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Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey

Artificial Intelligence in Medicine 2024年 151卷 102858-102858页

作者： Kosar, Amna Asif, Muhammad Ahmad, Maaz Bin Akram, Waseem Mahmood, Khalid Kumari, Saru Department of Computer Science Lahore Garrison University Lahore Pakistan Karachi Pakistan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Taiwan Douliu Taiwan Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan Departement of Mathematics Chaudhary Charan Singh University Meerut India

The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls. Around the globe, the total number of cases and deaths reported till November 12, 2022, were >600 million and 6.6 million, respectively. During the period of COVID-19, several diverse diagnostic techniques have been proposed. This work presents a systematic review of COVID-19 diagnostic techniques in response to such acts. Initially, these techniques are classified into different categories based on their working principle and detection modalities, i.e. chest X-ray imaging, cough sound or respiratory patterns, RT-PCR, antigen testing, and antibody testing. After that, a comparative analysis is performed to evaluate these techniques' efficacy which may help to determine an optimum solution for a particular scenario. The findings of the proposed work show that Artificial Intelligence plays a vital role in developing COVID-19 diagnostic techniques which support the healthcare system. The related work can be a footprint for all the researchers, available under a single umbrella. Additionally, all the techniques are long-lasting and can be used for future pandemics. © 2024 Elsevier B.V.

关键词： Coronavirus

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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy 23

ACGraph: Accelerating Streaming Graph Processing via Depende...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

ISBN: (纸本)9798350323481

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

关键词： graph processing

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods 3

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia

ISBN: (纸本)9798350306231

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier. © 2023 IEEE.

关键词： Support vector machines

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Graph Context Transformation Learning for Progressive Correspondence Pruning

arXiv

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arXiv 2023年

作者： Guo, Junwen Xiao, Guobao Wang, Shiping Yu, Jun School of Electronics and Information Engineering Tongji University China College of Computer and Data Science Fuzhou University China School of Computer Science and Technology Hangzhou Dianzi University China

Most of existing correspondence pruning methods only concentrate on gathering the context information as much as possible while neglecting effective ways to utilize such information. In order to tackle this dilemma, in this paper we propose Graph Context Transformation Network (GCT-Net) enhancing context information to conduct consensus guidance for progressive correspondence pruning. Specifically, we design the Graph Context Enhance Transformer which first generates the graph network and then transforms it into multibranch graph contexts. Moreover, it employs self-attention and cross-attention to magnify characteristics of each graph context for emphasizing the unique as well as shared essential information. To further apply the recalibrated graph contexts to the global domain, we propose the Graph Context Guidance Transformer. This module adopts a confident-based sampling strategy to temporarily screen high-confidence vertices for guiding accurate classification by searching global consensus between screened vertices and remaining ones. The extensive experimental results on outlier removal and relative pose estimation clearly demonstrate the superior performance of GCT-Net compared to state-of-the-art methods across outdoor and indoor datasets. The source code will be available at: https://***/guobaoxiao/GCT-Net/. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A New Federated Learning Framework Against Gradient Inversion Attacks 39

A New Federated Learning Framework Against Gradient Inversio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Pengxin Zeng, Shuang Chen, Wenhao Zhang, Xiaodan Ren, Weihong Zhou, Yuyin Qu, Liangqiong School of Computing and Data Science The University of Hong Kong Hong Kong Department of Mathematics The University of Hong Kong Hong Kong College of Computer Science Beijing University of Technology China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering UC Santa Cruz United States

ISBN: (纸本)157735897X

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively “breaks the direct connection” between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning 3

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

ISBN: (纸本)9798350306231

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%;Naive Bayes: 83%. © 2023 IEEE.

关键词： Support vector machines

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An IoT-Enabled Smart Network Traffic Signal Assistant System for Emergency Vehicles Using computer Vision 6th

An IoT-Enabled Smart Network Traffic Signal Assistant System...

