Article
Engineering, Civil
Asad Ali, Farhan Aadil, Muhammad Fahad Khan, Muazzam Maqsood, Sangsoon Lim
Summary: Vehicular ad-hoc networks (VANETs) pose challenges for robust and scalable communication. Existing clustering techniques generate excessive clusters, leading to resource consumption and increased communication overhead. To address this, a novel clustering algorithm based on the Harris Hawks Optimization algorithm (HHOCNET) is proposed. Simulations show that HHOCNET outperforms state-of-the-art schemes in optimizing the multi-objective clustering problem in VANETs.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Farhana Ajaz, Mohd Naseem, Gulfam Ahamad
Summary: In vehicular ad hoc networks (VANETs), communication between vehicles, vehicles to road side units, and vice-versa is essential for sharing and exchanging a huge amount of data and information. To facilitate efficient information sharing, a systematic and structured connection establishment algorithm is required. Additionally, a unique address assignment algorithm is needed to assign a unique address to each connected node in the network. This paper explores various IP address protocols in VANETs and discusses the advantages and disadvantages of existing IP address allocation protocols.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Bohan Li, Xinyang Song, Tianlun Dai, Wenlong Wu, Di Zhu, Xiangping Zhai, Hao Wen, Qinyong Lin, Huazhou Chen, Ken Cai
Summary: Vehicular ad hoc networks (VANETs) are essential for improving traffic efficiency and safety through real-time information sharing. Digital Twins (DT) have been used to facilitate the design and deployment of VANETs, but face interference from malicious vehicles. To address this, a decentralized trust management scheme embedded with blockchain is proposed to detect malicious DT-vehicles.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Sreelakshmi Pazhoor, Jesy Pachat, Anjana Ambika Mahesh, P. P. Deepthi, B. Sundar Rajan
Summary: In this work, a novel technique called index coded NOMA (IC-NOMA) is proposed, which combines NOMA and index coding to reduce the number of transmissions and improve spectral efficiency. Through detailed analytical studies, it is validated that the proposed transmission system provides improved spectral efficiency and power saving compared to conventional IC systems.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Engineering, Civil
Muhammad Asad Saleem, Xiong Li, Muhammad Faizan Ayub, Salman Shamshad, Fan Wu, Haider Abbas
Summary: The popularity of vehicles has led to the development of smart cities, making vehicular ad-hoc network (VANET) a widely used communication method for obtaining information about road conditions, speed, vehicle location, and traffic congestion. However, the security of private data in VANET is a critical task due to various security threats. In this article, a lightweight and secure privacy-preserving key agreement protocol for VANETs is proposed, which utilizes hashing technique for efficient and secure data transmission.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yongje Shin, Hyun-Seok Choi, Youngju Nam, Hyunchong Cho, Euisin Lee
Summary: This paper proposes a protocol called PSOstreaming, which utilizes particle swarm optimization and mobility prediction algorithms to provide high-quality video streaming services. Experimental results show that PSOstreaming is flexible and achieves a high frame delivery ratio in dynamic topology changes and QoS and QoE.
Article
Engineering, Civil
Shengchu Wang, Xianbo Jiang
Summary: In this paper, a three-dimensional universal cooperative localizer (3D UCL) is proposed for vehicular ad-hoc networks (VANETs), and a 3D geographical information enhanced UCL (3D GIE-UCL) is developed by combining 3D UCL with a NLOS identification mechanism assisted by geographical information. Both 3D UCL and 3D GIE-UCL show significant improvement in positioning performance after the application of acceleration techniques.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Azzam Mourad, Hanine Tout, Omar Abdel Wahab, Hadi Otrok, Toufic Dbouk
Summary: The article discusses the challenges of intrusion detection in Internet of Vehicles and vehicular networks, and proposes a vehicular-edge computing (VEC) fog-enabled scheme to offload intrusion detection tasks with minimal latency. The scheme aims to maximize offloading survivability while minimizing computation execution time and energy consumption.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Le Chang, Xia Deng, Jianping Pan, Yun Zhang
Summary: This article studies the problem of deploying edge servers in a metropolitan area. By analyzing the Shanghai Taxi Trace and building multiobjective optimization models, a heuristic multiobjective optimization method is proposed to address this problem. Numerical results demonstrate that this method achieves a desirable balance among delay, hand-offs, and cost.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Review
Computer Science, Interdisciplinary Applications
Faruk Baturalp Gunay, Ercument Ozturk, Tugrul Cavdar, Y. Sinan Hanay, Atta ur Rehman Khan
Summary: Localization has become a significant research area, particularly within Vehicular Ad Hoc Networks (VANET), where GPS technology and advancements in Internet of Things (IoT) have played crucial roles. Future research directions include examining the pros and cons of different methodologies.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2021)
Article
Computer Science, Information Systems
Leticia Lemus Cardenas, Ahmad Mohamad Mezher, Pablo Andres Barbecho Bautista, Juan Pablo Astudillo Leon, Monica Aguilar Igartua
Summary: Vehicular networks rely on intelligent routing protocols to enhance safety and efficiency, with an increasing trend towards using machine learning algorithms for data-driven predictions. The proposed ML-based routing protocol for VANETs demonstrates improved performance in urban scenarios, reducing packet losses and delays even in complex environments.
