Thematische Bibliographien / SUPERVISED TECHNOLOGY

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Auswahl der wissenschaftlichen Literatur zum Thema „SUPERVISED TECHNOLOGY“

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Veröffentlicht am 11. September 2023

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Zeitschriftenartikel zum Thema "SUPERVISED TECHNOLOGY"

Zohuri, Bahman. „The Evolution of Artificial Intelligence: From Supervised to Semi-Supervised and Ultimately Unsupervised Technology Trends“. Current Trends in Engineering Science (CTES) 3, Nr. 5 (22.08.2023): 1–4. http://dx.doi.org/10.54026/ctes/1040.

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The progression of Artificial Intelligence (AI) technology from supervised learning to semi-supervised methods and ultimately reaching the realm of unsupervised AI marks a remarkable evolution in the field. This article explores this captivating journey, tracing the development of AI from its roots in supervised learning, where models are trained using labeled data, to the innovative semi-supervised approach, which harnesses the power labeled and unlabeled data. The pinnacle of this evolution is unsupervised learning, where AI systems autonomously uncover hidden patterns and relationships within unlabeled data. The implications of this evolution are profound, shaping industries and sparking ethical conversations. This article delves into each stage, revealing the mechanics, applications, and potential societal impact of AI’s transformative trajectory. As we peer into the future, we anticipate an era of AI innovation characterized by unprecedented adaptability, creativity, and discovery.

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Sun, Tong He, und Guo Qing Yan. „Land Utilization and Classification Method Based on Remote Sensing Technology“. Applied Mechanics and Materials 239-240 (Dezember 2012): 501–6. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.501.

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This paper discusses land utilization classification based on remote sensing technology. Taking the Xinjiang Kulja county bureau department area remote sensing images as the basic information, and using ERDAS IMAGINE, this paper discusses non-supervised classification and supervised classification methods. The results show that remote sensing technology applies to land utilization situation and land classification, which has reference value.

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Abdullah, Khalid Murad, Bahaulddin Nabhan Adday, Refed Adnan Jaleel, Iman Mohammed Burhan, Mohanad Ahmed Salih und Musaddak Maher Abdul Zahra. „Integrating of Promising Computer Network Technology with Intelligent Supervised Machine Learning for Better Performance“. Webology 19, Nr. 1 (20.01.2022): 3792–99. http://dx.doi.org/10.14704/web/v19i1/web19249.

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The Software defined network (SDN) controller has such networks universal sight and allows for centralized management and control for the networks. The algorithms of Machine learning used alone or combined with the SDN controller's northbound applications in order to make intelligent SDN. SDN is such potential networking design that blends network's programmability with central administration. The control and the data planes are separated in SDN, and the network with central management point is called SDN controller, which may be programmed and utilized as a brain of the network. Lately, the community of researchers have shown a greater willingness to take advantage of current advances in artificial intelligence to give the SDN best decision making and learning skills. Our research found that combining SDN with Intelligent Supervised Machine Learning (ISML) is very important for performance improvement. ISML is the development of algorithms that can generate broad patterns and assumptions from external source instances in order to portend the predestination of future instances. The ISML algorithms of classification goal is to categorize data based on past information. In data science problems, classification is used rather frequently. To solve such problems, a number of successful approaches were already presented, including rule-based techniques, instance-based techniques, logic-based techniques, and stochastic techniques. This study examined the ISML algorithms' efficiency by checking the precision, accuracy, and with or without SDN recall.

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D M, Yashaswini. „Detection of Fake Online Reviews using Semi-supervised and Supervised learning“. International Journal for Research in Applied Science and Engineering Technology 10, Nr. 7 (31.07.2022): 789–96. http://dx.doi.org/10.22214/ijraset.2022.44368.

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Abstract: Nowadays, when somebody wants to make some decisions about a product or a service everyone goes with the reviews as it has become an essential part of decision making. When a customer wants to order a product on an e commerce website firstly everyone checks the review section in detail and further proceeds for decision making about the product. If the reviews posted were satisfactory for the customer he may order the product thus reviews become a reputed parameter for the businesses and companies and also a great source of information for the customers. Every customer thinks that the reviews he/she is seeing is authentic and any manipulation in that from any individuals or any rival companies which may lead to fake data which will be labeled as fake reviews. This type of attempt if not noticed may let us think about the gen-unity of the data. So these reviews are the most important parameter for the businesses and companies. There exist some groups or persons who make use of these reviews to forge customers for their own interest or damage their competitors reputation. In order to solve this problem we uses Machine learning techniques(Supervised and semi-supervised) to detect whether the given review is fake or not with high accuracy. Along with this objective we also focus on developing models which need less data to train.Since we can’t always be able to get labeled data we use semi-supervised machine learning to make use of unlabeled data.It is understandable our model should be capable of giving results in reasonably less time. .In this paper we proposed many classification algorithm like Support Vector Machine algorithm (SVM) , Random Forest algorithm (RF) and deep neural network

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Choi, Sungchul, Mokhammad Afifuddin und Wonchul Seo. „A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence“. IEEE Access 10 (2022): 19284–300. http://dx.doi.org/10.1109/access.2022.3151870.

