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A study on multi-agent reinforcement learning for autonomous distribution vehicles

Iran Journal of computer Science 2023年第4期6卷 297-305页

作者： Ergün, Serap Department of Computer Engineering Faculty of Technology Isparta University of Applied Sciences Isparta 32260 Turkey

A self-driving automobile, also known as an autonomous vehicle, can observe its surroundings and maneuver without the assistance of a driver thanks to software algorithms and a completely automated driving system. As a result, the car may react to the environment in a way that is comparable to a human driver. Road regulations and limitations are required to guarantee the security and effectiveness of delivery services, as well as to stop accidents and damage brought on by technology failures. This paper formulates the Autonomous Delivery Vehicles optimization problem and proposes a Multi-Agent Reinforcement Learning approach that makes use of shortest-path data that have been obtained analytically. This method allows for the training of multiple agents to work together and improve delivery. Using this strategy, autonomous delivery vehicles' efficiency and security can be markedly enhanced. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Autonomous vehicles Multi-agent systems Reinforcement learning Shortest-path information

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5G Slice Mutation to Overcome Distributed Denial of Service Attacks Using Reinforcement Learning 17

5G Slice Mutation to Overcome Distributed Denial of Service ...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Javadpour, Amir Ja'fari, Forough Taleb, Tarik Benzaid, Chafika Ictficial Oy Espoo Finland Sharif University of Technology Department of Computer Engineering Iran Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum Germany University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland

ISBN: (纸本)9798331509736

5G slices are susceptible to indirect Distributed Denial of Service (DDoS) attacks, where overwhelming traffic directed to one slice can also disrupt other slices sharing the same infrastructure Many current mitigation methods rely on a detection phase, which may not be effective against unknown or sophisticated attacks. Moving Target Defense (MTD) is a security mechanism that invalidates the adversary's collected information, and it can be deployed without the detection phase. In this paper, we propose a Slice Mutation technique based on Reinforcement Learning (SMRL) that reduces the impact of DDoS attacks on 5G slices while keeping the number of allocated slices acceptable. SMRL proposes a general RL model that considers ternary and ranking numbers to improve learning performance. We tested SMRL on computer networks attacked by a real botnet called Mirai and assessed its performance using various measures, including a new functionality analysis method The results indicate that SMRL decreases the number of slices impacted by a DDoS attack and enhances the distribution of slices among infrastructure resources by 46 % and 20 %, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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Alert Prediction in computer Networks Using Deep Graph Learning 10

Alert Prediction in Computer Networks Using Deep Graph Learn...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Nayeri, Zahra Makki Rezvani, Mohsen Shahrood University of Technology Faculty of Computer Engineering Shahrood Iran

ISBN: (纸本)9798331532543

Cyber-attacks have surged in intensity and complexity, often outpacing existing detection systems. Advanced attack prediction algorithms can help mitigate these threats, reducing their impact and strengthening network security. While prior research has advanced attack predictability and source estimation, deep graph modeling has yet to integrate such advancements fully. This article introduces a novel method for binary link prediction in cyber-attack networks, leveraging dynamic graph deep learning and temporal graph neural networks (TGN). The approach begins by constructing a time-evolving attack graph based on historical alert data within a specific time window. This dynamic graph is then fed into a TGN, which automatically extracts structural and temporal features. The model predicts the likelihood of future attack links between entities, enabling more effective identification of potential cyber threats before they occur. In this article, three different deep networks were evaluated for predicting attacks. The dynamic graph method demonstrated 10-18% better performance compared to the other methods, improving accuracy in link prediction. © 2024 IEEE.

关键词： Alert prediction binary link prediction deep neural networks graph neural networks

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Deep neural network-based tools for hotel marketing activities of online travel agents

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Multimedia Tools and Applications 2025年 1-24页

作者： Keles, Ali Keles, Ayturk Keles, Mustafa Berk Mutlu, Behçet Department of Computer Education and Instructional Technology Faculty of Education Agri Ibrahim Cecen University Agri04100 Turkey Machine Learning Engineer *** Istanbul Turkey Director of Technology R&D Center *** Istanbul Turkey Department of Computer Engineering Gebze Technical University Kocaeli Turkey

