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International Research Conference on Smart computing and Systems Engineering (SCSE)

作者： Hmkt Gunawardhana Btgs Kumara Kapila Rathnayake Prasad M. Jayaweera Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Department of Computer Science Faculty of Applied Sciences University of Sri Jayawardanapura Sri Lanka

For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study’s findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces.

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Performance of heat transfer in a porous square cavity filled with TiO2–H2O nanofluid by considering the impacts of magnetic field and heat absorption/generation with shape effects

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International Journal of Ambient Energy 2025年第1期46卷

作者： Santhosh, N. Subramanyam Reddy, A. Sivaraj, R. Rushi Kumar, B. Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Vellore India Department of Mathematics and Statistics School of Applied Science and Humanities Vignan's Foundation for Science Technology and Research Andhra Pradesh Guntur India Department of Mathematics and Computing Dr B R Ambedkar National Institute of Technology Punjab Jalandhar India

This research aims to optimise the fluid flow and heat transfer features of a porous square cavity filled with (Formula presented.) (titanium oxide-water) nanoliquid under the influence of magnetic field and the heat source or sink. The non-dimensional version of partial differential equations is solved using the marker-and-cell technique (MAC) and the finite difference methodology. The left and right walls are adiabatic. The top wall is cooled, and the bottom wall is heated completely. Spherical (Formula presented.) and non-spherical shapes (hexahedron, tetrahedron, column and lamina) nanoparticles are utilised to examine the optimal heat transference and fluid circulations within the enclosure. The impacts are graphically visualised and discussed with the following parameters nanoparticles volume fraction (Formula presented.), heat source or sink (Q), Darcy number (Da), Rayleigh number (Ra), and Hartmann number (Ha). Of the four non-spherical configurations of nanoparticles, lamina nanoparticles provide the best thermal transference and fluid velocity inside the cavity. The average heat transfer rate improved by (Formula presented.) when (Formula presented.) nanoparticles were added to the base fluid. The maximum heat transmission rate is provided by nanoparticles that are lamina-shaped. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Rayleigh number

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Support Vector Machine for Multiclass Classification of Redundant Instances

Support Vector Machine for Multiclass Classification of Redu...

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Intelligent computing and Networking - Proceedings of IC-ICN 2023

作者： Surbakti, Herison Kumar, Athmakuri Naveen Tara, Saikumar Keshta, Ismail khare, Amit Prasad, G.N.R. Faculty of Computing and Digital Technology HELP University Kuala Lumpur Malaysia Senior Software Engineer Glosoft Technologies Pvt Ltd Chennai India Department of ECE BVRIT Hyderabad College of Engineering for Women Hyderabad India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Technology JIET Institute of Design and Technology Rajasthan Jodhpur India Telangana Hyderabad500091 India

ISBN: (纸本)9789819931767

In recent years, support vector machine has become one of the most important classification techniques in pattern recognition, machine learning, and data mining due to its superior classification effect and solid theoretical base. However, its training time will increase dramatically as the number of samples increases, and training will become more sophisticated when dealing with problems involving multiple classifications. A quick training data reduction approach MOIS appropriate for multi-classification tasks is presented as a solution for the aforementioned issues. While eliminating redundant training samples, the boundary samples that play a vital role are chosen in order to considerably reduce training data and the problem of unequal distribution between categories. The experimental results demonstrate that MOIS may maintain or even improve the classification performance of support vector machines while substantially enhancing training efficiency. On the Opt digit dataset, the suggested method improves classification accuracy from 98.94% to 99.05%, while training time is reduced to 15% of the original;in HCL2000, the proposed method improves classification accuracy from 98.94% to 99.05%. When the accuracy rate is marginally increased (from 99.29% to 99.30%) on the first 100 categories dataset, the training time is dramatically reduced to less than 6% of the original. Additionally, MOIS has a high operational efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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FedHZone: Federated Learning Based Hazardous Zone Detection Using Internet of Drone Things

FedHZone: Federated Learning Based Hazardous Zone Detection ...

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2024 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Biswas, Payel De, Debashis Obaidat, Mohammad S. Hazra, Arnab Sadoun, Balqies Jogesh Chandra Chaudhuri College Department of Computer Science Kolkata India Maulana Abul Kalam Azad University of Technology Centre of Mobile Cloud Computing Department of Computer Science and Engineering West Bengal WB Nadia India School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing SRM University Department of Computational Intelligence SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Dr. Sudhir Chandra sur Institute of Technology & Sports Complex Department of Computer Science and Engineering Kolkata India College of Engineering Al Balqa ' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Air pollution can affect human health, so it is necessary to predict the air quality index (AQI) in advance. In this work, air quality data collected by the Internet of Drone Things (IoDT) is predicted and analyzed to detect highly polluted zones using a distributed privacy-preserving federated learning (FL) approach. Air quality data for a particular zone is collected and then fed into a Long short-Term memory (LSTM) based model to predict the pollution level in local training. Global aggregation is done by an IoDT-mounted central server to aggregate the received models from the local IoDT level. Privacy-preserving FedHZone predicts and analyses the AQI values from the past time series dataset in a multi-drone environment. This work detects the most hazardous zone in a larger region of Jaipur, Rajasthan using real-Time data. The result shows FedHZone with stacked LSTM having ''2%-7% better accuracy than the vanilla LSTM-based model. © 2024 IEEE.

