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2024 IEEE Sustainable Smart Lighting World Conference and Expo, LS24 2024

作者： Mahmoudi, Ali Ozcelebi, Tanir Meratnia, Nirvana Eindhoven University of Technology Math. and Computer Science Dept. Eindhoven Netherlands

ISBN: (纸本)9798331540197

Recognizing human activities is necessary to achieve fully context-aware lighting in a smart home. Ambient sensors suit this task as they are ubiquitous, non-intrusive, and non-obstructive. However, each home has a unique floor plan and a unique number and placement of these sensors. This leads to heterogeneity of input data spaces for machine learning models, which hinders their use in different homes. We propose that converting the data into the semantic input space of a language model can not only unify the input space across different houses but also improve the prediction performance thanks to the model's understanding of semantics. We compared a pre-trained BERT model with one trained from scratch to test the proposed semantic conversion on three datasets from CASAS. The results show that the pre-trained model performs better on accuracy and F1-score. © 2024 IEEE.

关键词： Semantics

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Optimization of Vehicular Congestion Control in Traffic Signals using YOLO algorithm

Optimization of Vehicular Congestion Control in Traffic Sign...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Sivaganesan, D. Kumar, S. Barath Krishnadharani, S. Rashmitha, G. PSG Institute of Technology and Applied Research Dept. of Computer Science and Engg Coimbatore India

ISBN: (纸本)9798350378177

The increase in the volume of vehicles utilizing our roads has highlighted the seriousness of the ongoing problem of traffic congestion. This problem is even worse at intersections, where a line of cars waits patiently for their turn to move forward. The traditional traffic light systems, which were originally intended to handle a less intense traffic load, are now having difficulty keeping up with the increasing number of vehicles, which further complicates the situation. We have adopted cutting-edge technologies, particularly those found in the fields of computer vision and machine learning, to tackle this modern dilemma. Our methodology is based on the application of a state-of-the-art system powered by a deep Convolutional Neural Network, which we call You Only Look Once (YOLO). With the help of this cutting-edge technology, we can quickly and thoroughly examine real-time data at signalized road crossings to gain a sophisticated understanding of the constantly changing traffic environment. Once we have a thorough understanding of the complex traffic dynamics thanks to YOLO's capabilities, we use this knowledge to make the most out of traffic light phases. We focus on closely examining important data elements, like the average wait time for a car and the number of cars in the line. The main goal is to create a coordinated and efficient traffic flow. This reduces the amount of time that drivers must wait in traffic overall and gives priority to safe and effective vehicle movement through intersections, resulting in a more responsive and fluid urban mobility experience. © 2024 IEEE.

关键词： Traffic congestion

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Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models

Drop the GAN: In Defense of Patches Nearest Neighbors as Sin...

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2022 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2022

作者： Granot, Niv Feinstein, Ben Shocher, Assaf Bagon, Shai Irani, Michal The Weizmann Institute of Science Dept. of Computer Science and Applied Math Israel Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image manipulation dates back long before the deep learning era. The classical prevailing approaches were based on maximizing patch similarity between the input and generated output. Recently, single-image GANs were introduced as a superior and more sophisticated solution to image manipulation tasks. Moreover, they offered the opportunity not only to manipulate a given image, but also to generate a large and diverse set of different outputs from a single natural image. This gave rise to new tasks, which are considered 'GAN-only'. However, despite their impressiveness, single-image GANs require long training time (usually hours) for each image and each task and often suffer from visual artifacts. In this paper we revisit the classical patch-based methods, and show that - unlike previously believed - classical methods can be adapted to tackle these novel 'GAN-only' tasks. Moreover, they do so better and faster than single-image GAN-based methods. More specifically, we show that: (i) by introducing slight modifications, classical patch-based methods are able to unconditionally generate diverse images based on a single natural image;(ii) the generated output visual quality exceeds that of single-image GANs by a large margin (confirmed both quantitatively and qualitatively);(iii) they are orders of magnitude faster (runtime reduced from hours to seconds). 22This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 788535), and the Carolito Stiftung. Dr Bagon is a Robin Chemers Neustein AI Fellow. © 2022 IEEE.

关键词： Training Deep learning Visualization Technological innovation computer vision Runtime Europe

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Vehicular Data Retrieval System

Vehicular Data Retrieval System

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Thajudheen, Shaeif Shanmugarathinum, G. Jesudoss, Angelin Gladys Presidency University Dept. of Computer Science Bengaluru India University of Technology and Applied Sciences Dept. of Computer Science Ibri Oman

ISBN: (纸本)9798350317060

The Vehicular Data Retrieval System is comprehensive research that focuses on collecting real-time data from vehicles and transmitting it to a remote server for storage and later visualization in a web application. The system incorporates advanced technologies such as GPS (Global Positioning System) for precise location tracking and data capturing. The primary aim of this research is to create an efficient and scalable solution that enables the retrieval of essential vehicular parameters including speed, engine temperature, fuel level, and coordinates. By harnessing this data, it becomes possible to visualize and analyze vehicle performance over time, leading to valuable insights for various applications, such as fleet management, transportation optimization, and environmental monitoring. This paper presents a detailed analysis of the research's requirements, an extensive literature review, an evaluation of existing systems, and the proposed system design and implementation. Furthermore, the paper discusses testing methodologies, the achieved outcomes, and recommendations for future enhancements to further enrich the vehicular data retrieval and visualization system. © 2023 IEEE.

