Journal articles: 'SUPERVISED TECHNOLOGY'

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Zohuri, Bahman. "The Evolution of Artificial Intelligence: From Supervised to Semi-Supervised and Ultimately Unsupervised Technology Trends." Current Trends in Engineering Science (CTES) 3, no. 5 (August 22, 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, and Guo Qing Yan. "Land Utilization and Classification Method Based on Remote Sensing Technology." Applied Mechanics and Materials 239-240 (December 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, and Musaddak Maher Abdul Zahra. "Integrating of Promising Computer Network Technology with Intelligent Supervised Machine Learning for Better Performance." Webology 19, no. 1 (January 20, 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, no. 7 (July 31, 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, and 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, and S. Bhanu Prakash. "Virtual Assistant Using Supervised Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 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, and Yutong Shi. "Supervised Character Resemble Substitution Personality Adversarial Method." Electronics 12, no. 4 (February 8, 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, no. 1 (June 14, 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, and 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, no. 4 (April 30, 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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Chettri, Ajanta, Amal George, Dr A. Rengarajan, and 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, no. 4 (April 30, 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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Qiu, Qingchen, Xuelian Wu, Zhi Liu, Bo Tang, Yuefeng Zhao, Xinyi Wu, Hongliang Zhu, and Yang Xin. "Survey of supervised classification techniques for hyperspectral images." Sensor Review 37, no. 3 (June 19, 2017): 371–82. http://dx.doi.org/10.1108/sr-07-2016-0124.

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Purpose This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI technology has been proposed for many years, and the applications of this technology were promoted by technical advancements. Design/methodology/approach First, the properties and current situation of hyperspectral technology are summarized. Then, this paper introduces a series of common classification approaches. In addition, a comparison of different classification approaches on real hyperspectral data is conducted. Finally, this survey presents a discussion on the classification results and points out the classification development tendency. Findings The core of this survey is to review of the state of the art of the classification for hyperspectral images, to study the performance and efficiency of certain implementation measures and to point out the challenges still exist. Originality value The study categorized the supervised classification for hyperspectral images, demonstrated the comparisons among these methods and pointed out the challenges that still exist.

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Kola, Lokesh. "A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Gestational Diabetes Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1001–5. http://dx.doi.org/10.22214/ijraset.2021.39434.

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Abstract: Diabetes is the deadliest chronic diseases in the world. According to World Health Organization (WHO) around 422 million people are currently suffering from diabetes, particularly in low and middle-income countries. Also, the number of deaths due to diabetes is close to 1.6 million. Recent research has proven that the occurrence of diabetes is likely to be seen in people aged between 18 and this has risen from 4.7 to 8.5% from 1980 to 2014. Early diagnosis is necessary so that the disease does not go into advanced stages which is quite difficult to cure. Significant research has been performed in diabetes predictions. As time passes, challenges keep increasing to build a system to detect diabetes systematically. The hype for Machine Learning is increasing day to day to analyse medical data to diagnose a disease. Previous research has focused on just identifying the diabetes without specifying its type. In this paper, we have we have predicted gestational diabetes (Type-3) by comparing various supervised and semi-supervised machine learning algorithms on two datasets i.e., binned and non-binned datasets and compared the performance based on evaluation metrics. Keywords: Gestational diabetes, Machine Learning, Supervised Learning, Semi-Supervised Learning, Diabetes Prediction

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Qiu, Dongwei, Haorong Liang, Zhilin Wang, Yuci Tong, and Shanshan Wan. "Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels." Applied Sciences 12, no. 22 (November 20, 2022): 11799. http://dx.doi.org/10.3390/app122211799.

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Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is placed on trains to implement structural health monitoring in tunnels. Through the image processing technology proposed in this paper, water leakage areas in subway tunnels can be found and repaired in real time. A lightweight automatic segmentation approach to water leakage using hybrid-supervised-deep-learning technology is proposed. This approach consists of the weakly supervised learning Water Leakage-CAM and fully supervised learning WRDeepLabV3+. The Water Leakage-CAM is used for the automatic labeling of data. The WRDeepLabV3+ is used for the accurate identification of water leakage areas in subway tunnels. Compared with other end-to-end semantic segmentation networks, the hybrid-supervised learning approach can more completely segment the water leakage region when dealing with water leakage in complex environments. The hybrid-supervised-deep-learning approach proposed in this paper achieves the highest MIoU of 82.8% on the experimental dataset, which is 6.4% higher than the second. The efficiency is also 25% higher than the second and significantly outperforms other end-to-end deep learning approaches.

