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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Chaudhari, Akhilesh Khobragade, Anish Ghumbre, Shashikant College of Engineering Pune Dept. of Computer Engineering and It Pune India Govt College of Engi and Research Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Knowledge graphs (KG) represent information in the form of graphs. Because heterogeneous and collaborative designs remain incomplete employing missing links between the entity pairs. KG embedding paradigm tries to reduce incompleteness by representing graphs in vector space. Attention-based learning provides the natural way of KG representation learning by deriving attention to each central entity from its neighbors. The attention mechanism decides which part to attend more to collect the most important information. This paper analyzes the attention-based models used to learn KG latent structure and is utilized to predict the missing link for KG completion. Many of them are neural network-based methods. Experimental results show that DisenKGAT and HOLE outperform rest methods in standard evaluation metrics on the two benchmark FB15k-237 and WN18RR datasets respectively. © 2023 IEEE.

关键词： embedding graph attention mechanism Knowledge Graph representation learning scoring function

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A HIGH-PRECISION 3D LASER MEASUREMENT METHOD BASED ON ESTIMATION OF TRUE VALUES BY HIGH-DENSITY MEASUREMENTS AND CALCULATION OF DIFFERENCES BETWEEN MEASUREMENT DATA OF TWO TIMES

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AIJ Journal of Technology and Design 2022年第68期28卷 515-520页

作者： Kakizaki, Ken'ichi Mukai, Tomohisa Araki, Shunsuke Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Dept. of Structural Engineering Building Research Institute

3D laser measurement has a problem that the measurement precision is not sufficient for damage analysis of buildings. In addition, when there is an unevenness in the measurement target, it is difficult to distinguish whether it is an initial shape or damage. In this paper, we propose a method to reduce the measurement error by processing the high-density laser measurement data with statistical methods. In addition, we propose a method to detect the damaged area by comparing the initial shape data of the measured object with the newly measured data. © 2022 Architectural Institute of Japan. All rights reserved.

关键词： Measurement errors

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Insight Now: A Cross-Platform News Application for Real-Time and Personalized News Aggregation 56

Insight Now: A Cross-Platform News Application for Real-Time...

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56th International Carnahan Conference on Security Technology, ICCST 2023

作者： Mohite, Sagar Jadhav, Swati Aggarwal, Aayush Shukla, Ananya Jain, Devashish Jaiswal, Simran College of Engineering Dept. of Computer Engineering Pune India BRACT's Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350315875

In an era characterized by rapid technological advancements, the ability to communicate instantly with others has become an integral part of our daily lives. With the ubiquity of mobile devices, tablets, and laptops, staying informed about the latest events and news has never been easier. This research article explores the development and impact of an Android news application that connects users to the latest news from over 120 reputable news organizations across India. The primary objective of this innovative application is to amalgamate global news sources and present information to users in an engaging and visually compelling format with minimal delay. One of the distinguishing features of this Android news app is its user-centric approach, allowing individuals to filter live news based on their preferences. Upon selecting a news article, users are seamlessly redirected to the corresponding news source, ensuring the integrity of the original reporting. The app is designed to deliver frequent updates on a wide range of topics, leveraging the capabilities of the NEWS API to source information from renowned news outlets worldwide, including BBC, CNN, The Guardian, and NDTV. This research anticipates a substantial increase in the speed at which information reaches the average person, thanks to the seamless integration of multiple news sources through this Android news application. This technological advancement promises to revolutionize the way individual's access and consume news, ensuring they stay well-informed in an increasingly fast-paced world. © 2023 IEEE.

关键词： Application programming interfaces (API)

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Analysis of Perturb and Observe MPPT under Varying PV Operating Climatic Conditions 21

Analysis of Perturb and Observe MPPT under Varying PV Operat...

