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Multi-label Feature Selection Based on Fuzzy Neighborhood Mutual Discrimination Index 12

Multi-label Feature Selection Based on Fuzzy Neighborhood Mu...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Wang, Chenxi Chen, E. Ren, Mengli Yu, Xiehua Lin, Yaojin Li, Shaozi Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China Minnan Science and Technology University School of Computer Science Lab of Data Science and Intelligence Application Quanzhou China Xiamen University School of Computer Science Lab of Data Science and Intelligence Application Xiamen China

ISBN: (纸本)9798350310153

Multi-label feature selection advances the performance of multi-label classification models by eliminating irrelevant and redundant features. Most multi-label feature selection algorithms assume that the training set containing logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances of labels. To solve the problem, we proposes a multi-label feature selection method based on fuzzy neighborhood mutual discriminant index. Firstly, logical labels are transformed into label distribution by label enhancement. Secondly, the information entropy is referenced to the label distribution learning, the label neighborhood similarity matrix is constructed to describe the ambiguity between labels. Finally, linking the fuzzy neighborhood discrimination index under the selected feature subset with label neighborhood similarity matrix is proposed, which is used for judging the distinguishing ability of the feature subset to label. Comprehensive experiments on 6 multi-label datasets show that the algorithm outperforms other algorithms in classification performance. © 2022 IEEE.

关键词： Feature Selection

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Automated Anomaly Detection in Medical Imaging Using Support Vector Machines 5th

Automated Anomaly Detection in Medical Imaging Using Support...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Goyal, Manish Kumar Shahid, Mohammad Karthikeyan, M.P. Kumar, Ritesh Department of Computer Science & Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Clinical imaging techniques such as X-ray, Magnetic Resonance Imaging (MRI), and laptop Tomography (CT) scanners are used to diagnose, compare, and deal with sufferers. Automatic Anomaly Detection is an automatic technique for identifying and diagnosing abnormalities in clinical imaging. It has been diagnosed that Support Vector Machines (SVM) can be effectively used to generate correct automatic anomaly detection for scientific imaging. SVM is an approach that uses supervised getting-to-know to create a choice boundary between classes, with the decision primarily based on the information to be had. The gain of SVM is that it plays an automatic function extracting from the medical imaging facts and uses those extracted capabilities for anomaly detection. It combines photograph segmentation and classification to classify the abnormal regions in an image. So one can enhance the accuracy of SVM;it's miles viable to use an expansion of kernel capabilities and unique parameter tuning. Moreover, introducing an automatic pipeline from training to deploying SVM reduces the efforts of manual feature extraction from photos. Consequently, aid Vector Machines is a feasible approach for automatic anomaly detection in scientific imaging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Nge-BatikVR: Innovative Batik Learning and Marketing Through Virtual Reality Serious Games with Immersive Hands-On Experience 15

Nge-BatikVR: Innovative Batik Learning and Marketing Through...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Ramdhan, Dimas Pratama, Galih Dea Wijaya, Michael Francis Syahputra, Muhammad Edo Wihendro Game Application & Technology Program School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350383829

Batik is a cultural heritage of Indonesia, recognized by WHO as an Intangible Cultural Heritage. Batik is dyed by skilled craftsmen who make patterns with dots and lines on the fabric from melted wax. The process is complicated, so few people can experience all the steps in crafting Batik. From these problems, immersive learning media are needed so everyone can learn and gain experience in Batik crafting from start to finish. In this study, we will present Nge-BatikVR, a serious game application that introduces Batik through Virtual Reality and offers an immersive experience of learning Batik from various regions with interactive hands-on feature aimed at people to better understand and learn Batik. Nge-BatikVR offers four main features called Sinau (Learn), Kuis (Quiz), Nge-Batik (Simulation) and Toko (Shopping), with the main purpose to present interactive and engaging media useful for introducing and learning Batik with a fully immersive experience. © 2023 IEEE.

关键词： Serious games

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YOLO: Redefining Real-Time Object Detection Accuracy and Efficiency 3

YOLO: Redefining Real-Time Object Detection Accuracy and Eff...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Verma, Shivam Gopikishan Maurya, Sudhanshu Pant, Himanshu Yadav, Ajay Kumar Thomas Varghese, Sneha Garg, Rachit Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University School of Computing Uttarakhand India United Institute of Management Department of Computer Application Uttar Pradesh Prayagraj India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350384598

Image classification, also known as object detection, is an advanced form of computer vision in which a network can predict objects in an image and point them out as bounding boxes. Classifying and recognizing objects in an image is just one aspect of object detection because it also involves localizing these objects and attracting bounding boxes around them. Since object detection involves image and video analysis, it has been of critical interest to researchers over the past few years. The previous object recognition method relies on manually created features and traditional and trainable algorithms. By using the YOLO (You Only Look Once) process, this research aims to perform real-time object detection. Regarding speed and performance, the YOLO algorithm is much more efficient than the existing model. Unlike existing algorithms that do not scan the entire image in single forward propagation, YOLO uses a neural network to predict binding boxes for the whole image. In comparison with existing algorithms, YOLO performs faster. © 2024 IEEE.