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AlZuhair, Mona Suliman, Mohamed Maher Ben Ismail, and Ouiem Bchir. "Soft Semi-Supervised Deep Learning-Based Clustering." Applied Sciences 13, no. 17 (August 27, 2023): 9673. http://dx.doi.org/10.3390/app13179673.

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Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the contributions made to enhance the state-of-the-art fully unsupervised clustering approaches. In this paper, we propose a novel semi-supervised deep clustering approach, named Soft Constrained Deep Clustering (SC-DEC), that aims to address the limitations exhibited by existing semi-supervised clustering approaches. Specifically, the proposed approach leverages a deep neural network architecture and generates fuzzy membership degrees that better reflect the true partition of the data. In particular, the proposed approach uses side-information and formulates it as a set of soft pairwise constraints to supervise the machine learning process. This supervision information is expressed using rather relaxed constraints named “should-link” constraints. Such constraints determine whether the pairs of data instances should be assigned to the same or different cluster(s). In fact, the clustering task was formulated as an optimization problem via the minimization of a novel objective function. Moreover, the proposed approach’s performance was assessed via extensive experiments using benchmark datasets. Furthermore, the proposed approach was compared to relevant state-of-the-art clustering algorithms, and the obtained results demonstrate the impact of using minimal previous knowledge about the data in improving the overall clustering performance.

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Sáiz-Manzanares, María Consuelo, Ismael Ramos Pérez, Adrián Arnaiz Rodríguez, Sandra Rodríguez Arribas, Leandro Almeida, and Caroline Françoise Martin. "Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques." Applied Sciences 11, no. 13 (July 2, 2021): 6157. http://dx.doi.org/10.3390/app11136157.

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In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The main goal of this study was to analyse the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (k-means ++, fuzzy k-means and DBSCAN). These algorithms provided the learning profile of the participants in three types (students over 50 years old; and students and teachers under 50 years of age). Therefore, the use of both types of data analysis is considered complementary.

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Hardy, Andy, Gregory Oakes, Juma Hassan, and Yussuf Yussuf. "Improved Use of Drone Imagery for Malaria Vector Control through Technology-Assisted Digitizing (TAD)." Remote Sensing 14, no. 2 (January 11, 2022): 317. http://dx.doi.org/10.3390/rs14020317.

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Drones have the potential to revolutionize malaria vector control initiatives through rapid and accurate mapping of potential malarial mosquito larval habitats to help direct field Larval Source Management (LSM) efforts. However, there are no clear recommendations on how these habitats can be extracted from drone imagery in an operational context. This paper compares the results of two mapping approaches: supervised image classification using machine learning and Technology-Assisted Digitising (TAD) mapping that employs a new region growing tool suitable for non-experts. These approaches were applied concurrently to drone imagery acquired at seven sites in Zanzibar, United Republic of Tanzania. Whilst the two approaches were similar in processing time, the TAD approach significantly outperformed the supervised classification approach at all sites (t = 5.1, p < 0.01). Overall accuracy scores (mean overall accuracy 62%) suggest that a supervised classification approach is unsuitable for mapping potential malarial mosquito larval habitats in Zanzibar, whereas the TAD approach offers a simple and accurate (mean overall accuracy 96%) means of mapping these complex features. We recommend that this approach be used alongside targeted ground-based surveying (i.e., in areas inappropriate for drone surveying) for generating precise and accurate spatial intelligence to support operational LSM programmes.

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Liu, MengYang, MingJun Li, and XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents." Computational Intelligence and Neuroscience 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.

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Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no set outcome from which they can learn. The predicting/forecasting column is not present in an unsupervised model, unlike in the supervised model. Supervised models use regression to predict continuous quantities and classification to predict discrete class labels; unsupervised models use clustering to group similar models and association learning to find associations between items. Unsupervised migration is a combination of the unsupervised learning method and migration. In unsupervised learning, there is no need to supervise the models. Migration is an effective tool in processing and imaging data. Unsupervised learning allows the model to work independently to discover patterns and information that were previously undetected. It mainly works on unlabeled data. Unsupervised learning can achieve more complex processing tasks when compared to supervised learning. The unsupervised learning method is more unpredictable when compared with other types of learning methods. Some of the popular unsupervised learning algorithms include k-means clustering, hierarchal clustering, Apriori algorithm, clustering, anomaly detection, association mining, neural networks, etc. In this research article, we implement this particular deep learning model in the marketing oriented asset allocation of high level accounting talents. When the proposed unsupervised migration algorithm was compared to the existing Fractional Hausdorff Grey Model, it was discovered that the proposed system provided 99.12% accuracy by the high level accounting talented candidate in market-oriented asset allocation.

