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Gradient Self-alignment in Private Deep learning 26th

Gradient Self-alignment in Private Deep Learning

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26th international conference on Medical Image Computing and computer-Assisted Intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Bani-Harouni, David Mueller, Tamara T. Rueckert, Daniel Kaissis, Georgios Tech Univ Munich Comp Aided Med Procedures Sch Computat Informat & Technol Garching Germany Tech Univ Munich Chair AI Med & Healthcare Sch Computat Informat & Technol Munich Germany Tech Univ Munich Sch Med Munich Germany Tech Univ Munich Dept Diagnost & Intervent Radiol Sch Med Munich Germany Imperial Coll London Dept Comp London England Helmholtz Zentrum Munich Inst Machine Learning Biomed Imaging Neuherberg Germany

ISBN: (纸本)9783031474002;9783031474019

Differential Privacy (DP) has become a gold-standard to preserve privacy in deep learning. Intuitively speaking, DP ensures that the output of a model is approximately invariant to the inclusion or exclusion of a single individual's data from the training set. there is, however, a trade-off between privacy and utility. DP models tend to perform worse than non-DP models trained on the same data. this is caused by the clipping of per-sample gradients and the addition of noise required for DP guarantees causing an obfuscation of the individual data point's contribution. In this work, we propose a method to reduce this discrepancy by improving the alignment between the per-sample gradients of each individual training sample with its non-DP gradient by increasing their cosine similarity. Optimizing the alignment in only a relevant subset of gradient dimensions, further improves the performance. We evaluate our method on CIFAR-10 and a pediatric pneumonia chest x-ray dataset.

关键词： Differential Privacy Private learning Gradient alignment

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Depth Estimation from Video using computer Vision and machine learning with Hyperparameter Optimization 4

Depth Estimation from Video using Computer Vision and Machin...

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4th international conference on Smart Sensors and application, ICSSA 2022

作者： Faisal, Fahim Salam, Md Abdus Habib, Mufti Bin Islam, Md. Shariful Nishat, Mirza Muntasir Islamic University of Technology Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (数字)9781665499811

ISBN: (纸本)9781665499811

this research work focuses on a widespread interpretation for the operation of depth estimation using machine learning (ML) algorithms and computer vision. Depth estimation, also known as abstraction, refers to the processes and systems used to describe the geometric features of a situation. computer vision is a branch of artificial intelligence that teaches advanced analytics and comprehends images. Based on the principle of disparity an algorithm was presented for the application of computer vision through which moving objects and human was detected using calibrated stereo camera. A correlation technique was used to identify the disparity as well as to detect, extract and match features of any given inputs. the experiment was performed on MATLAB environment by dint of a function of computer vision. Using this algorithm, detection and identification of a moving object was done and the depth of it was measured. the disparity map and reconstruction of the 3D image was presented as well as the distance of each person from the camera was calculated. Afterwards, a 3D mapping of that moving object was created using reconstruction. Moreover, monocular 3-D reconstruction was performed to provide nearly accurate measurement. © 2022 IEEE.

关键词： machine learning

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application of Deep learning for Wildfire Risk Management: Preliminary Results 1

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

作者： De Rango, Alessio D’Ambrosio, Donato Mendicino, Giuseppe Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (数字)9783031812446

ISBN: (纸本)9783031812439

Due to ongoing climate change and the related increased risk of drought, wildfires in the Mediterranean are expected to become more frequent and intense. this note introduces the use of Deep learning to produce susceptibility maps for wildfire risk assessment to tackle this scenario. Indeed, thanks to the improved computational capabilities of modern GPUs, recent literature has pointed out that it is possible to train Deep learning models on big datasets by outperforming standard approaches in several application domains, including the one object of this study. An Artificial Neural Network (ANN) model has been developed and applied to the Calabria region (southern Italy) study area. the network has been trained and validated over ten years of data by considering both meteorological and geomorphological data, and human factors. Preliminary results are promising, evidencing a higher network’s accuracy to the operational state-of-the-art Fire Weather Index (FWI) method, currently applied for forecasting and mitigation purposes in the study area. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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GAN-Based Data Augmentation for learning Behavior Analysis in MOOCs 4th

GAN-Based Data Augmentation for Learning Behavior Analysis ...

