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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sha, Xingchen Bao, Shuyu Chan, Hau Chau, Vincent Fong, Ken C.K. Li, Minming Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Computer Science and Engineering University of Nebraska-Lincoln United States School of Computer Science and Engineering Southeast University China Division of Artificial Intelligence School of Data Science Lingnan University Hong Kong Hong Kong

ISBN: (纸本)157735897X

We study the k-facility location games with optional preferences on the line. In the games, each strategic agent has a public location preference on the k facility locations and a private optional preference on the preferred/acceptable set of facilities out of the k facilities. Our goal is to design strategyproof mechanisms to elicit agents’ optional preferences and locate k facilities to minimize the social or maximum cost of agents based on their facility preferences and public agent locations. We consider two variants of the facility location games with optional preferences: the Min variant and the Max variant where the agent’s cost is defined as their distance to the closest acceptable facility and the farthest acceptable facility, respectively. For the Min variant, we present two deterministic strategyproof mechanisms to minimize the maximum cost and social cost with k ≥ 3 facilities and well-separated n agents, achieving approximation ratios of 3 and 2n + 1 respectively. We complement the results by establishing lower bounds of 23 and n4 for the approximation ratios achievable by any deterministic strategyproof mechanisms for the maximum cost and social cost, respectively. We then improve our results in a special setting of the Min variant where there are exactly three facilities and present two deterministic strategyproof mechanisms to minimize the maximum cost and social cost. For the Max variant, we present an optimal deterministic strategyproof mechanism for the maximum cost and a k-approximation deterministic strategyproof mechanism for the social cost. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 990-1004页

作者： Xiaohua Ge Qing-Long Han Jun Wang Xian-Ming Zhang IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Department of Computer Science and School of Data Science City University of Hong KongChina

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden *** MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external *** central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction *** this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle ***,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is *** is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is *** simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

关键词： Adaptive control collision avoidance distributed formation control multi-vehicle systems neural networks obstacle avoidance repulsive potential

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MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles 26

MapsTP: HD Map Images Based Multimodal Trajectory Prediction...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Sharma, Sushil Das, Arindam Sistu, Ganesh Halton, Mark Eising, Ciarán Department of Electronic & Computer Engineering University of Limerick Ireland SFI CRT Foundations in Data Science University of Limerick Ireland

ISBN: (纸本)9781837242672

Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances decision-making by considering multiple possible future trajectories based on diverse sources of environmental data. In this approach, we leverage ResNet-50 to extract image features from high-definition map data and use IMU sensor data to calculate speed, acceleration, and yaw rate. A temporal probabilistic network is employed to compute potential trajectories, selecting the most accurate and highly probable trajectory paths. This method integrates HD map data to improve the robustness and reliability of trajectory predictions for autonomous vehicles. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Autonomous Vehicles HDMap Images MultiModel Trajectory Prediction Probabilistic Network

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Recent Trends in Text-to-Image GAN

Recent Trends in Text-to-Image GAN

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Kumar, Varun Kumar, Dharmender Guru Jambheshwar University of Science and Technology Department of Computer Science and Technology Haryana Hisar India Guru Jambheshwar University of Science and Technology Department of Data Science and Artificial Intelligence Haryana Hisar India

ISBN: (纸本)9798350375237

Generating genuine images from textual description is challenging for both computer vision and linguistic representation in text-to-image synthesis. Generative adversarial networks (GAN) are an emerging generative model that has been producing great results by generating high-quality images with diverse images. The present review provides an overview of GAN with its background like architecture, game theory key ideas, loss functions, performance metrics and challenges. Recent and relevant text-to-image GAN models are discussed, including Gradual Refinement GAN (GR-GAN), Generative Adversarial CLIPS (GALIP), GigaGAN and StyleGAN-T with their architecture, dataset used highlighting limitations, strengths, year, and applications and comparing their performance metrics like Inception Score (IS) and Fréchet Inception Distance (FID) with identifying future directions, such as drawing boundaries for open research challenges. The present review functions as an extensive knowledge base and is highly valuable for researchers and practitioners who are interested in learning more about text-to-image using GANs. © 2024 IEEE.

关键词： Generative adversarial networks

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AI Sovereignty in Autonomous Driving: Exploring Needs and Possibilities for Overcoming Challenges

AI Sovereignty in Autonomous Driving: Exploring Needs and Po...

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Sinha, Koesha Misra, Bitan Chakraborty, Sayan Dey, Nilanjan Department of Statistics and Data Science Bangalore India Techno International New Town Department of Computer Science Engineering Kolkata India

ISBN: (纸本)9798350314144

With the development of artificial intelligence, advancements in navigation systems for self-driving cars have become a new direction over the last decade. The inclusion of AI-driven actuators in autonomous vehicles has broken the barriers in terms of real-time high-quality data processing resources, accuracy of decisive actions and generalization of environment-action pairs. Upgradation from a car with no automation to a car with minimal to no human intervention has become a boon of AI, as it resolves most of the transportation problems on roads, including human error, lack of visibility in adverse weather conditions, tiredness of drivers in long journeys, etc. This study focuses on AI-enabled tasks, including object detection and identification, lane detection, notification for lane departure and reinforcement learning from the operational environment. However, there exist serious issues in deploying AI-empowered modules in autonomous cars, as the consumer rights to explain, trustworthiness, and reliability of the machine have not yet met the requirements. Our work explores the needs and prospects of AI sovereignty in autonomous driving by overcoming the aforementioned issues so that the healthy progress of technological society can take care of the future world. © 2023 IEEE.

