標題: Titlebook: Explainable Machine Learning for Multimedia Based Healthcare Applications; M. Shamim Hossain,Utku Kose,Deepak Gupta Book 2023 The Editor(s [打印本頁] 作者: Clientele 時間: 2025-3-21 18:59
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書目名稱Explainable Machine Learning for Multimedia Based Healthcare Applications被引頻次學科排名
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書目名稱Explainable Machine Learning for Multimedia Based Healthcare Applications年度引用學科排名
書目名稱Explainable Machine Learning for Multimedia Based Healthcare Applications讀者反饋
書目名稱Explainable Machine Learning for Multimedia Based Healthcare Applications讀者反饋學科排名
作者: 點燃 時間: 2025-3-21 22:13 作者: allergen 時間: 2025-3-22 02:59 作者: 小歌劇 時間: 2025-3-22 04:53 作者: 地牢 時間: 2025-3-22 10:12
Sergej Sizov,Steffen Staab,Thomas Franzed using ensemble of optimized kernel fuzzy C means multilayer deep transfer convolutional learning (OpKFuzCMM-DTCL). The presented technique’s performance was assessed using a benchmark image Bone tumour dataset as well as Bone MRI images. When compared to current strategies in the literature, our 作者: 無情 時間: 2025-3-22 15:13 作者: 無情 時間: 2025-3-22 18:23
Merril Silverstein,Roseann Giarrussoartificial intelligence have played an important role in medicinal field in recent years. This study was conducted as a specific systematic literature review. Systematic research is a scientific synthesis that uses methods to identify and evaluate studies on the subject. The data part of the researc作者: 蝕刻 時間: 2025-3-22 22:07
https://doi.org/10.1007/978-3-540-31211-6xpose symptoms for COVID-19 diagnosis and forecasting and integrated comprehension of huge AI and ML continue to be challenges. As a result, the development and use of the Explainable Artificial Intelligence (XAI) concept provides a solution in these responses, where rule-based approaches are ideall作者: 口味 時間: 2025-3-23 02:56
G. N. Tiwari,Arvind Tiwari,Shyam the research field to handle those issues. It also presents the evaluation of the heart rate and other health vitals measuring tools present in the commercial market and compares the performance of these tools against a standard health monitoring device. Two commercially available applications Well作者: PON 時間: 2025-3-23 07:16 作者: 上腭 時間: 2025-3-23 12:56
Explainable Machine Learning (XML) for Multimedia-Based Healthcare Systems: Opportunities, Challengowever, this scenario also presents some significant problems. Interpretability and explainability of such models is one of the like issues, particularly for sophisticated nonlinear models. If these issues are not resolved in the healthcare system, adoption chances may be severely hampered, in actua作者: 變化無常 時間: 2025-3-23 16:12
Ensemble Deep Learning Architectures in Bone Cancer Detection Based on Medical Diagnosis in Explained using ensemble of optimized kernel fuzzy C means multilayer deep transfer convolutional learning (OpKFuzCMM-DTCL). The presented technique’s performance was assessed using a benchmark image Bone tumour dataset as well as Bone MRI images. When compared to current strategies in the literature, our 作者: expound 時間: 2025-3-23 19:45
Digital Dermatitis Disease Classification Utilizing Visual Feature Extraction and Various Machine Llearning methodologies..After the study’s results are analysed in detail with tables and figures, it is concluded that the artificial intelligence models created can be used in the classification of DD case photographs with a cumulative accuracy value more than 0.95. This conclusion was reached foll作者: acquisition 時間: 2025-3-24 00:34 作者: 跳動 時間: 2025-3-24 05:37
Application of Interpretable Artificial Intelligence Enabled Cognitive Internet of Things for COVIDxpose symptoms for COVID-19 diagnosis and forecasting and integrated comprehension of huge AI and ML continue to be challenges. As a result, the development and use of the Explainable Artificial Intelligence (XAI) concept provides a solution in these responses, where rule-based approaches are ideall作者: BIAS 時間: 2025-3-24 07:50 作者: 陳腐思想 時間: 2025-3-24 12:23
Book 2023 as future insights. In detail, a comprehensive topic coverage is ensured by focusing on remarkable healthcare problems solved with Artificial Intelligence. Because today’s conditions in medical data processing are often associated with multimedia, the book considers research studies with especially multimedia data processing..作者: CANON 時間: 2025-3-24 14:51
Book 2023 the content includes not only introductions for applied research efforts but also theoretical touches and discussions targeting open problems as well as future insights. In detail, a comprehensive topic coverage is ensured by focusing on remarkable healthcare problems solved with Artificial Intelli作者: chapel 時間: 2025-3-24 21:01
emarkable healthcare problems solved with Artificial Intelligence. Because today’s conditions in medical data processing are often associated with multimedia, the book considers research studies with especially multimedia data processing..978-3-031-38038-9978-3-031-38036-5作者: interlude 時間: 2025-3-25 01:56
An Introduction to the Handbook,lustrate how to leverage the WBCD dataset to build explainable machine learning to make an untrustworthy prediction trustworthy. The book chapter includes a case study that will be very useful to get more exposure to the same and helpful for the researchers working in the same field.作者: Decrepit 時間: 2025-3-25 03:26 作者: Decibel 時間: 2025-3-25 07:45
Software Engineering in the Cloud, subject, especially on explainable artificial intelligence, was carried out. It has been determined that the use of explainable artificial intelligence provides great advantages in terms of speed and cost in drug discovery.作者: 大溝 時間: 2025-3-25 14:57