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Liu, MengYang, MingJun Li, and XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents." Computational Intelligence and Neuroscience 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.

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Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no set outcome from which they can learn. The predicting/forecasting column is not present in an unsupervised model, unlike in the supervised model. Supervised models use regression to predict continuous quantities and classification to predict discrete class labels; unsupervised models use clustering to group similar models and association learning to find associations between items. Unsupervised migration is a combination of the unsupervised learning method and migration. In unsupervised learning, there is no need to supervise the models. Migration is an effective tool in processing and imaging data. Unsupervised learning allows the model to work independently to discover patterns and information that were previously undetected. It mainly works on unlabeled data. Unsupervised learning can achieve more complex processing tasks when compared to supervised learning. The unsupervised learning method is more unpredictable when compared with other types of learning methods. Some of the popular unsupervised learning algorithms include k-means clustering, hierarchal clustering, Apriori algorithm, clustering, anomaly detection, association mining, neural networks, etc. In this research article, we implement this particular deep learning model in the marketing oriented asset allocation of high level accounting talents. When the proposed unsupervised migration algorithm was compared to the existing Fractional Hausdorff Grey Model, it was discovered that the proposed system provided 99.12% accuracy by the high level accounting talented candidate in market-oriented asset allocation.

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Borda, Davide, Mattia Bergagio, Massimo Amerio, Marco Carlo Masoero, Romano Borchiellini, and Davide Papurello. "Development of Anomaly Detectors for HVAC Systems Using Machine Learning." Processes 11, no. 2 (February 10, 2023): 535. http://dx.doi.org/10.3390/pr11020535.

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Faults and anomalous behavior affect the operation of Heating, Ventilation and Air Conditioning (HVAC) systems. This causes performance loss, energy waste, noncompliance with regulations and discomfort among occupants. To prevent damage, automated, fast identification of faults in HVAC systems is needed. Fault Detection and Diagnosis (FDD) techniques are very effective for these purposes. The best FDD methods, in terms of cost effectiveness and data exploitation, are based on process history; i.e., on sensor data from automation systems. In this work, supervised and semi-supervised models were developed. Other than with regard to outdoor temperature and humidity, the input parameters of an HVAC system have few internal variables. Performance of traditional methods (e.g., VAR, Random Forest) is low, so Artificial Neural Networks (ANNs) were selected, since they can capture nonlinear relationships among features and are easily optimized. ANNs can detect simultaneous faults from different classes. ANN metrics are easily evaluated. The ground truth is obtained from process history (supervised case) and from a mix of deterministic methods and clustering (semi-supervised case). The derivation of the ground truth in the semi-supervised case, and extensive comparison with advanced supervised models, set this work apart from previous studies. The Mean Absolute Error (MAE) of the best supervised model was 0.032 over 15 min and 0.034 over 30 min. The Balanced Accuracy Score (BAS) of the best semi-supervised model was 86%.

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Tong, Yuerong, Jingyi Liu, Lina Yu, Liping Zhang, Linjun Sun, Weijun Li, Xin Ning, Jian Xu, Hong Qin, and Qiang Cai. "Technology investigation on time series classification and prediction." PeerJ Computer Science 8 (May 18, 2022): e982. http://dx.doi.org/10.7717/peerj-cs.982.

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Time series appear in many scientific fields and are an important type of data. The use of time series analysis techniques is an essential means of discovering the knowledge hidden in this type of data. In recent years, many scholars have achieved fruitful results in the study of time series. A statistical analysis of 120,000 literatures published between 2017 and 2021 reveals that the topical research about time series is mostly focused on their classification and prediction. Therefore, in this study, we focus on analyzing the technical development routes of time series classification and prediction algorithms. 87 literatures with high relevance and high citation are selected for analysis, aiming to provide a more comprehensive reference base for interested researchers. For time series classification, it is divided into supervised methods, semi-supervised methods, and early classification of time series, which are key extensions of time series classification tasks. For time series prediction, from classical statistical methods, to neural network methods, and then to fuzzy modeling and transfer learning methods, the performance and applications of these different methods are discussed. We hope this article can help aid the understanding of the current development status and discover possible future research directions, such as exploring interpretability of time series analysis and online learning modeling.

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Dhamelia, Hemin, and Riti Moradiya. "Unlocking Hidden Insights: Unleashing the Strength of Semi-Supervised Learning in Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 2049–57. http://dx.doi.org/10.22214/ijraset.2023.55468.

