標題: Titlebook: Explainable Edge AI: A Futuristic Computing Perspective; Aboul Ella Hassanien,Deepak Gupta,Ankit Garg Book 2023 The Editor(s) (if applicab [打印本頁] 作者: 兩邊在擴散 時間: 2025-3-21 17:43
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書目名稱Explainable Edge AI: A Futuristic Computing Perspective網絡公開度學科排名
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書目名稱Explainable Edge AI: A Futuristic Computing Perspective讀者反饋
書目名稱Explainable Edge AI: A Futuristic Computing Perspective讀者反饋學科排名
作者: CANON 時間: 2025-3-21 21:42
https://doi.org/10.1007/978-1-4614-9035-7AI is used in every field where AI can be used but with some modification or by adding some techniques of XAI as “SHAP (Shaply Additive exPlanations), DeepSHAP, DeepLIFT, CXplain, and LIME”. The main goal of this chapter is to provide a brief overview of XAI by covering almost every aspect of XAI. T作者: 分解 時間: 2025-3-22 03:59 作者: Melanoma 時間: 2025-3-22 07:47 作者: 有常識 時間: 2025-3-22 09:46
https://doi.org/10.1007/978-0-387-85731-2ery difficult to understand. It sometimes becomes a very hard task for the domain experts too to understand the ML algorithms of the black block models, so the need for the development of this type of technology was felt. Many times, results are developed with very high accuracy are quite easy to un作者: 托運 時間: 2025-3-22 15:24
Satellite in Real (Perturbed) Orbit,riteria. The Edge AI community, which spans several ICT, engineering, and perform computer science subfields, researches unique machine learning techniques for the edge computing environment. The objective is to offer a hypothetical roadmap that may unite important players and enablers in order to t作者: 托運 時間: 2025-3-22 20:19 作者: 自制 時間: 2025-3-22 22:48
Michael S. Ritsner,Irving I. Gottesmanstomer service. The best example of explainable edge AI is virtual assistants such as Alexa, google assistant. They learn from the user’s world and phrases and can store them directly on the device. These are just a few examples later, and we have possible applications in future works on the explain作者: Original 時間: 2025-3-23 02:51
George Foussias,Ofer Agid,Gary Remingtonof parameters, it is difficult to interpret the model creating a black box. The chapter aims to outline the explainability of data fusion at the edge. It highlights different data models of fusion, discusses a framework for AI and data fusion at the edge and identifies potential challenges and possi作者: AUGER 時間: 2025-3-23 08:50 作者: 教育學 時間: 2025-3-23 09:58
Karen Weston,Mary Ott,Susan Rodger, the node has to monitor the neighboring node parameters at regular intervals, which incurs a huge number of communication overhead. The nodes in sensor network can employ the learning strategy to determine its best possible action to enhance the network coverage as well as network lifetime. The ch作者: AFFIX 時間: 2025-3-23 14:18
Explainable Edge AI: A Futuristic Computing Perspective作者: 甜得發(fā)膩 時間: 2025-3-23 21:18
Book 2023 finally, it elaborates on the technicalities of explainability in edge AI. Owing to the quick transition in the current computing scenario and integration with the latest AI-based technologies, it is significant to facilitate people-centric computing through explainable edge AI. Explainable edge AI作者: instulate 時間: 2025-3-24 01:20
Explainable Artificial Intelligence: Concepts and Current Progression,AI is used in every field where AI can be used but with some modification or by adding some techniques of XAI as “SHAP (Shaply Additive exPlanations), DeepSHAP, DeepLIFT, CXplain, and LIME”. The main goal of this chapter is to provide a brief overview of XAI by covering almost every aspect of XAI. T作者: 山崩 時間: 2025-3-24 03:00
Explainable Artificial Intelligence (XAI): Understanding and Future Perspectives,ls. The distrust of totally non-human, autonomous artificial intelligence systems is established as the cornerstone of the movement. The root of distrust lies in a lack of knowledge as to why intelligent systems make certain decisions in certain situations. As a result, this problem has sparked a fr作者: 在駕駛 時間: 2025-3-24 06:46
Explainable Artificial Intelligence (XAI): Conception, Visualization and Assessment Approaches Towaifically, rule based models and expert systems). Models underlying this problem come within the so-called Explainable AI (XAI) field, which is extensively acknowledged as a racial feature for the practical deployment of AI models. As a result, explainable artificial intelligence (XAI) has turned int作者: embolus 時間: 2025-3-24 12:54 作者: 過多 時間: 2025-3-24 18:45
Recent Challenges on Edge AI with Its Application: A Brief Introduction,riteria. The Edge AI community, which spans several ICT, engineering, and perform computer science subfields, researches unique machine learning techniques for the edge computing environment. The objective is to offer a hypothetical roadmap that may unite important players and enablers in order to t作者: 大火 時間: 2025-3-24 19:38
Explainable Artificial Intelligence in Health Care: How XAI Improves User Trust in High-Risk Decisid eye. Many health care practitioners already use AI, but it is frequently difficult to understand, causing irritation among clinicians and patients, especially when making high-stakes decisions. That’s why the health-care business requires explainable AI (XAI). Significant AI recommendations, such 作者: gastritis 時間: 2025-3-24 23:29
