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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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A Review of Algorithms's Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms's Complexities on Different Valued So...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Majeed, Asad Iftikhar, Mahnoor Ali, Raja Hashim Arshad, Usama Shabbir, Muhammad Zeeshan Zeeshan Ijaz, Ali Ali, Nisar Aftab, Ali Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant. © 2023 IEEE.

关键词： Computational complexity

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Impact of Varying Laser Power in WDM Transmission Systems to Control Nonlinear Effects 1

Impact of Varying Laser Power in WDM Transmission Systems to...

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1st IEEE International Conference on Communication engineering and Emerging Technologies, ICoCET 2024

作者： Hammood, Dalal Abdulmohsin Galeb, Basim Al-Hilali, Aqeel Bashar, Mustafa Rosli, Siti Julia Electrical Engineering Technical College Middle Technical University Cybersecurity Technology Engineering Department Baghdad10022 Iraq Computer Technician Engineering Al Hikma University College Baghdad Iraq Medical Instrumentation Engineering Al-farahidi University Baghdad Iraq Uruk University Baghdad Iraq Faculty of Electronic Engineering Technology Perlis Arau02600 Malaysia

ISBN: (纸本)9798331504144

This study investigates how varied CW laser power broadcasts affect a sixteen-channel DWDM system. Constant transmission distance is sixty kilometers. The system can handle 0.649 Tbps. Launch power ranging from 0 to 10 dBm is studied to observe its effects on signal quality, crosstalk, and system efficiency. The QF assessment data shows that greater power levels boost optical signal-to-noise ratio (OSNR) and Q-factor. This improvement occurs at lower power levels. Compared to ambient noise, the signal is stronger. Due to increasing OSNR, Q-factor improves with power. When nonlinear effects become evident, progress may stagnate. Increased power levels may increase nonlinear processes including stimulated Brillouin scattering (SBS), four-wave mixing (FWM), and self-phase modulation (SPM). The Q-factor may decrease due to signal distortion and noise. According to the Bit Error Rate (BER) parameter, greater power increases signal intensity, which improves OSNR and reduces mistakes. Thus, BER drops. To increase an optical communication system's performance, laser power must be balanced to maximize the Q-factor, minimize the bit error rate (BER), and prevent substantial nonlinear aberrations. © 2024 IEEE.

关键词： Four wave mixing

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The Integration of Explainable AI Methods for the Classification of Medical Image Data

The Integration of Explainable AI Methods for the Classifica...

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Electrical, electronic and Computing engineering (IcETRAN), International Conference on

作者： Mina Nikolić Dragan Janković Aleksandar Stanimirović Leonid Stoimenov Computer Science Department Faculty of Electronic Engineering Niš Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

Deep convolutional neural network architectures have in recent years been widely used for enhancing various computer vision tasks, such as Image classification, Semantic Segmentation and Object detection. With great advancements in terms of quality of the obtained results, the path was paved for using these kinds of neural networks in the medical domain. But, when working with sensitive matters involving human lives, there is a need to consider the interpretability and explainability of these models and not just the typical evaluation metrics for the given task. To do such a thing, tools such as LIME and PyTorch Grad-CAM can be used, among many others. The integration of Explainable AI (XAI) methods proposed in this paper aims to enable the paradigm of XAI to be used in medical image classification tasks with the standardized MedMNIST dataset. By doing such an integration, a deeper analysis regarding the quality of the model can be enabled. In that way, instances that were misclassified can be visually examined and used to paint a clearer picture of the complete model's decision-making process.

关键词： Visualization Explainable AI Semantic segmentation Pipelines computer architecture Object detection Convolutional neural networks

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Performance Analysis of an Artificial Intelligence Nanosystem with Biological Internet of Nano Things

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computer Modeling in engineering & Sciences 2022年第10期133卷 111-131页

作者： Saied M.Abd El-atty Nancy A.Arafa Atef Abouelazm Osama Alfarraj Konstantinos A.Lizos Farid Shawki Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt Computer Science Department Community CollegeKing Saud UniversityRiyadhSaudi Arabia Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway

Artificial intelligence(AI)has recently been used in nanomedical applications,in which implanted intelligent nanosystems inside the human body were used to diagnose and treat a variety of ailments with the help of the Internet of biological Nano Things(IoBNT).Biological circuit engineering or nanomaterial-based architectures can be used to approach the *** nanomedical applications,the blood vascular medium serves as a communication channel,demonstrating a molecular communication system based on flow and *** paper presents a performance study of the channel capacity for flow-based-diffusive molecular communication nanosystems that takes into account the ligand-receptor binding *** earlier studies,we take into account the effects of biological physical characteristics such as blood pressure,blood viscosity,and vascular diameter on channel ***,in terms of drug transmission error probability,the inter-symbol interference(ISI)phenomenon is applied to the proposed *** numerical results show that the proposed AI nanosystems-based IoBNT technology can be successfully implemented in future nanomedicine.

