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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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All dielectric compact broadband silicon waveguide mode converters

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Optical and Quantum Electronics 2024年第12期56卷 1-21页

作者： Hassan, Samar Hassan A. Hameed, Mohamed Farhat O. Mahmoud, Korany R. Wageeh, Amr Obayya, S.S.A. Electronics and Communications Engineering Department Faculty of Engineering Helwan University Cairo Egypt Centre for Nanotechnology Zewail City of Science Technology and Innovation October Gardens 6Th of October City Giza12578 Egypt Nanotechnology and Nanoelectronics Engineering Program Zewail City of Science Technology and Innovation October Gardens 6Th of October City Giza12578 Egypt Mathematics and Engineering Physics Department Faculty of Engineering University of Mansoura Mansoura35516 Egypt Cairo Egypt Giza Egypt Centre for Photonics and Smart Materials Zewail City of Science Technology and Innovation October Gardens 6Th of October City Giza12578 Egypt Department of Electronics and Communications Engineering Faculty of Engineering University of Mansoura Mansoura35516 Egypt

Compact mode order converters (MOCs) operating at the mid-infrared regime are reported using silicon-on-calcium fluoride platform. The suggested designs convert the fundamental transverse electric (TE) mode to first and second order TE modes with total device lengths of only 5.89 μm and 5 μm, respectively, at λ = 3.14 µm. The full vectorial 3D finite-deference time-domain is utilized to analyze the TE0 → TE1 and TE0→TE2 MOCs. The proposed designs depend on using dielectric material of Si3N4 inside the Si core region to improve and facilitate the mode conversion process. The conversion efficiencies (inter-modal crosstalks [CTs]) are equal to 93.4% (− 19.2 dB), and 90% (− 17 dB) for TE0: TE1 and TE0:TE2 MOCs, respectively. Moreover, the reported designs have a wide wavelength range of 300 nm, where the conversion-efficiency (CE) and maximum CT are > 86% and 0: TE1 and TE0: TE2 MOCs, respectively, at λ = 1.55 µm. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Silicon photonics

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Evaluation of Transfer Learning Models on Traumatic Brain Injury Severity Classification 21

Evaluation of Transfer Learning Models on Traumatic Brain In...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Do, Ngoc Hier, Daniel B. Obafemi-Ajayi, Tayo Missouri State University Dept. of Mathematics SpringfieldMO United States Missouri University of Science & Technology Kummer Institute RollaMO United States Missouri State University Engineering Program SpringfieldMO United States

ISBN: (纸本)9798350356632

After traumatic brain injury (TBI), clinicians use the Glasgow Coma Scale (GCS) to classify patients by severity and radiologists use the Rotterdam score and the Marshall score to classify CT scans by severity. This work investigates a viable efficient low cost transfer learning model to use MRI images to predict GCS, Rotterdam, and Marshall severity class after TBI. The enhanced transfer learning model architecture integrates multiple fine-tuning steps in which a few layers of the pre-trained model is unfrozen in each iteration so that the neural network is able to learn layer by layer further aspects of the image for better classification. By conducting a thorough evaluation across multiple convolutional neural network (CNN) architectures, this work ascertains the sensitivity of CNN models in detecting anatomical changes presented in MRI images that are predictive of severity class after TBI. These models have potential to predict outcomes after TBI. We utilize both quantitative metrics and qualitative analysis to validate the clinical relevance of the models in predicting severity class on admission and outcome at 6 months. The residual network models (ResNet152 in particular) outperformed the other models in predicting initial severity class. © 2024 IEEE.

关键词： Convolutional neural networks

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Economic and Sensitivity Analyses for an Optimal Hybrid Power Generation for Stand-Alone Power Systems: Case of Klongrua, Phato, Chumphon, Thailand

Economic and Sensitivity Analyses for an Optimal Hybrid Powe...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Jeenthanom, Pathumwadee Saelao, Jeerawan Muangjai, Worrajak Thongporn, Jutturit Panlawan, Nattawat Somsak, Teerasak Rajamangala University of Technology Lanna Electrical Engineering Program Faculty of Engineering Chiang Mai Thailand Maejo University Division of Mathematics Faculty of Science Chiangmai Thailand Rajamangala University of Technology Lanna Collage of Integrated of Science and Technology Chiang Mai Thailand

ISBN: (纸本)9781665402064

This paper analyzes a hybrid power system for an independent power system for Baan Khlongrua community, which is a village far from the transmission line system so that the community has electricity available all the time. The designed system consists of solar cells, batteries, inverters and small power turbines. Therefore, this research assesses the economic value and analyzes the system's sensitivity. It is assumed that the electricity demand will increase by 4.25% per year. The analysis data found that in the first year, the cost of energy (COE) was 0.137 /kWh, the second year was 0.132 /kWh, and the third year was 0.127 /kWh. Year 1 has a high COE due to system design being approximately 10% larger than current demand, and in years 4, 5, 6, COE will increase. As a result of the designed system, it will not produce enough electricity to meet the electrical load of the village. Therefore, a diesel generator compensates during the power shortage, causing the CO2 value in the year 4,5,6 to 3,780 kg/yr 2,898 kg/yr 3,265 kg/yr increased. This CO2 value, therefore, affects the COE increase, and COE decreases from 0.137 to 0.127 /kWh. the payback period of the system can be quickly achieved. © 2022 IEEE.

