標(biāo)題: Titlebook: Data Management, Analytics and Innovation; Proceedings of ICDMA Neha Sharma,Amlan Chakrabarti,Jan Martinovic Conference proceedings 2021 Sp [打印本頁(yè)] 作者: Carter 時(shí)間: 2025-3-21 19:36
書目名稱Data Management, Analytics and Innovation影響因子(影響力)
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書目名稱Data Management, Analytics and Innovation網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Management, Analytics and Innovation被引頻次
書目名稱Data Management, Analytics and Innovation被引頻次學(xué)科排名
書目名稱Data Management, Analytics and Innovation年度引用
書目名稱Data Management, Analytics and Innovation年度引用學(xué)科排名
書目名稱Data Management, Analytics and Innovation讀者反饋
書目名稱Data Management, Analytics and Innovation讀者反饋學(xué)科排名
作者: 寡頭政治 時(shí)間: 2025-3-21 22:13 作者: hermetic 時(shí)間: 2025-3-22 04:11 作者: Insufficient 時(shí)間: 2025-3-22 07:29
Automatic Standardization of Data Based on Machine Learning and Natural Language Processingintegrated from various ocular sources and standardized to have a uniformity; The integrated standard data set is then processed and transformed to generate features for machine learning models automatically; The predictive machine learning models can be trained with the stratified random sampled da作者: 圓錐體 時(shí)間: 2025-3-22 11:16
Analysis of GHI Forecasting Using Seasonal ARIMA (GHI) is the strongest predictor of actual generation. Hence, the solar energy prediction problem can be attempted by predicting GHI. Auto-Regressive Integrated Moving Average (ARIMA) is one of the fundamental models for time series prediction. India is a country with significant solar energy possi作者: Monocle 時(shí)間: 2025-3-22 13:48 作者: Monocle 時(shí)間: 2025-3-22 20:52
Application of Bayesian Automated Hyperparameter Tuning on Classifiers Predicting Customer Retentiontomated Hyperparameter Tuning, with Tree-structured Parzen Estimator, has been performed on all of nine ML classifiers predicting the customers likely to be retained by the bank. After visualizing the nature of dataset and its constraints of class imbalance and limited training examples, Feature Eng作者: 大看臺(tái) 時(shí)間: 2025-3-22 22:38
Quantum Machine Learning: A Review and Current Statusum machine learning investigates how results from the quantum world can be used to solve problems from machine learning. The amount of data needed to reliably train a classical computation model is evergrowing and reaching the limits which normal computing devices can handle. In such a scenario, qua作者: 重畫只能放棄 時(shí)間: 2025-3-23 03:29 作者: defendant 時(shí)間: 2025-3-23 08:18
An Efficient Algorithm for Complete Linkage Clustering with a Merging Thresholdp at a high speed. Apart from collecting this avalanche of data, another major problem is extracting useful information from it. Clustering is a highly powerful data mining tool capable of finding hidden information from a totally unlabelled dataset. Complete Linkage Clustering is a distance-based H作者: 致命 時(shí)間: 2025-3-23 11:17
Analytics for In Silico Development of Inhibitors from Neem (,) Against Pantothenate Synthetase of de health threats. So there is an urgent need for the development of new drug for tuberculosis. Pantothenate synthase (PS) is a valid target for rational drug designing against . as it is absent in animal system. Neem (.) has been used as traditional Indian medicine since the ancient era. Preparatio作者: Foam-Cells 時(shí)間: 2025-3-23 15:52
A Machine Learning Model for Forecasting Wind Disasters for Farmersake precautionary actions that can reduce the damages caused by strong winds. The changes in wind speed are based on the various atmospheric parameters like relative-humidity, pressure, temperature, and wind direction. The values of these parameters can be obtained from the area weather stations for作者: inflame 時(shí)間: 2025-3-23 22:03
Prediction of Most Valuable Bowlers of Indian Premier League (IPL)e mainly two roles, namely, batsman and bowler. Top performing teams get to win the match; hence, it needs to be comprised of the best players. In such a scenario, the evaluation of a player profile for team selection plays a very crucial role. The work proposes an approach to predict bowler ranking作者: 連鎖,連串 時(shí)間: 2025-3-23 23:35
Categorizing Text Documents Using Na?ve Bayes, SVM and Logistic Regression mining Technology, Data extraction and Artificial Intelligence for text categorization. This paper will showcase the features of the technologies involved. There are three machine learning algorithms (SVM, Multinomial Na?ve Bayes and Logistic Regression) used in this paper for text categorization, 作者: nutrients 時(shí)間: 2025-3-24 06:09
