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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kumar Reddy, V Mahesh Malavika, K.V. Lokasree, B.S. Nanda Kumar, K. Ksrm College of Engineering Department of Electrical and Electronics Engineering Andhra Pradesh Kadapa India Hindustan Institute of Technology and Science Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Cmr Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350322309

The conventional electrical transmission and distribution network should be upgraded to an interactive network due to rising energy demand and consumption. A smart grid or a service network one of these is smart meters where smart grid solutions have been presented. In order to enable real-time data collection, processing of meter readings, real-time monitoring, and decision-making, smart metering over the air is a crucial component of a smart grid. In traditional energy monitoring systems, utility usages are written on paper by workers in traditional meter reading systems, and there is a high risk of human mistakes, which raises the cost to users/companies. And no real-time monitoring and data communication between utility and energy meters. This project aims to use an IoT-based smart meter to control the power system's power consumption. This system uses a power monitoring strategy and leads to better data processing, real-time energy monitoring, and data sharing. By doing this, the energy system's power usage can be adjusted, which lowers overall consumption and costs. A novel power theft idea is used, and the user may also turn on and off certain appliances based on power requirements. The smart meter is controlled by an Arduino micro controller. Embedded C Proteus software is used for simulation, while Node-red can be used for visualization, data storage, and remote access. © 2023 ieee.

关键词： Smart meters

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Dispersion-Controlled Unidirectional Magnetoinductive Waves 17

Dispersion-Controlled Unidirectional Magnetoinductive Waves

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17th International Congress on Artificial Materials for Novel Wave Phenomena, Metamaterials 2023

作者： Yan, J. Radkovskaya, A. Solymar, L. Stevens, C. Shamonina, E. University of Oxford Department of Engineering Science Parks Road OxfordOX1 3PJ United Kingdom

ISBN: (纸本)9798350332445

We review our recent work on unidirectional wave guiding due to interference of magnetoinductive waves. We derive selectivity rules for switchable unidirectional signal guiding and demonstrate that they are governed by dispersion relations. Expanding our analysis to diatomic structures capable of carrying both forward and backward waves, we realise a frequency-controlled switching of unidirectional wave guidance. A variety of scenarios including both 1D and 2D structures, operating either in the MHz or in the THz frequency range will be presented, with Poynting vector analysis used for visualization of unidirectional signal propagation. Our analytical model will be verified with numerical simulations (THz) and experimental data (MHz). © 2023 ieee.

关键词： Dispersion (waves)

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Assessment and Prediction of Urban Pollutants and its Influence on Human Health using Deep Learning Algorithm 9

Assessment and Prediction of Urban Pollutants and its Influe...

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9th ieee International Conference for Convergence in Technology, I2CT 2024

作者： Kavathekar, Vaishali Tripathy, Amiya Kumar Chettri, Sarat Kumar Bhanage, Vinayak Zaidansari, Mohammad Babu, Lavena Joshua, Allen Assam Don Bosco University Computer Science And Engineering Assam India Don Bosco Institute of Technology Mumbai India Don Bosco Institute of Technology Computer Engineering Mumbai India Assam Don Bosco University Computer Application Assam India Hiroshima University Advance Science And Engineering Hiroshima Japan Don Bosco Institute of Technology Information Technology Mumbai India

ISBN: (纸本)9798350394474

Mumbai, the financial center of India, is a thriving city where the urban environment is constantly changing. Degradation of the environment is a result of these changes. Recent research reveals a recurring problem, wherein pollutant levels regularly exceed advised thresholds, thus having an adverse effect on human health. The research makes use of meteorological data from four weather stations (Powai, Sion, Colaba and Borivali) that span the years 2000-2023. The chosen contaminants for study are PM2.5, PM10, and air temperature. All of the contaminants are growing exponentially, according to the study. For future prediction, deep learning techniques are used. In comparison to the other algorithms, the results show that Bi-LSTM is more accurate. Based on this model, it appears that regular places will soon become hotspots and maybe dangerous. With the use of this model, Mumbai metropolitan planners and authorities may make timely and well-informed decisions that would promote the city's sustainable growth and citizens well-being. © 2024 ieee.

关键词： data visualization

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Cloud-Based Smart Home Monitoring using ThingSpeak and ESP32 5

Cloud-Based Smart Home Monitoring using ThingSpeak and ESP32

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5th ieee International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Komitov, Nikolay Terziyska, Margarita Terziyski, Zhelyazko University of Food Technologies Information and Control Technology Dept. of Automation Plovdiv Bulgaria University of Food Technologies Physics and Information Technologies Dept. of Mathematics Plovdiv Bulgaria Trakia University Stara Dept. of Computer Science and Mathematics Zagora Bulgaria

ISBN: (纸本)9798350352863

This paper presents an approach to implementing a cloud-based smart home monitoring system using the ESP32 microcontroller and ThingSpeak as the cloud platform. The proposed system provides an efficient solution for real-time monitoring of various environmental parameters, which are collected through sensors connected to the ESP32 and transmitted wirelessly to the ThingSpeak cloud. By leveraging the Internet of Things (IoT), this system enables users to remotely monitor their home environment from anywhere using a web interface. The article discusses the hardware setup, software implementation, and data visualization techniques, offering a practical guide for those interested in smart home technology and IoT integration. © 2024 ieee.