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Ali, MD Mohsin, S. Vamshi, S. Shiva und S. Bhanu Prakash. „Virtual Assistant Using Supervised Learning“. International Journal for Research in Applied Science and Engineering Technology 11, Nr. 6 (30.06.2023): 3239–45. http://dx.doi.org/10.22214/ijraset.2023.54262.

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Abstract: Todays modern world, everything got evoluted including technology from vacuum tubes to nanoelectronics.Now a days the total world is in single palm of human in the form of mobile.But these days we aren’t using hands, instead we using voice commands to wake electronics devices like mobiles,it is not only in mobiles, also in every electronics.This type of usage is possible by embedding a virtual assistant into electronics. Virtual Assistants are software programs that help you ease your day to day tasks, such as showing weather report, creating reminders, making shopping lists etc. They can take commands via text (online chat bots) or by voice. This paper delivers steps to build a web based virtual assistant using supervised learning to achieve goals which cannot done by the VA’s in market.And as this model uses supervised learning, so there will be limited commands and data to maintain it in our control.

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Wang, Xiujuan, Siwei Cao, Kangfeng Zheng, Xu Guo und Yutong Shi. „Supervised Character Resemble Substitution Personality Adversarial Method“. Electronics 12, Nr. 4 (08.02.2023): 869. http://dx.doi.org/10.3390/electronics12040869.

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With the development of science and computer technology, social networks are changing our daily lives. However, this leads to new, often hidden dangers in areas such as cybersecurity. Of these, the most complex and harmful is the Advanced Persistent Threat attack (APT attack). The development of personality analysis and prediction technology provides the APT attack a good opportunity to infiltrate personality privacy. Malicious people can exploit existing personality classifiers to attack social texts and steal users’ personal information. Therefore, it is of high importance to hide personal privacy information in social texts. Based on the personality privacy protection technology of adversarial examples, we proposed a Supervised Character Resemble Substitution personality adversarial method (SCRS) in this paper, which hides personality information in social texts through adversarial examples to realize personality privacy protection. The adversarial examples should be capable of successfully disturbing the personality classifier while maintaining the original semantics without reducing human readability. Therefore, this paper proposes a measure index of “label contribution” to select the words that are important to the label. At the same time, in order to maintain higher readability, this paper uses character-level resemble substitution to generate adversarial examples. Experimental validation shows that our method is able to generate adversarial examples with good attack effect and high readability.

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Wang, Hanyun. „Comparing supervised and unsupervised learning in image denoising“. Applied and Computational Engineering 5, Nr. 1 (14.06.2023): 284–91. http://dx.doi.org/10.54254/2755-2721/5/20230581.

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Recent studies on unsupervised learning have attracted people's increasing attention. In particular, Deep learning has developed rapidly in recent years. With the development of media images, people's demand for image noise reduction is increasing, and the requirements are becoming more and more strict. The traditional methods used for image noise reduction are far from meeting people's requirements, and people are eager to find a more efficient image noise reduction technology. In recent years, the technology of using a convolutional neural network for image noise reduction has become more and more skilled. This paper explores the reliability of image noise reduction technology using a convolutional neural network as an autoencoder, and whether good performance is maintained without using clean images. The article aims to compare the performance with supervised learning and unsupervised learning by deep learning in image denoising.

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Chettri, Ajanta, Amal George, Dr A. Rengarajan und Feon Jaison. „Research Paper on Fake Online Reviews Detection using Semi-supervised and Supervised learning“. International Journal for Research in Applied Science and Engineering Technology 10, Nr. 4 (30.04.2022): 1973–79. http://dx.doi.org/10.22214/ijraset.2022.41687.

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Abstract: Today's business and commerce are heavily influenced by online reviews. Most online product purchase decisions are based on customer reviews. As a result, opportunistic individuals or groups seek to shake product reviews in their favor. Fake online reviews have a significant impact on the efficiency of online consumers, merchants and e-commerce markets. Despite academic efforts to study fake reviews, there remains a need for research that can systematically analyze and summarize their causes and consequences. This task provides a semi-supervised and supervised text mining model for detecting fake web reviews and comparing their effectiveness to hotel review datasets. Keywords: Semi-Supervised. Supervised, Detection, Fake Review, Marketing

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Dissertationen zum Thema "SUPERVISED TECHNOLOGY"

Persson, Travis. „Semi-Supervised Learning for Predicting Biochemical Properties“. Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447652.

