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SGformer:Boosting transformers for indoor lighting estimation from a single image

Computational Visual Media 2024年第4期10卷 671-686页

作者： Junhong Zhao Bing Xue Mengjie Zhang Centre for Data Science and Artificial Intelligence&School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Estimating lighting from standard images can effectively circumvent the need for resourceintensive high-dynamic-range(HDR)lighting ***,this task is often ill-posed and challenging,particularly for indoor scenes,due to the intricacy and ambiguity inherent in various indoor illumination *** propose an innovative transformer-based method called SGformer for lighting estimation through modeling spherical Gaussian(SG)distributions—a compact yet expressive lighting *** from previous approaches,we explore underlying local and global dependencies in lighting features,which are crucial for reliable lighting ***,we investigate the structural relationships spanning various resolutions of SG distributions,ranging from sparse to dense,aiming to enhance structural consistency and curtail potential stochastic noise stemming from independent SG component *** harnessing the synergy of local–global lighting representation learning and incorporating consistency constraints from various SG resolutions,the proposed method yields more accurate lighting estimates,allowing for more realistic lighting effects in object relighting and *** code and model implementing our work can be found at https://***/junhong-jennifer-zhao/SGformer.

关键词： lighting estimation transformer spherical Gaussian(SG) augmented reality

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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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CoverGAN: cover photo generation from text story using layout guided GAN

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Soft Computing 2025年第1期29卷 405-423页

作者： Cheema, Adeel Naeem, M. Asif Department of Data Science and Artificial Intelligence National University of Computer and Emerging Sciences Islamabad Pakistan

Generating cover photos from story text is a non trivial challenge to solve. Existing approaches focus on generating only images from given text prompt. To the best of our knowledge, non of these approaches focus on generating cover photos from a text story. The paper addresses this issue by introducing multi-object image generation with text title from a text story. We split the problem into three steps:- understanding semantics of text story, predicting layout of objects, and generating a cover photo. At start, a semantic relation was encoded between text story objects using a Scene graph, then features from graph neural network were concatenated with single object features from scene graph to create an object layout. All of these features were then passed on to the image generating part. Image generation was further divided into two phases. In the first phase, the image is generated using a scene graph image generation model. While in the second phase, the results of first phase were further enhanced using image translation model conditioned on the object layout. In final phase we generated title of the given story based on generated image. In our experiments, we used custom dataset of text stories with three animal categories along with the COCO dataset. For the image generating part, we evaluated our approach with state of the art models known as scene_gen and sg2im. Our method generated high-resolution informative cover photo with story title by positioning the objects at right locations as specified in the text story. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Graph neural networks

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Leveraging Advanced Deep Learning Architectures for Precise Knee Osteoporosis Prediction: A Paradigm Shift in Diagnostic Healthcare 2

Leveraging Advanced Deep Learning Architectures for Precise ...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Keerthika, S. Janani, A. Krishnasamy, Lalitha DevaBrindha, S. Priyanka, S. Dharun, S.R. Department of Artificial Intelligence Kongu Engineering College Erode India Department of Artificial Intelligence and Data Science Kongu Engineering College Erode India Department of Artificial Intelligence and Data Science Nandha Engineering College TamilNadu Perundurai India

ISBN: (纸本)9798331541217

Osteoporosis is a condition characterized by reduced bone mass and deterioration of bone tissue. It affects the people by increasing bone fragility and a higher risk of fractures. Globally, it affects approximately 200 million people, predominantly postmenopausal women and the elderly. Early detection of osteoporosis is crucial for reducing fracture risks, improving patient outcomes, and lowering healthcare costs. However, traditional diagnostic methods are often invasive and costly, particularly in developing regions. Current methods lack efficiency and affordability, underscoring the need for noninvasive and cost-effective diagnostic solutions. This paper explores the use of various deep learning techniques such as CNN, VGG16, ResNet, DenseNet, AlexNet, and GoogLeNet. Using deep learning models on knee X-ray images, this effort provides a simple, non-invasive approach to early osteoporosis detection. The models are assessed using accuracy metrics, and cross-validation on training, validation, and test datasets is used to validate their performance. The models' classification performance is also evaluated using F1-scores, accuracy, recall, and confusion matrices. The models are employed to analyze the images, with the aim of improving diagnostic accuracy and enabling early intervention. The overall efficiency is improved by using data augmentation approaches, optimizing the models and implementing efficient preprocessing steps. This study's primary objective is to develop and evaluate deep learning models that can use knee X-ray images to identify osteoporosis early, offering a quicker, non-invasive, and easier method of diagnosis. © 2024 IEEE.

关键词： Bone Fragility Deep Learning Early Detection Healthcare Efficiency Osteoporosis

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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Real-Time Driver Alertness Assessment Using Neural Networks

Real-Time Driver Alertness Assessment Using Neural Networks

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Yoga, M. Ramyasri, M.M. Lokitha, R. Jane Karunya, J. Jeeva, N. Kokila, M.M. Kongu Engineering College Department of Artificial Intelligence Tamilnadu India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamilnadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Drivers have profited significantly from developments in computer technology with the introduction of intelligent car systems. However, driver weariness is a key contributing cause to many car accidents. This research suggests an intelligent system for detecting driver tiredness based on vision, employing a convolutional neural network (CNN) to capture latent facial traits rather than relying on hand-engineered features like blink rate, eye closure, and yawning. The driver is classified as either sleepy or not by a softmax layer, which enables the system to alert the driver and avoid collisions. The proposed approach relies on extracting features from driver photos using a CNN and assessing eye states and facial expressions to reliably identify tiredness. The algorithm's excellent accuracy in identifying sleepy states is demonstrated by the results, underscoring its potential to improve road safety and lower the number of accidents caused by fatigued drivers. © 2025 IEEE.

