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Deep Learning Technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Bheemalingaiah, M. Srinivasan, R. Nathan, V. Vijey Kolluri, Raju Gupta, Amit J.B Institute of Engineering and Technology Department of CSE Telangana Hyderabad India School of Computer Science and Engineering Galgotia University Department of CSE U.P India SRM TRP Engineering College Department of CSE Tamilnadu Tiruchirappalli India Narasaraopet Engineering College Department of ECE Andhra Pradesh Narasaraopet India J.B Institute of Engineering and Technology Dep. of AI & ML Telangana Hyderabad India

ISBN: (纸本)9798350378092

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology. © 2024 IEEE.

关键词： Convolutional neural networks

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An Extensive Examination of Different Phases Available in Enhancing the Power of WSN

An Extensive Examination of Different Phases Available in En...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Rupali Gill Rakesh Kumar Shivani Pant A. Deepika Ramy Riad Al-Fatlawy Sanjeevkumar Angadi Centre of Research Impact and Outreach Chitkara University Rajpura Punjab India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Science engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of CSE(AI&ML) CMR College of Engineering & Technology The Islamic university Najaf Iraq Computer Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Pune Maharashtra India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In this whole test, we delve into the evolution and transformative ability of Wireless Sensor Networks (WSNs), emphasizing the pivotal position of graphical statistics in elucidating the technological improvements and stressful conditions interior this region. By harnessing the energy of the IEEE 802.15.4 general, this research provides an in-intensity analysis of WSNs’ operational efficiencies, energy consumption styles, and network resilience through meticulously designed graphs and information visualizations. These graphical representations now not only offer a easy and concise overview of WSN universal performance metrics but additionally spotlight the giant enhancements completed thru the implementation of a singular WSN framework. The examiner’s findings underscore the vital importance of empirical statistics visualization in advancing our knowledge of WSN technologies, offering valuable insights into the optimization strategies that may result in extra power-inexperienced, reliable, and scalable sensor networks. This research contributes to the continued communicate in the subject, paving the manner for future enhancements and programs of WSNs across several sectors.

关键词： IEEE 802.15 Standard Wireless communication Measurement Knowledge engineering Wireless sensor networks Visualization Energy consumption

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Hybrid Optimization Based Long Short-Term Memory for Android Malware Detection

Hybrid Optimization Based Long Short-Term Memory for Android...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Lande, Jayapal Maheswari, M. Kamali, S.M. Dineshkumar, R. Priyanka, M.S. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Institue of Science and Technology School of Computing Department of Computational Intelligence Chennai India Annapoorana Engineering College Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Department of Ece Sriperumbudur Thandalam India Nitte Meenakshi Institute of Technology Department of Ai and Ml Yelahanka Bengaluru India

ISBN: (纸本)9798350313413

Malware detection is process of detecting the suspicious files and activities, that can classify as benign and malicious depends on different characters like capability of infection, structure of malware and speed rate. In this paper, proposed a Particle Swarm Optimization and Ant Colony Optimization (HPSALO) based Long Short-Term Memory (LSTM) model for malware detection and classification. The combination of these two algorithms that leads to the balance between exploration and exploitation. The dataset utilized for the research is DREBIN dataset and process of data cleaning is performed by NumPy library. The feature selection is performed by combination of hybrid PSALO algorithms and selected features are given to Long Short-Term Memory (LSTM) classifier that classifies as benign and malware files. The performance of proposed method is estimated by performance metrics of accuracy, precision, recall and f1-score. The proposed method attained accuracy of 99.38%, precision of 99.30%, recall of 99.08% and f1-score of 99.42% which is comparatively higher than other existing methods like Whale Optimization Algorithm-LSTM (WOA-LSTM), Cat Swarm Optimization-LSTM (CSO-LSTM), Particle Swarm Optimization-LSTM and Ant Lion Optimization-LSTM. © 2023 IEEE.

关键词： Ant Lion Optimization Long Short-Term Memory Malware Detection NumPy and Particle Swarm Optimization

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Crop Disease Detection with Autonomous Robotic Systems

Crop Disease Detection with Autonomous Robotic Systems

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Women in Computing (InCoWoCo), International Conference for

作者： M Rithani KJ Shabarirajan B Shivanesh RS SyamDev Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of AI & ML New Horizon College of Engineering Bangalore India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Crop disease detection is an important maintenance aspect for the upkeep of agricultural productivity. This paper presents a novel autonomous robot system designed to navigate and monitor crop health. The system was developed within a ROS framework using a camera to capture real-time video feeds of crops and a YOLO v8 model for detecting the presence of three specific plant diseases. Such a configuration resembles a farm in the presence of crops and trees and hence can be termed as a dirty farm. Automated navigation will cover all the area. The detection model processes each frame to detect plant disease. Such an approach has the potential to make improvements in early detection of diseases, thus leading to proper management and informed decisions about precision agriculture.

