標(biāo)題: Titlebook: Advances in Machine Learning/Deep Learning-based Technologies; Selected Papers in H George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain Book [打印本頁] 作者: 可怖 時間: 2025-3-21 19:03
書目名稱Advances in Machine Learning/Deep Learning-based Technologies影響因子(影響力)
書目名稱Advances in Machine Learning/Deep Learning-based Technologies影響因子(影響力)學(xué)科排名
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書目名稱Advances in Machine Learning/Deep Learning-based Technologies網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advances in Machine Learning/Deep Learning-based Technologies被引頻次
書目名稱Advances in Machine Learning/Deep Learning-based Technologies被引頻次學(xué)科排名
書目名稱Advances in Machine Learning/Deep Learning-based Technologies年度引用
書目名稱Advances in Machine Learning/Deep Learning-based Technologies年度引用學(xué)科排名
書目名稱Advances in Machine Learning/Deep Learning-based Technologies讀者反饋
書目名稱Advances in Machine Learning/Deep Learning-based Technologies讀者反饋學(xué)科排名
作者: 津貼 時間: 2025-3-21 20:28 作者: LUCY 時間: 2025-3-22 03:21
Advances in Machine Learning/Deep Learning-based Technologies978-3-030-76794-5Series ISSN 2662-3447 Series E-ISSN 2662-3455 作者: 淘氣 時間: 2025-3-22 06:01 作者: 大火 時間: 2025-3-22 11:05 作者: 迎合 時間: 2025-3-22 13:30 作者: LIMIT 時間: 2025-3-22 20:28
Energy Efficiency Projects that Move Slowly, on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent a作者: LAITY 時間: 2025-3-23 01:17
A Representative Energy Efficiency Project,r equipment damages. Such a task becomes even harder for universities offering distance education, as their students visit less often their lab facilities and may have limited opportunities to become familiar with the respective instruments and equipment. For this purpose, the Hellenic Open Universi作者: indices 時間: 2025-3-23 03:22
https://doi.org/10.1007/978-1-4471-4516-5from educational data and enhancing the quality of learning. Predicting student learning outcomes is one of the most significant problems facing these fields. Addressing effectively a predictive problem comprises the training of a supervised learning algorithm on a given set of labeled data. The dif作者: 只有 時間: 2025-3-23 09:02
A Representative Energy Efficiency Project,hnologies that can detect hidden nuclear material before its use. The process of detecting and identifying nuclear materials for non-reported purposes is under the umbrella of nuclear security. Among several areas that contribute to the security and safeguards of nuclear materials, radiation data an作者: Ambiguous 時間: 2025-3-23 13:21 作者: Recess 時間: 2025-3-23 17:34 作者: 名字的誤用 時間: 2025-3-23 21:19
A Representative Energy Efficiency Project,e plots (RP) are a phase space visualization tool used for the analysis of dynamical systems. This approach takes advantage of recurrence plots that are used as input image representations for a class of deep learning algorithms called convolutional neural networks. We show that by leveraging recurr作者: Metastasis 時間: 2025-3-23 23:27 作者: 使虛弱 時間: 2025-3-24 05:17
Debashis Bhattacharya,Vamsi Boppanassion ratio while maintaining the same video quality. One of the advanced techniques that HEVC adopts is dividing the input signal into Coding Units (CU) with various sizes, which ensures bitrate reduction while preserving visual details. The process that determines the size of CU is known as the Co作者: 圖畫文字 時間: 2025-3-24 06:32
https://doi.org/10.1007/978-3-030-76794-5Computational Intelligence; Machine Learning; Deep Learning; Intelligent Systems; Nikolaos G; Bourbakis作者: 尖 時間: 2025-3-24 13:07
978-3-030-76796-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 某人 時間: 2025-3-24 18:12 作者: 別名 時間: 2025-3-24 20:06
A Representative Energy Efficiency Project,re used as input image representations for a class of deep learning algorithms called convolutional neural networks. We show that by leveraging recurrence plots with optimal embedding parameters, appropriate representations of underlying dynamics are obtained by the proposed autoregressive deep learning model to produce forecasts.作者: ineffectual 時間: 2025-3-24 23:48
Book 2022its sub-field of .Deep Learning.) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machi作者: Confirm 時間: 2025-3-25 05:07 作者: 運動吧 時間: 2025-3-25 11:34
Energy Efficiency Projects that Move Slowly,ames and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.作者: 彩色 時間: 2025-3-25 14:03 作者: Culmination 時間: 2025-3-25 17:21