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Abstract: Semi-supervised learning bridges supervised and unsupervised methods, utilizing limited labeled data alongside vast unlabeled data. This paper explores its foundations, algorithms, applications, challenges, and trends. It covers co-training, selftraining, multi-view learning, and generative approaches, addressing label scarcity, noisy data, and model robustness. The research offers insights into semi-supervised learning's transformative role in machine learning

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Sun, Tong He, and Guo Qing Yan. "Land Classification Method and Analysis Based on Remote Sensing Technology." Advanced Materials Research 726-731 (August 2013): 4582–86. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4582.

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In recent years, the research about land utilization changing already became one of the research about global changing's key topics, the land utilization classification, as its sub-topic, also attract men's high attention. Remote Sensing (RS) and the Geographic Information System (GIS) as the two big spatial technology tool to support modern geography, their union arouses people's universal interest and research. Because the remote sensing information has the advantage of covering wide area, timeliness and current situation, quick speed, short cycle and reliable accurate as well as time-saving, effort-saving, low status merit expense, it is widely used in the land resource and land utilization situation investigation at present, land utilization change monitor and so on. 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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Jiang, Yi, and Hui Sun. "Top-K Pseudo Labeling for Semi-Supervised Image Classification." International Journal of Data Warehousing and Mining 19, no. 2 (December 30, 2022): 1–18. http://dx.doi.org/10.4018/ijdwm.316150.

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In this paper, a top-k pseudo labeling method for semi-supervised self-learning is proposed. Pseudo labeling is a key technology in semi-supervised self-learning. Briefly, the quality of the pseudo label generated largely determined the convergence of the neural network and the accuracy obtained. In this paper, the authors use a method called top-k pseudo labeling to generate pseudo label during the training of semi-supervised neural network model. The proposed labeling method helps a lot in learning features from unlabeled data. The proposed method is easy to implement and only relies on the neural network prediction and hyper-parameter k. The experiment results show that the proposed method works well with semi-supervised learning on CIFAR-10 and CIFAR-100 datasets. Also, a variant of top-k labeling for supervised learning named top-k regulation is proposed. The experiment results show that various models can achieve higher accuracy on test set when trained with top-k regulation.

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Chen, Shouchun, Fei Wang, Yangqiu Song, and Changshui Zhang. "Semi-supervised ranking aggregation." Information Processing & Management 47, no. 3 (May 2011): 415–25. http://dx.doi.org/10.1016/j.ipm.2010.09.003.

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Song, Yide. "Weakly-Supervised and Unsupervised Video Anomaly Detection." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 160–70. http://dx.doi.org/10.54097/hset.v12i.1444.

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As surveillance technology is continuously improving, an ever-increasing number of cameras are being deployed everywhere. Relying on manual detection of anomalies through cameras may be unreliable and untimely. Therefore, the application of deep learning in video anomaly detection is being extensively studied. Anomaly Detection (AD) refers to identifying events that deviate from the desired actions. This article discusses representative unsupervised and weakly-supervised learning methods applied to various data types. In these machine learning methods, Generative Adversarial Network, Auto Encoder, Recurrent Neural Network, etc. are broadly adopted for AD. Some renowned and new datasets are reviewed. Furthermore, we also proposed several future directions of research in video anomaly detection.

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Wang, Jiahao, Junhao Zhao, Hong Sun, Xiao Lu, Jixia Huang, Shaohua Wang, and Guofei Fang. "Satellite Remote Sensing Identification of Discolored Standing Trees for Pine Wilt Disease Based on Semi-Supervised Deep Learning." Remote Sensing 14, no. 23 (November 23, 2022): 5936. http://dx.doi.org/10.3390/rs14235936.

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Pine wilt disease (PWD) is the most dangerous biohazard of pine species and poses a serious threat to forest resources. Coupling satellite remote sensing technology and deep learning technology for the accurate monitoring of PWD is an important tool for the efficient prevention and control of PWD. We used Gaofen-2 remote sensing images to construct a dataset of discolored standing tree samples of PWD and selected three semantic segmentation models—DeepLabv3+, HRNet, and DANet—for training and to compare their performance. To build a GAN-based semi-supervised semantic segmentation model for semi-supervised learning training, the best model was chosen as the generator of generative adversarial networks (GANs). The model was then optimized for structural adjustment and hyperparameter adjustment. Aimed at the characteristics of Gaofen-2 images and discolored standing trees with PWD, this paper adopts three strategies—swelling prediction, raster vectorization, and forest floor mask extraction—to optimize the image identification process and results and conducts an application demonstration study in Nanping city, Fujian Province. The results show that among the three semantic segmentation models, HRNet was the optimal conventional semantic segmentation model for identifying discolored standing trees of PWD based on Gaofen-2 images and that its MIoU value was 68.36%. Additionally, the GAN-based semi-supervised semantic segmentation model GAN_HRNet_Semi improved the MIoU value by 3.10%, and its recognition segmentation accuracy was better than the traditional semantic segmentation model. The recall rate of PWD discolored standing tree monitoring in the demonstration area reached 80.09%. The combination of semi-supervised semantic segmentation technology and high-resolution satellite remote sensing technology provides new technical methods for the accurate wide-scale monitoring, prevention, and control of PWD.

