Smart Transportation Systems

Advances in civil and industrial engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 254 - 283

Published: June 30, 2024

This chapter delves into the transformative potential of smart transportation systems (STS) within context sustainable urban mobility. As cities worldwide grapple with dual challenges rapid urbanization and environmental sustainability, STS emerge as a pivotal solution, harnessing power advanced technologies to optimize efficiency, reduce impact, enhance liability. aims dissect components, functionalities, benefits STS, illustrating how they serve backbone contemporary practices. The reviews development technological foundations emphasizing its key components applications. It provides examples applications in traffic management, public transport, logistics, planning illustrate their real impact. Overall, highlights role shaping future promoting mobility, improving quality life around world.

Language: Английский

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Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Polish Journal of Radiology, Journal Year: 2024, Volume and Issue: 89, P. 30 - 48

Published: Jan. 22, 2024

Ovarian cancer poses a major worldwide health issue, marked by high death rates and deficiency in reliable diagnostic methods. The precise prompt detection of ovarian holds great importance advancing patient outcomes determining suitable treatment plans. Medical imaging techniques are vital diagnosing cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as promising solution to improve the accuracy detection. <br /> This systematic review explores role DL improving for cancer. methodology involved establishment research questions, inclusion exclusion criteria, comprehensive search strategy across relevant databases. selected studies focused on applied diagnosis using medical modalities, well tumour differentiation radiomics. Data extraction, analysis, synthesis were performed summarize characteristics findings studies.<br emphasizes potential enhancing accelerating process offering more efficient solutions. models have demonstrated their effectiveness categorizing tissues comparable performance that experienced radiologists. integration into promise outcomes, refining approaches, supporting well-informed decision-making. Nevertheless, additional validation necessary ensure dependability applicability everyday clinical settings.

Language: Английский

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Integration of IoMT for Enhanced Healthcare

Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 70 - 95

Published: June 7, 2024

The internet of medical things (IoMT) has revolutionised modern healthcare. This is explored in detail the chapter. chapter examines how IoMT technologies are being applied three critical areas—sleep monitoring, body movement detection, and rehabilitation evaluation—and they may completely transform patient outcomes treatment. gives an overview healthcare introduction, highlighting value sleep improving well-being. also covers future trends, problems potential roadblocks to adoption, solutions privacy security issues. ends with a summary most important lessons learned, revolutionary contemporary urging more research into its possible uses.

Language: Английский

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Screening ovarian cancer by using risk factors: machine learning assists

BioMedical Engineering OnLine, Journal Year: 2024, Volume and Issue: 23(1)

Published: Feb. 12, 2024

Abstract Background and aim Ovarian cancer (OC) is a prevalent aggressive malignancy that poses significant public health challenge. The lack of preventive strategies for OC increases morbidity, mortality, other negative consequences. Screening through risk prediction could be leveraged as powerful strategy purposes have not received much attention. So, this study aimed to leverage machine learning approaches predictive assistance solutions screen high-risk groups achieve practical purposes. Materials methods As data-driven retrospective in nature, we 1516 suspicious women data from one concentrated database belonging six clinical settings Sari City 2015 2019. Six (ML) algorithms, including XG-Boost, Random Forest (RF), J-48, support vector (SVM), K-nearest neighbor (KNN), artificial neural network (ANN) were construct models OC. To choose the best model predicting OC, compared various built using area under receiver characteristic operator curve (AU-ROC). Results Current experimental results revealed XG-Boost with AU-ROC = 0.93 (0.95 CI [0.91–0.95]) was recognized best-performing Conclusions ML possess efficiency interoperability leveraging screening groups.

Language: Английский

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Cultivating Cultural Intelligence for the Global Workforce

Advances in higher education and professional development book series, Journal Year: 2024, Volume and Issue: unknown, P. 297 - 332

Published: Sept. 16, 2024

The chapter investigates the fundamental part of social insights (CQ) in planning people for victory today's interconnected and differing world. It starts by characterizing CQ its importance exploring cross-cultural intuition cultivating comprehensive situations. chapter, at that point, dives into four key components CQ: cognitive, metacognitive, motivational, behavioral, highlighting their significance creating intercultural competence. Through case considerations illustrations, outlines effect on worldwide commerce group flow. Also, it examines devices techniques surveying instructive settings, distinguishing ranges quality openings development among understudies. inventive educational strategies curricular approaches to advancement, emphasizing experiential learning multicultural ventures.

