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Face Image Recognition Using Deep learning Through Autoencoder Pre-Training

Face Image Recognition Using Deep learning Through Autoencod...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Solomon, Enoch Woubie, Abraham Emiru, Eyael Solomon Virginia State University Department of Computer Science RichmondVA United States Silo AI Helsinki Finland Engineering and Computer Science University of Trento Department of Information Italy

ISBN: (纸本)9798350393415

The primary objective of this work is to present an alternative approach aimed at reducing the dependency on labeled data. Our proposed method involves utilizing autoencoder pre-training within a face recognition task with two step processes. Initially, an autoencoder is trained in an unsupervised manner. Subsequently, a deep learning model is trained with initialized parameters from the pre-trained autoencoder. This deep learning training process is conducted in a supervised manner, employing relatively limited labeled training dataset. During evaluation phase, face image embeddings is generated as the output of deep neural network layer. Our training is executed on the CelebA dataset, while evaluation is performed using Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) datasets. Experimental results demonstrate that by initializing the deep neural network with pre-trained autoencoder parameters achieve comparable results to state-of-the-art methods. © 2023 IEEE.

关键词： Face recognition

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Soft Prompt Recovers Compressed LLMs, Transferably 41

Soft Prompt Recovers Compressed LLMs, Transferably

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41st International Conference on Machine Learning, ICml 2024

作者： Xu, Zhaozhuo Liu, Zirui Chen, Beidi Zhong, Shaochen Tang, Yuxin Wang, Jue Zhou, Kaixiong Hu, Xia Shrivastava, Anshumali Department of Computer Science Stevens Institute of Technology United States Department of Computer Science Rice University United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Together AI United States Department of Electrical and Computer Engineering North Carolina State University United States ThirdAI Corp. United States

Model compression is one of the most popular approaches to improve the accessibility of Large Language Models (LLMs) by reducing their memory footprint. However, the gaining of such efficiency benefits often simultaneously demands extensive engineering efforts and intricate designs to mitigate the performance decline. In this work, we leverage (Soft) Prompt Tuning in its most vanilla form and discover such conventionally learned soft prompts can recover the performance of compressed LLMs. More surprisingly, we observe such recovery effect to be transferable among different tasks and models (albeit natural tokenizer and dimensionality limitations), resulting in further overhead reduction and yet, subverting the common belief that learned soft prompts are task-specific. Our work is fully orthogonal and compatible with model compression frameworks such as pruning and quantization, where we enable up to 8× compressed LLM (with a joint 4-bit quantization and 50% weight pruning compression) to match its uncompressed counterparts on popular benchmarks. We note that we are the first to reveal vanilla Parameter-Efficient Fine-Tuning (PEFT) techniques have the potential to be utilized under a compression recovery context, opening a new line of opportunities for model accessibility advancement while freeing our fellow researchers from the previously present engineering burdens and constraints. The code is available at https://***/zirui-ray-liu/compress-thenprompt. Copyright 2024 by the author(s)

关键词： Benchmarking

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Deployment Of Machine Learning Model to Evaluate the Quality of Green Spacing

Deployment Of Machine Learning Model to Evaluate the Quality...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： M Vineela V Rama Krishna Bhaludra R Nadh Singh G. Divya Vani N. Sudha Laxmaiah Cherukupally Saritha Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Vinay Nagar Hyderabad India Department of CSE-AI&ML(CSM) Bhoj Reddy Engineering College for Women Vinay Nagar Hyderabad India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Green space is any vegetated infrastructure. Green space promotes mental and physical wellness, socialization, and environmental protection. The state of green space determines its quality. Based on machine learning research, trash, lack of upkeep, and dirtiness lower green space quality. These algorithms evaluate green areas without considering human perception. Domain-based approaches are laborious, tedious and difficult to implement across large areas. This study suggests applying transfer learning on pre-trained models to design, analyze, and implement a machine-learning technique for human-perception green space quality assessment. Precision, F1-score, accuracy, recollection, Cohen’s Kappa, and Regular ROC-AUC were high for the generated models. In addition, the models’ file sizes and inference times were analyzed to guarantee their use in real-world settings. Grad-CAM, which uses heat maps to assess a model’s learning performance, was also included in the study. ResNet50 was the top-performing model, with 99.99 per cent accuracy, 99.99 per cent precision, 99.99 per cent recall, 99.00 per cent F1-score,0.98 Cohen’s Kappa, and an Average ROC-AUC of 1.00. The ResNet50 model was the second-fastest predictor and had a reasonable file size. Grad-CAM imaginings reveal that ResNet50 can accurately pinpoint regions crucial to its learning. Finally, an interactive online application based on the ResNet50 model was released via the Stream lit cloud platform.

