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共 3334 条记录，以下是591-600 订阅

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Material Named Entity recognition (MNER) for Knowledge-Driven Materials Using Deep learning Approach 4th

Material Named Entity Recognition (MNER) for Knowledge-Drive...

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

作者： Saef Ullah Miah, M. Sulaiman, Junaida Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Universiti Malaysia Pahang Pahang Pekan26600 Malaysia

ISBN: (纸本)9789811994821

the scientific literature contains an abundance of cutting-edge knowledge in the field of materials science, as well as useful data (e.g., numerical values from experimental results, properties, and structure of materials). To speed up the identification of new materials, these data are essential for data-driven machine learning (ML) and deep learning (DL) techniques. Due to the large and growing amount of publications, it is difficult for humans to manually retrieve and retain this knowledge. In this context, we investigate a deep neural network model based on Bi-LSTM to retrieve knowledge from published scientific articles. the proposed deep neural network-based model achieves an F1 score of 9 ~ 7 % for the Material Named Entity recognition (MNER) task. the study addresses motivation, relevant work, methodology, hyperparameters, and overall performance evaluation. the analysis provides insight into the results of the experiment and points to future directions for current research. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022 - Proceedings

4th International Conference on Pattern Analysis and Intelli...

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

ISBN: (纸本)9781665461610

the proceedings contain 57 papers. the topics discussed include: a deep learning-based system for detection at-risk students;an explainable self-labeling grey-box model;development of a biometric authentication platform using voice recognition;binary Archimedes optimization algorithm based feature selection for regression problem;semantic segmentation of breast cancer histopathology images using deep learning;a biased random-key genetic algorithm for extractive single-document summarization;a virtual clustering for data dissemination in vehicular fog computing;a novel method of requirements analysis for big data projects;combination of closed-set and open-set biometric identification;a formal integrated approach for cyber physical systems;and testing cyber-physical production system: test methods categorization and dataset.

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Time Based Image Synthesis: Light and Color Conversion Using machine learning Techniques 4

Time Based Image Synthesis: Light and Color Conversion Using...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Labh, Jyoti Ranjan Dwivedi, R.K. College of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9798350387490

the progress made in camera technology and computational imaging has been astounding when it comes to photography. A specialized neural image signal processor is currently used by many top smartphone cameras and DSLR to convert chaotic raw sensor photos into the final, polished output. A sizable dataset of image pairs-one with a noisy, short-exposure raw picture and another with a clean, long-exposure ground-truth raw image-is needed to train these neural ISPs for low-light, processing It takes a lot of effort and time to capture these aligned image pairs, and motion blur in the long-exposure ground truth frequently makes the process difficult. Artificial nighttime datasets can be used to train various image processing algorithms and neural ISPs that are more resilient and effective. In order to overcome this difficulty, we proposed a model that uses to convert image colour and lighting of a raw picture to artificially create realistic image of different time zone raw photos, brighter photos are typically easier to take, have less noise, and are less likely to suffer from motion blur. the objective of the suggested processing framework is to transform raw photographs taken during the different time into simulated images of different timing with different levels of noise and other low-light features. More ease and flexibility in training neural ISPs for time-based rendering is made possible by this artificial time- based picture dataset. Additionally, in our proposed method the performance of the trained neural ISPs can resemble that of models trained only on real different timings photos if they mix their synthetic data with a tiny percentage of real data. the potential to produce accurate raw photos of midnight and different night time in a synthetic manner holds significant implications for computational photography and image processing applications and even object recognition under difficult nocturnal conditions. © 2024 IEEE.