关键词： Federated learning

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Quantum synchronization and dissipative quantum sensing

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Physical Review A 2025年第1期111卷 012410-012410页

作者： Gaurav M. Vaidya Simon B. Jäger Athreya Shankar Department of Physics Indian Institute of Technology Madras Chennai 600036 India Physics Department and Research Center OPTIMAS University of Kaiserslautern-Landau D-67663 Kaiserslautern Germany Department of Instrumentation and Applied Physics Indian Institute of Science Bangalore 560012 India Center for Quantum Information Communication and Computing Indian Institute of Technology Madras Chennai 600036 India

We study the phenomenon of quantum synchronization from the viewpoint of quantum metrology. By interpreting quantum self-sustained oscillators as dissipative quantum sensors, we develop a framework to characterize several aspects of quantum synchronization. We show that the quantum Fisher information (QFI) serves as a system-agnostic measure of quantum synchronization that also carries a clear operational meaning, viz., it quantifies the precision with which the amplitude of a weak synchronizing drive can be measured. We extend our analysis to study many-body oscillators subjected to multiple drives. We show how the QFI matrix can be used to determine the optimal drive that maximizes quantum synchronization, and also to quantitatively differentiate the synchronization responses induced by different drives. Our work highlights multiple connections between quantum synchronization and quantum metrology, paving a route toward finding quantum technological applications of quantum synchronization.

关键词： Fisher information matrix

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Smart Traffic Control: Adaptive Signal Management Based on Real-time Lane Detection using YOLOv8

Smart Traffic Control: Adaptive Signal Management Based on R...

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Automation and Computation (AUTOCOM), International Conference on

作者： M. Ravichandran Kumar Laxmikant Annamalai Muthu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electrical and Electronics Engineering University of Technology & Applied Science Nizwa Oman

As cities grow, handling traffic in big urban are-as becomes a huge proble-m. More cars on the road and not enough roads le-ad to heavy traffic jams. This increases trave-l time and harms our environment. Our study tackle-s these problems with a ne-w approach. We use real-time- lane detection with YOLOv8 and adaptable- traffic lights. Using sharp computer vision, our system pinpoints vehicle-s. It adjusts traffic light timings on-the-go to improve traffic flow. Our model works e-xtremely accurately. During the- learning phase, it achieve-d a mAP50 score of 99.3% and a mAP50-95 score of 87.4%. In the te-sting phase, it got a mAP score of 99.2% and a mAP50-95 score of 86.2%. The re-sults highlight how the system can improve city trave-l. It’s valuable for city planners and traffic officials. It helps the-m understand smart transportation systems bette-r.

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Cooperative Neural Synchronization between Mothers and Children: A Functional Near-Infrared Spectroscopy Hyperscanning Investigation

SSRN

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SSRN 2024年

作者： Asiry, Othman Abdullah Eltoukhy, Mohamed Meselhy Faye, Ibrahima Department of Information Technology College of Computing and Information Technology University of Jeddah Khulais Jeddah21959 Saudi Arabia Department of Fundamental and Applied Sciences University Teknologi PETRONAS Seri Iskandar Perak32610 Malaysia

This study delves into how the brains of mothers and children synchronize during collaborative and individual tasks, aiming to shed light on the neurological underpinnings of social interaction. By employing hyperscanning techniques, we gathered data to understand how brain activity changes when people work together compared to working alone. Our focus was on exploring how the brains of mothers and children interact during problem-solving tasks, both when they collaborate and when they work independently. Using robust statistical analyses like ANOVAs and T-tests, we identified specific brain regions involved in these tasks. Our results, depicted through tables and figures, demonstrate that cooperative problem-solving elicits stronger neuronal synchronization compared to individual efforts. We consistently observed significant effects in certain brain regions, indicating where collaboration had a greater impact on brain activity. These findings suggest that collaborative tasks activate distinct brain regions more intensely than individual tasks, providing insights into the cognitive mechanisms behind cooperative behavior. This study lays the foundation for further research into how to foster cooperative interactions in therapeutic and educational settings, leveraging our understanding of brain activity patterns during social tasks. © 2024, The Authors. All rights reserved.

关键词： Brain

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A Survey on Usage of Multimedia Databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance

A Survey on Usage of Multimedia Databases for Emotion Elicit...

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Proceedings of the International Convention MIPRO

作者： M. Horvat P. Jerčić Department of Applied Computing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors’ intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers’ opinions and experiences during that time. The complete aggregated results are publicly available at https://***/mhorvat/stimdbsurvey.

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FreqRISE: Explaining time series using frequency masking

arXiv

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arXiv 2024年

作者： Brüsch, Thea Wickstrøm, Kristoffer Knutsen Schmidt, Mikkel N. Alstrøm, Tommy Sonne Jenssen, Robert Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Physics and Technology UiT The Arctic University of Norway Norway Norwegian Computing Center Oslo Norway

Time-series data are fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. Current methods for obtaining saliency maps assume localized information in the raw input space. In this paper, we argue that the salient information of a number of time series is more likely to be localized in the frequency domain. We propose FreqRISE, which uses masking-based methods to produce explanations in the frequency and time-frequency domain, and outperforms strong baselines across a number of tasks. The source code is available here: https://***/theabrusch/FreqRISE © 2024, CC BY.

关键词： Time series

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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