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Peterson, Carsten, Stephen Redfield, James D. Keeler, and Eric Hartman. "An Optoelectronic Architecture for Multilayer Learning in a Single Photorefractive Crystal." Neural Computation 2, no. 1 (March 1990): 25–34. http://dx.doi.org/10.1162/neco.1990.2.1.25.

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We propose a simple architecture for implementing supervised neural network models optically with photorefractive technology. The architecture is very versatile: a wide range of supervised learning algorithms can be implemented including mean-field-theory, backpropagation, and Kanerva-style networks. Our architecture is based on a single crystal with spatial multiplexing rather than the more commonly used angular multiplexing. It handles hidden units and places no restrictions on connectivity. Associated with spatial multiplexing are certain physical phenomena, rescattering and beam depletion, which tend to degrade the matrix multiplications. Detailed simulations including beam absorption and grating decay show that the supervised learning algorithms (slightly modified) compensate for these degradations.

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Huang, Yanli. "Open Learning Environment for Multimodal Learning Based on Knowledge Base Technology." International Journal of Emerging Technologies in Learning (iJET) 18, no. 11 (June 7, 2023): 38–51. http://dx.doi.org/10.3991/ijet.v18i11.39397.

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With the development of Internet technology, multimodal data have become the main data resource in the information age. Multimodal learning mode, as an education and teaching mode developed on multimodal data technology, provides more convenience for multimedia teaching. However, many challenges persist in its actual development and application. Currently, the multimodal learning model is susceptible to classroom noise, lack of teaching information, and other factors, which adversely affect multimodal data collection, teaching application, and achievement output. Thus, this paper optimizes the multimodal learning model in the open learning environment and takes 120 engineering students from a university in Guangxi Province as the research object. First, a sequential modal extraction method is proposed by constructing a multimodal probability generation model and then the data are modeled. Semi-supervised learning is then achieved by analyzing and combining the supervised and unsupervised learning processes. Finally, the knowledge base technology with information fusion characteristics is applied to the multimodal teaching mode. This teaching mode has been proven to improve students’ learning ability and learning achievement and teachers’ teaching effectiveness.

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Zhao, Jianhua, and Ning Liu. "Semi-supervised Classification Based Mixed Sampling for Imbalanced Data." Open Physics 17, no. 1 (December 31, 2019): 975–83. http://dx.doi.org/10.1515/phys-2019-0103.

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Abstract In practical application, there are a large amount of imbalanced data containing only a small number of labeled data. In order to improve the classification performance of this kind of problem, this paper proposes a semi-supervised learning algorithm based on mixed sampling for imbalanced data classification (S2MAID), which combines semi-supervised learning, over sampling, under sampling and ensemble learning. Firstly, a kind of under sampling algorithm UD-density is provided to select samples with high information content from majority class set for semi-supervised learning. Secondly, a safe supervised-learning method is used to mark unlabeled sample and expand the labeled sample. Thirdly, a kind of over sampling algorithm SMOTE-density is provided to make the imbalanced data set become balance set. Fourthly, an ensemble technology is used to generate a strong classifier. Finally, the experiment is carried out on imbalanced data with containing only a few labeled samples, and semi-supervised learning process is simulated. The proposed S2MAID is verified and the experimental result shows that the proposed S2MAID has a better classification performance.

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Song, Xiao Na, Jun Zheng, Pei Li, Xiao Xia Hou, Jing Rong Zhang, Yan Ping Hu, and Ning Gao. "Design of Intelligent Supervised System Based on Internet of Things." Advanced Materials Research 816-817 (September 2013): 967–70. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.967.

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Based on technology of Internet of Things, an intelligent supervised system for housing is formed to monitor various parameters at home. This system adopts special sensors to sample the environmental data, and will send alarm information through the GPRS gateway to the mobile phone of the host once the data beyonds the allowable value. Through information exchange between host and the supervised system, people can check the real time information of home and commit intelligent control. This paper introduced the structure, the hardware and software design idea of the supervised system. Special designs ensure that the system has relatively low power consumption and can be easily used for housing supervision and control.