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6th International Conference on Intelligent Sustainable Systems, ICISS 2023

作者： Senthil, G.A. Prabha, R. Suganthi, S. Sridevi, S. Shanthi, N. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramachandra Engineering and Technology Chennai India Department of Electrical and Electronics Engineering Sri Sai Ram Institute of Technology Chennai India

ISBN: (纸本)9789819917259

The increase in vehicles on the road causes an increase in traffic conditions, which induces difficulty in clearing the path for emergency vehicles such as ambulances, rescue fire engine and emergence vehicles. To avoid difficulty wait in traffic signal for the emergency situation, an IoT sensor smart-based system is developed that identifies the arrival of an ambulance to the hotspots through the signals received from the ambulance and maintains the signals accordingly. The regulation of the signals based on the location of the ambulance paves the way for the ambulance and rescue fire engine to urge its destination. This can be achieved via a device that incorporates a camera and sensor placed on the hotspots, as a Smart Network Traffic Signal (SNTS) detects and locates the vehicle and performs formulations to regulate traffic before the traffic signal. Machine learning algorithms are implicated to develop the model for the detection of the ambulance through the signals received. Convolutional Neural Network (CNN) and MobileNet algorithms employed yield 81 and 99% efficiency and are considered accurate for this system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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A Novel Energy Efficient Sensor data Encryption in Fog Based IoT Networks

A Novel Energy Efficient Sensor Data Encryption in Fog Based...

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Next Generation Electronics (NEleX), International Conference on

作者： Raja M Manicka Kumar S Manoj B Kiruba Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India

Smart devices equipped with embedded systems, including CPUs, sensors, and communication hardware, utilize web connectivity to gather and relay data from their environment, forming a segment of the Internet of Things network. Sensors in an IoT network are resource-constrained devices, but traditional data security techniques use complicated security mechanisms with long processing and reaction times, which reduce the network's overall lifespan. As a consequence, we proposed an EELWEP (energy efficient light weight encryption process) system to keep the data acquired by each sensor node private. When using this strategy, the secret key is exchanged using the Diffie-Hellman method using the Pretty Good Privacy (PGP) program, and a large portion of the operations is carried out via the use of symmetric cryptography. The energy usage and calculation time on the sensor network are significantly reduced as a result of this method. The suggested system is simulated, and their results are analysed using a variety of parameters in comparison to existing benchmark schemes. Comparing the suggested technique to the current approaches reveals that it outperforms the alternatives in the vast majority of situations studied.

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Channel-wise attention based binarization for CNN Architectures 5

Channel-wise attention based binarization for CNN Architectu...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Paul, Ipsita Bandyopadhyay, Mainak Dash, Satya Ranjan Aurpa, Tanjim Taharat Kiit D.U. School of Computer Engineering Bhubaneswar India Kiit D.U. School of Computer Applications Bhubaneswar India Bsmr Digital University Department of Data Science Engineering Bangladesh

ISBN: (纸本)9798331522100

Attention mechanisms are known to effectively increase the performance of a model by focusing on specific sections of the input while executing a task. Squeeze and Excitation module (SE-Net) and Efficient channel attention module (ECA-Net) are well-known Channel Attention networks that only involve a few parameters to achieve a clear performance gain. In order to further improve the model's accuracy, Binarized Neural Networks (BNNs) can be introduced in the same. BNNs are deep learning models that use binarized values for activation functions and training weights, instead of fully precised values that leads to much faster computation, occupying less memory and power. Motivated by this, a method of binarization of the convolutional network is proposed in this paper that can enhance the overall model accuracy. In our work, we focused on evaluating the performance of our binarized attention module over the pre-existing ones. Thus, the objective of this work is to design, implement, and evaluate efficient neural network models that balance accuracy and computational efficiency for image classification tasks on datasets with differing complexity levels, such as CIFAR-10 and CIFAR-100. By exploring various backbone architectures (e.g., MobileNetV2 and ResNet50), attention mechanisms (e.g., ECANet and SENet), and binarization techniques, this study aims to understand the impact of these components on model performance and resource requirements. Ultimately, the goal is to develop optimized models that achieve high accuracy with minimal computational overhead, making them suitable for deployment in resource-constrained environments. © 2025 IEEE.

关键词： Convolutional neural networks

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Hybrid Visual Verification

Hybrid Visual Verification

引用

International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： S.Nikkath Bushra A. Syed Ismail P. Sasigresa Edwin Singh C Department of Computing Technologies SRM Institute of Scienece and Technology KattankulathurChengalpattu India Department of Data Science and Business Systems SRM Institute of Scienece and Technology KattankulathurChengalpattu India Department: Computer Science and Engineering Veltech University Department: Computer Science and Engineering Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

data security is becoming increasingly important as cloud computing advances. data security is the fundamental problem of all distributed computing systems. Cloud computing enables access to distributed applications and services belonging to various organizations spread across different locations. Cloud computing raises security concerns, because it shares data with distributed users via network in an open terrain. In this system, some of the most important security services like image-based security are handled by the cloud computing system. The proposed scheme takes care of several attacks like botnets, malware and guessing attacks. This scheme is contrasted withseveral established schemesand a new form of authentication is used for the purpose of storing files in a secure manner.

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