关键词： Color photography

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Explainable Process Deviance Discovery with data-Efficient Deep learning 4th

Explainable Process Deviance Discovery with Data-Efficient ...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Folino, Francesco Folino, Gianluigi Guarascio, Massimo Pontieri, Luigi Rende87036 Italy

ISBN: (纸本)9783031812408

Detecting deviant execution instances of a business process is an important issue in modern enterprises and organizations. Recently, learning deep Deviance Detection Models (DDMs) out of process traces via (semi-)supervised methods was shown to outperform traditional approaches based on standard machine learning solutions. However, deep models need to be trained with large enough amounts of data, which may not be available in several real-life settings, especially in Green-AI applications where restrictions to data access and data processing operations must be obeyed. To better suit such settings, a novel approach for discovering a deep DDM is proposed that compensates for the scarcity of training data by leveraging a complementary self-supervised learning task. Besides minimizing the deviance classification loss, the reconstruction loss of an auxiliary auto-encoder sub-net is exploited as an additional (self-)supervision signal. For the sake of explanation, the neural network is provided with a flat pattern-based encoding, on top of which two parallel stacks of feature-representation layers are learnt efficiently and robustly by leveraging residual-like skip connections. the approach effectively deals with the challenging combination of data-efficiency and explainability requirements on a case study concerning the execution traces of a real-life process. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Enhancing Customer Prediction Using machine learning with Feature Selection Approaches 1

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4th international conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Siva Subramanian, R. Maheswari, B. Nikkath Bushra, S. Nirmala, G. Anita, M. Department of CSE S. A. Engineering College Tamil Nadu Chennai India Department of CSE Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of IT St. Joseph’s Institute of Technology Tamil Nadu Chennai India R.M.D. Engineering College Tamil Nadu Kavaraipettai India

ISBN: (数字)9789811974021

ISBN: (纸本)9789811974014

In each enterprise, customer data is collected in large quantities and the analysis of such data aids in the development of more accurate insights into customer patterns and the identification of high-risk customers in the enterprise. In today’s world, machine learning approaches are being used more and more in customer pattern analysis and prediction, as well as in other areas. But in real-time, the consumer dataset often holds a poor quality of data such as irrelevant, corrected, and noisy variables. With these poor quality data, better prediction using the machine learning approaches cannot be witnessed. the removal of correlated variables and the selection of variables with high information content helps to improve the accuracy of customer predictions. With the help of feature selection mechanisms and machine learning techniques, we devise a framework for conducting more accurate customer pattern analysis in this study. the research investigation makes use of UCI’s customer dataset and compares machine learning results obtained with and without feature selection. Performing customer analysis helps businesses to improve their decision-making and marketing strategies, which in turn improves customer retention and satisfaction, as well as the enterprise’s overall business processes. the exploratory process is done with two distinct feature selection approaches, and feature subset captured is modeled using two different machine learning mechanisms. It is clear from the exploratory results that the use of feature selection allows for the removal of correlated variables and the selection of relevant variables, which in turn aids in the improvement of the classifier’s performance. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Deep learning-Based Pipeline for the Detection of Multiple Ocular Diseases 4th

Deep Learning-Based Pipeline for the Detection of Multipl...

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Angadi, Ananya Bhat, Aneesh N. Ankitha, P. Kumar, Parul S. Srinivasa, Gowri PES Center for Pattern Recognition Department of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9789819900466

Computer-aided diagnostic systems provide decision support for the early and accessible detection of ocular diseases. While predictive models exist for certain common diseases in isolation, there is a need to detect multiple diseases at the same time to ensure that rarer conditions do not go undiagnosed. In the present work, we propose a workflow to detect the presence of seven ocular diseases. We first determine the disease risk using EfficientNetB3 pre-trained on ImageNet. Next, for common diseases with a relatively larger representation of images in the dataset, we utilize a Vanilla CNN to classify each disease. For relatively less common diseases, characterized by fewer representative samples in the dataset, we employ transfer learning using EfficientNetB3 pre-trained on ImageNet. To deal with the class imbalance in the original data, we create representative datasets with respect to each disease. the resulting models were found to have high sensitivity (0.8–1.0) and promising accuracy (0.7–0.96). © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Automated Proctoring System to Detect Anomalous Behavior in E-learning During Times of Crisis 4th

Automated Proctoring System to Detect Anomalous Behavior in ...