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4th international conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Zouleikha, Betaitia Aida, Chefrour Samia, Drissi Computer Science Department Science and Technology Faculty Mohamed Cherif Messaadia University Souk Ahras Algeria LISCO Laboratory Badji Mokhtar University Annaba Algeria LiM Laboratory Faculty of Science and Technology Souk Ahras University Souk Ahras Algeria

ISBN: (纸本)9783031733437

the analysis of learning behavior in Massive Open Online Courses (MOOCs) holds immense potential for personalized learning and improving educational outcomes. While several MOOC datasets exist, such as KDD 2015 and Coursera, the learning Behavior Analytics Dataset presents a unique and rich source of information, encompassing diverse aspects of learner interaction and engagement. However, the limited size of this dataset poses a significant challenge for applying deep learning techniques, which typically require large amounts of data for effective training. To address this data scarcity issue, we propose the application of Generative Adversarial Networks (GANs) for data augmentation. By learning the underlying distribution of the original dataset, GANs can generate synthetic data points that retain the characteristics and patterns of the real data. Augmenting the learning Behavior Analytics Dataset with these synthetic samples allows for the training of more robust and generalizable deep learning models, enabling in-depth analysis of learning behaviors and unlocking new possibilities for personalized interventions and adaptive learning systems within MOOC environments. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Predicting Landslides and Floods with Deep learning 4

Predicting Landslides and Floods with Deep Learning

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Chaganti, Pavan Chandra Vishal Vasireddy, Kuladeep Reddy, Endurthy Rishik Dondeti, Revant Reddy Syama, S. Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Bengaluru India Amrita Vishwa Vidyapeetham Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350300093

Landslides are destructive natural disasters that have a strong destructive potential. the field of landslide disaster prevention is built around landslide detection mapping. Land slide detection is one of the fundamental studies in this subject since the goal of landslide analysis is to reduce the likelihood of landslide occurrence by physical intervention. this study summarizes pioneering studies in the area of landslide analysis and presents how to gather and use landslide data for deciliter and cubic centimeter techniques. the most popular analytical indexes for object detection and image segmentation are listed next. this work even suggests a system of algorithms for identifying the presence of floodwater (water hazard) in images taken using cell phones or other optical cameras. Of the several approaches tested, the pretrained VGG-16 network using a logistic regression classifier achieved the best results. Landslide and flood prediction research is conducted by interpreting aerial photographs and conducting field verification. Remote sensing technologies have enabled researchers to use pictures provided by high-resolution laser to identify landslides and flood brought on by significant events. © 2023 IEEE.

关键词： Deep neural networks

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application of Advanced machine learning Algorithms for Early Detection of Mild Cognitive Impairment and Alzheimer's Disease 11

Application of Advanced Machine Learning Algorithms for Earl...

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11th international Scientific conference on computer Science, COMSCI 2023

作者： Kepesiova, Zuzana Ruzicky, Eugen Kozak, Stefan Malaschitz, Richard Zimmermann, Alfred Pan-European University Faculty of Informatics Bratislava Slovakia Axon PRO S.r.o. Bratislava Slovakia

ISBN: (纸本)9798350325256

Alzheimer's disease (AD) is an incurable neurodegenerative disease that primarily affects the mental and linguistic abilities of the sufferer. the disease affects to a large extent not only the quality of life of the patients themselves, but also of their immediate environment. Diagnosing AD is a complex process that requires multiple resources, facilities and, above all, qualified experts. the whole process is time-consuming. In the early stages, such as mild cognitive impairment (MCI), it is difficult to diagnose the disease because of the very mild manifestations of the disease. To help medical specialists and researchers detect MCI and AD disease, we have developed a machine learning based approach. the patient is asked to describe in spoken word the offered images, either in a single word or a sentence description. this description is captured as an audio recording in a mobile app. the dataset of such recordings consists of more than 1500 recordings of different individuals, with approximately 9.49% of them having been diagnosed with MCI or AD. the dataset imbalanced nature calls for use of metrics such as Mathews correlation coefficient (MCC), sensitivity-specificity and ROC curve to evaluate the used inbtelligent algorithms. the case study resulted in a machine learning model with 79.07% (54.35%87.4%) MCC. © 2023 IEEE.

关键词： Neurodegenerative diseases

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CAPTAIN: An AI-Based Chatbot for Cyberbullying Prevention and Intervention 4th

CAPTAIN: An AI-Based Chatbot for Cyberbullying Prevention an...

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4th international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 25th international conference on Human-computer Interaction (HCII)

作者： Lian, Andrew T. Reyes, Alfredo Costilla Hu, Xia Kinkaid Sch Houston TX 77024 USA Rice Univ Dept Comp Sci Houston TX 77005 USA

ISBN: (纸本)9783031358937;9783031358944

Cyberbullying is a widespread and growing problem that can cause various psychological and health well-being outcomes in youth and is considered a serious public health threat. Cutting-edge informatics technology would enable us to identify and stop cyberbullying to prevent harm, death, and privacy violations. However, current cyberbullying prevention approaches offer limited interactions, individualized education, and in-time intervention. With the current emerging technologies in Artificial Intelligence (AI), the use of chatbots have become increasingly popular in health promotion. However, there are current technological challenges that need to be addressed, such as detecting and preventing cyberbullying in real-time, providing personalized responses and intervention, as well as developing the chatbot with a user-friendly interface. this paper introduces CAPTAIN (Cyberbullying Awareness and Prevention through Artificial INtelligence), an AI-based chatbot for cyberbullying prevention that can provide anytime interaction for personalized intervention.