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Zhang, Kun, Hai Feng Wang, and Zhuang Li. "Based on IDRISI Remote Sensing Images Land-Use Types of Supervised Classification Techniques." Applied Mechanics and Materials 415 (September 2013): 305–8. http://dx.doi.org/10.4028/www.scientific.net/amm.415.305.

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With remote sensing technology and computer technology, remote sensing classification technology has been rapid progress. In the traditional classification of remote sensing technology, based on the combination of today's technology in the field of remote sensing image classification, some new developments and applications for land cover classification techniques to make more comprehensive elaboration. Using the minimum distance classifier extracts of the study area land use types. Ultimately extracted land use study area distribution image and make its analysis and evaluation.

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Nivelkar, Mukta, and S. G. Bhirud. "Modeling of Supervised Machine Learning using Mechanism of Quantum Computing." Journal of Physics: Conference Series 2161, no. 1 (January 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2161/1/012023.

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Abstract Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models is very well set but it has more computational requirements based on complex and high-volume data processing. Supervised machine learning modelling using quantum computing deals with feature selection, parameter encoding and parameterized circuit formation. This paper highlights on integration of quantum computation and machine learning which will make sense on quantum machine learning modeling. Modelling of quantum parameterized circuit, Quantum feature set design and implementation for sample data is discussed. Supervised machine learning using quantum mechanism such as superposition and entanglement are articulated. Quantum machine learning helps to enhance the various classical machine learning methods for better analysis and prediction using complex measurement.

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Schneider, Tizian, Steffen Klein, and Andreas Schütze. "Machine learning in industrial measurement technology for detection of known and unknown faults of equipment and sensors." tm - Technisches Messen 86, no. 11 (November 26, 2019): 706–18. http://dx.doi.org/10.1515/teme-2019-0086.

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AbstractThis paper focuses on the application of novelty detection in combination with supervised fault classification for industrial condition monitoring. Its goal is to provide a guideline for engineers on how to apply novelty detection for outlier detection, monitoring of supervised classification and detection of unknown faults without the need of a data scientist. All guidelines are demonstrated by means of a publicly available condition monitoring dataset. In each application case the results achieved with different common novelty detection algorithms are compared, advantages and disadvantages of the respective algorithms are shown. To increase applicability of the suggested approach visualization of results is emphasized and all algorithms have been included in a publicly available data analysis software toolbox with graphical user interface.

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Wang, Cong, Wanshu Fan, Yutong Wu, and Zhixun Su. "Weakly supervised single image dehazing." Journal of Visual Communication and Image Representation 72 (October 2020): 102897. http://dx.doi.org/10.1016/j.jvcir.2020.102897.

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Kong, Yating, Jide Li, Liangpeng Hu, and Xiaoqiang Li. "Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps." Applied Sciences 13, no. 15 (July 26, 2023): 8616. http://dx.doi.org/10.3390/app13158616.

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Image matting methods based on deep learning have made tremendous success. However, the success of previous image matting methods typically relies on a massive amount of pixel-level labeled data, which are time-consuming and costly to obtain. This paper first proposes a semi-supervised deep learning matting algorithm based on semantic consistency of trimaps (Tri-SSL), which uses trimaps to provide weakly supervised signals for the unlabeled data, to reduce the labeling cost. Tri-SSL is a single-stage semi-supervised algorithm that consists of a supervised branch and a weakly supervised branch that share the same network in one iteration during training. The supervised branch is consistent with standard supervised matting methods. In the weakly supervised branch, trimaps of different granularities are used as weakly supervised signals for unlabeled images, and the two trimaps are naturally perturbed samples. Orientation consistency constraints are imposed on the prediction results of trimaps of different granuliarty and the intermediate features of the network. Experimental results show that Tri-SSL improves model performance by effectively utilizing unlabeled data.

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Zhao, Qingyu, Zixuan Liu, Ehsan Adeli, and Kilian M. Pohl. "Longitudinal self-supervised learning." Medical Image Analysis 71 (July 2021): 102051. http://dx.doi.org/10.1016/j.media.2021.102051.

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Hang, Feilu, Wei Guo, Hexiong Chen, Linjiang Xie, Xiaoyu Bai, and Yao Liu. "Network Intrusion Anomaly Detection Model Based on Multiclassifier Fusion Technology." Mobile Information Systems 2023 (April 8, 2023): 1–11. http://dx.doi.org/10.1155/2023/1594622.