Vacationers always want to choose the hotel that best meets their needs and expectations. For this, they typically use online travel agents (OTAs). OTAs must analyze millions of images from thousands of hotels quickly and classify them accurately to best meet demands. This is a highly complex and challenging task. OTAs also have to choose a default image to represent best way each hotel in order to best market hotels to vacationers and encourage click behavior. This is another challenge to overcome. The biggest problem in classifying millions of images from different areas of hotels is that these areas often have similar objects and environments. For this reason, fewer categories have been used in deep learning (DL) studies conducted so far. In this study, two deep neural networks (DNN) have been developed to accomplish these challenging tasks. The first network is the "hotel image classification network" (HICNet), which classifies images of different areas of hotels into 11 categories. In a multi-class classification task that includes closely related categories such as bar, food, and lobby, the HICNet network achieved very good results, reaching an average of 79% precision, sensitivity, and F1-score. The second network is the "hotel default image network" (HDINet), which enables the determination of a default image for each hotel on the OTA page. HDINet can accurately identify the default image of hotels with an average of 92% precision, sensitivity, and F1-score. These networks have been integrated into the OTA system named *** and are currently in active use. © The Author(s) 2025.

关键词： Hotels

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Banana Leaf Disease Detection using Advanced Convolutional Neural Network

Banana Leaf Disease Detection using Advanced Convolutional N...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Mohanraj, E. Hariharan, R. Keerthana, S. Cavin, K.B. Faculty of Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350333602

Any plant's ability to grow disease-free is crucial for both the environment and human existence. Nevertheless, various diseases, viruses, and fungi affect the plant and highly influence the yield quality and production quantity. If the disease and its origin are not appropriately diagnosed, illness control procedures might be a waste of effort and resources, causing additional plant losses. Plant disease tracking by manual process is quite challenging. It requires a considerable amount of work, expertise in plant diseases, and protracted processing durations. Traditional neural networks require a large number of parameters to train effectively, which can make them slow and computationally expensive. The previous study includes the leaf disease detection performed by CNN, R-CNN, and SVM models. Using the Faster R-CNN, which outperforms other models in terms of computation time and accuracy, this study intends to develop a banana leaf disease detection system. © 2023 IEEE.

关键词： Object detection

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Photonics Applications and Web engineering – WILGA 2024

Photonics Applications and Web Engineering – WILGA 2024

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Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024

作者： Romaniuk, Ryszard S. Smolarz, Andrzej Wójcik, Waldemar Warsaw University of Technology Institute of Electronic Systems Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Poland

ISBN: (数字)9781510685796

ISBN: (纸本)9781510685789

Wilga 2024 Summer Symposium on Photonics Applications and Web engineering was the 52th edition of the research and technical meetings series. Traditionally, the annual series of technical conferences and topical sessions lasted the whole week. They usually were split to one winter meeting in January and one or two summer events in May/June, and in September. The Covid epidemic and the war in Ukraine has changed a lot. The hybrid meetings were held in Wilga resort near Warsaw, owned by the Warsaw University of technology. Now the meetings since 2022 were shifted to Lublin. Nearly 100 participants took part in all of the Wilga 2022 events, most of them being young researchers active in all aspects of photonics, electronics and ICT science and technology. Over 60 participants took part in Wilga 2024 meetings. Wilga Symposium embraces hardware and software technologies associated with photonics like optics, optical engineering, optoelectronics, electronics and electrical engineering, mechatronics, chemical and material engineering, applied physics, industrial solutions and applications. Around 50 papers were presented during Wilga 2024, oral and poster. Out of this number, some papers chosen by authors are published in this volume of Proc. SPIE mostly related to photonics. The major change was that during Wilga 2020, 2021, 2022 and 2024 Symposia, during summer editions, no traditional large young researcher sessions at large were possible to be organized. We hope that this will change and Wilga 2025 will return to a good tradition to organize concurrent social research sessions full of young researchers like was during Wilga 2019 and earlier. © 2024 SPIE.