关键词： Image classification

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Improving Medical Image Classification Through Transfer Learning and Meta Learning 5th

Improving Medical Image Classification Through Transfer Lear...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Arora, Sonia Jebakumari, S. Adlin Singh, Vaishali Kharbas, Vikas Kumar Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

This paper presents an observation of improving clinical picture classification via transfer getting to know and meta-studying. Switching getting-to-know and meta-learning methods are usually employed in medical imaging class tasks. Transfer studying objectives to switch information and illustration from a properly-trained version and mildew that expertise to higher healthy the problem at hand, while meta-gaining knowledge is a version of switch gaining knowledge of which targets to seize and adapt the generalizable expertise of a project domain, and to allow a progressed impact for the goal tasks. Inside the clinical imaging scenario, the two procedures can dig deeper into the several layers which might be involved in gaining deep knowledge of algorithms. The authors compare the effectiveness of the proposed strategies in five publicly available medical imaging datasets. Effects have shown that the proposed techniques notably advanced the usual type accuracy for a few datasets. Furthermore, it was found that the 2-step technique showed higher performance than the one-step technique. This study presents a higher understanding of the efficacy of transfer learning and meta-mastering techniques for clinical photo-type tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Design of data processing scheme of 5G terminal protocol conformance test system 4

Design of data processing scheme of 5G terminal protocol con...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Jingyao, Cao Yadav, Amit Khan, Asif Sah, Abhishek Pratap Chengdu Neusoft University School of Computer and Software Chengdu China Charles Darwin University College of Engineering It and Environment NT Australia Integral University Department of Computer Application Lucknow India University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China

ISBN: (纸本)9798350343670

Considering the fact that the test case runs abnormally and the test case execution fails during the development and debugging of the 5G terminal protocol conformance test system. Here useful approach is implemented by embedding data copy and storage module in the system, which completes the encapsulation, copy and storage of massive data generated during the operation of the system, so that equipment developers can correctly locate the errors in the system according to the data, and repair the system loopholes to ensure the stability and accuracy of the system. © 2024 IEEE.

关键词： 5G Terminal copy and storage module protocol conformance

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Point Process based time sensitive personalised recommendation

Point Process based time sensitive personalised recommendati...

引用

2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Abbas, Khushnood Dong, Shi Khan, Asif School of Computer Science and Technology Zhoukou Normal University China Department of Computer Application Integral University UP Lucknow India

We frequently rely on suggestions in an online context, including those from search engine results, e-commerce product recommendations, movie recommendations, and so forth. These suggestions are made in response to our actions, such as when we search for products, songs, or movies, or at the very least, when we visit the website in order to receive a recommendation. These recommendations do not take into account time-sensitive factors, such as when we often utilise these services during the day or the week. We prefer to repeat some activities, like listening to music at a specific time of day. We presented a methodology in this paper that addresses two critical issues in personalised recommendation. 1) recommending the right items to the right people at the right time, and 2) estimating when a user will return to a service or product after performing repeated actions. The scholarly community has not yet examined this work in personalised recommendations. To do this, we offered the Hawkes process, in which we employed a customised initial intensity based on Hierarchical Poisson Factorization, and for a dynamic activity, we considered a sinusoidal function coupled with exponential effect decay. This is consistent with user activity cycles, such as music listening during a specific time of day. This is the first framework we've used, which is based on a probabilistic matrix factorization. © 2022 Elsevier B.V.. All rights reserved.

关键词： Machine learning

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Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather 2

Employing Sequence-to-Sequence Stacked LSTM Autoencoder Arch...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Maharatha, Arpita Das, Ratnakar Mishra, Jibitesh Nayak, Soumya Ranjan Aluvala, Srinivas Dept. of Computer Science & Application Odisha University of Technology and Research Bhubaneswar751029 India School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today's world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature. © 2024 Elsevier B.V.. All rights reserved.

关键词： Weather forecasting

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Applying Transfer Learning to Medical Image Classification Tasks 5th

Applying Transfer Learning to Medical Image Classification T...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Gupta, Manali Haripriya, V. Singh, Akhilendra Pratap Yadav, Surendra Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Transfer studying is a machine learning approach that permits a model to leverage know-how from a previous challenge to boost studying in the following tasks. It's been used increasingly in medical photo classification obligations, allowing area-particular knowledge to be leveraged from current fashions. This method can enhance the overall performance of clinical photograph-type structures and reduce the amount of labeled information required to analyze a brand-new venture. In this paper, we assess the present-day kingdom of switches, getting to know the scientific photo category and the results for clinical picture evaluation. We discuss present switch studying techniques and their influences on the accuracy of medical photo category responsibilities and the challenges related to constructing generalizable fashions. Eventually, we take a look at existing datasets and implementations available for the clinical photo category and factor into promising destiny studies guidelines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Understanding the Role of Data-Driven Communications Software Design in Networks 5th

Understanding the Role of Data-Driven Communications Softwar...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Singh, Vaishali Inzimam Dadhich, Shruti R, Murugan Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Facts-pushed verbal exchange software program layout is a critical element in the design of statistics networks. It encompasses the identification, control, and manipulate of network traffics, which includes the prioritization of communications and associated statistics, in addition to the regulation of connection distributions and assets over the complete community. Furthermore, information-pushed communique software program design encompasses algorithms designed to mitigate potential verbal exchange bottlenecks, via controlling the drift of information via the network, thereby improving its reliability and serviceability. Thru this technique of design, it's far possible to create a dynamically optimized community that is capable of speedy and successfully routing facts to its supposed vacation spot. Through making use of these standards, designers and engineers are able to create high-appearing networks that fulfill the demands located upon them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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