標(biāo)題: Titlebook: Deep Learning Techniques for Biomedical and Health Informatics; Sujata Dash,Biswa Ranjan Acharya,Arpad Kelemen Book 2020 Springer Nature S [打印本頁(yè)] 作者: ATE 時(shí)間: 2025-3-21 16:45
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics影響因子(影響力)
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics被引頻次
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics被引頻次學(xué)科排名
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics年度引用
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics年度引用學(xué)科排名
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics讀者反饋
書(shū)目名稱(chēng)Deep Learning Techniques for Biomedical and Health Informatics讀者反饋學(xué)科排名
作者: Pathogen 時(shí)間: 2025-3-21 23:41
Deep Learning Based Biomedical Named Entity Recognition Systemsal mission in linguistic communication process referring to artificial intelligence, information Retrieval and data Extraction. Linguistic communication process could be a subfield of engineering, computer science and data engineering that deals that the interaction between the pc and human language作者: Mere僅僅 時(shí)間: 2025-3-22 04:12 作者: 占卜者 時(shí)間: 2025-3-22 08:03
Applications of Deep Learning in Healthcare and Biomedicineount of data is collected and stored. With this change, there is a need for analytical and technological upgradation of existing systems and processes. Data collected is in the form of Electronic Health Data taken from individuals or patients which can be in the form of readings, texts, speeches or 作者: PLIC 時(shí)間: 2025-3-22 09:44 作者: CIS 時(shí)間: 2025-3-22 15:38
Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informaticsrequirements and availability of health records. With the improvement of machine learning techniques, the recommender system brings about several opportunities to the medical science. Systems can perform more efficiently and solve complex problems using deep learning, even when data set is diverse a作者: CIS 時(shí)間: 2025-3-22 18:26
Deep Learning and Explainable AI in Healthcare Using EHRlity of technology which could predict many different diseases risks. Patients Electronic Health Records (EHR) contains all different kinds of medical data for each patient, for each medical visit. Now there are many predictive models like random forests, boosted trees which provide high accuracy bu作者: patriot 時(shí)間: 2025-3-23 00:39 作者: 蟄伏 時(shí)間: 2025-3-23 05:08 作者: 山間窄路 時(shí)間: 2025-3-23 09:35
Intelligent, Secure Big Health Data Management Using Deep Learning and Blockchain Technology: An Overe. Such health monitoring and support are getting immensely popular among both patients and doctors as it does not require physical movement which is always not possible for elderly people who lives mostly alone in current socio-economic situations. Healthcare Informatics plays a key role in such c作者: 有害處 時(shí)間: 2025-3-23 11:21
Malaria Disease Detection Using CNN Technique with SGD, RMSprop and ADAM Optimizerse world. There exists many drugs which make malaria a curable disease but due to inadequate technologies and equipments, we are unable to detect and cure it. The method of diagnosing malaria involves counting of parasite and red blood cells drugs physically which is a labor-intensive and error-prone作者: 薄荷醇 時(shí)間: 2025-3-23 16:31 作者: Arroyo 時(shí)間: 2025-3-23 19:49 作者: ANTIC 時(shí)間: 2025-3-24 01:24
Diabetes Detection Using ECG Signals: An Overviewtion where high amount of glucose is present in the blood along with lack of insulin. The incidence of diabetes affected people is increasing every year. Diabetes cannot be cured. It can only be managed. If, not managed properly, it can lead to great complications which can be fatal. Therefore, time作者: gorgeous 時(shí)間: 2025-3-24 04:52
Deep Learning and the Future of Biomedical Image Analysisedical sciences. In the field of medical imaging for the diagnosis of disease, DL techniques are very helpful for early detection. Most important features of DL techniques are that they are uncomplicated with lower complexity, which ultimately saves the time and money and tackle many tough tasks sim作者: 基因組 時(shí)間: 2025-3-24 07:31
Automated Brain Tumor Segmentation in MRI Images Using Deep Learning: Overview, Challenges and Futurinitial stages which eventually improve treatment as well as survival chances of patient. Manual segmentation is highly dependent on doctor, it may vary from one expert to another as well as it is very time-consuming. On the other side, automated segmentation helps a doctor in quick decision making,作者: Pde5-Inhibitors 時(shí)間: 2025-3-24 13:23
