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Research on Face Attribute Privacy Protection Method Based on Multi task learning 5

Research on Face Attribute Privacy Protection Method Based o...

引用

5th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2023

作者： Gu, Fenfei Blancaflor, Eric Abisado, Mideth National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China Mapua University School of Information Technology Manila Philippines

ISBN: (纸本)9798350359930

With the widespread application of facial image recognition in fields such as finance, social media, and transportation, the privacy data of facial images is also frequently leaked. In order to effectively protect facial images, the protection of facial attributes is particularly important. therefore, this article combines the method of multi task learning to divide deep learning models into low-level models and top-level models. the underlying model serves as a data feature extractor;the top-level model serves as a classifier, specifically responsible for three tasks in multi task learning: business classification, privacy protection, and image restoration. the goal of the business classification task is to identify user facial attributes;the goal of privacy protection tasks is to minimize the inclusion of privacy related feature information in data features as much as possible;the goal of image restoration task is to make it difficult to recover the original image containing privacy attributes from the extracted data features. In order to measure the privacy of the data feature extractor, a loss function is set for each task, and finally, the loss functions of these three tasks are fused to obtain a data feature extractor that can protect privacy. Finally, through experimental data analysis, this method ensures both the accuracy of facial image recognition and the leakage of facial attribute privacy data. © 2023 IEEE.

关键词： data privacy

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machine learning Model Development Using Computational Neurology 1

引用

5th international conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Kanrar, Soumen Department of Computer Science and Engineering Amity University Jharkhand Ranchi801001 India Department of Computer Science Vidyasagar University West Bengal Midnapore India

ISBN: (数字)9789811986697

ISBN: (纸本)9789811986680

the ever-enhancement of techniques and technology helps computer scientists to collect voluminous data with specified resolution. the bottleneck in understanding how computational neurology works is consequently shifting away from the amount and type of voluminous data. the general query raised by everyone mined is, what do we do with that voluminous data? there has been a growing interest in leveraging the vast volume of data. Computational neurology gains more insight into the voluminous data with the help of various levels of analysis, measurement techniques, and experimental paradigms. this paper presents the algorithmic development, implementation, and validation of a machine learning model based on the spike response model (SRM-0) of neuronal dynamics. We have considered 'spike, time-dependent plasticity’ (STDP) as our strategy for reinforcement learning in this work. the performance of the model is exhibited through the pattern recognition task of recognizing handwritten digits from the MNIST dataset. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Coral Detection with machine learning 5

Coral Detection with Machine Learning

引用

5th international conference on Computer Communication and the Internet, ICCCI 2023

作者： Nan, Fan-Hua Hsieh, Yi-Zeng Chen, Hao-Ching National Taiwan Ocean University Department of Aquaculture Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350326956

Ocean exploration has always been a costly, high-risk, and slow process, but it is an essential part of the environment. In order to reduce dependence on human labor and increase the speed of data processing, we will introduce a method of using machine learning to assist in coral area surveys in this paper. We will first provide an introduction to the principles of coral image recognition and the structure of the SOLOv2 model, followed by a presentation of the method used to train the model to identify corals, the results of further optimizing the original SOLOv2 model, and an algorithm used to estimate area. © 2023 IEEE.

关键词： machine learning

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Morphological Similarity Based Livestock Prediction Using machine learning Technique 5

Morphological Similarity Based Livestock Prediction Using Ma...

引用

5th IEEE international conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Anita, C.S. Shobha Rani, P. Maddali, Masthan Lakshmi Sahithi Monika, J. Shaik, Rubeena R.M.D. Engineering College Department Of CSE Tamil Nadu Tiruvallur District601 206 India Virtusa Private Limited Full Stack Developer Tamil Nadu Chennai India

ISBN: (纸本)9798350351378

In the current technological world, detection of stock market through various software systems is in practice. Predicting stock prices through recorded information of previous years, has enhanced the future prediction. Stock value information is viewed as a common place time in time series. In the existing system, a recurrent neural network(RNN), Long momentary memory (LMM) and gated rent units(GRU) are considered for regularizing the stock values that deals with sort of information. Considering the individual examination of stocks, according to the relativity between the existing stock and the whole market price, the proposed system analyzes the strategy behind. the proposed model considers clustering strategy for mining comfortable stocks which consolidate morphological comparison comparability distance MSD and K-means clustering. Further the system uses hierarchal temporal memory (HTM) model to which high course of data is initially depicted to learn then using the design among the comparative stocks and its information construction of inadequate stock values are normalized. In terms of prediction accuracy the proposed KMSD-LSTM model is improved in the prediction of price. LSTM model neglects the patterns that are not significantly related and remembers the pattern that makes a change in the prediction process. © 2024 IEEE.