Machine Learning to Diagnose Common Diseases Based on Symptomsits symptoms. Early diagnosis is the key for effective treatment of a disease and better living of the people. Providing sophisticated and accurate algorithms and techniques to overcome this issue will be revolutionary. Though hospitals in urban areas are using advanced technology for diagnosis and 作者: Infect 時(shí)間: 2025-3-24 07:02
Efficacy of Oversampling Over Machine Learning Algorithms in Case of Sentiment Analysisbe called as sentiment analysis. Sentiment analysis is basically extracting the tone or emotion of the writer, by understanding the text sequence. This way of approach is to understand the sentiment of a text considering as a boon in the customer management system and can easily be applied to the so作者: 賞心悅目 時(shí)間: 2025-3-24 13:11 作者: RALES 時(shí)間: 2025-3-24 16:27 作者: 懦夫 時(shí)間: 2025-3-24 22:39
Data Management, Analytics and Innovation978-981-15-5619-7Series ISSN 2194-5357 Series E-ISSN 2194-5365 作者: 隱士 時(shí)間: 2025-3-25 02:18
https://doi.org/10.1007/978-1-4471-5490-7eatures which contribute most to our prediction variable or output in which we are interested. This subset of features has some very important benefits: it reduces the computational complexity of learning algorithms, saves time, improves accuracy, and the selected features can be insightful for the 作者: Decline 時(shí)間: 2025-3-25 05:53 作者: CANDY 時(shí)間: 2025-3-25 10:49 作者: artless 時(shí)間: 2025-3-25 11:40
The Problem of Linguistic Meaningintegrated from various ocular sources and standardized to have a uniformity; The integrated standard data set is then processed and transformed to generate features for machine learning models automatically; The predictive machine learning models can be trained with the stratified random sampled da作者: 范例 時(shí)間: 2025-3-25 19:54 作者: 運(yùn)動(dòng)吧 時(shí)間: 2025-3-25 21:03
Cognitive Linguistics and Language Teachingity system using which we utilize a variety of algorithms and techniques to detect security-related anomalies and threats in an enterprise network environment. The system helps in providing scores for anomalous activities and produces alerts. The system receives data from multiple intrusion detectio作者: Eviction 時(shí)間: 2025-3-26 00:59
John Sweller,Paul Ayres,Slava Kalyugatomated Hyperparameter Tuning, with Tree-structured Parzen Estimator, has been performed on all of nine ML classifiers predicting the customers likely to be retained by the bank. After visualizing the nature of dataset and its constraints of class imbalance and limited training examples, Feature Eng作者: expire 時(shí)間: 2025-3-26 07:59
John Sweller,Paul Ayres,Slava Kalyugaum machine learning investigates how results from the quantum world can be used to solve problems from machine learning. The amount of data needed to reliably train a classical computation model is evergrowing and reaching the limits which normal computing devices can handle. In such a scenario, qua作者: 諷刺 時(shí)間: 2025-3-26 10:20 作者: Cholecystokinin 時(shí)間: 2025-3-26 12:38 作者: 發(fā)展 時(shí)間: 2025-3-26 16:54 作者: 協(xié)定 時(shí)間: 2025-3-26 23:03 作者: 細(xì)絲 時(shí)間: 2025-3-27 04:09 作者: 引起痛苦 時(shí)間: 2025-3-27 05:21
Uwe Hentschel,Manfred Kie?ling,Arn Hosemann mining Technology, Data extraction and Artificial Intelligence for text categorization. This paper will showcase the features of the technologies involved. There are three machine learning algorithms (SVM, Multinomial Na?ve Bayes and Logistic Regression) used in this paper for text categorization, 作者: 形上升才刺激 時(shí)間: 2025-3-27 09:39
https://doi.org/10.1007/978-3-663-14658-2its symptoms. Early diagnosis is the key for effective treatment of a disease and better living of the people. Providing sophisticated and accurate algorithms and techniques to overcome this issue will be revolutionary. Though hospitals in urban areas are using advanced technology for diagnosis and 作者: 返老還童 時(shí)間: 2025-3-27 16:03
https://doi.org/10.1007/978-1-4614-4544-9be called as sentiment analysis. Sentiment analysis is basically extracting the tone or emotion of the writer, by understanding the text sequence. This way of approach is to understand the sentiment of a text considering as a boon in the customer management system and can easily be applied to the so作者: ETCH 時(shí)間: 2025-3-27 18:03