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With the increasing development of the industrial Internet, network security has attracted more and more attention. Among the numerous network security technologies, anomaly detection technology based on network traffic has become an important research field. At present, a large number of methods for network anomaly detection have been proposed. Most of the better performance detection methods are based on supervised machine learning algorithms, which require a large number of labelled data for model training. However, in a real network, it is impossible to manually filter and label large-scale traffic data. Network administrators can only use unsupervised machine learning algorithms for actual detection, and the detection effects are much worse than supervised learning algorithms. To improve the accuracy of the unsupervised detection methods, this study proposes a network anomaly detection model based on multiple classifier fusion technology, which applies different fusion techniques (such as Majority Vote, Weighted Majority Vote, and Naive Bayes) to fuse the detection results of the five best performing unsupervised anomaly detection algorithms. Comparative experiments are carried out on three public datasets. Experimental results show that, in terms of RECALL and AUC score, the fusion model proposed in this study achieves better performance than the five separate anomaly detection baseline algorithms, and it has better robustness and stability, which can be effectively applied to a wide range of network anomaly detection scenarios.

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Bordoloi, Monali, Preetam Chayan Chatterjee, Saroj Kumar Biswas, and Biswajit Purkayastha. "Keyword extraction using supervised cumulative TextRank." Multimedia Tools and Applications 79, no. 41-42 (August 21, 2020): 31467–96. http://dx.doi.org/10.1007/s11042-020-09335-1.

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Shu, Xin, Haiyan Jiang, and Huanliang Xu. "Graph regularized supervised cross-view hashing." Multimedia Tools and Applications 77, no. 21 (April 27, 2018): 28207–24. http://dx.doi.org/10.1007/s11042-018-5988-3.

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Yang, Haichuan, Xiao Bai, Yanzhen Liu, Yanyang Wang, Lu Bai, Jun Zhou, and Wenzhong Tang. "Maximum margin hashing with supervised information." Multimedia Tools and Applications 75, no. 7 (January 27, 2016): 3955–71. http://dx.doi.org/10.1007/s11042-015-3159-3.

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Ram, Nikhil Chandra Sai, Gowtham Vakati, Jagadesh Varma Nadimpall, Yash Sah, and Sai Karthik Datla. "Fake Reviews Detection Using Supervised Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3718–27. http://dx.doi.org/10.22214/ijraset.2022.43202.

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Abstract: With the continuous evolve of E-commerce systems, online reviews are mainly considered as a crucial factor for building and maintaining a good reputation. Moreover, they have an effective role in the decision making process for end users. Usually, a positive review for a target object attracts more customers and lead to high increase in sales. Nowadays, deceptive or fake reviews are deliberately written to build virtual reputation and attracting potential customers. Thus, identifying fake reviews is a vivid and ongoing research area. Identifying fake reviews depends not only on the key features of the reviews but also on the behaviours of the reviewers. This paper proposes a machine learning approach to identify fake reviews. In addition to the features extraction process of the reviews, this paper applies several features engineering to extract various behaviours of the reviewers. The paper compares the performance of several experiments done on a real Yelp dataset of restaurants reviews, we compare the performance of machine learning classifiers; KNN, Naive Bayes (NB), Logistic Regression. The results reveal that Logistic Regression outperforms the rest of classifiers in terms of accuracy achieving best. The results show that the system has better ability to detect a review as fake or original.

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Shin, Sungho, Jongwon Kim, Yeonguk Yu, Seongju Lee, and Kyoobin Lee. "Self-Supervised Transfer Learning from Natural Images for Sound Classification." Applied Sciences 11, no. 7 (March 29, 2021): 3043. http://dx.doi.org/10.3390/app11073043.

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We propose the implementation of transfer learning from natural images to audio-based images using self-supervised learning schemes. Through self-supervised learning, convolutional neural networks (CNNs) can learn the general representation of natural images without labels. In this study, a convolutional neural network was pre-trained with natural images (ImageNet) via self-supervised learning; subsequently, it was fine-tuned on the target audio samples. Pre-training with the self-supervised learning scheme significantly improved the sound classification performance when validated on the following benchmarks: ESC-50, UrbanSound8k, and GTZAN. The network pre-trained via self-supervised learning achieved a similar level of accuracy as those pre-trained using a supervised method that require labels. Therefore, we demonstrated that transfer learning from natural images contributes to improvements in audio-related tasks, and self-supervised learning with natural images is adequate for pre-training scheme in terms of simplicity and effectiveness.

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Caponi, Matteo, Adam Cox, and Siddharth Misra. "Viscosity prediction using image processing and supervised learning." Fuel 339 (May 2023): 127320. http://dx.doi.org/10.1016/j.fuel.2022.127320.

