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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 Istanbul34083 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 City320315 Taiwan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department Sarıyer İstanbul34396 Turkey University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of

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： Urban transportation

LSTM deep learning model for Alzheimer’s disease prediction based on cost-effective time series cognitive scores

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LSTM deep learning model for Alzheimer’s disease prediction...

Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Hager Saleh Nora El-Rashidy Tamer Abuhmed Shaker El-Sappagh Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University College of Computing and Informatics Sungkyunkwan University South Korea Faculty of Computer Science and Engineering Galala University Suez Egypt

Alzheimer’s disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance [1]. For instance, cognitive scores (CSs) including clinical dementia rating, and Alzheimer’s disease assessment scores have been integrated with other modalities like MRI to predict the future status of AD patients [2]. They enhanced the performance of DL models. These scores can be collected easily and in a cost-effective way in hospitals. No study in the literature has built a deep learning model to predict AD based on the cost-effective and multimodal time series data of CSs. In this study, we propose an LSTM-based DL architecture to predict AD based on multiple time series CSs. The proposed model has been optimized using a Bayesian optimizer to select the best architecture, and it has been compared with multiple classical machine learning models like random forest and others. The proposed LSTM architecture achieved better results than other models and provided stable and robust performance.

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

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Early Detection and Classification of Melanoma Based on Andr...

International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Jasmine Iswarini Dyah Prabandari Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Bilal R. Altamer Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant.

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Micro-ring-resonator based passive photonic spike-time-dependent-plasticity scheme for unsupervised learning in optical neural networks

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Micro-ring-resonator based passive photonic spike-time-depen...

Optical Fiber Communication Conference, OFC 2020

作者： Mesaritakis, Charis Skontranis, Menelaos Sarantoglou, George Bogris, Adonis University of the Aegean Department of Information and Communication Systems Engineering Karlovassi-Samos83200 Greece University of West Attica Department of Informatics and Computer Engineering Ag. Spyridonos Egaleo12243 Greece

In this work, a photonic spike-time-dependent-plasticity scheme based on high-order passive ring resonators is demonstrated. Numerical simulations confirmed the validity of the approach assuming post and pre-synaptic ... 详细信息

ISBN: (数字)9781943580712

ISBN: (纸本)9781943580712

关键词： Semiconductor quantum dots

An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection

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arXiv 2022年

作者： Li, Lingjie Xuan, Manlin Lin, Qiuzhen Jiang, Min Ming, Zhong Tan, Kay Chen The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Informatics Xiamen University Xiamen361005 China The Department of Computing The Hong Kong Polytechnic University Hong Kong

Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature Selection

Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems

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arXiv 2025年

作者： Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Ivković, Nikola Mihaljević, Ivan Mohamud, Mudathir Ahmed Kowrina, Ahmed Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Electrical-Electronics Engineering Biruni University Istanbul Turkey Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia Faculty of Computing SIMAD University Mogadishu252 Somalia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia Département d’Informatique et Mathématiques Faculté des Sciences et Techniques Université de Nouakchott Al Aasriya Nouakchott880 Mauritania

In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures. © 2025, CC BY.

关键词： Queueing theory

Introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization 15th

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Introducing QuantConn: Overcoming Challenging Diffusion Acqu...

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Newlin, Nancy Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Yang, Joseph Yuan-Mou Wu, Ye Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Consagra, William Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’ Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Paediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA Santa Clara United States Learning Research and Development Center University of Pittsburgh Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathmatics Eindhoven University of Technology Eindhoven Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla y León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031869198

White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

A new approach for solving global optimization and engineering problems based on modified Sea Horse Optimizer

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arXiv 2024年

作者： Hashim, Fatma A. Mostafa, Reham R. Khurma, Ruba Abu Qaddoura, Raneem Castillo, P.A. Faculty of Engineering Helwan University Egypt University of Sharjah Sharjah27272 United Arab Emirates Department of Information System Faculty of Computers and Information Sciences Mansoura University Mansoura35516 Egypt MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan School of Computing and Informatics Al Hussein Technical University Amman Jordan Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

Sea Horse Optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named mSHO. The enhancement primarily focuses on bolstering SHO’s exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm’s search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon’s rank-sum and Friedman’s tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and effic

关键词： Global optimization

Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions

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arXiv 2024年

作者： Alimisis, Panagiotis Mademlis, Ioannis Radoglou-Grammatikis, Panagiotis Sarigiannidis, Panagiotis Papadopoulos, Georgios Th. Department of Informatics and Telematics Harokopio University of Athens Thiseos 70 Attiki AthensGR 17676 Greece Department of Electrical and Computer Engineering University of Western Macedonia Active Urban Planning Zone KozaniGR 50150 Greece K3Y Studentski district Vitosha quarter bl. 9 Sofia City Province SofiaBG 1700 Bulgaria

Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets;thereby, improving the performance and robustness of machine learning models in downstream tasks. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and semantics-aware way. Diffusion Models (DMs), which comprise one of the most recent and highly promising classes of methods in the field of generative Artificial Intelligence (AI), have emerged as a powerful tool for image data augmentation, capable of generating realistic and diverse images by learning the underlying data distribution. The current study realizes a systematic, comprehensive and in-depth review of DM-based approaches for image augmentation, covering a wide range of strategies, tasks and applications. In particular, a comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is initially performed. Subsequently, a taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks. Then, performance assessment methodologies and respective evaluation metrics are analyzed. Finally, current challenges and future research directions in the field are discussed. © 2024, CC BY.

关键词： Semantics