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The predictive performance of supervised learning methods relies on large amounts of labeled data. Data sets used in Quantitative Structure Activity Relationship modeling often contain a limited amount of labeled data, while unlabeled data is abundant. Semi-supervised learning can improve the performance of supervised methods by incorporating a larger set of unlabeled samples with fewer labeled instances. A semi-supervised learning method known as Label Spreading was compared to a Random Forest in its effectiveness for correctly classifying the binding properties of molecules on ten different sets of compounds. Label Spreading using a k-Nearest Neighbors (LS-KNN) kernel was found to, on average, outperform the Random Forest. Using a randomly sampled labeled data set of sizes 50 and 100, LS-KNN achieved a mean accuracy of 4.03% and 1.97% higher than that of the Random Forest.The outcome was similar for the mean area under the Receiver Operating Characteristic curve (AUC). For large sets of labeled data, the performances between the methods were indistinguishable. It was also found that sampling labeled data from generated clusters using a k-Means clustering algorithm, as opposed to random sampling, increased the performance of all applied methods. For a labeled data set ofsize 50, Label Spreading using a Radial Basis Function kernel increased its meanaccuracy and AUC by 7.52% and 3.08%, respectively, when sampling from clusters. In conclusion, semi-supervised learning could be beneficial when applied to similar modeling scenarios. However, the improvements heavily depend on the underlying data, suggesting that there is no one-size-fits-all method.

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Kola, Lokesh, und Vigneshwar Muriki. „A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Diabetes Prediction“. Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21816.

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Background: The main cause of diabetes is due to high sugar levels in the blood. There is no permanent cure for diabetes. However, it can be prevented by early diagnosis. In recent years, the hype for Machine Learning is increasing in disease prediction especially during COVID-19 times. In the present scenario, it is difficult for patients to visit doctors. A possible framework is provided using Machine Learning which can detect diabetes at early stages. Objectives: This thesis aims to identify the critical features that impact gestational (Type-3) diabetes and experiments are performed to identify the efficient algorithm for Type-3 diabetes prediction. The selected algorithms are Decision Trees, RandomForest, Support Vector Machine, Gaussian Naive Bayes, Bernoulli Naive Bayes, Laplacian Support Vector Machine. The algorithms are compared based on the performance. Methods: The method consists of gathering the dataset and preprocessing the data. SelectKBestunivariate feature selection was performed for selecting the important features, which influence the Type-3 diabetes prediction. A new dataset was created by binning some of the important features from the original dataset, leading to two datasets, non-binned and binned datasets. The original dataset was imbalanced due to the unequal distribution of class labels. The train-test split was performed on both datasets. Therefore, the oversampling technique was performed on both training datasets to overcome the imbalance nature. The selected Machine Learning algorithms were trained. Predictions were made on the test data. Hyperparameter tuning was performed on all algorithms to improve the performance. Predictions were made again on the test data and accuracy, precision, recall, and f1-score were measured on both binned and non-binned datasets. Results: Among selected Machine Learning algorithms, Laplacian Support Vector Machineattained higher performance with 89.61% and 86.93% on non-binned and binned datasets respectively. Hence, it is an efficient algorithm for Type-3 diabetes prediction. The second best algorithm is Random Forest with 74.5% and 72.72% on non-binned and binned datasets. The non-binned dataset performed well for the majority of selected algorithms. Conclusions: Laplacian Support Vector Machine scored high performance among the other algorithms on both binned and non-binned datasets. The non-binned dataset showed the best performance in almost all Machine Learning algorithms except Bernoulli naive Bayes. Therefore, the non-binned dataset is more suitable for the Type-3 diabetes prediction.

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Aboushady, Moustafa. „Semi-supervised learning with HALFADO: two case studies“. Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425888.

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This thesis studies the HALFADO algorithm[1], a semi-supervised learning al- gorithm designed for detecting anomalies in complex information flows. This report assesses HALFADO’s performance in terms of detection capabilities (pre- cision and recall) and computational requirements. We compare the result of HALFADO with a standard supervised and unsupervised learning approach.The results of two case studies are reported: (1) HALFADO as applied to a FinTech example with a flow of financial transactions, and (2) HALFADO as applied to detecting hate speech in a social media feed. Those results point to the benefits of using HALFADO in environments where one has only modest computational resources.