关键词： Nanosystem AI Internet of biological Nano Things(IoBNT) channel capacity

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting 6

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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6th IEEE Symposium on computers and Informatics, ISCI 2024

作者： Omar, Mas Yakub, Fitri Talip, Mohamad Sofian Abu Maslan, Mohd Nazmin Sinha, Vijay Kumar Muljono, Muljono Universiti Teknologi Malaysia Wee Laboratory Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Department of Electronic System Engineering Kuala Lumpur Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Universiti Teknikal Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Melaka Malaysia Chitkara University School of Engineering & Technology Department of Computer Science & Engineering Himachal Pradesh India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350353853

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement System. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications. © 2024 IEEE.

关键词： Mean square error

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Hardware-Limited Non-Uniform Task-Based Quantizers 48

Hardware-Limited Non-Uniform Task-Based Quantizers

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Bernardo, Neil Irwin Zhu, Jingge Eldar, Yonina C. Evans, Jamie University of Melbourne Department of Electrical and Electronic Engineering Australia University of the Philippines Diliman Electrical and Electronics Engineering Institute Philippines Weizmann Institute of Science Faculty of Math and Computer Science Israel

ISBN: (纸本)9781728163277

Hardware-limited task-based quantization is a new design paradigm for data acquisition systems equipped with scalar analog-to-digital converters using a small number of bits. By taking into account the system task, task-based quantizers can efficiently recover the desired parameters from the low-bit quantized observation. Current design and analysis frameworks for hardware-limited task-based quantization are only applicable to inputs with bounded support and uniform quantizers with non-subtractive dithering. In this paper, we propose a new framework based on generalized Bussgang decomposition that enables the design and analysis of hardware-limited task-based quantizers equipped with non-uniform scalar quantizers or have inputs with unbounded support. We consider the scenario in which the task is linear. Under this scenario, we derive new pre-quantization and post-quantization mappings for task-based quantizers with mean squared error (MSE) that closely matches the theoretical MSE. © 2023 IEEE.

关键词： Analog-to-digital conversion Quantization

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Hybrid of Distributed Cumulative Histograms and Classification Model for Attack Detection

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computer Systems Science & engineering 2023年第5期45卷 2235-2247页

作者： Mostafa Nassar Anas M.Ali Walid El-Shafai Adel Saleeb Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Hossam Eldin H.Ahmed Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Computer Science Security Engineering LaboratoryPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Traditional security systems are exposed to many various attacks,which represents a major challenge for the spread of the Internet in the *** techniques have been suggested for detecting attacks using machine learning and deep *** significant advantage of deep learning is that it is highly efficient,but it needs a large training time with a lot of ***,in this paper,we present a new feature reduction strategy based on Distributed Cumulative Histograms(DCH)to distinguish between dataset features to locate the most effective *** histograms assess the dataset instance patterns of the applied features to identify the most effective attributes that can significantly impact the classification *** different models for detecting attacks using Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)are also *** accuracy test of attack detection using the hybrid model was 98.96%on the UNSW-NP15 *** proposed model is compared with wrapper-based and filter-based Feature Selection(FS)*** proposed model reduced classification time and increased detection accuracy.

关键词： Feature selection DCH LSTM CNN security systems

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Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

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computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil Radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MRI images automatic left ventricle localization deep learning models faster R-CNN

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Modifying Quasi Z-Source Network for an Innovative Extendable Switched-Capacitor Boost Multilevel Inverter Topology

Modifying Quasi Z-Source Network for an Innovative Extendabl...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Sia, Yew Wei Law, Kah Haw Hann Lim, Garenth King Sim, Sy Yi Kiam Yeo, Kenneth Siok Curtin University Malaysia Department of Electrical and Computer Engineering Sarawak Malaysia Universiti Teknologi Brunei Electrical and Electronic Engineering Programme Area Bandar Seri Begawan Brunei University Tun Hussein Onn Malaysia Faculty of Engineering Technology Johor Batu Pahat86400 Malaysia

ISBN: (纸本)9798350357905

The emergence of Hybrid Multilevel Inverters (HMLIs) represents a significant advancement in addressing the limitations of traditional multilevel inverter (MLI) topologies. In recent years, various concepts and topologies have been proposed to enhance HMLIs, with a primary focus on generating a greater number of AC voltage levels while minimizing component count and utilizing isolated DC sources. Traditionally, these HMLI topologies required two-stage configurations involving DC-to-DC converters, leading to increased costs and complexity. This paper highlights the transition to single-stage DC-AC inverters, which offers substantial benefits, including high boost gain, superior conversion efficiency, and a reduced reliance on passive components. Furthermore, the paper introduces a groundbreaking solution - a 57-level switched-capacitor boost multilevel inverter (SCBMLI), derived from the quasi z-source inverter (qZSI). The proposed SCBMLI offers a multitude of advantages, including the generation of high voltage levels, balancing the voltage of capacitor in the circuit without the need for any control system, and achieving low Total Harmonic Distortion (THD) in high voltage applications. © 2024 IEEE.

关键词： Topology

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