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Implementation of Multilevel Monitoring and Control System for Mission Smart Villages 15th

Implementation of Multilevel Monitoring and Control System f...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Hemalatha, B. Karthik, B. Department of Electronics and Communication Engineering Bharath Institute of Higher Education and Research Chennai India

ISBN: (纸本)9783031810855

the study and creation of the village as a smart village is the focus of this research. the smart villages can achieve various successes for making livelihood better. Villages can be able to develop to support value added services for various attributes to the village and for the people. the Indian national and state governments have embraced the idea of the "smart village". It is an initiative and a large-scale, intricate task for comprehensive rural development. Implementing IOT, by creating multilevel monitoring system using GSM module through connecting them to Arduino Uno Microcontroller, it can lead the issues to be solved. Creating multilevel networking system between each server and each house connected in the village will be monitored by host server. It will analyze and predict the issues and leads the solution towards the particular consumer. this paper will address the technical solutions for energy management, alerting on emergency services, optimized irrigation system, creating awareness on electronics from rural to urban in their day-to-day life. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Risk management

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A Deep Neural networks model for Restaurant Recommendation systems in thailand 2022

A Deep Neural Networks model for Restaurant Recommendation s...

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14th international conference on Machine Learning and Computing, ICMLC 2022

作者： Saelim, Apisara Kijsirikul, Boonserm Computer Engineering Chulalongkorn University Thailand

ISBN: (纸本)9781450395700

In the age of flooded information, Recommender systems play a crucial role as long as consumers consume more content and submit more data. Many businesses have implemented Recommender systems to assist users find items based on their previous interactions. Deep neural networks have demonstrated promising results in a variety of disciplines, including recommendation systems in the past few years. However, such studies ignore auxiliary information input. In this work, we purpose a deep recommendation system with neural networks which consists of deep collaborative filtering to learn user and item interaction latent factor and enrich the performance with textual information by using multi-layer perceptrons and combining these two models under our framework, called DNNRecs. Apart from our model framework, we also contribute a feature engineering method to create new features from review text by using technique tf-idf. Extensive experiments on one real-life dataset in thailand demonstrate the effectiveness of the proposed model. © 2022 ACM.

关键词： Recommender systems

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High-Speed Long-Haul Multi-Channel Nonlinear Optical communication systems Influenced by Equalization Enhanced Phase Noise 14

High-Speed Long-Haul Multi-Channel Nonlinear Optical Communi...

引用

14th IEEE international conference on Advanced Infocomm Technology (ICAIT)

作者： Jin, Cenqin Tan, Mingming Shevchenko, Nikita A. Liu, Zheng Li, Zhe Popov, Sergei Chen, Yunfei Xu, Tianhua Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England Aston Univ Aston Inst Photon Technol Birmingham B4 7ET W Midlands England Univ Cambridge Dept Engn Elect EnginecringDiv Fibre Opt Communicationtem Lab FOCSLab Cambridge CB3 0FA England Tianjin Univ Tianjin 300072 Peoples R China Cisco Syst Maynard MA 01754 USA KTH Royal Inst Technol S-16440 Stockholm Sweden UCL London WC1E 6BT England

ISBN: (数字)9781665471565

ISBN: (纸本)9781665471565

In this work, the performance of high-speed long-haul nonlinear Nyquist-spaced multi-channel coherent optical fiber communication systems utilizing electronic dispersion compensation and digital nonlinearity compensation is explored taking into consideration the enhanced equalization phase noise. the analytical model has also been developed to estimate the system performance under different transmission scenarios.

关键词： optical fiber communication analytical model laser phase noise electronic dispersion compensation equalization enhanced phase noise

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Predicting Targeted Violence from Social Media communication 14

Predicting Targeted Violence from Social Media Communication

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14th IEEE/ACM international conference on Advances in Social networks Analysis and Mining, ASONAM 2022

作者： Kaati, Lisa Shrestha, Amendra Akrami, Nazar Stockholm University Stockholm Sweden Mind Intelligence Lab Uppsala Sweden Uppsala University Uppsala Sweden