关键词： Photonic devices

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Influencing Factors in Facial Recognition and Verification: A Siamese Neural Network Hyperparameter Study

Influencing Factors in Facial Recognition and Verification: ...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Maaz, Ahmad Arshad, Usama Ahmad, Tafreed Abbas, Shaheer Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

There are many practical uses for facial verification and recognition jobs. In this study, we look into the developments and difficulties faced in facial recognition technologies, especially in the application of Siamese neural networks. This study converges around optimizing hyperparameters to improve Siamese network accuracy and performance. We evaluated the impact of different optimizers, loss functions, and data preprocessing techniques on Siamese network performance through a chain of experiments. The results demonstrated the importance of choosing the right hyperparameters, demonstrating how decisions such as the Adam optimizer and Binary Cross-Entropy as the loss function may greatly enhance the system's capability to precisely identify and validate faces. This study also overlooks the importance of diverse and high-quality datasets and states how these elements affect the model's performance. With this work, we hope to give a thorough manual for fine-Tuning Siamese neural networks for use in facial recognition applications, along with helpful advice on how to overcome the difficulties posed by dataset variability and hyperparameter selection. In addition to adding to the theoretical knowledge of facial verification technologies, our research offers useful suggestions for enhancing the conception and application of these systems in actual settings. © 2024 IEEE.

关键词： Face recognition

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NeuralMango: Advanced Mango Classification and Price Prediction

NeuralMango: Advanced Mango Classification and Price Predict...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Peerzada, Shuhrahbeel Saud, Muhammad Ramiz Javed, Danish Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

The mislabeling of mangoes in local Pakistani markets unfairly impacts buyers, who frequently end up paying a premium for a cheaper type of mango fruit. To address this issue, our study employs advanced deep learning techniques, specifically leveraging the EfficientBNet2 model. This model aids in providing consumers with accurate information about the mangoes they purchase, including details on daily government-set prices for these varieties. We've developed a user-friendly Flutter app supported by the predictive capabilities of the EfficientBNet2 model, enabling buyers to make informed decisions. Our research aims to bridge the gap caused by mango mislabeling in Pakistani markets, ultimately preventing buyers from falling victim to price discrepancies in various types of mangoes. By utilizing EfficientBNet2 and TensorFlow, our goal is to furnish users with real-Time pricing data and comprehensive product information. The creation of the Flutter app serves to empower buyers by putting this predictive technology directly into their hands. The significance of this study lies in its potential to simplify the buying process for everyday consumers through the application of deep learning models. Furthermore, by providing accurate pricing information, we anticipate contributing to greater stability within the local mango market. © 2024 IEEE.

关键词： Costs

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Comparative Analysis of Machine Learning Algorithms for Classification of Thai Fake News 9

Comparative Analysis of Machine Learning Algorithms for Clas...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Siriphanpornchana, Phumchai Kanjanawattana, Sarunya Institute of Engineering Suranaree University of Technology Faculty of Computer Engineering Nakhon Ratchasima30000 Thailand

ISBN: (纸本)9798350311402

Social media is an important source of false news because of its accessibility, usability, and large community. Individuals who trust the fabricated news may be misinformed. Classifying true and false information among data is a challenging task for the ordinary person. Hence, machine learning is the best solution for supporting human judgment. In this study, eleven machine learning algorithms were employed to evaluate the performance of models utilizing a Thai fake news dataset. In addition, we designed experiments to demonstrate how stop words influence performance. This study found that stop words had no effect on the performance of the model while classifying Thai fake news, and that SVM with an RBF kernel provided the best performance. © 2023 IEEE.

关键词： Classification (of information)

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Crime Type Prediction based on Various Occurrence using Parallel LSTM

Crime Type Prediction based on Various Occurrence using Para...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Saradha, M. Nithesh Priyan, T. Shreeram, D.U. Viknesh, S. Faculty of Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350333602

Crime is a widespread societal issue that has a negative impact on people's standard of living and the nation's prosperity. It's a major consideration for potential residents and tourists alike when deciding whether or not to settle in a given area. As crime rates rise, police departments have a growing need for cutting-edge GIS and data mining tools to enhance crime analytics and strengthen public safety. The suggested method includes preprocessing, feature selection, and evaluating the model's performance. We begin by cleaning up the raw crime statistics. For more predictable signals, this comprises both spatial and temporal regularization. Feature selection is performed using a rough spanning tree. To measure the effectiveness of the model, we employ parallel LSTM. When compared to two established approaches, the new strategy fares quite well. © 2023 IEEE.

关键词： Long short-term memory

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