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21st International Multi-Conference on systems, Signals and Devices, SSD 2024

作者： Addawe, Abdirahim Mahmood, Musaria K. Karim, Sulaiman M. Nezam, Ahmad M. Ankara Yildirim Beyazit University Dept. of Energy Systems Engineering Ankara Turkey Jannat Iraq College Dept. of Medical Instr. Tech. Eng. Ramadi Iraq Al-Hadba'a University College Dept. of Computer Technology Engineering Mosul Iraq Karabuk Üniversitesi Dept. of Computer Engineering Karabuk Turkey

ISBN: (纸本)9798350374131

Improving the efficiency of photovoltaic (PV) sys-tems has emerged as a critical priority. Achieving optimal power production from solar modules requires operating at maximum power point (MPP) under various climatic conditions. This paper describes the operation of the Perturb and Observe (P&O) as an efficient Maximum Power-Point Tracking (MPPT) algorithm. Simulations of various operating scenarios are performed using MATLAB to assess the algorithm efficiency under both Standard Test Conditions (STC) and Partial Shading Conditions (PSC). This study considers factors such as output power efficiency, time response, and steady-state power oscillation. The simulation results show a fast-tracking, lower oscillation in the case of STC but fail to track the MPP in the case of multi-panel under PSC. © 2024 IEEE.

关键词： MATLAB

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A Sequential Pattern Mining Approach for Situation-Aware Human Activity Projection

A Sequential Pattern Mining Approach for Situation-Aware Hum...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： D'Aniello, Giuseppe Falcone, Roberto Gaeta, Matteo Rehman, Zia Ur Fortino, Giancarlo University of Salerno Dept. Information and Electrical Engineering and Applied Mathematics SA Fisciano Italy University of Calabria Dept. Computer Engineering Modeling Electronics and Systems Cs Rende Italy

ISBN: (纸本)9781665410205

Human activity prediction has become increasingly prevalent in a plethora of time-critical applications. To realize accurate identification and prediction of human behaviour, we propose a situation-aware wearable computing system. A wearable computing system has the capability to perceive, comprehend and project situations by analyzing the human behavioral patterns in different environments. In particular, this work proposes a situation-aware human activity prediction (SA-HAP) approach based on sequential pattern mining that aims to anticipate future activities and tailor its responses according to situations by analyzing frequent sequential patterns and their correlations to understand how these situations are interrelated. The approach not only improves prediction accuracy but also provide the foundation for a more informed decision-making process, as the projected situations can be explained using the identified behavioral patterns. The approach is compared with other traditional techniques for activity prediction (LSTM and HMM), achieving better performance on the Extrasensory dataset. © 2024 IEEE.

关键词： Prediction models

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Flood Detection from Satellite Images Using Self-Attention Empowered EfficientNetV2 Mechanism 7

Flood Detection from Satellite Images Using Self-Attention E...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Selva Jeba, G. Chitra, P. Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India Thiagarajar College of Engineering Dept. of Computer Applications Madurai India

ISBN: (纸本)9798350304268

Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classifying images. As a result, the utilization of deep learning in remote sensing, especially for the crucial task of flood detection, has become an increasingly prominent and promising area of research and application. This study proposes a lightweight yet powerful feature extraction framework by leveraging a self-attention(SA) based EfficientNetV2. The EfficientNetV2 architecture serves as the backbone of the model and has exhibited effectiveness in image classification tasks. By incorporating self-attention mechanisms, the model can capture spatial relationships within remote sensing images, enhancing its accuracy and efficiency in flood detection. The model was trained and assessed using the SEN12-FLOOD dataset, and the outcomes demonstrate that the suggested framework exhibits enhanced flood detection performance, achieving an accuracy of 95.9%. © 2023 IEEE.

