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COVID-19 Improved Diagnoses Based on the Open-morphology Filter and Deep-learning

Recent Patents on engineering 2022年第3期16卷 122-130页

作者： Younus, Majid D. Zedan, Mohammad J. M. Malallah, Fahad L. Saeed, Mustafa G. Computer and Information Engineering Department College of Electronics Engineering Ninevah University Mosul Iraq Computer Science Dept. College of Science Cihan-Slemani University Slimani Iraq

Background: Coronavirus (COVID-19) has appeared first time in Wuhan, China, as an acute respiratory syndrome and spread rapidly. It has been declared a pandemic by the WHO. Thus, there is an urgent need to develop an accurate computer-aided method to assist clinicians in identifying COVID-19-infected patients by computed tomography CT images. The contribution of this paper is that it proposes a pre-processing technique that increases the recognition rate compared to the techniques existing in the literature. Methods: The proposed pre-processing technique, which consists of both contrast enhancement and open-morphology filter, is highly effective in decreasing the diagnosis error rate. After carrying out pre-processing, CT images are fed to a 15-layer convolution neural network (CNN) as deep-learning for the training and testing operations. The dataset used in this research has been publically published, in which CT images were collected from hospitals in Sao Paulo, Brazil. This dataset is composed of 2482 CT scans images, which include 1252 CT scans of SARS-CoV-2 infected patients and 1230 CT scans of non-infected SARS-CoV-2 patients. Results: The proposed detection method achieves up to 97.8% accuracy, which outperforms the reported accuracy of the dataset by 97.3%. Conclusion: The performance in terms of accuracy has been improved up to 0.5% by the proposed methodology over the published dataset and its method. © 2022 Bentham science Publishers.

关键词： COVID-19

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A Comprehensive Analysis of Identifying Patients with Chronic Kidney Disease 2

A Comprehensive Analysis of Identifying Patients with Chroni...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Geetha, S. Marimuthu, C.N. Maheswari, S. Deepa, B. Anna University Faculty of Information and Communication Engineering Chennai600025 India Nandha Engineering College Department of Electronics and Communication Engineering Erode638052 India Nandha Engineering College Department of Computer Science and Engineering Erode638052 India

ISBN: (纸本)9798350387933

Chronic Kidney Disease (CKD) is a prevalent and progressive health condition affecting millions of individuals worldwide. Early identification and management of CKD are critical to mitigate its adverse effects on patients' health and reduce the burden on healthcare systems. This study presents a comprehensive analysis of various methodologies and approaches for identifying patients with Chronic Kidney Disease. The research encompasses a detailed review of traditional and emerging diagnostic techniques, by exploring the sensitivity, specificity, and accuracy of these methods, considering their applicability in diverse patient populations and healthcare settings. Furthermore, this analysis delves into the role of artificial intelligence (AI) and machine learning in enhancing CKD detection and risk prediction. The synthesis of this comprehensive analysis provides valuable insights into the current landscape of CKD identification and offers recommendations for optimizing diagnostic strategies. © 2024 IEEE.

关键词： Risk assessment

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Graph Complemented Latent Representation for Few-Shot Image Classification

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IEEE Transactions on Multimedia 2023年 25卷 1979-1990页

作者： Zhong, Xian Gu, Cheng Ye, Mang Huang, Wenxin Lin, Chia-Wen Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Peking University School of Electronic Engineering and Computer Science Beijing100091 China ZhongNeng Power-Tech Development Company Ltd Beijing100034 China Wuhan University School of Computer Science Wuhan430072 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan National Tsing Hua University Institute of Communications Engineering Hsinchu30013 Taiwan

Few-shot learning is a tough topic to solve since obtaining a large number of training samples in real applications is challenging. It has attracted increasing attention recently. Meta-learning is a prominent way to address this issue, intending to adapt predictors as base-learners to new tasks swiftly. However, a key challenge of meta-learning is its lack of expressive capacity, which stems from the difficulty of extracting general information from a small number of training samples. As a result, the generalizability of meta-learners trained from high-dimensional parameter spaces is frequently limited. To learn a better representation, we propose a graph complemented latent representation (GCLR) network for few-shot image classification. In particular, we embed the representation into a latent space, in which the latent codes are reconstructed using variational information to enrich the representation. In this way, the latent representation can achieve better generalizability. Another benefit is that, because the latent space is formed using variational inference, it cooperates well with various base-learners, boosting robustness. To make full use of the relation between samples in each category, a graph neural network (GNN) is also incorporated to improve relation mining. Consequently, our end-to-end framework delivers competitive performance on three few-shot learning benchmarks for image classification. © 2021 IEEE.