Article
Computer Science, Information Systems
Mohammed Alzahrani, Mohd Yazid Idris, Fuad A. Ghaleb, Rahmat Budiarto
Summary: Vehicular Ad Hoc Networks (VANETs) aim to improve road safety, traffic efficiency, and passenger comfort. However, misbehaving vehicles that send false information can hinder the potential of VANETs. This study proposes an improved Robust Misbehavior Detection Scheme (iRMDS) by using a machine learning-based classifier instead of a statistics-based detection threshold. Results show that the proposed solution outperforms related work, indicating promising potential for reliable VANET applications.
Article
Computer Science, Information Systems
Ammar Hawbani, Xingfu Wang, Ahmed Al-Dubai, Liang Zhao, Omar Busaileh, Ping Liu, Mohammed A. A. Al-Qaness
Summary: This study proposes a solution to the problem of multicriteria multihop routing in vehicular ad hoc networks (VANETs) and introduces the HERO protocol, which shows promising performance in terms of delivery success ratio, delivery delay, and communication overhead.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Civil
Zhenyu Lu, Wanneng Shu, Yan Li
Summary: The rapid increase in the number of motor vehicles has led to a corresponding increase in traffic congestion, resulting in a significant impact on humans. Intelligent anti-collision control using inter-vehicle communication technology can improve road utilization and traffic efficiency. In this paper, a deep learning image estimation model based on joint attention mechanism is applied, which combines a deep estimation network Yolov5 and a location-based VANET information fast transmission strategy. The proposed algorithm achieves the desired control goal, demonstrating its effectiveness in vehicle collision prevention.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Fillipe Santos, Andre L. L. Aquino, Edmundo R. M. Madeira, Raquel S. Cabral
Summary: This study proposed a method using both temporal graphs and temporal measures to model VANETs applications, with comparison showing that the aggregated model is inefficient in modeling the temporal aspects. Network evaluation through simulation revealed the impact of temporal modeling on RSUs deployment, demonstrating that temporal measures had better coverage area results.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jose F. Aldana-Martin, Jose Garcia-Nieto, Maria del Mar Roldan-Garcia, Jose F. Aldana-Montes
Summary: Remote sensing technology provides a technological framework for advanced applications in various fields, with Earth Observation becoming increasingly important. Knowledge-driven approaches remain a challenge in remote sensing, with semantic technologies showing high success in knowledge representation in the Earth Observation domain.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Biology
Hossein Nematzadeh, Jose Garcia-Nieto, Ismael Navas-Delgado, Jose F. Aldana-Montes
Summary: Explainable Artificial Intelligence (CAI) makes complex and opaque models understandable to human users. This paper proposes an Ensemble-based Genetic Algorithm Explainer (EGAE) for melanoma cancer detection, which automatically detects and presents informative sections of the image. Experimental results show that EGAE improves the accuracy of explanation compared to LIME efficiently.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Multidisciplinary
Manuel Paneque, Maria del Mar Roldan-Garcia, Jose Garcia-Nieto
Summary: In current Open Banking services, the PSD2 directive allows for secure collection of bank customer information to analyze their financial status and needs. The massive number of daily transactions between Fintech entities requires effective data management, which can be achieved through common data integration schemes and Semantic Web technologies. This work proposes an ontology approach to serve as a semantic data mediator in open banking operations, using semantic reconciliation mechanisms to align transaction data with invoice information and infer financial solvency classification and transaction concept suggestions.
APPLIED SCIENCES-BASEL
(2023)
Article
Biology
Adrian Segura-Ortiz, Jose Garcia-Nieto, Jose F. Aldana-Montes, Ismael Navas-Delgado
Summary: Gene regulatory networks play a crucial role in understanding disease triggers and developing new therapeutic targets. This study proposes GENECI, an evolutionary machine learning approach, to construct ensembles of inference results and optimize the consensus network based on confidence levels and topological characteristics. The proposed method is proven to be robust and accurate, with the ability to generalize to multiple datasets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Manuel Paneque, Maria del Mar Roldan-Garcia, Jose Garcia-Nieto
Summary: Learning management systems play an important role in online education. Academic institutions generate large volumes of learning-related data, which can be challenging to manage. Semantic web technologies provide an effective framework for integrating and querying these data, and the e-LION semantic model serves as a data consolidation approach.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Antonio J. Nebro, Manuel Lopez-Ibanez, Jose Garcia-Nieto, Carlos A. Coello Coello
Summary: Research in multi-objective particle swarm optimizers (MOPSOs) usually proposes one new algorithm at a time without clear understanding of crucial components. To address this issue, the authors propose AutoMOPSO, a flexible algorithm template that can generate numerous potential MOPSOs, and use the automatic configuration tool irace to search for high-performing designs. The results demonstrate that AutoMOPSO outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families, and identifies the key design choices and parameters of the winning MOPSO.
