Impacts of intelligent transportation systems on energy conservation and emission reduction of transport systems: A comprehensive review

Green Technologies and Sustainability, Год журнала: 2022, Номер 1(1), С. 100002 - 100002

Опубликована: Ноя. 16, 2022

With the development of smart cities, new requirements have been put forward for control carbon emissions (CEs) in transportation system. Intelligent systems (ITS) provide a solution to problems traffic congestion and CEs caused by rapid increase number vehicles. This work aims at impact ITS on energy conservation emission reduction (ECER) networks. screened more than 100 pieces relevant literature ECER from Google Scholar classified analyzed them one one. First, system is studied. The monitoring network developing brilliant direction, initial induction coil detector current ITS. Then, realization path studied three aspects: transportation, organization management, upgrading replacement. Finally, different domains summarized. results indicate that has undergone traditional video surveillance become increasingly intelligent. Visually presenting data management plays very influential role alleviating vehicle ECER. However, selecting best parameters necessary realize transport At same time, implementation these energy-saving measures requires traction promotion government policies order fulfill its potential role.

Язык: Английский

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High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images

Computers in Biology and Medicine, Год журнала: 2023, Номер 155, С. 106646 - 106646

Опубликована: Фев. 10, 2023

In this study, multiple lung diseases are diagnosed with the help of Neural Network algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia, Pneumothorax, Atelectasis, Edema, Effusion, Hernia, Cardiomegaly, Pulmonary Fibrosis, Nodule, and Consolidation, studied from ChestX-ray14 dataset. A proposed fine-tuned MobileLungNetV2 model is employed for analysis. Initially, pre-processing done on X-ray images dataset using CLAHE to increase image contrast. Additionally, a Gaussian Filter, denoise images, data augmentation methods used. The pre-processed fed into several transfer learning models; such as InceptionV3, AlexNet, DenseNet121, VGG19, MobileNetV2. Among these models, MobileNetV2 performed highest accuracy 91.6% in overall classifying lesions Chest Images. This then optimise model. On data, model, MobileLungNetV2, achieves an extraordinary classification 96.97%. Using confusion matrix all classes, it determined that has high precision, recall, specificity scores 96.71%, 96.83% 99.78% respectively. study employs Grad-cam output determine heatmap disease detection. shows promising results images.

Язык: Английский

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A scoping review of fair machine learning techniques when using real-world data

Journal of Biomedical Informatics, Год журнала: 2024, Номер 151, С. 104622 - 104622

Опубликована: Март 1, 2024

The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI ML take important roles care, there are concerns about associated fairness bias. That is, an tool may have a disparate impact, with its benefits drawbacks unevenly distributed across societal strata subpopulations, potentially exacerbating existing inequities. Thus, the objectives this scoping review were summarize literature identify gaps topic tackling algorithmic bias optimizing AI/ML models using real-world data (RWD) domains. We conducted thorough techniques for assessing model when RWD focus lies on appraising different quantification metrics accessing fairness, publicly accessible datasets research, mitigation approaches. identified 11 papers that focused applications. current research mitigating issues limited, both terms disease variety applications, well accessibility public research. Existing studies often indicate positive outcomes pre-processing address There remain unresolved questions within field require further which includes pinpointing root causes models, broadening use exploring implications healthcare settings, evaluating addressing multi-modal data. This paper provides useful reference material insights researchers regarding reveals field. Fair burgeoning requires heightened cover diverse applications types RWD.

Язык: Английский

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A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System

Sensors, Год журнала: 2022, Номер 22(20), С. 8068 - 8068

Опубликована: Окт. 21, 2022

The emerging field of eXplainable AI (XAI) in the medical domain is considered to be utmost importance. Meanwhile, incorporating explanations with respect legal and ethical necessary understand detailed decisions, results, current status patient's conditions. Successively, we will presenting a survey for XAI model enhancements, evaluation methods, significant overview case studies open box architecture, datasets, future improvements. Potential differences methods are provided recent stated as (i) local global preprocessing, (ii) knowledge base distillation algorithms, (iii) interpretable machine learning. characteristics details healthcare explainability included prominently, whereas pre-requisite provides insights brainstorming sessions before beginning project. Practical study determines progress leading advance developments within field. Ultimately, this proposes critical ideas surrounding user-in-the-loop approach, an emphasis on human-machine collaboration, better produce explainable solutions. feedback system human rating-based intelligible into constructive method enforced explanation feedback. For long time, limitations ratings, scores grading present. Therefore, novel recommendation scoring designed approached from work. Additionally, paper encourages importance implementing solutions high impact

Язык: Английский

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Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2)

Health and Technology, Год журнала: 2022, Номер 12(5), С. 923 - 929

Опубликована: Авг. 12, 2022

This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, adapting healthcare system to cope with future pandemics. The core objective is develop a concept supported by autonomous that can use edge health devices real-time data. constructs two case scenarios applying cybersecurity (1) self-optimising predictive cyber risk analytics of failures in systems during Disease X event (i.e., undefined pandemic), (2) self-adaptive forecasting medical production supply chain bottlenecks To construct testing scenarios, uses Covid-19 synthesise data algorithms i.e., optimising securing digital anticipation disease X. are built tackle logistical challenges disruption complex chains vaccine distribution optimisation algorithms.