关键词： Smart homes

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Foot Traffic Analysis Using Wi-Fi Sensor During the Tokyo 2020 Olympics and Paralympics 7

Foot Traffic Analysis Using Wi-Fi Sensor During the Tokyo 20...

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7th International Conference on Information Technology, InCIT 2023

作者： Traganmaturapot, Peerada Sonehara, Noboru Hiruma, Nobuharu Cooharojananone, Nagul Jirapongwanich, Jirakit Yodsakuntip, Kanat Atchariyachanvanich, Kanokwan Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Institute for Policy Studies Tsuda University Tokyo Japan AREAPORTAL Inc Tokyo Japan King Mongkut's Institute of Technology School of Information Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798350358698

Various real-world factors, such as time, weather, distance, environment, the COVID-19 pandemic, or even protests, can all impact human decision-making. However, restrictions and unexpected occurrences may also influence people's decisions regarding their path at any given time. These factors can lead to challenges in managing foot traffic at largescale events. In response to these challenges, this paper proposes a data-driven web-based foot traffic management supporting dashboard for large-scale events based on limited pedestrian count data, consisting of sensor name, latitude, longitude, MAC address, Datetime, and RSSI, collected by Wi-Fi sensors around the Sendagaya area during the Tokyo 2020 Olympics and Paralympics. The results confirmed that our proposed web-based dashboard contributes to human behavior understanding and decision-supporting policymaking for foot traffic management, which improves the design of spectator movement between transportation and venues in large-scale events. Furthermore, the dashboard is valuable from various perspectives, including preventing crowd crushing, redesigning areas to increase engagement in the shopping district, and improving traffic management. © 2023 ieee.

关键词： data visualization

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Exploring Social Media Trends - A Kannada dataset Analysis

Exploring Social Media Trends - A Kannada Dataset Analysis

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Dey, Amritangshu Aishwaryasri, J. Jai Surya, R. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Department of Computer Science Engineering Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

ISBN: (纸本)9798350313413

Platforms for social media are dynamic tools that help ideas spread and develop quickly. This study introduces a novel way of locating popular Kannada-language subjects on social networking sites. This study also involves data preprocessing, feature extraction, and data visualization approaches to reveal underlying patterns and insights using a sizable dataset made up of Kannada text, tweets, hashtags, and news headlines. This method efficiently incorporates both sophisticated Machine Learning models, such as N-grams and word tokenization, and Deep Learning models, including sentence transformers and U-map embeddings. The examination of coherence and silhouette scores is used to validate the models. The main goal of this research is to offer an in-depth analysis of issues that regularly come up in Kannada debates on social media. This enables organizations, researchers, and content producers to make well-informed decisions, comprehend user opinion more thoroughly, and keep up with rapidly changing technological developments. In essence, this study helps provide a thorough understanding of the constantly changing digital ecosystem. © 2023 ieee.

关键词： Bidirectional Encoder Representations from Transformers (BERT) Kannada language Latent Dirichlet Allocation (LDA) Natural Language Processing (NLP) social media Topic modelling Transformer trend

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Unraveling the Potential of Machine Learning in Lung Cancer Prediction

Unraveling the Potential of Machine Learning in Lung Cancer ...

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2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Gupta, Dakshesh Saini, Jyoti Rana, Meenakshi Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350380880

Lung cancer is a disease in which some undesired cells grow inside the lungs and as they grow older they get transferred to nearby lymph nodes and then to different parts of the body. Lung cancer is a very common disease that affects people of all ages. Its causes can be many either due to smoking or peer pressure. Cancer is a type of disease that spreads very fast in different parts of the body but cannot be easily identified. There are various researches previously done but the model on which they are trained is not giving much accurate results. There are different Machine Learning models used to predict lung cancer such as Tree, Random Forest, and Neural Network. The Neural Network was one of the supervised learning models which provided us with the most accuracy and precision. The best fit is calculated by the Area under Curve, Classification Accuracy, Precision, Fl Score, and Recall. In this discussion, it is all about various Machine Learning techniques, Orange software used for data prediction, and Tableau software used for data visualization are done. The model is trained on the dataset of 310 rows and 16 columns. The value of each columns decides how will are model behave for different unknown values. The results of the model are also shown, including its accuracy, and the results are visualized through graphs that help to predict the disease which is better than all the existing model's results. It will let the readers be able to understand lung cancer more accurately and what data to include to get the most accurate results for lung cancer prediction. © 2023 ieee.