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Rollenhagen, Svante. „Classification of social gestures : Recognizing waving using supervised machinelearning“. Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230678.

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This paper presents an approach to gesture recognition including the use of a tool in order to extract certain key-points of the human body in each frame, and then processing this data and extracting features from this. The gestures recognized were two-handed waving and clapping. The features used were the maximum co-variance from a sine-fit to time-series of arm angles, as well as the max and min of this fitted sinus function. A support vector machine was used for the learning. The result was a promising accuracy of 93% ± 4% using 5-fold cross-validation. The limitations of the methods used are then discussed, which includes lack of support for more than one gesture in the data as well as some lack of generality in means of the features used. Finally some suggestions are made as to what improvements and further explorations could be made.
I den har rapporten presenteras ett försök att göra gestigenkanning av gesterna vinkning samt handklappning med hjälp av ett verktyg som kan kanna igen ett antal punkter hos den mänskliga kroppen från videodata. At- tributen som användes är den maximala kovariansen från en sinus-anpassning till vinkeldata, samt det maximala och minimala värdet av anpassningen. En stodvektormaskin (Support Vector Machine) användes for inlärningen. Resultatet var en precision på 93% ± 4% där femdelad korsvalidering användes. Begränsningarna hos de använda metoderna diskuteras sedan, vilket inkluderar: brist på support for mer an en gest i video-datan, samt brister i generalitet nar det kommer till vilka attribut som anvandes. Slutligen ges förslag på framtida utvecklingar och förbättringar.

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Eggertsson, Gunnar Atli. „Classification of Seismic Body Wave Phases Using Supervised Learning“. Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-423977.

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The task of accurately distinguishing between arrivals of different types of seismic waves is a common and important task within the field of seismology. For data generated by seismic stations operated by SNSN this task generally requires manual effort. In this thesis, two automatic classification models which distinguish between two types of body waves, P- and S-waves, are implemented and compared, with the aim of reducing the need for manual input. The algorithms are logistic regression and feed-forward artificial neural network. The applied methods use labelled historical data from seismological events in Sweden to train a set of classifiers, with a unique classifier associated with each seismic station. When evaluated on test data, the logistic regression classifiers achieve a mean accuracy of approximately 96% over all stations compared to approximately 98% for the neural network classifiers. The results suggest that both implemented classifiers represent a good option for automatic body wave classification in Sweden.

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Elf, Sebastian, und Christopher Öqvist. „Comparison of supervised machine learning models forpredicting TV-ratings“. Thesis, KTH, Hälsoinformatik och logistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278054.

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Abstract Manual prediction of TV-ratings to use for program and advertisement placement can be costly if they are wrong, as well as time-consuming. This thesis evaluates different supervised machine learning models to see if the process of predicting TV-ratings can be automated with better accuracy than the manual process. The results show that of the two tested supervised machine learning models, Random Forest and Support Vector Regression, Random Forest was the better model. Random Forest was better on both measurements, mean absolute error and root mean squared error, used to compare the models. The conclusion is that Random Forest, evaluated with the dataset and methods used, are not accurate enough to replace the manual process. Even though this is the case, it could still potentially be used as part of the manual process to ease the workload of the employees. Keywords Machine learning, supervised learning, TV-rating, Support Vector Regression, Random Forest.
SammanfattningAtt manuellt förutsäga tittarsiffor för program- och annonsplacering kan vara kostsamt och tidskrävande om de är fel. Denna rapport utvärderar olika modeller som utnyttjar övervakad maskininlärning för att se om processen för att förutsäga tittarsiffror kan automatiseras med bättre noggrannhet än den manuella processen. Resultaten visar att av de två testade övervakade modellerna för maskininlärning, Random Forest och Support Vector Regression, var Random Forest den bättre modellen. Random Forest var bättre med båda de två mätningsmetoder, genomsnittligt absolut fel och kvadratiskt medelvärde fel, som används för att jämföra modellerna. Slutsatsen är att Random Forest, utvärderad med de data och de metoderna som används, inte är tillräckligt exakt för att ersätta den manuella processen. Även om detta är fallet, kan den fortfarande potentiellt användas som en del av den manuella processen för att underlätta de anställdas arbetsbelastning.Nyckelord Maskininlärning, övervakad inlärning, tittarsiffror, Support Vector Regression, Random Forest.

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Pein, Raoul Pascal. „Semi-supervised image classification based on a multi-feature image query language“. Thesis, University of Huddersfield, 2010. http://eprints.hud.ac.uk/id/eprint/9244/.

