while GA-BP model and single BP model achieved a higher R2 value of 0.92 and 0.84. The most striking thing is that in the classification task, the accuracy of PSO-BP neural network reaches 91.7526%, which is 2.3253% and 12.0174% higher than GA-BP model and pure BP model. These results show that the BP neural network optimized by particle swarm optimization algorithm has higher accuracy and better performance than the unoptimized BP model and GA-BP model. To sum up, this study has made some contributions in the field of machine learning, and provided an important method for improving prediction performance and optimizing neural network models. © 2023 IEEE.

关键词： Machine learning

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Automating Maritime Risk Data Collection and Identification Leveraging Large Language Models 24

Automating Maritime Risk Data Collection and Identification ...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Huang, Donghao Fu, Xiuju Yin, Xiaofeng Pen, Haibo Wang, Zhaoxia Singapore Management University School of Computing and Information Systems Singapore Mastercard Research and Development Singapore Agency for Science Technology and Research Institute of High Performance Computing Singapore Tianjin University School of Electrical and Information Engineering China

ISBN: (纸本)9798331530631

Maritime risk research is crucial yet challenging for improving safety, efficiency, and sustainability in maritime operations. This paper presents an innovative method for automating the collection and identification of risk data related to global maritime risks from news sources, addressing the limitations of traditional manual methods. To evaluate the proposed method, different learning-based models, including conventional machine learning approaches and advanced Large Language Models (LLMs) such as GPT-4 and LLaMA-3.1, are comprehensively studied for comparison. In addition, not only do we use popular evaluation metrics to assess the proposed method, but we also introduce a new evaluation metric, called the "Ratio of Valid Categories (RVC),"to evaluate model reliability. The merits of the proposed method are demonstrated across different evaluation metrics. The research results show that the proposed LLM-based methods, particularly the GPT-4-based method, consistently outperform traditional models, significantly improving both the efficiency and accuracy of maritime risk data collection and identification. Our findings contribute to the expanding literature on LLM applications in risk management, demonstrating their potential to transform data collection and identification practices. © 2024 IEEE.

关键词： Contrastive Learning

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DPT‐tracker:Dual pooling transformer for efficient visual tracking

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CAAI Transactions on Intelligence technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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Physical Layer Security for RIS-Assisted Dual-Hop PLC/RF Communication Systems 23

Physical Layer Security for RIS-Assisted Dual-Hop PLC/RF Com...

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23rd IEEE International Conference on Communication technology, ICCT 2023

作者： Wu, Jingjing Yang, Jing Ding, Jie School of Information Engineering Yangzhou University Yangzhou China School of Computing Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798350325959

In this paper, we discuss the physical layer security of reconfigurable intelligent surface (RIS) assisted power line communication (PLC)/radio frequency (RF) communication system. The source S communicates with the destination D with the help of a decode-and-forward relay and an RIS. Specifically, the S transmits the signal to R through PLC link, and the R decode and forwards the received signal to the RIS by RF link. Then, the RIS reflects the received signal to D. An eavesdropper attempts to eavesdrop information from the relay R's broadcast signal. It is assumed that the PLC channel follows log-normal fading, while the RF link undergoes Rayleigh fading. To evaluate the system security performance, the closed-form expressions for the secrecy outage probability, the ergodic secrecy capacity and the effective secrecy throughput are derived. Numerical simulations results show that reducing the arrival probability of impulse noise, increasing the number of reflective elements, and increasing the transmit signal-to-noise ratio can effectively improve the safety performance of the system. © 2023 IEEE.

关键词： physical layer security power line communication reconfigurable intelligent surface secrecy outage probability

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An Evolutionary Algorithm for Adversarial SQL Injection Attack Generation 20

An Evolutionary Algorithm for Adversarial SQL Injection Atta...

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20th IEEE International Conference on Intelligence and Security Informatics, ISI 2023

作者： Issakhani, Maryam Huang, Mufeng Tayebi, Mohammad A. Lashkari, Arash Habibi School of Information Technology York University Toronto Canada School of Computing Science Simon Fraser University Burnaby Canada

ISBN: (纸本)9798350337730

Web application security poses ongoing challenges for organizations, and researchers have increasingly turned to machine and deep learning techniques to address vulnerabilities such as SQL injection and Cross Site Scripting. While these studies have made notable progress, the vulnerability to adversarial attacks remains a significant challenge. In this work, we aim to explore the impact of adversarial examples on machine and deep learning applications for detecting web vulnerabilities. We propose an evolutionary algorithm to create effective adversarial samples for bypassing various detection systems by iterative exploration of the search space. The effectiveness of our proposed approach is validated through rigorous testing on different diverse SQL injection detection systems. © 2023 IEEE.

关键词： Evolutionary algorithms

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Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling 30

Beyond Linguistic Cues: Fine-grained Conversational Emotion ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Xu, Bo Li, Longjiao Luo, Wei Naseriparsa, Mehdi Zhao, Zhehuan Lin, Hongfei Xia, Feng School of Software Dalian University of Technology Dalian China School of Information Technology Deakin University Geelong Australia Federation University Australia Ballarat Australia School of Computer Science and Technology Dalian University of Technology Dalian China School of Computing Technologies RMIT University Melbourne Australia

ISBN: (纸本)9782493814104

Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers. Although previous studies have made significant progress, accurate recognition and interpretation of similar fine-grained emotion properly accounting for individual variability remains a challenge. One particular under-explored area is the role of individual beliefs and desires in modelling emotion. Inspired by the Belief-Desire Theory of Emotion, we propose a novel method for conversational emotion recognition that incorporates both belief and desire to accurately identify emotions. We extract emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations. By applying message passing between nodes, our graph effectively models the utterance context, speaker's global state, and the interaction between emotional beliefs, desires, and utterances. We evaluate our model's performance by conducting extensive experiments on four popular ERC datasets and comparing it with multiple state-of-the-art models. The experimental results demonstrate the superiority of our proposed model and validate the effectiveness of each module in the model. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Emotion Recognition

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 111-136页

作者： SwornaKokila, M.L. Venkatarathinam, R. Rose Bindu, Joseph P. Manivasagam, M.A. Kishore, Kakarla Hari Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology Sri BalajiChockalingam Engineering College A.C.S. Nagar Irumbedu Thiruvannamalai District Tamil Nadu Arni India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Computer Science and Engineering Siddartha Institute of Science and Technology Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time ( © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Smart power grids

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Efficient Power Control for UAV Based on Trajectory and Game Theory

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Computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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