Explainable Machine Learning in Healthcare,lustrate how to leverage the WBCD dataset to build explainable machine learning to make an untrustworthy prediction trustworthy. The book chapter includes a case study that will be very useful to get more exposure to the same and helpful for the researchers working in the same field.作者: alliance 時間: 2025-3-25 17:45 作者: 游行 時間: 2025-3-25 22:58
Using Explainable Artificial Intelligence in Drug Discovery: A Theoretical Research, subject, especially on explainable artificial intelligence, was carried out. It has been determined that the use of explainable artificial intelligence provides great advantages in terms of speed and cost in drug discovery.作者: MEN 時間: 2025-3-26 02:20 作者: 減至最低 時間: 2025-3-26 06:05
re inherently “black box” and lack the ability to explain how a decision is made. In order to fill this gap, the concept of explainable artificial intelligence (XAI) is proposed. This chapter aims at providing a brief review of XAI terminologies, review studies, and methods.作者: 膽小懦夫 時間: 2025-3-26 09:50
A Novel Approach of COVID-19 Estimation Using GIS and Kmeans Clustering: A Case of GEOAI,ted cases in India. Four states are in the red zone?Viz. Maharashtra (44582), Tamil Nadu (14753), Gujarat (13268), and Delhi (12319). This paper aims to study the Covid-19 using Geographic Information Systems?models and Artificial Intelligence?algorithms.作者: 交響樂 時間: 2025-3-26 14:47 作者: Aura231 時間: 2025-3-26 19:30
al knowledge and technical details for understanding the tarThis book covers the latest research studies regarding Explainable Machine Learning used in multimedia-based healthcare applications. In this context, the content includes not only introductions for applied research efforts but also theoret作者: Affluence 時間: 2025-3-26 22:15 作者: OMIT 時間: 2025-3-27 02:27 作者: Tonometry 時間: 2025-3-27 06:18 作者: 直覺沒有 時間: 2025-3-27 10:44
https://doi.org/10.1007/978-3-031-38036-5machine learning; deep learning; multimedia; artificial intelligence; interpretable machine learning; exp作者: 粘 時間: 2025-3-27 16:30
Mike Friedrichsen,Wolfgang Mühl-Benninghauss a tool in the analysis of medical images and videos, can facilitate the diagnosis process and contribute to increasing the accuracy in the decision-making stages of the experts. In addition, the evaluation of medical data, which requires experience and expertise, is achieved with the help of deep 作者: 勤勉 時間: 2025-3-27 21:46
ements in data processing have made this process possible, and the advancement of technology for network infrastructures. In the healthcare systems, where data abundance has generated a rush of new techniques for effective data collection and processing, this new predicament is particularly noticeab作者: 過份艷麗 時間: 2025-3-27 23:25 作者: appall 時間: 2025-3-28 05:49
Understanding Status as a Social Resources (DD), a malady that is frequent in dairy cattle and causes significant economic losses. For the purpose of the research, a member of the teaching staff who specialises in podiatry organised photographs of lesions caused by DD collected from 168 Holstein cows into groups based on the size of the le作者: discord 時間: 2025-3-28 08:52
An Introduction to the Handbook, of information for cancer researchers. Machine learning models used in healthcare, like those used in other fields, are still mostly unknown. Understanding the rationale behind machine learning model predictions is critical in deciding trust if a clinician wants to initiate cancer treatment action 作者: SUE 時間: 2025-3-28 13:47
Gordon Parker,Gemma L. Gladstoneng this malignancy early can save lives. More than 120 distinct tumors and related hereditary illnesses have individualized resources available, according to .. To diagnose breast cancer, machine learning techniques are mostly used. This research projects the use of eight machine learning (ML) appro作者: 一大塊 時間: 2025-3-28 17:41 作者: judicial 時間: 2025-3-28 21:24
rt of people’s daily lives. Due to the growing interest in AI, the number of research on the topic has increased significantly in recent years. AI based methods are used to reveal information, make decisions, and detect data behaviors. Albeit AI-based models outperform traditional techniques, they a作者: irreducible 時間: 2025-3-29 02:39 作者: arterioles 時間: 2025-3-29 04:30 作者: Neutral-Spine 時間: 2025-3-29 11:16
https://doi.org/10.1007/978-3-540-31211-6 Coronavirus (COVID-19) pandemic globally. Their use has proved effective in managing the pandemic by exploiting huge amount of data brought about with the use of IoT and cloud-based storage. Their implementation has provided various advantages like collection of huge data, scalability, real-time mo作者: 放肆的我 時間: 2025-3-29 13:43 作者: insipid 時間: 2025-3-29 19:22
Automatic Fetal Motion Detection from Trajectory of US Videos Based on YOLOv5 and LSTM,s a tool in the analysis of medical images and videos, can facilitate the diagnosis process and contribute to increasing the accuracy in the decision-making stages of the experts. In addition, the evaluation of medical data, which requires experience and expertise, is achieved with the help of deep 作者: Sciatica 時間: 2025-3-29 22:02
Explainable Machine Learning (XML) for Multimedia-Based Healthcare Systems: Opportunities, Challengements in data processing have made this process possible, and the advancement of technology for network infrastructures. In the healthcare systems, where data abundance has generated a rush of new techniques for effective data collection and processing, this new predicament is particularly noticeab作者: commute 時間: 2025-3-30 03:26
Ensemble Deep Learning Architectures in Bone Cancer Detection Based on Medical Diagnosis in Explain radiologists to make better decisions. The majority of early deaths worldwide are attributed to a group of diseases known as bone cancers, which are characterised by unchecked cell development. To treat the patient, early diagnosis and classification of the bone tumour are now necessary. This resea
