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Promoting sustainable urban mobility: An integrated fuzzy decision-making model for assessing autonomous bus alternatives

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Görçün, Ömer Faruk Özçalıcı, Mehmet Gurler, Hasan Emin Pamucar, Dragan Simic, Vladimir Department of Business Administration Faculty of Economics Administrative and Social Sciences at Kadir Has University Cibali Av. Kadir Has St. Fatih Istanbul 34083 Turkey Department of International Trade and Logistics Faculty of Economics and Administrative Sciences Kilis 7 Aralık University Kilis Turkey Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan City 320315 Taiwan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department Sarıyer İstanbul 34396 Turkey University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 South Korea

Today, in addition to the increasing pressures on urban transportation authorities to achieve sustainability goals, it has become necessary to comprehensively evaluate innovative transportation technologies such as autonomous (driverless) buses due to the increasing demand for public transportation applications that will contribute to making urban transportation more sustainable with its environmental, social, economic and social dimensions. In addition, the reservations and hesitations of decision-makers about integrating autonomous buses into urban transportation systems have not been eliminated. These hesitations and reservations are mainly due to critical research, theoretical gaps, and limitations in practice. Considering these gaps, this study presents an innovative model that integrates the fuzzy logarithm methodology of additive weights (LMAW) method and the fuzzy Dombi Bonferroni (DOBI) method to evaluate and rank 20 different autonomous bus alternatives with 33 sustainability criteria. The proposed integrated decision-making procedure can effectively manage complex uncertainties while examining whether autonomous bus alternatives can be integrated into urban transportation systems based on sustainability, considering four-dimensional sustainability criteria. This finding indicates that urban transportation's user-oriented and reliable nature is critical to achieving sustainability goals. In addition, the Proterra Catalyst (A9) is the autonomous bus with the highest sustainability performance for use in urban transport, followed by the Mercedes-Benz Future Bus (A18) and Mercedes-Benz eCitaro (A8). These results regarding alternatives underline the importance of advances in autonomous vehicle technology and making these vehicles more sustainable in evaluation processes. © 2025 Elsevier Ltd

关键词： Autonomous buses Fuzzy Dombi Bonferroni method Fuzzy logarithm methodology of additive weights Sustainability Urban transportation

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A dual GAN with identity blocks and pancreas-inspired loss for renewable energy optimization

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Scientific Reports 2025年第1期15卷 1-32页

作者： Elbaz, Mostafa Said, Wael Mahmoud, Gamal M. Marie, Hanaa Salem Department of Computer Science Faculty of Computers and Informatics Kafrelsheikh University Kafrelsheikh Egypt Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig 44511 Egypt Department of Electrical Engineering Pharos University in Alexandria Alexandria Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa 35712 Egypt

Integrating energy and solar imagery is essential for electrical engineers in renewable energy prediction, consumption analysis, regression modeling, and fault detection applications. A significant challenge in these areas is the limited availability of high-quality datasets, which can hinder the accuracy of the predictive models. To address this issue, this paper proposes leveraging Generative Adversarial Networks (GANs) to generate synthetic samples for training. Despite their potential, traditional GAN face challenges such as mode collapse, vanishing gradients, and pixel integrity issues. This paper introduces a novel architecture, Penca-GAN, which enhances GANs through three key modifications: (1) dual loss functions to ensure pixel integrity and promote diversity in augmented images, effectively mitigating mode collapse and improving the quality of synthetic data;(2) the integration of an identity block to stabilize training, preserving essential input features and facilitating smoother gradient flow;and (3) a pancreas-inspired metaheuristic loss function that dynamically adapts to variations in training data to maintain pixel coherence and diversity. Extensive experiments on three renewable energy datasets—SKY images, Solar images, and Wind Turbine images—demonstrate the effectiveness of the Penca-GAN architecture. Our comparative analysis revealed that Penca-GAN consistently achieved the lowest Fréchet Inception Distance (FID) scores (164.45 for SKY, 113.54 for Solar, and 109.34 for Wind Turbine), indicating superior image quality compared to other architectures. Additionally, it attains the highest Inception Score (IS) across all datasets, scoring 71.43 for SKY, 87.65 for Solar, and 90.32 for Wind Turbine. Furthermore, the application of Penca-GAN significantly enhanced the fault detection capabilities, achieving accuracy improvements from 85.92 to 90.04% for solar panels and from 86.06 to 90.43% for wind turbines. These results underscore Penca-GAN’s robust

