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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Ritesh Bishnoi, Sunita Manivasagam Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Chemistry Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Lightwave-included Networks (LINs), based totally on incorporated optics, have emerged as viable, robust, and financial solutions for high-potential communique. LINs allow for the combination of loads of optical wavelengths onto a single medium, which allows the communique of huge quantities of records. These networks leverage evanescently coupled waveguides to combine, separate, and manual light waves for transmission in a particular device. Incorporated optic additives, which include multiplexers, demultiplexers, modulators, and switches, are used to establish paths between nodes. This actively controlled multimode community effects in procedures which might be greater green than conventional single-mode networks while additionally appreciably decreasing fee. Furthermore, LINs are less sensitive to noise in comparison to conventional transmission lines for the reason that light is used for verbal exchange. moreover, the optical isolation of LINs allows conversation at extremely high frequencies, imparting an opportunity to grow the verbal exchange pace inside the community. The LIN setup also affords tolerance and simplicity of renovation, permitting nodes to be remoted for debugging or protection without compromising the general network country. Moreover, in step with wavelength, electricity manipulation may be applied to optimize community visitors and decorate the overall performance. © 2024 IEEE.

关键词： Communique Financial Furthermore Incorporated Lightwave Networks

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MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning 39

MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zheng, Shangfei Wang, Weiqing Qu, Jianfeng Yin, Hongzhi Chen, Wei Zhao, Lei Soochow University School of Computer Science and Technology China Monash University Department of Data Science and Ai Australia The University of Queensland School of Information Technology and Electrical Engineering Australia

ISBN: (纸本)9798350322279

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleteness has significantly hindered the applications of MKGs. To tackle the problem, existing studies employ the embedding-based reasoning models to infer the missing knowledge after fusing the multi-modal features. However, the reasoning performance of these methods is limited due to the following problems: (1) ineffective fusion of multi-modal auxiliary features;(2) lack of complex reasoning ability as well as inability to conduct the multi-hop reasoning which is able to infer more missing knowledge. To overcome these problems, we propose a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning). Specifically, the model contains the following two components: (1) a unified gate-attention network which is designed to generate effective multi-modal complementary features through sufficient attention interaction and noise reduction;(2) a complementary feature-aware reinforcement learning method which is proposed to predict missing elements by performing the multi-hop reasoning process, based on the features obtained in component (1). The experimental results demonstrate that MMKGR outperforms the state-of-the-art approaches in the MKG reasoning task. © 2023 IEEE.

关键词： Knowledge graph

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k-anonymity based location privacy protection method for location-based services in Internet of Thing

k-anonymity based location privacy protection method for loc...

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作者： Wang, Bo Guo, Yina Li, Hongtao Li, Zhiying School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China College of Mathematics and Computer Science Shanxi Normal University Linfen China

The wide application of Internet of Things technology has promoted the application of location-based services (LBS). Users can enjoy various conveniences brought by LBS. However, mobile users should submit their location information to LBS which may lead to privacy disclosure. Therefore, users need to protect their privacy while enjoying the service. If the privacy protection problem cannot be solved, the development of mobile internet business will be greatly affected. The existing location privacy protection methods take little account of real-time road conditions and are unable to resist link attacks. In this article, a k-anonymous location privacy protection method based on Voronoi map was proposed. First, the Voronoi diagram was divided according to road network structure and real-time road network data, and Voronoi unit was established with the intersection as the base point. Then, the base point is sent as an anchor instead of the user's actual location to the third-party server. The Voronoi diagram generated by the k-anonymity constituted an anonymous space to resist multi-query attacks and protected the location. k-1 user location points that are communicating were selected to form k-anonymous set with the current user, and send the anonymous set to LBS. Theoretical analysis and experimental results show that the proposed method not only efficiently protected location privacy, but also provided high-quality service. © 2021 John Wiley & Sons, Ltd.