Language: Английский

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From Data to Decisions

Advances in environmental engineering and green technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: Jan. 24, 2025

In this chapter, we'll take a deep dive into how artificial intelligence (AI) is stepping up to tackle some of our biggest environmental problems. With AI and data technology advancing rapidly, we now have an incredible opportunity use these tools protect planet. We'll explore can gather, analyze, understand massive amounts data, giving us valuable insights make smarter decisions. Through real-world examples stories, see being used model climate change, track wildlife, detect pollution, manage precious natural resources. By showing the power combining with smart choices, chapter aims highlight playing crucial role in building better, greener future for all.

Language: Английский

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A Review of Deep Learning Models for Early Detection and Diagnosis of Ovarian Cancer

International Research Journal of Multidisciplinary Technovation, Journal Year: 2025, Volume and Issue: unknown, P. 123 - 137

Published: Jan. 27, 2025

Ovarian cancer ranks seventh worldwide and is the third most common type of diagnosed in women India. Numerous studies have demonstrated that number people affected by ovarian expected to rise significantly future. Proactive measures for early detection are essential prevent death recurrence. This paper attempts review various deep learning (DL) models diagnosis, including detecting risk factors, analyzing genomic data sets, predicting disease progression, recurrence, mortality rates, identifying correlations patterns. The patient's electronic health records contain effective analytics on imaging other types may open door more accurate or identification cancer. taxonomy several ways DL aids detection, treatment will be compiled this article. As per reviews, research examined Convolutional Neural Networks (CNNs) approach Early Detection Diagnosis Cancer. because CNNs a popular potent architecture image classification tasks their capacity learn spatial hierarchical features from images effectively. article seeks give future topics assess state-of-the-art application algorithms diagnosis.

Language: Английский

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OVision A raspberry Pi powered portable low cost medical device framework for cancer diagnosis

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 28, 2025

Cancer remains a major global health challenge, with significant disparities in access to advanced diagnostic and prognostic technologies, especially resource-constrained settings. Existing medical treatments devices for cancer diagnosis are often prohibitively expensive, limiting their reach impact. Pathologists' scarcity exacerbates accuracy, elevating mortality risks. To address these critical issues, this study presents OVision - low cost, deep learning-powered framework developed assist histopathological diagnosis. The key objective is leverage the portable, low-power computing Raspberry Pi. By designing standalone that eliminate need internet connectivity high-end infrastructure, we can dramatically reduce costs while maintaining accuracy. As proof of concept, demonstrated viability through compact, self-contained device capable accurately detecting ovarian subtypes 95% on par traditional methods, costing small fraction price. This off-grid solution has immense potential improve precision diagnostics, underserved regions world lack resources deploy infrastructure-heavy technologies. In addition, by classifying each tile, tool provide percentages histologic subtype detected within slide. capability enhances precision, offering detailed overview heterogeneity tissue sample, helps understanding complexity tailoring personalized treatment plans. conclusion, work proposes transformative model developing affordable, accessible bring healthcare benefits all, laying foundation more equitable, inclusive future medicine.

Language: Английский

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Histopathological Deep Learning: Exploring Ovarian Cancer Subtypes with Image Analysis

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Machine learning use in early ovarian cancer detection

Deleted Journal, Journal Year: 2025, Volume and Issue: 2(1)

Published: March 11, 2025

Ovarian cancer remains a significant public health challenge due to the difficulty of early detection. This review explores promising potential machine learning (ML) algorithms in this domain. We analyze studies that investigate application ML for ovarian The highlights effectiveness various algorithms, including support vector machines (SVMs), random forests, and XGBoost, achieving high diagnostic accuracy. Studies exploring diverse data sources, such as blood tests, genetic data, medical images, demonstrate versatility Notably, ability tailor models specific risk groups disease stages is crucial advancement with further improve However, challenges related quality, standardization, ethical considerations require attention. concludes by emphasizing need future research focused on refining existing models, deep techniques, incorporating multi-omics data. Additionally, addressing quality bias essential ensuring equitable ML-based tools. Overall, underscores transformative enhancing accuracy detection, ultimately leading improved patient outcomes.

Language: Английский

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