Machine learning empowered computer networks

Cerquitelli, Tania; Meo, Michela; Curado, Marília; Skorin‐Kapov, Lea; Tsiropoulou, Eirini Eleni

doi:10.1016/j.comnet.2023.109807

Cited by 23 publications

(3 citation statements)

References 17 publications

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“…Significant contributions to CP research include the following: Seong-Woo Kim et al [16][17][18], who created a framework to extend perception beyond line-of-sight, a cooperative driving system using CP, and methods for improving AD safety and smoothness; Pierre Merdiganc et al [19], who integrated perception and vehicle-to-pedestrian communication to enhance Vulnerable Road Users' (VRUs) safety; Aaron Miller et al [20], who developed a perception and localization system allowing vehicles with basic sensors to leverage data from those with advanced sensors, thus elevating AD capabilities; Xiaboo Chen et al [21,22], who proposed a recursive Bayesian framework for more reliable cooperative tracking, and a robust framework for multi-vehicle tracking under inaccurate self-localization; Adamey et al [23], who introduced a method for collaborative vehicle tracking in mixed-traffic settings; Francesco Biral et al [24], who demonstrated how the SAFE STRIP EU project technology aids in deploying the LDM for Cooperative ITS safety applications; and Stefano Masi et al [25], who developed a cooperative roadside vision system to enhance the perception capabilities of an AV; Sumbal Malik et al [26], who highlight the need for advanced CP to overcome challenges in achieving level 5 AD; Tania Cerquitelli et al [27], who discussed in a special issue the integration of machine learning and artificial intelligence technologies to empower network communication, analysing how computer networks can become smarter; Andrea Piazzoni et al [28], who discuss how to model CP errors in AD, focusing on the impact of occlusion on safety and how CP may address it; Zhiying Song et al [29], who presented a framework for evaluating CP in connected AVs, emphasizing the importance of CP in increasing vehicle awareness beyond sensor FoV; Mao Shan et al [30], who introduced a novel framework for enhancing CP in Connected AVs by probabilistically fusing V2X data, improving perception range and decision-making in complex environments.…”

Section: Introductionmentioning

confidence: 99%

Multi-Layered Local Dynamic Map for a Connected and Automated In-Vehicle System

Taddei,

Visintainer,

Stoffella

et al. 2024

Sustainability

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Automated Driving (AD) has been receiving considerable attention from industry, the public, and researchers for its ability to reduce accidents, emissions, and congestion. The purpose of this study is to extend the standardized Local Dynamic Map (LDM) by adding two new layers, and develop efficient and accurate algorithms designed to enhance AD by exploiting the LDM coupled with Cooperative Perception (CP). The LDM is implemented as a Neo4j graph database and extends the standard four-layer structure by adding a detection layer and a prediction layer. A custom Application Programming Interface (API) manages all incoming data, generates the LDM, and runs the algorithms. Currently, the API can match detected entities coming from different sources, correctly position them on the map even in the presence of high uncertainties in the data, and predict their future actions. We tested the developed LDM with real-world data, which we collected using a prototype vehicle from Centro Ricerche FIAT (CRF) Trento Branch—the supporting research center for this work—in urban, suburban, and highway areas of Trento, Italy. The results show that the developed solution is capable of accurately matching and predicting detected entities, is robust to high uncertainties in the data, and is efficient, achieving real-time performance in all scenarios. From these results we can conclude that the LDM and CP have the potential to be core parts of AD, bringing improvements to the development process.

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Section: Introductionmentioning

confidence: 99%

Multi-Layered Local Dynamic Map for a Connected and Automated In-Vehicle System

Taddei,

Visintainer,

Stoffella

et al. 2024

Sustainability

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“…This influence stems from ML's adeptness at discerning intricate patterns and extracting insights from convoluted datasets. Notably, graph-based deep learning has been employed for medical diagnostic purposes [17], while inverse reinforcement learning (IRL) algorithms have demonstrated efficacy in optimizing performance within intricate systems [18]. The progress witnessed in ML exhibits promise in a myriad of medical applications [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Statistical Methods and Machine Learning Algorithms for Investigating Metabolic Syndrome in Temporomandibular Disorders: A Nationwide Study

Chweidan,

Rudyuk,

Tzur

et al. 2024

Bioengineering

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The objective of this study was to analyze the associations between temporomandibular disorders (TMDs) and metabolic syndrome (MetS) components, consequences, and related conditions. This research analyzed data from the Dental, Oral, Medical Epidemiological (DOME) records-based study which integrated comprehensive socio-demographic, medical, and dental databases from a nationwide sample of dental attendees aged 18–50 years at military dental clinics for 1 year. Statistical and machine learning models were performed with TMDs as the dependent variable. The independent variables included age, sex, smoking, each of the MetS components, and consequences and related conditions, including hypertension, hyperlipidemia, diabetes, impaired glucose tolerance (IGT), obesity, cardiac disease, obstructive sleep apnea (OSA), nonalcoholic fatty liver disease (NAFLD), transient ischemic attack (TIA), stroke, deep venous thrombosis (DVT), and anemia. The study included 132,529 subjects, of which 1899 (1.43%) had been diagnosed with TMDs. The following parameters retained a statistically significant positive association with TMDs in the multivariable binary logistic regression analysis: female sex [OR = 2.65 (2.41–2.93)], anemia [OR = 1.69 (1.48–1.93)], and age [OR = 1.07 (1.06–1.08)]. Features importance generated by the XGBoost machine learning algorithm ranked the significance of the features with TMDs (the target variable) as follows: sex was ranked first followed by age (second), anemia (third), hypertension (fourth), and smoking (fifth). Metabolic morbidity and anemia should be included in the systemic evaluation of TMD patients.

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“…Using these strategies speeds up the diagnosis process and reduces human error. Utilizing the proven effectiveness of machine learning and deep learning techniques in various applications [23,24], the researchers used these techniques on dermoscopy images to examine skin lesions [25,26]. Since 2015, dermoscopic image analysis (DIA) has relied primarily on convolutional neural networks (CNNs) as classifiers, with advanced computer-aided diagnosis research emphasizing the importance of CNN in achieving superior results in image classification, detection, and segmentation in complex scenarios [27].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework

Ali,

Khalid,

Alshanbari

et al. 2023

Bioengineering

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The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions. In the first stage, a CNN model was developed to classify skin lesion images into two classes, namely benign and malignant. In the second stage, the model was then used with the transfer learning concept to further categorize benign lesions into five subcategories (melanocytic nevus, actinic keratosis, benign keratosis, dermatofibroma, and vascular) and malignant lesions into two subcategories (melanoma and basal cell carcinoma). The frozen weights of the CNN developed–trained with correlated images benefited the transfer learning using the same type of images for the subclassification of benign and malignant classes. The proposed multistage and multiclass technique improved the classification accuracy of the online ISIC2018 skin lesion dataset by up to 93.4% for benign and malignant class identification. Furthermore, a high accuracy of 96.2% was achieved for subclassification of both classes. Sensitivity, specificity, precision, and F1-score metrics further validated the effectiveness of the proposed multistage and multiclass framework. Compared to existing CNN models described in the literature, the proposed approach took less time to train and had a higher classification rate.

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Machine learning empowered computer networks

Cited by 23 publications

References 17 publications

Multi-Layered Local Dynamic Map for a Connected and Automated In-Vehicle System

Multi-Layered Local Dynamic Map for a Connected and Automated In-Vehicle System

Statistical Methods and Machine Learning Algorithms for Investigating Metabolic Syndrome in Temporomandibular Disorders: A Nationwide Study

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework

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