SWARM INTELLIGENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jamal Toutouh, Francisco Chicano, Rodrigo Gil-Merino
Summary: Security and emergency services are major concerns for authorities and citizens, and adapting them to inhabitants is crucial for smart cities. This paper proposes two bi-objective formulations for the police patrol routing problem, which is different from vehicle routing and travelling salesman problems. The experimental analysis shows that the relaxed formulation can compute the complete Pareto front, providing valuable information for solving the precise formulation in future work.
SMART CITIES, ICSC-CITIES 2022
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Lucas Gonzalez, Jamal Toutouh, Sergio Nesmachnow
Summary: This article explores the application of deep neural networks architectures for automatic building extraction, which is crucial for urban city planning and management. The results show that UNet-based architectures provide the most accurate predictions.
SMART CITIES, ICSC-CITIES 2022
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Patricia Cobelli, Sergio Nesmachnow, Jamal Toutouh
Summary: This article compares Generative Adversarial Networks for the image super-resolution problem, presenting a four-step research approach. The main findings indicate that both models are capable of producing accurate results with a reasonable deviation from the state-of-the-art, and possess good transfer capabilities.
2022 IEEE LATIN AMERICAN CONFERENCE ON COMPUTATIONAL INTELLIGENCE (LA-CCI)
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Sandro Hurtado, Jose Garcia-Nieto, Ismael Navas-Delgado
Summary: This paper introduces FIMED 2.0, a service for flexible management and analysis of heterogeneous clinical data. It allows flexible clinical data management from multiple trials, improving data quality and simplifying clinical trials. The service is built on top of a NoSQL Database (MongoDB) and enables dynamic and incremental collection and integration of clinical data based on research needs. This new version of the tool expands on the previous version by including gene regulatory network analysis and gene functionality annotation through data visualization.
BIOINFORMATICS AND BIOMEDICAL ENGINEERING, PT I
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Sandro Hurtado, Hossein Nematzadeh, Jose Garcia-Nieto, Miguel-Angel Berciano-Guerrero, Ismael Navas-Delgado
Summary: In recent years, eXplainable Artificial Intelligence (XAI) has gained attention in data analytics, especially in the field of medical problems. This paper investigates the interpretability of modern XAI methods LIME and SHAP on a Melanoma image classification dataset. The results show that XAI methods have advantages in interpreting the results of Melanoma image classification, with LIME performing better in terms of reproducibility and execution time.
BIOINFORMATICS AND BIOMEDICAL ENGINEERING, PT I
(2022)
Proceedings Paper
Computer Science, Cybernetics
Diana Flores, Erik Hemberg, Jamal Toutouh, Una-May O'reily
Summary: In medical image processing, Generative Adversarial Networks (GANs) offer a method for data augmentation to improve the robustness of computer-aided diagnosis systems. This paper introduces the Lipizzaner framework, which combines spatial coevolution and gradient-based learning to enhance performance and mitigate GAN training difficulties. Experimental analysis demonstrates significant improvements in training with Lipizzaner on HPC systems.
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'22)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Claudio Risso, Christian Cintrano, Jamal Toutouh, Sergio Nesmachnow
Summary: This article presents an exact approach for solving the problem of locating electric vehicle charging stations in a city. It introduces mixed integer programming formulations for two variants of the problem and evaluates the approach using a real-world case study in Malaga, Spain. The results demonstrate the effectiveness of the proposed approach in dealing with a large number of variables and improving the quality of solutions compared to a previous metaheuristic approach.
SMART CITIES (ICSC-CITIES 2021)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Jamal Toutouh, Irene Lebrusan, Christian Cintrano
Summary: The increase in life expectancy is an achievement for society, and therefore age considerations should be included when planning cities and their services. This is particularly important for providing and expanding access to public transport for the elderly. This study objectively evaluates the quality of bus service in Melilla, Spain, using available socio-demographic and mobility open data. The research finds that the bus service significantly reduces journey times for the elderly compared to non-elderly passengers.
SMART CITIES (ICSC-CITIES 2021)
(2022)
Article
Mathematical & Computational Biology
Diego G. Rossit, Segio Nesmachnow, Jamal Toutouh, Francisco Luna
Summary: This article presents an optimization model to schedule deferrable appliances in households, aiming to minimize the cost of electricity bill and maximize users' satisfaction with the consumed energy. The study applies stochastic resolution methods to account for the variability in users' satisfaction. The competitive performance of the proposed approach, compared with a greedy heuristic, is demonstrated using real-world data and different household types.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
