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7th international conference on Smart Trends in Computing and Communications, SmartCom 2023

7th International Conference on Smart Trends in Computing an...

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7th international conference on Smart Trends in Computing and Communications, SmartCom 2023

ISBN: (纸本)9789819908370

the proceedings contain 144 papers. the special focus in this conference is on Smart Trends in Computing and Communications. the topics include: Semi-Supervised Medical Image Segmentation on Data from Different Distributions;profiling Network Traffic by Using Classification Techniques in Machine Learning;identification of Cracks in High-Rise Buildings Using Drones and Deep Learning;wine Quality Prediction Using Machine Learning Techniques;wearable Technology with Location Tracking, Health Monitoring, and Attendance Tracking for Employees;analysis of Path Loss of Wireless Channel with Hata Model;a Study on Cross Site Scripting Attacks;Solving Text-to-SQL Task through Machine Translation;Deep Learning Framework for Liver CT Image Segmentation and Risk Prediction;evolution, Trends, and Future Developments of Business Intelligence;Face Recognition Using Convolutional Neural Network (CNN);Design and Development of a Cross-Platform Application: MSCW App;improving Naturalness in Speech Synthesis Using Fuzzy Logic;online Handwriting Recognition in Multiple Languages;playful thinking as a Strategy to Assess Mathematical Skills in Primary School;anomaly Detection in Medical IoT Devices Using Federated Learning;comparative Analysis of Image Augmentation and Data Deduplication Techniques;Identification of Spoofing URLs Using Hybrid algorithms;Role of PRI Functionaries in Adaptation of e-Governance at Gram Panchayat: A Study of Two States;a Discrete Firefly Algorithm Applied to Structural Bridge Truss Optimization;a Secure Protocol for Authentication and Data Storage for Healthcare System;implementation of Real Time Face Mask Recognition Technique Using Keras, TensorFlow;Factors Affecting the Integration of Educational Technologies in Academic Delivery by Management Teachers During COVID-19 Times;face Recognition at Various Angles.

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A Pedestrian Detection Method for Highways Based on Lightweight FCOS and Hybrid Feature Pyramid Networks

A Pedestrian Detection Method for Highways Based on Lightwei...

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Robotics, Control and Automation Engineering (RCAE), international conference on

作者： ZhiHeng Cai WeiMing Wang School of Computer Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

With the rapid development of the transportation industry, pedestrian detection algorithms on highway roads have become an important research direction. To solve the problem of a large number of model parameters and a large amount of calculation, this paper proposes a road pedestrian detection method based on lightweight FCOS. In this method, the hybrid feature pyramid networks(HyFpn) is introduced by improving the neck feature pyramid networks, and the lighter mobilenetV3 is used as the backbone network. Experimental results show that the proposed method significantly reduces the size and computational complexity of the model while maintaining high detection accuracy, which is conducive to real-time pedestrian detection on resource-constrained equipment. Compared with the existing methods, the proposed method achieves a better balance between accuracy and speed.

关键词： Training Pedestrians Accuracy Computational modeling Roads Transportation industry Predictive models Feature extraction Mobile handsets Computational complexity

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7th international conference on Computing inEngineering and Technology, ICCET 2022

7th International Conference on Computing inEngineering and ...

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7th international conference on Computing in Engineering and Technology, ICCET 2022

ISBN: (纸本)9781839537042

the proceedings contain 65 papers. the topics discussed include: enhancing sentiment analysis performance with blend of features and deep learning algorithms;a deep learning based approach for mask detection;breast cancer detection using optimized hidden convolutional neural network;plant disease detection and management using deep learning approach;remote sensing image fusion using machine learning and deep learning: a systematic review;smart tour advisor using machine learning and natural language processing;image analysis using convolutional neural network to detect bird species;secure image watermarking using MLDWT and SVD for secure medical data transmission model in healthcare systems;and automatic detection of tuberculosis with the help of image processing and machine learning classifier.

关键词： Image fusion

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Fast Clustering for Redundancy Removal in Audio Anomaly Detection 7

Fast Clustering for Redundancy Removal in Audio Anomaly Dete...