关键词： Training Analytical models Visualization Green products Transfer learning Streaming media Security

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Nearest Neighbor Based Unsupervised Deep Learning Image Recognition Method

Nearest Neighbor Based Unsupervised Deep Learning Image Reco...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Solomon, Enoch Woubie, Abraham Emiru, Eyael Solomon Virginia State University Department of Computer Science RichmondVA United States Silo AI Helsinki Finland University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350393415

Although deep learning are commonly employed for image recognition, usually huge amount of labeled training data is required, which may not always be readily available. This leads to a noticeable performance disparity when compared to state-of-The-Art unsupervised face verification techniques. In this work, we propose a method to narrow this gap by leveraging an autoencoder to convert the face image vector into a novel representation. Notably, the autoencoder is trained to reconstruct neighboring face image vectors rather than the original input image vectors. These neighbor face image vectors are chosen through an unsupervised process based on the highest cosine scores with the training face image vectors. The proposed method achieves a relative improvement of 56% in terms of EER over the baseline system on Labeled Faces in the Wild (LFW) dataset. This has successfully narrowed down the performance gap between cosine and PLDA scoring systems. © 2023 IEEE.

关键词： Face recognition

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Enhancing Human-Robot Interaction through Natural Language Processing and Speech Recognition Techniques

Enhancing Human-Robot Interaction through Natural Language P...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Thotakura Veeranna K. Pavan Kumar Mahadeva Aruna Amit Verma Thanu Kurian Department of CSE(AI&ML) Sai Spurthi Institute of Technology B. Gangaram India Department of Electronics & Communication Engineering (ECE) K.S.R.M College of Engineering(Autonomous) Kadapa India Computer Science and Engineering Sai Spurthi Institute of Technology B. Gangaram India Department of Computer Science and Engineering Dr. K N Modi University Newai Rajasthan India Department of AI &ML New Horizon College of Engineering Bangalore India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The rapid development of artificial intelligence (ai) has resulted in the gradual integration of robots into individuals' everyday lives and professional environments. The use of image recognition employing the robot's human-machine interface technology has become widespread. However, there are still some unresolved problems with the methods used for interaction between robots and machines, such as natural language processing (NLP) and speech recognition. Thus, building on previous research in the field, this work suggests a novel approach to robot-machine interaction by integrating the perceptron lexicon analysis model with the transfer dependence syntactic analysis model. To enhance interaction performance, researchers need to delve further into language-based human-machine interaction systems. An experimental evaluation of the structural perceptron model reveals a recall rate of 82%, an F1 value of 83%, and a testing accuracy of 96%. A maximum analysis of data speed of 751K/s is achieved using the data transfer dependence syntax analysis model. With superior robustness and reaction sensitivity, the novel technique for robot-machine interaction achieves a 93% accuracy rate in comparison to previous methods that have been tested in simulations. To conclude, research methodologies provide the foundation, both theoretically and practically, for better human-machine cooperation and enhanced robot interaction skills)

关键词： Human computer interaction Analytical models Adaptation models Accuracy Human-machine systems Speech recognition Syntactics Robot sensing systems Natural language processing Testing

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Revolutionizing Healthcare with Wearable Devices and IoT Integration for Personalized Health Management in Next-Generation Health Informatics

Revolutionizing Healthcare with Wearable Devices and IoT Int...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Parthipan, V. Ramesh, P.S. Gururama Senthilvel, P. Dineshkumar, R. Vennila, G. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India

ISBN: (纸本)9798350388602

This work outlines a paradigm shift that is created by the integration of wearable devices with the Internet of Things for health consciousness. With the help of the proposed system, the user is given the constant readings of the key heath indicators including heart rate, blood pressure, blood glucose level, physical activity, and the quality of sleep, all the data are transferred in real-time to an IoT platform, and the data analysis supported by machine learning algorithms provide the user with individually tailored heath reports. Wearable Medical today carried out a 90 persons trial over a six-month long program and noted changes in health parameters, user interactions and results. Several client outcomes highlighted that clients' heart rate, blood pressure and glucose levels, physical activity, and sleep quality improved. The system also provided strong control in data protection and the compliance with different regulations which is needed for any company. Although there are issues with device and network integration and the supporting architecture, the study demonstrates the system's capability of decreasing hospital attendance, increasing medication compliance and quality of life, thereby opening up possibilities for exploring wearable health technologies in clinical environments in the future. © 2024 IEEE.

关键词： Electronic health record

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Self-supervised Deep Learning Based End-to-End Face Verification Method using Siamese Network 17

Self-supervised Deep Learning Based End-to-End Face Verifica...

