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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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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

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computer Modeling in engineering & sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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Integration of MCC to detect disease in plants with DL Health Industry 4

Integration of MCC to detect disease in plants with DL Healt...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bewerwal, Anubhav Habelalmateen, Mohammed I. Alsailawi, H.A. Venkatesan, D. Dhabli, Dharmesh Jabbar, Qasim Abdul Bader, Nabaa M. Alwan, Ali Saad Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq St.Martin's Engineering College Dept.of Ai&ml Secundrabad India Vishwakarma Institute of Information Technology Pune India Alkunooze University College Medical Instrument Department Basra Iraq National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

Mobile gadgets are progressively assuming a vital role in individuals' everyday routines as they streamline several practical activities. Mobile cloud computing overcomes the constraints of memory, CPU, and battery life, and enhances its functionalities by integrating mobile and cloud computing. Big data analytics solutions facilitate the extraction of value from data by considering four key factors: volume, diversity, velocity, and validity. The study explores the use of mobile cloud computing and big data analytics in the context of networked healthcare. The focus is on the justification and development of interconnected healthcare applications and systems, highlighting the rise of cloud computing in this domain. The paper presents a cloudlet-based mobile cloud computing architecture designed specifically for big data applications in the healthcare sector. In the proposed framework the study assess two types of models that is cloud-based model and Wi-fi Cloudlet-based model, the working mechanism model describe the differentiation in processing. Furthermore, this study evaluates the results obtained and compares both mechanism based on latency in processing. © 2024 IEEE.

关键词： Electronic health record

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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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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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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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FEDERATED RECOMMENDATION WITH ADDITIVE PERSONALIZATION 12

FEDERATED RECOMMENDATION WITH ADDITIVE PERSONALIZATION

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12th International Conference on Learning Representations, ICLR 2024

作者： Li, Zhiwei Long, Guodong Zhou, Tianyi Australian AI Institute Faculty of Engineering and IT University of Technology Sydney Australia Department of Computer Science University of Maryland College Park United States

Building recommendation systems via federated learning (FL) is a new emerging challenge for next-generation Internet service. Existing FL models share item embedding across clients while keeping the user embedding private and local on the client side. However, identical item embedding cannot capture users' individual differences in perceiving the same item and may lead to poor personalization. Moreover, dense item embedding in FL results in expensive communication costs and latency. To address these challenges, we propose Federated Recommendation with Additive Personalization (FedRAP), which learns a global view of items via FL and a personalized view locally on each user. FedRAP encourages a sparse global view to save FL's communication cost and enforces the two views to be complementary via two regularizers. We propose an effective curriculum to learn the local and global views progressively with increasing regularization weights. To produce recommendations for a user, FedRAP adds the two views together to obtain a personalized item embedding. FedRAP achieves the best performance in FL setting on multiple benchmarks. It outperforms recent federated recommendation methods and several ablation study baselines. Our code is available at https://***/mtics/FedRAP. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Embeddings

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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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