关键词： Image reconstruction

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Analysis of Various Motion Detection and Facial Recognition Based Home Security Systems 2nd

Analysis of Various Motion Detection and Facial Recognition ...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Karthikeyan, P. Nidhi, Shashank Vishnu Kumar, S.R. Karunakaran, V. Velliangiri, S. Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Computer Science and Engineering-AI and ML Jain Deemed to be University Bangalore India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9783031689079

A combination of AI and IoT makes a great home security system. IoT collects data from the surroundings, while AI takes the data and makes predictions accordingly. In this case, these predictions help in creating alerts. CCTV cameras were traditionally used for home security systems, with attendees required to monitor the footage. Because of technological advancements, this process can be completed with the least amount of human intervention. Other work has been done in the field of home security systems that use facial recognition and motion detection. Those systems don’t exhibit good accuracy in detecting multiple human faces. This work focuses on a detailed survey of different facial recognition techniques along with motion detection techniques as per the requirements of a home security system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motion tracking

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Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's Disease Progression Analysis 4

Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's D...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Selvam, L. Dhamotharan, R. Krishnan, R. Santhana Francis Raj, J. Relin Sankar, G. Ram Yamini, G. Srm Madurai College for Engineering and Technology Department of Computer Science and Engineering Pottapalayam Sivaganga India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of English Chennai India

ISBN: (纸本)9798331523923

Alzheimer's disease detection and classification using deep learning techniques offer significant advancements in early diagnosis and progression analysis of this neurodegenerative disorder. This approach leverages various medical imaging modalities such as MRI, PET, CT, and fMRI, alongside clinical data like cognitive test results and biomarkers, to assess brain changes associated with Alzheimer's. The focus is on classifying the disease into three stages: Mild, Moderate, and Severe, using advanced machine learning models. Deep learning techniques, such as EfficientNet B7 for 2D imaging and 3D CNNs for volumetric data, enable efficient feature extraction from medical scans. Additionally, Recurrent Neural Networks (RNNs) with GRU layers are used to capture temporal patterns from longitudinal data, improving predictive accuracy. Hybrid models, combining CNNs with attention mechanisms, further enhance classification. The model uses a multi-class classification approach with Softmax activation to predict the likelihood of each stage of Alzheimer's disease. Performance evaluation includes key metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). AUC can be calculated in two ways: individual AUC scores for each disease stage or a combined AUC for overall classification. This analysis provides a comprehensive understanding of the model's effectiveness in distinguishing between different stages of Alzheimer's. The results can be integrated into clinical workflows to assist healthcare professionals in diagnosing and monitoring Alzheimer's disease progression, offering a potential path toward more efficient and early intervention strategies. © 2025 IEEE.

关键词： Recurrent neural networks

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Multi-agent Simulation for Mass School Shootings

Multi-agent Simulation for Mass School Shootings

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Dai, Wei Singh, Yash Pratap Zhang, Rui Purdue University Northwest Department of Computer Science HammondIN United States Purdue University Northwest Department of Electrical and Computer Engineering HammondIN United States

ISBN: (纸本)9798350362480

The increasing frequency of school shootings in the United States has been raised as a critical concern. Active shooters kill innocent students and educators in schools. These incidents highlight the urgent need for effective strategies to minimize casualties. This study aims to address the challenge of simulating and assessing potential mitigation measures by developing a multi-agent simulation model. Our model is designed to estimate casualty rates and evacuation efficiency during active shooter scenarios within school buildings. The simulation evaluates the impact of a gun detection system on safety outcomes. By simulating school shooting incidents with and without this system, we observe a significant improvement in evacuation rates, which increased from 16.6% to 66.6%. Furthermore, the Gun Detection System reduced the average casualty rate from 24.0% to 12.2% within a period of six minutes, based on a simulated environment with 100 students. We conducted a total of 48 simulations across three different floor layouts, varying the number of students and time intervals to assess the system's adaptability. We anticipate that the research will provide a starting point for demonstrating that a gunshot detection system can significantly improve both evacuation rates and casualty reduction. © 2024 IEEE.