关键词： Autonomous vehicles

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Cancer Diseases Diagnosis Using Deep Transfer Learning Architectures 1st

Cancer Diseases Diagnosis Using Deep Transfer Learning Archi...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Promy, Tania Ferdousey Joya, Nadia Islam Turna, Tasfia Haque Sukhi, Zinia Nawrin Ashraf, Faisal Bin Uddin, Jia Department of Computer Science and Engineering School of Data Science Brac University Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031346187

Cancer is a lethal disease among the diseases in the world. It is clinically known as ‘Malignant Neoplasm’ which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. There are many medical procedures to identify the cancer cell, such as mammography, MRI, CT scan, which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of medical image classification using deep learning, our recommended initiative targets to analyze the performance of different deep transfer learning models for cancer cell diagnosis. In this paper, we have used VGG16, Inception V3 and MobileNet V2 deep architectures to diagnosis the breast cancer (KAU-BCMD dataset), lung cancer (IQ-OTH/NCCD dataset) and skin cancerHam10000). Experimental results demonstrate that VGG16 architecture shows comparatively higher accuracy by exhibiting 98.5% of accuracy for breast cancer, 99.90% for lung cancer and 93% for skin cancer dataset. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Diseases

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Guest Editorial: Special Issue on Evolutionary Computation for Games

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IEEE Transactions on Games 2023年第1期15卷 1-4页

作者： Schrum, Jacob Liu, Jialin Browne, Cameron Ekart, Aniko Gallagher, Marcus Southwestern University Department of Mathematics and Computer Science GeorgetownTX United States Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands Aston University Department of Computer Science United Kingdom The University of Queensland School of Information Technology and Electrical Engineering Australia

The eight papers in this special section focus on applications of evolutionary computation to games to demonstrate several ways in which evolution can push boundaries and explore new areas of what is possible in the realm of games research, with a focus on game-playing, automatic agent parameter tuning, automatic game testing, and procedural content generation.

关键词： Special issues and sections Evolutionary computation Games

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Cloud Computing Malware Detection Using Feature Selection Based on Optimized White Shark Algorithm (WSO) 2

Cloud Computing Malware Detection Using Feature Selection Ba...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Mustafa, Hossam M. J. Al-Zyod, Mohammad H. University Of Petra Faculty Of Information Technology Department Of Data Science And Artificial Intelligence Amman Jordan Al-Balqa Applied University Computer Science Department Salt Jordan

ISBN: (纸本)9798350394962

The majority of information technology organizations nowadays prefer cloud computing as their main choice due to improvements in information technology. However, as a result of cloud computing's vast and open architecture, which makes it vulnerable to hacker attacks, the security of data remains an important concern. As a consequence, ensuring the security and integrity of data has become crucial while constructing an abnormal intrusion detection system utilizing machine learning algorithms represents a method to potentially detect intrusions. Recurring clouding as well as prevention. Using machine learning algorithms and revised intrusion datasets is necessary to overcome the challenge of finding effective and optimal network intrusion detection systems. The White Shark Optimization approach works along with Support Vector Machines (SVM). The suggested algorithm's effectiveness is assessed using metrics including accuracy and recall. When results of previous research employing both of the two datasets (NSL-KDD and Kyoto) for predicting the detection of abnormal infiltration are compared to the proposed algorithm based on SVM, it is determined that the white shark optimization algorithm produced the best accuracy, at 99.8%. © 2024 IEEE.

关键词： Support vector machines

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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Integrating Deep Learning with Case-based Reasoning for Enhanced Brain Tumor Detection: A Review 2

Integrating Deep Learning with Case-based Reasoning for Enha...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Krishna, Redrouthu Vamsi Dhatterwal, Jagjit Singh Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Vaddeswaram India Department of Artificial Intelligence & Data Science Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350368413

Detecting brain tumors (BT) early is vital for effective treatment planning and improved patient outcomes. The fusion of Deep Learning (DL) and case-based reasoning (CBR) presents a promising solution for enhancing BT detection accuracy and reliability. DL methods, such as Convolutional Neural Networks (CNNs), excel in automatically extracting discriminative features from medical images, bolstering diagnostic capabilities. Case-based reasoning, on the other hand, leverages past diagnostic cases to provide context-aware decision support for new cases. This review study provides an overview of recent advancements in the integration of DL with CBR for brain tumor detection. Then discuss the strengths and limitations of both approaches and highlight how their integration can address key challenges in brain tumor detection, such as limited labeled data, interpretability of DL models, and the need for personalized diagnosis. Furthermore, review existing studies that have applied DLand CBR techniques individually and in combination for brain tumor detection. Finally, discuss challenges and future research directions and opportunities for leveraging this integrated approach to improve brain tumor diagnosis and patient care. In this, review total 55 papers are taken based on the Integrating DL with Case-Based Reasoning for Enhanced Brain Tumor Detection. In this, the research articles published between 2021 and early 2024 were taken into consideration for this study. © 2024 IEEE.

关键词： Convolutional neural networks

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