关键词： attention mechanism deep learning EfficientNetV2 flood detection remote sensing

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Acoustic data acquisition and integration for semantic organization of sentimental data and analysis in a PWSN

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Multimedia Tools and Applications 2024年 1-23页

作者： Das, Sushovan Mondal, Uttam Kr. Dept. of CSE College of Engineering and Mgmt WB Kolaghat721171 India Dept. of Computer Science Vidyasagar University WB Midnapur721102 India

In an acoustic pervasive wireless sensor network (PWSN), the BASE station plays a vital role in gathering and integrating acoustic sensor data from various nodes, including end and router devices tracking time-driven events. The semantic BASE station is crucial in the IoT landscape as it consolidates data from these networks, enabling thorough sentiment analysis of acoustic signals and yielding insights across domains. A semantic processor at the BASE station is essential for an energy-efficient and intelligent PWSN, managing data collection, integration, signal feature extraction, and publication for model training and sentiment analysis. This paper introduces a novel approach to designing a semantic BASE station, focusing on ontology generation, evaluation, and updates to bolster pervasive wireless sensors in capturing and depicting events and time through an ontological framework. The study addresses challenges in efficiently gathering, integrating, and processing acoustic data from pervasive nodes, proposing a semantic processor at the BASE station to enhance feature extraction and metadata publication. The semantic organization of feature-extracted labeled metadata enables the analysis of comprehensive machine learning (ML) applications such as sentiment analysis, type detection, and environment detection by generating confusion matrix. Evaluation includes performance metrics (NEEN, LSNS, BDAS) as well as accuracy, precision, sensitivity, and specificity for sentimental data analysis to validate the proposed technique’s efficacy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sensor nodes

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MoFLeuR: Motion-based Federated Learning Gesture Recognition 4

MoFLeuR: Motion-based Federated Learning Gesture Recognition

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4th IEEE International Conference on Human-Machine systems, ICHMS 2024

作者： Jamal Seyedmohammadi, S. Sheikholeslami, S. Mohammad Abouei, Jamshid Mohammadi, Arash Plataniotis, Konstantinos N. Concordia University Inst. Of Information Systems Engineering Montreal Canada University Of Toronto Dept. Of Electrical And Computer Engineering Toronto Canada Yazd University Dept. Of Electrical Engineering Yazd Iran

ISBN: (纸本)9798350315790

Most of the recent works on Human Gesture Recognition (HGR) using motion data rely on gathering a dataset, that faces two major challenges: a) the datasets are originally stored on the smart devices at the end-users, and gathering them in one place is not feasible due to communication limitations, and b) clients are reluctant to share their private data with a central server due to privacy concerns. In this paper, we address these issues and propose a privacy-preserving framework based on Federated Learning (FL) for HGR using motion data. Furthermore, we consider data heterogeneity which have destructive effects on the performance of the global model. Accordingly, we propose a communication and computation-efficient client selection method, mitigating the impact of data heterogeneity in the FL process. In the proposed framework, clients are not requested to share sensitive information about their local datasets with the edge server. Simulation results show that the proposed MoFLeuR algorithm improves the performance of the global model in the presence of different degrees of data heterogeneity, and it outperforms the baseline algorithms in terms of different metrics, namely accuracy, convergence speed, and communication and computation efficiency. © 2024 IEEE.

关键词： Internet of things

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

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17th IEEE International Conference on Advanced Networks and Telecommunications systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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UAV-based Firefighting by Multi-agent Reinforcement Learning 13

UAV-based Firefighting by Multi-agent Reinforcement Learning

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Tanha, Reza Shami Hooshmand, Mohsen Afsharchi, Mohsen Dept. Computer Science and It Zanjan Iran University of Zanjan Dept. Electrical and Computer Engineering Zanjan Iran

ISBN: (纸本)9798350330151

Wildfire in the forest leads to a huge amount of financial and human losses. That is it causes damage to the forest and the life of firefighters. To reduce the amount of such damage, unmanned aerial vehicles are among the best options to do the job of firefighting. Their orchestration of effective fire extinguishing is a must and essential. This work proposes a multi-agent reinforcement learning method using an actor-critic structure to extinguish the fire. While the learning is centralized, their execution is decentralized. Moreover, we define two models of fire spreading based on the discrete-time Markov process. The results show that the proposed method outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： Reinforcement learning

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