关键词： Diseases

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Adversarial Representation Mechanism Learning for Network Embedding

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第2期35卷 1200-1213页

作者： He, Dongxiao Wang, Tao Zhai, Lu Jin, Di Yang, Liang Huang, Yuxiao Feng, Zhiyong Yu, Philip S. S. Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300131 Peoples R China George Washington Univ Data Sci Washington DC 20052 USA Univ Illinois Dept Comp Sci Chicago IL 60607 USA Tsinghua Univ Inst Data Sci Beijing Peoples R China

Network embedding which is to learn a low dimensional representation of nodes in a network has been used in many network analysis tasks. Some network embedding methods, including those based on Generative Adversarial Networks (GAN) (a promising deep learning model), have been proposed recently. Existing GAN-based methods typically use GAN to learn a Gaussian distribution as a prior for network embedding, which makes it difficult to distinguish the node representation from Gaussian distribution. It did not apply the adversarial learning strategy on the representation mechanism but just on representation results. Thus, it does not make full use of the essential advantage of GAN, and leads to compromised performance of the method. To address this problem, we propose a novel adversarial learning framework consisting of three players for network embedding, which applies the adversarial learning strategy on the representation mechanism, called Adversarial representation mechanism GAN (ArmGAN). Specifically, the first two players, named encoder and competitor, aim to learn two different representation mechanisms (i.e., two ways projecting data onto latent space). They compete with each other to improve their representation mechanisms. The third player is the discriminator, which discriminate the representation mechanism of the encoder from that of the competitor. In addition, we design a perturbation strategy to produce fake networks from the original network, and feed the fake networks to the competitor to obtain a "fake " representation mechanism. We evaluated ArmGAN on a variety of tasks including node clustering, node classification, link prediction and visualization. Moreover, we compared ArmGAN with 10 state-of-the-art methods (including DGI, which is well-known for its high accuracy) on 7 real-world networks. The experimental results show the significant superiority of ArmGAN over the existing methods.

关键词： Generative adversarial networks Gaussian distribution Topology Network topology Generators Games Task analysis Network embedding generative adversarial network graph neural network social network analysis

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Rice Disease Image Classification using MobileNetV2 Pretrained Model with Attention visualization using Gradient-weighted Class Activation Mapping (Grad-CAM) 6

Rice Disease Image Classification using MobileNetV2 Pretrain...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Fiqri, Fahmi Noor Setyaningsih, Sri Saepulrohman, Asep Ipb University Faculty of Science and Mathematics Department of Computer Science Bogor Indonesia Pakuan University Faculty of Science and Mathematics Department of Computer Science Bogor Indonesia

ISBN: (纸本)9798350315639

Rice is one of the staple foods in Asia, particularly in Indonesia. With an ever-increasing demand of food, a stable rice production is mandatory and pest is one of the major challenges faced in sustainable rice production. In this research, we proposed an image classification model based on the MobileNetV2 pretrained model combined with a visual explanation based on the gradient-weighted class activation (Grad-CAM) algorithm to build a robust and accurate classification of rice diseases. The model is based on convolutional neural network (CNN) architecture. First, transfer learning is done from the MobileNetV2 pretrained model to create the classification model, followed by Grad-CAM to produce the visual explanation of the CNN. Finally, the model is trained on 7,077 rice images containing four different diseases (bacterial blight, blast, brown spot, and tungro) with a data augmentation process to increase the dataset's overall variance. This process yields a model with a classification accuracy of up to 99,9%, combined with visual feature explanation making this model a robust and efficient classification model. © 2023 ieee.

关键词： Convolutional neural networks

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Architecture and Implementation of High-Fidelity Digital Twins for Industrial Robots

Architecture and Implementation of High-Fidelity Digital Twi...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： Liu, Yongkui Wang, Xinyu Yang, Kang Pan, Yaduo Wang, Qianji Xidian University School of Mechano-Electronic Engineering Xian China Lanzhou Jiaotong University School of Mechanical Engineering Lanzhou China Wuhan University of Science and Technology School of Information Science and Engineering Wuhan China

ISBN: (纸本)9798350301113

Digital twin is an important technology emerging in recent years. Industrial robots are an important type of equipment for the manufacturing industry. Construction of digital twins for industrial robots is of great significance. Fidelity of digital twins of robots is important as it determines their usability in manufacturing. In order to enable transparent management and intelligent applications of industrial robots, this paper proposes an architecture and a method of building high-fidelity digital twins for robots. First, a high-fidelity model of industrial robots is proposed. Then, logical architecture and functional architecture of the high-fidelity digital twins of industrial robots are proposed. The logical architecture comprises physical robot, virtual robot as well as communication and interaction between them. The functional architecture consists of five layers, including equipment layer, transmission layer, digital twin layer, application layer, and industry layer. Finally, based on the model and architecture, and taking the SD3/500 industrial robot as an example, a digital twin system is developed, which implements functions of virtual-real synchronization, real-time monitoring, kinematic simulation, data visualization, and real-time finite element analysis. The digital twin system verifies the feasibility of the proposed model and architecture. © 2023 ieee.

关键词： Kinematics

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