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7th international conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Hong, Renhang Zhou, Yang Shi, Shenghe Zhang, Bo Nankai University College of Software Tianjin China Chongqing Datang International Pengshu Hydropower Development Co. Ltd Chongqing China

ISBN: (纸本)9781665487115

the objective of this work is to detect anomalies of a group of hydro-Turbines and electric generators of a power plant based on their audio signals when they are running. Autoencoders are utilized for this task. In this approach, neural networks learn the time-frequency distribution of normal audio signals, and they are then used to predict the normal components of testing signals. If spectral features are used, the residuals produced by subtracting the predictions from the inputs can be regarded as estimations of anomaly signals in the inputs, and their energies can be used as the basis to make the normal/abnormal decision. When the approach was used in our project, we encountered a problem of large but imbalance training data. the training data has quite large a size(about 12,890 hours) but is highly redundant(caused by the fact that at most time devices run in stationary states). To make the training feasible, we propose a new clustering algorithm which is specially designed to quickly remove redundant data in the large database. the algorithm is significantly faster than the conventional k-means algorithm or its improved variants. the redundant data of the training database was removed by the algorithm. Autoencoders trained on the shrinked database can detect all the real anomalies recorded in the past months. When tested on a database of simulated anomalies, the average precision for the ten devices is 96.9 percent, showing the effectiveness of the algorithm. © 2022 IEEE.

关键词： Training Time-frequency analysis Databases Redundancy Clustering algorithms Training data Detectors

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A Tool for Simulating Demand Responsive Transport Systems in SUMO 7

A Tool for Simulating Demand Responsive Transport Systems in...

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7th international conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Armellini, Maria Giuliana German Aerosp Ctr DLR Inst Transportat Syst Berlin Germany

ISBN: (纸本)9781728189956

Demand responsive transport (DRT) has become very important in the last years and is being tested in different cities worldwide. Due to the complexity and diversity of these services, their planning is quite challenging. this paper introduces a tool to simulate different types of static DRT services, using the open source micro-simulation Eclipse SUMO (Simulation of Urban MObility) as a framework. the tool was written in Python and calculates the best routes for each DRT vehicle based on information about requests, vehicle fleet and network. As test case, multiple requests between a peri-urban area and the city center of Brunswick (Germany) were simulated for different DRT services. the results show the practicality of the tool as a form of analysis and planning of DRT systems.

关键词： Urban areas Tools Planning complexity theory Intelligent transportation systems

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Artificial Intelligence Based Predictive Analytics for Website Performance Optimization 7

Artificial Intelligence Based Predictive Analytics for Websi...

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7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mathur, Sandeep Hasan, Yazdani Bhargava, Deepshikha Bhattacharjee, Subhangee Rana, Ajay Amity University Uttar Pradesh Greater Noida India Noida International University Uttar Pradesh Greater Noida India Gisma University of Applied Sciences Potsdam Germany

ISBN: (纸本)9798350350067

In the contemporary digital landscape, the efficacy of websites as vital platforms for businesses cannot be overstated. they serve as primary interfaces for interaction with customers, facilitating transactions, brand representation, and engagement. However, optimizing website performance to ensure maximal user engagement and satisfaction presents a formidable challenge. Traditional methods of website optimization often rely on retrospective analysis of Past data, which might not fully convey the subtle dynamics of user behaviour and market trends. this paper poses an innovative approach to address this challenge by integrating AI (Artificial Intelligence) - based predictive analytics into website optimization strategies. With the utilization of machine learning algorithms, our approach aims to analyse extensive datasets encompassing user interactions, website metrics, and external market dynamics. By leveraging predictive analytics, we anticipate user behaviour, predict future trends, and create data-driven decisions for the optimization of website content, layout, and features in real-time. this proactive method allows organizations to stay ahead of the curve, anticipate user needs, and deliver personalized experiences tailored to individual preferences and behaviours. the effectiveness of our approach is demonstrated through a series of experiments, showcasing improvements in key website performance metrics such as user engagement, conversion rates, and overall effectiveness. By empowering organizations to anticipate user behaviour and adapt their strategies accordingly, our method holds promise for revolutionizing website management practices in the digital age. © 2024 IEEE.

关键词： Customer satisfaction

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Multi-Objective Evolutionary Algorithm for Construction Branched Pipe Routing

Multi-Objective Evolutionary Algorithm for Construction Bran...

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IEEE international conference on Information Communication and Signal Processing (ICICSP)

作者： Yizhe Liu Weize Sun Moufei Cai Garfield Xianzhang Guan Pui-Lam Ng Dept. of Electronics and Information Engineering Shenzhen University Shenzhen China Dept. of G&K CONSULTANCY Ltd. Hong Kong China Dept. of Civil Engineering The University of Hong Kong China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Due to the complexity of layout spaces in constructions, the arrangement of pipelines is a laborious task. However, the majority of existing pipeline layout algorithms, which are based on objective weighting, primarily focus on single pipeline layout. Moreover, in practical engineering scenarios, branched pipelines outnumber single pipelines by a considerable margin. therefore, this paper proposes a method based on an improved A* algorithm and the NSGA-II algorithm to solve the problem of branched pipeline layout. First, mathematical models of layout space and optimization algorithms are established, and optimization objectives are defined. Second, the key technologies of the algorithm are detailed, including the improved A* algorithm, two different genetic crossover strategies, and a strategy for preserving population diversity, aimed at enhancing the algorithm's optimization capability. third, a method to optimize the overall overlap of branch pipelines is proposed. Finally, the effectiveness and efficiency of this method are verified through experiments.