关键词： Nonbibliographic retrieval systems

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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach

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Neutrosophic Sets and Systems 2025年 81卷 479-502页

作者： Abdo, Doaa A. Salama, A.A. Abdelmegaly, Alaa A. Mahmoud, Hanan Khadari Mahdi Applied statistics and insurance department faculty of commerce Mansoura university Mansoura Egypt Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Higher Institute of Advanced Management Sciences and Computers Al-Buhayrah Egypt Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura Egypt

This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values). Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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Blockchain-based federated learning with checksums to increase security in Internet of Things solutions

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4685-4694页

作者： Prokop, Katarzyna Polap, Dawid Srivastava, Gautam Lin, Jerry Chun-Wei Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland Dept. of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Western Norway University of Applied Sciences Bergen Norway Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon

Federated learning is becoming a practical solution for machine learning (ML) in industry. This is due to the possibility of implementing artificial intelligence (AI) systems and training its models on private data sets. However, this is not an ideal solution as it is possible to manipulate or even intercept the model during its transmission between the server and workers. In this paper, we propose a solution to ensure the security of the model transmitted between units in FL. The given model is encrypted with AES, DES, RSA algorithms, then a checksum is determined. This checksum with a private key is stored as a transaction on a blockchain. In the case of sending the model and its modification, the recipient can easily verify whether it is correct. The proposed solution has been described, tested, and compared to indicate its advantages and disadvantages. Conducted experiments were based on analyzing the communication time between participants, the accuracy of machine learning models, and attack detection. In terms of attack detection on the blockchain, we reached 81% thanks to the checksum mechanism. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blockchain

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Data Augmentation for Messenger Phishing Detection Using Large Language Models

Data Augmentation for Messenger Phishing Detection Using Lar...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Noh, Keonwoong Oh, Seokjin Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

In this study, we introduce a new dataset specifically designed for detecting messenger phishing, an increasingly significant issue in cybercrime. To overcome the scarcity of labeled phishing data, we employ large language models (LLMs) to generate synthetic data, thereby expanding the dataset and improving detection capabilities. Our experimental results show that a model trained exclusively on synthetic data performs comparably to those trained with labeled data. Furthermore, combining synthetic data with labeled data achieves superior F1 and accuracy scores compared to using labeled data only while reducing misclassification errors. © 2025 IEEE.

关键词： Phishing

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Improving Messenger Phishing Detection Using Heterogeneous Phishing Data

Improving Messenger Phishing Detection Using Heterogeneous P...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Oh, Seokjin Noh, Keonwoong Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

Recently, as messenger phishing has been occurring more frequently, the need for its detection has increased;however, datasets for messenger phishing detection are publicly unavailable. In this paper, we address the data scarcity problem of the newly collected messenger phishing dataset by leveraging various types of pre-existing auxiliary phishing data. Experimental results demonstrate that the error rate decreased by up to 1.81 % and the F1 score improved by 7.35% when smishing and voice phishing data are used. These findings confirm that integrating heterogeneous phishing data can mitigate the data scarcity problem and enhance messenger phishing detection performance. © 2025 IEEE.

关键词： Phishing

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Centralized and Decentralized Federated Learning in Autonomous Swarm Robots: Approaches, Algorithms, Optimization Criteria and Challenges : The Sixth Edition of International Conference on Pattern Analysis and Intelligent Systems (PAIS'24) 6

Centralized and Decentralized Federated Learning in Autonomo...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Hafid, Aicha Hocine, Riadh Guezouli, Lahcene Abdessemed, Mohamed. R University of Batna2 Dept. Math And Computer Science Batna05000 Algeria

ISBN: (纸本)9798350350265

Fault tolerance and energy consumption optimization are critical issues in swarm robotics. This study examines recent approaches to address these challenges, focusing on a comparative analysis between Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL). CFL requires a centralized access point for model aggregation, while DFL eliminates the need for a central server, enabling aggregation at each node according to a specific architecture. The analysis of the results reveals that other approaches, such as Hybrid Federated Learning (HFL), more effectively meet the needs of intelligent agents (swarm robots). This effectiveness is particularly enhanced when HFL is combined with Deep Reinforcement Learning (DRL), resulting in Deep Hybrid Federated Reinforcement Learning (DHFRL). The results demonstrate that, although DFL eliminates the necessity of a central server, hybrid approaches are more efficient, especially when combined with Deep Reinforcement Learning (DRL), thus forming Deep Hybrid Federated Reinforcement Learning (DHFRL). © 2024 IEEE.

关键词： Reinforcement learning

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Proposed Deep Learning Approach for Plant Diseases Detection 4

Proposed Deep Learning Approach for Plant Diseases Detection

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Abden, Sofiane Bendjima, Mostefa Benkrama, Soumia Gasbaoui, Mohammed El Amin Kourtiche, Ikram Tahri Mohamed University Ltit Lab Dept. of Math and Computer Science Bechar Algeria

ISBN: (数字)9798331519872

ISBN: (纸本)9798331519872

Farmers in most agricultural countries usually experience high annual losses due to crop diseases and plant infections, which threatens global food security and worsens the famine situation. Researchers have focused on trying to detect the earliest symptoms of tomato plant diseases due to the multilateral effort to find the potential cure to further prevent plant decay cases and decrease the food shortage risk worldwide. On the global market, tomatoes are one of the most grown crops and offer substantial value from sales. In this paper, we are proposing a model based on a convolutional neural network and using the Very Deeper VGG16 architecture for tomato plant disease detection and classification. We have employed our method to identified and classified ten classes, nine for various disease types and one for healthy tomato leaves, a dataset of 16,012 images of tomato leaves from PlantVillage with ten diverse categories has employed. The comparison results showed that VGG16 achieved the highest accuracy of 98%, outperforming the CNN by 2.2%. And we compared the results with some recent related works. © 2024 IEEE.

关键词： Deep learning Plant diseases Accuracy Plants (biology) Crops Globalization Food security Feature extraction Real-time systems Convolutional neural networks

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