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The area of Content-Based Image Retrieval (CBIR) deals with a wide range of research disciplines. Being closely related to text retrieval and pattern recognition, the probably most serious issue to be solved is the so-called \semantic gap". Except for very restricted use-cases, machines are not able to recognize the semantic content of digital images as well as humans. This thesis identifies the requirements for a crucial part of CBIR user interfaces, a multimedia-enabled query language. Such a language must be able to capture the user's intentions and translate them into a machine-understandable format. An approach to tackle this translation problem is to express high-level semantics by merging low-level image features. Two related methods are improved for either fast (retrieval) or accurate(categorization) merging. A query language has previously been developed by the author of this thesis. It allows the formation of nested Boolean queries. Each query term may be text- or content-based and the system merges them into a single result set. The language is extensible by arbitrary new feature vector plug-ins and thus use-case independent. This query language should be capable of mapping semantics to features by applying machine learning techniques; this capability is explored. A supervised learning algorithm based on decision trees is used to build category descriptors from a training set. Each resulting \query descriptor" is a feature-based description of a concept which is comprehensible and modifiable. These descriptors could be used as a normal query and return a result set with a high CBIR based precision/recall of the desired category. Additionally, a method for normalizing the similarity profiles of feature vectors has been developed which is essential to perform categorization tasks. To prove the capabilities of such queries, the outcome of a semi-supervised training session with \leave-one-object-out" cross validation is compared to a reference system. Recent work indicates that the discriminative power of the query-based descriptors is similar and is likely to be improved further by implementing more recent feature vectors.

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Persson, Martin. „Semantic Mapping using Virtual Sensors and Fusion of Aerial Images with Sensor Data from a Ground Vehicle“. Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-2186.

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Hussein, Abdul Aziz. „Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning“. University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042.

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Chetry, Roshan. „Web genre classification using feature selection and semi-supervised learning“. Kansas State University, 2011. http://hdl.handle.net/2097/8855.

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Master of Science
Department of Computing and Information Sciences
Doina Caragea
As the web pages continuously change and their number grows exponentially, the need for genre classification of web pages also increases. One simple reason for this is given by the need to group web pages into various genre categories in order to reduce the complexities of various web tasks (e.g., search). Experts unanimously agree on the huge potential of genre classification of web pages. However, while everybody agrees that genre classification of web pages is necessary, researchers face problems in finding enough labeled data to perform supervised classification of web pages into various genres. The high cost of skilled manual labor, rapid changing nature of web and never ending growth of web pages are the main reasons for the limited amount of labeled data. On the contrary unlabeled data can be acquired relatively inexpensively in comparison to labeled data. This suggests the use of semi-supervised learning approaches for genre classification, instead of using supervised approaches. Semi-supervised learning makes use of both labeled and unlabeled data for training - typically a small amount of labeled data and a large amount of unlabeled data. Semi-supervised learning have been extensively used in text classification problems. Given the link structure of the web, for web-page classification one can use link features in addition to the content features that are used for general text classification. Hence, the feature set corresponding to web-pages can be easily divided into two views, namely content and link based feature views. Intuitively, the two feature views are conditionally independent given the genre category and have the ability to predict the class on their own. The scarcity of labeled data, availability of large amounts of unlabeled data, richer set of features as compared to the conventional text classification tasks (specifically complementary and sufficient views of features) have encouraged us to use co-training as a tool to perform semi-supervised learning. During co-training labeled examples represented using the two views are used to learn distinct classifiers, which keep improving at each iteration by sharing the most confident predictions on the unlabeled data. In this work, we classify web-pages of .eu domain consisting of 1232 labeled host and 20000 unlabeled hosts (provided by the European Archive Foundation [Benczur et al., 2010]) into six different genres, using co-training. We compare our results with the results produced by standard supervised methods. We find that co-training can be an effective and cheap alternative to costly supervised learning. This is mainly due to the two independent and complementary feature sets of web: content based features and link based features.

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Bücher zum Thema "SUPERVISED TECHNOLOGY"

United States. Congress. Office of Technology Assessment., Hrsg. The Electronic supervisor: New technology, new tensions. Washington, D.C: Congress of the U.S., Office of Technology Assessment, 1987.

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United States. Congress. Office of Technology Assessment., Hrsg. The Electronic supervisor: New technology, new tensions. Washington, D.C: Congress of the United States, Office of Technology Assessment, 1987.

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United States. Congress. Office of Technology Assessment., Hrsg. The Electronic supervisor: New technology, new tensions. Washington, D.C: U.S. Dept. of Education, Office of Educational Research and Improvement, 1987.

