標(biāo)題: Titlebook: Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen; Aditya Khamparia,Deepak Gupta,Valent [打印本頁(yè)] 作者: EFFCT 時(shí)間: 2025-3-21 17:14
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen影響因子(影響力)
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen影響因子(影響力)學(xué)科排名
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen被引頻次
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen被引頻次學(xué)科排名
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen年度引用
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen年度引用學(xué)科排名
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen讀者反饋
書(shū)目名稱Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen讀者反饋學(xué)科排名
作者: 能夠支付 時(shí)間: 2025-3-21 23:13
Modeling of Explainable Artificial Intelligence with Correlation-Based Feature Selection Approach fe of fuzzy k-nearest neighbor classifier (FKNN), and the parameter tuning of this model is performed utilizing black widow optimization (BWO) approach. The experimental result analysis of the XAICFS-BDA technique is carried out using distinct benchmark biomedical dataset. Extensive comparative analy作者: 繁榮中國(guó) 時(shí)間: 2025-3-22 01:11
Explainable Artificial Intelligence with Metaheuristic Feature Selection Technique for Biomedical Dork (DNN) is exploited for medical data classification, and its efficiency can be further improved by the use of Nadam-optimizer-based hyperparameter tuning process. The performance validation of the XAIMFS-BMC technique is tested using distinct benchmark medical dataset, and the results are inspect作者: flourish 時(shí)間: 2025-3-22 07:43
Design of Multimodal Fusion-Based Deep Learning Approach for COVID-19 Diagnosis Using Chest X-Ray Iused together to increase the classification performance. Finally, multilayer perceptron (MLP) is applied to detect and classify the input images into distinct class labels. In order to examine the effective classifier outcome of the MMFBDL model, a comprehensive set of simulations takes place and t作者: Restenosis 時(shí)間: 2025-3-22 10:54 作者: fixed-joint 時(shí)間: 2025-3-22 15:00
Rethinking the Transfer Learning Architecture for Respiratory Diseases and COVID-19 Diagnosis,and normal occurrences was used to diagnose coronavirus disease automatically. A dataset has been used in this experiment comprising 76 image samples showing verified COVID-19 illness, 2786 images showing bacterial pneumonia, 1504 images showing viral pneumonia, and 1583 images showing normal circum作者: glacial 時(shí)間: 2025-3-22 21:07
Arithmetic Optimization Algorithm with Explainable Artificial Intelligence Technique for Biomedicalignals, where the AOA can be utilized for effectively selecting the weight and bias values of the SVM model. For ensuring the enhanced performance of the AOA-XAI approach, a series of simulations can be implemented against the benchmark dataset. The experimental results reported the supremacy of the作者: Neonatal 時(shí)間: 2025-3-22 23:10
Unkonventionell gegen Konventionenemble learning model. Moreover, the parameter tuning of the BWELM model takes place by the use of chaotic starling particle swarm optimization (CSPSO), where the inertia weight and acceleration coefficient of the PSO algorithm are modified via logistic chaotic map. The application of CSPSO algorithm作者: HERE 時(shí)間: 2025-3-23 01:29
Unkonventionell gegen Konventionene of fuzzy k-nearest neighbor classifier (FKNN), and the parameter tuning of this model is performed utilizing black widow optimization (BWO) approach. The experimental result analysis of the XAICFS-BDA technique is carried out using distinct benchmark biomedical dataset. Extensive comparative analy作者: 有權(quán)威 時(shí)間: 2025-3-23 08:54
Deepak Vaid,Sundance Bilson-Thompsonork (DNN) is exploited for medical data classification, and its efficiency can be further improved by the use of Nadam-optimizer-based hyperparameter tuning process. The performance validation of the XAIMFS-BMC technique is tested using distinct benchmark medical dataset, and the results are inspect作者: Constitution 時(shí)間: 2025-3-23 12:37
Optimum Location for Relay Node in LTE-A,used together to increase the classification performance. Finally, multilayer perceptron (MLP) is applied to detect and classify the input images into distinct class labels. In order to examine the effective classifier outcome of the MMFBDL model, a comprehensive set of simulations takes place and t作者: 辯論的終結(jié) 時(shí)間: 2025-3-23 16:24 作者: 褪色 時(shí)間: 2025-3-23 19:50
Signals and Communication Technologyand normal occurrences was used to diagnose coronavirus disease automatically. A dataset has been used in this experiment comprising 76 image samples showing verified COVID-19 illness, 2786 images showing bacterial pneumonia, 1504 images showing viral pneumonia, and 1583 images showing normal circum作者: Intervention 時(shí)間: 2025-3-23 23:44