关键词： YOLO Productivity Precision agriculture Plant diseases Navigation Robot vision systems Crops Streaming media Real-time systems Monitoring

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Detection and Quantification of TB Bacilli in Sputum Smear Images

Detection and Quantification of TB Bacilli in Sputum Smear I...

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Women in Computing (InCoWoCo), International Conference for

作者： M Rithani Jeevanantham B Sona RS SyamDev Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of AI \& ML New Horizon College of Engineering Bangalore India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Early and precise detection is essential to stop the spread of tuberculosis (TB) and enhance patient outcomes. This research aims to create an automated system that employs cutting-edge image processing and machine-learning approaches to identify tuberculosis (TB) bacilli in sputum smear microscopy pictures. An automated solution is required to increase diagnostic accuracy and efficiency because traditional diagnostic methods are labor-intensive, time-consuming, and prone to human mistakes. In the end, we optimize the best-performing model to reduce processing time while preserving diagnostic accuracy. Our goal is to assess the performance of several models in terms of training time, testing time, and accuracy. By enabling the use of sophisticated diagnostic technologies in environments with limited resources, the suggested method supports international efforts to combat tuberculosis.

关键词： Training Solid modeling Image segmentation Accuracy Tuberculosis Microscopy Computational modeling Neural networks Machine learning Testing

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Advanced Detection of Cyber-Physical Attacks in Manufacturing Using LSTM-KNN

Advanced Detection of Cyber-Physical Attacks in Manufacturin...

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Data science and Network Security (ICDSNS), International Conference on

作者： N Penchalaiah Arvind Kumar Chaithrashree. A Aravindh Saivaraju V. Bhoopathy Anil Kumar Lamba Department of AI&ML Annamacharya University Andhra Pradesh India SCA MRIIRS Faridabad Haryana India Department of Computer Science and Engineering Brindavan College of Engineering Bangalore India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sree Rama Engineering College Tirupathi India Department of Computer Science and Engineering Geeta University Delhi NCR Panipat India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Cyber Manufacturing System (CMS) is an assembly system that could be used in the future. There will be an increase in cyberattacks like the Stuxnet strike as the number of connected devices continues to grow. Current cyber security measures are inadequate to protect CMSs from new cyber-physical threats. This new security flaw can be better understood with the help of CMS's taxonomy for cyber-physical hazards, which is part of the agency's mandate. Preprocessing, feature selection, and training the model are the three steps that make up this proposed method. The data collected from the sensors show the greatest distance between two nearby locations after any outliers have been removed by preprocessing. Feature selection makes advantage of dimensional reduction derived from geometric feature distribution. When training the model, an LSTM-KNN was used. The average accuracy of the suggested method is 93.46%, which is higher than that of LSTM and KNN.

关键词： Training Accuracy Taxonomy Nearest neighbor methods Network security Feature extraction Sensors computer crime Long short term memory Manufacturing systems

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Harnessing Tech Innovations for Diversified Multigenerational Work Force and Work Life Balance

Harnessing Tech Innovations for Diversified Multigenerationa...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Mudit Mittal Vishal Sharma Neeraj Varshney Sunil Prashanth Kumar Nandini Shirish Boob R. Akhilesh Reddy Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara India Department of Computer Engineering and Applications GLA University Mathura Department of Mechanical Engineering New Horizon College of Engineering Bangalore Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

This research aims to examine how technologically innovative breakthroughs may help a multigenerational heterogeneous workforce to maintain a good work and life balance. We investigate how technology can be used to strategically raise up the rate of participation and align with the particular needs of different age groups. The research findings show how ai powered personal learning, collaborative digital communication tools and flexible remote working solutions promote a healthy and mature culture in the workplace. The findings explain how key it is to utilize technology in a refined manner such that all workers can thrive and maintain a sustainable balance between their work and home life.

关键词： Training Productivity Technological innovation Employment Companies Learning (artificial intelligence) Digital communication Remote working Information and communication technology Business

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Enhancing Supply Chain Management Efficiency with IoT and Machine Learning Integration

Enhancing Supply Chain Management Efficiency with IoT and Ma...