ISBN: (纸本)9781665456616

For decades, threat assessment professionals have used structured professional judgment instruments to make decisions about, for example, the likelihood of violent behavior of an individual. However, with the increased use of social media, most people use online digital platforms to communicate, which is also the case for potential violent offenders. For example, many mass shootings in recent years have been preceded by communication in online forums. In this paper, we introduce methods to identify markers of the warning behaviors Leakage, Fixation, Identification, and Affiliation and examine their discriminant validity. Our results show that violent offenders score higher on these markers and that these markers were present among a significantly higher proportion of violent offenders as compared to the normal population. We argue that our method can be used to predict potential planned, purposeful, or instrumental targeted violence in written communication. Automated methods for detecting warning behavior from written communication can serve as a complement to traditional threat assessment and provides unique opportunities for threat assessment beyond traditional methods. © 2022 IEEE.

关键词： Social networking (online)

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Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder through Gene Expression Analysis 15th

Breast Cancer Diagnosis Using Elephant Herding Optimization ...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. this research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

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Lora-Enabled System for Smart Farming Management and Oversight, Utilizing Wireless Sensor networks 14

Lora-Enabled System for Smart Farming Management and Oversig...

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14th IEEE international conference on communication systems and Network Technologies, CSNT 2025

作者： Elumalai, J. Kumuthapriya, K. Hema, R. Sundaram, P. Sakthi Shunmuga Department of Electronics and Communication Engineering S.K.R Engineering College Ponamallee Chennai600123 India Department of Electronics and Communication Engineering Tagore Engineering College Rathinamangalam Kelambakkam - Vandalur Rd Tamil Nadu Chennai600127 India

ISBN: (纸本)9798331531935

the purpose of this study is to create a LoRa-based smart farming management and surveillance system. Utilizing Wireless Sensor networks (WSNs) in rural settings, with the aim of replacing the existing technology in agricultural monitoring systems. A dedicated private network server is established and connected to a gateway, which gathers information or signals from the nodes at the end and sends this data to the cloud without the need for routers. this data can then be utilized for various applications by the end users. the system overcomes issues related to communication failures and energy-efficient data transmission. this advanced farming platform enhances the effectiveness of farming methods. the proposed methodology is the application of resources including as water, fertilizers, and pesticides should be optimized in order to achieve maximum efficiency while simultaneously minimizing waste. Farming productivity and sustainability are both significantly improved because of its contribution, notably in terms of the efficient management of resources. And intelligent cities used to improve agriculture-comparing the planned weather station and the numerous developed patents. the technique here is like a cutting-edge, reasonably priced choice for local weather monitoring. © 2025 IEEE.

关键词： Agricultural robots

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Predictive Maintenance in Photovoltaic systems Using Ensemble ML Empirical Analysis 14

Predictive Maintenance in Photovoltaic Systems Using Ensembl...

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14th international conference on Ubiquitous and Future networks, ICUFN 2023

作者： Mofidul, Raihan Bin Alam, Syed Samiul Chakma, Arbil Chung, ByungDeok Jang, Yeong Min Kookmin University Dept. of Electronics Engineering Seoul Korea Republic of ENS. Co. Ltd Ansan Korea Republic of

ISBN: (纸本)9798350335385

this paper aims to enhance the effectiveness and sustainability of photovoltaic (PV) systems by employing ensemble machine learning empirical analysis (EMLEA) to predict regular maintenance schedules using a minimal set of features. the research utilizes a 99.9kW PV system dataset, with a diverse set of features such as DC voltage, DC current, instantaneous power generation, power factor, and frequency. Ensemble machine learning (ML) algorithms including RandomForest, XGBoost, CatBoost, and LightGBM were deployed to forecast the regular maintenance needs of the PV system. the proposed EMLEA system identified DC voltage and DC current as key determining factors for the system's regular maintenance needs. Using these essential features, the maintenance schedule was estimated, and the performance metrics were evaluated by the EMLEA. Out of all models tested, XGBoost emerged as the superior model with an impressive accuracy of 98.62% and a precision of 94.37%. © 2023 IEEE.

关键词： Maintenance

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A Novel Channel Identification Architecture for mmWave systems Based on Eigen Features 14

A Novel Channel Identification Architecture for mmWave Syste...

