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15th IEEE International Conference on Cloud Computing Technology and science, CloudCom 2024

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering Al-Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331507589

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction. © 2024 IEEE.

关键词： Electronic health record

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Multimodal Learning Approach for Multi-Topic Twitter Summarization 7

Multimodal Learning Approach for Multi-Topic Twitter Summari...

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7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Cao, Yangyang Wu, Yanmin Qi, Jinli Chen, Zizhong Chongqing Key Lab. of Compl. Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing401331 China University of California Riverside Department of Computer Science and Engineering RiversideCA United States

ISBN: (纸本)9781665487115

Twitter summary is designed to filter out the content summary from lots of noisy tweets, which can be used for Twitter search, public opinion analysis, hot topic discovery, etc. But the existing Twitter summary methods mostly focus on individual topics or hot events, and there are few studies on Twitter summary with multi-Topic posts. For the sake of resolve this problem, we propose a Twitter summary method based on social network information and manifold learning, which can extract tweets covering multiple topics, and better integrates the topic information and structural information of the tweets. First, the multi-modal manifold learning method is improved, and the social network information is integrated. Second, in order to test the relevance of the tweet summary and the topic, a test method of topic membership is designed. What's more, due to the lack of evaluation data sets, the author built a manual evaluation dataset. Our experiments on large-scale real tweets demonstrate the effectiveness of our framework. © 2022 IEEE.

关键词： Cloud computing Social networking (online) Blogs Manuals big data Manifold learning Noise measurement

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G-Bot: Revealing Government Programs with Smarted Assistance 2

G-Bot: Revealing Government Programs with Smarted Assistance

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Ramathilagam, A. Gunasekaran, Kavitha Pandian, Elamparithi Kumar, M. Satheesh Ponkumar, D. David Neels Saravanakumar, R. P.S.R. Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350366570

Residents have had trouble getting accurate state program information for years. G-bot's innovative technology employs NLP to link customers to state-level policies and activities to bridge this knowledge gap. G-bot's core NLP module is taught to deliver reliable and trustworthy information to help people make healthy and progressing decisions. G-bot provides real-time, reliable data to promote public awareness, transparency, and involvement in government processes. Users may explore the enormous state program and regulatory information using G-bot's simple interface. G-bot's Flask integration helps users locate vital information. G-bot enhances user-centric apps beyond information retrieval. AI-powered language processing ushers in a new era, letting individuals shape their communities. G-bot uses NER to identify government entities. This tool simplifies identifying organizations, programs, laws, agencies, and people, improving search results and customizing insights for better decision-making. © 2024 IEEE.

关键词： Bot (Internet)

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Enhanced Synthetic Images Classification with Vision Transformers and Effective data Augmentation Techniques

Enhanced Synthetic Images Classification with Vision Transfo...

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IEEE International Conference on Multi Topic

作者： Khadija Haider Muhammad Sohail Abbas Asif Muhammad Muhid Qaiser Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Software Engineering National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Synthetic images have made significant advancements, resulting in highly realistic and high-quality visuals generated at an unprecedented speed. However, the proliferation of synthetic images raises growing concerns regarding their potential misuse, ethical implications, and associated risks in various sectors. This study focuses on leveraging data augmentation techniques to enhance the accuracy and reliability of synthetic image detection. In our study, we fine-tuned a Vision Transformer (ViT) on the CIFAKE dataset, comprising real and synthetic images, augmented using various techniques such as transformations, photometric adjustments, geometric changes, and noise addition. The best results were obtained with ViT fine-tuned by distortions and noise augmentation, achieving an accuracy rate of 98.66%, demonstrating that sophisticated augmentation techniques improve performance. data augmentation plays a crucial role in handling the increasing realism and sophistication of synthetic images, contributing to more effective content moderation, digital forensics efforts, and the mitigation of potential risks associated with synthetic media.