关键词： Pipelines Layout Signal processing algorithms Evolutionary computation Signal processing Genetics Routing Mathematical models complexity theory Optimization

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An Overlapping Community Discovery Algorithm Based on Cycle Structure and Label Optimization

An Overlapping Community Discovery Algorithm Based on Cycle ...

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international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Miaomiao Liu WenQing Zhang Jingfeng Guo Yuchen Liu School of Computer and Information Technology Northeast Petroleum University Daqing Heilongjiang China College of Information Science and Engineering Yanshan University Qinhuangdao Hebei China

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

An overlapping community discovery algorithm integrating cycle structure and label optimization strategies is proposed. Initially, the node superiority index is defined, and the seed node set is selected based on a threshold. then labels of non-seed nodes are updated through node similarity to decrease the number of initial labels, which helps enhance the stability of the algorithm and reduce computational complexity. Furthermore, based on cycle structure, an initial label updating sequence is generated from the descending order of node cycle ratios, subsequently combining node importance and node similarity to define label updating rules. Moreover, in cases where indicators are identical, the quantity of shared cycles is used to determine the category of labels, thereby increasing the accuracy of community partitioning. Experiments indicate that compared with five other algorithms, the proposed algorithm demonstrates highest up to 64.1% and 41.5% improvement in EQ and NMI.

关键词： Accuracy Stability criteria Stars Big Data Partitioning algorithms Indexes Computational complexity

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ICMSSP 2022 - 2022 7th international conference on Multimedia Systems and Signal Processing

ICMSSP 2022 - 2022 7th International Conference on Multimedi...

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7th international conference on Multimedia Systems and Signal Processing, ICMSSP 2022

ISBN: (纸本)9781450396424

the proceedings contain 15 papers. the topics discussed include: application of color filtering and image filtering algorithms in circuit diagnosis of smart meter box;research on brick carving based on 3D digital modeling technology — take the example of Linxia brick sculpture art ‘River and Mountain’;improving identity-relevant deepfake video detection in real-world with adversarial data augmentation;land evolution analysis of Waisanding sandbar using Google Earth;research on course clustering based on semantic;application study of mobile technology assisted translation teaching mode in applied universities;on the influence and application of artificial intelligence on China’s commercial sports industry — empirical analysis based on artificial intelligence coupling model;efficiency enhancement and mode change of intelligent media communication under the interaction of artificial intelligence technology;and on the mechanism of artificial intelligence affecting sports tourism industry.

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IoT Revolutionization using Federated Learning and Decentralized AI on Edge Devices 7

IoT Revolutionization using Federated Learning and Decentral...

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7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Chaudhary, Diwakar Verma, S.K. Pundir, Sumit Ranganathan, S. Umadevi, V. Srinivasan, K. Mangalmay Institute of Management & Technology Department of Management Uttar Pradesh Greater Noida India Noida International University School of Business Management Uttar Pradesh Greater Noida India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Affiliated to Bharathidasan University Department of Computer Science Tamil Nadu Trichy India Saveetha College of Liberal Arts and Sciences Simats Department of Commerce General Tamil Nadu Chennai India

ISBN: (纸本)9798350350067

this research proposes a novel strategy for addressing the limitations of centralized architectures in IoT data processing. Traditional systems experience significant bandwidth use, privacy difficulties, and scalability issues. To address these difficulties, the proposed approach combines Federated Learning (FL) with Decentralized AI on Edge Devices (DAI-ED). By decentralizing processing duties and utilizing FL methods, data is kept local to edge devices, lowering latency and improving privacy. Deployment entails choosing AI-capable edge devices and configuring them for seamless integration. Secure communication techniques provide privacy throughout FL operations. Model optimization approaches help to improve efficiency even further. Compared to centralized systems, the results show higher model accuracy, decreased communication overhead, and increased resource utilization. the proposed system has an overall accuracy of 95%, with edge device accuracy at 94.5% and central server accuracy at 96%. Communication overhead during the training and inference phases is greatly minimized, with the proposed system requiring 150 MB per device against 400 MB in centralized systems. the proposed system has low memory consumption and computational complexity, but it is very scalable. © 2024 IEEE.

关键词： Federated learning

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