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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： Srinivas D N. Kirthiga V Divya Vani Vijilius Helena Raj Ginni Nijhawan Joshuva Arockia Dhanraj School of Business SR University Warangal Telangana India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhuangar Tamil Nadu Institute of Aeronautical Engineering Hyderabad Department of Applied Sciences New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Bengaluru Karnataka India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

In order to increase force chain operation efficacy, this exploration article looks into the synergistic integration of machine literacy algorithms with Internet of impacts (IoT) technology. This paper highlights the dynamic and more complicated character of contemporary force networks, and it suggests a novel method that uses bias and Internet of Things detectors to collect data in real time across the force chain network. In addition, machine literacy algorithms are used to analyse this data, allowing for the optimisation of colourful force chain operations, anomaly detection, and prophetic perception. Through the utilisation of advanced analytics and IoT-generated data, associations can improve their force chain's visibility, traceability, and decision-making powers. In addition to offering an abstract framework for the integration of IoT and machine literacy in force chain operations, the study offers a thorough survey of the existing literature and addresses the obstacles, opportunities, and areas for future research in this rapidly developing topic.

关键词： Surveys Supply chain management Heuristic algorithms Force Supply chains Dynamics Machine learning

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Machine Learning Prediction Model for Heart Disease Data Based on Feature engineering

Machine Learning Prediction Model for Heart Disease Data Bas...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Md. Aamir Sohail Kunwar Babar Ali Satvik Vats Akhilesh Pandey Aditya Verma Department of CSE (AI) Meerut Institute of Engineering and Technology Meerut India Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE (AI and ML) Meerut Institute of Engineering and Technology Meerut India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Growing health risks has brought up remarkable impact in the mind of people to find the most important factors responsible for diseases like diabetes, heart disease and so on. In this study, authors have gone through a set of factors associated with daily habits of people around us and used those factors to classify the risk of heart diseases by introducing feature engineering concepts in the form of grouping of categorical and numerical features. the paper focuses on the improvement of results in the form of accuracy through the introduction of a new model framework whose mathematical expression and detailed explanation is described in Methodology section. Apart from classification, it has also been studied about the inter-dependence of available attributes in the data. The grouping of features have been done through a well-defined algorithm described in this study.

关键词： Heart Accuracy Computational modeling Training data Machine learning Predictive models Encoding Data models Mathematical models Diseases

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Autoencoder-LSTM Ensembles for Predictive Analysis of Financial Churn Patterns

Autoencoder-LSTM Ensembles for Predictive Analysis of Financ...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Raynukaazhakarsamy Rahul Pradhan Nadhema Ahmed Jaff G Venu Gopal Mariam Fadel Ali P. Nagaraj Department of Computer Applications Nehru Arts and Science College (Autonomous) Coimbatore India Department of Computer Engineering & Applications GLA University Mathura India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Computer Science and Engineering (AI & ML) Geethanjali College of Engineering and Technology Telangana India Department of Radiology Techniques Uruk University Baghdad Iraq Department of AI & ML K. Ramakrishnan College of Engineering Trichy Tamilnadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

In the financial sector, customer churn is a significant problem that influences the organization’s revenue growth. Churn means that customers have left their current providers which is a negative factor for the providers or financial institutions; therefore, accurate churn prediction helps institutions to increase customer loyalty and satisfaction by improving the quality of service. In this paper, a new method of using Autoencoder and LSTM network for predicting financial churn patterns is introduced. The Autoencoder is used to reduce dimensions and for unsupervised learning acts as a pass through that allows the model to learn the latent patterns in customer behaviour and transactions. Since the LSTM has demonstrated its efficacy in analyzing temporal dependencies of a sequence in time series, it is used to predict churn by analyzing sequential customer activity. The ensemble approach combines the merits of the two models improving the predictive power and especially the stability in a situation of data scarcity and high noise levels. Experimental comparison with conventional predictive models such as decision trees and logistic regression reveal that the Autoencoder-LSTM is more accurate and sensitive than traditional models by comparison. In other words, the proposed model enhances the early identification of potential churners and such institutions can then take appropriate measures of containing it, and in the process, offer relevant retention solutions. The paper describes the benefits of utilizing deep learning models for churn prediction for the financial industry and provides information on how such methods might be used to improve customer retention. Possible future research avenues consist of improving the model interpretability and generalizing the approach to other industries with comparable churn prediction requirements. The accuracy obtained from the proposed method is higher at 99.7% compared to other methods.

关键词： Accuracy Computational modeling Autoencoders Time series analysis Predictive models Stability analysis Churn Predictive analytics Long short term memory Unsupervised learning

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