关键词： Sensitivity analysis

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UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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Computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Optimal Resource Allocation for NOMA Wireless Networks

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Computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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Integrated microalgae culture with food processing waste for wastewater remediation and enhanced biomass productivity

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Chinese Chemical Letters 2023年第2期34卷 636-643页

作者： Angela Paul Peter Kit Wayne Chew Apurav Krishna Koyande Heli Siti Halimatul Munawaroh Amit Bhatnagar Yang Tao Chihe Sun Fubao Sun Zengling Ma Pau Loke Show Department of Chemical and Environmental Engineering Faculty of Science and EngineeringUniversity of Nottingham MalaysiaJalan Broga43500 SemenyihSelangor Darul EhsanMalaysia Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection Wenzhou UniversityWenzhou 325035China Study Program of Chemistry Department of Chemistry EducationFaculty of Mathematics and Science EducationUniversitas Pendidikan IndonesiaBandung 40154Indonesia Department of Separation Science LUT School of Engineering ScienceLUT UniversitySammonkatu 12FI 50130MikkeliFinland College of Food Science and Technology Nanjing Agricultural UniversityNanjing 210095China Key Laboratory of Industrial Biotechnology Ministry of EducationSchool of BiotechnologyJiangnan UniversityWuxi 214122China Department of Sustainable Engineering Saveetha School of EngineeringSIMATSChennai 602105India

Waste generation from food manufacturing facilities poses a serious hazard like environmental degradation, water pollution, and land pollution due to its high nutrient composition. Specifically, solid waste(powder) disposal requires additional energy sources in terms of scientific treatment, structured collection, and disposal packaging according to the safety regulation. Thus, this research discusses the viewpoint of integrating food processing waste as an organic carbon source with BG-11 medium for Chlorella vulgaris(FSP-E) growth. The food processing waste powders investigated in this study were obtained from milk, and biscuit manufacturing facilities. The culture medium was modified by combining both BG-11 and food processing waste powders to identify the optimal algal growth and biochemical *** to the microalgae grown in BG-11 alone(IBG), the combination of biscuit waste and IBG produced higher biomass concentration(44%), with increased lipid(11%), protein(20%), and carbohydrate(57%) contents. Chlorella vulgaris was able to uptake nutrients from the culture medium with combination of food processing waste and IBG thus enhancing its growth. The results obtained also indicate that an integrated culture system using food processing waste and synthetic sources can generate energy out of waste by improving the bio-composition of the microalgae biomass.

关键词： Medium contamination Food processing Waste powder Organic medium Waste remediation

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Optimizing Insurance Strategies through Disaster Risk Assessment Models: A Case Study of Earthquakes in Japan

Optimizing Insurance Strategies through Disaster Risk Assess...

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Computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Qiyuan Luo Xingnan Zhang Yuanxin Zhang Burra Venkata Durga Kumar Meifeng Liu Program in Computer Science & Technology Xiamen University Malaysia Kuala Lumplur Malaysia Program in Software Engineering Xiamen University Malaysia Kuala Lumplur Malaysia Program in Mathematics and Applied Mathematics Xiamen University Malaysia Kuala Lumplur Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

With the increasing frequency of extreme weather events and natural disasters, insurance providers around the world are facing enormous challenges. This study addresses insurance claims resulting from these disasters by developing a comprehensive disaster insurance risk assessment model, focusing on Japan, a country prone to frequent earthquakes. The model integrates a Catastrophe Model, the Analytic Hierarchy Process (AHP), and an expert scoring method to optimize the risk assessment and compensation process for historic buildings. The results show that the model has strong predictive power, which provides valuable insights for insurance companies, policymakers, and urban planners in mitigating the impact of disasters and protecting cultural heritage.

关键词： Analytical models Disasters Earthquakes Buildings Insurance Predictive models Risk management Cultural differences Meteorology Context modeling

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Neural Bayes estimators for censored inference with peaks-over-threshold models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19070-19118页

作者： Jordan Richards Matthew Sainsbury-Dale Andrew Zammit-Mangion Raphaël Huser School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK and Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia and School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.

关键词： censored data convolutional neural network likelihood-free inference neural estimation spatial extremes threshold exceedance

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