关键词： Location based services

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An Integrated Machine Learning and IoT Based Approach for Enhanced Healthcare Efficiency and Personalized Treatment 23th

An Integrated Machine Learning and IoT Based Approach for En...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Singh, Jagendra Singh, Navneet Pratap Hasan, Dler Salih Shelke, Nitin Arvind Namdev, Arpit Girdhar, Nancy School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Information Technology University Institute of Technology RGPV Bhopal India University of La Rochelle La Rochelle France

ISBN: (纸本)9783031648120

This research suggests a comprehensive healthcare system that successfully blends machine learning (ML) and the internet of things (IoT) in order to increase healthcare efficiency and provide customized treatment in emergency ward situations. We employ a range of sensors, including those that detect pressure, temperature, oxygen levels, and heart rate, to continuously collect real-time patient data. In the outcomes of our trials, the ML models (SVM, ANN, RF, and DT) displayed varying degrees of accuracy, precision, recall, and F1 score. The most effective model, ANN, obtained an astounding accuracy rate of 92%. SVM came in second with an accuracy of 89%, followed by RF and DT in third and fourth, respectively, with 86% and 84% accuracy. Real-time data and predictive analytics are now available to healthcare professionals, which has a significant beneficial influence on efficiency and enables the customization of patient treatment plans. The number of emergencies was dramatically reduced as a result of these foresights, which improved patient outcomes. This study shows the potential for a patient-centered, technology-driven healthcare system that elevates the bar for healthcare delivery. The study results not only demonstrate the viability of our approach but also point out significant advancements in medical technology, paving the way for more effective, flexible, and patient-focused healthcare systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Exploring Neural Networks for Time Series Classification in Communication Networks

Exploring Neural Networks for Time Series Classification in ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Chaudhary, Pavan Singh, Vishvendra Adlin Jebakumari, S. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Agriculture Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

paper explores the utility of diverse neural community architectures to time series category issues in verbal exchange networks. The authors first compare diverse architectures primarily based on their simple idea of characteristics for categories in conversation networks. Various methods were investigated to acquire better consequences, including fitting, batch normalization, dropout, and switch learning. The consequences acquired from the experiments confirmed that the distinct architectures supply better performance compared to the conventional strategies in the time collection type. The authors also discussed several upgrades incrementally from the viewpoint of communication networks while offering a perception of their workings. The research outcomes can be a doubtlessly valuable device to help communications networks improve their performance and safety. Exploring Neural Networks for the Time series category in communication Networks is the latest research topic focusing on growing gadget learning fashions for time-structured facts in communication networks. The goal of this study is to benefit a higher knowledge of the shape of the time collection and allow green classification of the records. Primarily, neural networks, customarily used for sample popularity, can be implemented to deal with time series class obligations in communication networks. By using training neural networks on time series information, it is viable to examine patterns and find correlations among them. The resulting fashions are then used to classify unseen records factors and discover anomalies within the facts. © 2024 IEEE.

关键词： acquired consequences conventional experiments networks

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Typhoon Track Image Prediction Using a Comprehensive LSTM Attention Model in Deep Learning 4

Typhoon Track Image Prediction Using a Comprehensive LSTM At...

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4th International Conference on Neural Networks, information and Communication engineering, NNICE 2024

作者： Jiang, Yiping Yu, Yi Ma, Yuelong Zhuang, Jiahao Ye, Xianze Yuan, Yifeng Su, Guanpeng Guangdong University of Science and Technology School of Computer Science Guangdong Dongguan China University of Macau Faculty of Science and Technology Fst China South China Normal University School of Information and Optoelectronic Science and Engineering Guangdong Guangzhou China Macao Polytechnic University Faculty of Applied Sciences China Zhuhai College of Science and Technology School of Computer Guangdong Zhuhai China

ISBN: (纸本)9798350394375

Typhoons are exceptionally destructive tropical cyclones that possess the capacity to inflict significant harm upon society. For institutions engaged in risk assessment and disaster mitigation, accurately forecasting typhoon paths and trajectories is of paramount importance. In this study, we propose a Typhoon Track Image Prediction model based on a Comprehensive Long Short-Term Memory (LSTM) Attention Model in the field of Deep Learning. This model significantly improves the accuracy of typhoon track prediction. To assess its performance, we utilize typhoon data from the China Typhoon Network spanning from 1949 to 2022. The Comprehensive LSTM Attention Model is compared against other popular models such as Temporal Convolutional Networks (TCN), traditional LSTM, and Seq2Seq models for forecasting the typhoon track over a 12-hour period. The results demonstrate the superiority of our proposed model, showcasing its effectiveness in terms of superior accuracy in typhoon track prediction. © 2024 IEEE.