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Huff, Stephen. Supervised Learning with Linear Regression: An Executive Review of Hot Technology. Independently Published, 2018.

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Feierherd, Guillermo Eugenio, Patricia Pesado und Osvaldo Mario Spositto, Hrsg. Computer Science & Technology Series. Editorial de la Universidad Nacional de La Plata (EDULP), 2015. http://dx.doi.org/10.35537/10915/48825.

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CACIC’14 was the twentieth Congress in the CACIC series. It was organized by the Department of Engineering and Technological Research at the La Matanza National University in La Matanza, Buenos Aires. The Congress included 13 Workshops with 135 accepted papers, 3 Conferences, 3 technical panels, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2014 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 230 submissions. An average of 2.5 review reports were collected for each paper, for a grand total of 594 review reports that involved about 206 different reviewers. A total of 135 full papers, involving 445 authors and 78 Universities, were accepted and 24 of them were selected for this book.

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Finochietto, Jorge Raúl, und Patricia Mabel Pesado, Hrsg. Computer Science & Technology Series. Editorial de la Universidad Nacional de La Plata (EDULP), 2014. http://dx.doi.org/10.35537/10915/58553.

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CACIC’13 was the nineteenth Congress in the CACIC series. It was organized by the Department of Computer Systems at the CAECE University in Mar del Plata. The Congress included 13 Workshops with 165 accepted papers, 5 Conferences, 3 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2013 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 247 submissions. An average of 2.5 review reports were collected for each paper, for a grand total of 676 review reports that involved about 210 different reviewers. A total of 165 full papers, involving 489 authors and 80 Universities, were accepted and 25 of them were selected for this book.

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Feierherd, Guillermo Eugenio, Patricia Mabel Pesado und Claudia Cecilia Russo, Hrsg. Computer Science & Technology Series. Editorial de la Universidad Nacional de La Plata (EDULP), 2016. http://dx.doi.org/10.35537/10915/58554.

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CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.

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Simari, Guillermo, und Hugo Padovani, Hrsg. Computer Science & Technology Series. Editorial de la Universidad Nacional de La Plata (EDULP), 2011. http://dx.doi.org/10.35537/10915/18411.

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CACIC’10 was the sixteenth Congress in the CACIC series. It was organized by the School of Computer Science of the University of Moron. The Congress included 10 Workshops with 104 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. (<a href="http://www.cacic2010.edu.ar/">http://www.cacic2010.edu.ar/</a>). CACIC 2010 was organized following the traditional Congress format, with 10 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 195 submissions. An average of 2.6 review reports were collected for each paper, for a grand total of 507 review reports that involved about 300 different reviewers. A total of 104 full papers were accepted and 20 of them were selected for this book.

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Buchteile zum Thema "SUPERVISED TECHNOLOGY"

Bhattacharyya, Debnath, Poulami Das, Samir Kumar Bandyopadhyay und Tai-hoon Kim. „Grayscale Image Classification Using Supervised Chromosome Clustering“. In Security Technology, 64–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10847-1_9.

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Huang, Feifei, Yan Yang, Tao Li, Jinyuan Zhang, Tonny Rutayisire und Amjad Mahmood. „Semi-supervised Hierarchical Co-clustering“. In Rough Sets and Knowledge Technology, 310–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31900-6_39.

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Guo, Xiyue, und Tingting He. „Leveraging Chinese Encyclopedia for Weakly Supervised Relation Extraction“. In Semantic Technology, 127–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31676-5_9.

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Zhang, Luhao, Linmei Hu und Chuan Shi. „Incorporating Instance Correlations in Distantly Supervised Relation Extraction“. In Semantic Technology, 177–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41407-8_12.

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Hu, Guoping, Jingjing Liu, Hang Li, Yunbo Cao, Jian-Yun Nie und Jianfeng Gao. „A Supervised Learning Approach to Entity Search“. In Information Retrieval Technology, 54–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880592_5.

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Makino, Takuya, und Tomoya Iwakura. „A Boosted Supervised Semantic Indexing for Reranking“. In Information Retrieval Technology, 16–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70145-5_2.

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Xu, Wei-ran, Dong-xin Liu, Jun Guo, Yi-chao Cai und Ri-le Hu. „Supervised Dual-PLSA for Personalized SMS Filtering“. In Information Retrieval Technology, 254–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04769-5_22.

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Màrquez, Lluís, Gerard Escudero, David Martínez und German Rigau. „Supervised Corpus-Based Methods for WSD“. In Text, Speech and Language Technology, 167–216. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-4809-8_7.

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Lu, Wei, und Min-Yen Kan. „Supervised Categorization of JavaScriptTM Using Program Analysis Features“. In Information Retrieval Technology, 160–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_13.