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Zhou, Ruixu, Wensheng Gao, Dengwei Ding, and Weidong Liu. "Supervised dimensionality reduction technology of generalized discriminant component analysis and its kernelization forms." Pattern Recognition 124 (April 2022): 108450. http://dx.doi.org/10.1016/j.patcog.2021.108450.

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Cheng, Ning, Hongpo Zhang, and Zhanbo Li. "Data sanitization against label flipping attacks using AdaBoost-based semi-supervised learning technology." Soft Computing 25, no. 23 (October 18, 2021): 14573–81. http://dx.doi.org/10.1007/s00500-021-06384-y.

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Lopane, Giovanna, Sabato Mellone, Mattia Corzani, Lorenzo Chiari, Pietro Cortelli, Giovanna Calandra-Buonaura, and Manuela Contin. "Supervised versus unsupervised technology-based levodopa monitoring in Parkinson’s disease: an intrasubject comparison." Journal of Neurology 265, no. 6 (March 29, 2018): 1343–52. http://dx.doi.org/10.1007/s00415-018-8848-1.

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Huang, Ri Sheng. "Information Technology in an Improved Supervised Locally Linear Embedding for Recognizing Speech Emotion." Advanced Materials Research 1014 (July 2014): 375–78. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.375.

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To improve effectively the performance on speech emotion recognition, it is needed to perform nonlinear dimensionality reduction for speech feature data lying on a nonlinear manifold embedded in high-dimensional acoustic space. This paper proposes an improved SLLE algorithm, which enhances the discriminating power of low-dimensional embedded data and possesses the optimal generalization ability. The proposed algorithm is used to conduct nonlinear dimensionality reduction for 48-dimensional speech emotional feature data including prosody so as to recognize three emotions including anger, joy and neutral. Experimental results on the natural speech emotional database demonstrate that the proposed algorithm obtains the highest accuracy of 90.97% with only less 9 embedded features, making 11.64% improvement over SLLE algorithm.

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Rana, Soumya Prakash, Maitreyee Dey, Riccardo Loretoni, Michele Duranti, Mohammad Ghavami, Sandra Dudley, and Gianluigi Tiberi. "Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model." Tomography 9, no. 1 (January 12, 2023): 105–29. http://dx.doi.org/10.3390/tomography9010010.

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Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.

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Liu, Chuang, Kang Su, Long Yang, Jie Li, and Jingbo Guo. "Detection of Complex Features of Car Body-in-White under Limited Number of Samples Using Self-Supervised Learning." Coatings 12, no. 5 (April 29, 2022): 614. http://dx.doi.org/10.3390/coatings12050614.

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The measurement and monitoring of the dimensional characteristics of the body-in-white is an important part of the automobile manufacturing process. The process of using key point regression technology to perform online detection of complex features on body-in-white currently faces a bottleneck problem, namely limited training samples. Under the condition that the number of labeled normal map samples is limited, this paper proposes a framework for domain-independent self-supervised learning using a large number of original images. Under this framework, a self-supervised pre-order task is designed, which uses a large number of easily accessible unlabeled original images for characterization learning as well as a domain discriminator to conduct adversarial training on the feature extractor, so that the extracted representation is domain-independent. Finally, in the key point regression task of five different complex features, a series of comparative experiments were carried out between the method in this paper and benchmark methods such as supervised learning, conventional self-supervised learning, and domain-related self-supervised learning. The results show that the method proposed in this paper has achieved significant performance advantages. In the principal component analysis of extracting features, the representation extracted by the method in this paper does not show obvious domain information.

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Wang, Jiayan, Zongmin Li, Xujian Qiao, Baodi Liu, and Yu Zhao. "Semi-Supervised Few-shot Image Classification Based on Subspace Learning." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012063. http://dx.doi.org/10.1088/1742-6596/2171/1/012063.

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Abstract Few-shot image classification is an image classification technology that uses minimal sample data to train the classifier or classification network and achieves a sure classification accuracy. Compared with the image classification technology based on big data, it has the advantages of an unlimited number of samples and fast processing speed. The purpose of subspace learning is to learn good mapping. This mapping maps high-dimensional image data from visual to low-dimensional semantic information space, which is convenient for information recognition, processing, and classification. Therefore, this paper proposes to use the subspace learning method to extract image features. On this basis, studies semi-supervised few-shot image classification, the self-training semi-supervised algorithm is improved to overcome the performance degradation caused by adding wrong pseudo label samples to the self-training algorithm. It is trained and tested on four mainstream few-shot learning datasets. It has a specific significance for the research and development of few-shot image classification.

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Journal articles on the topic 'SUPERVISED TECHNOLOGY'

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