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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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Proceedings - 2023 international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

Proceedings - 2023 International Conference on Advances in E...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

ISBN: (纸本)9798350308082

the proceedings contain 159 papers. the topics discussed include: cloud edge interaction protocol detection system for distribution IoT based on metadata definition;a data mining method based on natural language processing and machine learning;simulation and intelligent fault diagnosis of bidirectional LLC resonant converter;an autonomous exploration method integrating local multi-point TOF data and edge following;regional integrated energy system control strategy considering orderly charging and discharging of electric vehicles;photogrammetric method for calculating spatial coordinates of transmission line galloping;analysis of the mechanism of soft fault in cables based on transmission line theory;short-term photovoltaic power prediction based on similar daily clustering and ISSA optimized neural network;and leveraging profiling for facial recognition optimization.

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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 ...

引用

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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Analysis of User's Power Consumption Behavior Based on K-means 4

Analysis of User's Power Consumption Behavior Based on K-mea...

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4th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2022

作者： Wang, Xinmeng Li, Haiqi Yi, Xiaoguang Kong, Jing Wang, Xinling State Grid Shandong Electric Power Company Heze Power Supply Company Marketing Department Shandong China

ISBN: (纸本)9798350333947

With the continuous development of energy Internet and smart grid technologies, the potential value of power big data is constantly being mined. User behavior of power consumption is of great significance to power companies, consumers and power systems. In this paper, through the mining and analysis of the data on the power consumption side, the K- means clustering method is used to search the similarity of the samples, and the users are classified according to the characteristics of the power consumption behavior. Finally, four different types of users are obtained, and the family structure composition and economic status of related users are analyzed. through this method, the user's power consumption behavior pattern is analyzed, which provides a decision-making basis for the grid management side, and improves the service quality of electricity sellers to users. © 2022 IEEE.

关键词： data mining

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Deep learning and machine Vision based Robot for Fire Detection and Control 4

Deep Learning and Machine Vision based Robot for Fire Detect...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Tephila, M.Benedict Aswini, P.M. Abhinandhan, S. Arjun, K.K. Sri Krishna College of Engineering and Technology Department of Ece Coimbatore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Robotics and deep learning algorithms have emerged as a crucial part of our everyday life. Most of the recent image recognition innovations use deep learning, big data analysis, and artificial intelligence as their core platform. Hence in this work, a firefighting robot that uses deep learning is introduced to detect fire. On detection, the type of fire is also identified with the help of a combination of Imagenet and Alexnet. A Raspberry Pi is used to execute the proposed work and is programmed to function efficiently to detect and classify fire. Based on the results obtained, it is concluded that classification accuracy stands at 92%, while the detection of fire is accurate up to 97.75%. the proposed work shows a dynamic improvement in the accuracy of detecting fire with the help of robots and further classifying them using machine vision. © 2022 IEEE.

关键词： Fires

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Classification of Inflammatory Gene Expression patterns with machine learning Models

Classification of Inflammatory Gene Expression Patterns with...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yeonglong Ay Yit Hong Choo Asim Bhatti Chee Peng Lim Department of Neurology and Neurological Science Stanford University Stanford U.S.A Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia

Dementia is a complex neurological disorder characterized by a progressive decline in cognitive functions. Recent studies have suggested a link between neuroinflammation and the development of dementia, particularly in Alzheimer’s disease. this study focuses on the analysis of inflammatory gene expression patterns in the parietal cortex (PCx) and temporal cortex (TCx) from a human brain RNA sequence data set, aiming to derive insights into the underlying genetic link associated with dementia, as well as prediction of dementia. Using five machine learning and statistical methods, our study reveals better detection and classification of dementia patients using PCx-related gene patterns, as compared with those from the TCx. Our findings support the hypothesis that the PCx plays a significant role in neuroinflammatory responses associated with dementia. Based on this preliminary study, machine learning models offer a useful approach to recognize inflammatory gene expression patterns and providing a valuable biomarker analysis and prediction for dementia diagnosis.

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