Role of Explainable Edge AI to Resolve Real Time Problem,stomer service. The best example of explainable edge AI is virtual assistants such as Alexa, google assistant. They learn from the user’s world and phrases and can store them directly on the device. These are just a few examples later, and we have possible applications in future works on the explain作者: 積習難改 時間: 2025-3-25 04:43 作者: 蹣跚 時間: 2025-3-25 07:39
Trust Model Based Data Fusion in Explainable Artificial Intelligence for Edge Computing Using Securial discriminant auto encoder in which the improvement of data accuracy, as well as for the maximizing of Edge-cloud based sensor networks lifespan. The fusion of edge cloud data has been carried out using discriminant auto encoder which is integrated with distributed edge cloud users, where the sec作者: guzzle 時間: 2025-3-25 13:14
A Deep Learning Based Target Coverage Protocol for Edge Computing Enabled Wireless Sensor Networks,, the node has to monitor the neighboring node parameters at regular intervals, which incurs a huge number of communication overhead. The nodes in sensor network can employ the learning strategy to determine its best possible action to enhance the network coverage as well as network lifetime. The ch作者: Facet-Joints 時間: 2025-3-25 19:43 作者: Autobiography 時間: 2025-3-25 20:02
Book 2023ability, interpretability, data-fusion, and comprehensibility that are significant for edge AI are being addressed in this book through explainable models and techniques. The concept of explainable edge AI is new in front of the academic and research community, and consequently, it will undoubtedly 作者: RUPT 時間: 2025-3-26 01:13 作者: 發(fā)酵劑 時間: 2025-3-26 06:40
Explainable Edge AI: A Futuristic Computing Perspective978-3-031-18292-1Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: bile648 時間: 2025-3-26 09:01 作者: UNT 時間: 2025-3-26 16:25 作者: 加入 時間: 2025-3-26 19:00 作者: Missile 時間: 2025-3-26 23:50 作者: obstruct 時間: 2025-3-27 04:22
https://doi.org/10.1007/978-0-387-85731-2able to that of humans. But the development of this kind of technology model that mimics humans involves a lot of complex calculations and complex algorithms that are difficult to explain and understand. For this problem, the concept of explainable artificial intelligence (XAI) is developed and intr作者: 相一致 時間: 2025-3-27 08:30 作者: 暴露他抗議 時間: 2025-3-27 11:06
Stanley L. Brodsky,H. O’Neal Smithermanency to AI algorithms so that their predictions can be justified. AI models, their predicted impact, and any biases may all be described using XAI. Human specialists can grasp the forecasts generated by this technology and have trust in the results. Medical AI applications must be transparent in ord作者: 送秋波 時間: 2025-3-27 14:25
Michael S. Ritsner,Irving I. Gottesmandevices. Artificial Intelligence algorithms are processed on edge or the devices of users. Edge Computing based on the same premise, stores, processes, and manages data directly at Internet of Things (IoT) endpoints. Edge artificial intelligence uses the device‘s hardware to process data and perform作者: 爵士樂 時間: 2025-3-27 20:18 作者: 博識 時間: 2025-3-28 00:58
Paul H. Lysaker,Molly A. Ericksono become an inherent concern in complex networks as a result of this increase of data. The practise of assessing trust using attributes that influence trust is known as trust evaluation. It is confronted with a number of serious challenges, including a shortage of critical assessment data, a require作者: 厚顏 時間: 2025-3-28 04:29
Karen Weston,Mary Ott,Susan Rodger. The sensor nodes are usually characterized as having scarce resources; hence energy efficient mechanisms which can enhance the resource utilization are of great significance. The integration of edge computing framework with the sensor network can aid in the data collection, dissemination and decis作者: colostrum 時間: 2025-3-28 09:25 作者: jealousy 時間: 2025-3-28 13:22
Studies in Computational Intelligencehttp://image.papertrans.cn/e/image/319296.jpg作者: 減至最低 時間: 2025-3-28 17:50 作者: 的’ 時間: 2025-3-28 20:46
Explainable Artificial Intelligence (XAI): Understanding and Future Perspectives, of the transition towards a more algorithmic society. The availability of enormous datasets, as well as recent advancements in deep learning methods, are allowing AI systems to perform at or even beyond the level of human performance on an expanding variety of challenging tasks. However, because of作者: 直言不諱 時間: 2025-3-29 01:46 作者: Cardioplegia 時間: 2025-3-29 04:25 作者: jagged 時間: 2025-3-29 10:18 作者: 爭吵加 時間: 2025-3-29 14:59
Explainable Artificial Intelligence in Health Care: How XAI Improves User Trust in High-Risk Decisiency to AI algorithms so that their predictions can be justified. AI models, their predicted impact, and any biases may all be described using XAI. Human specialists can grasp the forecasts generated by this technology and have trust in the results. Medical AI applications must be transparent in ord作者: rectum 時間: 2025-3-29 16:03 作者: 細絲 時間: 2025-3-29 22:18
Explainable Data Fusion on Edge: Challenges and Opportunities, nowadays available in nearly every field, be it science or social domain. As much of these collected data are time-sensitive, they need to be utilized timely to give an effective outcome. Data fusion is of paramount significance in enhancing the collected data‘s effectiveness. But, today, as AI has