关键词： Convolutional neural networks

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Enabling secure modern web browsers against cache-based timing attacks

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International Journal of Security and Networks 2024年第1期19卷 43-54页

作者： Ganesan, Sangeetha Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu Tiruvallur India

Web applications have grown to be the foundation of any kind of system, ranging from cloud services to the internet of things (IoT) systems. As a huge amount of sensitive data is processed in web applications, user privacy shows as the most important concern in web security. In the virtualisation system, cache side channel (CSC) attack techniques have become popular to retrieve the secret information of other users. This paper presents a run-time detection and prevention mechanism, called browser watcher (BW), for time-driven CSC attacks. The computation overhead of the proposed BW java script engine is monitored and tabulated for the different domains. The average cache miss rate is measured from 23% to 89%. Once the BW system identifies the attacker, then it prevents stealing the secret information of the victim. This makes it very hard for the attacker to find the memory access pattern of the victim. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Side channel attack

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Revolutionizing Breast Cancer Detection: Leveraging EfficientNetB3 for Accuracy and Scalability 3

Revolutionizing Breast Cancer Detection: Leveraging Efficien...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhuvaneswari, S. Dhivya, G. Veronica Philo Vimal, J. Veera Anusuya, V. Kalaivani, V. National Engineering College Department of Artificial Intelligence and Data Science Kovilpatti India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

One of the largest health issues facing women worldwide is breast cancer, with a growing need for early and accurate diagnosis to improve the patient outcome. Traditional approaches like mammography and biopsy often come with limitations like interpretive variability and accessibility issues. In this paper, let's approach the identification of breast cancer using CNN by focusing on the EfficientNetb3 architecture. The proposed method utilizes the advanced feature extraction capabilities and computational efficiency of EfficientNetb3 to analyze mammographic images. The model achieves high accuracy and sensitivity with class imbalance and overfitting issues using a well-structured dataset and robust preprocessing techniques. This study opens the door for more efficient and widely available AI-driven healthcare solutions by showcasing the potential of EfficientNet - based CNN models to improve diagnostic precision and scalability. © 2025 IEEE.

关键词： Convolutional neural networks

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Genetic Algorithm Powered Bivariate Function Encryption for Robust data Security 4

Genetic Algorithm Powered Bivariate Function Encryption for ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Hariharan, N.R. Ritheshwar, R.Sai Suseendran, R. Meena, R. Kalaiselvi, T. Easwari Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331523923

This paper introduces a new encryption technique which bases its operations on advanced bivariate mathematical functions and uses a genetic algorithm to optimize these along with random key generation techniques. This, in turn, allows for the enhancement of cryptographic complexity and better security features with fortified protection against any possible attacks. The encryption mechanism integrates random keys along with the evolved bivariate functions, and RSA protocol is employed for securing the channel of exchanging keys, ensuring confidentiality at each stage of the transmission process. The integration of these methodologies has created a two-layer security framework that combines the efficiency of symmetric encryption with the robustness of RSA. Advanced symmetric encryption combined with RSA significantly increases the security measures provided by defending against brute force attacks, cryptanalysis, and intercept threats. Through the combination of symmetric encryption and an asymmetric key exchange protocol, this strategy fully addresses the disadvantages of the typical cryptographic system. It forms an effective solution in meeting the contemporary security demands for digital communication environments. © 2025 IEEE.

关键词： Encryption algorithms

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Yoga Posture Analysis and Feedback System Using Mediapipe

Yoga Posture Analysis and Feedback System Using Mediapipe

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Snigdha, D. Tejasree, S. Rithvik, N. Shanmuagasundaram, Hariharan Motikar, Siddharth Reddy, S. Srinivas Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331509828

This project integrates Mediapipe with Python to implement a yoga posture analysis system for evaluating and providing efficacious feedback on human poses. The system establishes ideal joint landmarks using reference images, which are compared with uploaded analysis images over multiple simulated epochs. Angles of key joints, such as the elbow, shoulder, hip and knee are calculated and annotated onto the analyzed images for visual feedback. Deviations of analyzed images are identified and corrective suggestions are provided. Comprehensive graphs of accuracy and loss for each epoch are displayed to visualize the trends in accuracy and pose improvement. Accuracy reflects how well the model predicts the correct output, while loss indicates areas for improvement. In addition, for easy reference, all epoch data are tabulated to summarize overall performance, and a final model accuracy percentage is computed. The project also gives visual presentations of accuracy and loss over each epoch. The system serves as an effective tool for posture correction, making it suitable for fitness applications, physiotherapy, and ergonomic evaluations, with its detailed visual and numerical feedback. © 2025 IEEE.

关键词： Joints (anatomy)

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