Book 2020re different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health...It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in t作者: 粘土 時(shí)間: 2025-3-24 16:37 作者: 腐蝕 時(shí)間: 2025-3-24 22:36
https://doi.org/10.1007/978-3-540-72962-4 approach i.e. Bidirectional Long Short Term Memory (Bi-LSTM), It mistakenly labeled a gene entity “BRCA1” as a disease entity which is “BRCA1 abnormality” or “Braca1-deficient” present in the training dataset. Similarly, “VHL (Von Hippel-Lindau disease),” which is one of the genes named labeled as 作者: 占線 時(shí)間: 2025-3-25 01:13
Basic Tools of Multivariate Matchings. It is an Artificial Neural Network that designs models computationally that are composed of many processing layers, in order to learn data representations with numerous levels of abstraction. Research suggests that deep learning might have benefits over previous algorithms of machine learning and作者: languor 時(shí)間: 2025-3-25 06:36
Design of Observational Studiesng the quality of clinical healthcare enormously. Such kind of intelligent decision making in healthcare and clinical practice is also expected to result in holistic treatment. In this chapter, we review and accumulate various existing DL techniques and their applications for decision support in cli作者: jumble 時(shí)間: 2025-3-25 08:08 作者: Figate 時(shí)間: 2025-3-25 11:38 作者: CLAIM 時(shí)間: 2025-3-25 16:17 作者: colony 時(shí)間: 2025-3-25 21:25 作者: contrast-medium 時(shí)間: 2025-3-26 03:56
OTFT Modelling and Characteristicsm that consists of exercises and preferable sports. We try to exploit an “Actor-Critic” model for enhancing the ability of the model to continuously update information seeking strategies based on user’s real-time feedback. Health industry usually deals with long-term issues. Traditional recommender 作者: 說(shuō)明 時(shí)間: 2025-3-26 05:38
https://doi.org/10.1007/978-3-319-21188-6aracteristics fit right to the nature of deep learning. Therefore, we believe it is the right time to summarize the current status, to review and learn from the state-of-the-art medical-based NLP techniques. Different from the existing reviews, we examine and categorize the current deep learning-bas作者: 招人嫉妒 時(shí)間: 2025-3-26 08:45
https://doi.org/10.1007/978-3-030-80139-7ery effectively so as to identify the correlation between the presence of diabetes and HRV signal variations in the most accurate and fast manner. We discuss several deep learning architectures which can be effectively used for HRV signal analysis for the purpose of detection of diabetes. It can be 作者: 改變立場(chǎng) 時(shí)間: 2025-3-26 13:25
Reliability and Congestion Controlsent complex structures, self-learning and efficiently process large amounts of MRI-based image data. Initially the chapter starts with brain tumor introduction and its various types. In the next section, various preprocessing techniques are discussed. Preprocessing is a crucial step for the correct作者: 博識(shí) 時(shí)間: 2025-3-26 18:51
Deep Learning Techniques for Biomedical and Health Informatics作者: innovation 時(shí)間: 2025-3-26 22:12
2197-6503 irect impact on improving the human life and health...It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in t978-3-030-33968-5978-3-030-33966-1Series ISSN 2197-6503 Series E-ISSN 2197-6511 作者: 束縛 時(shí)間: 2025-3-27 03:45
Deep Learning Based Biomedical Named Entity Recognition Systemscommunication. The various varieties of named entities includes person name, association name, place name, numbers etc. During this book chapter we tend to area unit solely handling medicine named entity recognition (Bio-NER) that could be a basic assignment within the conducting of medicine text te作者: 付出 時(shí)間: 2025-3-27 05:57
Disambiguation Model for Bio-Medical Named Entity Recognition approach i.e. Bidirectional Long Short Term Memory (Bi-LSTM), It mistakenly labeled a gene entity “BRCA1” as a disease entity which is “BRCA1 abnormality” or “Braca1-deficient” present in the training dataset. Similarly, “VHL (Von Hippel-Lindau disease),” which is one of the genes named labeled as 作者: Accrue 時(shí)間: 2025-3-27 10:50
Applications of Deep Learning in Healthcare and Biomedicines. It is an Artificial Neural Network that designs models computationally that are composed of many processing layers, in order to learn data representations with numerous levels of abstraction. Research suggests that deep learning might have benefits over previous algorithms of machine learning and作者: 整潔漂亮 時(shí)間: 2025-3-27 16:08
Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcareng the quality of clinical healthcare enormously. Such kind of intelligent decision making in healthcare and clinical practice is also expected to result in holistic treatment. In this chapter, we review and accumulate various existing DL techniques and their applications for decision support in cli作者: 過(guò)渡時(shí)期 時(shí)間: 2025-3-27 18:45 作者: Incommensurate 時(shí)間: 2025-3-27 22:28 作者: Amenable 時(shí)間: 2025-3-28 05:46 作者: 規(guī)范就好 時(shí)間: 2025-3-28 09:32 作者: VEN 時(shí)間: 2025-3-28 13:05
Deep Reinforcement Learning Based Personalized Health Recommendationsm that consists of exercises and preferable sports. We try to exploit an “Actor-Critic” model for enhancing the ability of the model to continuously update information seeking strategies based on user’s real-time feedback. Health industry usually deals with long-term issues. Traditional recommender 作者: Criteria 時(shí)間: 2025-3-28 18:11 作者: 意外的成功 時(shí)間: 2025-3-28 20:04 作者: 感染 時(shí)間: 2025-3-29 01:10
Automated Brain Tumor Segmentation in MRI Images Using Deep Learning: Overview, Challenges and Futursent complex structures, self-learning and efficiently process large amounts of MRI-based image data. Initially the chapter starts with brain tumor introduction and its various types. In the next section, various preprocessing techniques are discussed. Preprocessing is a crucial step for the correct作者: 乳白光 時(shí)間: 2025-3-29 03:08
https://doi.org/10.1007/978-3-540-72962-4ing Part of Speech Tagging, Chunking, and Entity Recognition on clinical texts. The sequence modeler in MedNLU is an integrated framework of Convolutional Neural Network, Conditional Random Fields and Bi-directional Long-Short Term Memory network.作者: 使增至最大 時(shí)間: 2025-3-29 09:51 作者: 別炫耀 時(shí)間: 2025-3-29 13:35
Implementing the ST-II Protocol, image analysis, computer aided diagnosis (CAD), image registration and, image-guided therapy and many more. The aim of writing this chapter is to describe the DL methods and, the future of biomedical imaging using DL in detail and discuss the issues and challenges.作者: 壟斷 時(shí)間: 2025-3-29 15:45 作者: 令人悲傷 時(shí)間: 2025-3-29 21:14
Malaria Disease Detection Using CNN Technique with SGD, RMSprop and ADAM Optimizersnormal blood films. The experimental result show our model works well on microscopic image and achieves an accuracy of 96.62% and the model has a lower model complexity are requires less computation time. Thus outperforming the state of art used previously.作者: Priapism 時(shí)間: 2025-3-30 01:03 作者: Indicative 時(shí)間: 2025-3-30 07:30 作者: Licentious 時(shí)間: 2025-3-30 09:49
Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informaticsnd unstructured. Here we present a comprehensive overview of the challenges associated with the existing recommender systems. Machine learning and deep learning techniques that are generally applied for health recommender system are discussed in detail along with their application to health informatics.作者: 優(yōu)雅 時(shí)間: 2025-3-30 14:01 作者: 確定方向 時(shí)間: 2025-3-30 18:14 作者: 細(xì)絲 時(shí)間: 2025-3-30 20:41 作者: Talkative 時(shí)間: 2025-3-31 01:37 作者: 整潔 時(shí)間: 2025-3-31 08:58 作者: BAN 時(shí)間: 2025-3-31 10:30
https://doi.org/10.1007/978-3-030-33966-1Biomedical Engineering; Health Informatics; Deep Learning; Machine Learning; Medical Imaging; Health Reco作者: epinephrine 時(shí)間: 2025-3-31 13:30
978-3-030-33968-5Springer Nature Switzerland AG 2020作者: 老巫婆 時(shí)間: 2025-3-31 20:04 作者: 難管 時(shí)間: 2025-4-1 01:04 作者: 去世 時(shí)間: 2025-4-1 03:59
https://doi.org/10.1007/978-3-540-72962-4es are present, including disease, chemical, gene, and protein. To find these entities, currently, a deep learning-based approach applied into the Biomedical Named Entity Recognition (Bio_NER) which gives prominent results. Although deep learning-based approach gives a satisfactory result, still a t作者: fiscal 時(shí)間: 2025-4-1 07:09
Basic Tools of Multivariate Matchingount of data is collected and stored. With this change, there is a need for analytical and technological upgradation of existing systems and processes. Data collected is in the form of Electronic Health Data taken from individuals or patients which can be in the form of readings, texts, speeches or