关键词： Commerce

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Smart Home Automation with IoT-driven IR Blasters: Enhancing Control of Legacy Appliances with Predictive Usage pattern using machine learning 5

Smart Home Automation with IoT-driven IR Blasters: Enhancing...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Saligue, Emannuel T. Saligue, Rosemarie Y. Olaybar, Jimson A. Sy, Keano Nikko L. Cerna, Patrick D. Southern Leyte State University Faculty of Computer Studies and Information Technology San Juan Campus Southern Leyte San Juan Philippines State University of Northern Negros Graduate School Southern Leyte Philippines

ISBN: (纸本)9798331504403

this research study demonstrates an advanced smart home automation system with IoT based IR blasters to control conventional appliances to improve their usage models. data were collected from an IoT driven Blaster and supplemented with synthetic dataset. By performing predictive analytics with RandomForestClassifier, the system has managed to realize a high accuracy rate of predicting appliance use pattern. the correlation analysis reveals significant relationships among usage features, highlighting patterns such as the positive correlation between frequency of use and total usage time. Time series analysis revealed stabilized trends on the utilization of products for the recent years whereas distribution analysis showed a prevalent behavior regarding usage. In predicting the lifespan of appliances, XGBoost model was utilized demonstrating minimal variation and high confidence in predictions, ensuring reliable appliance management. © 2024 IEEE.

关键词： Smart homes

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A FUTURISTIC APPRAISAL OF MALWARE DETECTION BY EMPLOYING data mining 5

A FUTURISTIC APPRAISAL OF MALWARE DETECTION BY EMPLOYING DAT...

引用

5th international conference on Computing and Informatics (IEEE ICCI)

作者： Mira, Fahad Technol Univ Bedfordshire Sch Comp Sci Luton Beds England

ISBN: (纸本)9781665499736;9781665499729

the fourth industrial revolution is the artificial intelligence and digitization of everything, which turns the world into a global hub. the vast use of technology is essential, but it accompanies malwares that destroy confidential information. To overcome these challenges, the latest data mining techniques have been used. It squeezes all invisible information and analyzes massive databases. Malware is unstructured material that can't be determined directly. Various researches have endeavored to turn unstructured data into structured data to overcome this challenge. Due to the rapidly changing and complex pattern of Malware, data mining techniques are used to detect Malware. this survey is furnished with the latest apparatus and pledge information of Malware detection by opting the techniques of data mining. Besides this, it categorizes mainly into two Approaches for-instance signature-based method and the Behaviour-based method. the Focused points of this survey are to layout these First, A a detailed description of Challenges in malware detection by using data mining - Second A complete framework of latest approaches to machine learning mechanism - third, a search for an efficient method to detect Malware-Fourth, canvassing of all factor to determine malware approaches in data mining. Moreover, a detailed contrast of detected approaches is also present to facilitate the researchers. Evolution method, advantages and disadvantages are discussed to describe the proficiency. this survey is fully equipped with the latest techniques. In, this report we observed KNN with 22% MLB 16% MLA 12% NB 9% and other SVM 9% KM 7% API 7% and others are less than 5% in detecting malware in data mining., So, we find KNN approach with high accuracy.

关键词： Malware detection data mining malware attacks invisible attacks IOT

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Knowledge Visualization of Student Performance Based on Attendance by using Prescriptive Analytic 5

Knowledge Visualization of Student Performance Based on Atte...