关键词： Performance evaluation computer vision Visualization Accuracy Computational modeling Transformers Distortion data augmentation Reliability Image classification

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HiPoser: 3D Human Pose Estimation with Hierarchical Shared Learning at Parts-Level Using Inertial Measurement Units 39

HiPoser: 3D Human Pose Estimation with Hierarchical Shared L...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Liao, Guorui Zheng, Chunyuan Cheng, Li Xie, Haoyu Huang, Shanshan Liao, Jun Li, Haoxuan Liu, Li School of Big Data & Software Engineering Chongqing University China School of Mathematical Sciences Peking University China Department of Electrical and Computer Engineering University of Alberta Canada Center for Data Science Peking University China

ISBN: (纸本)157735897X

This paper considers the challenging problem of 3D Human Pose Estimation (HPE) from a sparse set of Inertial Measurement Units (IMUs). Existing efforts typically reconstruct a pose sequence by either directly tackling whole-body motions or focusing on distinctive spatio-temporal features of local body parts. Unfortunately, these methods ignore existing interdependent motor synergies amongst body parts, which may lead to pose estimation with ambiguous local parts. This observation motivates us to propose a hierarchical learning-based approach, HiPoser, which utilizes a hierarchical shared structure using Mamba blocks as the backbone to focus on the following estimation tasks, involving: 1) torso pose, 2) lower limbs pose, 3) upper limbs pose, and finally 4) global translation. These tasks selectively incorporate body motion states and are to be carried out sequentially in reconstructing part-based poses, which are amalgamated to estimate the final full-body pose with the global translation that satisfies inter-part consistencies. Our hierarchical structure allows HiPoser the flexibility in prioritizing different aspects of pose estimation, to emphasize more on detail or stability. Empirical evaluations over three benchmark datasets demonstrate the superiority of HiPoser over existing state-of-the-art models, suggesting that analyzing the synergistic movement of body parts is indeed important for advancing IMU-based 3D HPE. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Metric system

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TezNet: An Efficient Convolutional Neural Network for Optical Coherence Tomography Classification

TezNet: An Efficient Convolutional Neural Network for Optica...

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International Conference on Robotics and Artificial intelligence (ICRAI)

作者： Muhid Qaiser Muhammad Sohail Abbas Asif Muhammad Khadija Haider Haroon Khan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Software Engineering National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Retinal diseases such as Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME) can severely impair vision and are challenging to reverse if detected late. Optical Coherence Tomography (OCT) serves as a non-invasive imaging modality to diagnose these conditions through cross-sectional retinal images. Current advancements in retinal disease detection predominantly focus on fine-tuning larger models to achieve high accuracy, often at the expense of substantial training time and data requirements. We introduce an innovative Convolutional Neural Network (CNN) named TezNet that is deeper and wider than existing custom CNNs for the dataset used in this research yet maintains a lightweight architecture with only 3.6 million parameters. This high efficiency is accomplished by leveraging depthwise and spatial separable convolutional techniques without employing preprocessing techniques or noise reduction filters. The data augmentation techniques generate diverse image qualities, ensuring robust predictions across various scenarios. By training exclusively on a single dataset, our model achieves rapid training times and optimal resource utilization, minimizing the risk of overfitting. This blend of advanced parameter optimization, minimal preprocessing, and strategic data augmentation underscores the potential of our CNN model for swift, precise, and resource-efficient retinal disease detection. TezNet model achieves a remarkable 96% accuracy, competing closely with state-of-the-art networks like ResNet50 (over 23M parameters), InceptionV3 (over 25M parameters), and ResNeXt (89M parameters).

关键词： Training Accuracy Computational modeling Optical coherence tomography Retina data augmentation data models Convolutional neural networks Optimization Diseases

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective 24

A Deep Learning Approach Towards Student Performance Predict...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Western University Electrical and Computer Engineering Department LondonON Canada Zarqa University Faculty of Information Technology Data Science and Artificial Intelligence Department Zarqa Jordan Jadara University Faculty of Science and Information Technology Irbid Jordan Zarqa University Faculty of Information Technology Computer Science Department Zarqa Jordan Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan

ISBN: (纸本)9798350384307

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. © 2023 IEEE.