关键词： Long short-term memory

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A Particle Swarm Optimization Algorithm Based on Multi-Population Mutual Learning for SNP-SNP Interaction Detection

A Particle Swarm Optimization Algorithm Based on Multi-Popul...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Linqian Li, Yahan Shang, Junliang Ren, Qianqian Zhang, Yuanyuan Liu, Jin-Xing Qufu Normal University School of Computer Science Rizhao276826 China Qingdao University of Technology School of Information and Control Engineering Qingdao266520 China University of Health and Rehabilitation Sciences School of Health and Life Science Qingdao266113 China

ISBN: (纸本)9798350386226

Single nucleotide polymorphism (SNPs) data have become abundant thanks to the quick advancement of high-throughput sequencing technology, which provides convenience for genome-wide association studies. Single SNPs have been proven to be the cause of some diseases, and the emergence of complex diseases is often thought to be the result of the interaction of multiple SNPs. However, the possible interaction of millions of SNPs imposes a heavy computational burden for uncovering complex disease mechanisms. The existing SNP-SNP interaction detection algorithms frequently have flaws including high computation complexity and poor optimization effectiveness. In this study, a particle swarm optimization algorithm based on multi-population mutual learning (PSOMPML) is proposed to detect SNP-SNP interactions. In this algorithm, the mutual learning strategy is introduced to deal with different particles in different sub-populations to facilitate knowledge exchange. In addition, the elite preservation mechanism is incorporated into PSOMPML, to better preserve the good SNPs in the elite particles. The promising region local search strategy searches the optimal solution along the target solution and its near space to increase the convergence speed of the proposed algorithm. Experiments on simulated data sets and real data also demonstrate the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

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computers, Materials & Continua 2022年第8期72卷 2467-2482页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Shady Y.El-Mashad Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Mechanical Engineering Department Qassim UniversityBuraidah51452Saudi Arabia Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt

The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing ***(HGB)is a critical component of the human body because it transports oxygen from the lungs to the body’s tissues and returns carbon dioxide from the tissues to the *** the HGB level is a critical step in any blood analysis *** often indicate whether a person is anemic or polycythemia *** ensemble models by combining two or more base machine learning(ML)models can help create a more improved *** purpose of this work is to present a weighted average ensemble model for predicting hemoglobin *** optimization method is utilized to get the ensemble’s optimum *** optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search(SCSFS).The proposed SCSFS ensemble is compared toDecision Tree,Multilayer perceptron(MLP),Support Vector Regression(SVR)and Random Forest Regressors as model-based approaches and the average ensemble *** SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.

关键词： Sine cosine optimization metaheuristics optimization hemoglobin estimation weight average ensemble

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine 13

Development of a Model for Discriminating Electroencephalogr...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Tanabe, Akifumi Nakatani, Kyosuke Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Kamae-Shigenobu, Kazue Shinosaki, Kazuhiro Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Aino University Department of Medical Engineering Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Aomori University Faculty of Software and Information Technology Aomori Japan Osaka University Cyber Media Center Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (纸本)9798350373332

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients. © 2024 IEEE.

关键词： Electroencephalography

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Automated Static Analysis with Bayesian Inference for Interference Mitigation in 5G Cloud Networks

Automated Static Analysis with Bayesian Inference for Interf...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shankar, Shiv Shankar Mehta, Deepak Singh, Vaishali Vivekananda Global University Department of Mathematics Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper researches modern automated Static evaluation software with Bayesian Inference for interference mitigation in 5G cloud networks. The automatic static analysis approach affords a novel technique to discover the interference between the community nodes. The proposed studies a new modern Bayesian Inference to confirm the interference degree between nodes after the automated static evaluation method is widely recognized. The Bayesian Inference method is particularly beneficial, as it may provide sturdy statistical records approximately the level of contemporary interference among nodes. The proposed research evaluated the use of a couple of experiments, demonstrating the state-of-the-art method's accuracy and effectiveness. The outcome of today's research affords an efficient approach for interference mitigation in 5G cloud networks. It presents an automatic Static analysis with Bayesian Inference for Interference Mitigation in 5G Cloud Networks, primarily based on system-gaining knowledge state modern and superior evaluation of modern 5G networks. The principle objective present day this technique is to reduce the signal-to-interference ratio (SIR) so that you can improve the overall performance of ultra-modern 5G cloud networks. Especially the proposed method of brand new Bayesian Inference to successfully examine the signal propagation sample state-of-the-art 5G network. Then, the learned values from the Bayesian Inference are used to optimize the inter-cell interference cancellation (IICC), inter-cell interference coordination (ICIC), and interference mitigation algorithms. ultimately, experimental effects reveal the effectiveness of today's the proposed method in terms of modern-day advanced community performance and decreased SIR. The outcomes recommend that the proposed automated static analysis with Bayesian Inference for Interference Mitigation in 5G Cloud Networks can offer an efficient and dependable answer for optimizing overall performance in 5G

关键词： automatic Bayesian contemporary evaluation mitigation

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