引用

5th international conference on Artificial Intelligence and data Sciences, AiDAS 2024

作者： Khamis, Shakiroh Ahmad, Mazida Bin Zolkafly, Ahmad Subhi Cheng, Soong Hoong Awang, Hapini Mansor, Nur Suhaili Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Perak Malaysia Institute of Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Kedah Sintok Malaysia

ISBN: (纸本)9798331528553

In the contemporary educational landscape, student performance evaluation stands as a pivotal facet of pedagogical improvement. Recognizing the significance of attendance as a determinant of academic success has not been well evaluated. thus, this project embarks on a comprehensive investigation to analyze, visualize, and enhance the understanding of the intricate relationship between attendance and student performance. In this study, the visual analytic tool that combines data visualization and machine learning techniques to perform some visual analysis of students' attendance and its impact on student performance are presented. the research methodology of this project involves a comprehensive data Science Life Cycle method that consists of six steps. an in-depth analysis of attendance data collected from diverse Higher learning Institutions, encompassing different grade levels and disciplines. through rigorous statistical analysis and data mining techniques, aim to uncover meaningful patterns, correlations, and insights that illuminate the impact of attendance on student outcomes. Simultaneously, leveraging the robust features of Power BI, the project translates these findings into interactive, visually-rich dashboards and reports, facilitating a deeper understanding of the attendance-performance relationship. this holistic approach aims to empower educators, administrators, and students with the means to make informed decisions, optimize attendance strategies, and ultimately enhance academic outcomes in the ever-evolving educational ecosystem. © 2024 IEEE.

关键词： Contrastive learning

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dataset Bias in Deception Detection 26

Dataset Bias in Deception Detection

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Mambreyan, Ara Punskaya, Elena Gunes, Hatice Univ Cambridge Dept Engn Cambridge England Univ Cambridge Dept Comp Sci & Technol Cambridge England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

With the advances in machine learning, lie detection technology gained significant attention. In recent years, several multi-modal techniques achieved as high as 99% accuracy results using the Real-life Trial dataset with only 121 data points. this led to considerable media hype and research interest in lie detection with machine learning. In this paper, we analyze the effect of dataset bias in deception detection. More specifically, we train a classifier to predict the sex of the identity appearing in the video. On a test data point, we use the sex predictor to predict sex which we use as a proxy for predicting deception, predicting lie for females and truth for males. this lie predictor simulates a classifier that uses nothing but dataset bias. Nevertheless, we find that the performance of this biased classifier is comparable to those of state-of-the-art papers. More specifically, when using IDT features, our biased classifier achieves 64.6% and 59.3% AUC while a classifier trained normally on truth/lie labels achieves 57.4% accuracy and 69.3% AUC. We perform similar experiments on the Bag-of-Lies dataset and show that it too is biased with respect to sex. In addition, we apply the state-of-the-art techniques on an unbiased dataset and show that their performance is no better than chance. Our experiments strongly suggest that the results of recent deception detection techniques can be explained by the bias inherent in the datasets.

关键词： Measurement Industries Ethics machine learning algorithms Government machine learning Detectors

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Speeding up Heterogeneous Federated learning with Sequentially Trained Superclients 26

Speeding up Heterogeneous Federated Learning with Sequential...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zaccone, Riccardo Rizzardi, Andrea Caldarola, Debora Ciccone, Marco Caputo, Barbara Politecnico Torino Turin Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Federated learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. this approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, Le. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios.(1)

关键词： Training Performance evaluation Federated learning Computational modeling Neural networks Statistical distributions data models

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Smart Agriculture: machine learning Approach for Tea Leaf Disease Detection 1

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5th international conference on Communication and Intelligent Systems (ICCIS)

作者： Rajagopal, Manikandan Sivasakthivel, Ramkumar Pandey, Megha Christ Univ Sch Business & Management Bangalore Karnataka India Christ Univ Sch Sci Dept Comp Sci Bangalore Karnataka India

ISBN: (数字)9789819720538

ISBN: (纸本)9789819720521;9789819720538

Across the globe, plant infections from pathogens such as fungi, bacteria and viruses are the major issues in the agricultural sector. Agricultural productivity is one of the most important things on which the nations' economy highly depends. the detection of diseases in plants plays a major role in the agricultural field. this study proposes a multi-stage network involving Convolutional neural network, pattern identification and Classification using Siamese network. the main objective behind this study is to enhance the disease detection technique performance. the image data of Tea leaves chosen for this study will be gathered. the algorithms based on techniques of image processing would be designed. the proposed algorithm was tested on the following diseases namely Red rust, Blister blight, Twig dieback, Stem canker, Grey Blight, Brown Blight, Brown root rot disease and Red root rot disease in Tea leaves.

关键词： machine learning Agriculture Tea leafs Convolutional neural network pattern identification

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