https://doi.org/10.1007/978-1-4471-4516-5tistical AI tool for the recognition of complex human activities. The formal model is associated with three context-free languages at body, motion, and activity levels. The statistical model is an artificial network that learns the activity of the human body.作者: PALL 時間: 2025-3-25 22:47
Closing the Achievement Gap in Singaporethe two “dueling” indexes; a t-norm operator-based feature importance index enables the appropriate feature set selection. Experimental results on social message streams show the method’s effectiveness in supporting those emotions the human considers relevant in the textual context.作者: 空氣 時間: 2025-3-26 03:08
anhand von Fallbeispielen exemplarisch illustriert.S?mtlich.Der Ausgangspunkt dieser Neusortierung ist: Wissenschaftskommunika-tion ist die Kontaktaufnahme und -pflege der Wissenschaft mit der Nichtwissenschaft, also mit ihrer Umwelt. Damit wird das Verst?ndnis von Wissenschaftskom-munikation sowoh作者: 殺蟲劑 時間: 2025-3-26 05:04
Computer-Human Mutual Training in a Virtual Laboratory Environmentich auf eine Analyse der um soziale Belohnungen, Reputation und Mittel ganz allgemein konkurrierenden Wissenschaftlers. und impliziert, wie an anderer Stelle gezeigt wurdet., ein unproblematisches Wesen des Inhalts der Wissenschaft.. Die zweite der beiden M?glichkeiten untersucht die Konkurrenz von 作者: Proponent 時間: 2025-3-26 10:00
lung der Wissenschaften voraus. Ihre vorg?ngige Erfahrung ist der Aufschwung der Naturwissenschaften. Ein zentrales, besonders in den folgenden Untersuchungen interessierendes Thema der Wissenschaftslehre ist der Erkenntnisfortschritt.. Die derzeit wohl bedeutendste Wissenschaftslehre ist mit dem Na作者: avenge 時間: 2025-3-26 14:10
AI for Cybersecurity: ML-Based Techniques for Intrusion Detection Systemsiss es keiner! Im Rahmen dieses Buches soll eine m?gliche Antwort auf die Frage – Was ist Konstruieren? – gegeben werden. Mit diesem Buch wird das spezielle Ziel verfolgt, die wissenschaftstheoretischen Grundlagen des Konstruierens zu erarbeiten, um hiermit sowohl eine Grundlage für zukünftige CAD–S作者: harpsichord 時間: 2025-3-26 19:43 作者: Negotiate 時間: 2025-3-26 23:11 作者: PANEL 時間: 2025-3-27 04:04 作者: Vital-Signs 時間: 2025-3-27 08:45 作者: certain 時間: 2025-3-27 09:52 作者: dearth 時間: 2025-3-27 15:38 作者: Bronchial-Tubes 時間: 2025-3-27 17:46 作者: Immortal 時間: 2025-3-28 01:58
A Comparison of Contemporary Methods on Univariate Time Series Forecastingichen Vorg?nge, Ereignisse, Funktionen abspielen, das alle m?glichen Eigenschaften hat oder alle m?glichen Akte vollzieht, von dem aber niemand mehr sagen — sondern nur theoretisch konstruieren — kann, wie es mit einem ?Objekt ‘zusammentreffen und mit anderen Subjekten kommunizieren und sich verst?n作者: STANT 時間: 2025-3-28 03:29 作者: MAUVE 時間: 2025-3-28 08:40
Book 2022 at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for作者: 真繁榮 時間: 2025-3-28 11:53
Introduction to Advances in Machine Learning/Deep Learning-Based Technologies,978-3-658-11354-4作者: BABY 時間: 2025-3-28 16:26
Semi-supervised Feature Selection Method for Fuzzy Clustering of Emotional States from Social Stream978-3-658-27226-5作者: tattle 時間: 2025-3-28 18:52
Exploiting Semi-supervised Learning in the Education Field: A Critical Survey978-3-476-04367-2作者: 貪心 時間: 2025-3-29 00:05 作者: Fallibility 時間: 2025-3-29 04:05 作者: syring 時間: 2025-3-29 07:54
A Formal and Statistical AI Tool for Complex Human Activity Recognition978-3-658-07036-6作者: 嗎啡 時間: 2025-3-29 11:44 作者: aggrieve 時間: 2025-3-29 17:11
A Representative Energy Efficiency Project,in which the computer evaluates the performance of the human user with respect to the completion of an experiment, contributing further to an effective learning process. Hence, in order for the performance assessment to be accurate, two separate machine learning techniques, a genetic algorithm and b作者: 過去分詞 時間: 2025-3-29 22:53
https://doi.org/10.1007/978-1-4471-4516-5r for building highly accurate and robust learning models. Over the last few years, a plethora of Semi Supervised Learning algorithms have been developed and implemented with great success for solving a variety of problems in many scientific fields, among which the education field as well. Following作者: nocturia 時間: 2025-3-30 01:55
https://doi.org/10.1007/978-1-4471-4516-5data that cover a 1-year period were used. A python repository of automated time series forecasting models (AtsPy) was exploited to run the experiments. For the final comparison three different metrics (RMSE, MAE and MAPE) were taken into consideration. The results of this extended experimental proc