Язык: Английский

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Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model

Remote Sensing, Год журнала: 2022, Номер 14(6), С. 1523 - 1523

Опубликована: Март 21, 2022

Olive trees, which are planted widely in China, economically significant. Timely and accurate acquisition of olive tree crown information is vital monitoring growth accurately predicting its fruit yield. The advent unmanned aerial vehicles (UAVs) deep learning (DL) provides an opportunity for rapid parameters the crown. In this study, we propose a method automatically extracting (crown number area tree), combining visible-light images captured by consumer UAV new model, U2-Net, with deeply nested structure. Firstly, data set (OTC) was constructed, further processed ESRGAN model to enhance image resolution augmented (geometric transformation spectral transformation) enlarge increase generalization ability model. Secondly, four typical subareas (A–D) study were selected evaluate performance U2-Net extraction different scenarios, compared three current mainstream models (i.e., HRNet, U-Net, DeepLabv3+) remote sensing segmentation effect. results showed that achieved high accuracy numbers mean intersection over union (IoU), overall (OA), F1-Score 92.27%, 95.19%, 95.95%, respectively. Compared other models, IoU, OA, increased 14.03–23.97 percentage points, 7.57–12.85 8.15–14.78 addition, had consistency between predicted measured crown, it lower error rate root squared (RMSE) 4.78, magnitude relative (MRE) 14.27%, coefficient determination (R2) higher than 0.93 all subareas, suggesting extracted best profile integrity most consistent actual situation. This indicates UVA RGB can provide highly robust result crowns helpful dynamic management orchard trees.

Язык: Английский

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A Survey of Convolutional Neural Network in Breast Cancer

Computer Modeling in Engineering & Sciences, Год журнала: 2022, Номер 136(3), С. 2127 - 2172

Опубликована: Ноя. 22, 2022

For people all over the world, cancer is one of most feared diseases. Cancer major obstacles to improving life expectancy in countries around world and biggest causes death before age 70 112 countries. Among kinds cancers, breast common for women. The data showed that female had become cancers.

Язык: Английский

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An innovative decision making method for air quality monitoring based on big data-assisted artificial intelligence technique

Journal of Innovation & Knowledge, Год журнала: 2023, Номер 8(2), С. 100294 - 100294

Опубликована: Март 1, 2023

This work dissects the application of big data and artificial intelligence (AI) technology in environmental protection monitoring. The principle collection is analysed based on atmospheric science AI technology. In addition, a combined model air quality forecasting machine learning proposed to resolve real monitoring challenges protection, namely, improved complete ensemble empirical mode decomposition with adaptive noise-whale optimization algorithm-extreme (ICEEMDAN-WOA-ELM). On this basis, deep introduced establish learning-based time-space-type-meteorology (TSTM) predict quality. Finally, verified by experiments. results demonstrate that ICEEMDAN-WOA-ELM significantly outperforms single forecasting. five evaluation index values are 14.187, 17.235, 0.140, 0.067, 0.946, which higher than those other models. single-step accuracy average TSTM heavily polluted weather forecast almost reached full marks, maximum 1.00. performance also decreases growth step size but remains above 0.86. It can be seen no longer meet requirements has advantages effective for protection.

Язык: Английский

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Exploring the current and prospective role of artificial intelligence in disease diagnosis

Annals of Medicine and Surgery, Год журнала: 2024, Номер 86(2), С. 943 - 949

Опубликована: Янв. 4, 2024

Artificial intelligence (AI) refers to the simulation of human processes by machines, especially computer systems, providing assistance in a variety patient care and health systems. The aim this review is contribute valuable insights ongoing discourse on transformative potential AI healthcare, nuanced understanding its current applications, future possibilities, associated challenges. authors conducted literature search role disease diagnosis possible applications using PubMed, Google Scholar, ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning deep-learning techniques, has become integral facilitating immediate access evidence-based guidelines, latest medical literature, tools for generating differential diagnoses. However, our research also acknowledges limitations methodologies explores uncertainties obstacles with complete integration into clinical practice. This highlighted critical significance integrating healthcare framework meticulously examined evolutionary trajectory healthcare-oriented from inception, delving state development projecting extent reliance future. have found central study exploration how strategic can accelerate diagnostic process, heighten accuracy, enhance overall operational efficiency, concurrently relieving burdens faced practitioners.

Язык: Английский

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Simultaneous forecasting of wind speed for multiple stations based on attribute-augmented spatiotemporal graph convolutional network and tree-structured parzen estimator

Energy, Год журнала: 2024, Номер 295, С. 131058 - 131058

Опубликована: Март 20, 2024

Язык: Английский

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