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Li, Haibo, Yutaka Matsuo und Mitsuru Ishizuka. „Semantic Relation Extraction Based on Semi-supervised Learning“. In Information Retrieval Technology, 270–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_26.

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Konferenzberichte zum Thema "SUPERVISED TECHNOLOGY"

Ramachandran, Akshat, und Rizwan Ahmed Ansari. „Self-Supervised Depth Enhancement“. In 2022 International Conference for Advancement in Technology (ICONAT). IEEE, 2022. http://dx.doi.org/10.1109/iconat53423.2022.9726086.

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Ferrer, Miguel, Marcos Faundez-Zanuy, Carlos Travieso, Joan Fabregas und Jesus Alonso. „Evaluation of supervised vs. non supervised databases for hand geometry identification“. In Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology. IEEE, 2006. http://dx.doi.org/10.1109/ccst.2006.313447.

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Cho, Gyusang, und Chan-Hyun Youn. „Supervised vs. Self-supervised Pre-trained models for Hand Pose Estimation“. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2022. http://dx.doi.org/10.1109/ictc55196.2022.9953011.

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Min Kye, Seong, Joon Son Chung und Hoirin Kim. „Supervised Attention for Speaker Recognition“. In 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2021. http://dx.doi.org/10.1109/slt48900.2021.9383579.

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Marques, Filipe, Pedro Costa, Filipa Castro und Manuel Parente. „Self-Supervised Subsea SLAM for Autonomous Operations“. In Offshore Technology Conference. Offshore Technology Conference, 2019. http://dx.doi.org/10.4043/29602-ms.

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Huang, Luzhe, Hanlong Chen, Tairan Liu und Aydogan Ozcan. „Self-supervised neural network for holographic microscopy“. In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_at.2023.atu3q.4.

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We present a self-supervised hologram reconstruction neural network trained using a physics-consistency loss, which achieves superior generalization to reconstruct holograms of various samples, without previously having/seeing any experimental data or prior knowledge regarding the samples.

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Jain, Swapnesh, Ruchi Patel, Shubham Gupta und Tanu Dhoot. „FAKE NEWS DETECTION USING SUPERVISED LEARNING METHOD“. In ETHICS AND INFORMATION TECHNOLOGY. VOLKSON PRESS, 2020. http://dx.doi.org/10.26480/etit.02.2020.104.108.

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Du, Weizhi, Qichen Fu und Zhengyu Huang. „A Self-Supervised Deep Model for Focal Stacking“. In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_at.2022.jth3a.10.

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We propose to train a self-supervised autoencoder to extract image features and fuse focal stack images. Numerical experiments show the proposed method achieves better fusion performance, compared to traditional fusion method using Laplacian operator.

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O'Shea, Timothy J., Nathan West, Matthew Vondal und T. Charles Clancy. „Semi-supervised radio signal identification“. In 2017 19th International Conference on Advanced Communication Technology (ICACT). IEEE, 2017. http://dx.doi.org/10.23919/icact.2017.7890052.

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Inoue, Tomoya, Yujin Nakagawa, Ryota Wada, Keisuke Miyoshi, Shungo Abe, Kouhei Kuroda, Masatoshi Nishi, Hakan Bilen und Konda Reddy Mopuri. „Early Stuck Detection Using Supervised and Unsupervised Machine Learning Approaches“. In Offshore Technology Conference Asia. OTC, 2022. http://dx.doi.org/10.4043/31376-ms.

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Abstract The early detection of a stuck pipe event is crucial as it is one of the major incidents resulting in nonproductive time. An ordinary supervised machine learning approach has been adopted to achieve the detection of stuck pipe in some previous studies. However, for early detection before stuck occurs with this approach, there are challenging issues such as limited stuck pipe data, various causes of stuck, and the lack of a prior exact "stuck sign" which should be a label in the training dataset. In this study, the surface drilling data is first collected from multiple agencies to enhance the training dataset. Subsequently, a supervised machine learning algorithm with ordinary binary classification methodologies, such as support vector machines and neural networks is adopted. The supervised machine learning approach presents good performance for stuck pipe event detection. However, it detects "stuck has already occurred", and it cannot effectively predict the stuck pipe because there is no exact sign for stuck pipe which is mandatory as label for training data. This study also adopts an unsupervised machine learning algorithm which employs architectures that include an autoencoder with long short-term memory, as well as a multiple prediction model to improve the expressiveness. The unsupervised machine learning process typically involves learning the features of normal activities, whereby the created model can represent only these activities. When stuck occurs or will occur, as such data are not represented by the created model, it should be detected. The performance of the early stuck pipe event detection using supervised and unsupervised machine learning approaches is analyzed, and the results demonstrate that the unsupervised machine learning approach presents a better early stuck pipe detection capability. The proposed machine learning algorithm will be further improved in the future and the prediction result will be validated through actual operation.