Xuesong Feng,Haidong Liu,Keqi Wuignals, where the AOA can be utilized for effectively selecting the weight and bias values of the SVM model. For ensuring the enhanced performance of the AOA-XAI approach, a series of simulations can be implemented against the benchmark dataset. The experimental results reported the supremacy of the作者: 男學(xué)院 時(shí)間: 2025-3-24 02:47
Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen作者: 控訴 時(shí)間: 2025-3-24 09:44 作者: 補(bǔ)助 時(shí)間: 2025-3-24 10:55 作者: oracle 時(shí)間: 2025-3-24 18:21
Book 2022ntages in dealing with big and complex data by using explainable AI concepts in the field of biomedical sciences. The book explains both positive as well as negative findings obtained by explainable AI techniques. It features real time experiences by physicians and medical staff for applied deep lea作者: 松軟無(wú)力 時(shí)間: 2025-3-24 22:42
Deepak Vaid,Sundance Bilson-Thompsonds to interpret deep neural networks using a game theory concept known as Shapley values. We also discuss how to introduce interpretability in existing deep learning model systems non-intrusively, making the transition from “black box” to interpretable deep neural networks.作者: –吃 時(shí)間: 2025-3-25 02:49
Explainable AI in Neural Networks Using Shapley Values,ds to interpret deep neural networks using a game theory concept known as Shapley values. We also discuss how to introduce interpretability in existing deep learning model systems non-intrusively, making the transition from “black box” to interpretable deep neural networks.作者: Adornment 時(shí)間: 2025-3-25 03:49 作者: 壯觀的游行 時(shí)間: 2025-3-25 11:16 作者: 欺騙手段 時(shí)間: 2025-3-25 12:35 作者: Ventricle 時(shí)間: 2025-3-25 18:29 作者: 心胸狹窄 時(shí)間: 2025-3-25 22:12 作者: Allodynia 時(shí)間: 2025-3-26 01:23 作者: 跑過(guò) 時(shí)間: 2025-3-26 07:29 作者: Seizure 時(shí)間: 2025-3-26 08:28
Explainable AI in Neural Networks Using Shapley Values,networks, it is difficult to comprehend and trust the decisions made by them. Even though DNNs are highly accurate, they cannot be adopted easily in domains such as medical and finance, where trust and transparency are non-negotiable. Explainable Artificial Intelligence (XAI) is the field of study i作者: delta-waves 時(shí)間: 2025-3-26 14:10 作者: Leisureliness 時(shí)間: 2025-3-26 19:12
ECG Classification and Analysis for Heart Disease Prediction Using XAI-Driven Machine Learning Algo is the electrical activity of the heart. It generates in form like electrical indication at a greater extent number of people are suffering from heart disease. Envision of heart sicknesses in clinical focuses is huge to recognize if the individual has heart sickness or not. Information mining is ut作者: Grievance 時(shí)間: 2025-3-27 00:25 作者: Gratulate 時(shí)間: 2025-3-27 02:11 作者: DEBT 時(shí)間: 2025-3-27 09:02 作者: conspicuous 時(shí)間: 2025-3-27 09:57
Intelligent Systems Reference Libraryhttp://image.papertrans.cn/b/image/188004.jpg作者: 空中 時(shí)間: 2025-3-27 15:07
Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligen978-981-19-1476-8Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: subordinate 時(shí)間: 2025-3-27 19:10 作者: 值得贊賞 時(shí)間: 2025-3-27 22:44
978-981-19-1478-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: 憤慨點(diǎn)吧 時(shí)間: 2025-3-28 03:38
Unkonventionell gegen Konventionenof handling massive and complicated data, XAI concept finds useful in several applications, particularly health care. With the developments of machine learning (ML) and XAI, healthcare service quality can be considerably improved. This article designs an optimal boosting label weighting extreme lear作者: Redundant 時(shí)間: 2025-3-28 10:18
Unkonventionell gegen Konventionenlainable artificial intelligence (XAI) tools can be applied for the effective examination of biomedical data and perform classification process. Besides, the high dimensionality of the medical data requires proper selection of features to reduce the complexity level. This paper presents an explainab作者: Aggrandize 時(shí)間: 2025-3-28 10:30
Umbau Zahnlabor DDT GmbH Georg DICKes, the person affected is not usually able to regulate activity. Individuals affected by Parkinson‘s disease display noticeable effects at varying stages of the condition, such as gait impairments and tremor events. Medical diagnosis is an important yet difficult job which should be carried out cor作者: 黃油沒(méi)有 時(shí)間: 2025-3-28 17:47
Deepak Vaid,Sundance Bilson-Thompsons, particularly health care. The application of XAI approaches can be used to investigate the biomedical data for effective disease diagnosis and classification. At the same time, the high dimensionality of the healthcare data poses a curse of dimensionality problem which can be solved by the design作者: corporate 時(shí)間: 2025-3-28 20:26