关键词： Deep Learning e-Learning Online Courses Student Performance Prediction

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MOT: A Multi-Omics Transformer for Multiclass Classification Tumour Types Predictions

MOT: A Multi-Omics Transformer for Multiclass Classification...

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16th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2023

作者： Abiodoun Osseni, Mazid Tossou, Prudencio Laviolette, FrancOis Corbeil, Jacques GRAAL Institute Intelligence and Data Department of Computer Science and Software Engineering Université Laval QuebecQC Canada Valence AI Discovery MontréalQC Canada Department of Molecular Medicine Université Laval QuebecQC Canada

Motivation: Breakthroughs in high-throughput technologies and machine learning methods have enabled the shift towards multi-omics modelling as the preferred means to understand the mechanisms underlying biological processes. Machine learning enables and improves complex disease prognosis in clinical settings. However, most multi-omic studies primarily use transcriptomics and epigenomics due to their over-representation in databases and their early technical maturity compared to others omics. For complex phenotypes and echanisms, not leveraging all the omics despite their varying degree of availability can lead to a failure to understand the underlying biological mechanisms and leads to less robust classifications and predictions. Results: We proposed MOT (Multi-Omic Transformer), a deep learning based model using the transformer architecture, that discriminates complex phenotypes (herein cancer types) based on five omics data types: transcriptomics (mRNA and miRNA), epigenomics (DNA methylation), copy number variations (CNVs), and proteomics. This model achieves an F1-score of 98:37% among 33 tumour types on a test set without missing omics views and an F1-score of 96:74% on a test set with missing omics views. It also identifies the required omic type for the best prediction for each phenotype and therefore could guide clinical decision-making when acquiring data to confirm a diagnostic. The newly introduced model can integrate and analyze five or more omics data types even with missing omics views and can also identify the essential omics data for the tumour multiclass classification tasks. It confirms the importance of each omic view. Combined, omics views allow a better differentiation rate between most cancer diseases. Our study emphasized the importance of multi-omic1 data to obtain a better multiclass cancer classification. Availability and implementation: MOT source code is available at predictions. © 2023 by SCITEPRESS – science and Technology Publications,

关键词： Diseases

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Exploring the Tangible Impact of Artificial intelligence and Machine Learning: Bridging the Gap between Hype and Reality 1

Exploring the Tangible Impact of Artificial Intelligence and...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dattangire, Rahul Vaidya, Ruchika Biradar, Divya Joon, Ashish Publicis Sapient Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India University of Texas at Arlington Computer Science ArlingtonTX76019 United States Andhus Technologies Inc Software Developer PensacolaFL United States

ISBN: (纸本)9798350367720

Beyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI's development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions. © 2024 IEEE.

关键词： Adversarial machine learning

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EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network

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Mathematical Biosciences and engineering 2024年第4期21卷 5712-5734页

作者： Qamar, Hafiz Ghulam Murtza Qureshi, Muhammad Farrukh Mushtaq, Zohaib Zubariah, Zubariah Rehman, Muhammad Zia ur Samee, Nagwan Abdel Mahmoud, Noha F. Gu, Yeong Hyeon Al-Masni, Mohammed A. School of Electrical Engineering Yanshan University Hebei Qinhuangdao066104 China Department of Electrical Engineering Riphah International University Islamabad44000 Pakistan Department of Electrical Electronics and Computer Systems College of Engineering and Technology University of Sargodha Sargodha40100 Pakistan Department of Physiotherapy Isfandyar Bukhari Civil Hospital District Headquarter Hospital Attock43600 Pakistan Department of Biomedical Engineering Riphah International University Islamabad44000 Pakistan Department of Health Science and Technology Aalborg University Aalborg9220 Denmark Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Artificial Intelligence Data Science College of Software & Convergence Technology Sejong University Seoul05006 Korea Republic of

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN’s superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control. © 2024 the Author(s), licensee AIMS Press.

关键词： Convolutional neural networks

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