关键词： Generative adversarial networks (GANs) Identity block Image augmentation Metaheuristic loss Renewable energy

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AI4FIDS: Multimodal Federated Intrusion Detection

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Radoglou-Grammatikis, Panagiotis Bouzinis, Pavlos S. Makris, Ioannis Lagkas, Thomas Argyriou, Vasileios Papadopoulos, Georgios Th Fouliras, Panagiotis Seritan, George Sarigiannidis, Panagiotis University of Western Macedonia Department of Electrical and Computer Engineering Campus ZEP Kozani Kozani50100 Greece K3Y Ltd William Gladstone 31 Sofia1000 Bulgaria MetaMind Innovation P.C. Kila Kozani Kozani50100 Greece Democritus University of Thrace Department of Computer Science Kavala Campus Kavala65404 Greece Kingston University London Department of Networks and Digital Media Penrhyn Road Kingston upon Thames Surrey KT1 2EE London United Kingdom Harokopio University of Athens Department of Informatics and Telematics Omirou 9 Tavros AthensGR17778 Greece University of Macedonia Department of Applied Informatics 156 Egnatia Street ThessalonikiGR54-636 Greece Politehnica University of Bucharest Electrical Engineering Faculty Bucharest Romania

The rapid progression of smart technologies creates several advantages like enhanced connectivity, personalisation solutions and environmental sustainability. However, this revolution creates also several cyber risks. In particular, the attackers have the ability to synthesise and automate advanced attack scenarios over time, while it is evident that Artificial Intelligence (AI) allows the composition of intelligent attack vectors that can adapt in real-time to conventional countermeasures. Despite the fact that AI can also benefit defensive mechanisms, there are still functional and privacy issues that need to be resolved. First, AI requires appropriate datasets that can differ from environment to environment. In addition, these datasets usually are not available due to privacy issues. Finally, adversarial attacks have the ability to target and affect the AI-based decision-making process. Therefore, in light of the previous remarks, we provide AI4FIDS, a multimodal Intrusion Detection System (IDS) for critical infrastructures. AI4FIDS leverages Federated learning (FL) and combines multiple data sources, thus allowing cooperative intelligence across multiple domains in a private manner and minimising the impact of potential adversarial attacks. In this paper, we present in detail the architectural design and specifications of AI4FIDS, while the evaluation results demonstrate their detection performance, taking into account several datasets and aggregation strategies. Finally, based on the evaluation results, we discuss how the overall reliability and detection capabilities (in terms of detecting multi-step attack scenarios) of AI4FIDS can be improved by combining the detection outcomes of the components behind AI4FIDS. © 2013 IEEE.

关键词： Federated learning

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Higher-Order Color Voronoi Diagrams and the Colorful Clarkson–Shor Framework

arXiv

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arXiv 2025年

作者： Bae, Sang Won Oliver, Nicolau Papadopoulou, Evanthia Division of AI Computer Science and Engineering Kyonggi University Suwon Korea Republic of Faculty of Informatics Università della Svizzera italiana Lugano Switzerland

Given a set S of n colored sites, each s ∈ S associated with a distance-to-site function δs : R2 → R, we consider two distance-to-color functions for each color: one takes the minimum of δs for sites s ∈ S in that color and the other takes the maximum. These two sets of distance functions induce two families of higher-order Voronoi diagrams for colors in the plane, namely, the minimal and maximal order-k color Voronoi diagrams, which include various well-studied Voronoi diagrams as special cases. In this paper, we derive an exact upper bound 4k(n − k) − 2n on the total number of vertices in both the minimal and maximal order-k color diagrams for a wide class of distance functions δs that satisfy certain conditions, including the case of point sites S under convex distance functions and the Lp metric for any 1 ⩽ p ⩽ ∞. For the L1 (or, L∞) metric, and other convex polygonal metrics, we show that the order-k minimal diagram of point sites has O(min{k(n− k), (n− k)2 }) complexity, while its maximal counterpart has O(min{k(n − k), k2 }) complexity. To obtain these combinatorial results, we extend the Clarkson–Shor framework to colored objects, and demonstrate its application to several fundamental geometric structures, including higher-order color Voronoi diagrams, colored j-facets, and levels in the arrangements of piecewise linear/algebraic curves/surfaces. We also present an iterative approach to compute higher-order color Voronoi diagrams. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph theory