Neha Sharma,Amlan Chakrabarti,Jan MartinovicPresents cutting-edge research in the fields of data management, analytics, and innovation.Gathers the outcomes of ICDMAI 2020, held in New Delhi, India.Offers a valuable reference resource for resear作者: Nmda-Receptor 時(shí)間: 2025-3-28 01:33
Advances in Intelligent Systems and Computinghttp://image.papertrans.cn/d/image/262878.jpg作者: 切掉 時(shí)間: 2025-3-28 02:10
Automatic Standardization of Data Based on Machine Learning and Natural Language Processingnerate features for machine learning models automatically; The predictive machine learning models can be trained with the stratified random sampled data and ranked features from the transformed datasets.作者: Prophylaxis 時(shí)間: 2025-3-28 07:50 作者: Presbyopia 時(shí)間: 2025-3-28 11:37 作者: Genistein 時(shí)間: 2025-3-28 14:55
Conference proceedings 2021ytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses c作者: Substance-Abuse 時(shí)間: 2025-3-28 22:37
Dania Marabissi,Romano Fantacciovements, designing appropriate action plans aligned to specific objectives and tracking and monitoring activities. Besides, visualizations tool is used to present outcome of the analytics in a way that explains future actions. Thus, preparing the library department for digital-future.作者: 采納 時(shí)間: 2025-3-29 02:41 作者: Eructation 時(shí)間: 2025-3-29 03:49
John Sweller,Paul Ayres,Slava Kalyugams, on the other hand, produce atypical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Here, we review the previous literature on quantum machine learning and provide the current status of it.作者: HUMP 時(shí)間: 2025-3-29 09:03
https://doi.org/10.1057/978-1-137-57895-2o reduce the impact of those future wind disasters on crops and agricultural fields. Weather data from past history is used to train the machine learning model to perform more accurate wind speed predictions.作者: 平庸的人或物 時(shí)間: 2025-3-29 11:31
https://doi.org/10.1007/978-1-4614-4544-9t vector machine(SVM), and XGBoost, to check if they can be as good as LSTM in any case. Also, as we discover the data distribution problem in our datasets, so we will be applying oversampling to make the distribution in a stabilized form.作者: 人造 時(shí)間: 2025-3-29 19:11
Digital Transformation of Information Resource Center of an Enterprise Using Analytical Servicesovements, designing appropriate action plans aligned to specific objectives and tracking and monitoring activities. Besides, visualizations tool is used to present outcome of the analytics in a way that explains future actions. Thus, preparing the library department for digital-future.作者: SLAG 時(shí)間: 2025-3-29 21:15
Scoring Algorithm Identifying Anomalous Behavior in Enterprise Networkanalytics (UEBA) to analyze network traffic logs and user activity data to learn from user behavior to indicate a malicious presence in your environment, whether the threat is previously known or not. Graph-based anomalous detection technique has been applied in this approach, the graph represents each entity’s behavior.作者: 不如樂(lè)死去 時(shí)間: 2025-3-30 03:57
Quantum Machine Learning: A Review and Current Statusms, on the other hand, produce atypical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Here, we review the previous literature on quantum machine learning and provide the current status of it.作者: Mettle 時(shí)間: 2025-3-30 04:03
A Machine Learning Model for Forecasting Wind Disasters for Farmerso reduce the impact of those future wind disasters on crops and agricultural fields. Weather data from past history is used to train the machine learning model to perform more accurate wind speed predictions.作者: ETCH 時(shí)間: 2025-3-30 10:56 作者: anaphylaxis 時(shí)間: 2025-3-30 15:48 作者: 和平主義 時(shí)間: 2025-3-30 17:33
Survey of Transfer Learning and a Case Study of Emotion Recognition Using Inductive Approachish and German, where knowledge of emotion identification exists in English language and is extended to learn emotions in German speech. We have attempted to uncover latent features of one language in another language.作者: sed-rate 時(shí)間: 2025-3-31 00:42
2194-5357 Delhi, India.Offers a valuable reference resource for researThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research paper作者: 天氣 時(shí)間: 2025-3-31 03:54 作者: BET 時(shí)間: 2025-3-31 06:53
The Microgenesis of Schizophrenic Symptomsh a scenario, the evaluation of a player profile for team selection plays a very crucial role. The work proposes an approach to predict bowler ranking by evaluation of a player’s past profile. We have used a supervised machine learning technique on past IPL data to predict top bowlers.作者: Ige326 時(shí)間: 2025-3-31 11:51