关键词： Students

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus 25

Transformer-based Text Classification on Unified Bangla Mult...

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25th International Arab Conference on information Technology, ACIT 2024

作者： Sourav, Md Sakib Ullah Mahmud, Mohammad Sultan Zheng, Hua Aljaidi, Mohammad Talukder, Md. Simul Hasan Bin Sulaiman, Rejwan Nur, Abdullah Hafez Al-Qerem, Ahmad Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shenzhen University College of Computer Science and Software Engineering Senzhen China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou510990 China Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Dhaka University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Northumbria University Newcastle Tayne School of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world's most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available. © 2024 IEEE.

关键词： Classification (of information)

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Toward a smart multi-unmanned aerial vehicle system

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IEEE Potentials 2022年第1期41卷 22-25页

作者： Jaiswal, Shashwat Sidhanta, Subhajit Department of Electrical Engineering and Computer Science Indian Institute of Technology Bhilai492015 India

Drones, or unmanned aerial vehicles (UAVs) in general, have been increasingly gaining popularity and attention in both the commercial and nonprofit sectors. They have particularly been envisioned to be of critical importance in military and disaster zones for many purposes, including surveying, relief, and combat. © 1988-2012 IEEE.

关键词： Antennas

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Hand-Powered Point-of-Care: Centrifugal Microfluidic Platform for Urine Routine Examination (μCUREX)

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Langmuir 2023年第5期39卷 1897-1904页

作者： Kuo, Shao-Hsuan Peraro, Alberto Lin, Hsiu-Pen Chang, Chun-Hao Li, Bor-Ran Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Biomedical Engineering University of Padua Padova35122 Italy Center for Emergent Functional Matter Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan

Urinalysis is one of the simplest and most common medical tests in modern cities. With the assistance of professional technicians and equipment, people in metropolitan areas can effortlessly acquire information about their physiological conditions from traditional clinical laboratories. However, the threshold, including precise benchtop equipment and well-trained personnel, still remains a considerable dilemma for residents in healthcare-poor areas. Hence, it is a crucial and urgent topic to develop a smart and affordable widget to address this challenge. To improve the healthcare rights of residents, we proposed a disposable centrifugal microfluidic urine routine examination platform (named μCUREX) actuated with a modified hand-powered fan. Two parts of urinalysis (sediment test and chemical strip test) were integrated into the μCUREX disc. The influence on sedimentation by variant hand-powered manipulation was simulated using COMSOL. As a result, more than 70% of the sediment can be collected. Moreover, the color change of chemical strip papers (indicators for glucose, pH, protein, and occult blood) was recorded with a 3D-printed studio and analyzed after reaction with chemical-spiked and pH-adjusted artificial and human urine specimens. The whole process can be completed within 10 min, with only 200 μL of urine needed. In conclusion, we successfully constructed an ultra-low-cost point-of-care platform for urinalysis in extremely resource-poor settings. The handy size, high affordability, and user-friendliness of the μCUREX disc provide strong potential and feasibility in solving problems in resource-poor settings. Furthermore, we highly expect the μCUREX platform to improve the level of healthcare in resource-limited areas. © 2023 American Chemical Society.

关键词： Microfluidics

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Short-term passenger flow prediction strategy of urban rail transit based on ridge regression and LSTM

Short-term passenger flow prediction strategy of urban rail ...

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2023 IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Liu, Hongkun Ren, Jinshan Wang, Zixuan University of Jinan School of Information Science and Engineering Jinan250022 China Jiangsu University of Science and Technology School of Computer Science Zhenjiang212000 China Shandong University of Science and Technology Department of Electrical Engineering and Information Technology Jinan250031 China

ISBN: (纸本)9798350399028

In order to reduce the interference of noise in the short-term passenger flow prediction of urban rail transit, an efficient and accurate traffic flow prediction scheme has become the most concerned issue for traffic participants. This paper focuses on the application of ridge regression and LSTM neural network in the short-term passenger flow of urban rail transit. This paper firstly performs data cleaning, missing processing and normalization. Then, we choose LSTM as the core and use the ridge regression-assisted model to predict the short-term passenger flow of urban rail transit. Ridge regression is used for prediction when there is less data, and LSTM with high fitting degree is used for medium and long-term prediction. The model has a high fitting effect on the randomly divided test set and real data. © 2023 IEEE.

关键词： Forecasting

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