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Miniaturized Quad-band Implantable Antenna for Wireless Capsule Endoscopy and Deep Tissue Monitoring Applications

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IEEE Transactions on Antennas and Propagation 2025年

作者： Nasir, Jamal Zada, Muhammad Shah, Syed Manaf Ali Basir, Abdul Shah, Izaz Ali Khan, Shahid Koziel, Slawomir Gdansk University of Technology Faculty of Electronics Telecommunication and Informatics Gdansk 80-233 Poland Department of Health Sciences and Technology ETH Zurich Biomedical and Mobile Health Technology Lab Lengghalde 5 Zurich 8008 Switzerland COMSATS University Department of Electrical and Computer Engineering Abbottabad Campus Islamabad 22060 Pakistan Tampere University Faculty of Information Technology and Communication Sciences Tampere 33720 Finland Hanyang University Department of Electronic Engineering Seoul 04763 South Korea Reykjavik University Department of Engineering Reykjavik 102 Iceland

Miniaturized multiband antennas are essential components of implantable biomedical devices (IMDs), enabling them to perform multiple tasks simultaneously in different biomedical applications. This study proposes a highly miniaturized implantable antenna with quad-band characteristics. The implantable antenna’s volume is 6 mm × 6 mm × 0.6 mm. The proposed antenna operates in the Medical Implant Communication System (MICS) band (401–406 MHz), Wireless Medical Telemetry Service (WMTS) (1427—1432 MHz) and Industrial, Scientific, and Medical (ISM) bands (433.1–434.8 MHz, 902–928 MHz, 2400–2483.5 MHz) for multi-tasking, such as data telemetry, wireless power transfer, and control signalling. This design achieves miniaturization using meandered lines, eliminating the need for shortening vias or ground plane slots, typically employed in implantable antennas for size reduction. This arrangement simplifies the structure and minimizes back radiation, enhancing overall performance. The proposed antenna was thoroughly analyzed through simulations integrated into a wireless capsule endoscopy device. A fabricated prototype embedded in minced pork was experimentally validated, demonstrating excellent agreement with the simulated parameters. The presented antenna exhibits a measured -10 dB bandwidth of 76.8, 72, 125, and 215.4 MHz at 402/433, 915, 1410, and 2450 MHz, respectively. Furthermore, it maintains stable measured gains of -44.7, -34, -30.6, and -29.7 dBi across the respective bands of interest. Specific absorption rates in heterogeneous stomach tissues were evaluated at the four resonance frequencies to ensure human safety, showing compliance with the IEEE standards. Furthermore, to evaluate the range of effective communication, a link budget analysis was performed at data rates of 1, 12, and 78 Mbps, demonstrating a minimum effective transmission range of 4.7 m. These features make the proposed miniaturized implantable antenna suitable for various biomedical applications.

关键词： Implantable antenna implantable biomedical devices (IMDs) link budget specific absorption rate (SAR) wireless capsule endoscopy (WCE)

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From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPT

arXiv

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arXiv 2025年

作者： Hamed, Ahmed Abdeen Crimi, Alessandro Misiak, Magdalena M. Lee, Byung Suk MGEN College of Engineering Northeastern University Miami MiamiFL33127 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States CASCI Laboratory Binghamtom University BinghamtonNY13902 United States AGH University of Krakow Faculty of Informatics Krakow30-059 Poland Department of Physiology and Biophysics Howard University WashingtonDC20059 United States

The generative capabilities of LLM models offer opportunities for accelerating tasks but raise concerns about the authenticity of the knowledge they produce. To address these concerns, we present a computational approach that evaluates the factual accuracy of biomedical knowledge generated by an LLM. Our approach consists of two processes: generating disease-centric associations and verifying these associations using the semantic framework of biomedical ontologies. Using ChatGPT, as the selected LLM, we designed prompt-engineering processes to establish linkages between diseases and related drugs, symptoms, and genes, and assessed consistency across multiple ChatGPT models (e.g., GPT-turbo, GPT-4, etc.) Experimental results demonstrate high accuracy in identifying disease terms (88%–97%), drug names (90%–91%), and genetic information (88%–98%). However, symptom term identification was notably lower (49%–61%), due to the informal and verbose nature of symptom descriptions, which hindered effective semantic matching with the formal language of specialized ontologies. Verification of associations reveals literature coverage rates of 89%–91% for disease-drug and disease-gene pairs, while symptom-related associations exhibit lower coverage (49%–62%). Despite the high accuracy in identifying term names, our observations revealed that the generated term IDs were invalid and, in some cases, redundant. This issue may impede knowledge integration across multiple ontologies, as term IDs are essential for establishing cross-references. Overall, these findings suggest that GenAI tools (e.g., ChatGPT) can generate more reliable biomedical knowledge if extra care is taken. Employing strategies such as Retrieval Augmented Generation (RAG) and incorporating knowledge from literature or ontology terms, as supervised in-context prompting, may further enhance the accuracy and reliability of GenAI-generated biomedical knowledge. Further work is needed to investigate these directions. ©