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Berichte der Organisationen zum Thema "SUPERVISED TECHNOLOGY"

Korchin, Howard. Development of a Comprehensive Supervisor Training Program for Advanced Manufacturing Technology. Fort Belvoir, VA: Defense Technical Information Center, Juni 1991. http://dx.doi.org/10.21236/ada243533.

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Mahdavian, Farnaz. Germany Country Report. University of Stavanger, Februar 2022. http://dx.doi.org/10.31265/usps.180.

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Germany is a parliamentary democracy (The Federal Government, 2021) with two politically independent levels of 1) Federal (Bund) and 2) State (Länder or Bundesländer), and has a highly differentiated decentralized system of Government and administration (Deutsche Gesellschaft für Internationale Zusammenarbeit, 2021). The 16 states in Germany have their own government and legislations which means the federal authority has the responsibility of formulating policy, and the states are responsible for implementation (Franzke, 2020). The Federal Government supports the states in dealing with extraordinary danger and the Federal Ministry of the Interior (BMI) supports the states' operations with technology, expertise and other services (Federal Ministry of Interior, Building and Community, 2020). Due to the decentralized system of government, the Federal Government does not have the power to impose pandemic emergency measures. In the beginning of the COVID-19 pandemic, in order to slowdown the spread of coronavirus, on 16 March 2020 the federal and state governments attempted to harmonize joint guidelines, however one month later State governments started to act more independently (Franzke & Kuhlmann, 2021). In Germany, health insurance is compulsory and more than 11% of Germany’s GDP goes into healthcare spending (Federal Statistical Office, 2021). Health related policy at the federal level is the primary responsibility of the Federal Ministry of Health. This ministry supervises institutions dealing with higher level of public health including the Federal Institute for Drugs and Medical Devices (BfArM), the Paul-Ehrlich-Institute (PEI), the Robert Koch Institute (RKI) and the Federal Centre for Health Education (Federal Ministry of Health, 2020). The first German National Pandemic Plan (NPP), published in 2005, comprises two parts. Part one, updated in 2017, provides a framework for the pandemic plans of the states and the implementation plans of the municipalities, and part two, updated in 2016, is the scientific part of the National Pandemic Plan (Robert Koch Institut, 2017). The joint Federal-State working group on pandemic planning was established in 2005. A pandemic plan for German citizens abroad was published by the German Foreign Office on its website in 2005 (Robert Koch Institut, 2017). In 2007, the federal and state Governments, under the joint leadership of the Federal Ministry of the Interior and the Federal Ministry of Health, simulated influenza pandemic exercise called LÜKEX 07, and trained cross-states and cross-department crisis management (Bundesanstalt Technisches Hilfswerk, 2007b). In 2017, within the context of the G20, Germany ran a health emergency simulation exercise with representatives from WHO and the World Bank to prepare for future pandemic events (Federal Ministry of Health et al., 2017). By the beginning of the COVID-19 pandemic, on 27 February 2020, a joint crisis team of the Federal Ministry of the Interior (BMI) and the Federal Ministry of Health (BMG) was established (Die Bundesregierung, 2020a). On 4 March 2020 RKI published a Supplement to the National Pandemic Plan for COVID-19 (Robert Koch Institut, 2020d), and on 28 March 2020, a law for the protection of the population in an epidemic situation of national scope (Infektionsschutzgesetz) came into force (Bundesgesundheitsministerium, 2020b). In the first early phase of the COVID-19 pandemic in 2020, Germany managed to slow down the speed of the outbreak but was less successful in dealing with the second phase. Coronavirus-related information and measures were communicated through various platforms including TV, radio, press conferences, federal and state government official homepages, social media and applications. In mid-March 2020, the federal and state governments implemented extensive measures nationwide for pandemic containment. Step by step, social distancing and shutdowns were enforced by all Federal States, involving closing schools, day-cares and kindergartens, pubs, restaurants, shops, prayer services, borders, and imposing a curfew. To support those affected financially by the pandemic, the German Government provided large economic packages (Bundesministerium der Finanzen, 2020). These measures have adopted to the COVID-19 situation and changed over the pandemic. On 22 April 2020, the clinical trial of the corona vaccine was approved by Paul Ehrlich Institute, and in late December 2020, the distribution of vaccination in Germany and all other EU countries

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