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17th IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2023

作者： Solomon, Enoch Woubie, Abraham Emiru, Eyael Solomon Virginia State University Department of Computer Science RichmondVA United States Silo Ai Helsinki Finland University of Trento Engineering and Computer Science Department of Information Italy

ISBN: (纸本)9798350396003

Achieving state-of-the-art results in face verification systems typically hinges on the availability of labeled face training data, a resource that often proves challenging to acquire in substantial quantities. In this research endeavor, we proposed employing Siamese networks for face recognition, eliminating the need for labeled face images. We achieve this by strategically leveraging negative samples alongside nearest neighbor counter-parts, thereby establishing positive and negative pairs through an unsupervised methodology. The architectural framework adopts a VGG encoder, trained as a double branch siamese network. Our primary aim is to circumvent the necessity for labeled face image data, thus proposing the generation of training pairs in an entirely unsupervised manner. Positive training data are selected within a dataset based on their highest cosine similarity scores with a designated anchor, while negative training data are culled in a parallel fashion, though drawn from an alternate dataset. During training, the proposed siamese network conducts binary classification via cross-entropy loss. Subsequently, during the testing phase, we directly extract face verification scores from the network's output layer. Experimental results reveal that the proposed unsupervised system delivers a performance on par with a similar but fully supervised baseline. © 2023 IEEE.

关键词： Face recognition

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Neuromorphic Computing Architectures for EnergyEfficient Edge Devices in Autonomous Vehicles 3

Neuromorphic Computing Architectures for EnergyEfficient Edg...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Pradhan, Rahul Gummadi, Annapurna Tanusha, P. Sowmya, Gandla Vaitheeshwari, S. Ponni Valavan, M. Gla University Department of Computer Science and Engineering Uttar Pradesh Mathura India Hyderabad India Kl University Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Nri Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science Chennai Avadi India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

Autonomous vehicles hold the promise of significantly reducing road accidents caused by human error, such as distracted driving or impaired judgment. However, the widespread adoption of autonomous vehicles faces challenges, including regulatory frameworks, liability concerns, ethical considerations, and the need for comprehensive infrastructure updates. This research focuses on the development of neuromorphic computing architectures tailored for energyefficient edge devices in autonomous vehicles. The significance of this study lies in addressing the escalating computational demands of real-time data processing in autonomous driving scenarios while mitigating the inherent power constraints of edge computing environments. The conventional approaches in autonomous vehicle computing, often centralized and powerintensive, pose challenges in terms of energy consumption and real-time responsiveness. The proposed neuromorphic computing architectures leverage spiking neural networks and event-driven processing to emulate the brain's efficiency, offering a novel solution to enhance both computational performance and energy efficiency. The research addresses prior issues related to the power-hungry nature of traditional computing paradigms in autonomous vehicles and emphasizes the need for more sustainable and responsive edge computing solutions. The novelty of this research lies in the integration of neuromorphic principles into edge devices, enabling onboard processing that is not only energy-efficient but also well-suited for the sporadic and dynamic nature of real-world driving environments. This study paves the way for advancements in autonomous vehicle technology, promising enhanced energy efficiency, reduced latency, and improved overall system performance. © 2024 IEEE.

关键词： Energy efficiency

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using ai Algorithm 5

A Detailed Structured Monitoting System for ICU Patient Mort...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Joshi, Shivani Kumar, Rajiv Rai, Vipin Dwivedi, Avinash Tripathi, Vikash Chandra G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Nvidia Ai and Learning & Research Center Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model's training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously © 2024 IEEE.

关键词： accuracy Artificial Neural Networks (ANN) exploratory data analysis high-risk patients ICU patient admission medical technology mortality outcomes periodic updates prediction model

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Employee and Client Analytical Tool 11th

Employee and Client Analytical Tool

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Gupta, Manan Rastogi, Sanchit Sharma, Puneet Arora, Deepak Department of Computer Science and Engineering Amity University Uttar Pradesh Noida India School of Computer Science and AI SR University Telangana Warangal India Department of CSE Amity University Uttar Pradesh Lucknow Campus India

ISBN: (纸本)9789819717231

Today, data analytics has become quite useful for everyone. Using the right tools, decision-makers can extract different insights of data which could help to optimize various parameters. It can help to recognize several patterns from the given loads of information. These kinds of insights can be quite useful to increase the overall performance of any company. According to this research and looking at the future scope of data science and analytics field, our team has used Qliksense software for creating an analytical tool known as "E&C AniTool" which is an advanced kind of dashboard consisting of various features and parameters for doing advance evaluation and analysis of any company data. This analytical tool can help manage the big data on a daily basis for any company using various visualization techniques which can be quite useful for any company. Qliksense is a software for visualizing data and discovering many products/tools that allow us to create user-friendly and communal visualizations that lead us to meaningful processes and decisions. The main reason to use Qliksense is that it can be accessed anywhere, anytime, and through any device. It is a modern data analytical software which is being used by many multinational corporations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Visualization

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