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the 4th international conference on data Science, machine learning 2022

作者： Manjunathswamy, B.E. Rathod, Ranjeet Managave, Padmaraj D. Sagar, Karthik S. Gowda, R. Rakshith Don Bosco Institute of Technology Bengaluru India

ISBN: (纸本)9789819920570

A boom in remote learning has happened recently. However, there has not been a good answer for academic exams. Some institutions use online copy-and-paste assignments, while others use remote proctoring, where a human proctor maintains an eye on students’ activities. there must be a solution as if way we live is to become the new norm. In this essay, we have put out a plan for creating an integrated AI-based system that can aid in discouraging exam cheating. the method recognizes fraud and records the evidence. this technology will be both affordable and secure. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Comparative Analysis of Convolutional and Long Term Short Memory Architectures in machine learning 1

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4th international conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Venugopal, Anita Kapil, Anil Kumar Dhofar University Dhofar Oman Surajmal University Uttarakhand India

ISBN: (数字)9789811998881

ISBN: (纸本)9789811998874

Training machines to develop intelligence is one of the popular studies of computer science. As long as machines with human intelligence is used for the benefits of humans, this technology will flourish civilization. Digital data capturing may process numerous challenges as documents may contain a combination of multiple unstructured data types. Such documents are easily readable to a human but machines may be unable to interpret the data as humans do. Neural network approaches like multilayer perceptron, CNN, RNN and LSTN are effective recognition methods as it can effectively deal with complex computing problems related to training networks and in the process of automating and producing predictive results. In deep neural networks, machines can detect prominent features of a given object without human interventions. the classification and recognition rate of all present algorithms depend chiefly on input images. this paper presents two approaches of deep learning for document recognition problem, convolutional neural network and the result of CNN is continued to RNN and LSTM approaches for synthesizing document. the study attempts to compare the results of CNN vs CNN-LSTM in terms of precision rate. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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4th international conference on Electrical, Computer and Telecommunication Engineering, ICECTE 2022

4th International Conference on Electrical, Computer and Tel...

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4th international conference on Electrical, Computer and Telecommunication Engineering, ICECTE 2022

ISBN: (纸本)9798350320541

the proceedings contain 63 papers. the topics discussed include: transfer learning model in deep neural network for COVID-19 prediction from CT images;a kernel based support vector machine approach: predicting psychiatric disorders using classified psychiatric features;hyper deep learning based load forecasting;vulnerability detection in popular android applications;deep convolutional neural network based automatic COVID-19 detection from chest x-ray images;identification of influential features for diabetes prediction at early stage;prediction of student performance in higher academy through different data mining techniques;Bangla language dialect classification using machine learning;and design and optimization of a passive micromixer with a double-layered structure for rapid mixing.

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An Enhanced Text Classification Using machine learning 1

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4th international conference on Computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Sunkari, Venkateswarlu Rao, Gudikandhula Narasimha Srinagesh, A. Belay, Elefelious G. School of Information Technology and Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Andhra University Andhra Pradesh Visakhapatnam India Department of CSE RVR & JC College of Engineering Andhra Pradesh Guntur India School of Information Technology and Engineering Addis Ababa Institute of Technology Addis Ababa Ethiopia

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

Content can be grouped using predefined classifications that are applied to plain language content. It's a "pack of words" representation;historical reports include a term that has values and indicate how frequently it appears in the record or not. In any case, because they include abundant or inconsequential data, large reports may face several problems. the impact of several attributes that convey the appropriation of a word in the archive is examined in this research. We refer to these attributes as distributional highlights. Every highlight is chosen based on the tfidf style criteria, and AI techniques are used to combine these highlights. One of the primary factors in distributional highlights—which hold a weighted object set—is term recurrence. Finding unusual information connections is more interesting than mining continuous ones when it comes time to limit a certain score works. the problem of locating rare weighted thing sets, also known as the rare weighted thing set mining problem, is addressed in this study. the classifier selected for order is the one that produces the more precise result. the distributional highlights are useful for text classification, according to experiments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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