关键词： Cyberbullying prevention machine learning Topic Modeling Chatbot

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machine learning-Based Research on Reserve Prediction of Natural-Gas-Hydrates 4

Machine Learning-Based Research on Reserve Prediction of Nat...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Ye, Sumin Mai, Shuxian Niu, Wenbo Yang, Tao Ye, Lei Zhang, Kun Tang, Huimin Li, Chenglong Peng, Boyang Yu, Qiannan College of Energy and Power Engineering Guangdong University of Petrochemical Technology China Co. Ltd. China Heilongjiang Key Laboratory of Reservoir Physics and Fluid Mechanics in Porous Medium China Geological Research Institute No. 4 Oil Production Plant of Daqing Oilfield Limited Company China

ISBN: (纸本)9798400708831

Currently, there are challenges in reserve prediction of natural-gas-hydrates, including difficulties in obtaining parameters, limited calculation accuracy, and complex processes. machine learning is a data-driven predictive method with strong generalization capabilities, capable of abstracting and inducing knowledge and rules from basic data witch has made significant advancements in various fields and has been preliminarily applied in fields of earth sciences and energy exploration and development. Various machine learning algorithms for determining the parameters of gas hydrate reserves were introduced in this paper. the support vector machine algorithm was employed to calculate effective area and thickness, while neural network algorithm was introduced to calculate porosity, saturation, and decomposition coefficient for the determination of parameters of natural-gas-hydrates reserves in Shenhu area of China's offshore, and then the feasibility and accuracy of the method was verified. Compared to evaluation results based on trial mining or numerical simulation, the machine learning-based reserve prediction method demonstrates high predictive accuracy, clear processes, and simple structure, making it highly valuable for future research on storage and transportation of natural-gas-hydrates and provides foundation for future industrial applications. © 2023 ACM.

关键词： Forecasting

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A Hybrid Deep learning Neural Network for Recognizing Exercise Activity Using Inertial Sensor and Motion Capture System 4

A Hybrid Deep Learning Neural Network for Recognizing Exerci...

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4th international conference on Big Data Analytics and Practices, IBDAP 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350300192

the recognition of human activities holds great significance in the fields of AI and human-computer interaction studies. Human activity recognition (HAR) finds extensive application, including in behavioral assessment, gesture identification, fall detection, and gait evaluation. Recent strides in wearable technology and AI have showcased the potential to accurately classify movement events for HAR, achieved by analyzing time-series data collected from wearable sensors. this study focuses on deep learning networks, particularly CNN and LSTM, to extract spatial and temporal features from wearable sensor data for efficient exercise activity classification. A comprehensive assessment compared two sensor data types: 3-axis linear acceleration from IMUs and 3D acceleration from MOCAP markers. Findings highlight comparable accuracy between IMUs and MOCAP in exercise identification. the hybrid CNN-LSTM demonstrated high efficacy, achieving remarkable F1-scores of 98.85% for IMU and 98.23% for MOCAP data. © 2023 IEEE.

关键词： Wearable sensors

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Non-intrusive Status Prediction of Residential Appliances using Supervised learning Techniques 6

Non-intrusive Status Prediction of Residential Appliances us...

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6th international conference on Energy Conservation and Efficiency, ICECE 2023

作者： Abbas, Muhammad Zaigham Sajjad, Intisar Ali Liaqat, Rehan Khursheed, Tanveer Awais, Muhammad Javed, Muhammad Saqib University of Engineering & Technology Electrical Engineering Department Taxila Pakistan Department of Electrical & Computer Engineering Comsat University Islamabad Wah Campus Wah Cantonment Pakistan Pakistan

ISBN: (纸本)9798350332193

Recent demand side management (DSM) research have focused a lot of attention on energy conservation due to environmental concerns and growing energy costs. Several studies have shown that educating users about energy-hungry equipment can result in significant energy savings. In non-intrusive load monitoring (NILM), aggregate demand, often acquired from the smart meter, is used to calculate the electricity usage of the various appliances. Using aggregate consumer data from the smart meter, this study proposes six supervised learning algorithms (Decision Tree, Linear Discriminant, k-Nearest Neighbors, Quadratic Discriminant, Support Vector machine, Ensemble) for predicting the working condition of various household appliances. the typical Pecan Street data is utilized to test the proposed methodologies, and several assessment measures are employed to compare the outcomes. K Nearest Neighbors and Support Vector machine outperform other proposed algorithms, according on comparison results. this approach can help with energy prediction of individual loads based on aggregate demand, which is beneficial for getting consumers to adopt energy-saving measures. © 2023 IEEE.

关键词： Support vector machines

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