关键词： Gene Ontology

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“Is It Future or Is It Past?”: From Self-contained Microtasks to AI-driven Collaborative Crowdsourcing

“Is It Future or Is It Past?”: From Self-contained Microta...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Daniel Schneider Marcos Antonio De Almeida Ramon Chaves Benjamim Fonseca Hesam Mohseni António Correia Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

关键词： Crowdsourcing Human computer interaction Collaboration Artificial intelligence Mediation Robots Optimization

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Context-aware Rate Adaptation for Predictive Flying Networks using Contextual Bandits

arXiv

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arXiv 2025年

作者： Queiros, Ruben Kaneko, Megumi Fontes, Helder Campos, Rui INESC TEC Faculty of Engineering University of Porto Porto Portugal National Institute of Informatics Dept. of Computer Science The University of Tokyo Tokyo Japan

The increasing complexity of wireless technologies, such as Wi-Fi, presents significant challenges for Rate Adaptation (RA) due to the large configuration space of transmission parameters. While extensive research has been conducted on RA for low-mobility networks, existing solutions fail to adapt in flying networks, where high mobility and dynamic wireless conditions introduce additional uncertainty. We propose Linear Upper Confidence Bound for RA (LinRA), a novel Contextual Bandit-based approach that leverages real-time link context to optimize transmission rates. Designed for predictive flying networks, where future trajectories are known, LinRA proactively adapts to obstacles affecting channel quality. Simulation results demonstrate that LinRA converges 5.2× faster than state-of-the-art benchmarks and improves throughput by 80% in Non Line-of-Sight (NLoS) conditions, matching the performance of ideal algorithms. With low time complexity, LinRA is a scalable and efficient RA solution for predictive flying networks. © 2025, CC BY.

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Measurement and Analysis of Ultra wideband Channel Coefficients through Crude Oils in a Multipath Environment

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Australian Journal of Electrical and Electronics engineering 2019年第3期16卷 195-205页

作者： Alshabo, Ahmed Ros, Montserrat Vial, Peter James Stirling, David Nikolic, Sasha Wysocki, Beata Wysocki, Tadeusz School of Electrical Computer and Telecommunications Engineering in the Faculty of Engineering & Informatics MIEEE University of Wollongong Wollongong Australia School of Electrical Computer and Telecommunications Engineering in the Faculty of Engineering & Informatics SMIEEE University of Wollongong Wollongong Australia Faculty of Engineering & Informatics SMIEEE University of Wollongong Wollongong Australia Department of Biology University of Nebraska-Lincoln LincolnNE United States SMIEEE UTP University Bydgoszcz Poland Department of Electrical & Computer Engineering University of Nebraska-Lincoln LincolnNE United States

A new application of K-NN was implemented to classify various substances using measured UWB channel complex coefficients. This study provides a low-cost system to detect different substances by applying a data mining technique to the collected frequency traces. Measurements of UWB channel coefficients were conducted for two types of crude oil and ocean water, independently contained within 5 and 10-l PVC containers. These containers were located as the device under test between directional transmitting and receiving ultra wideband antennas and were both connected via the Vector Network Analyser to measure their channel frequency responses over frequency bands of 300 MHz to 8 GHz. Magnitude differences were subsequently taken between the various baselines (empty PVC containers) and filled containers of the crude oil types (denoted A and B) or ocean water. Random noise was also subsequently added to this data to test the robustness of the classification method. After applying K-NN to the collected data it was found that our classification results were 100% for the sets of 5 l and 10-l crude oil samples but reduced to 56.66% and 60.66% when the random noise with standard deviations of one was added to the recorded data. © 2019, © 2019 Engineers Australia.

关键词： Microwave antennas

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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