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14th IEEE international conference on Wireless communications and Signal Processing (WCSP)

作者： Zhang, Yibin Sun, Jinlong Gui, Guan Gacanin, Haris Adachi, Fumiyuki NJUPT Coll Telecommun & Informat Engn Nanjing Peoples R China Rhein Westfal TH Aachen Inst Commun Technol & Embedded Syst Aachen Germany Tohoku Univ Res Org Elect Commun Sendai Japan

ISBN: (纸本)9781665450850

Millimeter wave (mmWave) communication technique has been developed rapidly because of many advantages of high speed, large bandwidth, and ultra-low delay. However, mmWave communications systems suffer from fast fading and frequent blocking. Hence, the ideal communication environment for mmWave is line of sight (LOS) channel. To improve the efficiency and capacity of mmWave system, and to better build the Internet of Everything (IoE) service network, this paper focuses on the channel identification technique in LOS and non-line of sight (NLOS) environments. Considering the limited computing ability of user equipments (UEs), this paper proposes a novel channel identification architecture based on eigen features, i.e. eigenmatrix and eigenvector (EMEV) of channel state information (CSI). Furthermore, this paper explores clustered delay line (CDL) channel identification with mmWave, which is defined by the 3rd generation partnership project (3GPP). the experimental results show that the EMEV based scheme can achieve identification accuracy of 99.88% assuming perfect CSI. In the robustness test, the maximum noise can be tolerated is SNR= 16 dB, with the threshold acc >= 95%. What is more, the novel architecture based on EMEV feature will reduce the comprehensive overhead by about 90%.

关键词： Channel identification millimeter wave clustered delay line eigenmatrix and eigenvector

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An Interactive Manta Ray Foraging Optimization Algorithm with Hierarchical Population Structure 14

An Interactive Manta Ray Foraging Optimization Algorithm wit...

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14th international conference on Intelligent Human-Machine systems and Cybernetics, IHMSC 2022

作者： Zheng, Tao Wang, Kaiyu Li, Jiayi Todo, Yuki Gao, Shangce University of Toyama Faculty of Engineering Toyama930-8555 Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Ishikawa9201192 Japan

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

the manta ray foraging optimization (MRFO) algorithm is a meta-heuristic method derived by imitating the behavior inspired by manta rays foraging. However, due to the lack of communication among individuals in the population, it suffers from the local optimum trapping problem. In this study, we innovatively propose a hierarchical population structure for it by adding an information interaction layer to the original MRFO population, namely an interactive manta rays foraging optimization (IMRFO) algorithm. In it, the hierarchical population structure successfully realizes the balance between local exploitation and global exploration. the superiority of IMRFO is confirmed in terms of solution quality, convergence speed, population diversity, and search trajectory by experimental findings based on thirty IEEE CEC2017 benchmark functions in comparison with other state-of-the-art meta-heuristic methods. © 2022 IEEE.

关键词： Optimization

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Fault Location Technology of Information System Across Isolation Devices 14

Fault Location Technology of Information System Across Isola...

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14th IEEE international conference on communication systems and Network Technologies, CSNT 2025

作者： Wang, Yao He, Yunrui Li, Zhihong Yan, Yiying Le, Xinyi Information and Communication Branch State Grid Corporation Limited Beijing100761 China

ISBN: (纸本)9798331531935

Fault location across isolated devices remains one of the most challenging tasks for modern information systems. Traditional linear regression fault location technology results in highly inaccurate and unproductive diagnoses due to the complex nature of the network environments and multiple layers of isolation. this paper aims at studying the fault location technology of information systems across isolated devices, specifying the improvement expected for diagnosis in both accuracy and speed. System logs and network traffic data are collected by data acquisition tools, which are then processed by feature extraction technology to individualize the key fault features. the convolutional neural networks (CNN) are then applied to identify and locate the patterns of fault. the experimental results indicate a maximum accuracy of 93% for this method in locating faults, with diagnosis time being reduced by 34% on average compared to the linear regression method. the CNN-based fault location technology appears to be capable of significantly improving the capability of fault diagnosis for information systems across isolated devices and providing a new solution for the fault management of complex network environments. © 2025 IEEE.

关键词： Information management

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